Package 'httk'

Title: High-Throughput Toxicokinetics
Description: Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).
Authors: John Wambaugh [aut, cre] , Sarah Davidson-Fritz [aut] , Robert Pearce [aut] , Caroline Ring [aut] , Greg Honda [aut] , Mark Sfeir [aut], Matt Linakis [aut] , Dustin Kapraun [aut] , Nathan Pollesch [ctb] , Miyuki Breen [ctb] , Shannon Bell [ctb] , Xiaoqing Chang [ctb] , Todor Antonijevic [ctb] , Jimena Davis [ctb], Elaina Kenyon [ctb] , Katie Paul Friedman [ctb] , Meredith Scherer [ctb] , James Sluka [ctb] , Noelle Sinski [ctb], Nisha Sipes [ctb] , Barbara Wetmore [ctb] , Lily Whipple [ctb], Woodrow Setzer [ctb]
Maintainer: John Wambaugh <[email protected]>
License: GPL-3
Version: 2.4.0
Built: 2024-11-16 06:08:45 UTC
Source: https://github.com/usepa/comptox-expocast-httk

Help Index

Add a table of chemical information for use in making httk predictions.

Description

This function adds chemical-specific information to the table chem.physical_and_invitro.data. This table is queried by the model parameterization functions when attempting to parameterize a model, so adding sufficient data to this table allows additional chemicals to be modeled.

Usage

add_chemtable(
  new.table,
  data.list,
  current.table = NULL,
  reference = NULL,
  species = NULL,
  overwrite = FALSE,
  sig.fig = 4,
  clint.pvalue.overwrite = TRUE,
  allow.na = FALSE
)

Arguments

new.table

Object of class data.frame containing one row per chemical, with each chemical minimally described by a CAS number.
data.list

This list identifies which properties are to be read from the table. Each item in the list should point to a column in the table new.table. Valid names in the list are: 'Compound', 'CAS', 'DSSTox.GSID' 'SMILES.desalt', 'Reference', 'Species', 'MW', 'logP', 'pKa_Donor', 'pKa_Accept', 'logMA', 'Clint', 'Clint.pValue', 'Funbound.plasma', 'Fabs', 'Fgut', 'Rblood2plasma'.
current.table

This is the table to which data are being added.
reference

This is the reference for the data in the new table. This may be omitted if a column in data.list gives the reference value for each chemical.
species

This is the species for the data in the new table. This may be omitted if a column in data.list gives the species value for each chemical or if the data are not species-specific (e.g., MW).
overwrite

If overwrite=TRUE then data in current.table will be replaced by any data in new.table that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way.
sig.fig

Sets the number of significant figures stored (defaults to 4)
clint.pvalue.overwrite

If TRUE then the Cl_int p-value is set to NA when the Cl_int value is changed unless a new p-value is provided. (defaults to TRUE)
allow.na

If TRUE (default is FALSE) then NA values are written to the table, otherwise they are ignored.

Value

data.frame

A new data.frame containing the data in current.table augmented by new.table

Author(s)

John Wambaugh

Examples

library(httk)

my.new.data <- as.data.frame(c("A","B","C"),stringsAsFactors=FALSE)
my.new.data <- cbind(my.new.data,as.data.frame(c(
                     "111-11-2","222-22-0","333-33-5"),
                     stringsAsFactors=FALSE))
my.new.data <- cbind(my.new.data,as.data.frame(c("DTX1","DTX2","DTX3"),
                    stringsAsFactors=FALSE))
my.new.data <- cbind(my.new.data,as.data.frame(c(200,200,200)))
my.new.data <- cbind(my.new.data,as.data.frame(c(2,3,4)))
my.new.data <- cbind(my.new.data,as.data.frame(c(0.01,0.02,0.3)))
my.new.data <- cbind(my.new.data,as.data.frame(c(0,10,100)))
colnames(my.new.data) <- c("Name","CASRN","DTXSID","MW","LogP","Fup","CLint")

chem.physical_and_invitro.data <- add_chemtable(my.new.data,
                                  current.table=
                                    chem.physical_and_invitro.data,
                                  data.list=list(
                                  Compound="Name",
                                  CAS="CASRN",
                                  DTXSID="DTXSID",
                                  MW="MW",
                                  logP="LogP",
                                  Funbound.plasma="Fup",
                                  Clint="CLint"),
                                  species="Human",
                                  reference="MyPaper 2015")
parameterize_steadystate(chem.name="C")  
calc_css(chem.name="B")                                

# Initialize a column describing proton donors ("acids")
my.new.data$pka.a <- NA 
# set chemical C to an acid (pKa_donor = 5):
my.new.data[my.new.data$Name=="C","pka.a"] <- "5"
chem.physical_and_invitro.data <- add_chemtable(my.new.data,
                                  current.table=
                                    chem.physical_and_invitro.data,
                                 data.list=list(
                                 Compound="Name",
                                 CAS="CASRN",
                                 DTXSID="DTXSID",
                                 pKa_Donor="pka.a"),
                                 species="Human",
                                 reference="MyPaper 2015") 

# Note Rblood2plasma and hepatic bioavailability change (relative to above):
parameterize_steadystate(chem.name="C")  

# Initialize a column describing proton acceptors ("bases")
my.new.data$pka.b <- NA 
# set chemical B to a base with multiple pka's (pKa_accept = 7 and 8):
my.new.data[my.new.data$Name=="B","pka.b"] <- "7;8"
chem.physical_and_invitro.data <- add_chemtable(my.new.data,
                                  current.table=
                                    chem.physical_and_invitro.data,
                                 data.list=list(
                                 Compound="Name",
                                 CAS="CASRN",
                                 DTXSID="DTXSID",
                                 pKa_Accept="pka.b"),
                                 species="Human",
                                 reference="MyPaper 2015") 
# Note that average and max change (relative to above):
calc_css(chem.name="B")

Draws ages from a smoothed distribution for a given gender/race combination

Description

This function should usually not be called directly by the user. It is used by httkpop_generate() in "virtual-individuals" mode.

Usage

age_draw_smooth(gender, reth, nsamp, agelim_months, nhanes_mec_svy)

Arguments

gender

Gender. Either 'Male' or 'Female'.
reth

Race/ethnicity. One of 'Mexican American', 'Other Hispanic', 'Non-Hispanic Black', 'Non-Hispanic White', 'Other'.
nsamp

Number of ages to draw.
agelim_months

Two-element numeric vector giving the minimum and maximum ages in months to include.
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Value

A named list with members 'ages_months' and 'ages_years', each numeric of length nsamp, giving the sampled ages in months and years.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Correct the measured intrinsive hepatic clearance for fraction free

Description

This function uses the free fraction estimated from Kilford et al. (2008) to increase the in vitro measure intrinsic hepatic clearance. The assumption that chemical that is bound in vitro is not available to be metabolized and therefore the actual rate of clearance is actually faster. Note that in most high throughput TK models included in the package this increase is offset by the assumption of "restrictive clearance" – that is, the rate of hepatic metabolism is slowed to account for the free fraction of chemical in plasma. This adjustment was made starting in Wetmore et al. (2015) in order to better predict plasma concentrations.

Usage

apply_clint_adjustment(
  Clint,
  Fu_hep = NULL,
  Pow = NULL,
  pKa_Donor = NULL,
  pKa_Accept = NULL,
  suppress.messages = FALSE
)

Arguments

Clint

In vitro measured intrinsic hepatic clearance in units of (ul/min/million hepatocytes).
Fu_hep

Estimated fraction of chemical free for metabolism in the in vitro assay, estimated by default from the method of Kilford et al. (2008) using calc_hep_fu
Pow

The octanal:water equilibrium partition coefficient
pKa_Donor

A string containing hydrogen donor ionization equilibria, concatenated with commas. Can be "NA" if none exist.
pKa_Accept

A string containing hydrogen acceptance ionization equilibria, concatenated with commas. Can be "NA" if none exist.
suppress.messages

Whether or not the output message is suppressed.

Value

Intrinsic hepatic clearance increased to take into account binding in the in vitro assay

Author(s)

John Wambaugh

References

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197. Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, others (2015). “Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing.” Toxicological Sciences, 148(1), 121–136.

See Also

calc_hep_fu

Correct the measured fraction unbound in plasma for lipid binding

Description

This function uses the lipid binding correction estimated by Pearce et al. (2017) to decrease the fraction unbound in plasma (fup). This correction assumes that there is additional in vivo binding to lipid, which has a greater impact on neutral lipophilic compounds.

Usage

apply_fup_adjustment(
  fup,
  fup.correction = NULL,
  Pow = NULL,
  pKa_Donor = NULL,
  pKa_Accept = NULL,
  suppress.messages = FALSE,
  minimum.Funbound.plasma = 1e-04
)

Arguments

fup

In vitro measured fraction unbound in plasma
fup.correction

Estimated correction to account for additional lipid binding in vivo (Pearce et al., 2017) from calc_fup_correction
Pow

The octanal:water equilibrium partition coefficient
pKa_Donor

A string containing hydrogen donor ionization equilibria, concatenated with commas. Can be "NA" if none exist.
pKa_Accept

A string containing hydrogen acceptance ionization equilibria, concatenated with commas. Can be "NA" if none exist.
suppress.messages

Whether or not the output message is suppressed.
minimum.Funbound.plasma fup

is not allowed to drop below this value (default is 0.0001).

Value

Fraction unbound in plasma adjusted to take into account binding in the in vitro assay

Author(s)

John Wambaugh

References

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197. Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, others (2015). “Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing.” Toxicological Sciences, 148(1), 121–136.

See Also

calc_fup_correction

Estimate well surface area

Description

Estimate geometry surface area of plastic in well plate based on well plate format suggested values from Corning. option.plastic == TRUE (default) give nonzero surface area (sarea, m^2) option.bottom == TRUE (default) includes surface area of the bottom of the well in determining sarea. Optionally include user values for working volume (v_working, m^3) and surface area.

Usage

armitage_estimate_sarea(
  tcdata = NA,
  this.well_number = 384,
  this.cell_yield = NA,
  this.v_working = NA
)

Arguments

tcdata

A data table with well_number corresponding to plate format, optionally include v_working, sarea, option.bottom, and option.plastic
this.well_number

For single value, plate format default is 384, used if is.na(tcdata)==TRUE
this.cell_yield

For single value, optionally supply cell_yield, otherwise estimated based on well number
this.v_working

For single value, optionally supply working volume, otherwise estimated based on well number (m^3)

Value

A data table composed of any input data.table tcdata with only the following columns either created or altered by this function:

Column Name Description Units
well_number number of wells on plate
sarea surface area m^2
cell_yield number of cells cells
v_working working (filled) volume of each well uL
v_total total volume of each well uL

Author(s)

Greg Honda

References

Armitage JM, Arnot JA, Wania F, Mackay D (2013). “Development and evaluation of a mechanistic bioconcentration model for ionogenic organic chemicals in fish.” Environmental toxicology and chemistry, 32(1), 115–128.

Evaluate the updated Armitage model

Description

Evaluate the Armitage model for chemical distributon in vitro. Takes input as data table or vectors of values. Outputs a data table. Updates over the model published in Armitage et al. (2014) include binding to plastic walls and lipid and protein compartments in cells.

Usage

armitage_eval(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  casrn.vector = NA_character_,
  nomconc.vector = 1,
  this.well_number = 384,
  this.FBSf = NA_real_,
  tcdata = NA,
  this.sarea = NA_real_,
  this.v_total = NA_real_,
  this.v_working = NA_real_,
  this.cell_yield = NA_real_,
  this.Tsys = 37,
  this.Tref = 298.15,
  this.option.kbsa2 = FALSE,
  this.option.swat2 = FALSE,
  this.pseudooct = 0.01,
  this.memblip = 0.04,
  this.nlom = 0.2,
  this.P_nlom = 0.035,
  this.P_dom = 0.05,
  this.P_cells = 1,
  this.csalt = 0.15,
  this.celldensity = 1,
  this.cellmass = 3,
  this.f_oc = 1,
  this.conc_ser_alb = 24,
  this.conc_ser_lip = 1.9,
  this.Vdom = 0,
  this.pH = 7,
  restrict.ion.partitioning = FALSE
)

Arguments

chem.cas

A single or vector of Chemical Abstracts Service Registry Number(s) (CAS-RN) of desired chemical(s).
chem.name

A single or vector of name(s)) of desired chemical(s).
dtxsid

A single or vector ofEPA's DSSTox Structure ID(s) (https://comptox.epa.gov/dashboard)
casrn.vector

A deprecated argument specifying a single or vector of Chemical Abstracts Service Registry Number(s) (CAS-RN) of desired chemical(s).
nomconc.vector

For vector or single value, micromolar (uM = mol/L) nominal concentration (e.g. AC50 value)
this.well_number

For single value, plate format default is 384, used if is.na(tcdata)==TRUE. This value chooses default surface area settings for armitage_estimate_sarea based on the number of plates per well.
this.FBSf

Fraction fetal bovine serum, must be entered by user.
tcdata

A data.table with casrn, nomconc, MP, gkow, gkaw, gswat, sarea, v_total, v_working. Otherwise supply single values to this.params (e.g., this.sarea, this.v_total, etc.). Chemical parameters are taken from chem.physical_and_invitro.data.
this.sarea

Surface area per well (m^2)
this.v_total

Total volume per well (uL)
this.v_working

Working volume per well (uL)
this.cell_yield

Number of cells per well
this.Tsys

System temperature (degrees C)
this.Tref

Reference temperature (degrees K)
this.option.kbsa2

Use alternative bovine-serum-albumin partitioning model
this.option.swat2

Use alternative water solubility correction
this.pseudooct

Pseudo-octanol cell storage lipid content
this.memblip

Membrane lipid content of cells
this.nlom

Structural protein content of cells
this.P_nlom

Proportionality constant to octanol structural protein
this.P_dom

Proportionality constant to dissolve organic material
this.P_cells

Proportionality constant to octanol storage lipid
this.csalt

Ionic strength of buffer (M = mol/L)
this.celldensity

Cell density kg/L, g/mL
this.cellmass

Mass per cell, ng/cell
this.f_oc

Everything assumed to be like proteins
this.conc_ser_alb

Mass concentration of albumin in serum (g/L)
this.conc_ser_lip

Mass concentration of lipids in serum (g/L)
this.Vdom

0 ml, the volume of dissolved organic matter (DOM)
this.pH

7.0, pH of cell culture
restrict.ion.partitioning

FALSE, Should we restrict the chemical available to partition to only the neutral fraction?

Value

Param Description Units
casrn Chemical Abstracts Service Registry Number character
nomconc Nominal Concentration uM=umol/L
well_number Number of wells in plate (used to set default surface area) unitless
sarea Surface area of well m^2
v_total Total volume of well uL
v_working Filled volume of well uL
cell_yield Number of cells cells
gkow The log10 octanol to water (PC) (logP) log10 unitless ratio
logHenry The log10 Henry's law constant ' log10 unitless ratio
gswat The log10 water solubility (logWSol) log10 mg/L
MP The chemical compound melting point degrees Kelvin
MW The chemical compound molecular weight g/mol
gkaw The air to water PC unitless ratio
dsm
duow
duaw
dumw
gkmw log10
gkcw The log10 cell/tissue to water PC log10 unitless ratio
gkbsa The log10 bovine serum albumin to water partitiion coefficient unitless
gkpl log10
ksalt Setschenow constant L/mol
Tsys System temperature degrees C
Tref Reference temperature degrees K
option.kbsa2 Use alternative bovine-serum-albumin partitioning model logical
option.swat2 Use alternative water solubility correction logical
FBSf Fraction fetal bovine serum unitless
pseudooct Pseudo-octanol cell storage lipid content
memblip Membrane lipid content of cells
nlom Structural protein content of cells
P_nlom Proportionality constant to octanol structural protein unitless
P_dom Proportionality constant to dissolved organic material (DOM) unitless
P_cells Proportionality constant to octanol storage lipid unitless
csalt Ionic strength of buffer M=mol/L
celldensity Cell density kg/L, g/mL
cellmass Mass per cell ng/cell
f_oc
cellwat
Tcor
Vm Volume of media L
Vwell Volume of medium (aqueous phase only) L
Vair Volume of head space L
Vcells Volume of cells/tissue L
Valb Volume of serum albumin L
Vslip Volume of serum lipids L
Vdom Volume of dissolved organic matter L
F_ratio
gs1.GSE
s1.GSE
gss.GSE
ss.GSE
kmw
kow The octanol to water PC (i.e., 10^gkow) unitless
kaw The air to water PC (i.e., 10^gkaw) unitless
swat The water solubility (i.e., 10^gswat) mg/L
kpl
kcw The cell/tissue to water PC (i.e., 10^gkcw) unitless
kbsa The bovine serum albumin to water PC unitless
swat_L
soct_L
scell_L
cinit Initial concentration uM=umol/L
mtot Total micromoles umol
cwat Total concentration in water uM=umol/L
cwat_s Dissolved concentration in water uM=umol/L
csat Is the solution saturated (1/0) logical
activity
cair Concentration in head space uM=umol/L
calb Concentration in serum albumin uM=umol/L
cslip Concentration in serum lipids uM=umol/L
cdom Concentration in dissolved organic matter uM=umol/L
ccells Concentration in cells uM=umol/L
cplastic Concentration in plastic uM=umol/m^2
mwat_s Mass dissolved in water umols
mair Mass in air/head space umols
mbsa Mass bound to bovine serum albumin umols
mslip Mass bound to serum lipids umols
mdom Mass bound to dissolved organic matter umols
mcells Mass in cells umols
mplastic Mass bond to plastic umols
mprecip Mass precipitated out of solution umols
xwat_s Fraction dissolved in water fraction
xair Fraction in the air fraction
xbsa Fraction bound to bovine serum albumin fraction
xslip Fraction bound to serum lipids fraction
xdom Fraction bound to dissolved organic matter fraction
xcells Fraction within cells fraction
xplastic Fraction bound to plastic fraction
xprecip Fraction precipitated out of solution fraction
eta_free Effective availability ratio fraction
cfree.invitro Free concentration in the in vitro media (use for Honda1 and Honda2) fraction

Author(s)

Greg Honda

References

Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol. 2014, 48, 9770-9779. https://doi.org/10.1021/es501955g

Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Examples

library(httk)

# Check to see if we have info on the chemical:
"80-05-7" %in% get_cheminfo()

#We do:
temp <- armitage_eval(casrn.vector = c("80-05-7", "81-81-2"), this.FBSf = 0.1,
this.well_number = 384, nomconc = 10)
print(temp$cfree.invitro)

# Check to see if we have info on the chemical:
"793-24-8" %in% get_cheminfo()

# Since we don't have any info, let's look up phys-chem from dashboard:
cheminfo <- data.frame(
  Compound="6-PPD",
  CASRN="793-24-8",
  DTXSID="DTXSID9025114",
  logP=4.27, 
  logHenry=log10(7.69e-8),
  logWSol=log10(1.58e-4),
  MP=	99.4,
  MW=268.404
  )
  
# Add the information to HTTK's database:
chem.physical_and_invitro.data <- add_chemtable(
 cheminfo,
 current.table=chem.physical_and_invitro.data,
 data.list=list(
 Compound="Compound",
 CAS="CASRN",
  DTXSID="DTXSID",
  MW="MW",
  logP="logP",
  logHenry="logHenry",
  logWSol="logWSol",
  MP="MP"),
  species="Human",
  reference="CompTox Dashboard 31921")

# Run the Armitage et al. (2014) model:
out <- armitage_eval(
  casrn.vector = "793-24-8", 
  this.FBSf = 0.1,
  this.well_number = 384, 
  nomconc = 10)
  
print(out)

Armitage et al. (2014) Model Inputs from Honda et al. (2019)

Description

Armitage et al. (2014) Model Inputs from Honda et al. (2019)

Usage

armitage_input

Format

A data frame with 53940 rows and 10 variables:

MP
MW
casrn
compound_name
gkaw
gkow
gswat

Author(s)

Greg Honda

Source

https://www.diamondse.info/

References

Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol. 2014, 48, 9770-9779. dx.doi.org/10.1021/es501955g Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Add a parameter value to the chem.physical_and_invitro.data table

Description

This internal function is used by add_chemtable to add a single new parameter to the table of chemical parameters. It should not be typically used from the command line.

Usage

augment.table(
  this.table,
  this.CAS,
  compound.name = NULL,
  this.property,
  value,
  species = NULL,
  reference,
  overwrite = FALSE,
  sig.fig = 4,
  clint.pvalue.overwrite = TRUE,
  allow.na = FALSE
)

Arguments

this.table

Object of class data.frame containing one row per chemical.
this.CAS

The Chemical Abstracts Service registry number (CAS-RN) correponding to the parameter value
compound.name

A name associated with the chemical (defaults to NULL)
this.property

The property being added/modified.
value

The value being assigned to this.property.
species

This is the species for the data in the new table. This may be omitted if a column in data.list gives the species value for each chemical or if the data are not species-specific (e.g., MW).
reference

This is the reference for the data in the new table. This may be omitted if a column in data.list gives the reference value for each chemical.
overwrite

If overwrite=TRUE then data in current.table will be replaced by any data in new.table that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way.
sig.fig

Sets the number of significant figures stored (defaults to 4)
clint.pvalue.overwrite

If TRUE then the Cl_int p-value is set to NA when the Cl_int value is changed unless a new p-value is provided. (defaults to TRUE)
allow.na

If TRUE (default is FALSE) then NA values are written to the table, otherwise they are ignored.

Value

data.frame

A new data.frame containing the data in current.table augmented by new.table

Author(s)

John Wambaugh

Find the best available ratio of the blood to plasma concentration constant.

Description

This function finds the best available constant ratio of the blood concentration to the plasma concentration, using get_rblood2plasma and calc_rblood2plasma.

Usage

available_rblood2plasma(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  adjusted.Funbound.plasma = TRUE,
  class.exclude = TRUE,
  suppress.messages = FALSE
)

Arguments

chem.cas

Either the CAS number or the chemical name must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
adjusted.Funbound.plasma

Whether or not to use Funbound.plasma adjustment if calculating Rblood2plasma.
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
suppress.messages

Whether or not to display relevant warning messages to user.

Details

Either retrieves a measured blood:plasma concentration ratio from the chem.physical_and_invitro.data table or calculates it using the red blood cell partition coefficient predicted with Schmitt's method

If available, in vivo data (from chem.physical_and_invitro.data) for the given species is returned, substituting the human in vivo value when missing for other species. In the absence of in vivo data, the value is calculated with calc_rblood2plasma for the given species. If Funbound.plasma is unvailable for the given species, the human Funbound.plasma is substituted. If none of these are available, the mean human Rblood2plasma from chem.physical_and_invitro.data is returned. details than the description above ~~

Value

The blood to plasma chemical concentration ratio – measured if available, calculated if not.

Author(s)

Robert Pearce

See Also

calc_rblood2plasma

get_rblood2plasma

Examples

available_rblood2plasma(chem.name="Bisphenol A",adjusted.Funbound.plasma=FALSE)
available_rblood2plasma(chem.name="Bisphenol A",species="Rat")

Aylward et al. 2014

Description

Aylward et al. (2014) compiled measurements of the ratio of maternal to fetal cord blood chemical concentrations at birth for a range of chemicals with environmental routes of exposure, including bromodiphenyl ethers, fluorinated compounds, organochlorine pesticides, polyaromatic hydrocarbons, tobacco smoke components, and vitamins.

Usage

aylward2014

Format

data.frame

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Aylward LL, Hays SM, Kirman CR, Marchitti SA, Kenneke JF, English C, Mattison DR, Becker RA (2014). “Relationships of chemical concentrations in maternal and cord blood: a review of available data.” Journal of Toxicology and Environmental Health, Part B, 17(3), 175–203. doi:10.1080/10937404.2014.884956.

Assess the current performance of httk relative to historical benchmarks

Description

The function performs a series of "sanity checks" and predictive performance benchmarks so that the impact of changes to the data, models, and implementation of the R package can be tested. Plots can be generated showing how the performance of the current version compares with past releases of httk.

Usage

benchmark_httk(
  basic.check = TRUE,
  calc_mc_css.check = TRUE,
  in_vivo_stats.check = TRUE,
  tissuepc.check = TRUE,
  suppress.messages = TRUE,
  make.plots = TRUE
)

Arguments

basic.check

Whether to run the basic checks, including uM and mg/L units for calc_analytic_css, calc_mc_css, and solve_pbtk as well as the number of chemicals with sufficient data to run the steady_state model (defaults to TRUE)
calc_mc_css.check

Whether to check the Monte Carlo sample. A comparison of the output of calc_mc_css to the SimCyp outputs reported in the Wetmore et al. (2012,2015) papers is performed. A comparison between the output of calc_analytic_css (no Monte Carlo) to the median of the output of calc_mc_css is also performed. (defaults to TRUE)
in_vivo_stats.check

Whether to compare the outputs of calc_mc_css and calc_tkstats to in vivo measurements of Css, AUC, and Cmax collected by Wambaugh et al. (2018). (defaults to TRUE)
tissuepc.check

Whether to compare the tissue-specific partition coefficient predictions from the calibrated Schmitt (2008) model to the in vivo data-derived estimates compiled by Pearce et al. (2017). (defaults to TRUE)
suppress.messages

Whether or not output messages are suppressed (defaults to TRUE)
make.plots

Whether current benchmarks should be plotted with historical performance (defaults to TRUE)

Details

Historically some refinements made to one aspect of httk have unintentionally impacted other aspects. Most notably errors have occasionally been introduced with respect to units (v1.9, v2.1.0). This benchmarking tool is intended to reduce the chance of these errors occurring in the future.

Past performance was retroactively evaluated by manually installing previous versions of the package from https://cran.r-project.org/src/contrib/Archive/httk/ and then adding the code for benchmark_httk at the command line interface.

The basic tests are important – if the output units for key functions are wrong, not much can be right. Past unit errors were linked to an incorrect unit conversions made within an individual function. Since the usage of convert_units became standard throughout httk, unit problems are hopefully less likely.

There are two Monte Carlo tests. One compares calc_mc_css 95th percentile steady-state plasma concentrations for a 1 mg/kg/day exposure against the Css values calculated by SimCyp and reported in Wetmore et al. (2012,2015). These have gradually diverged as the assumptions for httk have shifted to better describe non-pharmaceutical, commercial chemicals.

The in vivo tests are in some ways the most important, as they establish the overall predictability for httk for Cmax, AUC, and Css. The in vivo statistics are currently based on comparisons to the in vivo data compiled by Wambaugh et al. (2018). We see that when the tissue partition coefficient calibrations were introduced in v1.6 that the overall predictability for in vivo endpoints was reduced (increased RMSLE). If this phenomena continues as new in vivo evaluation data become available, we may need to revisit whether evaluation against experimentally-derived partition coefficients can actually be used for calibration, or just merely for establishing confidence intervals.

The partition coefficient tests provide an important check of the httk implementation of the Schmitt (2008) model for tissue:plasma equilibrium distribution. These predictions heavily rely on accurate description of tissue composition and the ability to predict the ionization state of the compounds being modeled.

Value

named list, whose elements depend on the selected checks

basic A list with four metrics: N.steadystate -- Number of chemicals with sufficient data for steady-state IVIVE calc_analytic.units -- Ratio of mg/L to uM * 1000 / molecular weight -- should be 1 calc_mc.units -- Ratio should be 1 solve_pbtk.units -- Ratio should be 1
calc_mc_css A list with four metrics: RMSLE.Wetmore -- Root mean squared log10 error (RMSLE) in predicted Css between literature values (SimCyp, Wetmore et al. 2012,2015) and calc_mc_css N.Wetmore -- Number of chemicals in Wetmore comparison RMSLE.noMC -- RMSLE between calc_analytic_css and calc_mc_css N.noMC -- Number of chemicals in noMC comparison
in_vivo_stats A list with two metrics: RMSLE.InVivoCss -- RMSLE between the predictions of calc_analytic_css and in vivo estimates of Css N.InVivoCss -- Number of chemicals in comparison
units.plot A ggplot2 figure showing units tests of various functions. Output is generated for mg/L and uM, and then the ratio mg/L/uM*1000/MW is calculated. If the units are correct the ratio should be 1 (within the precision of the functions -- usually four significant figures).
invivo.rmsle.plot A ggplot2 figure comparing model predictions to in vivo measured values. Output generated is the root mean square log10 error for parameters estimated by the package.
model.rmsle.plot A ggplot2 figure comparing various functions values against values predicted by other models (chiefly SimCyp predictions from Wetmore et al. 2012 and 2015. Output generated is the root mean square log10 error for parameters estimated by the package.
count.plot A ggplot2 figure showing count of chemicals of various functions. Output generated is a count of the chemicals available for the each of the parameters estimated by and used for benchmarking the package.

Author(s)

John Wambaugh

References

Davidson-Fritz SE, Evans MV, Chang X, Breen M, Honda GS, Kenyon E, Linakis MW, Meade A, Pearce RG, Purucker T, Ring CL, Sfeir MA, Setzer RW, Sluka JP, Vitense K, Devito MJ, Wambaugh JF (2023). “Transparent and Evaluated Toxicokinetic Models for Bioinformatics and Public Health Risk Assessment.” Unpublished.

Find average blood masses by age.

Description

If blood mass from blood_weight is negative or very small, then just default to the mean blood mass by age. (Geigy Scientific Tables, 7th ed.)

Usage

blood_mass_correct(blood_mass, age_months, age_years, gender, weight)

Arguments

blood_mass

A vector of blood masses in kg to be replaced with averages.
age_months

A vector of ages in months.
age_years

A vector of ages in years.
gender

A vector of genders (either 'Male' or 'Female').
weight

A vector of body weights in kg.

Value

A vector of blood masses in kg.

Author(s)

Caroline Ring

References

Geigy Pharmaceuticals, "Scientific Tables", 7th Edition, John Wiley and Sons (1970)

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict blood mass.

Description

Predict blood mass based on body surface area and gender, using equations from Bosgra et al. 2012

Usage

blood_weight(BSA, gender)

Arguments

BSA

Body surface area in m^2. May be a vector.
gender

Either 'Male' or 'Female'. May be a vector.

Value

A vector of blood masses in kg the same length as BSA and gender.

Author(s)

Caroline Ring

References

Bosgra, Sieto, et al. "An improved model to predict physiologically based model parameters and their inter-individual variability from anthropometry." Critical reviews in toxicology 42.9 (2012): 751-767.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

CDC BMI-for-age charts

Description

Charts giving the BMI-for-age percentiles for boys and girls ages 2-18

Usage

bmiage

Format

A data.table with 434 rows and 5 variables:

Sex

Female or Male

Agemos

Age in months

P5

The 5th percentile BMI for the corresponding sex and age

P85

The 85th percentile BMI for the corresponding sex and age

P95

The 95th percentile BMI for the corresponding sex and age

Details

For children ages 2 to 18, weight class depends on the BMI-for-age percentile.

Underweight

<5th percentile

Normal weight

5th-85th percentile

Overweight

85th-95th percentile

Obese

>=95th percentile

Author(s)

Caroline Ring

Source

https://www.cdc.gov/growthcharts/data/zscore/bmiagerev.csv

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict body surface area.

Description

Predict body surface area from weight, height, and age, using Mosteller's formula for age>18 and Haycock's formula for age<18

Usage

body_surface_area(BW, H, age_years)

Arguments

BW

A vector of body weights in kg.
H

A vector of heights in cm.
age_years

A vector of ages in years.

Value

A vector of body surface areas in cm^2.

Author(s)

Caroline Ring

References

Mosteller, R. D. "Simplified calculation of body surface area." N Engl J Med 317 (1987): 1098..

Haycock, George B., George J. Schwartz, and David H. Wisotsky. "Geometric method for measuring body surface area: a height-weight formula validated in infants, children, and adults." The Journal of pediatrics 93.1 (1978): 62-66.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict bone mass

Description

Predict bone mass from age_years, height, weight, gender, using logistic equations fit to data from Baxter-Jones et al. 2011, or for infants < 1 year, using equation from Koo et al. 2000 (See Price et al. 2003)

Usage

bone_mass_age(age_years, age_months, height, weight, gender)

Arguments

age_years

Vector of ages in years.
age_months

Vector of ages in months.
height

Vector of heights in cm.
weight

Vector of body weights in kg.
gender

Vector of genders, either 'Male' or 'Female'.

Value

Vector of bone masses.

Author(s)

Caroline Ring

References

Baxter-Jones, Adam DG, et al. "Bone mineral accrual from 8 to 30 years of age: an estimation of peak bone mass." Journal of Bone and Mineral Research 26.8 (2011): 1729-1739.

Koo, Winston WK, and Elaine M. Hockman. "Physiologic predictors of lumbar spine bone mass in neonates." Pediatric research 48.4 (2000): 485-489.

Price, Paul S., et al. "Modeling interindividual variation in physiological factors used in PBPK models of humans." Critical reviews in toxicology 33.5 (2003): 469-503.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict brain mass.

Description

Predict brain mass from gender and age.

Usage

brain_mass(gender, age_years)

Arguments

gender

Vector of genders, either 'Male' or 'Female'
age_years

Vector of ages in years.

Value

A vector of brain masses in kg.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Calculate the analytic steady state plasma concentration.

Description

This function calculates the analytic steady state plasma or venous blood concentrations as a result of infusion dosing for the three compartment and multiple compartment PBTK models.

Usage

calc_analytic_css(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "human",
  daily.dose = NULL,
  dose = 1,
  dose.units = "mg/kg/day",
  route = "oral",
  output.units = "uM",
  model = "pbtk",
  concentration = "plasma",
  suppress.messages = FALSE,
  tissue = NULL,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  IVIVE = NULL,
  Caco2.options = list(),
  parameterize.args = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk (for model = 'pbtk'), parameterize_3comp (for model = '3compartment), parameterize_1comp(for model = '1compartment') or parameterize_steadystate (for model = '3compartmentss'), overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
daily.dose

Total daily dose, mg/kg BW.
dose

The amount of chemial to which the individual is exposed.
dose.units

The units associated with the dose received.
route

Route of exposure (either "oral", "iv", or "inhalation" default "oral").
output.units

Units for returned concentrations, defaults to uM (specify units = "uM") but can also be mg/L.
model

Model used in calculation,'gas_pbtk' for the gas pbtk model, 'pbtk' for the multiple compartment model, '3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model.
concentration

Desired concentration type: 'blood','tissue', or default 'plasma'. In the case that the concentration is for plasma, selecting "blood" will use the blood:plasma ratio to estimate blood concentration. In the case that the argument 'tissue' specifies a particular tissue of the body, concentration defaults to 'tissue' – that is, the concentration in the If cocentration is set to 'blood' or 'plasma' and 'tissue' specifies a specific tissue then the value returned is for the plasma or blood in that specific tissue.
suppress.messages

Whether or not the output message is suppressed.
tissue

Desired steady state tissue concentration. Default is of NULL typically gives whole body plasma concentration.
restrictive.clearance

If TRUE (default), then only the fraction of chemical not bound to protein is available for metabolism in the liver. If FALSE, then all chemical in the liver is metabolized (faster metabolism due to rapid off-binding).
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
IVIVE

Honda et al. (2019) identified four plausible sets of assumptions for in vitro-in vivo extrapolation (IVIVE) assumptions. Argument may be set to "Honda1" through "Honda4". If used, this function overwrites the tissue, restrictive.clearance, and bioactive.free.invivo arguments. See Details below for more information.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
parameterize.args

List of arguments passed to model's associated parameterization function, including default.to.human, adjusted.Funbound.plasma, regression, and minimum.Funbound.plasma. The default.to.human argument substitutes missing animal values with human values if true, adjusted.Funbound.plasma returns adjusted Funbound.plasma when set to TRUE along with parition coefficients calculated with this value, regression indicates whether or not to use the regressions in calculating partition coefficients, and minimum.Funbound.plasma is the value to which Monte Carlo draws less than this value are set (default is 0.0001 – half the lowest measured Fup in our dataset).
...

Additional parameters passed to parameterize function if parameters is NULL.

Details

Concentrations are calculated for the specifed model with constant oral infusion dosing. All tissues other than gut, liver, and lung are the product of the steady state plasma concentration and the tissue to plasma partition coefficient.

Only four sets of IVIVE assumptions that performed well in Honda et al. (2019) are currently included in honda.ivive: "Honda1" through "Honda4". The use of max (peak) concentration can not be currently be calculated with calc_analytic_css. The httk default settings correspond to "Honda3":

In Vivo Conc. Metabolic Clearance Bioactive Chemical Conc. In Vivo TK Statistic Used* Bioactive Chemical Conc. In Vitro
Honda1 Veinous (Plasma) Restrictive Free Mean Conc. In Vivo Free Conc. In Vitro
Honda2 Veinous Restrictive Free Mean Conc. In Vivo Nominal Conc. In Vitro
Honda3 Veinous Restrictive Total Mean Conc. In Vivo Nominal Conc. In Vitro
Honda4 Target Tissue Non-restrictive Total Mean Conc. In Vivo Nominal Conc. In Vitro

"Honda1" uses plasma concentration, restrictive clearance, and treats the unbound invivo concentration as bioactive. For IVIVE, any input nominal concentration in vitro should be converted to cfree.invitro using armitage_eval, otherwise performance will be the same as "Honda2".

Value

Steady state plasma concentration in specified units

Author(s)

Robert Pearce, John Wambaugh, Greg Honda, Miyuki Breen

References

Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

See Also

calc_css

Examples

calc_analytic_css(chem.name='Bisphenol-A',output.units='mg/L',
                 model='3compartment',concentration='blood')


calc_analytic_css(chem.name='Bisphenol-A',tissue='liver',species='rabbit',
                 parameterize.args = list(
                                default.to.human=TRUE,
                                adjusted.Funbound.plasma=TRUE,
                                regression=TRUE,
                                minimum.Funbound.plasma=1e-4),daily.dose=2)

calc_analytic_css(chem.name="bisphenol a",model="1compartment")

calc_analytic_css(chem.cas="80-05-7",model="3compartmentss")

params <- parameterize_pbtk(chem.cas="80-05-7") 

calc_analytic_css(parameters=params,model="pbtk")

# Try various chemicals with differing parameter sources/issues:
calc_analytic_css(chem.name="Betaxolol")
calc_analytic_css(chem.name="Tacrine",model="pbtk")
calc_analytic_css(chem.name="Dicofol",model="1compartment")
calc_analytic_css(chem.name="Diflubenzuron",model="3compartment")
calc_analytic_css(chem.name="Theobromine",model="3compartmentss")

Calculate the analytic steady state concentration for the one compartment model.

Description

This function calculates the analytic steady state plasma or venous blood concentrations as a result of infusion dosing.

Usage

calc_analytic_css_1comp(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  dosing = list(daily.dose = 1),
  hourly.dose = NULL,
  dose.units = "mg",
  concentration = "plasma",
  suppress.messages = FALSE,
  recalc.blood2plasma = FALSE,
  tissue = NULL,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  Caco2.options = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk (for model = 'pbtk'), parameterize_3comp (for model = '3compartment), parameterize_1comp(for model = '1compartment') or parameterize_steadystate (for model = '3compartmentss'), overrides chem.name and chem.cas.
dosing

List of dosing metrics used in simulation, which includes the namesake entries of a model's associated dosing.params. For steady-state calculations this is likely to be either "daily.dose" for oral exposures or "Cinhaled" for inhalation.
hourly.dose

Hourly dose rate mg/kg BW/h.
dose.units

The units associated with the dose received.
concentration

Desired concentration type, 'blood' or default 'plasma'.
suppress.messages

Whether or not the output message is suppressed.
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters. Use this if you have altered hematocrit, Funbound.plasma, or Krbc2pu.
tissue

Desired tissue conentration (defaults to whole body concentration.)
restrictive.clearance

If TRUE (default), then only the fraction of chemical not bound to protein is available for metabolism in the liver. If FALSE, then all chemical in the liver is metabolized (faster metabolism due to rapid off-binding).
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Additional parameters passed to parameterize function if parameters is NULL.

Value

Steady state plasma concentration in mg/L units

Author(s)

Robert Pearce and John Wambaugh

See Also

calc_analytic_css

parameterize_1comp

Calculate the analytic steady state concentration for model 3compartment

Description

This function calculates the analytic steady state plasma or blood concentrations as a result of constant oral infusion dosing. The three compartment model (Pearce et al. 2017) describes the amount of chemical in three key tissues of the body: the liver, the portal vein (essentially, oral absorption from the gut), and a systemic compartment ("sc") representing the rest of the body. See solve_3comp for additional details. The analytical steady-state solution for the the three compartment model is:

Cplasmass=dosefup∗QGFR+Clh+ClhQlfupRb:pQGFRC^{ss}_{plasma} = \frac{dose}{f_{up}*Q_{GFR} + Cl_{h} + \frac{Cl_{h}}{Q_{l}}\frac{f_{up}}{R_{b:p}}Q_{GFR}}

Cbloodss=Rb:p∗CplasmassC^{ss}_{blood} = R_{b:p}*C^{ss}_{plasma}

where Q_GFR is the glomerular filtration rate in the kidney, Q_l is the total liver blood flow (hepatic artery plus total vein), Cl_h is the chemical-specific whole liver metabolism clearance (scaled up from intrinsic clearance, which does not depend on flow), f_up is the chemical-specific fraction unbound in plasma, R_b:p is the chemical specific ratio of concentrations in blood:plasma.

Usage

calc_analytic_css_3comp(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  dosing = list(daily.dose = 1),
  hourly.dose = NULL,
  dose.units = "mg",
  concentration = "plasma",
  suppress.messages = FALSE,
  recalc.blood2plasma = FALSE,
  tissue = NULL,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  Caco2.options = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk (for model = 'pbtk'), parameterize_3comp (for model = '3compartment), parameterize_1comp(for model = '1compartment') or parameterize_steadystate (for model = '3compartmentss'), overrides chem.name and chem.cas.
dosing

List of dosing metrics used in simulation, which includes the namesake entries of a model's associated dosing.params. For steady-state calculations this is likely to be either "daily.dose" for oral exposures or "Cinhaled" for inhalation.
hourly.dose

Hourly dose rate mg/kg BW/h.
dose.units

The units associated with the dose received.
concentration

Desired concentration type, 'blood' or default 'plasma'.
suppress.messages

Whether or not the output message is suppressed.
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters. Use this if you have altered hematocrit, Funbound.plasma, or Krbc2pu.
tissue

Desired tissue conentration (defaults to whole body concentration.)
restrictive.clearance

If TRUE (default), then only the fraction of chemical not bound to protein is available for metabolism in the liver. If FALSE, then all chemical in the liver is metabolized (faster metabolism due to rapid off-binding).
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Additional parameters passed to parameterize function if parameters is NULL.

Value

Steady state plasma concentration in mg/L units

Author(s)

Robert Pearce and John Wambaugh

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

calc_analytic_css

parameterize_3comp

Calculate the analytic steady state concentration for the three compartment steady-state model

Description

This function calculates the steady state plasma or venous blood concentrations as a result of constant oral infusion dosing. The equation, initally used for high throughput in vitro-in vivo extrapolation in (Rotroff et al. 2010) and later given in (Wetmore et al. 2012), assumes that the concentration is the inverse of the total clearance, which is the sum of hepatic metabolism and renal filatrion:

Cplasmass=dosefup∗QGFR+ClhC^{ss}_{plasma} = \frac{dose}{f_{up}*Q_{GFR}+Cl_{h}}

Cbloodss=Rb:p∗CplasmassC^{ss}_{blood} = R_{b:p}*C^{ss}_{plasma}

where Q_GFR is the glomerular filtration rate in the kidney, Cl_h is the chemical-specific whole liver metabolism clearance (scaled up from intrinsic clearance, which does not depend on flow), f_up is the chemical-specific fraction unbound in plasma, R_b:p is the chemical specific ratio of concentrations in blood:plasma.

Usage

calc_analytic_css_3compss(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  dosing = list(daily.dose = 1),
  hourly.dose = NULL,
  dose.units = "mg",
  concentration = "plasma",
  suppress.messages = FALSE,
  recalc.blood2plasma = FALSE,
  tissue = NULL,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  Caco2.options = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk (for model = 'pbtk'), parameterize_3comp (for model = '3compartment), parameterize_1comp(for model = '1compartment') or parameterize_steadystate (for model = '3compartmentss'), overrides chem.name and chem.cas.
dosing

List of dosing metrics used in simulation, which includes the namesake entries of a model's associated dosing.params. For steady-state calculations this is likely to be either "daily.dose" for oral exposures or "Cinhaled" for inhalation.
hourly.dose

Hourly dose rate mg/kg BW/h.
dose.units

The units associated with the dose received.
concentration

Desired concentration type, 'blood' or default 'plasma'.
suppress.messages

Whether or not the output message is suppressed.
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters. Use this if you have 'altered hematocrit, Funbound.plasma, or Krbc2pu.
tissue

Desired tissue concentration (defaults to whole body concentration.)
restrictive.clearance

If TRUE (default), then only the fraction of chemical not bound to protein is available for metabolism in the liver. If FALSE, then all chemical in the liver is metabolized (faster metabolism due to rapid off-binding).
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Additional parameters passed to parameterize function if parameters is NULL.

Details

This equation is a simplification of the steady-state plasma concentration in the three-comprtment model (see solve_3comp), neglecting a higher order term that causes this Css to be higher for very rapidly cleared chemicals.

Value

Steady state plasma concentration in mg/L units

Author(s)

Robert Pearce and John Wambaugh

References

Rotroff DM, Wetmore BA, Dix DJ, Ferguson SS, Clewell HJ, Houck KA, LeCluyse EL, Andersen ME, Judson RS, Smith CM, others (2010). “Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening.” Toxicological Sciences, 117(2), 348–358.

Wetmore BA, Wambaugh JF, Ferguson SS, Sochaski MA, Rotroff DM, Freeman K, Clewell III HJ, Dix DJ, Andersen ME, Houck KA, others (2012). “Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment.” Toxicological Sciences, 125(1), 157–174.

See Also

calc_analytic_css

parameterize_steadystate

Calculate the analytic steady state plasma concentration for model pbtk.

Description

This function calculates the analytic steady state concentration (mg/L) as a result of constant oral infusion dosing. Concentrations are returned for plasma by default, but various tissues or blood concentrations can also be given as specified.

Usage

calc_analytic_css_pbtk(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  dosing = list(daily.dose = 1),
  hourly.dose = NULL,
  dose.units = "mg",
  concentration = "plasma",
  suppress.messages = FALSE,
  recalc.blood2plasma = FALSE,
  tissue = NULL,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  Caco2.options = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk (for model = 'pbtk'), parameterize_3comp (for model = '3compartment), parameterize_1comp(for model = '1compartment') or parameterize_steadystate (for model = '3compartmentss'), overrides chem.name and chem.cas.
dosing

List of dosing metrics used in simulation, which includes the namesake entries of a model's associated dosing.params. For steady-state calculations this is likely to be either "daily.dose" for oral exposures or "Cinhaled" for inhalation.
hourly.dose

Hourly dose rate mg/kg BW/h.
dose.units

The units associated with the dose received.
concentration

Desired concentration type, 'blood', 'tissue', or default 'plasma'.
suppress.messages

Whether or not the output message is suppressed.
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters. Use this if you have altered hematocrit, Funbound.plasma, or Krbc2pu.
tissue

Desired tissue conentration (defaults to whole body concentration.)
restrictive.clearance

If TRUE (default), then only the fraction of chemical not bound to protein is available for metabolism in the liver. If FALSE, then all chemical in the liver is metabolized (faster metabolism due to rapid off-binding).
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Additional parameters passed to parameterize function if parameters is NULL.

Details

The PBTK model (Pearce et al. 2017) predicts the amount of chemical in various tissues of the body. A system of ordinary differential equations describes how the amounts in each tissue change as a function of time. The analytic steady-state equation was found by algebraically solving for the tissue concentrations that result in each equation being zero – thus determining the concentration at which there is no change over time as the result of a fixed infusion dose rate.

The analytical solution is:

Cvenss=doserate∗Qliver+QgutfupRb:p∗Clmetabolism+(Qliver+Qgut)Qcardiac−(Qliver+Qgut)2fupRb:p∗Clmetabolism+(Qliver+Qgut)−(Qkidney)2fupRb:p∗QGFR+Qkideny−QrestC^{ss}_{ven} = \frac{dose rate * \frac{Q_{liver} + Q_{gut}}{\frac{f_{up}}{R_{b:p}}*Cl_{metabolism} + (Q_{liver}+Q_{gut})}}{Q_{cardiac} - \frac{(Q_{liver} + Q_{gut})^2}{\frac{f_{up}}{R_{b:p}}*Cl_{metabolism} + (Q_{liver}+Q_{gut})} - \frac{(Q_{kidney})^2}{\frac{f_{up}}{R_{b:p}}*Q_{GFR}+Q_{kideny}}-Q_{rest}}

Cplasmass=CvenssRb:pC^{ss}_{plasma} = \frac{C^{ss}_{ven}}{R_{b:p}}

Ctissuess=Ktissue:fuplasma∗fupRb:p∗CvenssC^{ss}_{tissue} = \frac{K_{tissue:fuplasma}*f_{up}}{R_{b:p}}*C^{ss}_{ven}

where Q_cardiac is the cardiac output, Q_gfr is the glomerular filtration rate in the kidney, other Q's indicate blood flows to various tissues, Cl_metabolism is the chemical-specific whole liver metabolism clearance, f_up is the chemical-specific fraction unbound in plasma, R_b2p is the chemical specific ratio of concentrations in blood:plasma, K_tissue2fuplasma is the chemical- and tissue-specific equilibrium partition coefficient and dose rate has units of mg/kg/day.

Value

Steady state plasma concentration in mg/L units

Author(s)

Robert Pearce and John Wambaugh

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

calc_analytic_css

parameterize_pbtk

Find the steady state concentration and the day it is reached.

Description

This function finds the day a chemical comes within the specified range of the analytical steady state venous blood or plasma concentration(from calc_analytic_css) for the multiple compartment, three compartment, and one compartment models, the fraction of the true steady state value reached on that day, the maximum concentration, and the average concentration at the end of the simulation.

Usage

calc_css(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  f = 0.01,
  daily.dose = 1,
  doses.per.day = 3,
  dose.units = "mg/kg",
  route = "oral",
  days = 21,
  output.units = "uM",
  suppress.messages = FALSE,
  tissue = NULL,
  model = "pbtk",
  default.to.human = FALSE,
  f.change = 1e-05,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  well.stirred.correction = TRUE,
  restrictive.clearance = TRUE,
  dosing = NULL,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or parameters must be specified.
chem.cas

Either the chemical name, CAS number, or parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk function, overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
f

Fractional distance from the final steady state concentration that the average concentration must come within to be considered at steady state.
daily.dose

Total daily dose, mg/kg BW.
doses.per.day

Number of oral doses per day.
dose.units

The units associated with the dose received.
route

Route of exposure (either "oral", "iv", or "inhalation" default "oral").
days

Initial number of days to run simulation that is multiplied on each iteration.
output.units

Units for returned concentrations, defaults to uM (specify units = "uM") but can also be mg/L.
suppress.messages

Whether or not to suppress messages.
tissue

Desired tissue concentration (default value is NULL, will depend on model – see steady.state.compartment in model.info file for further details.)
model

Model used in calculation, 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, and '1compartment' for the one compartment model.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
f.change

Fractional change of daily steady state concentration reached to stop calculating.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
well.stirred.correction

Uses correction in calculation of hepatic clearance for well-stirred model if TRUE for model 1compartment elimination rate. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
dosing

The dosing object for more complicated scenarios. Defaults to repeated daily.dose spread out over doses.per.day
...

Additional arguments passed to model solver (default of solve_pbtk).

Value

frac

Ratio of the mean concentration on the day steady state is reached (baed on doses.per.day) to the analytical Css (based on infusion dosing).
max

The maximum concentration of the simulation.
avg

The average concentration on the final day of the simulation.
the.day

The day the average concentration comes within 100 * p percent of the true steady state concentration.

Author(s)

Robert Pearce, John Wambaugh

See Also

calc_analytic_css

Examples

calc_css(chem.name='Bisphenol-A',doses.per.day=5,f=.001,output.units='mg/L')


parms <- parameterize_3comp(chem.name='Bisphenol-A')
parms$Funbound.plasma <- .07
calc_css(chem.name='Bisphenol-A',parameters=parms,model='3compartment')

out <- solve_pbtk(chem.name = "Bisphenol A",
  days = 50,                                   
  daily.dose=1,
  doses.per.day = 3)
plot.data <- as.data.frame(out)

css <- calc_analytic_css(chem.name = "Bisphenol A")
library("ggplot2")
c.vs.t <- ggplot(plot.data,aes(time, Cplasma)) + geom_line() +
geom_hline(yintercept = css) + ylab("Plasma Concentration (uM)") +
xlab("Day") + theme(axis.text = element_text(size = 16), axis.title =
element_text(size = 16), plot.title = element_text(size = 17)) +
ggtitle("Bisphenol A")

print(c.vs.t)

Calculate the distribution coefficient

Description

This function estimates the ratio of the equilibrium concentrations of a compound in octanol and water, taking into account the charge of the compound. Given the pH, we assume the neutral (uncharged) fraction of compound partitions according to the hydrophobicity (Pow). We assume that only a fraction alpha (defaults to 0.001 – Schmitt (2008)) of the charged compound partitions into lipid (octanol): Dow = Pow*(Fneutral + alpha*Fcharged) Fractions charged are calculated according to hydrogen ionization equilibria (pKa_Donor, pKa_Accept) using calc_ionization.

Usage

calc_dow(
  Pow = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  pH = NULL,
  pKa_Donor = NULL,
  pKa_Accept = NULL,
  fraction_charged = NULL,
  alpha = 0.001
)

Arguments

Pow

Octanol:water partition coefficient (ratio of concentrations)
chem.cas

Either the chemical name or the CAS number must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from a parameterize_MODEL function, overrides chem.name and chem.cas.
pH

pH where ionization is evaluated.
pKa_Donor

Compound H dissociation equilibirum constant(s). Overwrites chem.name and chem.cas.
pKa_Accept

Compound H association equilibirum constant(s). Overwrites chem.name and chem.cas.
fraction_charged

Fraction of chemical charged at the given pH
alpha

Ratio of Distribution coefficient D of totally charged species and that of the neutral form

Value

Distribution coefficient (numeric)

Author(s)

Robert Pearce and John Wambaugh

References

Schmitt, Walter. "General approach for the calculation of tissue to plasma partition coefficients." Toxicology in vitro 22.2 (2008): 457-467.

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of Pharmacokinetics and Pharmacodynamics 44.6 (2017): 549-565.

Strope, Cory L., et al. "High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling." Science of The Total Environment 615 (2018): 150-160.

See Also

calc_ionization

Calculate the elimination rate for a one compartment model

Description

This function calculates an elimination rate from the three compartment steady state model where elimination is entirely due to metablism by the liver and glomerular filtration in the kidneys.

Usage

calc_elimination_rate(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  suppress.messages = TRUE,
  default.to.human = FALSE,
  restrictive.clearance = TRUE,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  regression = TRUE,
  well.stirred.correction = TRUE,
  clint.pvalue.threshold = 0.05,
  minimum.Funbound.plasma = 1e-04
)

Arguments

chem.cas

Either the cas number or the chemical name must be specified.
chem.name

Either the chemical name or the cas number must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_steadystate or 1compartment function, overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
suppress.messages

Whether or not the output message is suppressed.
default.to.human

Substitutes missing animal values with human values if true.
restrictive.clearance

In calculating elimination rate, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
regression

Whether or not to use the regressions in calculating partition coefficients.
well.stirred.correction

Uses correction in calculation of hepatic clearance for -stirred model if TRUE. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).

Details

Elimination rate calculated by dividing the total clearance (using the default -stirred hepatic model) by the volume of distribution. When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

Elimination rate

Units of 1/h.

Author(s)

John Wambaugh

References

Schmitt, Walter. "General approach for the calculation of tissue to plasma partition coefficients." Toxicology in vitro 22.2 (2008): 457-467.

Dawson DE, Ingle BL, Phillips KA, Nichols JW, Wambaugh JF, Tornero-Velez R (2021). “Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.” Environmental Science & Technology, 55(9), 6505-6517. doi:10.1021/acs.est.0c06117, PMID: 33856768, https://doi.org/10.1021/acs.est.0c06117.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

Examples

calc_elimination_rate(chem.name="Bisphenol A")
## Not run: 
calc_elimination_rate(chem.name="Bisphenol A",species="Rat")
calc_elimination_rate(chem.cas="80-05-7")

## End(Not run)

Functions for calculating the bioavaialble fractions from oral doses

Description

These functions calculate the fraction of chemical absorbed from the gut based upon in vitro measured Caco-2 membrane permeability data. Caco-2 permeabilities (10−610^{-6} cm/s) are related to effective permeability based on Yang et al. (2007). These functions calculate the fraction absorbed (calc_fabs.oral – S Darwich et al. (2010) and Yu and Amidon (1999)), the fraction surviving first pass gut metabolism (calc_fgut.oral), and the overall systemic oral bioavailability (calc_fbio.oral). Note that the first pass hepatic clearance is calculated within the parameterization and other functions. using calc_hep_bioavailability Absorption rate is calculated according to Fick's law (LennernÄs (1997)) assuming low blood concentrations.

Usage

calc_fbio.oral(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  suppress.messages = FALSE,
  restrictive.clearance = FALSE
)

calc_fabs.oral(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  suppress.messages = FALSE,
  Caco2.Pab.default = 1.6
)

calc_peff(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  suppress.messages = FALSE,
  Caco2.Pab = NULL
)

calc_kgutabs(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  suppress.messages = FALSE
)

calc_fgut.oral(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  suppress.messages = FALSE,
  Caco2.Pab.default = 1.6
)

Arguments

parameters

(List) A list of the parameters (Caco2.Pab, Funbound.Plasma, Rblood2plasma, Clint, BW, Qsmallintestine, Fabs, Fgut) used in the calculation, either supplied by user or calculated in parameterize_steadystate.
chem.cas

(Character) Chemical CAS number. (Defaults to 'NULL'.) (Note: Either the chemical name, CAS number, or EPA's DSSTox Structure ID must be specified).
chem.name

(Character) Chemical name. (Defaults to 'NULL'.) (Note: Either the chemical name, CAS number, or EPA's DSSTox Structure ID must be specified).
dtxsid

(Character) EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard). (Defaults to 'NULL'.) (Note: Either the chemical name, CAS number, or EPA's DSSTox Structure ID must be specified).
species

(Character) Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

(Logical) Substitutes missing rat values with human values if TRUE. (Not applicable for 'calc_fabs.oral'.) (Defaults to 'FALSE'.)
suppress.messages

(Logical) Whether or not the output message is suppressed. (Defaults to 'FALSE'.)
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
Caco2.Pab.default

(Numeric) Caco2 apical to basolateral data. (Defaults to 1.6.) (Not applicable for 'calc_fbio.oral'.)
Caco2.Pab

(Numeric) Caco2 apical to basolaterial permeability used by calc_peff

Details

We assume that systemic oral bioavailability (FbioF_{bio}) consists of three components: (1) the fraction of chemical absorbed from intestinal lumen into enterocytes (FabsF_{abs}), (2) the fraction surviving intestinal metabolism (FgutF_{gut}), and (3) the fraction surviving first-pass hepatic metabolism (FhepF_{hep}). This function returns (Fabs∗FgutF_{abs}*F_{gut}).

We model systemic oral bioavailability as Fbio=Fabs∗Fgut∗FhepF_{bio}=F_{abs}*F_{gut}*F_{hep}. FhepF_{hep} is estimated from in vitro TK data using calc_hep_bioavailability. If FbioF_{bio} has been measured in vivo and is found in table chem.physical_and_invitro.data then we set Fabs∗FgutF_{abs}*F_{gut} to the measured value divided by FhepF_{hep}. Otherwise, if Caco2 membrane permeability data or predictions are available FabsF_{abs} is estimated using calc_fgut.oral. Intrinsic hepatic metabolism is used to very roughly estimate (FgutF_{gut}) using calc_fgut.oral. If argument keepit100 is used then there is complete absorption from the gut (that is, Fabs=Fgut=1F_{abs}=F_{gut}=1).

Value

fbio.oral

Oral bioavailability, the fraction of oral dose reaching systemic distribution in the body.
fabs.oral

Fraction of dose absorbed, i.e. the fraction of the dose that enters the gutlumen.
fgut.oral

Fraction of chemical surviving first pass metabolism in the gut.
fhep.oral

Fraction of chemical surviving first pass hepatic clearance.
kgutabs

Rate of absorption from gut (1/h).

Functions

  • calc_fabs.oral(): Calculate the fraction absorbed in the gut (Darwich et al., 2010)

  • calc_peff(): Calculate the effective gut permeability rate (10^-4 cm/s)

  • calc_kgutabs(): Calculate the gut absorption rate (1/h)

  • calc_fgut.oral(): Calculate the fraction of chemical surviving first pass metabolism in the gut

Author(s)

Gregory Honda and John Wambaugh

References

S Darwich A, Neuhoff S, Jamei M, Rostami-Hodjegan A (2010). “Interplay of metabolism and transport in determining oral drug absorption and gut wall metabolism: a simulation assessment using the 'Advanced Dissolution, Absorption, Metabolism (ADAM)' model.” Current drug metabolism, 11(9), 716–729. Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A (2007). “Prediction of intestinal first-pass drug metabolism.” Current drug metabolism, 8(7), 676–684. Honda G, Kenyon EM, Davidson-Fritz SE, Dinallo R, El-Masri H, Korel-Bexell E, Li L, Paul-Friedman K, Pearce R, Sayre R, Strock C, Thomas R, Wetmore BA, Wambaugh JF (2023). “Impact of Gut Permeability on Estimation of Oral Bioavailability for Chemicals in Commerce and the Environment.” Unpublished. Yu LX, Amidon GL (1999). “A compartmental absorption and transit model for estimating oral drug absorption.” International journal of pharmaceutics, 186(2), 119–125. LennernÄs H (1997). “Human jejunal effective permeability and its correlation with preclinical drug absorption models.” Journal of Pharmacy and Pharmacology, 49(7), 627–638.

Calculate maternal-fetal physiological parameters

Description

This function uses the equations from Kapraun (2019) to calculate chemical- independent physiological paramreters as a function of gestational age in weeks.

Usage

calc_fetal_phys(week = 12, ...)

Arguments

week

Gestational week
...

Additional arguments to parameterize_fetal_pbtk

Details

BW=prepregnantBW+BWcubictheta1∗tw+BWcubictheta2∗tw2+BWcubictheta3∗tw3BW = pre_pregnant_BW + BW_cubic_theta1 * tw + BW_cubic_theta2 * tw^2 + BW_cubic_theta3 * tw^3

Wadipose=Wadiposelineartheta0+Wadiposelineartheta1∗tw;Wadipose = Wadipose_linear_theta0 + Wadipose_linear_theta1 * tw ;

Wfkidney=0.001∗Wfkidneygompertztheta0∗exp(Wfkidneygompertztheta1/Wfkidneygompertztheta2∗(1−exp(−Wfkidneygompertztheta2∗tw)));Wfkidney = 0.001 * Wfkidney_gompertz_theta0 * exp ( Wfkidney_gompertz_theta1 / Wfkidney_gompertz_theta2 * ( 1 - exp ( - Wfkidney_gompertz_theta2 * tw ) ) ) ;

Wfthyroid=0.001∗Wfthyroidgompertztheta0∗exp(Wfthyroidgompertztheta1/Wfthyroidgompertztheta2∗(1−exp(−Wfthyroidgompertztheta2∗tw)));Wfthyroid = 0.001 * Wfthyroid_gompertz_theta0 * exp ( Wfthyroid_gompertz_theta1 / Wfthyroid_gompertz_theta2 * ( 1 - exp ( - Wfthyroid_gompertz_theta2 * tw ) ) ) ;

Wfliver=0.001∗Wflivergompertztheta0∗exp(Wflivergompertztheta1/Wflivergompertztheta2∗(1−exp(−Wflivergompertztheta2∗tw)));Wfliver = 0.001 * Wfliver_gompertz_theta0 * exp ( Wfliver_gompertz_theta1 / Wfliver_gompertz_theta2 * ( 1 - exp ( - Wfliver_gompertz_theta2 * tw ) ) ) ;

Wfbrain=0.001∗Wfbraingompertztheta0∗exp(Wfbraingompertztheta1/Wfbraingompertztheta2∗(1−exp(−Wfbraingompertztheta2∗tw)));Wfbrain = 0.001 * Wfbrain_gompertz_theta0 * exp ( Wfbrain_gompertz_theta1 / Wfbrain_gompertz_theta2 * ( 1 - exp ( - Wfbrain_gompertz_theta2 * tw ) ) ) ;

Wfgut=0.001∗Wfgutgompertztheta0∗exp(Wfgutgompertztheta1/Wfgutgompertztheta2∗(1−exp(−Wfgutgompertztheta2∗tw)));Wfgut = 0.001 * Wfgut_gompertz_theta0 * exp ( Wfgut_gompertz_theta1 / Wfgut_gompertz_theta2 * ( 1 - exp ( - Wfgut_gompertz_theta2 * tw ) ) ) ;

Wflung=0.001∗Wflunggompertztheta0∗exp(Wflunggompertztheta1/Wflunggompertztheta2∗(1−exp(−Wflunggompertztheta2∗tw)));Wflung = 0.001 * Wflung_gompertz_theta0 * exp ( Wflung_gompertz_theta1 / Wflung_gompertz_theta2 * ( 1 - exp ( - Wflung_gompertz_theta2 * tw ) ) ) ;

hematocrit=(hematocritquadratictheta0+hematocritquadratictheta1∗tw+hematocritquadratictheta2∗pow(tw,2))/100;hematocrit = ( hematocrit_quadratic_theta0 + hematocrit_quadratic_theta1 * tw + hematocrit_quadratic_theta2 * pow ( tw , 2 ) ) / 100 ;

Rblood2plasma=1−hematocrit+hematocrit∗Krbc2pu∗Fractionunboundplasma;Rblood2plasma = 1 - hematocrit + hematocrit * Krbc2pu * Fraction_unbound_plasma ;

fhematocrit=(fhematocritcubictheta1∗tw+fhematocritcubictheta2∗pow(tw,2)+fhematocritcubictheta3∗pow(tw,3))/100;fhematocrit = ( fhematocrit_cubic_theta1 * tw + fhematocrit_cubic_theta2 * pow ( tw , 2 ) + fhematocrit_cubic_theta3 * pow ( tw , 3 ) ) / 100 ;

Rfblood2plasma=1−fhematocrit+fhematocrit∗Kfrbc2pu∗Fractionunboundplasmafetus;Rfblood2plasma = 1 - fhematocrit + fhematocrit * Kfrbc2pu * Fraction_unbound_plasma_fetus ;

fBW=0.001∗fBWgompertztheta0∗exp(fBWgompertztheta1/fBWgompertztheta2∗(1−exp(−fBWgompertztheta2∗tw)));fBW = 0.001 * fBW_gompertz_theta0 * exp ( fBW_gompertz_theta1 / fBW_gompertz_theta2 * ( 1 - exp ( - fBW_gompertz_theta2 * tw ) ) ) ;

Vplacenta=0.001∗(Vplacentacubictheta1∗tw+Vplacentacubictheta2∗pow(tw,2)+Vplacentacubictheta3∗pow(tw,3));Vplacenta = 0.001 * ( Vplacenta_cubic_theta1 * tw + Vplacenta_cubic_theta2 * pow ( tw , 2 ) + Vplacenta_cubic_theta3 * pow ( tw , 3 ) ) ;

Vamnf=0.001∗Vamnflogistictheta0/(1+exp(−Vamnflogistictheta1∗(tw−Vamnflogistictheta2)));Vamnf = 0.001 * Vamnf_logistic_theta0 / ( 1 + exp ( - Vamnf_logistic_theta1 * ( tw - Vamnf_logistic_theta2 ) ) ) ;

Vplasma=Vplasmamodlogistictheta0/(1+exp(−Vplasmamodlogistictheta1∗(tw−Vplasmamodlogistictheta2)))+Vplasmamodlogistictheta3;Vplasma = Vplasma_mod_logistic_theta0 / ( 1 + exp ( - Vplasma_mod_logistic_theta1 * ( tw - Vplasma_mod_logistic_theta2 ) ) ) + Vplasma_mod_logistic_theta3 ;

Vrbcs=hematocrit/(1−hematocrit)∗Vplasma;Vrbcs = hematocrit / ( 1 - hematocrit ) * Vplasma ;

Vven=venousbloodfraction∗(Vrbcs+Vplasma);Vven = venous_blood_fraction * ( Vrbcs + Vplasma ) ;

Vart=arterialbloodfraction∗(Vrbcs+Vplasma);Vart = arterial_blood_fraction * ( Vrbcs + Vplasma ) ;

Vadipose=1/adiposedensity∗Wadipose;Vadipose = 1 / adipose_density * Wadipose ;

Vffmx=1/ffmxdensity∗(BW−Wadipose−(fBW+placentadensity∗Vplacenta+amnfdensity∗Vamnf));Vffmx = 1 / ffmx_density * ( BW - Wadipose - ( fBW + placenta_density * Vplacenta + amnf_density * Vamnf ) ) ;

Vallx=Vart+Vven+Vthyroid+Vkidney+Vgut+Vliver+Vlung;Vallx = Vart + Vven + Vthyroid + Vkidney + Vgut + Vliver + Vlung ;

Vrest=Vffmx−Vallx;Vrest = Vffmx - Vallx ;

Vfart=0.001∗arterialbloodfraction∗fbloodweightratio∗fBW;Vfart = 0.001 * arterial_blood_fraction * fblood_weight_ratio * fBW ;

Vfven=0.001∗venousbloodfraction∗fbloodweightratio∗fBW;Vfven = 0.001 * venous_blood_fraction * fblood_weight_ratio * fBW ;

Vfkidney=1/kidneydensity∗Wfkidney;Vfkidney = 1 / kidney_density * Wfkidney ;

Vfthyroid=1/thyroiddensity∗Wfthyroid;Vfthyroid = 1 / thyroid_density * Wfthyroid ;

Vfliver=1/liverdensity∗Wfliver;Vfliver = 1 / liver_density * Wfliver ;

Vfbrain=1/braindensity∗Wfbrain;Vfbrain = 1 / brain_density * Wfbrain ;

Vfgut=1/gutdensity∗Wfgut;Vfgut = 1 / gut_density * Wfgut ;

Vflung=1/lungdensity∗Wflung;Vflung = 1 / lung_density * Wflung ;

Vfrest=fBW−(Vfart+Vfven+Vfbrain+Vfkidney+Vfthyroid+Vfliver+Vfgut+Vflung);Vfrest = fBW - ( Vfart + Vfven + Vfbrain + Vfkidney + Vfthyroid + Vfliver + Vfgut + Vflung ) ;

Qcardiac=24∗(Qcardiaccubictheta0+Qcardiaccubictheta1∗tw+Qcardiaccubictheta2∗pow(tw,2)+Qcardiaccubictheta3∗pow(tw,3));Qcardiac = 24 * ( Qcardiac_cubic_theta0 + Qcardiac_cubic_theta1 * tw + Qcardiac_cubic_theta2 * pow ( tw , 2 ) + Qcardiac_cubic_theta3 * pow ( tw , 3 ) ) ;

Qgut=0.01∗(Qgutpercentinitial+(Qgutpercentterminal−Qgutpercentinitial)/term∗tw)∗Qcardiac;Qgut = 0.01 * ( Qgut_percent_initial + ( Qgut_percent_terminal - Qgut_percent_initial ) / term * tw ) * Qcardiac ;

Qkidney=24∗(Qkidneycubictheta0+Qkidneycubictheta1∗tw+Qkidneycubictheta2∗pow(tw,2)+Qkidneycubictheta3∗pow(tw,3));Qkidney = 24 * ( Qkidney_cubic_theta0 + Qkidney_cubic_theta1 * tw + Qkidney_cubic_theta2 * pow ( tw , 2 ) + Qkidney_cubic_theta3 * pow ( tw , 3 ) ) ;

Qliver=0.01∗(Qliverpercentinitial+(Qliverpercentterminal−Qliverpercentinitial)/term∗tw)∗Qcardiac;Qliver = 0.01 * ( Qliver_percent_initial + ( Qliver_percent_terminal - Qliver_percent_initial ) / term * tw ) * Qcardiac ;

Qthyroid=0.01∗(Qthyroidpercentinitial+(Qthyroidpercentterminal−Qthyroidpercentterminal)/term∗tw)∗Qcardiac;Qthyroid = 0.01 * ( Qthyroid_percent_initial + ( Qthyroid_percent_terminal - Qthyroid_percent_terminal ) / term * tw ) * Qcardiac ;

Qplacenta=24∗Qplacentalineartheta1∗1000∗Vplacenta;Qplacenta = 24 * Qplacenta_linear_theta1 * 1000 * Vplacenta ;

Qadipose=0.01∗(Qadiposepercentinitial+(Qadiposepercentterminal−Qadiposepercentinitial)/term∗tw)∗Qcardiac;Qadipose = 0.01 * ( Qadipose_percent_initial + ( Qadipose_percent_terminal - Qadipose_percent_initial ) / term * tw ) * Qcardiac ;

Qrest=Qcardiac−(Qgut+Qkidney+Qliver+Qthyroid+Qplacenta+Qadipose);Qrest = Qcardiac - ( Qgut + Qkidney + Qliver + Qthyroid + Qplacenta + Qadipose ) ;

Qgfr=60∗24∗0.001∗(Qgfrquadratictheta0+Qgfrquadratictheta1∗tw+Qgfrquadratictheta2∗pow(tw,2));Qgfr = 60 * 24 * 0.001 * ( Qgfr_quadratic_theta0 + Qgfr_quadratic_theta1 * tw + Qgfr_quadratic_theta2 * pow ( tw , 2 ) ) ;

Qfrvtl=60∗24∗0.001∗Qfrvtllogistictheta0/(1+exp(−Qfrvtllogistictheta1∗(tw−Qfrvtllogistictheta2)));Qfrvtl = 60 * 24 * 0.001 * Qfrvtl_logistic_theta0 / ( 1 + exp ( - Qfrvtl_logistic_theta1 * ( tw - Qfrvtl_logistic_theta2 ) ) ) ;

Qflvtl=60∗24∗0.001∗Qflvtllogistictheta0/(1+exp(−Qflvtllogistictheta1∗(tw−Qflvtllogistictheta2)));Qflvtl = 60 * 24 * 0.001 * Qflvtl_logistic_theta0 / ( 1 + exp ( - Qflvtl_logistic_theta1 * ( tw - Qflvtl_logistic_theta2 ) ) ) ;

Qfda=60∗24∗0.001∗Qfdalogistictheta0/(1+exp(−Qfdalogistictheta1∗(tw−Qfdalogistictheta2)));Qfda = 60 * 24 * 0.001 * Qfda_logistic_theta0 / ( 1 + exp ( - Qfda_logistic_theta1 * ( tw - Qfda_logistic_theta2 ) ) ) ;

Qfartb=Qflvtl+Qfda;Qfartb = Qflvtl + Qfda ;

Qfcardiac=Qfartb;Qfcardiac = Qfartb ;

Qflung=Qfrvtl−Qfda;Qflung = Qfrvtl - Qfda ;

Qfplacenta=60∗24∗0.001∗Qfplacentalogistictheta0/(1+exp(−Qfplacentalogistictheta1∗(tw−Qfplacentalogistictheta2)));Qfplacenta = 60 * 24 * 0.001 * Qfplacenta_logistic_theta0 / ( 1 + exp ( - Qfplacenta_logistic_theta1 * ( tw - Qfplacenta_logistic_theta2 ) ) ) ;

Qfdv=60∗24∗0.001∗Qfdvgompertztheta0∗exp(Qfdvgompertztheta1/Qfdvgompertztheta2∗(1−exp(−Qfdvgompertztheta2∗tw)));Qfdv = 60 * 24 * 0.001 * Qfdv_gompertz_theta0 * exp ( Qfdv_gompertz_theta1 / Qfdv_gompertz_theta2 * ( 1 - exp ( - Qfdv_gompertz_theta2 * tw ) ) ) ;

Qfgut=Qfgutpercent/Qfnonplacentalpercent∗(1−Qfplacenta/Qfartb)∗Qfartb;Qfgut = Qfgut_percent / Qfnonplacental_percent * ( 1 - Qfplacenta / Qfartb ) * Qfartb ;

Qfkidney=Qfkidneypercent/Qfnonplacentalpercent∗(1−Qfplacenta/Qfartb)∗Qfartb;Qfkidney = Qfkidney_percent / Qfnonplacental_percent * ( 1 - Qfplacenta / Qfartb ) * Qfartb ;

Qfbrain=Qfbrainpercent/Qfnonplacentalpercent∗(1−Qfplacenta/Qfartb)∗Qfartb;Qfbrain = Qfbrain_percent / Qfnonplacental_percent * ( 1 - Qfplacenta / Qfartb ) * Qfartb ;

Qfliver=Qfliverpercent/(100−(Qbrainpercent+Qkidneypercent+Qgutpercent))∗(1−(Qfbrainpercent+Qfkidneypercent+Qfgutpercent)/Qfnonplacentalpercent)∗(1−Qfplacenta/Qfartb)∗Qfartb;Qfliver = Qfliver_percent / ( 100 - ( Qbrain_percent + Qkidney_percent + Qgut_percent ) ) * ( 1 - ( Qfbrain_percent + Qfkidney_percent + Qfgut_percent ) / Qfnonplacental_percent ) * ( 1 - Qfplacenta / Qfartb ) * Qfartb ;

Qfthyroid=Qfthyroidpercent/(100−(Qbrainpercent+Qkidneypercent+Qgutpercent))∗(1−(Qfbrainpercent+Qfkidneypercent+Qfgutpercent)/Qfnonplacentalpercent)∗(1−Qfplacenta/Qfartb)∗Qfartb;Qfthyroid = Qfthyroid_percent / ( 100 - ( Qbrain_percent + Qkidney_percent + Qgut_percent ) ) * ( 1 - ( Qfbrain_percent + Qfkidney_percent + Qfgut_percent ) / Qfnonplacental_percent ) * ( 1 - Qfplacenta / Qfartb ) * Qfartb ;

Qfrest=Qfcardiac−(Qfplacenta+Qfgut+Qfliver+Qfthyroid+Qfkidney+Qfbrain);Qfrest = Qfcardiac - ( Qfplacenta + Qfgut + Qfliver + Qfthyroid + Qfkidney + Qfbrain ) ;

Qfbypass=Qfcardiac−Qflung;Qfbypass = Qfcardiac - Qflung ;

Value

list containing:

BW

Maternal body weight, kg
Wadipose

Maternal adipose fraction of total weight
Wfkidney

Fetal kidney fraction of total weight
Wfthyroid

Fetal thyroid fraction of total weight
Wfliver

Fetal liver fraction of total weight
Wfbrain

Fetal brain fraction of total weight
Wfgut

Fetal gut fraction of total weight
Wflung

Fetal lung fraction of total weight
hematocrit

Maternal hematocrit fraction of blood
Rblood2plasma

Maternal Rblood2plasma
fhematocrit

Fetal hematocrit fraction of blood
Rfblood2plasma

Fetal Rfblood2plasma
fBW

Fetal body weight, kg
Vplacenta

Volume of Vplacenta, L
Vamnf

Volume of amniotic fluid, L
Vplasma

Maternal volume of plasma, L
Vrbcs

Maternal volume of red blood cells, L
Vven

Maternal volume of venous blood, L
Vart

Maternal volume of arterial blood, L
Vadipose

Maternal volume of adipose, L
Vffmx

Fetal volume ofVffmx, L
Vallx

Vallx, L
Vrest

Maternal volume of rest of body, L
Vfart

Fetal volume of arterial blood, L
Vfven

Fetal volume of venous blood, L
Vfkidney

Fetal volume of kidney, L
Vfthyroid

Fetal volume of thyroid, L
Vfliver

Fetal volume of liver, L
Vfbrain

Fetal volume of brain, L
Vfgut

Fetal volume of gut, L
Vflung

Fetal volume of lung, L
Vfrest

Fetal volume of rest of body, L
Qcardiac

Maternal cardiac output blood flow, L/day
Qgut

Maternal blood flow to gut, L/day
Qkidney

Maternal blood flow to kidney, L/day
Qliver

Maternal blood flow to liver, L/day
Qthyroid

Maternal blood flow to thyroid, L/day
Qplacenta

Maternal blood flow to placenta, L/day
Qadipose

Maternal blood flow to adipose, L/day
Qrest

Maternal blood flow to rest, L/day
Qgfr

Maternal glomerular filtration rate in kidney, L/day
Qfrvtl

Fetal blood flow to right ventricle, L/day
Qflvtl

Fetal blood flow to left ventircle, L/day
Qfda

Fetal blood flow to Qfda, L/day
Qfartb

Fetal blood flow to Qfartb, L/day
Qfcardiac

Fetal cardiac output blood flow, L/day
Qflung

Fetal blood flow to lung, L/day
Qfplacenta

Fetal blood flow to placenta, L/day
Qfdv

Fetal blood flow to Qfdv, L/day
Qfgut

Fetal blood flow to gut, L/day
Qfkidney

Fetal blood flow to kidney, L/day
Qfbrain

Fetal blood flow to brain, L/day
Qfliver

Fetal blood flow to liver, L/day
Qfthyroid

Fetal blood flow to thyroid, L/day
Qfrest

Fetal blood flow to rest, L/day
Qfbypass

Fetal blood flow to Qfbypass, L/day

Author(s)

John Wambaugh

References

Kapraun DF, Wambaugh JF, Setzer RW, Judson RS (2019). “Empirical models for anatomical and physiological changes in a human mother and fetus during pregnancy and gestation.” PLOS ONE, 14(5), 1-56. doi:10.1371/journal.pone.0215906.

Calculate the correction for lipid binding in plasma binding assay

Description

Poulin and Haddad (2012) observed "...that for a highly lipophilic compound, the calculated fup is by far [less than] the experimental values observed under in vitro conditions." Pearce et al. (2017) hypothesized that there was additional lipid binding in vivo that acted as a sink for lipophilic compounds, reducing the effective fup in vivo. It is possible that this is due to the binding of lipophilic compounds on the non plasma-side of the rapid equilibrium dialysis plates (Waters et al., 2008). Pearce et al. (2017) compared predicted and observed tissue partition coefficients for a range of compounds. They showed that predictions were improved by adding additional binding proportional to the distribution coefficient Dow (calc_dow) and the fractional volume of lipid in plasma (Flipid). We calculate Flipid as the sum of the physiological plasma neutral lipid fractional volume and 30 percent of the plasma neutral phospholipid fractional volume. We use values from Peyret et al. (2010) for rats and Poulin and Haddad (2012) for humans. The estimate of 30 percent of the neutral phospholipid volume as neutral lipid was used for simplictity's sake in place of our membrane affinity predictor. To account for additional binding to lipid, plasma to water partitioning (Kplasma:water = 1/fup) is increased as such: Kcorrectedplasma:water = 1/fcorrectedup = 1/fin vitroup + Dow*Flipid

Usage

calc_fup_correction(
  fup = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  Flipid = NULL,
  plasma.pH = 7.4,
  dow74 = NULL,
  species = "Human",
  default.to.human = FALSE,
  force.human.fup = FALSE,
  suppress.messages = FALSE
)

Arguments

fup

Fraction unbound in plasma, if provided this argument overides values from argument parameters and chem.physical_and_invitro.data
chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
Flipid

The fractional volume of lipid in plasma (from physiology.data)
plasma.pH

pH of plasma (default 7.4)
dow74

The octanol-water distribution ratio (DOW).
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing fraction of unbound plasma with human values if true.
force.human.fup

Returns human fraction of unbound plasma in calculation for rats if true. When species is specified as rabbit, dog, or mouse, the human unbound fraction is substituted.
suppress.messages

Whether or not the output message is suppressed.

Details

Note that octanal:water partitioning above 1:1,000,000 (LogDow > 6) are truncated at 1:1,000,000 because greater partitioning would likely take longer than protein binding assay itself.

Value

A numeric fraction unpbound in plasma between zero and one

Author(s)

John Wambaugh

References

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Peyret T, Poulin P, Krishnan K (2010). “A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals.” Toxicology and applied pharmacology, 249(3), 197–207.

Poulin P, Haddad S (2012). “Advancing prediction of tissue distribution and volume of distribution of highly lipophilic compounds from a simplified tissue-composition-based model as a mechanistic animal alternative method.” Journal of pharmaceutical sciences, 101(6), 2250–2261.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Waters, Nigel J., et al. "Validation of a rapid equilibrium dialysis approach for the measurement of plasma protein binding." Journal of pharmaceutical sciences 97.10 (2008): 4586-4595.

See Also

apply_fup_adjustment

calc_dow

Calculates the half-life for a one compartment model.

Description

This function calculates the half life from the three compartment steady state model where elimination is entirely due to metabolism by the liver and glomerular filtration in the kidneys.

Usage

calc_half_life(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  suppress.messages = TRUE,
  default.to.human = FALSE,
  restrictive.clearance = TRUE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  well.stirred.correction = TRUE,
  clint.pvalue.threshold = 0.05,
  minimum.Funbound.plasma = 1e-04
)

Arguments

chem.cas

Either the cas number or the chemical name must be specified.
chem.name

Either the chemical name or the cas number must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_steadystate or 1compartment function, overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
suppress.messages

Whether or not the output message is suppressed.
default.to.human

Substitutes missing animal values with human values if true.
restrictive.clearance

In calculating elimination rate, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
well.stirred.correction

Uses correction in calculation of hepatic clearance for -stirred model if TRUE. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).

Details

Half life is calculated by dividing the natural-log of 2 by the elimination rate from the one compartment model.

Value

Half life

Units of h.

Author(s)

Sarah E. Davidson

See Also

[calc_elimination_rate()] for the elimination rate calculation

Examples

calc_half_life(chem.name="Bisphenol A")

calc_half_life(chem.name="Bisphenol A",species="Rat")
calc_half_life(chem.cas="80-05-7")

Calculate first pass heaptic metabolism

Description

For models that don't described first pass blood flow from the gut, need to cacluate a hepatic bioavailability, that is, the fraction of chemical systemically available after metabolism during the first pass through the liver (Rowland, 1973 Equation 29, where k21 is blood flow through the liver and k23 is clearance from the liver in Figure 1 in that paper).

Usage

calc_hep_bioavailability(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  restrictive.clearance = TRUE,
  flow.34 = TRUE,
  suppress.messages = FALSE,
  species = "Human"
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
flow.34

A logical constraint
suppress.messages

Whether or not to suppress the output message.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").

Value

A data.table whose columns are the parameters of the HTTK model specified in model.

Author(s)

John Wambaugh

References

Rowland, Malcolm, Leslie Z. Benet, and Garry G. Graham. Rowland M, Benet LZ, Graham GG (1973). “Clearance concepts in pharmacokinetics.” Journal of pharmacokinetics and biopharmaceutics, 1(2), 123–136.

Calculate the hepatic clearance.

Description

This function calculates the hepatic clearance in plasma for using the "well-stirred" model by default. Other scaling options from (Ito and Houston 2004) are also available. Parameters for scaling from flow-free intrinsic-hepatic clearance to whole-liver metabolism rate are taken from (Carlile et al. 1997). In vitro measured hepatic clearace is corrected for estimated binding in the in vitro clearance assay using the model of (Kilford et al. 2008). The agument restrictive.clearance (defaults to TRUE) describes the significance (or lack thereof) of plasma protein binding in metabolism. Restrictive clearance assumes that only the free fraction of chemical in plasma is available for metabolism. Non-restrictive clearance assumes that the compound is weakly bound to plasma protein and any free chemical metabolized is instantly replaced. For non-restrictive clearance the effective fup = 1.

Usage

calc_hep_clearance(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  hepatic.model = "well-stirred",
  suppress.messages = FALSE,
  well.stirred.correction = TRUE,
  restrictive.clearance = TRUE,
  species = "Human",
  adjusted.Funbound.plasma = TRUE,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_steadystate function, overrides chem.name and chem.cas.
hepatic.model

Model used in calculating hepatic clearance, unscaled, parallel tube, dispersion, or default well-stirred.
suppress.messages

Whether or not to suppress the output message.
well.stirred.correction

Uses the (Yang et al. 2007) blood:plasma ratio correction in the calculation of hepatic clearance for well-stirred model if TRUE if argument hepatic.model = "well-stirred".
restrictive.clearance

If TRUE (default) the rate of metabolism is restricted to the unbound fraction of chemical. If FALSE the free fraction is set to 1 (that is, plasma protein binding is weak and metabolzied chemical is rapidly replaced)
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
adjusted.Funbound.plasma

Uses the (Pearce et al. 2017) lipid binding adjustment for Funbound.plasma (which also impacts partition coefficients such as blood:plasma ratio) when set to TRUE (Default).
...

Additional parameters passed to parameterize_steadystate if parameters is NULL.

Value

Hepatic Clearance

Units of L/h/kg BW.

Author(s)

John Wambaugh and Robert Pearce

References

Carlile DJ, Zomorodi K, Houston JB (1997). “Scaling factors to relate drug metabolic clearance in hepatic microsomes, isolated hepatocytes, and the intact liver: studies with induced livers involving diazepam.” Drug metabolism and disposition, 25(8), 903–911.

Ito K, Houston JB (2004). “Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes.” Pharmaceutical research, 21, 785–792.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Yang J, Jamei M, Yeo KR, Rostami-Hodjegan A, Tucker GT (2007). “Misuse of the well-stirred model of hepatic drug clearance.” Drug Metabolism and Disposition, 35(3), 501–502.

Examples

calc_hep_clearance(chem.name="Ibuprofen",hepatic.model='unscaled')
calc_hep_clearance(chem.name="Ibuprofen",well.stirred.correction=FALSE)

Calculate the free chemical in the hepaitic clearance assay

Description

This function uses the method from Kilford et al. (2008) to calculate the fraction of unbound chemical in the hepatocyte intrinsic clearance assay. The bound chemical is presumed to be unavailable during the performance of the assay, so this fraction can be used to increase the apparent clearance rate to better estimate in vivo clearance. For bases, the fraction of chemical unbound in hepatocyte clearance assays (fuhep) is calculated in terms of logPow but for neutrual and acidic compounds we use logDow (from calc_dow). Here we denote the appropriate partition coefficient as "logP/D". Kilford et al. (2008) calculates fuhep = 1/(1 + 125*VR*10^(0.072*logP/D2 + 0.067*logP/D-1.126))

Usage

calc_hep_fu(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  Vr = 0.005,
  pH = 7.4
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
Vr

Ratio of cell volume to incubation volume. Default (0.005) is taken from
pH

pH of the incupation medium.

Details

Note that octanal:water partitioning above 1:1,000,000 (LogPow > 6) are truncated at 1:1,000,000 because greater partitioning would likely take longer than hepatocyte assay itself.

Value

A numeric fraction between zero and one

Author(s)

John Wambaugh and Robert Pearce

References

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, others (2015). “Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing.” Toxicological Sciences, 148(1), 121–136.

See Also

apply_clint_adjustment

Calculate the hepatic clearance (deprecated).

Description

This function is included for backward compatibility. It calls calc_hep_clearance which calculates the hepatic clearance in plasma for a well-stirred model or other type if specified. Based on Ito and Houston (2004)

Usage

calc_hepatic_clearance(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  default.to.human = FALSE,
  hepatic.model = "well-stirred",
  suppress.messages = FALSE,
  well.stirred.correction = TRUE,
  restrictive.clearance = TRUE,
  adjusted.Funbound.plasma = TRUE,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_steadystate function, overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing animal values with human values if true.
hepatic.model

Model used in calculating hepatic clearance, unscaled, parallel tube, dispersion, or default well-stirred.
suppress.messages

Whether or not to suppress the output message.
well.stirred.correction

Uses correction in calculation of hepatic clearance for well-stirred model if TRUE for hepatic.model well-stirred. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
adjusted.Funbound.plasma

Whether or not to use Funbound.plasma adjustment if calculating Rblood2plasma.
...

Additional parameters passed to parameterize_steadystate if parameters is NULL.

Value

Hepatic Clearance

Units of L/h/kg BW.

Author(s)

John Wambaugh and Robert Pearce

References

Ito, K., & Houston, J. B. (2004). "Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes." Pharmaceutical Tesearch, 21(5), 785-792.

Examples

calc_hep_clearance(chem.name="Ibuprofen",hepatic.model='unscaled')
calc_hep_clearance(chem.name="Ibuprofen",well.stirred.correction=FALSE)

Calculate the ionization.

Description

This function calculates the ionization of a compound at a given pH. The pKa's are either entered as parameters or taken from a specific compound in the package. The arguments pKa_Donor and pKa_Accept may be single numbers, characters, or vectors. We support characters because there are many instances with multiple predicted values and all those values can be included by concatenating with commas (for example, pKa_Donor = "8.1,8.6". Finally, pka_Donor and pKa_Accept may be vectors of characters representing different chemicals or instances of chemical parameters to allow for uncertainty analysis. A null value for pKa_Donor or pKa_Accept is interpretted as no argument provided, while NA is taken as no equlibria

Usage

calc_ionization(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  pH = NULL,
  pKa_Donor = NULL,
  pKa_Accept = NULL
)

Arguments

chem.cas

Either the chemical name or the CAS number must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from a parameterize_MODEL function, overrides chem.name and chem.cas.
pH

pH where ionization is evaluated.
pKa_Donor

Compound H dissociation equilibirum constant(s). Overwrites chem.name and chem.cas.
pKa_Accept

Compound H association equilibirum constant(s). Overwrites chem.name and chem.cas.

Details

The fractions are calculated by determining the coefficients for each species and dividing the particular species by the sum of all three. The positive, negative and zwitterionic/neutral coefficients are given by:

zwitter/netural=1zwitter/netural = 1

for(iin1:pkabove)negative=negative+10(i∗pH−pKa1−...−pKai)for(i in 1:pkabove) negative = negative + 10^(i * pH - pKa1 - ... - pKai)

for(iin1:pkbelow)positive=positive+10(pKa1+...+pKai−i∗pH)for(i in 1:pkbelow) positive = positive + 10^(pKa1 + ... + pKai - i * pH)

where i begins at 1 and ends at the number of points above(for negative) or below(for positive) the neutral/zwitterionic range. The neutral/zwitterionic range is either the pH range between 2 pKa's where the number of acceptors above is equal to the number of donors below, everything above the pKa acceptors if there are no donors, or everything below the pKa donors if there are no acceptors. Each of the terms in the sums represent a different ionization.

Value

fraction_neutral

fraction of compound neutral
fraction_charged

fraction of compound charged
fraction_negative

fraction of compound negative
fraction_positive

fraction of compound positive
fraction_zwitter

fraction of compound zwitterionic

Author(s)

Robert Pearce and John Wambaugh

References

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of Pharmacokinetics and Pharmacodynamics 44.6 (2017): 549-565.

Strope, Cory L., et al. "High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling." Science of The Total Environment 615 (2018): 150-160.

Examples

# Donor pKa's 9.78,10.39 -- Should be almost all neutral at plasma pH:
out <- calc_ionization(chem.name='bisphenola',pH=7.4)
print(out)
out[["fraction_neutral"]]==max(unlist(out))

# Donor pKa's 9.78,10.39 -- Should be almost all negative (anion) at higher pH:
out <- calc_ionization(chem.name='bisphenola',pH=11)
print(out)
out[["fraction_negative"]]==max(unlist(out))

# Fictitious compound, should be almost all all negative (anion):
out <- calc_ionization(pKa_Donor=8,pKa_Accept="1,4",pH=9)
print(out)
out[["fraction_negative"]]>0.9

# Donor pKa 6.54 -- Should be mostly negative (anion):
out <- calc_ionization(chem.name='Acephate',pH=7)
print(out)
out[["fraction_negative"]]==max(unlist(out))

#Acceptor pKa's "9.04,6.04"  -- Should be almost all positive (cation) at plasma pH:
out <- calc_ionization(chem.cas="145742-28-5",pH=7.4)
print(out)
out[["fraction_positive"]]==max(unlist(out))

#Fictious Zwitteron:
out <- calc_ionization(pKa_Donor=6,pKa_Accept="8",pH=7.4)
print(out)
out[["fraction_zwitter"]]==max(unlist(out))

Calculate air:matrix partition coefficients

Description

This function uses the methods colleced by Linakis et al. (2020) to calculate air partition coefficients for blood, water, and mucus.

Usage

calc_kair(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  adjusted.Funbound.plasma = TRUE,
  default.to.human = FALSE,
  suppress.messages = FALSE,
  pH = 7.4,
  alpha = 0.001
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model. Can include parameters "logHenry" and "body_temp", but if not included standard values are looked up from httk tables.
species

Species used for body temperature, defaults to "Human"
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
default.to.human

Substitutes missing species-specific values with human values if TRUE (default is FALSE).
suppress.messages

Whether or not the output messages are suppressed.
pH

pH where ionization is evaluated.
alpha

Ratio of Distribution coefficient D of totally charged species and that of the neutral form

Details

The blood:air partition coefficient (PB:A) was calculated as PB:A = PB:A * RB:P / fup where P_B:A is the blood:air partition, RB:P is the blood:plasma partition ratio, fup is the fraction unbound in the plasma, and P_W:A is the water:air partition coefficient: R * Tbody / HLC / P where R is the gas constant (8.314 J/mol/K), T_body is the species-specific body temperature (K) from physiology.data, HLC is the Henry's Law Constant (atm*m^3 / mol), and P is conversion factor from atmospheres to Pascals (1 atm = 101325 Pa).

In the isopropanol PBTK model published by Clewell et al. (2001) it was noted that certain chemicals are likely to be absorbed into the mucus or otherwise trapped in the upper respiratory tract (URT). Following Scott (2014), the air:mucus partition coefficient (PA:M) calculated as log10(1/Kwater2air) - (log10(Pow) - 1) * 0.524 where Pow is the octanol/water partition coefficient

Value

A named list containing the blood:air, water:air, and mucus:air partition coefficients

Author(s)

John Wambaugh and Matt Linakis

References

Linakis, Matthew W., et al. "Development and evaluation of a high throughput inhalation model for organic chemicals." Journal of exposure Science & Environmental Epidemiology 30.5 (2020): 866-877.

Clewell III, Harvey J., et al. "Development of a physiologically based pharmacokinetic model of isopropanol and its metabolite acetone." Toxicological Sciences 63.2 (2001): 160-172.

Scott, John W., et al. "Tuning to odor solubility and sorption pattern in olfactory epithelial responses." Journal of Neuroscience 34.6 (2014): 2025-2036.

See Also

calc_dow

Back-calculates the Red Blood Cell to Unbound Plasma Partition Coefficient

Description

Given an observed ratio of chemical concentration in blood to plasma, this function calculates a Red Blood Cell to unbound plasma (Krbc2pu) partition coefficient that would be consistent with that observation.

Usage

calc_krbc2pu(
  Rb2p,
  Funbound.plasma,
  hematocrit = NULL,
  default.to.human = FALSE,
  species = "Human",
  suppress.messages = TRUE
)

Arguments

Rb2p

The chemical blood:plasma concentration ratop
Funbound.plasma

The free fraction of chemical in the presence of plasma protein Rblood2plasma.
hematocrit

Overwrites default hematocrit value in calculating Rblood2plasma.
default.to.human

Substitutes missing animal values with human values if true.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
suppress.messages

Determine whether to display certain usage feedback.

Value

The red blood cell to unbound chemical in plasma partition coefficient.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Ruark, Christopher D., et al. "Predicting passive and active tissue: plasma partition coefficients: interindividual and interspecies variability." Journal of pharmaceutical sciences 103.7 (2014): 2189-2198.

Calculate the membrane affinity

Description

Membrane affinity (MA) is the membrane:water partition coefficient. MA chacterizes chemical partitioning into membranes formed from neutral phospholipids (KnPL). Pearce et al. (2017) compared five different methods for predicting membrane affinity using measured data for 59 compounds. The method of Yun and Edgington (2013) was identified as the best: MA = 10^(1.294 + 0.304 * log10(Pow)

Usage

calc_ma(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  suppress.messages = FALSE
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
suppress.messages

Whether or not the output message is suppressed.

Value

A numeric fraction unpbound in plasma between zero and one

Author(s)

John Wambaugh

References

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Yun, Y. E., and A. N. Edginton. "Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters." Xenobiotica 43.10 (2013): 839-852.

Calculate maternal body weight

Description

This function initializes the parameters needed in the functions solve_fetal_pbtk by calling solve_pbtk and adding additional parameters.

Usage

calc_maternal_bw(week = 12)

Arguments

week

Gestational week

Details

BW <- params$pre_pregnant_BW + params$BW_cubic_theta1 * tw + params$BW_cubic_theta2 * tw^2 + params$BW_cubic_theta3 * tw^3

Value

BW

Maternal Body Weight, kg.

Author(s)

John Wambaugh

References

Kapraun DF, Wambaugh JF, Setzer RW, Judson RS (2019). “Empirical models for anatomical and physiological changes in a human mother and fetus during pregnancy and gestation.” PLOS ONE, 14(5), 1-56. doi:10.1371/journal.pone.0215906.

Distribution of chemical steady state concentration with uncertainty and variability

Description

For a given chemical and fixed dose rate this function determines a distribution of steady-state concentrations reflecting measurement uncertainty an population variability. Uncertainty and variability are simulated via the Monte Carlo method – many sets of model parameters are drawn according to probability distributions described in Ring et al. (2017) (doi:10.1016/j.envint.2017.06.004) for human variability and Wambaugh et al. (2019) (doi:10.1093/toxsci/kfz205) for measurement uncertainty. Monte Carlo samples are generated by the function create_mc_samples. To allow rapid application of the Monte Carlo method we make use of analytical solutions for the steady-state concentration for a particular model via a given route (when available) as opposed to solving the model numerically (that is, using differential equations). For each sample of the Monte Carlo method (as specified by argument samples) the parameters for the analytical solution are varied. An ensemble of steady-state predictions are produced, though by default only the quantiles specified by argument which.quantile are provided. If the full set of predicted values are desired use set the argument return.samples to TRUE.

Usage

calc_mc_css(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  samples = 1000,
  which.quantile = 0.95,
  species = "Human",
  daily.dose = 1,
  suppress.messages = FALSE,
  model = "3compartmentss",
  httkpop = TRUE,
  httkpop.dt = NULL,
  invitrouv = TRUE,
  calcrb2p = TRUE,
  censored.params = list(),
  vary.params = list(),
  return.samples = FALSE,
  tissue = NULL,
  concentration = "plasma",
  output.units = "mg/L",
  invitro.mc.arg.list = NULL,
  httkpop.generate.arg.list = list(method = "direct resampling"),
  convert.httkpop.arg.list = NULL,
  parameterize.arg.list = NULL,
  calc.analytic.css.arg.list = NULL,
  Caco2.options = NULL
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
samples

Number of samples generated in calculating quantiles.
which.quantile

Which quantile from Monte Carlo simulation is requested. Can be a vector.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Species must be set to "Human" to run httkpop model.
daily.dose

Total daily dose, mg/kg BW.
suppress.messages

Whether or not to suppress output message.
model

Model used in calculation,'gas_pbtk' for the gas pbtk model, 'pbtk' for the multiple compartment model, '3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model. This only applies when httkpop=TRUE and species="Human", otherwise '3compartmentss' is used.
httkpop

Whether or not to use population generator and sampler from httkpop. This is overwrites censored.params and vary.params and is only for human physiology. Species must also be set to 'Human'.
httkpop.dt

A data table generated by httkpop_generate. This defaults to NULL, in which case httkpop_generate is called to generate this table.
invitrouv

Logical to indicate whether to include in vitro parameters in uncertainty and variability analysis
calcrb2p

Logical determining whether or not to recalculate the chemical ratio of blood to plasma
censored.params

The parameters listed in censored.params are sampled from a normal distribution that is censored for values less than the limit of detection (specified separately for each parameter). This argument should be a list of sublists. Each sublist is named for a parameter in "parameters" and contains two elements: "CV" (coefficient of variation) and "LOD" (limit of detection, below which parameter values are censored. New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Censored values are sampled on a uniform distribution between 0 and the limit of detection. Not used with httkpop model.
vary.params

The parameters listed in vary.params are sampled from a normal distribution that is truncated at zero. This argument should be a list of coefficients of variation (CV) for the normal distribution. Each entry in the list is named for a parameter in "parameters". New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Not used with httkpop model.
return.samples

Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.
tissue

Desired steady state tissue concentration. Default is of NULL typically gives whole body plasma concentration.
concentration

Desired concentration type: 'blood','tissue', or default 'plasma'. In the case that the concentration is for plasma, selecting "blood" will use the blood:plasma ratio to estimate blood concentration. In the case that the argument 'tissue' specifies a particular tissue of the body, concentration defaults to 'tissue' – that is, the concentration in the If cocentration is set to 'blood' or 'plasma' and 'tissue' specifies a specific tissue then the value returned is for the plasma or blood in that specific tissue.
output.units

Plasma concentration units, either uM or default mg/L.
invitro.mc.arg.list

List of additional parameters passed to invitro_mc
httkpop.generate.arg.list

Additional parameters passed to httkpop_generate.
convert.httkpop.arg.list

Additional parameters passed to the convert_httkpop_* function for the model.
parameterize.arg.list

A list of arguments to be passed to the model parameterization function (that is, parameterize_MODEL) corresponding to argument "model". (Defaults to NULL.)
calc.analytic.css.arg.list

Additional parameters passed to
Caco2.options

Arguments describing how to handle Caco2 absorption data that are passed to invitro_mc and the parameterize_[MODEL] functions. See get_fbio for further details.

calc_analytic_css.

Details

The chemical-specific steady-state concentration for a dose rate of 1 mg/kg body weight/day can be used for in in vitro-in vivo extrapolation (IVIVE) of a bioactive in vitro concentration by dividing the in vitro concentration by the steady-state concentration to predict a dose rate (mg/kg/day) that would produce that concentration in plasma. Using quantiles of the distribution (such as the upper 95th percentile) allow incorporation of uncertainty and variability into IVIVE.

Reverse Dosimetry Toxicodynamic IVIVE

Reverse Dosimetry Toxicodynamic IVIVE

Figure from Breen et al. (2021) (doi:10.1080/17425255.2021.1935867) shows reverse dosimetry toxicodynamic IVIVE. Equivalent external dose is determined by solving the TK model in reverse by deriving the external dose (that is, TK model input) that produces a specified internal concentration (that is, TK model output). Reverse dosimetry and IVIVE using HTTK relies on the linearity of the models. We calculate a scaling factor to relate in vitro concentrations (uM) to administered equivalent doses (AED). The scaling factor is the inverse of the steady state plasma concentration (Css) predicted for a 1 mg/kg/day exposure dose rate. We use Monte Carlo to simulate variability and propagate uncertainty; for example, to calculate an upper 95th percentile Css,95 for individuals who get higher plasma concentrations from the same exposure.

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

httk-pop is used only for humans. For non-human species biological variability is simulated by drawing parameters from uncorellated log-normal distributions.

Chemical-specific httk data are available primarily for human and for a few hundred chemicals in rat. All in silico predictions are for human. Thus, when species is specified as rabbit, dog, or mouse, the user can choose to set the argument default.to.human to TRUE so that this function uses the appropriate physiological data (volumes and flows) but substitutes human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

If the argument tissue is used, the steady-state concentration in that tissue, if available, is provided. If that tissue is included in the model used (specified by arguement model) then the actual tissue concentration is provided. Otherwise, the tissue-specific partition coefficient is used to estimate the concentration from the plasma.

The six sets of plausible IVIVE assumptions identified by Honda et al. (2019) (doi:10.1371/journal.pone.0217564) are:

in vivo Conc. Metabolic Clearance Bioactive Chemical Conc. TK Statistic Used*
Honda1 Veinous (Plasma) Restrictive Free Mean Conc.
Honda2 Veinous Restrictive Free Max Conc.
Honda3 Veinous Non-restrictive Total Mean Conc.
Honda4 Veinous Non-restrictive Total Max Conc.
Honda5 Target Tissue Non-restrictive Total Mean Conc.
Honda6 Target Tissue Non-restrictive Total Max Conc.

*Assumption is currently ignored because analytical steady-state solutions are currently used by this function.

Value

Quantiles (specified by which.quantile) of the distribution of plasma steady-stae concentration (Css) from the Monte Carlo simulation

Author(s)

Caroline Ring, Robert Pearce, John Wambaugh, Miyuki Breen, and Greg Honda

References

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences 147.1 (2015): 55-67.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Rowland M, Benet LZ, Graham GG (1973). “Clearance concepts in pharmacokinetics.” Journal of pharmacokinetics and biopharmaceutics, 1(2), 123–136.

See Also

calc_analytic_css

create_mc_samples

Examples

# Set the number of samples (NSAMP) low for rapid testing, increase NSAMP 
# for more stable results. Default value is 1000:
NSAMP = 10

# Basic in vitro - in vivo extrapolation with httk, convert 3 uM in vitro
# concentration of chemical with CAS 2451-62-9 to mg/kg/day:
set.seed(1234)
3/calc_mc_css(chem.cas="2451-62-9", samples=NSAMP, output.units="uM")
# The significant digits should give the same answer as:
set.seed(1234)
calc_mc_oral_equiv(chem.cas="2451-62-9", conc=3, samples=NSAMP)  

 set.seed(1234)
 calc_mc_css(chem.name='Bisphenol A', output.units='uM',
             samples=NSAMP, return.samples=TRUE)

 set.seed(1234)
 calc_mc_css(chem.name='Bisphenol A', output.units='uM',
             samples=NSAMP,
             httkpop.generate.arg.list=list(method='vi'))
                          
 # The following example should result in an error since we do not 
 # estimate tissue partitioning with '3compartmentss'.                         
 set.seed(1234)
 try(calc_mc_css(chem.name='2,4-d', which.quantile=.9,
             samples=NSAMP,
             httkpop=FALSE, tissue='heart'))
 
# But heart will work with PBTK, even though it's lumped since we estimate
# a partition coefficient before lumping:
 set.seed(1234)
 calc_mc_css(chem.name='2,4-d', model='pbtk',
             samples=NSAMP,
             which.quantile=.9, httkpop=FALSE, tissue='heart')

 set.seed(1234)
 calc_mc_css(chem.cas = "80-05-7", which.quantile = 0.5,
             output.units = "uM", samples = NSAMP,
             httkpop.generate.arg.list=list(method='vi', gendernum=NULL, 
             agelim_years=NULL, agelim_months=NULL,
             weight_category = c("Underweight","Normal","Overweight","Obese")))

 params <- parameterize_pbtk(chem.cas="80-05-7")
 set.seed(1234)
 calc_mc_css(parameters=params,model="pbtk", samples=NSAMP)

 set.seed(1234)
 # Standard HTTK Monte Carlo 
 calc_mc_css(chem.cas="90-43-7", model="pbtk", samples=NSAMP)
 set.seed(1234)
 # HTTK Monte Carlo with no measurement uncertainty (pre v1.10.0):
 calc_mc_css(chem.cas="90-43-7",
 model="pbtk",
 samples=NSAMP,
 invitro.mc.arg.list = list(
   adjusted.Funbound.plasma = TRUE,
   poormetab = TRUE, 
   fup.censored.dist = FALSE, 
   fup.lod = 0.01, 
   fup.meas.cv = 0.0, 
   clint.meas.cv = 0.0, 
   fup.pop.cv = 0.3, 
   clint.pop.cv = 0.3))

 # HTTK Monte Carlo with no HTTK-Pop physiological variability):
 set.seed(1234)
 calc_mc_css(chem.cas="90-43-7",model="pbtk",samples=NSAMP,httkpop=FALSE)

 # HTTK Monte Carlo with no in vitro uncertainty and variability):
 set.seed(1234)
 calc_mc_css(chem.cas="90-43-7",model="pbtk",samples=NSAMP,invitrouv=FALSE)

 # HTTK Monte Carlo with no HTTK-Pop and no in vitro uncertainty and variability):
 set.seed(1234)
 calc_mc_css(chem.cas="90-43-7" ,model="pbtk",
             samples=NSAMP, httkpop=FALSE, invitrouv=FALSE)

 # Should be the same as the mean result:
 calc_analytic_css(chem.cas="90-43-7",model="pbtk",output.units="mg/L")

 # HTTK Monte Carlo using basic Monte Carlo sampler:
 set.seed(1234)
 calc_mc_css(chem.cas="90-43-7",
             model="pbtk",
             samples=NSAMP,
             httkpop=FALSE,
             invitrouv=FALSE,
             vary.params=list(Pow=0.3))
 
# We can also use the Monte Carlo functions by passing a table
# where each row represents a different Monte Carlo draw of parameters:
p <- create_mc_samples(chem.cas="80-05-7")
# Use data.table for steady-state plasma concentration (Css) Monte Carlo:
calc_mc_css(parameters=p)
# Using the same table gives the same answer:
calc_mc_css(parameters=p)
# Use Css for 1 mg/kg/day for simple reverse toxicokinetics 
# in Vitro-In Vivo Extrapolation to convert 15 uM to mg/kg/day:
15/calc_mc_css(parameters=p, output.units="uM")
# Can do the same with calc_mc_oral_equiv:
calc_mc_oral_equiv(15, parameters=p)

Calculate Monte Carlo Oral Equivalent Dose

Description

This function converts a chemical plasma concentration to an oral adminstered equivalent dose (AED) using a concentration obtained from calc_mc_css. This function uses reverse dosimetry-based 'in vitro-in vivo extrapolation (IVIVE) for high throughput risk screening. The user can input the chemical and in vitro bioactive concentration, select the TK model, and then automatically predict the in vivo AED which would produce a body concentration equal to the in vitro bioactive concentration. This function relies on the Monte Carlo method (via funcion create_mc_samples to simulate both uncertainty and variability so that the result is a distribution of equivalent doses, from which we provide specific quantiles (specified by which.quantile), though the full set of predictions can be obtained by setting return.samples to TRUE.

Usage

calc_mc_oral_equiv(
  conc,
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  which.quantile = 0.95,
  species = "Human",
  input.units = "uM",
  output.units = "mgpkgpday",
  suppress.messages = FALSE,
  return.samples = FALSE,
  restrictive.clearance = TRUE,
  bioactive.free.invivo = FALSE,
  tissue = NULL,
  concentration = "plasma",
  IVIVE = NULL,
  model = "3compartmentss",
  Caco2.options = list(),
  calc.analytic.css.arg.list = list(),
  ...
)

Arguments

conc

Bioactive in vitro concentration in units of uM.
chem.name

Either the chemical name or the CAS number must be specified.
chem.cas

Either the CAS number or the chemical name must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
which.quantile

Which quantile from Monte Carlo steady-state simulation (calc_mc_css) is requested. Can be a vector. Note that 95th concentration quantile is the same population as the 5th dose quantile.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
input.units

Units of given concentration, default of uM but can also be mg/L.
output.units

Units of dose, default of 'mgpkgpday' for mg/kg BW/ day or 'umolpkgpday' for umol/ kg BW/ day.
suppress.messages

Suppress text messages.
return.samples

Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
bioactive.free.invivo

If FALSE (default), then the total concentration is treated as bioactive in vivo. If TRUE, the the unbound (free) plasma concentration is treated as bioactive in vivo. Only works with tissue = NULL in current implementation.
tissue

Desired steady state tissue concentration. Default is of NULL typically gives whole body plasma concentration.
concentration

Desired concentration type: 'blood','tissue', or default 'plasma'. In the case that the concentration is for plasma, selecting "blood" will use the blood:plasma ratio to estimate blood concentration. In the case that the argument 'tissue' specifies a particular tissue of the body, concentration defaults to 'tissue' – that is, the concentration in the If cocentration is set to 'blood' or 'plasma' and 'tissue' specifies a specific tissue then the value returned is for the plasma or blood in that specific tissue.
IVIVE

Honda et al. (2019) identified six plausible sets of assumptions for in vitro-in vivo extrapolation (IVIVE) assumptions. Argument may be set to "Honda1" through "Honda6". If used, this function overwrites the tissue, restrictive.clearance, and bioactive.free.invivo arguments. See Details below for more information.
model

Model used in calculation,'gas_pbtk' for the gas pbtk model, 'pbtk' for the multiple compartment model, '3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model. This only applies when httkpop=TRUE and species="Human", otherwise '3compartmentss' is used.
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
calc.analytic.css.arg.list

A list of options to pass to the analytic steady-state calculation function. This includes 'restrictive.clearance', 'bioactive.free.invivo', 'IVIVE', 'wellstirred.correction', and 'adjusted.Funbound.plasma'.
...

Additional parameters passed to calc_mc_css for httkpop and variance of parameters.

Details

The chemical-specific steady-state concentration for a dose rate of 1 mg/kg body weight/day can be used for in IVIVE of a bioactive in vitro concentration by dividing the in vitro concentration by the steady-state concentration to predict a dose rate (mg/kg/day) that would produce that concentration in plasma. Using quantiles of the distribution (such as the upper 95th percentile) allow incorporation of uncertainty and variability into IVIVE.

This approach relies on thelinearity of the models to calculate a scaling factor to relate in vitro concentrations (uM) with AED. The scaling factor is the inverse of the steady-state plasma concentration (Css) predicted for a 1 mg/kg/day exposure dose rate where in vitro concentration [X] and Css must be in the same units. Note that it is typical for in vitro concentrations to be reported in units of uM and Css in units of mg/L, in which case one must be converted to the other.

Reverse Dosimetry Toxicodynamic IVIVE

Reverse Dosimetry Toxicodynamic IVIVE

Figure from Breen et al. (2021) (doi:10.1080/17425255.2021.1935867) shows reverse dosimetry toxicodynamic IVIVE. Equivalent external dose is determined by solving the TK model in reverse by deriving the external dose (that is, TK model input) that produces a specified internal concentration (that is, TK model output). Reverse dosimetry and IVIVE using HTTK relies on the linearity of the models. We calculate a scaling factor to relate in vitro concentrations (uM) to AEDs. The scaling factor is the inverse of the Css predicted for a 1 mg/kg/day exposure dose rate. We use Monte Carlo to simulate variability and propagate uncertainty; for example, to calculate an upper 95th percentile Css,95 for individuals who get higher plasma concentrations from the same exposure.

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

All arguments after httkpop only apply if httkpop is set to TRUE and species to "Human".

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitutes human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Tissue concentrations are calculated for the pbtk model with oral infusion dosing. All tissues other than gut, liver, and lung are the product of the steady state plasma concentration and the tissue to plasma partition coefficient.

The six sets of plausible IVIVE assumptions identified by Honda et al. (2019) (doi:10.1371/journal.pone.0217564) are:

in vivo Conc. Metabolic Clearance Bioactive Chemical Conc. TK Statistic Used*
Honda1 Veinous (Plasma) Restrictive Free Mean Conc.
Honda2 Veinous Restrictive Free Max Conc.
Honda3 Veinous Non-restrictive Total Mean Conc.
Honda4 Veinous Non-restrictive Total Max Conc.
Honda5 Target Tissue Non-restrictive Total Mean Conc.
Honda6 Target Tissue Non-restrictive Total Max Conc.

*Assumption is currently ignored because analytical steady-state solutions are currently used by this function.

Value

Equivalent dose in specified units, default of mg/kg BW/day.

Author(s)

John Wambaugh

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, others (2015). “Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing.” Toxicological Sciences, 148(1), 121–136.

Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Rowland M, Benet LZ, Graham GG (1973). “Clearance concepts in pharmacokinetics.” Journal of pharmacokinetics and biopharmaceutics, 1(2), 123–136.

See Also

calc_mc_css

create_mc_samples

Examples

# Set the number of samples (NSAMP) low for rapid testing, increase NSAMP 
# for more stable results. Default value is 1000:
NSAMP = 10

# Basic in vitro - in vivo extrapolation with httk, convert 0.5 uM in vitro
# concentration of chemical Surinabant to mg/kg/day:
set.seed(1234)
0.5/calc_mc_css(chem.name="Surinabant", samples=NSAMP, output.units="uM")

# The significant digits should give the same answer as:
set.seed(1234)
calc_mc_oral_equiv(chem.name="Surinabant",conc=0.5,samples=NSAMP)  

# Note that we use set.seed to get the same sequence of random numbers for
# the two different function calls (calc_mc_css and calc_mc_oral_equiv)

# The following example should result in an error since we do not 
# estimate tissue partitioning with '3compartmentss'. 
set.seed(1234)                        
try(calc_mc_oral_equiv(0.1, chem.cas="34256-82-1",
                       which.quantile=c(0.05,0.5,0.95),
                       samples=NSAMP,
                       tissue='brain'))
       
set.seed(1234)
calc_mc_oral_equiv(0.1,chem.cas="34256-82-1", model='pbtk',
                   samples=NSAMP,
                   which.quantile=c(0.05,0.5,0.95), tissue='brain')
 
# We can also use the Monte Carlo functions by passing a table
# where each row represents a different Monte Carlo draw of parameters:
p <- create_mc_samples(chem.cas="80-05-7")
# Use data.table for steady-state plasma concentration (Css) Monte Carlo:
calc_mc_css(parameters=p)
# Using the same table gives the same answer:
calc_mc_css(parameters=p)
# Use Css for 1 mg/kg/day for simple reverse toxicokinetics 
# in Vitro-In Vivo Extrapolation to convert 15 uM to mg/kg/day:
15/calc_mc_css(parameters=p, output.units="uM")
# Can do the same with calc_mc_oral_equiv:
calc_mc_oral_equiv(15, parameters=p)

Conduct multiple TK simulations using Monte Carlo

Description

This function finds the analytical steady state plasma concentration(from calc_analytic_css) using a monte carlo simulation (monte_carlo).

Usage

calc_mc_tk(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  samples = 1000,
  species = "Human",
  suppress.messages = FALSE,
  model = "pbtk",
  httkpop = TRUE,
  httkpop.dt = NULL,
  invitrouv = TRUE,
  calcrb2p = TRUE,
  censored.params = list(),
  vary.params = list(),
  return.samples = FALSE,
  tissue = NULL,
  output.units = "mg/L",
  solvemodel.arg.list = list(times = c(0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5)),
  Caco2.options = list(),
  invitro.mc.arg.list = NULL,
  httkpop.generate.arg.list = list(method = "direct resampling"),
  convert.httkpop.arg.list = NULL,
  parameterize.arg.list = NULL,
  return.all.sims = FALSE
)

Arguments

chem.cas

Either the CAS number, parameters, or the chemical name must be specified.
chem.name

Either the chemical parameters, name, or the CAS number must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from parameterize_steadystate. Not used with httkpop model.
samples

Number of samples generated in calculating quantiles.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Species must be set to "Human" to run httkpop model.
suppress.messages

Whether or not to suppress output message.
model

Model used in calculation: 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model. This only applies when httkpop=TRUE and species="Human", otherwise '3compartmentss' is used.
httkpop

Whether or not to use population generator and sampler from httkpop. This is overwrites censored.params and vary.params and is only for human physiology. Species must also be set to 'Human'.
httkpop.dt

A data table generated by httkpop_generate. This defaults to NULL, in which case httkpop_generate is called to generate this table.
invitrouv

Logical to indicate whether to include in vitro parameters in uncertainty and variability analysis
calcrb2p

Logical determining whether or not to recalculate the chemical ratio of blood to plasma
censored.params

The parameters listed in censored.params are sampled from a normal distribution that is censored for values less than the limit of detection (specified separately for each parameter). This argument should be a list of sub-lists. Each sublist is named for a parameter in "parameters" and contains two elements: "CV" (coefficient of variation) and "LOD" (limit of detection, below which parameter values are censored. New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Censored values are sampled on a uniform distribution between 0 and the limit of detection. Not used with httkpop model.
vary.params

The parameters listed in vary.params are sampled from a normal distribution that is truncated at zero. This argument should be a list of coefficients of variation (CV) for the normal distribution. Each entry in the list is named for a parameter in "parameters". New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Not used with httkpop model.
return.samples

Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.
tissue

Desired steady state tissue conentration.
output.units

Plasma concentration units, either uM or default mg/L.
solvemodel.arg.list

Additional arguments ultimately passed to solve_model
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.default = 2, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings.
invitro.mc.arg.list

List of additional parameters passed to invitro_mc
httkpop.generate.arg.list

Additional parameters passed to httkpop_generate.
convert.httkpop.arg.list

Additional parameters passed to the convert_httkpop_* function for the model.
parameterize.arg.list

Additional parameters passed to the parameterize_* function for the model.
return.all.sims

Logical indicating whether to return the results of all simulations, in addition to the default toxicokinetic statistics

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

All arguments after httkpop only apply if httkpop is set to TRUE and species to "Human".

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Tissue concentrations are calculated for the pbtk model with oral infusion dosing. All tissues other than gut, liver, and lung are the product of the steady state plasma concentration and the tissue to plasma partition coefficient.

The six sets of plausible in vitro-in vivo extrpolation (IVIVE) assumptions identified by Honda et al. (2019) (doi:10.1371/journal.pone.0217564) are:

in vivo Conc. Metabolic Clearance Bioactive Chemical Conc. TK Statistic Used*
Honda1 Veinous (Plasma) Restrictive Free Mean Conc.
Honda2 Veinous Restrictive Free Max Conc.
Honda3 Veinous Non-restrictive Total Mean Conc.
Honda4 Veinous Non-restrictive Total Max Conc.
Honda5 Target Tissue Non-restrictive Total Mean Conc.
Honda6 Target Tissue Non-restrictive Total Max Conc.

*Assumption is currently ignored because analytical steady-state solutions are currently used by this function.

Value

If return.all.sims == FALSE (default) a list with:

means

The mean concentration for each model compartment as a function of time across the Monte Carlo simulation
sds

The standard deviation for each model compartment as a function of time across the Monte Carlo simulation

If return.all.sums == TRUE then a list is returned with:

stats

The list of means and sds from return.all.sums=FALSE
sims

The concentration vs. time results for each compartment for every (samples) set of parameters in the Monte Carlo simulation

Author(s)

John Wambaugh

See Also

create_mc_samples

Examples

NSAMP <- 50
chemname="Abamectin"
times<- c(0,0.25,0.5,0.75,1,1.5,2,2.5,3,4,5)
age.ranges <- seq(6,80,by=10)
forward <- NULL
for (age.lower in age.ranges)
{
  label <- paste("Ages ",age.lower,"-",age.lower+4,sep="")
  set.seed(1234)
  forward[[label]] <- calc_mc_tk(
                        chem.name=chemname,
                        samples=NSAMP,
                        httkpop.generate.arg.list=list(
                          method="d",
                          agelim_years = c(age.lower, age.lower+9)),
                        solvemodel.arg.list = list(
                          times=times))
}

Calculate the constant ratio of the blood concentration to the plasma concentration.

Description

This function calculates the constant ratio of the blood concentration to the plasma concentration.

Usage

calc_rblood2plasma(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  hematocrit = NULL,
  Krbc2pu = NULL,
  Funbound.plasma = NULL,
  default.to.human = FALSE,
  species = "Human",
  adjusted.Funbound.plasma = TRUE,
  class.exclude = TRUE,
  suppress.messages = TRUE
)

Arguments

chem.cas

Either the CAS number or the chemical name must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from parameterize_schmitt
hematocrit

Overwrites default hematocrit value in calculating Rblood2plasma.
Krbc2pu

The red blood cell to unbound plasma chemical partition coefficient, typically from predict_partitioning_schmitt
Funbound.plasma

The fraction of chemical unbound (free) in the presence of plasma protein
default.to.human

Substitutes missing animal values with human values if true.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
adjusted.Funbound.plasma

Whether or not to use Funbound.plasma adjustment.
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
suppress.messages

Determine whether to display certain usage feedback.

Details

The red blood cell (RBC) parition coefficient as predicted by the Schmitt (2008) method is used in the calculation. The value is calculated with the equation: 1 - hematocrit + hematocrit * Krbc2pu * Funbound.plasma, summing the red blood cell to plasma and plasma:plasma (equal to 1) partition coefficients multiplied by their respective fractional volumes. When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data (hematocrit and temperature), but substitutes human fraction unbound and tissue volumes.

Value

The blood to plasma chemical concentration ratio

Author(s)

John Wambaugh and Robert Pearce

References

Schmitt W. "General approach for the calculation of tissue to plasma partition coefficients." Toxicology In Vitro, 22, 457-467 (2008).

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Ruark, Christopher D., et al. "Predicting passive and active tissue: plasma partition coefficients: interindividual and interspecies variability." Journal of pharmaceutical sciences 103.7 (2014): 2189-2198.

Examples

calc_rblood2plasma(chem.name="Bisphenol A")
calc_rblood2plasma(chem.name="Bisphenol A",species="Rat")

Calculate toxicokinetic summary statistics (deprecated).

Description

#' This function is included for backward compatibility. It calls calc_tkstats which calculates the area under the curve, the mean, and the peak values for the venous blood or plasma concentration of a specified chemical or all chemicals if none is specified for the multiple compartment model with a given number of days, dose, and number of doses per day.

Usage

calc_stats(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  route = "oral",
  stats = c("AUC", "peak", "mean"),
  species = "Human",
  days = 28,
  daily.dose = 1,
  dose = NULL,
  doses.per.day = 1,
  output.units = "uM",
  concentration = "plasma",
  tissue = "plasma",
  model = "pbtk",
  default.to.human = FALSE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  suppress.messages = FALSE,
  ...
)

Arguments

chem.name

Name of desired chemical.
chem.cas

CAS number of desired chemical.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk function, overrides chem.name and chem.cas.
route

String specification of route of exposure for simulation: "oral", "iv", "inhalation", ...
stats

Desired values (either 'AUC', 'mean', 'peak', or a vector containing any combination).
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
days

Length of the simulation.
daily.dose

Total daily dose, mg/kg BW.
dose

Amount of a single dose at time zero, mg/kg BW.
doses.per.day

Number of doses per day.
output.units

Desired units (either "mg/L", "mg", "umol", or default "uM").
concentration

Desired concentration type, 'blood' or default 'plasma'.
tissue

Desired steady state tissue conentration.
model

Model used in calculation, 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
suppress.messages

Whether to suppress output message.
...

Arguments passed to solve function.

Details

Default value of 0 for doses.per.day solves for a single dose.

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

AUC

Area under the plasma concentration curve.
mean.conc

The area under the curve divided by the number of days.
peak.conc

The highest concentration.

Author(s)

Robert Pearce and John Wambaugh

Calculate toxicokinetic summary statistics.

Description

This function calculates the area under the curve, the mean, and the peak values for the venous blood or plasma concentration of a specified chemical or all chemicals if none is specified for the multiple compartment model with a given number of days, dose, and number of doses per day.

Usage

calc_tkstats(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  route = "oral",
  stats = c("AUC", "peak", "mean"),
  species = "Human",
  days = 28,
  daily.dose = 1,
  dose = NULL,
  forcings = NULL,
  doses.per.day = 1,
  output.units = "uM",
  concentration = "plasma",
  tissue = "plasma",
  model = "pbtk",
  default.to.human = FALSE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  suppress.messages = FALSE,
  ...
)

Arguments

chem.name

Name of desired chemical.
chem.cas

CAS number of desired chemical.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_pbtk function, overrides chem.name and chem.cas.
route

String specification of route of exposure for simulation: "oral", "iv", "inhalation", ...
stats

Desired values (either 'AUC', 'mean', 'peak', or a vector containing any combination).
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
days

Length of the simulation.
daily.dose

Total daily dose, mg/kg BW.
dose

Amount of a single dose at time zero, mg/kg BW.
forcings

Manual input of 'forcings' data series argument for ode integrator, defaults is NULL. Then other input parameters (see exp.start.time, exp.conc, exp.duration, and period) provide the necessary information to assemble a forcings data series.
doses.per.day

Number of doses per day.
output.units

Desired units (either "mg/L", "mg", "umol", or default "uM").
concentration

Desired concentration type, 'blood' or default 'plasma'.
tissue

Desired steady state tissue conentration.
model

Model used in calculation, 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
suppress.messages

Whether to suppress output message.
...

Arguments passed to solve function.

Details

Default value of 0 for doses.per.day solves for a single dose.

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

AUC

Area under the plasma concentration curve.
mean.conc

The area under the curve divided by the number of days.
peak.conc

The highest concentration.

Author(s)

Robert Pearce and John Wambaugh

Examples

calc_tkstats(chem.name='Bisphenol-A',days=100,stats='mean',model='3compartment')


calc_tkstats(chem.name='Bisphenol-A',days=100,stats=c('peak','mean'),species='Rat')

triclosan.stats <- calc_tkstats(days=10, chem.name = "triclosan")

Calculate the total plasma clearance.

Description

This function calculates the total clearance rate for a one compartment model for plasma where clearance is entirely due to metablism by the liver and glomerular filtration in the kidneys, identical to clearance of three compartment steady state model.

Usage

calc_total_clearance(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  suppress.messages = FALSE,
  default.to.human = FALSE,
  well.stirred.correction = TRUE,
  restrictive.clearance = TRUE,
  adjusted.Funbound.plasma = TRUE,
  ...
)

Arguments

chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
chem.name

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_steadystate function, overrides chem.name and chem.cas.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
suppress.messages

Whether or not the output message is suppressed.
default.to.human

Substitutes missing animal values with human values if true.
well.stirred.correction

Uses correction in calculation of hepatic clearance for well-stirred model if TRUE. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
restrictive.clearance

Protein binding is not taken into account (set to 1) in liver clearance if FALSE.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE.
...

Additional parameters passed to parameterize_steadystate if parameters is NULL.

Value

Total Clearance

Units of L/h/kg BW.

Author(s)

John Wambaugh

Examples

calc_total_clearance(chem.name="Ibuprofen")

Calculate the volume of distribution for a one compartment model.

Description

This function predicts partition coefficients for all tissues using predict_partitioning_schmitt, then lumps them into a single compartment.

Usage

calc_vdist(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  default.to.human = FALSE,
  species = "Human",
  suppress.messages = FALSE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  minimum.Funbound.plasma = 1e-04
)

Arguments

chem.cas

Either the CAS number or the chemical name must be specified when Funbound.plasma is not given in parameter list.
chem.name

Either the chemical name or the CAS number must be specified when Funbound.plasma is not given in parameter list.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from parameterize_3comp, parameterize_pbtk or predict_partitioning_schmitt.
default.to.human

Substitutes missing animal values with human values if true.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
suppress.messages

Whether or not the output message is suppressed.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with parition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).

Details

The effective volume of distribution is calculated by summing each tissues volume times it's partition coefficient relative to plasma. Plasma, and the paritioning into RBCs are also added to get the total volume of distribution in L/KG BW. Partition coefficients are calculated using Schmitt's (2008) method. When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

Volume of distribution

Units of L/ kg BW.

Author(s)

John Wambaugh and Robert Pearce

References

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Peyret T, Poulin P, Krishnan K (2010). “A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals.” Toxicology and applied pharmacology, 249(3), 197–207.

See Also

predict_partitioning_schmitt

tissue.data

physiology.data

Examples

calc_vdist(chem.cas="80-05-7")
calc_vdist(chem.name="Bisphenol A")
calc_vdist(chem.name="Bisphenol A",species="Rat")

Test the check digit of a CAS number to confirm validity

Description

Chemical abstracts services registry numbers (CAS-RN) include a final digit as a "checksum" to test for validity (that is, that the number has not been corrupted).

Usage

CAS.checksum(CAS.string)

Arguments

CAS.string

A character string of three numbers separated by two dashes

Details

The check digit (final number) is calculated by working from right to left, starting with the second to last digit of the CAS-RN. We multiply each digit by an increasing digit (1, 2, 3...) and sum as we work from right to left. The check digit should equal the final digit of the sum.

Value

logical (TRUE if final digit of CAS is consistent with other digits)

Author(s)

John Wambaugh

Check for sufficient model parameters

Description

This function halt model evaluation if not all the needed parameters (as specified in the modelinfo_[MODEL].r file) are available. The function uses get_cheminfo, so if the chemical has been checked against that function already then evaluation should proceed as expected. If you do not have the parameters you need and are using a non-human species try default.to.human = TRUE (there are many more values for human than any other species). If working in human, try first using load_dawson2021, load_sipes2017, or load_pradeep2020.

Usage

check_model(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  model = NULL,
  species = NULL,
  class.exclude = TRUE,
  default.to.human = FALSE
)

Arguments

chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
model

Model to be checked, modelinfo files specify the requrements of each model.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
default.to.human

Substitutes missing fraction of unbound plasma with human values if true.

Value

Stops code from running if all parameters needed for model are not available, otherwise does nothing.

Author(s)

john Wambaugh

See Also

get_cheminfo

Parameter Estimates from Wambaugh et al. (2018)

Description

This table includes 1 and 2 compartment fits of plasma concentration vs time data aggregated from chem.invivo.PK.data, performed in Wambaugh et al. 2018. Data includes volume of distribution (Vdist, L/kg), elimination rate (kelim, 1/h), gut absorption rate (kgutabs, 1/h), fraction absorbed (Fabsgut), and steady state concentration (Css, mg/L).

Usage

chem.invivo.PK.aggregate.data

Format

data.frame

Author(s)

John Wambaugh

Source

Wambaugh et al. 2018 Toxicological Sciences, in press

Published toxicokinetic time course measurements

Description

This data set includes time and dose specific measurements of chemical concentration in tissues taken from animals administered control doses of the chemicals either orally or intravenously. This plasma concentration-time data is from rat experiments reported in public sources. Toxicokinetic data were retrieved from those studies by the Netherlands Organisation for Applied Scientific Research (TNO) using curve stripping (TechDig v2). This data is provided for statistical analysis as in Wambaugh et al. 2018.

Usage

chem.invivo.PK.data

Format

A data.frame containing 597 rows and 13 columns.

Author(s)

Sieto Bosgra

Source

Wambaugh et al. 2018 Toxicological Sciences, in press

References

Aanderud L, Bakke OM (1983). Pharmacokinetics of antipyrine, paracetamol, and morphine in rat at 71 ATA. Undersea Biomed Res. 10(3):193-201. PMID: 6636344

Aasmoe L, Mathiesen M, Sager G (1999). Elimination of methoxyacetic acid and ethoxyacetic acid in rat. Xenobiotica. 29(4):417-24. PMID: 10375010

Ako RA. Pharmacokinetics/pharmacodynamics (PK/PD) of oral diethylstilbestrol (DES) in recurrent prostate cancer patients and of oral dissolving film (ODF)-DES in rats. PhD dissertation, College of Pharmacy, University of Houston, USA, 2011.

Anadon A, Martinez-Larranaga MR, Fernandez-Cruz ML, Diaz MJ, Fernandez MC, Martinez MA (1996). Toxicokinetics of deltamethrin and its 4'-HO-metabolite in the rat. Toxicol Appl Pharmacol. 141(1):8-16. PMID: 8917670

Binkerd PE, Rowland JM, Nau H, Hendrickx AG (1988). Evaluation of valproic acid (VPA) developmental toxicity and pharmacokinetics in Sprague-Dawley rats. Fundam Appl Toxicol. 11(3):485-93. PMID: 3146521

Boralli VB, Coelho EB, Cerqueira PM, Lanchote VL (2005). Stereoselective analysis of metoprolol and its metabolites in rat plasma with application to oxidative metabolism. J Chromatogr B Analyt Technol Biomed Life Sci. 823(2):195-202. PMID: 16029965

Chan MP, Morisawa S, Nakayama A, Kawamoto Y, Sugimoto M, Yoneda M (2005). Toxicokinetics of 14C-endosulfan in male Sprague-Dawley rats following oral administration of single or repeated doses. Environ Toxicol. 20(5):533-41. PMID: 16161119

Cruz L, Castaneda-Hernandez G, Flores-Murrieta FJ, Garcia-Lopez P, Guizar-Sahagun G (2002). Alteration of phenacetin pharmacokinetics after experimental spinal cord injury. Proc West Pharmacol Soc. 45:4-5. PMID: 12434508

Della Paschoa OE, Mandema JW, Voskuyl RA, Danhof M (1998). Pharmacokinetic-pharmacodynamic modeling of the anticonvulsant and electroencephalogram effects of phenytoin in rats. J Pharmacol Exp Ther. 284(2):460-6. PMID: 9454785

Du B, Li X, Yu Q, A Y, Chen C (2010). Pharmacokinetic comparison of orally disintegrating, beta-cyclodextrin inclusion complex and conventional tablets of nicardipine in rats. Life Sci J. 7(2):80-4.

Farris FF, Dedrick RL, Allen PV, Smith JC (1993). Physiological model for the pharmacokinetics of methyl mercury in the growing rat. Toxicol Appl Pharmacol. 119(1):74-90. PMID: 8470126

Hays SM, Elswick BA, Blumenthal GM, Welsch F, Conolly RB, Gargas ML (2000). Development of a physiologically based pharmacokinetic model of 2-methoxyethanol and 2-methoxyacetic acid disposition in pregnant rats. Toxicol Appl Pharmacol. 163(1):67-74. PMID: 10662606

Igari Y, Sugiyama Y, Awazu S, Hanano M (1982). Comparative physiologically based pharmacokinetics of hexobarbital, phenobarbital and thiopental in the rat. J Pharmacokinet Biopharm. 10(1):53-75. PMID: 7069578

Ito K, Houston JB (2004). Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res. 21(5):785-92. PMID: 15180335

Jia L, Wong H, Wang Y, Garza M, Weitman SD (2003). Carbendazim: disposition, cellular permeability, metabolite identification, and pharmacokinetic comparison with its nanoparticle. J Pharm Sci. 92(1):161-72. PMID: 12486692

Kawai R, Mathew D, Tanaka C, Rowland M (1998). Physiologically based pharmacokinetics of cyclosporine A: extension to tissue distribution kinetics in rats and scale-up to human. J Pharmacol Exp Ther. 287(2):457-68. PMID: 9808668

Kim YC, Kang HE, Lee MG (2008). Pharmacokinetics of phenytoin and its metabolite, 4'-HPPH, after intravenous and oral administration of phenytoin to diabetic rats induced by alloxan or streptozotocin. Biopharm Drug Dispos. 29(1):51-61. PMID: 18022993

Kobayashi S, Takai K, Iga T, Hanano M (1991). Pharmacokinetic analysis of the disposition of valproate in pregnant rats. Drug Metab Dispos. 19(5):972-6. PMID: 1686245

Kotegawa T, Laurijssens BE, Von Moltke LL, Cotreau MM, Perloff MD, Venkatakrishnan K, Warrington JS, Granda BW, Harmatz JS, Greenblatt DJ (2002). In vitro, pharmacokinetic, and pharmacodynamic interactions of ketoconazole and midazolam in the rat. J Pharmacol Exp Ther. 302(3):1228-37. PMID: 12183684

Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K, Baquie M, Waldmann T, Ensenat-Waser R, Jagtap S, Evans RM, Julien S, Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou I, Heke M, Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout J, Verwei M, Vilo J, Kortenkamp A, Hescheler J, Hothorn L, Bremer S, van Thriel C, Krause KH, Hengstler JG, Rahnenfuhrer J, Leist M, Sachinidis A (2013). Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol. 87(1):123-43. PMID: 23179753

Leon-Reyes MR, Castaneda-Hernandez G, Ortiz MI (2009). Pharmacokinetic of diclofenac in the presence and absence of glibenclamide in the rat. J Pharm Pharm Sci. 12(3):280-7. PMID: 20067705

Nagata M, Hidaka M, Sekiya H, Kawano Y, Yamasaki K, Okumura M, Arimori K (2007). Effects of pomegranate juice on human cytochrome P450 2C9 and tolbutamide pharmacokinetics in rats. Drug Metab Dispos. 35(2):302-5. PMID: 17132763

Okiyama M, Ueno K, Ohmori S, Igarashi T, Kitagawa H (1988). Drug interactions between imipramine and benzodiazepines in rats. J Pharm Sci. 77(1):56-63. PMID: 2894451

Pelissier-Alicot AL, Schreiber-Deturmeny E, Simon N, Gantenbein M, Bruguerolle B (2002). Time-of-day dependent pharmacodynamic and pharmacokinetic profiles of caffeine in rats. Naunyn Schmiedebergs Arch Pharmacol. 365(4):318-25. PMID: 11919657

Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van der Linden SC, Man H, Roelofs MJ, Schulpen SH, Schwarz M, Uibel F, van Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B (2013). Evaluation of an alternative in vitro test battery for detecting reproductive toxicants. Reprod Toxicol. 38:53-64. PMID: 23511061

Pollack GM, Li RC, Ermer JC, Shen DD (1985). Effects of route of administration and repetitive dosing on the disposition kinetics of di(2-ethylhexyl) phthalate and its mono-de-esterified metabolite in rats. Toxicol Appl Pharmacol. Jun 30;79(2):246-56. PMID: 4002226

Saadeddin A, Torres-Molina F, Carcel-Trullols J, Araico A, Peris JE (2004). Pharmacokinetics of the time-dependent elimination of all-trans-retinoic acid in rats. AAPS J. 6(1):1-9. PMID: 18465253

Satterwhite JH, Boudinot FD (1991). Effects of age and dose on the pharmacokinetics of ibuprofen in the rat. Drug Metab Dispos. 19(1):61-7. PMID: 1673423

Szymura-Oleksiak J, Panas M, Chrusciel W (1983). Pharmacokinetics of imipramine after single and multiple intravenous administration in rats. Pol J Pharmacol Pharm. 35(2):151-7. PMID: 6622297

Tanaka C, Kawai R, Rowland M (2000). Dose-dependent pharmacokinetics of cyclosporin A in rats: events in tissues. Drug Metab Dispos. 28(5):582-9. PMID: 10772639

Timchalk C, Nolan RJ, Mendrala AL, Dittenber DA, Brzak KA, Mattsson JL (2002). A Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and humans. Toxicol Sci. Mar;66(1):34-53. PMID: 11861971

Tokuma Y, Sekiguchi M, Niwa T, Noguchi H (1988). Pharmacokinetics of nilvadipine, a new dihydropyridine calcium antagonist, in mice, rats, rabbits and dogs. Xenobiotica 18(1):21-8. PMID: 3354229

Treiber A, Schneiter R, Delahaye S, Clozel M (2004). Inhibition of organic anion transporting polypeptide-mediated hepatic uptake is the major determinant in the pharmacokinetic interaction between bosentan and cyclosporin A in the rat. J Pharmacol Exp Ther. 308(3):1121-9. PMID: 14617681

Tsui BC, Feng JD, Buckley SJ, Yeung PK (1994). Pharmacokinetics and metabolism of diltiazem in rats following a single intra-arterial or single oral dose. Eur J Drug Metab Pharmacokinet. 19(4):369-73. PMID: 7737239

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences (2015): 228-237.

Wang Y, Roy A, Sun L, Lau CE (1999). A double-peak phenomenon in the pharmacokinetics of alprazolam after oral administration. Drug Metab Dispos. 27(8):855-9. PMID: 10421610

Wang X, Lee WY, Or PM, Yeung JH (2010). Pharmacokinetic interaction studies of tanshinones with tolbutamide, a model CYP2C11 probe substrate, using liver microsomes, primary hepatocytes and in vivo in the rat. Phytomedicine. 17(3-4):203-11. PMID: 19679455

Yang SH, Lee MG (2008). Dose-independent pharmacokinetics of ondansetron in rats: contribution of hepatic and intestinal first-pass effects to low bioavailability. Biopharm Drug Dispos. 29(7):414-26. PMID: 18697186

Yeung PK, Alcos A, Tang J (2009). Pharmacokinetics and Hemodynamic Effects of Diltiazem in Rats Following Single vs Multiple Doses In Vivo. Open Drug Metab J. 3:56-62.

Summary of published toxicokinetic time course experiments

Description

This data set summarizes the time course data in the chem.invivo.PK.data table. Maximum concentration (Cmax), time integrated plasma concentration for the duration of treatment (AUC.treatment) and extrapolated to zero concentration (AUC.infinity) as well as half-life are calculated. Summary values are given for each study and dosage. These data can be used to evaluate toxicokinetic model predictions.

Usage

chem.invivo.PK.summary.data

Format

A data.frame containing 100 rows and 25 columns.

Author(s)

John Wambaugh

Source

Wambaugh et al. 2018 Toxicological Sciences, in press

References

Aanderud L, Bakke OM (1983). Pharmacokinetics of antipyrine, paracetamol, and morphine in rat at 71 ATA. Undersea Biomed Res. 10(3):193-201. PMID: 6636344

Aasmoe L, Mathiesen M, Sager G (1999). Elimination of methoxyacetic acid and ethoxyacetic acid in rat. Xenobiotica. 29(4):417-24. PMID: 10375010

Ako RA. Pharmacokinetics/pharmacodynamics (PK/PD) of oral diethylstilbestrol (DES) in recurrent prostate cancer patients and of oral dissolving film (ODF)-DES in rats. PhD dissertation, College of Pharmacy, University of Houston, USA, 2011.

Anadon A, Martinez-Larranaga MR, Fernandez-Cruz ML, Diaz MJ, Fernandez MC, Martinez MA (1996). Toxicokinetics of deltamethrin and its 4'-HO-metabolite in the rat. Toxicol Appl Pharmacol. 141(1):8-16. PMID: 8917670

Binkerd PE, Rowland JM, Nau H, Hendrickx AG (1988). Evaluation of valproic acid (VPA) developmental toxicity and pharmacokinetics in Sprague-Dawley rats. Fundam Appl Toxicol. 11(3):485-93. PMID: 3146521

Boralli VB, Coelho EB, Cerqueira PM, Lanchote VL (2005). Stereoselective analysis of metoprolol and its metabolites in rat plasma with application to oxidative metabolism. J Chromatogr B Analyt Technol Biomed Life Sci. 823(2):195-202. PMID: 16029965

Chan MP, Morisawa S, Nakayama A, Kawamoto Y, Sugimoto M, Yoneda M (2005). Toxicokinetics of 14C-endosulfan in male Sprague-Dawley rats following oral administration of single or repeated doses. Environ Toxicol. 20(5):533-41. PMID: 16161119

Cruz L, Castaneda-Hernandez G, Flores-Murrieta FJ, Garcia-Lopez P, Guizar-Sahagun G (2002). Alteration of phenacetin pharmacokinetics after experimental spinal cord injury. Proc West Pharmacol Soc. 45:4-5. PMID: 12434508

Della Paschoa OE, Mandema JW, Voskuyl RA, Danhof M (1998). Pharmacokinetic-pharmacodynamic modeling of the anticonvulsant and electroencephalogram effects of phenytoin in rats. J Pharmacol Exp Ther. 284(2):460-6. PMID: 9454785

Du B, Li X, Yu Q, A Y, Chen C (2010). Pharmacokinetic comparison of orally disintegrating, beta-cyclodextrin inclusion complex and conventional tablets of nicardipine in rats. Life Sci J. 7(2):80-4.

Farris FF, Dedrick RL, Allen PV, Smith JC (1993). Physiological model for the pharmacokinetics of methyl mercury in the growing rat. Toxicol Appl Pharmacol. 119(1):74-90. PMID: 8470126

Hays SM, Elswick BA, Blumenthal GM, Welsch F, Conolly RB, Gargas ML (2000). Development of a physiologically based pharmacokinetic model of 2-methoxyethanol and 2-methoxyacetic acid disposition in pregnant rats. Toxicol Appl Pharmacol. 163(1):67-74. PMID: 10662606

Igari Y, Sugiyama Y, Awazu S, Hanano M (1982). Comparative physiologically based pharmacokinetics of hexobarbital, phenobarbital and thiopental in the rat. J Pharmacokinet Biopharm. 10(1):53-75. PMID: 7069578

Ito K, Houston JB (2004). Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res. 21(5):785-92. PMID: 15180335

Jia L, Wong H, Wang Y, Garza M, Weitman SD (2003). Carbendazim: disposition, cellular permeability, metabolite identification, and pharmacokinetic comparison with its nanoparticle. J Pharm Sci. 92(1):161-72. PMID: 12486692

Kawai R, Mathew D, Tanaka C, Rowland M (1998). Physiologically based pharmacokinetics of cyclosporine A: extension to tissue distribution kinetics in rats and scale-up to human. J Pharmacol Exp Ther. 287(2):457-68. PMID: 9808668

Kim YC, Kang HE, Lee MG (2008). Pharmacokinetics of phenytoin and its metabolite, 4'-HPPH, after intravenous and oral administration of phenytoin to diabetic rats induced by alloxan or streptozotocin. Biopharm Drug Dispos. 29(1):51-61. PMID: 18022993

Kobayashi S, Takai K, Iga T, Hanano M (1991). Pharmacokinetic analysis of the disposition of valproate in pregnant rats. Drug Metab Dispos. 19(5):972-6. PMID: 1686245

Kotegawa T, Laurijssens BE, Von Moltke LL, Cotreau MM, Perloff MD, Venkatakrishnan K, Warrington JS, Granda BW, Harmatz JS, Greenblatt DJ (2002). In vitro, pharmacokinetic, and pharmacodynamic interactions of ketoconazole and midazolam in the rat. J Pharmacol Exp Ther. 302(3):1228-37. PMID: 12183684

Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K, Baquie M, Waldmann T, Ensenat-Waser R, Jagtap S, Evans RM, Julien S, Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou I, Heke M, Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout J, Verwei M, Vilo J, Kortenkamp A, Hescheler J, Hothorn L, Bremer S, van Thriel C, Krause KH, Hengstler JG, Rahnenfuhrer J, Leist M, Sachinidis A (2013). Human embryonic stem cell-derived test systems for developmental neurotoxicity: a transcriptomics approach. Arch Toxicol. 87(1):123-43. PMID: 23179753

Leon-Reyes MR, Castaneda-Hernandez G, Ortiz MI (2009). Pharmacokinetic of diclofenac in the presence and absence of glibenclamide in the rat. J Pharm Pharm Sci. 12(3):280-7. PMID: 20067705

Nagata M, Hidaka M, Sekiya H, Kawano Y, Yamasaki K, Okumura M, Arimori K (2007). Effects of pomegranate juice on human cytochrome P450 2C9 and tolbutamide pharmacokinetics in rats. Drug Metab Dispos. 35(2):302-5. PMID: 17132763

Okiyama M, Ueno K, Ohmori S, Igarashi T, Kitagawa H (1988). Drug interactions between imipramine and benzodiazepines in rats. J Pharm Sci. 77(1):56-63. PMID: 2894451

Pelissier-Alicot AL, Schreiber-Deturmeny E, Simon N, Gantenbein M, Bruguerolle B (2002). Time-of-day dependent pharmacodynamic and pharmacokinetic profiles of caffeine in rats. Naunyn Schmiedebergs Arch Pharmacol. 365(4):318-25. PMID: 11919657

Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van der Linden SC, Man H, Roelofs MJ, Schulpen SH, Schwarz M, Uibel F, van Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B (2013). Evaluation of an alternative in vitro test battery for detecting reproductive toxicants. Reprod Toxicol. 38:53-64. PMID: 23511061

Pollack GM, Li RC, Ermer JC, Shen DD (1985). Effects of route of administration and repetitive dosing on the disposition kinetics of di(2-ethylhexyl) phthalate and its mono-de-esterified metabolite in rats. Toxicol Appl Pharmacol. Jun 30;79(2):246-56. PMID: 4002226

Saadeddin A, Torres-Molina F, Carcel-Trullols J, Araico A, Peris JE (2004). Pharmacokinetics of the time-dependent elimination of all-trans-retinoic acid in rats. AAPS J. 6(1):1-9. PMID: 18465253

Satterwhite JH, Boudinot FD (1991). Effects of age and dose on the pharmacokinetics of ibuprofen in the rat. Drug Metab Dispos. 19(1):61-7. PMID: 1673423

Szymura-Oleksiak J, Panas M, Chrusciel W (1983). Pharmacokinetics of imipramine after single and multiple intravenous administration in rats. Pol J Pharmacol Pharm. 35(2):151-7. PMID: 6622297

Tanaka C, Kawai R, Rowland M (2000). Dose-dependent pharmacokinetics of cyclosporin A in rats: events in tissues. Drug Metab Dispos. 28(5):582-9. PMID: 10772639

Timchalk C, Nolan RJ, Mendrala AL, Dittenber DA, Brzak KA, Mattsson JL (2002). A Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and humans. Toxicol Sci. Mar;66(1):34-53. PMID: 11861971

Tokuma Y, Sekiguchi M, Niwa T, Noguchi H (1988). Pharmacokinetics of nilvadipine, a new dihydropyridine calcium antagonist, in mice, rats, rabbits and dogs. Xenobiotica 18(1):21-8. PMID: 3354229

Treiber A, Schneiter R, Delahaye S, Clozel M (2004). Inhibition of organic anion transporting polypeptide-mediated hepatic uptake is the major determinant in the pharmacokinetic interaction between bosentan and cyclosporin A in the rat. J Pharmacol Exp Ther. 308(3):1121-9. PMID: 14617681

Tsui BC, Feng JD, Buckley SJ, Yeung PK (1994). Pharmacokinetics and metabolism of diltiazem in rats following a single intra-arterial or single oral dose. Eur J Drug Metab Pharmacokinet. 19(4):369-73. PMID: 7737239

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences (2015): 228-237.

Wang Y, Roy A, Sun L, Lau CE (1999). A double-peak phenomenon in the pharmacokinetics of alprazolam after oral administration. Drug Metab Dispos. 27(8):855-9. PMID: 10421610

Wang X, Lee WY, Or PM, Yeung JH (2010). Pharmacokinetic interaction studies of tanshinones with tolbutamide, a model CYP2C11 probe substrate, using liver microsomes, primary hepatocytes and in vivo in the rat. Phytomedicine. 17(3-4):203-11. PMID: 19679455

Yang SH, Lee MG (2008). Dose-independent pharmacokinetics of ondansetron in rats: contribution of hepatic and intestinal first-pass effects to low bioavailability. Biopharm Drug Dispos. 29(7):414-26. PMID: 18697186

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Physico-chemical properties and in vitro measurements for toxicokinetics

Description

This data set contains the necessary information to make basic, high-throughput toxicokinetic (HTTK) predictions for compounds, including Funbound.plasma, molecular weight (g/mol), logP, logMA (membrane affinity), intrinsic clearance(uL/min/10^6 cells), and pKa. These data have been compiled from multiple sources, and can be used to parameterize a variety of toxicokinetic models. See variable EPA.ref for information on the reference EPA.

Usage

chem.physical_and_invitro.data

Format

A data.frame containing 9411 rows and 54 columns.

Column Name Description Units
Compound The preferred name of the chemical compound none
CAS The preferred Chemical Abstracts Service Registry Number none
CAS.Checksum A logical indicating whether the CAS number is valid none
DTXSID DSSTox Structure ID (https://comptox.epa.gov/dashboard) none
Formula The proportions of atoms within the chemical compound none
All.Compound.Names All names of the chemical as they occured in the data none
logHenry The log10 Henry's law constant log10(atmosphers*m^3/mole)
logHenry.Reference Reference for Henry's law constant
logP The log10 octanol:water partition coefficient (PC) log10 unitless ratio
logP.Reference Reference for logPow
logPwa The log10 water:air PC log10 unitless ratio
logPwa.Reference Reference for logPwa
logMA The log10 phospholipid:water PC or "Membrane affinity" unitless ratio
logMA.Reference Reference for membrane affinity
logWSol The log10 water solubility log10(mole/L)
logWSol.Reference Reference for logWsol
MP The chemical compound melting point degrees Celsius
MP.Reference Reference for melting point
MW The chemical compound molecular weight g/mol
MW.Reference Reference for molecular weight
pKa_Accept The hydrogen acceptor equilibria concentrations logarithm
pKa_Accept.Reference Reference for pKa_Accept
pKa_Donor The hydrogen acceptor equilibria concentrations logarithm
pKa_Donor.Reference Reference for pKa_Donor
All.Species All species for which data were available none
DTXSID.Reference Reference for DTXSID
Formula.Reference Reference for chemical formulat
[SPECIES].Clint (Primary hepatocyte suspension) intrinsic hepatic clearance. Entries with comma separated values are Bayesian estimates of the Clint distribution - displayed as the median, 95th credible interval (that is quantile 2.5 and 97.5, respectively), and p-value. uL/min/10^6 hepatocytes
[SPECIES].Clint.pValue Probability that there is no clearance observed. Values close to 1 indicate clearance is not statistically significant. none
[SPECIES].Clint.pValue.Ref Reference for Clint pValue
[SPECIES].Clint.Reference Reference for Clint
[SPECIES].Caco2.Pab Caco-2 Apical-to-Basal Membrane Permeability 10^-6 cm/s
[SPECIES].Caco2.Pab.Reference Reference for Caco-2 Membrane Permeability
[SPECIES].Fabs In vivo measured fraction of an oral dose of chemical absorbed from the gut lumen into the gut unitless fraction
[SPECIES].Fabs.Reference Reference for Fabs
[SPECIES].Fgut In vivo measured fraction of an oral dose of chemical that passes gut metabolism and clearance unitless fraction
[SPECIES].Fgut.Reference Reference for Fgut
[SPECIES].Foral In vivo measued fractional systemic bioavailability of an oral dose, modeled as he product of Fabs * Fgut * Fhep (where Fhep is first pass hepatic metabolism). unitless fraction
[SPECIES].Foral.Reference Reference for Foral
[SPECIES].Funbound.plasma Chemical fraction unbound in presence of plasma proteins (fup). Entries with comma separated values are Bayesian estimates of the fup distribution - displayed as the median and 95th credible interval (that is quantile 2.5 and 97.5, respectively). unitless fraction
[SPECIES].Funbound.plasma.Ref Reference for Funbound.plasma
[SPECIES].Rblood2plasma Chemical concentration blood to plasma ratio unitless ratio
[SPECIES].Rblood2plasma.Ref Reference for Rblood2plasma
Chemical.Class All classes to which the chemical has been assigned

Details

In some cases the rapid equilbrium dailysis method (Waters et al., 2008) fails to yield detectable concentrations for the free fraction of chemical. In those cases we assume the compound is highly bound (that is, Fup approaches zero). For some calculations (for example, steady-state plasma concentration) there is precendent (Rotroff et al., 2010) for using half the average limit of detection, that is 0.005. We do not recomend using other models where quantities like partition coefficients must be predicted using Fup. We also do not recomend including the value 0.005 in training sets for Fup predictive models.

Note that in some cases the Funbound.plasma and the intrinsic clearance are provided as a series of numbers separated by commas. These values are the result of Bayesian analysis and characterize a distribution: the first value is the median of the distribution, while the second and third values are the lower and upper 95th percentile (that is qunatile 2.5 and 97.5) respectively. For intrinsic clearance a fourth value indicating a p-value for a decrease is provided. Typically 4000 samples were used for the Bayesian analusis, such that a p-value of "0" is equivale to "<0.00025". See Wambaugh et al. (2019) for more details.

Any one chemical compound may have multiple ionization equilibria (see Strope et al., 2018) may both for donating or accepting a proton (and therefore changing charge state). If there are multiple equlibria of the same type (donor/accept])the are concatonated by commas.

All species-specific information is initially from experimental measurements. The functions load_sipes2017, load_pradeep2020, and load_dawson2021 may be used to add in silico, structure-based predictions for many thousands of additional compounds to this table.

Author(s)

John Wambaugh

Source

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences (2015): 228-237.

References

CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard)

EPI Suite, https://www.epa.gov/opptintr/exposure/pubs/episuite.htm

Brown, Hayley S., Michael Griffin, and J. Brian Houston. "Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance." Drug metabolism and disposition 35.2 (2007): 293-301.

Gulden, Michael, et al. "Impact of protein binding on the availability and cytotoxic potency of organochlorine pesticides and chlorophenols in vitro." Toxicology 175.1-3 (2002): 201-213.

Hilal, S., Karickhoff, S. and Carreira, L. (1995). A rigorous test for SPARC's chemical reactivity models: Estimation of more than 4300 ionization pKas. Quantitative Structure-Activity Relationships 14(4), 348-355.

Honda, G. S., Pearce, R. G., Pham, L. L., Setzer, R. W., Wetmore, B. A., Sipes, N. S., ... & Wambaugh, J. F. (2019). Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions. PloS one, 14(5), e0217564.

Ito, K. and Houston, J. B. (2004). Comparison of the use of liver models for predicting drug clearance using in vitro kinetic data from hepatic microsomes and isolated hepatocytes. Pharm Res 21(5), 785-92.

Jones, O. A., Voulvoulis, N. and Lester, J. N. (2002). Aquatic environmental assessment of the top 25 English prescription pharmaceuticals. Water research 36(20), 5013-22.

Jones, Barry C., et al. "An investigation into the prediction of in vivo clearance for a range of flavin-containing monooxygenase substrates." Drug metabolism and disposition 45.10 (2017): 1060-1067.

Lau, Y. Y., Sapidou, E., Cui, X., White, R. E. and Cheng, K. C. (2002). Development of a novel in vitro model to predict hepatic clearance using fresh, cryopreserved, and sandwich-cultured hepatocytes. Drug Metabolism and Disposition 30(12), 1446-54.

Linakis, M. W., Sayre, R. R., Pearce, R. G., Sfeir, M. A., Sipes, N. S., Pangburn, H. A., ... & Wambaugh, J. F. (2020). Development and evaluation of a high-throughput inhalation model for organic chemicals. Journal of Exposure Science & Environmental Epidemiology, 1-12.

Lombardo, F., Berellini, G., & Obach, R. S. (2018). Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 1352 drug compounds. Drug Metabolism and Disposition, 46(11), 1466-1477.

McGinnity, D. F., Soars, M. G., Urbanowicz, R. A. and Riley, R. J. (2004). Evaluation of fresh and cryopreserved hepatocytes as in vitro drug metabolism tools for the prediction of metabolic clearance. Drug Metabolism and Disposition 32(11), 1247-53, 10.1124/dmd.104.000026.

Naritomi, Y., Terashita, S., Kagayama, A. and Sugiyama, Y. (2003). Utility of Hepatocytes in Predicting Drug Metabolism: Comparison of Hepatic Intrinsic Clearance in Rats and Humans in Vivo and in Vitro. Drug Metabolism and Disposition 31(5), 580-588, 10.1124/dmd.31.5.580.

Obach, R. S. (1999). Prediction of human clearance of twenty-nine drugs from hepatic microsomal intrinsic clearance data: An examination of in vitro half-life approach and nonspecific binding to microsomes. Drug Metabolism and Disposition 27(11), 1350-9.

Paini, Alicia; Cole, Thomas; Meinero, Maria; Carpi, Donatella; Deceuninck, Pierre; Macko, Peter; Palosaari, Taina; Sund, Jukka; Worth, Andrew; Whelan, Maurice (2020): EURL ECVAM in vitro hepatocyte clearance and blood plasma protein binding dataset for 77 chemicals. European Commission, Joint Research Centre (JRC) [Dataset] PID: https://data.europa.eu/89h/a2ff867f-db80-4acf-8e5c-e45502713bee

Paixao, P., Gouveia, L. F., & Morais, J. A. (2012). Prediction of the human oral bioavailability by using in vitro and in silico drug related parameters in a physiologically based absorption model. International journal of pharmaceutics, 429(1), 84-98.

Pirovano, Alessandra, et al. "QSARs for estimating intrinsic hepatic clearance of organic chemicals in humans." Environmental toxicology and pharmacology 42 (2016): 190-197.

Riley, Robert J., Dermot F. McGinnity, and Rupert P. Austin. "A unified model for predicting human hepatic, metabolic clearance from in vitro intrinsic clearance data in hepatocytes and microsomes." Drug Metabolism and Disposition 33.9 (2005): 1304-1311.

Schmitt, W. (2008). General approach for the calculation of tissue to plasma partition coefficients. Toxicology in vitro : an international journal published in association with BIBRA 22(2), 457-67, 10.1016/j.tiv.2007.09.010.

Shibata, Y., Takahashi, H., Chiba, M. and Ishii, Y. (2002). Prediction of Hepatic Clearance and Availability by Cryopreserved Human Hepatocytes: An Application of Serum Incubation Method. Drug Metabolism and Disposition 30(8), 892-896, 10.1124/dmd.30.8.892.

Sohlenius-Sternbeck, Anna-Karin, et al. "Practical use of the regression offset approach for the prediction of in vivo intrinsic clearance from hepatocytes." Xenobiotica 42.9 (2012): 841-853.

Tonnelier, A., Coecke, S. and Zaldivar, J.-M. (2012). Screening of chemicals for human bioaccumulative potential with a physiologically based toxicokinetic model. Archives of Toxicology 86(3), 393-403, 10.1007/s00204-011-0768-0.

Uchimura, Takahide, et al. "Prediction of human blood-to-plasma drug concentration ratio." Biopharmaceutics & drug disposition 31.5-6 (2010): 286-297.

Wambaugh, J. F., Wetmore, B. A., Ring, C. L., Nicolas, C. I., Pearce, R. G., Honda, G. S., ... & Badrinarayanan, A. (2019). Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization. Toxicological Sciences, 172(2), 235-251.

Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Sochaski, M. A., Rotroff, D. M., Freeman, K., Clewell, H. J., 3rd, Dix, D. J., Andersen, M. E., Houck, K. A., Allen, B., Judson, R. S., Singh, R., Kavlock, R. J., Richard, A. M. and Thomas, R. S. (2012). Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicological sciences : an official journal of the Society of Toxicology 125(1), 157-74, 10.1093/toxsci/kfr254.

Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Li, L., Clewell, H. J., Judson, R. S., Freeman, K., Bao, W., Sochaski, M. A., Chu, T.-M., Black, M. B., Healy, E., Allen, B., Andersen, M. E., Wolfinger, R. D. and Thomas, R. S. (2013). Relative Impact of Incorporating Pharmacokinetics on Predicting In Vivo Hazard and Mode of Action from High-Throughput In Vitro Toxicity Assays. Toxicological Sciences 132(2), 327-346, 10.1093/toxsci/kft012.

Wetmore BA, Wambaugh JF, Allen B, Ferguson SS, Sochaski MA, Setzer RW, Houck KA, Strope CL, Cantwell K, Judson RS, others (2015). “Incorporating high-throughput exposure predictions with dosimetry-adjusted in vitro bioactivity to inform chemical toxicity testing.” Toxicological Sciences, 148(1), 121–136.

F. L. Wood, J. B. Houston and D. Hallifax 'Drug Metabolism and Disposition November 1, 2017, 45 (11) 1178-1188; DOI: https://doi.org/10.1124/dmd.117.077040

See Also

get_physchem_param

get_invitroPK_param

add_chemtable

CKD-EPI equation for GFR.

Description

Predict GFR from serum creatinine, gender, and age.

Usage

ckd_epi_eq(scr, gender, reth, age_years, ckd_epi_race_coeff = FALSE)

Arguments

scr

Vector of serum creatinine values in mg/dL.
gender

Vector of genders (either 'Male' or 'Female').
reth

Vector of races/ethnicities. Not used unless ckd_epi_race_coeff is TRUE.
age_years

Vector of ages in years.
ckd_epi_race_coeff

Whether to use the "race coefficient" in the CKD-EPI equation. Default is FALSE.

Details

From Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006

Value

Vector of GFR values in mL/min/1.73m^2.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Concentration data involved in Linakis 2020 vignette analysis.

Description

These rat and human TK concentration vs. time (CvT) data are drawn from the CvTdb (Sayre et el., 2020). Concentrations have all been converted to the units of uM. All data are from inhalation studies.

Usage

concentration_data_Linakis2020

Format

A data.frame containing 2142 rows and 16 columns.

Details

Abbreviations used for sampling matrix: BL : blood EEB : end-exhaled breath MEB : mixed exhaled breath VBL : venous blood ABL : arterial blood EB : unspecified exhaled breath sample (assumed to be EEB) PL: plasma +W with work/exercise

Column Name Description
PREFERRED_NAME Substance preferred name
DTXSID Identifier for CompTox Chemical Dashboard
CASRN Chemical abstracts service registration number
AVERAGE_MASS Substance molecular weight g/mol
DOSE DOSE_U Inhalation exposure concentration in parts per million
EXP_LENGTH Duration of inhalation exposur
TIME Measurment time
TIME_U Time units for all times reported
CONC_SPECIES Species for study
SAMPLING_MATRIX Matrix analyzed
SOURCE_CVT Data source identifier within CvTdb
ORIG_CONC_U Original reported units for concentration
CONCENTRATION Analyte concentration in uM units

Author(s)

Matt Linakis

Source

Matt Linakis

References

Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, Gearhart JM, Wambaugh JF (2020). “Development and evaluation of a high-throughput inhalation model for organic chemicals.” Journal of exposure science & environmental epidemiology, 30(5), 866–877. Sayre RR, Wambaugh JF, Grulke CM (2020). “Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals.” Scientific data, 7(1), 122.

convert_solve_x

Description

This function is designed to convert compartment values estimated from one of the HTTK models (e.g. "1compartment) using the solve_model function. It takes the HTTK model output matrix, model name, desired output units, and compound information to perform the conversion default model units to user specified units.

Usage

convert_solve_x(
  model.output.mat,
  model = NULL,
  output.units = NULL,
  MW = NULL,
  vol = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  monitor.vars = NULL,
  suppress.messages = FALSE,
  verbose = FALSE,
  ...
)

Arguments

model.output.mat

Matrix of results from HTTK solve_model function.
model

Specified model to use in simulation: "pbtk", "3compartment", "3compartmentss", "1compartment", "schmitt", ...
output.units

Output units of interest for the compiled components. Defaults to NULL, and will provide values in model units if unspecified.
MW

Molecular weight of substance of interest in g/mole
vol

Volume for the target tissue of interest in liters (L). NOTE: Volume should not be in units of per BW, i.e. "kg".
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
chem.name

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID . (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs.
parameters

A set of model parameters, especially a set that includes MW (molecular weight) for our conversions.
monitor.vars

A vector of character strings indicating the model component variables to retain in the conversion factor table (assuming suppress.messages == FALSE). It should also be noted this option does NOT exclude columns from the input matrix provided in the 'model.output.mat' parameter. (Default is NULL, i.e. conversion factors for all model components are included in the reporting matrix.)
suppress.messages

Whether or not the output messages are suppressed. (Default is FALSE, i.e. show messages.)
verbose

Whether or not to display the full conversion factor table. (Default is FALSE, i.e. only include rows where the conversion factor is 1.)
...

Other parameters that can be passed to convert_units, e.g. temperature and compound state. See details in convert_units.

Details

The function can be used to convert all compartments to a single unit, only units for a single model compartment, or units for a set of model compartments.

More details on the unit conversion can be found in the documentation for convert_units.

Value

'new.ouput.matrix' A matrix with a column for time (in days), each compartment, and the area under the curve (AUC) and a row for each time point. The compartment and AUC columns are converted from model specified units to user specified units.

'output.units.vector' A vector of character strings providing the model compartments and their corresponding units after convert_solve_x.

Author(s)

Sarah E. Davidson

See Also

convert_units

Examples

output.mat <- solve_1comp(dtxsid = "DTXSID0020573",days=1)
new.output.mat <- convert_solve_x(output.units = "mg",
                                  model.output.mat = output.mat,
                                  model = "1compartment",
                                  dtxsid = "DTXSID0020573")

convert_units

Description

This function is designed to accept input units, output units, and the molecular weight (MW) of a substance of interest to then use a table lookup to return a scaling factor that can be readily applied for the intended conversion. It can also take chemical identifiers in the place of a specified molecular weight value to retrieve that value for its own use.

Usage

convert_units(
  input.units = NULL,
  output.units = NULL,
  MW = NULL,
  vol = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  temp = 25,
  liquid.density = 1,
  state = "liquid"
)

Arguments

input.units

Assigned input units of interest
output.units

Desired output units
MW

Molecular weight of substance of interest in g/mole
vol

Volume for the target tissue of interest in liters (L). NOTE: Volume should not be in units of per BW, i.e. "kg".
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
chem.name

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

A set of model parameters, especially a set that includes MW (molecular weight) for our conversions
temp

Temperature for conversions (default = 25 degreees C)
liquid.density

Density of the specified chemical in liquid state, numeric value, (default 1.0 g/mL).
state

Chemical state (gas or default liquid).

Details

If input or output units not contained in the table are queried, it gives a corresponding error message. It gives a warning message about the handling of 'ppmv,' as the function is only set up to convert between ppmv and mass-based units (like mg/m3 or umol/L) in the context of ideal gases.

convert_units is not directly configured to accept and convert units based on BW, like mg/kg. For this purpose, see scale_dosing.

The function supports a limited set of most relevant units across toxicological models, currently including umol, uM, mg, mg/L, mg/m3 or umol/L), and in the context of gases assumed to be ideal, ppmv.

Andersen and Clewell's Rules of PBPK Modeling:

  1. Check Your Units

  2. Check Your Units

  3. Check Mass Balance

Author(s)

Mark Sfeir, John Wambaugh, and Sarah E. Davidson

Examples

# MW BPA is 228.29 g/mol
# 1 mg/L -> 1/228.29*1000 = 4.38 uM
convert_units("mg/L","uM",chem.cas="80-05-7")

# MW Diclofenac is 296.148 g/mol
# 1 uM -> 296.148/1000 =  0.296
convert_units("uM","mg/L",chem.name="diclofenac")

# ppmv only works for gasses:
try(convert_units("uM","ppmv",chem.name="styrene"))
convert_units("uM","ppmv",chem.name="styrene",state="gas")

# Compare with https://www3.epa.gov/ceampubl/learn2model/part-two/onsite/ia_unit_conversion.html
# 1 ug/L Toluene -> 0.263 ppmv
convert_units("ug/L","ppmv",chem.name="toluene",state="gas")
# 1 pppmv Toluene, 0.0038 mg/L
convert_units("ppmv","mg/L",chem.name="toluene",state="gas")

MW_pyrene <- get_physchem_param(param='MW', chem.name='pyrene')
conversion_factor <- convert_units(input.units='mg/L', output.units ='uM',
  MW=MW_pyrene)

calc_mc_oral_equiv(15, parameters=p)

Create a table of parameter values for Monte Carlo

Description

This is the HTTK master function for creating a data table for use with Monte Carlo methods to simulate parameter uncertainty and variabilit. Each column of the output table corresponds to an HTTK model parameter and each row corresponds to a different random draw (for example, different individuals when considering biological variability). This function call three different key functions to simulate parameter parameter uncertainty and/or variability in one of three ways. First parameters can be varied in an uncorrelated manner using truncated normal distributions by the function monte_carlo. Then, physiological parameters can be varied in a correlated manner according to the Ring et al. (2017) (doi:10.1016/j.envint.2017.06.004) httk-pop approach by the function httkpop_mc. Next, both uncertainty and variability of in vitro HTTK parameters can be simulated by the function invitro_mc as described by Wambaugh et al. (2019) (doi:10.1093/toxsci/kfz205). Finally, tissue-specific partition coefficients are predicted for each draw using the Schmitt (2008) (doi:10.1016/j.tiv.2007.09.010) method as calibrated to in vivo data by Pearce et al. (2017) (doi:10.1007/s10928-017-9548-7) and implemented in predict_partitioning_schmitt.

Usage

create_mc_samples(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  samples = 1000,
  species = "Human",
  suppress.messages = FALSE,
  model = "3compartmentss",
  httkpop = TRUE,
  invitrouv = TRUE,
  calcrb2p = TRUE,
  censored.params = list(),
  vary.params = list(),
  return.samples = FALSE,
  tissue = NULL,
  httkpop.dt = NULL,
  invitro.mc.arg.list = NULL,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  httkpop.generate.arg.list = list(method = "direct resampling"),
  convert.httkpop.arg.list = NULL,
  propagate.invitrouv.arg.list = NULL,
  parameterize.arg.list = NULL,
  Caco2.options = NULL
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Parameters from the appropriate parameterization function for the model indicated by argument model
samples

Number of samples generated in calculating quantiles.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Species must be set to "Human" to run httkpop model.
suppress.messages

Whether or not to suppress output message.
model

Model used in calculation: 'pbtk' for the multiple compartment model,'3compartment' for the three compartment model, '3compartmentss' for the three compartment steady state model, and '1compartment' for one compartment model. This only applies when httkpop=TRUE and species="Human", otherwise '3compartmentss' is used.
httkpop

Whether or not to use the Ring et al. (2017) "httkpop" population generator. Species must be 'Human'.
invitrouv

Logical to indicate whether to include in vitro parameters such as intrinsic hepatic clearance rate and fraction unbound in plasma in uncertainty and variability analysis
calcrb2p

Logical determining whether or not to recalculate the chemical ratio of blood to plasma
censored.params

The parameters listed in censored.params are sampled from a normal distribution that is censored for values less than the limit of detection (specified separately for each parameter). This argument should be a list of sub-lists. Each sublist is named for a parameter in "parameters" and contains two elements: "CV" (coefficient of variation) and "LOD" (limit of detection, below which parameter values are censored. New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Censored values are sampled on a uniform distribution between 0 and the limit of detection. Not used with httkpop model.
vary.params

The parameters listed in vary.params are sampled from a normal distribution that is truncated at zero. This argument should be a list of coefficients of variation (CV) for the normal distribution. Each entry in the list is named for a parameter in "parameters". New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the CV. Not used with httkpop model.
return.samples

Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.
tissue

Desired steady state tissue conentration.
httkpop.dt

A data table generated by httkpop_generate. This defaults to NULL, in which case httkpop_generate is called to generate this table.
invitro.mc.arg.list

Additional parameters passed to invitro_mc.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
httkpop.generate.arg.list

Additional parameters passed to httkpop_generate.
convert.httkpop.arg.list

Additional parameters passed to the convert_httkpop_* function for the model.
propagate.invitrouv.arg.list

Additional parameters passed to model's associated in vitro uncertainty and variability propagation function
parameterize.arg.list

Additional parameters passed to the parameterize_* function for the model.
Caco2.options

Arguments describing how to handle Caco2 absorption data that are passed to invitro_mc and the parameterize_[MODEL] functions. See get_fbio for further details.

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (2022).

We aim to make any function that uses chemical identifiers (name, CAS, DTXSID) also work if passed a complete vector of parameters (that is, a row from the table generated by this function). This allows the use of Monte Carlo to vary the parameters and therefore vary the function output. Depending on the type of parameters (for example, physiological vs. in vitro measurements) we vary the parameters in different ways with different functions.

Value

A data table where each column corresponds to parameters needed for the specified model and each row represents a different Monte Carlo sample of parameter values.

Author(s)

Caroline Ring, Robert Pearce, and John Wambaugh

References

Breen M, Wambaugh JF, Bernstein A, Sfeir M, Ring CL (2022). “Simulating toxicokinetic variability to identify susceptible and highly exposed populations.” Journal of Exposure Science & Environmental Epidemiology, 32(6), 855–863.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce RG, Honda GS, Dinallo R, Angus D, Gilbert J, Sierra T, others (2019). “Assessing toxicokinetic uncertainty and variability in risk prioritization.” Toxicological Sciences, 172(2), 235–251.

Examples

# We can use the Monte Carlo functions by passing a table
# where each row represents a different Monte Carlo draw of parameters:
p <- create_mc_samples(chem.cas="80-05-7")

# Use data.table for steady-state plasma concentration (Css) Monte Carlo:
calc_mc_css(parameters=p)

# Using the same table gives the same answer:
calc_mc_css(parameters=p)

# Use Css for 1 mg/kg/day for simple reverse toxicokinetics 
# in Vitro-In Vivo Extrapolation to convert 15 uM to mg/kg/day:
15/calc_mc_css(parameters=p, output.units="uM")

# Can do the same with calc_mc_oral_equiv:
calc_mc_oral_equiv(15, parameters=p)

#Generate a population using the virtual-individuals method,
#including 80 females and 20 males,
#including only ages 20-65,
#including only Mexican American and
#Non-Hispanic Black individuals,
#including only non-obese individuals
set.seed(42)
mypop <- httkpop_generate(method = 'virtual individuals',
                          gendernum=list(Female=80,
                          Male=20),
                          agelim_years=c(20,65),
                          reths=c('Mexican American',
                          'Non-Hispanic Black'),
                          weight_category=c('Underweight',
                          'Normal',
                          'Overweight'))
# Including a httkpop.dt argument will overwrite the number of sample and
# the httkpop on/off logical switch:
samps1 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE,
                           httkpop.dt=mypop)
samps2 <- create_mc_samples(chem.name="bisphenola",
                           httkpop.dt=mypop)
# But we can turn httkpop off altogether if desired:
samps3 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE)

Dawson et al. 2021 data

Description

This table includes QSAR (Random Forest) model predicted values for unbound fraction plasma protein (fup) and intrinsic hepatic clearance (clint) for a subset of chemicals in the Tox21 library (see https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21).

Usage

dawson2021

Format

data.frame

Details

Predictions were made with a set of Random Forest QSAR models, as reported in Dawson et al. (2021).

Author(s)

Daniel E. Dawson

References

Dawson DE, Ingle BL, Phillips KA, Nichols JW, Wambaugh JF, Tornero-Velez R (2021). “Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.” Environmental Science & Technology, 55(9), 6505-6517. doi:10.1021/acs.est.0c06117, PMID: 33856768, https://doi.org/10.1021/acs.est.0c06117.

See Also

load_dawson2021

Reference for EPA Physico-Chemical Data

Description

The physico-chemical data in the chem.phys_and_invitro.data table are obtained from EPA's Comptox Chemicals dashboard. This variable indicates the date the Dashboard was accessed.

Usage

EPA.ref

Format

An object of class character of length 1.

Author(s)

John Wambaugh

Source

https://comptox.epa.gov/dashboard

Predict GFR.

Description

Predict GFR using CKD-EPI equation (for adults) or BSA-based equation (for children).

Usage

estimate_gfr(gfrtmp.dt, gfr_resid_var = TRUE, ckd_epi_race_coeff = FALSE)

Arguments

gfrtmp.dt

A data.table with columns gender, reth, age_years, age_months, BSA_adj, serum_creat.
gfr_resid_var

Logical value indicating whether or not to include residual variability when generating GFR values. (Default is TRUE.)
ckd_epi_race_coeff

Logical value indicating whether or not to use the "race coefficient" from the CKD-EPI equation when estimating GFR values. (Default is FALSE.)

Details

Add residual variability based on reported residuals for each equation.

Value

The same data.table with a gfr_est column added, containing estimated GFR values.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict GFR in children.

Description

BSA-based equation from Johnson et al. 2006, Clin Pharmacokinet 45(9) 931-56. Used in Wetmore et al. 2014.

Usage

estimate_gfr_ped(BSA)

Arguments

BSA

Vector of body surface areas in m^2.

Value

Vector of GFRs in mL/min/1.73m^2.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Generate hematocrit values for a virtual population

Description

Predict hematocrit from age using smoothing splines and kernel density estimates of residual variability fitted to NHANES data, for a given combination of gender and NHANES race/ethnicity category.

Usage

estimate_hematocrit(gender, reth, age_years, age_months, nhanes_mec_svy)

Arguments

gender

Gender for which to generate hematocrit values ("Male" or "Female")
reth

NHANES race/ethnicity category for which to generate serum creatinine values ("Mexican American", "Non-Hispanic Black", "Non-Hispanic White", "Other", or "Other Hispanic")
age_years

Vector of ages in years for individuals for whom to generate hematocrit values (corresponding to age_months)
age_months

vector of ages in months for individuals for whom to generate hematocrit values (between 0-959 months)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Details

This function should usually not be called directly by the user. It is used by httkpop_generate() in "virtual-individuals" mode, after drawing gender, NHANES race/ethnicity category, and age from their NHANES proportions/distributions.

Value

A vector of numeric generated hematocrit values (blood percentage red blood cells by volume).

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

SEEM Example Data We can grab SEEM daily intake rate predictions already in RData format from https://github.com/HumanExposure/SEEM3RPackage/tree/main/SEEM3/data Download the file Ring2018Preds.RData

Description

We do not have the space to distribute all the SEEM predictions within this R package, but we can give you our "Intro to IVIVE" example chemicals

Usage

example.seem

Format

data.frame

References

Ring CL, Arnot JA, Bennett DH, Egeghy PP, Fantke P, Huang L, Isaacs KK, Jolliet O, Phillips KA, Price PS, others (2018). “Consensus modeling of median chemical intake for the US population based on predictions of exposure pathways.” Environmental science & technology, 53(2), 719–732.

ToxCast Example Data The main page for the ToxCast data is here: https://www.epa.gov/comptox-tools/exploring-toxcast-data Most useful to us is a single file containing all the hits across all chemcials and assays: https://clowder.edap-cluster.com/datasets/6364026ee4b04f6bb1409eda?space=62bb560ee4b07abf29f88fef

Description

As of November, 2022 the most recent version was 3.5 and was available as an .Rdata file (invitrodb_3_5_mc5.Rdata)

Usage

example.toxcast

Format

data.frame

Details

Unfortunately for this vignette there are too many ToxCast data to fit into a 5mb R package. So we will subset to just the shemicals for the "Intro to IVIVE" vignette and distribute only those data. In addition, out of 78 columns in the data, we will keep only eight.

Export model to jarnac.

Description

This function exports the multiple compartment PBTK model to a jarnac file.

Usage

export_pbtk_jarnac(
  chem.cas = NULL,
  chem.name = NULL,
  species = "Human",
  initial.amounts = list(Agutlumen = 0),
  filename = "default.jan",
  digits = 4
)

Arguments

chem.cas

Either the chemical name or CAS number must be specified.
chem.name

Either the chemical name or CAS number must be specified.
species

Species desired (either "Rat", "Rabbit", "Dog", or default "Human").
initial.amounts

Must specify initial amounts in units of choice.
filename

The name of the jarnac file containing the model.
digits

Desired number of decimal places to round the parameters.

Details

Compartments to enter into the initial.amounts list includes Agutlumen, Aart, Aven, Alung, Agut, Aliver, Akidney, and Arest.

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

Text containing a Jarnac language version of the PBTK model.

Author(s)

Robert Pearce

Examples

export_pbtk_jarnac(chem.name='Nicotine',initial.amounts=list(Agutlumen=1),filename='PBTKmodel.jan')

Export model to sbml.

Description

This function exports the multiple compartment PBTK model to an sbml file.

Usage

export_pbtk_sbml(
  chem.cas = NULL,
  chem.name = NULL,
  species = "Human",
  initial.amounts = list(Agutlumen = 0),
  filename = "default.xml",
  digits = 4
)

Arguments

chem.cas

Either the chemical name or CAS number must be specified.
chem.name

Either the chemical name or CAS number must be specified.
species

Species desired (either "Rat", "Rabbit", "Dog", or default "Human").
initial.amounts

Must specify initial amounts in units of choice.
filename

The name of the jarnac file containing the model.
digits

Desired number of decimal places to round the parameters.

Details

Compartments to enter into the initial.amounts list includes Agutlumen, Aart, Aven, Alung, Agut, Aliver, Akidney, and Arest.

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

Text describing the PBTK model in SBML.

Author(s)

Robert Pearce

Examples

export_pbtk_sbml(chem.name='Nicotine',initial.amounts=list(Agutlumen=1),filename='PBTKmodel.xml')

Fetal Partition Coefficients

Description

Partition coefficients were measured for tissues, including placenta, in vitro by Csanady et al. (2002) for Bisphenol A and Diadzen. Curley et al. (1969) measured the concentration of a variety of pesticides in the cord blood of newborns and in the tissues of infants that were stillborn.

Usage

fetalpcs

Format

data.frame

Details

Three of the chemicals studied by Curley et al. (1969) were modeled by Weijs et al. (2013) using the same partition coefficients for mother and fetus. The values used represented "prior knowledge" summarizing the available literature.

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Csanady G, Oberste-Frielinghaus H, Semder B, Baur C, Schneider K, Filser J (2002). “Distribution and unspecific protein binding of the xenoestrogens bisphenol A and daidzein.” Archives of toxicology, 76(5-6), 299–305. Curley A, Copeland MF, Kimbrough RD (1969). “Chlorinated hydrocarbon insecticides in organs of stillborn and blood of newborn babies.” Archives of Environmental Health: An International Journal, 19(5), 628–632. Weijs L, Yang RS, Das K, Covaci A, Blust R (2013). “Application of Bayesian population physiologically based pharmacokinetic (PBPK) modeling and Markov chain Monte Carlo simulations to pesticide kinetics studies in protected marine mammals: DDT, DDE, and DDD in harbor porpoises.” Environmental science & technology, 47(9), 4365–4374.

Literature In Vivo Data on Doses Causing Neurological Effects

Description

Studies were selected from Table 1 in Mundy et al., 2015, as the studies in that publication were cited as examples of compounds with evidence for developmental neurotoxicity. There were sufficient in vitro toxicokinetic data available for this package for only 6 of the 42 chemicals.

Usage

Frank2018invivo

Format

A data.frame containing 14 rows and 16 columns.

Author(s)

Timothy J. Shafer

References

Frank, Christopher L., et al. "Defining toxicological tipping points in neuronal network development." Toxicology and Applied Pharmacology 354 (2018): 81-93.

Mundy, William R., et al. "Expanding the test set: Chemicals with potential to disrupt mammalian brain development." Neurotoxicology and Teratology 52 (2015): 25-35.

Generate demographic parameters for a virtual population

Description

Generate gender, NHANES race/ethnicity category, ages, heights, and weights for a virtual population, based on NHANES data.

Usage

gen_age_height_weight(
  nsamp = NULL,
  gendernum = NULL,
  reths,
  weight_category,
  agelim_years,
  agelim_months,
  nhanes_mec_svy
)

Arguments

nsamp

The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum

Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
reths

Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
weight_category

Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
agelim_years

Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
agelim_months

Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Details

This function should usually not be called directly by the user. It is used by httkpop_generate() in "virtual-individuals" mode.

Value

A data.table containing variables

gender

Gender of each virtual individual

reth

Race/ethnicity of each virtual individual

age_months

Age in months of each virtual individual

age_years

Age in years of each virtual individual

weight

Body weight in kg of each virtual individual

height

Height in cm of each virtual individual

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

importFrom survey svymean

Generate heights and weights for a virtual population.

Description

Predict height and weight from age using smoothing splines, and then add residual variability from a 2-D KDE, both fitted to NHANES data, for a given combination of gender and NHANES race/ethnicity category.

Usage

gen_height_weight(gender, reth, age_months, nhanes_mec_svy)

Arguments

gender

Gender for which to calculate height/weight ("Male" or "Female")
reth

NHANES race/ethnicity category for which to calculate height/weight ("Mexican American", "Non-Hispanic Black", "Non-Hispanic White", "Other", or "Other Hispanic")
age_months

vector of ages in months for individuals for whom to calculate height/weight (between 0-959 months)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Details

This function should usually not be called directly by the user. It is used by httkpop_generate() in "virtual-individuals" mode, after drawing gender, NHANES race/ethnicity category, and age from their NHANES proportions/distributions.

Value

A list containing two named elements, weight and height, each of which is a numeric vector. weight gives individual body weights in kg, and height gives individual heights in cm, corresponding to each item in the input age_months.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Generate serum creatinine values for a virtual population.

Description

Predict serum creatinine from age using smoothing splines and kernel density estimates of residual variability fitted to NHANES data,, for a given combination of gender and NHANES race/ethnicity category.

Usage

gen_serum_creatinine(gender, reth, age_years, age_months, nhanes_mec_svy)

Arguments

gender

Gender for which to generate serum creatinine values ("Male" or "Female")
reth

NHANES race/ethnicity category for which to generate serum creatinine values ("Mexican American", "Non-Hispanic Black", "Non-Hispanic White", "Other", or "Other Hispanic")
age_years

Vector of ages in years for individuals for whom to generate serum creatinine values (corresponding to age_months)
age_months

vector of ages in months for individuals for whom to generate serum creatinine values (between 0-959 months)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Details

This function should usually not be called directly by the user. It is used by httkpop_generate() in "virtual-individuals" mode, after drawing gender, NHANES race/ethnicity category, and age from their NHANES proportions/distributions.

Value

A vector of numeric generated serum creatinine values (mg/dL).

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Retrieve in vitro measured Caco-2 membrane permeabilit

Description

This function checks for chemical-specific in vitro measurements of the Caco-2 membrane permeability in the chem.physical_and_invitro.data table. If no value is available argument Caco2.Pab.default is returned. Anywhere that the values is reported by three numbers separated by a comma (this also happens for plasma protein binding) the three values are: median, lower 95 percent confidence intervals, upper 95 percent confidence interval. Unless you are doing monte carlo work it makes sense to ignore the second and third values.

Usage

get_caco2(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  Caco2.Pab.default = 1.6,
  suppress.messages = FALSE
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
Caco2.Pab.default

sets the default value for Caco2.Pab if Caco2.Pab is unavailable.
suppress.messages

Whether or not the output message is suppressed.

Author(s)

John Wambaugh

Retrieve chemical identity from HTTK package

Description

Given one of chem.name, chem.cas (Chemical Abstract Service Registry Number), or DTXSID (DSStox Substance Identifier https://comptox.epa.gov/dashboard) this function checks if the chemical is available and, if so, returns all three pieces of information.

Usage

get_chem_id(chem.cas = NULL, chem.name = NULL, dtxsid = NULL)

Arguments

chem.cas

CAS regstry number
chem.name

Chemical name
dtxsid

DSSTox Substance identifier

Value

A list containing the following chemical identifiers:

chem.cas

CAS registry number
chem.name

Name
dtxsid

DTXSID

Author(s)

John Wambaugh and Robert Pearce

Retrieve chemical information available from HTTK package

Description

This function lists information on all the chemicals within HTTK for which there are sufficient data for the specified model and species. By default the function returns only CAS (that is, info="CAS"). The type of information available includes chemical identifiers ("Compound", "CAS", "DTXSID"), in vitro measurements ("Clint", "Clint.pvalue", "Funbound plasma", "Rblood2plasma"), and physico-chemical information ("Formula", "logMA", "logP", "MW", "pKa_Accept", "pKa_Donor"). The argument "info" can be a single type of information, "all" information, or a vector of specific types of information. The argument "model" defaults to "3compartmentss" and the argument "species" defaults to "human". Since different models have different requirements and not all chemicals have complete data, this function will return different numbers of chemicals depending on the model specified. If a chemical is not listed by get_cheminfo then either the in vitro or physico-chemical data needed are currently missing (but could potentially be added using add_chemtable.

Usage

get_cheminfo(
  info = "CAS",
  species = "Human",
  fup.lod.default = 0.005,
  model = "3compartmentss",
  default.to.human = FALSE,
  median.only = FALSE,
  fup.ci.cutoff = TRUE,
  clint.pvalue.threshold = 0.05,
  physchem.exclude = TRUE,
  class.exclude = TRUE,
  suppress.messages = FALSE
)

Arguments

info

A single character vector (or collection of character vectors) from "Compound", "CAS", "DTXSID, "logP", "pKa_Donor"," pKa_Accept", "MW", "Clint", "Clint.pValue", "Funbound.plasma","Structure_Formula", or "Substance_Type". info="all" gives all information for the model and species.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
fup.lod.default

Default value used for fraction of unbound plasma for chemicals where measured value was below the limit of detection. Default value is 0.0005.
model

Model used in calculation, 'pbtk' for the multiple compartment model, '1compartment' for the one compartment model, '3compartment' for three compartment model, '3compartmentss' for the three compartment model without partition coefficients, or 'schmitt' for chemicals with logP and fraction unbound (used in predict_partitioning_schmitt).
default.to.human

Substitutes missing values with human values if true.
median.only

Use median values only for fup and clint. Default is FALSE.
fup.ci.cutoff

Cutoff for the level of uncertainty in fup estimates. This value should be between (0,1). Default is 'NULL' specifying no filtering.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
physchem.exclude

Exclude chemicals on the basis of physico-chemical properties (currently only Henry's law constant) as specified by the relevant modelinfo_[MODEL] file (default TRUE).
class.exclude

Exclude chemical classes identified as outside of domain of applicability by the relevant modelinfo_[MODEL] file (default TRUE).
suppress.messages

Whether or not the output messages are suppressed (default FALSE).

Details

When default.to.human is set to TRUE, and the species-specific data, Funbound.plasma and Clint, are missing from chem.physical_and_invitro.data, human values are given instead.

In some cases the rapid equilibrium dialysis method (Waters et al., 2008) fails to yield detectable concentrations for the free fraction of chemical. In those cases we assume the compound is highly bound (that is, Fup approaches zero). For some calculations (for example, steady-state plasma concentration) there is precedent (Rotroff et al., 2010) for using half the average limit of detection, that is, 0.005 (this value is configurable via the argument fup.lod.default). We do not recommend using other models where quantities like partition coefficients must be predicted using Fup. We also do not recommend including the value 0.005 in training sets for Fup predictive models.

Note that in some cases the Funbound.plasma (fup) and the intrinsic clearance (clint) are provided as a series of numbers separated by commas. These values are the result of Bayesian analysis and characterize a distribution: the first value is the median of the distribution, while the second and third values are the lower and upper 95th percentile (that is quantile 2.5 and 97.5) respectively. For intrinsic clearance a fourth value indicating a p-value for a decrease is provided. Typically 4000 samples were used for the Bayesian analysis, such that a p-value of "0" is equivalent to "<0.00025". See Wambaugh et al. (2019) for more details. If argument median.only == TRUE then only the median is reported for parameters with Bayesian analysis distributions. If the 95 credible interval is larger than fup.ci.cutoff (defaults to NULL) then the Fup is treated as too uncertain and the value NA is given.

Value

vector/data.table

Table (if info has multiple entries) or vector containing a column for each valid entry specified in the argument "info" and a row for each chemical with sufficient data for the model specified by argument "model":

Column Description units
Compound The preferred name of the chemical compound none
CAS The preferred Chemical Abstracts Service Registry Number none
DTXSID DSSTox Structure ID (https://comptox.epa.gov/dashboard) none
logP The log10 octanol:water partition coefficient log10 unitless ratio
MW The chemical compound molecular weight g/mol
pKa_Accept The hydrogen acceptor equilibria concentrations logarithm
pKa_Donor The hydrogen donor equilibria concentrations logarithm
[SPECIES].Clint (Primary hepatocyte suspension) intrinsic hepatic clearance. Entries with comma separated values are Bayesian estimates of the Clint distribution - displayed as the median, 95th credible interval (that is quantile 2.5 and 97.5, respectively), and p-value. uL/min/10^6 hepatocytes
[SPECIES].Clint.pValue Probability that there is no clearance observed. Values close to 1 indicate clearance is not statistically significant. none
[SPECIES].Funbound.plasma Chemical fraction unbound in presence of plasma proteins (fup). Entries with comma separated values are Bayesian estimates of the fup distribution - displayed as the median and 95th credible interval (that is quantile 2.5 and 97.5, respectively). unitless fraction
[SPECIES].Rblood2plasma Chemical concentration blood to plasma ratio unitless ratio

Author(s)

John Wambaugh, Robert Pearce, and Sarah E. Davidson

References

Rotroff, Daniel M., et al. "Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening." Toxicological Sciences 117.2 (2010): 348-358.

Waters, Nigel J., et al. "Validation of a rapid equilibrium dialysis approach for the measurement of plasma protein binding." Journal of pharmaceutical sciences 97.10 (2008): 4586-4595.

Wambaugh, John F., et al. "Assessing toxicokinetic uncertainty and variability in risk prioritization." Toxicological Sciences 172.2 (2019): 235-251.

Examples

# List all CAS numbers for which the 3compartmentss model can be run in humans: 
get_cheminfo()

get_cheminfo(info=c('compound','funbound.plasma','logP'),model='pbtk') 
# See all the data for humans:
get_cheminfo(info="all")

TPO.cas <- c("741-58-2", "333-41-5", "51707-55-2", "30560-19-1", "5598-13-0", 
"35575-96-3", "142459-58-3", "1634-78-2", "161326-34-7", "133-07-3", "533-74-4", 
"101-05-3", "330-54-1", "6153-64-6", "15299-99-7", "87-90-1", "42509-80-8", 
"10265-92-6", "122-14-5", "12427-38-2", "83-79-4", "55-38-9", "2310-17-0", 
"5234-68-4", "330-55-2", "3337-71-1", "6923-22-4", "23564-05-8", "101-02-0", 
"140-56-7", "120-71-8", "120-12-7", "123-31-9", "91-53-2", "131807-57-3", 
"68157-60-8", "5598-15-2", "115-32-2", "298-00-0", "60-51-5", "23031-36-9", 
"137-26-8", "96-45-7", "16672-87-0", "709-98-8", "149877-41-8", "145701-21-9", 
"7786-34-7", "54593-83-8", "23422-53-9", "56-38-2", "41198-08-7", "50-65-7", 
"28434-00-6", "56-72-4", "62-73-7", "6317-18-6", "96182-53-5", "87-86-5", 
"101-54-2", "121-69-7", "532-27-4", "91-59-8", "105-67-9", "90-04-0", 
"134-20-3", "599-64-4", "148-24-3", "2416-94-6", "121-79-9", "527-60-6", 
"99-97-8", "131-55-5", "105-87-3", "136-77-6", "1401-55-4", "1948-33-0", 
"121-00-6", "92-84-2", "140-66-9", "99-71-8", "150-13-0", "80-46-6", "120-95-6",
"128-39-2", "2687-25-4", "732-11-6", "5392-40-5", "80-05-7", "135158-54-2", 
"29232-93-7", "6734-80-1", "98-54-4", "97-53-0", "96-76-4", "118-71-8", 
"2451-62-9", "150-68-5", "732-26-3", "99-59-2", "59-30-3", "3811-73-2", 
"101-61-1", "4180-23-8", "101-80-4", "86-50-0", "2687-96-9", "108-46-3", 
"95-54-5", "101-77-9", "95-80-7", "420-04-2", "60-54-8", "375-95-1", "120-80-9",
"149-30-4", "135-19-3", "88-58-4", "84-16-2", "6381-77-7", "1478-61-1", 
"96-70-8", "128-04-1", "25956-17-6", "92-52-4", "1987-50-4", "563-12-2", 
"298-02-2", "79902-63-9", "27955-94-8")
httk.TPO.rat.table <- subset(get_cheminfo(info="all",species="rat"),
 CAS %in% TPO.cas)
 
httk.TPO.human.table <- subset(get_cheminfo(info="all",species="human"),
 CAS %in% TPO.cas)
 
# create a data.frame with all the Fup values, we ask for model="schmitt" since
# that model only needs fup, we ask for "median.only" because we don't care
# about uncertainty intervals here:
fup.tab <- get_cheminfo(info="all",median.only=TRUE,model="schmitt")
# calculate the median, making sure to convert to numeric values:
median(as.numeric(fup.tab$Human.Funbound.plasma),na.rm=TRUE)
# calculate the mean:
mean(as.numeric(fup.tab$Human.Funbound.plasma),na.rm=TRUE)
# count how many non-NA values we have (should be the same as the number of 
# rows in the table but just in case we ask for non NA values:
sum(!is.na(fup.tab$Human.Funbound.plasma))

Retrieve and parse intrinsic hepatic clearance

Description

This function retrieves the chemical- and species-specific intinsic hepatic clearance (Clint, inits of uL/min/million hepatocytes) from chem.physical_and_invitro.data. If that parameter is described by a distribution (that is, a median, lower-, upper-95th percentile and p-value separated by commas) this function splits those quantiles into separate values. Most Clint values have an accompanying p-value indicating the probability that no decrease was observed. If the p-values exceeds a threhsold (default 0.05) the clearance is set to zero (no clearance). Some values extracted from the literature do not have a p-value.

Usage

get_clint(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  force.human.clint = FALSE,
  suppress.messages = FALSE,
  clint.pvalue.threshold = 0.05
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing hepatic clearance with human values if true.
force.human.clint

If a non-human species value (matching argument species) is available, it is ignored and the human intrinsic clearance is used
suppress.messages

Whether or not the output message is suppressed.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.

Value

list containing:

CLint.point

Point estimate (central tendency) of the intrinsic hepatic clearance
Clint.dist

Quantiles of a distribution (median, lower, upper 95th percentiles) and pvalue
Clint.pvalue

pvalue for whether disapperance of parent compound was observed

Author(s)

John Wambaugh

See Also

chem.physical_and_invitro.data

Retrieve or calculate fraction of chemical absorbed from the gut

Description

This function checks for chemical-specific in vivo measurements of the fraction absorbed from the gut in the chem.physical_and_invitro.data table. If in vivo data are unavailable (or keepit100 == TRUE) we attempt to use in vitro Caco-2 membrane permeability to predict the fractions according to calc_fbio.oral.

Usage

get_fbio(
  parameters = NULL,
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  Caco2.Pab.default = 1.6,
  Caco2.Fgut = TRUE,
  Caco2.Fabs = TRUE,
  overwrite.invivo = FALSE,
  keepit100 = FALSE,
  suppress.messages = FALSE
)

Arguments

parameters

A list of the parameters (Caco2.Pab, Funbound.Plasma, Rblood2plasma, Clint, BW, Qsmallintestine, Fabs, Fgut) used in the calculation, either supplied by user or calculated in parameterize_steady_state.
chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing rat values with human values if true.
Caco2.Pab.default

sets the default value for Caco2.Pab if Caco2.Pab is unavailable.
Caco2.Fgut

= TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut.
Caco2.Fabs

= TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs.
overwrite.invivo

= TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available.
keepit100

TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings.
suppress.messages

Whether or not the output message is suppressed.

Author(s)

Greg Honda and John Wambaugh

Retrieve and parse fraction unbound in plasma

Description

This function retrieves the chemical- and species-specific fraction unbound in plasma (fup) from chem.physical_and_invitro.data. If that parameter is described by a distribution (that is, a median, lower-, and upper-95th percentile separated by commas) this function splits those quantiles into separate values.

Usage

get_fup(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  force.human.fup = FALSE,
  suppress.messages = FALSE,
  minimum.Funbound.plasma = 1e-04
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing fraction of unbound plasma with human values if true.
force.human.fup

If a non-human species value (matching argument species) is available, it is ignored and the human fraction unbound is returned
suppress.messages

Whether or not the output message is suppressed.
minimum.Funbound.plasma fup

is not allowed to drop below this value (default is 0.0001).

Value

list containing:

Funbound.plasma.point

Point estimate (central tendency) of the Unbound fraction in plasma
Funbound.plasma.dist

Quantiles of a distribution (median, lower and upper 95th percentiles) for the unbound fraction

Author(s)

John Wambaugh

See Also

chem.physical_and_invitro.data

Categorize kidney function by GFR.

Description

For adults: In general GFR > 60 is considered normal 15 < GFR < 60 is considered kidney disease GFR < 15 is considered kidney failure

Usage

get_gfr_category(age_years, age_months, gfr_est)

Arguments

age_years

Vector of ages in years.
age_months

Vector of ages in months.
gfr_est

Vector of estimated GFR values in mL/min/1.73m^2.

Details

These values can also be used for children 2 years old and greater (see PEDIATRICS IN REVIEW Vol. 29 No. 10 October 1, 2008 pp. 335-341 (doi: 10.1542/pir.29-10-335))

Value

Vector of GFR categories: 'Normal', 'Kidney Disease', 'Kidney Failure'.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Retrieve species-specific in vitro data from chem.physical_and_invitro.data table

Description

This function retrieves in vitro PK data (for example, intrinsic metabolic clearance or fraction unbound in plasma) for the the chemical specified by argument "chem.name", "dtxsid", or chem.cas from the table chem.physical_and_invitro.data. This function looks for species-specific values based on the argument "species".

Usage

get_invitroPK_param(
  param,
  species,
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL
)

Arguments

param

The desired parameters, a vector or single value.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
chem.name

The chemical names that you want parameters for, a vector or single value
chem.cas

The chemical CAS numbers that you want parameters for, a vector or single value
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard)

Details

Note that this function works with a local version of the chem.physical_and_invitro.data table to allow users to add/modify chemical data (for example, adding new data via add_chemtable or loading in silico predictions distributed with httk via load_sipes2017, load_pradeep2020, load_dawson2021, or load_honda2023).

User can request via argument param (case-insensitive):

Parameter Description Units
[SPECIES].Clint (Primary hepatocyte suspension) intrinsic hepatic clearance. Entries with comma separated values are Bayesian estimates of the Clint distribution - displayed as the median, 95th credible interval (that is quantile 2.5 and 97.5, respectively), and p-value. uL/min/10^6 hepatocytes
[SPECIES].Clint.pValue Probability that there is no clearance observed. Values close to 1 indicate clearance is not statistically significant. none
[SPECIES].Caco2.Pab Caco-2 Apical-to-Basal Membrane Permeability 10^-6 cm/s
[SPECIES].Fabs In vivo measured fraction of an oral dose of chemical absorbed from the gut lumen into the gut unitless fraction
[SPECIES].Fgut In vivo measured fraction of an oral dose of chemical that passes gut metabolism and clearance unitless fraction
[SPECIES].Foral In vivo measued fractional systemic bioavailability of an oral dose, modeled as he product of Fabs * Fgut * Fhep (where Fhep is first pass hepatic metabolism). unitless fraction
[SPECIES].Funbound.plasma Chemical fraction unbound in presence of plasma proteins (fup). Entries with comma separated values are Bayesian estimates of the fup distribution - displayed as the median and 95th credible interval (that is quantile 2.5 and 97.5, respectively). unitless fraction
[SPECIES].Rblood2plasma Chemical concentration blood to plasma ratio unitless ratio

Value

The parameters, either a single value, a named list for a single chemical, or a list of lists

Author(s)

John Wambaugh and Robert Pearce

See Also

chem.physical_and_invitro.data

get_invitroPK_param

add_chemtable

Get literature Chemical Information.

Description

This function provides the information specified in "info=" for all chemicals with data from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_lit_cheminfo(info = "CAS", species = "Human")

Arguments

info

A single character vector (or collection of character vectors) from "Compound", "CAS", "MW", "Raw.Experimental.Percentage.Unbound", "Entered.Experimental.Percentage.Unbound", "Fub", "source_PPB", "Renal_Clearance", "Met_Stab", "Met_Stab_entered", "r2", "p.val", "Concentration..uM.", "Css_lower_5th_perc.mg.L.", "Css_median_perc.mg.L.", "Css_upper_95th_perc.mg.L.", "Css_lower_5th_perc.uM.","Css_median_perc.uM.","Css_upper_95th_perc.uM.", and "Species".
species

Species desired (either "Rat" or default "Human").

Value

info

Table/vector containing values specified in "info" for valid chemicals.

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

get_lit_cheminfo()
get_lit_cheminfo(info=c('CAS','MW'))

Get literature Css

Description

This function retrieves a steady-state plasma concentration as a result of infusion dosing from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_lit_css(
  chem.cas = NULL,
  chem.name = NULL,
  daily.dose = 1,
  which.quantile = 0.95,
  species = "Human",
  clearance.assay.conc = NULL,
  output.units = "mg/L",
  suppress.messages = FALSE
)

Arguments

chem.cas

Either the cas number or the chemical name must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
daily.dose

Total daily dose infused in units of mg/kg BW/day. Defaults to 1 mg/kg/day.
which.quantile

Which quantile from the SimCYP Monte Carlo simulation is requested. Can be a vector.
species

Species desired (either "Rat" or default "Human").
clearance.assay.conc

Concentration of chemical used in measureing intrinsic clearance data, 1 or 10 uM.
output.units

Returned units for function, defaults to mg/L but can also be uM (specify units = "uM").
suppress.messages

Whether or not the output message is suppressed.

Value

A numeric vector with the literature steady-state plasma concentration (1 mg/kg/day) for the requested quantiles

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

get_lit_css(chem.cas="34256-82-1")

get_lit_css(chem.cas="34256-82-1",species="Rat",which.quantile=0.5)

get_lit_css(chem.cas="80-05-7", daily.dose = 1,which.quantile = 0.5, output.units = "uM")

Get Literature Oral Equivalent Dose

Description

This function converts a chemical plasma concetration to an oral equivalent dose using the values from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_lit_oral_equiv(
  conc,
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  suppress.messages = FALSE,
  which.quantile = 0.95,
  species = "Human",
  input.units = "uM",
  output.units = "mg",
  clearance.assay.conc = NULL,
  ...
)

Arguments

conc

Bioactive in vitro concentration in units of specified input.units, default of uM.
chem.name

Either the chemical name or the CAS number must be specified.
chem.cas

Either the CAS number or the chemical name must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
suppress.messages

Suppress output messages.
which.quantile

Which quantile from the SimCYP Monte Carlo simulation is requested. Can be a vector. Papers include 0.05, 0.5, and 0.95 for humans and 0.5 for rats.
species

Species desired (either "Rat" or default "Human").
input.units

Units of given concentration, default of uM but can also be mg/L.
output.units

Units of dose, default of 'mg' for mg/kg BW/ day or 'mol' for mol/ kg BW/ day.
clearance.assay.conc

Concentration of chemical used in measureing intrinsic clearance data, 1 or 10 uM.
...

Additional parameters passed to get_lit_css.

Value

Equivalent dose in specified units, default of mg/kg BW/day.

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

table <- NULL
for(this.cas in sample(get_lit_cheminfo(),50)) table <- rbind(table,cbind(
as.data.frame(this.cas),as.data.frame(get_lit_oral_equiv(conc=1,chem.cas=this.cas))))




get_lit_oral_equiv(0.1,chem.cas="34256-82-1")

get_lit_oral_equiv(0.1,chem.cas="34256-82-1",which.quantile=c(0.05,0.5,0.95))

Get physico-chemical parameters from chem.physical_and_invitro.data table

Description

This function retrieves physico-chemical properties ("param") for the chemical specified by chem.name or chem.cas from the table chem.physical_and_invitro.data. This function is distinguished from get_invitroPK_param in that there are no species-specific values. Physically meaningful values for ionization equilibria are NA/none (that is, no ionization), a single value, or a series of values separated by commas. If logMA (log10 membrane affinity) is NA, we use calc_ma() to predict it later on in the model parameterization functions.

Usage

get_physchem_param(param, chem.name = NULL, chem.cas = NULL, dtxsid = NULL)

Arguments

param

The desired parameters, a vector or single value.
chem.name

The chemical names that you want parameters for, a vector or single value
chem.cas

The chemical CAS numbers that you want parameters for, a vector or single value
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs

Details

Note that this function works with a local version of the chem.physical_and_invitro.data table to allow users to add/modify chemical data (for example, adding new data via add_chemtable or loading in silico predictions distributed with httk via load_sipes2017, load_pradeep2020, load_dawson2021, or load_honda2023).

User can request the following via argument param (case-insensitive):

Parameter Description Units
MW Molecular weight g/mole
pKa_Donor Hydrogen donor ionization equilibria (acidic pKa) pH
pKa_Accept Hyrdogen acceptor ionization equilibria (basic pKa pH
logMA log10 Membrane Affinity unitless
logP log10 Octanol:Water Partition Coefficient (hydrophobicity) unitless
logPwa log10 Water:Air Partition Coefficient unitless
logHenry log10 Henry's Law Constant atm-m3/mole
logWSol log10 Water Solubility moles/L: Water solubility at 25C
MP Melting point deg C

Value

The parameters, either a single value, a named list for a single chemical, or a list of lists

Author(s)

John Wambaugh and Robert Pearce

See Also

chem.physical_and_invitro.data

get_invitroPK_param

add_chemtable

Examples

get_physchem_param(param = 'logP', chem.cas = '80-05-7')
get_physchem_param(param = c('logP','MW'), chem.cas = c('80-05-7','81-81-2'))
# This function should be case-insensitive:
try(get_physchem_param(chem.cas="80-05-7","LogP"))
# Asking for a parameter we "don't" have produces an error:
try(get_physchem_param(chem.cas="80-05-7","MA"))
get_physchem_param(chem.cas="80-05-7","logMA")
# Ionization equilibria can be NA/none, a single value, or a series of values
# separated by commas:
get_physchem_param(chem.cas="80-05-7","pKa_Donor")
get_physchem_param(chem.cas="80-05-7","pKa_Accept")
get_physchem_param(chem.cas="71751-41-2","pKa_Donor")
get_physchem_param(chem.cas="71751-41-2","pKa_Accept")
# If logMA (log10 membrane affinity) is NA, we use calc_ma() to predict it
# in the parameterization functions:
get_physchem_param(chem.cas="71751-41-2","logMA")
parameterize_steadystate(chem.cas="71751-41-2")

Get ratio of the blood concentration to the plasma concentration.

Description

This function attempts to retrieve a measured species- and chemical-specific blood:plasma concentration ratio.

Usage

get_rblood2plasma(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE
)

Arguments

chem.name

Either the chemical name or the CAS number must be specified.
chem.cas

Either the CAS number or the chemical name must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing animal values with human values if true.

Details

A value of NA is returned when the requested value is unavailable. Values are retrieved from chem.physical_and_invitro.data. details than the description above ~~

Value

A numeric value for the steady-state ratio of chemical concentration in blood to plasma

Author(s)

Robert Pearce

Examples

get_rblood2plasma(chem.name="Bisphenol A")
get_rblood2plasma(chem.name="Bisphenol A",species="Rat")

Assign weight class (underweight, normal, overweight, obese)

Description

Given vectors of age, BMI, recumbent length, weight, and gender, categorizes weight classes using CDC and WHO categories.

Usage

get_weight_class(age_years, age_months, bmi, recumlen, weight, gender)

Arguments

age_years

A vector of ages in years.
age_months

A vector of ages in months.
bmi

A vector of BMIs.
recumlen

A vector of heights or recumbent lengths in cm.
weight

A vector of body weights in kg.
gender

A vector of genders (as 'Male' or 'Female').

Details

According to the CDC (https://www.cdc.gov/ncbddd/disabilityandhealth/obesity.html), adult weight classes are defined using BMI as follows:

Underweight

BMI less than 18.5

Normal

BMI between 18.5 and 25

Overweight

BMI between 25 and 30

Obese

BMI greater than 30

For children ages 2 years and older, weight classes are defined using percentiles of sex-specific BMI for age, as follows (Barlow et al., 2007):

Underweight

Below 5th percentile BMI for age

Normal

5th-85th percentile BMI for age

Overweight

85th-95th percentile BMI for age

Obese

Above 95th percentile BMI for age

For children birth to age 2, weight classes are defined using percentiles of sex-specific weight-for-length (Grummer-Strawn et al., 2009). Weight above the 97.7th percentile, or below the 2.3rd percentile, of weight-for-length is considered potentially indicative of adverse health conditions. Here, weight below the 2.3rd percentile is categorized as "Underweight" and weight above the 97.7th percentile is categorized as "Obese."

Value

A character vector of weight classes. Each element will be one of 'Underweight', 'Normal', 'Overweight', or 'Obese'.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Barlow SE. Expert committee recommendations regarding the prevention, assessment, and treatment of child and adolescent overweight and obesity: summary report. Pediatrics. 2007;120 Suppl 4. doi:10.1542/peds.2007-2329C

Grummer-Strawn LM, Reinold C, Krebs NF. Use of World Health Organization and CDC growth charts for children Aged 0-59 months in the United States. Morb Mortal Wkly Rep. 2009;59(RR-9). https://www.cdc.gov/mmwr/preview/mmwrhtml/rr5909a1.htm

Get literature Chemical Information. (deprecated).

Description

This function is included for backward compatibility. It calls get_lit_cheminfo which provides the information specified in "info=" for all chemicals with data from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_wetmore_cheminfo(
  info = "CAS",
  species = "Human",
  suppress.messages = FALSE
)

Arguments

info

A single character vector (or collection of character vectors) from "Compound", "CAS", "MW", "Raw.Experimental.Percentage.Unbound", "Entered.Experimental.Percentage.Unbound", "Fub", "source_PPB", "Renal_Clearance", "Met_Stab", "Met_Stab_entered", "r2", "p.val", "Concentration..uM.", "Css_lower_5th_perc.mg.L.", "Css_median_perc.mg.L.", "Css_upper_95th_perc.mg.L.", "Css_lower_5th_perc.uM.","Css_median_perc.uM.","Css_upper_95th_perc.uM.", and "Species".
species

Species desired (either "Rat" or default "Human").
suppress.messages

Whether or not the output message is suppressed.

Value

info

Table/vector containing values specified in "info" for valid chemicals.

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

get_lit_cheminfo()
get_lit_cheminfo(info=c('CAS','MW'))

Get literature Css (deprecated).

Description

This function is included for backward compatibility. It calls get_lit_css which retrieves a steady-state plasma concentration as a result of infusion dosing from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_wetmore_css(
  chem.cas = NULL,
  chem.name = NULL,
  daily.dose = 1,
  which.quantile = 0.95,
  species = "Human",
  clearance.assay.conc = NULL,
  output.units = "mg/L",
  suppress.messages = FALSE
)

Arguments

chem.cas

Either the cas number or the chemical name must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
daily.dose

Total daily dose infused in units of mg/kg BW/day. Defaults to 1 mg/kg/day.
which.quantile

Which quantile from the SimCYP Monte Carlo simulation is requested. Can be a vector.
species

Species desired (either "Rat" or default "Human").
clearance.assay.conc

Concentration of chemical used in measureing intrinsic clearance data, 1 or 10 uM.
output.units

Returned units for function, defaults to mg/L but can also be uM (specify units = "uM").
suppress.messages

Whether or not the output message is suppressed.

Value

A numeric vector with the literature steady-state plasma concentration (1 mg/kg/day) for the requested quantiles

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

get_lit_css(chem.cas="34256-82-1")

get_lit_css(chem.cas="34256-82-1",species="Rat",which.quantile=0.5)

get_lit_css(chem.cas="80-05-7", daily.dose = 1,which.quantile = 0.5, output.units = "uM")

Get Literature Oral Equivalent Dose (deprecated).

Description

This function is included for backward compatibility. It calls get_lit_oral_equiv which converts a chemical plasma concetration to an oral equivalent dose using the values from the Wetmore et al. (2012) and (2013) publications and other literature.

Usage

get_wetmore_oral_equiv(
  conc,
  chem.name = NULL,
  chem.cas = NULL,
  suppress.messages = FALSE,
  which.quantile = 0.95,
  species = "Human",
  input.units = "uM",
  output.units = "mg",
  clearance.assay.conc = NULL,
  ...
)

Arguments

conc

Bioactive in vitro concentration in units of specified input.units, default of uM.
chem.name

Either the chemical name or the CAS number must be specified.
chem.cas

Either the CAS number or the chemical name must be specified.
suppress.messages

Suppress output messages.
which.quantile

Which quantile from the SimCYP Monte Carlo simulation is requested. Can be a vector. Papers include 0.05, 0.5, and 0.95 for humans and 0.5 for rats.
species

Species desired (either "Rat" or default "Human").
input.units

Units of given concentration, default of uM but can also be mg/L.
output.units

Units of dose, default of 'mg' for mg/kg BW/ day or 'mol' for mol/ kg BW/ day.
clearance.assay.conc

Concentration of chemical used in measureing intrinsic clearance data, 1 or 10 uM.
...

Additional parameters passed to get_lit_css.

Value

Equivalent dose in specified units, default of mg/kg BW/day.

Author(s)

John Wambaugh

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Li, L., Clewell, H.J. III, Judson, R.S., Freeman, K., Bao, W, Sochaski, M.A., Chu T.-M., Black, M.B., Healy, E, Allen, B., Andersen M.E., Wolfinger, R.D., and Thomas R.S., "The Relative Impact of Incorporating Pharmacokinetics on Predicting in vivo Hazard and Mode-of-Action from High-Throughput in vitro Toxicity Assays" Toxicological Sciences, 132:327-346 (2013).

Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S., LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015). "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological Sciences, kfv171.

Examples

table <- NULL
for(this.cas in sample(get_lit_cheminfo(),50)) table <- rbind(table,cbind(
as.data.frame(this.cas),as.data.frame(get_lit_oral_equiv(conc=1,chem.cas=this.cas))))




get_lit_oral_equiv(0.1,chem.cas="34256-82-1")

get_lit_oral_equiv(0.1,chem.cas="34256-82-1",which.quantile=c(0.05,0.5,0.95))

KDE bandwidths for residual variability in hematocrit

Description

Bandwidths used for a one-dimensional kernel density estimation of the distribution of residual errors around smoothing spline fits of hematocrit vs. age for NHANES respondents in each of ten combinations of sex and race/ethnicity categories.

Usage

hct_h

Format

A named list with 10 elements, each a numeric value. Each list element corresponds to, and is named for, one combination of NHANES sex categories (Male and Female) and NHANES race/ethnicity categories (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other).

Details

Each matrix is the standard deviation for a normal distribution: this is the bandwidth to be used for a kernel density estimation (KDE) (using a normal kernel) of the distribution of residual errors around smoothing spline fits of hematocrit vs. age for NHANES respondents in the specified sex and race/ethnicity category. Optimal bandwidths were pre-calculated by doing the smoothing spline fits, getting the residuals, then calling kde on the residuals (which calls hpi to compute the plug-in bandwidth).

Used by HTTK-Pop only in "virtual individuals" mode (i.e. httkpop_generate with method = "v"), in estimate_hematocrit.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict hematocrit in infants under 1 year old.

Description

For infants under 1 year, hematocrit was not measured in NHANES. Assume a log-normal distribution where plus/minus 1 standard deviation of the underlying normal distribution is given by the reference range. Draw hematocrit values from these distributions by age.

Usage

hematocrit_infants(age_months)

Arguments

age_months

Vector of ages in months; all must be <= 12.

Details

Age Reference range
<1 month 31-49
1-6 months 29-42
7-12 months 33-38

Value

Vector of hematocrit percentages corresponding to the input vector of ages.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Return the assumptions used in Honda et al. 2019

Description

This function returns four of the better performing sets of assumptions evaluated in Honda et al. 2019 (https://doi.org/10.1371/journal.pone.0217564).These include four different combinations of hepatic clearance assumption, in vivo bioactivity assumption, and relevant tissue assumption. Generally, this function is not called directly by the user, but instead called by setting the IVIVE option in calc_mc_oral_equiv, calc_mc_css, and calc_analytic functions. Currently, these IVIVE option is not implemented the solve_1comp etc. functions.

Usage

honda.ivive(method = "Honda1", tissue = "liver")

Arguments

method

This is set to one of "Honda1", "Honda2", "Honda3", or "Honda4".
tissue

This is only relevant to "Honda4" and indicates the relevant tissue compartment.

Details

Only four sets of IVIVE assumptions that performed well in Honda et al. (2019) are currently included: "Honda1" through "Honda4". The use of max (peak) concentration can not be currently be calculated with calc_analytic_css. The httk default settings correspond to "Honda3":

In Vivo Conc. Metabolic Clearance Bioactive Chemical Conc. In Vivo TK Statistic Used* Bioactive Chemical Conc. In Vitro
Honda1 Veinous (Plasma) Restrictive Free Mean Conc. In Vivo Free Conc. In Vitro
Honda2 Veinous Restrictive Free Mean Conc. In Vivo Nominal Conc. In Vitro
Honda3 Veinous Restrictive Total Mean Conc. In Vivo Nominal Conc. In Vitro
Honda4 Target Tissue Non-restrictive Total Mean Conc. In Vivo Nominal Conc. In Vitro

"Honda1" uses plasma concentration, restrictive clearance, and treats the unbound invivo concentration as bioactive. For IVIVE, any input nominal concentration in vitro should be converted to cfree.invitro using armitage_eval, otherwise performance will be the same as "Honda2".

Value

A list of tissue, bioactive.free.invivo, and restrictive.clearance assumptions.

Author(s)

Greg Honda and John Wambaugh

References

Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Examples

honda.ivive(method = "Honda1", tissue = NULL)

Measured Caco-2 Apical-Basal Permeability Data

Description

In vitro Caco-2 membrane permeabilities characterize how readily absobed/transported a chemical is. These measurements are all for the apical-to-basal Caco-2 orientation. These data were either measured by EPA or collected by other others, as indicated by the column 'Data Origin'. Anywhere that the values is reported by three numbers separated by a comma (this also happens for plasma protein binding) the three values are: median, lower 95 percent confidence intervals, upper 95 percent confidence interval. Unless you are doing monte carlo work it makes sense to ignore the second and third values.

Usage

honda2023.data

Format

An object of class data.frame with 634 rows and 5 columns.

Details

Column Name Description Units
DTXSID EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard)
Pab Apical-to-basal Caco-2 permeability 10^-6 cm/s
Data Origin The reference which collected/generated the measurement
Test Whether (1) or not (0) the data was withheld from model building to be used in the QSPR test set
CAS Chemical Abstracts Service Registry Number

References

Obringer C, Manwaring J, Goebel C, Hewitt NJ, Rothe H (2016). “Suitability of the in vitro Caco-2 assay to predict the oral absorption of aromatic amine hair dyes.” Toxicology in Vitro, 32, 1–7.

Lanevskij K, Didziapetris R (2019). “Physicochemical QSAR analysis of passive permeability across Caco-2 monolayers.” Journal of Pharmaceutical Sciences, 108(1), 78–86.

Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, others (2012). “ChEMBL: a large-scale bioactivity database for drug discovery.” Nucleic Acids Research, 40(D1), D1100–D1107.

Honda G, Kenyon EM, Davidson-Fritz SE, Dinallo R, El-Masri H, Korel-Bexell E, Li L, Paul-Friedman K, Pearce R, Sayre R, Strock C, Thomas R, Wetmore BA, Wambaugh JF (2023). “Impact of Gut Permeability on Estimation of Oral Bioavailability for Chemicals in Commerce and the Environment.” Unpublished.

Predicted Caco-2 Apical-Basal Permeabilities

Description

Honda et al. (2023) describes the construction of a machine-learning quantitative structure-property relationship (QSPR )model for in vitro Caco-2 membrane permeabilites. That model was used to make chemical-specific predictions provided in this table.

Usage

honda2023.qspr

Format

An object of class data.frame with 14033 rows and 5 columns.

Details

Column Name Description Units
DTXSID EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard)
Pab.Class.Pred Predicted Pab rate of slow (1), moderate (2), or fast (3)
Pab.Pred.AD Whether (1) or not (0) the chemical is anticipated to be withing the QSPR domain of applicability
CAS Chemical Abstracts Service Registry Number
Pab.Quant.Pred Median and 95-percent interval for values within the predicted class's training data moderate (2), or fast (3) 10^-6 cm/s

References

Honda G, Kenyon EM, Davidson-Fritz SE, Dinallo R, El-Masri H, Korel-Bexell E, Li L, Paul-Friedman K, Pearce R, Sayre R, Strock C, Thomas R, Wetmore BA, Wambaugh JF (2023). “Impact of Gut Permeability on Estimation of Oral Bioavailability for Chemicals in Commerce and the Environment.” Unpublished.

See Also

load_honda2023

Howgate 2006

Description

This data set is only used in Vignette 5.

Usage

howgate

Format

A data.table containing 24 rows and 11 columns.

Author(s)

Caroline Ring

References

Howgate, E. M., et al. "Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability." Xenobiotica 36.6 (2006): 473-497.

Historical Performance of R Package httk

Description

This table records the historical performance and other metrics of the R package "httk" as profiled with the function benchmark_httk. There is a row for each version and a column for each benchmark or metric. This table is used to generate graphs comparing the current version to past performance in order to help identify unintended degradtion of package capabilities.

Usage

httk.performance

Format

An object of class data.frame with 25 rows and 18 columns.

Details

Column Name Description
Version The release of httk (major.minor.patch)
N.steadystate The number of chemicals for which Css can be predicted for the steady-state model
calc_analytic.units The ratio of the output of calc_analytic_css in mg/L to uM multiplied by 1000/MW (should be 1)
calc_mc.units The ratio of the output of calc_mc_css in mg/L to uM multiplied by 1000/MW (should be 1)
solve_pbtk.units The ratio of a Cplasma value from solve_pbtk in mg/L to uM multiplied by 1000/MW (should be 1)
RMSLE.Wetmore Root mean squared log10 error between Css predictions from httk and published values from Wetmore papers (both 95th percentile)
N.Wetmore Number of chemicals used in RMSLE evaluation
RMSLE.noMC RMSLE between 95th percentile Css prediction and median prediction
N.noMC Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCss RMSLE for predictions of in vivo measured Css
N.InVivoCss Number of chemicals used in RMSLE evaluation
RMSLE.InVivoAUC RMSLE for predictions of in vivo measured AUCs
N.InVivoAUC Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCmax RMSLE for predictions of in vivo measured Cmax
N.InVivoCmax Number of chemicals used in RMSLE evaluation
RMSLE.TissuePC RMSLE for predicted tissue:plasma partition coefficients
N.TissuePC Number of chemicals used in RMSLE evaluation
Notes Why benchmarks/metrics may have changed

References

Davidson-Fritz SE, Evans MV, Chang X, Breen M, Honda GS, Kenyon E, Linakis MW, Meade A, Pearce RG, Purucker T, Ring CL, Sfeir MA, Setzer RW, Sluka JP, Vitense K, Devito MJ, Wambaugh JF (2023). “Transparent and Evaluated Toxicokinetic Models for Bioinformatics and Public Health Risk Assessment.” Unpublished.

See Also

benchmark_httk

httkpop: Virtual population generator for HTTK.

Description

The httkpop package generates virtual population physiologies for use in population TK.

Details

To simulate inter-individual variability in the TK model, a MC approach is used: the model parameters are sampled from known or assumed distributions, and the model is evaluated for each sampled set of parameters. To simulate variability across subpopulations, the MC approach needs to capture the parameter correlation structure. For example, kidney function changes with age (Levey et al., 2009), thus the distribution of GFR is likely different in 6-year-olds than in 65-yearolds. To directly measure the parameter correlation structure, all parameters need to be measured in each individual in a representative sample population. Such direct measurements are extremely limited. However, the correlation structure of the physiological parameters can be inferred from their known individual correlations with demographic and anthropometric quantities for which direct population measurements do exist. These quantities are sex, race/ethnicity, age, height, and weight (Howgate et al., 2006; Jamei et al., 2009a; Johnson et al., 2006; McNally et al., 2014; Price et al., 2003). Direct measurements of these quantities in a large, representative sample of the U.S. population are publicly available from NHANES. NHANES also includes laboratory measurements, including both serum creatinine, which can be used to estimate GFR (Levey et al., 2009), and hematocrit. For conciseness, sex, race/ethnicity, age, height, weight, serum creatinine, and hematocrit will be called the NHANES quantities.

HTTK-Pop's correlated MC approach begins by sampling from the joint distribution of the NHANES quantities to simulate a population. Then, for each individual in the simulated population, HTTKePop predicts the physiological parameters from the NHANES quantities using regression equations from the literature (Barter et al., 2007; Baxter-Jones et al., 2011; Bosgra et al., 2012; Koo et al., 2000; Levey et al., 2009; Looker et al., 2013; McNally et al., 2014; Ogiu et al., 1997; Price et al., 2003; Schwartz and Work, 2009; Webber and Barr 2012). Correlations among the physiological parameters are induced by their mutual dependence on the correlated NHANES quantities. Finally, residual variability is added to the predicted physiological parameters using estimates of residual marginal variance (i.e., variance not explained by the regressions on the NHANES quantities) (McNally et al., 2014).

Data were combined from the three most recent publicly-available NHANES cycles: 2007-2008, 2009-2010, and 2011-2012. For each cycle, some NHANES quantities - height, weight, serum creatinine, and hematocrit - were measured only in a subset of respondents. Only these subsets were included in HTTKePop. The pooled subsets from the three cycles contained 29,353 unique respondents. Some respondents were excluded from analysis: those with age recorded as 80 years (because all NHANES respondents 80 years and older were marked as "80"); those with missing height, weight or hematocrit data; and those aged 12 years or older with missing serum creatinine data. These criteria excluded 4807 respondents, leaving 24,546 unique respondents. Each NHANES respondent was assigned a cycle-specific sample weight, which can be interpreted as the number of individuals in the total U.S. population represented by each NHANES respondent in each cycle (Johnson et al., 2013). Because data from three cycles were combined, the sample weights were rescaled (divided by the number of cycles being combined, as recommended in NHANES data analysis documentation) (Johnson et al., 2013). To handle the complex NHANES sampling structure, the R survey package was used to analyze the NHANES data (Lumley, 2004).

To allow generation of virtual populations specified by weight class, we coded a categorical variable for each NHANES respondent. The categories Underweight, Normal, Overweight, or Obese were assigned based on weight, age, and height/length (Grummer-Strawn et al., 2010; Kuczmarski et al., 2002; Ogden et al., 2014; WHO, 2006, 2010). We implemented two population simulation methods within HTTK-Pop: the direct-resampling method and the virtual-individuals method. The direct-resampling method simulated a population by sampling NHANES respondents with replacement, with probabilities proportional to the sample weights. Each individual in the resulting simulated population was an NHANES respondent, identified by a unique NHANES sequence number. By contrast, the second method generates "virtual individuals" - sets of NHANES quantities that obey the approximate joint distribution of the NHANES quantities (calculated using weighted smoothing functions and kernel density estimators), but do not necessarily correspond to any particular NHANES respondent. The direct-resampling method removed the possibility of generating unrealistic combinations of the NHANES quantities; the virtual-individuals method allowed the use of interpolation to simulate subpopulations represented by only a small number of NHANES respondents.

For either method, HTTK-Pop takes optional specifications about the population to be simulated and then samples from the appropriate conditional joint distribution of the NHANES quantities.

Once HTTK-Pop has simulated a population characterized by the NHANES quantities, the physiological parameters of the TK model are predicted from the NHANES quantities using regression equations from the literature. Liver mass was predicted for individuals over age 18 using allometric scaling with height from Reference Man (Valentin, 2002), and for individuals under 18 using regression relationships with height and weight published by Ogiu et al. (1997). Residual marginal variability was added for each individual as in PopGen (McNally et al., 2014). Similarly, hepatic portal vein blood flows (in L/h) are predicted as fixed fractions of a cardiac output allometrically scaled with height from Reference Man (Valentin, 2002), and residual marginal variability is added for each individual (McNally et al., 2014). Glomerular filtration rate (GFR) (in L/h/1.73 m2 body surface area) is predicted from age, race, sex, and serum creatinine using the CKD-EPI equation, for individuals over age 18 (Levey et al., 2009). For individuals under age 18, GFR is estimated from body surface area (BSA) (Johnson et al., 2006); BSA is predicted using Mosteller's formula (Verbraecken et al., 2006) for adults and Haycock's formula (Haycock et al., 1978) for children. Hepatocellularity (in millions of cells per gram of liver tissue) is predicted from age using an equation developed by Barter et al. (2007). Hematocrit is estimated from NHANES data for individuals 1 year and older. For individuals younger than 1 year, for whom NHANES did not measure hematocrit directly, hematocrit was predicted from age in months, using published reference ranges (Lubin, 1987).

In addition to the HTTK physiological parameters, the HTTK models include chemical-specific parameters representing the fraction of chemical unbound in plasma (Fup) and intrinsic clearance (CLint). Because these parameters represent interactions of the chemical with the body, their values will vary between individuals. To simulate this variability, Fub and CLint were included in MC simulations, by sampling from estimated or assumed distributions for the parameters defining them.

Variability in hematocrit was simulated either using NHANES data (for individuals ages 1 and older) or using age-based reference ranges (for individuals under age 1). Fup was treated as a random variable obeying a distribution censored below the average limit of quantification (LOQ) of the in vitro assay. Specifically, Fup was assumed to obey a normal distribution truncated below at 0 and above at 1, centered at the Fup value measured in vitro, with a 30 the average LOQ (0.01), Fup was instead drawn from a uniform distribution between 0 and 0.01. Fup was assumed to be independent of all other parameters. This censored normal distribution was chosen to match that used in Wambaugh et al. (2015).

Variability in hepatocellularity (106 cells/g liver) and Mliver (kg) were simulated. The remaining source of variability in CLint,h is variability in CLint, which was simulated using a Gaussian mixture distribution to represent the population proportions of poor metabolizers (PMs) and non-PMs of each substance. The true prevalence of PMs is isozyme-specific (Ma et al., 2002; Yasuda et al., 2008); however, isozyme- specific metabolism data were not available for the majority of chemicals considered. We therefore made a simplifying assumption that 5 slower than average. With 95 a normal distribution truncated below at zero, centered at the value measured in vitro, with a 30 CLint was drawn from a PM distribution: a truncated normal distribution centered on one-tenth of the in vitro value with 30 Both CLint itself and the probability of being a PM were assumed to be independent of all other parameters. The truncated normal nonePM distribution was chosen because it has been used (with 100 in previous work (Rotroff et al., 2010; Wambaugh et al., 2015; Wetmore et al., 2014; Wetmore et al., 2015; Wetmore et al., 2012); the PM distribution was chosen to comport with the nonePM distribution.

Main function to generate a population

If you just want to generate a table of (chemical-independent) population physiology parameters, use httkpop_generate.

Using HTTK-Pop with HTTK

To generate a population and then run an HTTK model for that population, the workflow is as follows:

  1. Generate a population using httkpop_generate.

  2. For a given HTTK chemical and general model, convert the population data to corresponding sets of HTTK model parameters using httkpop_mc.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Levey, A.S., Stevens, L.A., Schmid, C.H., Zhang, Y.L., Castro, A.F., Feldman, H.I., et al., 2009. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 150, 604-612.

Howgate, E., Rowland-Yeo, K., Proctor, N., Tucker, G., Rostami-Hodjegan, A., 2006. Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica 36, 473-497.

Jamei, M., Dickinson, G.L., Rostami-Hodjegan, A., 2009a. A framework for assessing inter-individual variability in pharmacokinetics using virtual human populations and integrating general knowledge of physical chemistry, biology, anatomy, physiology and genetics: a tale of 'bottom-up' vs 'top-down' recognition of covariates. Drug Metab. Pharmacokinet. 24, 53-75.

Johnson, T.N., Rostami-Hodjegan, A., Tucker, G.T., 2006. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45, 931-956.

McNally, K., Cotton, R., Hogg, A., Loizou, G., 2014. PopGen: a virtual human population generator. Toxicology 315, 70-85.

Price, P.S., Conolly, R.B., Chaisson, C.F., Gross, E.A., Young, J.S., Mathis, E.T., et al., 2003. Modeling interindividual variation in physiological factors used in PBPK models of humans. Crit. Rev. Toxicol. 33, 469-503.

Barter, Z.E., Bayliss, M.K., Beaune, P.H., Boobis, A.R., Carlile, D.J., Edwards, R.J., et al., 2007. Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human micro-somal protein and hepatocellularity per gram of liver. Curr. Drug Metab. 8, 33-45.

Baxter-Jones, A.D., Faulkner, R.A., Forwood, M.R., Mirwald, R.L., Bailey, D.A., 2011. Bone mineral accrual from 8 to 30 years of age: an estimation of peak bone mass. J. Bone Miner. Res. 26, 1729-1739.

Bosgra, S., van Eijkeren, J., Bos, P., Zeilmaker, M., Slob, W., 2012. An improved model to predict physiologically based model parameters and their inter-individual variability from anthropometry. Crit. Rev. Toxicol. 42, 751-767.

Koo, W.W., Walters, J.C., Hockman, E.M., 2000. Body composition in human infants at birth and postnatally. J. Nutr. 130, 2188-2194.

Looker, A., Borrud, L., Hughes, J., Fan, B., Shepherd, J., Sherman, M., 2013. Total body bone area, bone mineral content, and bone mineral density for individuals aged 8 years and over: United States, 1999-2006. In: Vital and health statistics Series 11, Data from the National Health Survey, pp. 1-78.

Ogiu, N., Nakamura, Y., Ijiri, I., Hiraiwa, K., Ogiu, T., 1997. A statistical analysis of the internal organ weights of normal Japanese people. Health Phys. 72, 368-383.

Schwartz, G.J., Work, D.F., 2009. Measurement and estimation of GFR in children and adolescents. Clin. J. Am. Soc. Nephrol. 4, 1832-1843.

Webber, C.E., Barr, R.D., 2012. Age-and gender-dependent values of skeletal muscle mass in healthy children and adolescents. J. Cachex. Sarcopenia Muscle 3, 25-29.

Johnson, C.L., Paulose-Ram, R., Ogden, C.L., Carroll, M.D., Kruszon-Moran, D., Dohrmann, S.M., et al., 2013. National health and nutrition examination survey: analytic guidelines, 1999-2010. Vital and health statistics Series 2. Data Eval. Methods Res. 1-24.

Lumley, T., 2004. Analysis of complex survey samples. J. Stat. Softw. 9, 1-19.

Grummer-Strawn, L.M., Reinold, C.M., Krebs, N.F., Control, C.f.D.; Prevention, 2010. Use of World Health Organization and CDC Growth Charts for Children Aged 0-59 Months in the United States. Department of Health and Human Services, Centers for Disease Control and Prevention.

Kuczmarski, R.J., Ogden, C.L., Guo, S.S., Grummer-Strawn, L.M., Flegal, K.M., Mei, Z., et al., 2002. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat. Series 11, Data from the national health survey 246, 1-190.

Ogden, C.L., Carroll, M.D., Kit, B.K., Flegal, K.M., 2014. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 311, 806-814.

WHO, 2006. In: WHO D.o.N.f.H.a.D. (Ed.), WHO Child Growth Standards: Length/Heightfor- Age, Weight-for-Age, Weight-for-Length, Weight-for-Height and Body Mass Indexfor- Age: Methods and Development.

WHO, 2010. In: (WHO) W.H.O. (Ed.), WHO Anthro for Personal Computers Manual: Software for Assessing Growth and Development of the World's Children, Version 3.2.2, 2011. WHO, Geneva.

Valentin, J., 2002. Basic anatomical and physiological data for use in radiological protection: reference values: ICRP publication 89. Ann. ICRP 32, 1-277.

Johnson, T.N., Rostami-Hodjegan, A., Tucker, G.T., 2006. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45, 931-956.

Verbraecken, J., Van de Heyning, P., De Backer, W., Van Gaal, L., 2006. Body surface area in normal-weight, overweight, and obese adults. A comparison study. Metabolism 55, 515-524

Haycock, G.B., Schwartz, G.J., Wisotsky, D.H., 1978. Geometric method for measuring body surface area: a height-weight formula validated in infants, children, and adults. J. Pediatr. 93, 62-66.

Lubin, B., 1987. Reference values in infancy and childhood. In: Nathan, D., Oski, F. (Eds.), Hematology of Infancy and Childhood.

Wambaugh, J.F., Wetmore, B.A., Pearce, R., Strope, C., Goldsmith, R., Sluka, J.P., et al., 2015. Toxicokinetic triage for environmental chemicals. Toxicol. Sci. 147, 55-67

Ma, M.K., Woo, M.H., Mcleod, H.L., 2002. Genetic basis of drug metabolism. Am. J. Health Syst. Pharm. 59, 2061-2069.

Yasuda, S.U., Zhang, L., Huang, S.M., 2008. The role of ethnicity in variability in response to drugs: focus on clinical pharmacology studies. Clin. Pharmacol. Ther. 84, 417-423.

Rotroff, D.M., Wetmore, B.A., Dix, D.J., Ferguson, S.S., Clewell, H.J., Houck, K.A., et al., 2010. Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. Toxicol. Sci. 117, 348-358.

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., et al., 2012. Integration of dosimetry, exposure, and high-throughput screening data in chemical toxicity assessment. Toxicol. Sci. 125, 157-174.

Wetmore, B.A., Allen, B., Clewell 3rd, H.J., Parker, T., Wambaugh, J.F., Almond, L.M., et al., 2014. Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicol. Sci. 142, 210-224.

Wetmore, B.A., Wambaugh, J.F., Allen, B., Ferguson, S.S., Sochaski, M.A., Setzer, R.W., et al., 2015. Incorporating high-throughput exposure predictions with Dosimetryadjusted in vitro bioactivity to inform chemical toxicity testing. Toxicol. Sci. 148, 121-136.

Convert HTTK-Pop-generated parameters to HTTK physiological parameters

Description

Convert HTTK-Pop-generated parameters to HTTK physiological parameters

Usage

httkpop_biotophys_default(indiv_dt)

Arguments

indiv_dt

The data.table object returned by httkpop_generate()

Value

A data.table with the physiological parameters expected by any HTTK model, including body weight (BW), hematocrit, tissue volumes per kg body weight, tissue flows as fraction of CO, CO per (kg BW)^3/4, GFR per (kg BW)^3/4, portal vein flow per (kg BW)^3/4, and liver density.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Generate a virtual population by directly resampling the NHANES data.

Description

Generate a virtual population by directly resampling the NHANES data.

Usage

httkpop_direct_resample(
  nsamp = NULL,
  gendernum = NULL,
  agelim_years = NULL,
  agelim_months = NULL,
  weight_category = c("Underweight", "Normal", "Overweight", "Obese"),
  gfr_category = c("Normal", "Kidney Disease", "Kidney Failure"),
  reths = c("Mexican American", "Other Hispanic", "Non-Hispanic White",
    "Non-Hispanic Black", "Other"),
  gfr_resid_var = TRUE,
  ckd_epi_race_coeff = FALSE,
  nhanes_mec_svy
)

Arguments

nsamp

The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum

Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
agelim_years

Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
agelim_months

Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
weight_category

Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
gfr_category

The kidney function categories to include in the population. Default is c('Normal','Kidney Disease', 'Kidney Failure') to include all kidney function levels.
reths

Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
gfr_resid_var

Logical value indicating whether or not to include residual variability when generating GFR values. (Default is TRUE.)
ckd_epi_race_coeff

Logical value indicating whether or not to use the "race coefficient" from the CKD-EPI equation when estimating GFR values. (Default is FALSE.)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Value

A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Inner loop function called by httkpop_direct_resample.

Description

Inner loop function called by httkpop_direct_resample.

Usage

httkpop_direct_resample_inner(
  nsamp,
  gendernum,
  agelim_months,
  agelim_years,
  reths,
  weight_category,
  gfr_resid_var,
  ckd_epi_race_coeff,
  nhanes_mec_svy
)

Arguments

nsamp

The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum

Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
agelim_months

Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
agelim_years

Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
reths

Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
weight_category

Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
gfr_resid_var

Logical value indicating whether or not to include residual variability when generating GFR values. (Default is TRUE, passed from 'httkpop_direct_resample'.)
ckd_epi_race_coeff

Logical value indicating whether or not to use the "race coefficient" from the CKD-EPI equation when estimating GFR values. (Default is FALSE, passed from 'httkpop_direct_resample'.)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate)

Value

A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Generate a virtual population for PBTK

Description

Generate a virtual population characterized by demographic, anthropometric, and physiological parameters relevant to PBTK.

Usage

httkpop_generate(
  method,
  nsamp = NULL,
  gendernum = NULL,
  agelim_years = NULL,
  agelim_months = NULL,
  weight_category = c("Underweight", "Normal", "Overweight", "Obese"),
  gfr_category = c("Normal", "Kidney Disease", "Kidney Failure"),
  reths = c("Mexican American", "Other Hispanic", "Non-Hispanic White",
    "Non-Hispanic Black", "Other"),
  gfr_resid_var = TRUE,
  ckd_epi_race_coeff = FALSE
)

Arguments

method

The population-generation method to use. Either "virtual individuals" or "direct resampling." Short names may be used: "d" or "dr" for "direct resampling", and "v" or "vi" for "virtual individuals".
nsamp

The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum

Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
agelim_years

Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If only a single value is provided, both minimum and maximum ages will be set to that value; e.g. agelim_years=3 is equivalent to agelim_years=c(3,3). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
agelim_months

Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If only a single value is provided, both minimum and maximum ages will be set to that value; e.g. agelim_months=36 is equivalent to agelim_months=c(36,36). If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
weight_category

Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
gfr_category

The kidney function categories to include in the population. Default is c('Normal','Kidney Disease', 'Kidney Failure') to include all kidney function levels.
reths

Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
gfr_resid_var

TRUE to add residual variability to GFR predicted from serum creatinine; FALSE to not add residual variability
ckd_epi_race_coeff

TRUE to use the CKD-EPI equation as originally published (with a coefficient changing predicted GFR for individuals identified as "Non-Hispanic Black"); FALSE to set this coefficient to 1.

Details

Demographic and anthropometric (body measures) variables, along with serum creatinine and hematocrit, are generated from survey data from the Centers for Disease Control's National Health and Nutrition Examination Survey (NHANES). Those data are stored in the object nhanes_mec_svy (a survey.design object, see package survey). With method = "d", these variables will be sampled with replacement directly from NHANES data. Each NHANES respondent's likelihood of being sampled is given by their sample weight. With method = "v", these variables will be sampled from distributions fitted to NHANES data. Tissue masses and flows are generated based on demographic, body measures, and serum creatinine values, using regression equations from the literature and/or allometric scaling based on height. Extensive details about how each of these parameters are generated are available in the supplemental material of Ring et al. (2017) (see References for full citation).

Value

A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter. Details of the parameters returned and their units are in the following tables.

Demographic variables

Name Definition Units
seqn NHANES unique identifier (only included if method = "direct resampling") NA
gender Sex: "Male" or "Female" NA
reth Race/ethnicity: "Non-Hispanic Black", "Non-Hispanic white", "Mexican American", "Other Hispanic", or "Other". NA
age_years Age (0-79 years) years
age_months Age (0-959 months) months

Body measures and laboratory measurements

Name Definition Units
height Height cm
weight Body weight kg
serum_creat Serum creatinine mg/dL
hematocrit Hematocrit (percentage by volume of red blood cells in blood) %

Tissue masses

Name Definition Units
Blood_mass Mass of blood kg
Brain_mass Mass of brain kg
Gonads_mass Mass of gonads kg
Heart_mass Mass of heart kg
Kidneys_mass Mass of kidneys kg
Large_intestine_mass Mass of large intestine kg
Liver_mass Mass of liver kg
Lung_mass Mass of lungs kg
Muscle_mass Mass of skeletal muscle kg
Pancreas_mass Mass of pancreas kg
Skeleton_mass Mass of skeleton (including bone, red and yellow marrow, cartilage, periarticular tissue) kg
Skin_mass Mass of skin kg
Small_intestine_mass Mass of small intestine kg
Spleen_mass Mass of spleen kg
Stomach_mass Mass of stomach tissue kg
Other_mass Mass of GI tract contents (1.4% of body weight) and tissues not otherwise enumerated (3.3% of body weight). kg
org_mass_sum Sum of the above tissue masses. A check to ensure this is less than body weight. kg
Adipose_mass Mass of adipose tissue. Assigned as weight - org_mass_sum. kg

Tissue flows

Name Definition Units
Adipose_flow Blood flow to adipose tissue L/h
Brain_flow Blood flow to brain tissue L/h
CO Cardiac output L/h
Gonads_flow Blood flow to gonads tissue L/h
Heart_flow Blood flow to heart tissue L/h
Kidneys_flow Blood flow to kidneys tissue (not for glomerular filtration!) L/h
Large_intestine_flow Blood flow to large intestine tissue L/h
Liver_flow Blood flow to liver tissue L/h
Lung_flow Blood flow to lung tissue L/h
Muscle_flow Blood flow to skeletal muscle tissue L/h
Pancreas_flow Blood flow to pancreas tissue L/h
Skeleton_flow Blood flow to skeleton L/h
Skin_flow Blood flow to skin L/h
Small_intestine_flow Blood flow to small intestine L/h
Spleen_flow Blood flow to spleen L/h
Stomach_flow Blood flow to stomach L/h
org_flow_check Sum of blood flows as a fraction of cardiac output (CO). A check to make sure this is less than 1. Unitless fraction

Adjusted variables

Name Definition Units
weight_adj Adjusted body weight: Sum of all tissue masses. kg
BSA_adj Adjusted body surface area, based on height and weight_adj. cm^2
million.cells.per.gliver Hepatocellularity 1e6 cells/g liver
gfr_est Glomerular filtration rate (GFR) estimated using either the CKD-EPI equation (for adults) or a body-surface-area-based equation (for children). mL/min/1.73 m^2 body surface area
bmi_adj Body mass index (BMI), adjusted to match weight_adj and height. kg/m^2
weight_class Weight category based on bmi_adj: "Underweight" (BMI < 18.5), "Normal" (18.5 < BMI < 24.9), "Overweight" (25.0 < BMI < 29.9), or "Obese" (BMI >= 30) Unitless category
gfr_class Kidney function category based on GFR: "Normal" (GFR >=60 mL/min/1.73 m^2), "Kidney Disease" (15 <= GFR <= 60), or "Kidney Failure" (GFR < 15). Unitless category

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Examples

#Simply generate a virtual population of 100 individuals,
 #using the direct-resampling method
 set.seed(42)
httkpop_generate(method='direct resampling', nsamp=100)

#Generate a population using the virtual-individuals method,
#including 80 females and 20 males,
#including only ages 20-65,
#including only Mexican American and
#Non-Hispanic Black individuals,
#including only non-obese individuals
set.seed(42)
mypop <- httkpop_generate(method = 'virtual individuals',
                          gendernum=list(Female=80,
                          Male=20),
                          agelim_years=c(20,65),
                          reths=c('Mexican American',
                          'Non-Hispanic Black'),
                          weight_category=c('Underweight',
                          'Normal',
                          'Overweight'))
# Including a httkpop.dt argument will overwrite the number of sample and
# the httkpop on/off logical switch:
samps1 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE,
                           httkpop.dt=mypop)
samps2 <- create_mc_samples(chem.name="bisphenola",
                           httkpop.dt=mypop)
samps3 <- create_mc_samples(chem.name="bisphenola",
                           httkpop=FALSE)
# Now run calc_mc_oral equiv on the same pop for two different chemcials:
calc_mc_oral_equiv(conc=10,
                   chem.name="bisphenola",
                   httkpop.dt=mypop,
                   return.samples=TRUE)
calc_mc_oral_equiv(conc=2,
                   chem.name="triclosan",
                   httkpop.dt=mypop,
                   return.samples=TRUE)

httk-pop: Correlated human physiological parameter Monte Carlo

Description

This is the core function for httk-pop correlated human physiological variability simulation as described by Ring et al. (2017) (doi:10.1016/j.envint.2017.06.004). This functions takes the data table of population biometrics (one individual per row) generated by httkpop_generate, and converts it to the corresponding table of HTTK model parameters for a specified HTTK model.

Usage

httkpop_mc(model, samples = 1000, httkpop.dt = NULL, ...)

Arguments

model

One of the HTTK models: "1compartment", "3compartmentss", "3compartment", or "pbtk".
samples

The number of Monte Carlo samples to use (can often think of these as separate individuals)
httkpop.dt

A data table generated by httkpop_generate. This defaults to NULL, in which case httkpop_generate is called to generate this table.
...

Additional arugments passed on to httkpop_generate.

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (submitted).

Value

A data.table with a row for each individual in the sample and a column for each parater in the model.

Author(s)

Caroline Ring and John Wambaugh

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Breen M, Wambaugh JF, Bernstein A, Sfeir M, Ring CL (2022). “Simulating toxicokinetic variability to identify susceptible and highly exposed populations.” Journal of Exposure Science & Environmental Epidemiology, 32(6), 855–863.

Rowland M, Benet LZ, Graham GG (1973). “Clearance concepts in pharmacokinetics.” Journal of pharmacokinetics and biopharmaceutics, 1(2), 123–136.

Examples

set.seed(42)
indiv_examp <- httkpop_generate(method="d", nsamp=10)

httk_param <- httkpop_mc(httkpop.dt=indiv_examp, 
                        samples=10,
                        model="1compartment")

Generate a virtual population by the virtual individuals method.

Description

Generate a virtual population by the virtual individuals method.

Usage

httkpop_virtual_indiv(
  nsamp = NULL,
  gendernum = NULL,
  agelim_years = NULL,
  agelim_months = NULL,
  weight_category = c("Underweight", "Normal", "Overweight", "Obese"),
  gfr_category = c("Normal", "Kidney Disease", "Kidney Failure"),
  reths = c("Mexican American", "Other Hispanic", "Non-Hispanic White",
    "Non-Hispanic Black", "Other"),
  gfr_resid_var = TRUE,
  ckd_epi_race_coeff = FALSE,
  nhanes_mec_svy
)

Arguments

nsamp

The desired number of individuals in the virtual population. nsamp need not be provided if gendernum is provided.
gendernum

Optional: A named list giving the numbers of male and female individuals to include in the population, e.g. list(Male=100, Female=100). Default is NULL, meaning both males and females are included, in their proportions in the NHANES data. If both nsamp and gendernum are provided, they must agree (i.e., nsamp must be the sum of gendernum).
agelim_years

Optional: A two-element numeric vector giving the minimum and maximum ages (in years) to include in the population. Default is c(0,79). If agelim_years is provided and agelim_months is not, agelim_years will override the default value of agelim_months.
agelim_months

Optional: A two-element numeric vector giving the minimum and maximum ages (in months) to include in the population. Default is c(0, 959), equivalent to the default agelim_years. If agelim_months is provided and agelim_years is not, agelim_months will override the default values of agelim_years.
weight_category

Optional: The weight categories to include in the population. Default is c('Underweight', 'Normal', 'Overweight', 'Obese'). User-supplied vector must contain one or more of these strings.
gfr_category

The kidney function categories to include in the population. Default is c('Normal','Kidney Disease', 'Kidney Failure') to include all kidney function levels.
reths

Optional: a character vector giving the races/ethnicities to include in the population. Default is c('Mexican American','Other Hispanic','Non-Hispanic White','Non-Hispanic Black','Other'), to include all races and ethnicities in their proportions in the NHANES data. User-supplied vector must contain one or more of these strings.
gfr_resid_var

Logical value indicating whether or not to include residual variability when generating GFR values. (Default is TRUE.)
ckd_epi_race_coeff

Logical value indicating whether or not to use the "race coefficient" from the CKD-EPI equation when estimating GFR values. (Default is FALSE.)
nhanes_mec_svy

surveydesign object created from mecdt using svydesign (this is done in httkpop_generate, which calls this function)

Value

A data.table where each row represents an individual, and each column represents a demographic, anthropometric, or physiological parameter.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

KDE bandwidth for residual variability in height/weight

Description

Bandwidths used for a two-dimensional kernel density estimation of the joint distribution of residual errors around smoothing spline fits of height vs. age and weight vs. age for NHANES respondents in each of ten combinations of sex and race/ethnicity categories.

Usage

hw_H

Format

A named list with 10 elements, each a matrix with 2 rows and 2 columns. Each list element corresponds to, and is named for, one combination of NHANES sex categories (Male and Female) and NHANES race/ethnicity categories (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other).

Details

Each matrix is a variance-covariance matrix for a two-dimensional normal distribution: this is the bandwidth to be used for a two-dimensional kernel density estimation (KDE) (using a two-dimensional normal kernel) of the joint distribution of residual errors around smoothing spline fits of height vs. age and weight vs. age for NHANES respondents in the specified sex and race/ethnicity category. Optimal bandwidths were pre-calculated by doing the smoothing spline fits, getting the residuals, then calling kde on the residuals (which calls Hpi to compute the plug-in bandwidth).

Used by HTTK-Pop only in "virtual individuals" mode (i.e. httkpop_generate with method = "v"), in gen_height_weight.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Convenience Boolean (yes/no) functions to identify chemical membership in several key lists.

Description

These functions allow easy identification of whether or not a chemical CAS is included in various research projects. While it is our intent to keep these lists up-to-date, the information here is only for convenience and should not be considered to be definitive.

Usage

in.list(chem.cas = NULL, which.list = "ToxCast")

Arguments

chem.cas

The Chemical Abstracts Service Resgistry Number (CAS-RN) corresponding to the chemical of interest.
which.list

A character string that can take the following values: "ToxCast", "Tox21", "ExpoCast", "NHANES", ""NHANES.serum.parent", "NHANES.serum.analyte","NHANES.blood.parent","NHANES.blood.analyte", "NHANES.urine.parent","NHANES.urine.analyte"

Details

Tox21: Toxicology in the 21st Century (Tox21) is a U.S. federal High Throughput Screening (HTS) collaboration among EPA, NIH, including National Center for Advancing Translational Sciences and the National Toxicology Program at the National Institute of Environmental Health Sciences, and the Food and Drug Administration. (Bucher et al., 2008)

ToxCast: The Toxicity Forecaster (ToxCast) is a HTS screening project led by the U.S. EPA to perform additional testing of a subset of Tox21 chemicals. (Judson et al. 2010)

ExpoCast: ExpoCast (Exposure Forecaster) is an U.S. EPA research project to generate tenetative exposure estimates (e.g., mg/kg BW/day) for thousands of chemicals that have little other information using models and informatics. (Wambaugh et al. 2014)

NHANES: The U.S. Centers for Disease Control (CDC) National Health and Nutrition Examination Survery (NHANES) is an on-going survey to characterize the health and biometrics (e.g., weight, height) of the U.S. population. One set of measurments includes the quantification of xenobiotic chemicals in various samples (blood, serum, urine) of the thousands of surveyed individuals. (CDC, 2014)

Value

logical

A Boolean (1/0) value that is TRUE if the chemical is in the list.

Author(s)

John Wambaugh

References

Bucher, J. R. (2008). Guest Editorial: NTP: New Initiatives, New Alignment. Environ Health Perspect 116(1).

Judson, R. S., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M. and Dix, D. J. (2010). In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project. Environmental Health Perspectives 118(4), 485-492.

Wambaugh, J. F., Wang, A., Dionisio, K. L., Frame, A., Egeghy, P., Judson, R. and Setzer, R. W. (2014). High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals. Environmental Science & Technology, 10.1021/es503583j.

CDC (2014). National Health and Nutrition Examination Survey. Available at: https://www.cdc.gov/nchs/nhanes.htm.

See Also

is.httk for determining inclusion in httk project

Examples

httk.table <- get_cheminfo(info=c("CAS","Compound"))
httk.table[,"Rat"] <- ""
httk.table[,"NHANES"] <- ""
httk.table[,"Tox21"] <- ""
httk.table[,"ToxCast"] <- ""
httk.table[,"ExpoCast"] <- ""
httk.table[,"PBTK"] <- ""
# To make this example run quickly, this loop is only over the first five 
# chemicals. To build a table with all available chemicals use:
# for (this.cas in httk.table$CAS)
for (this.cas in httk.table$CAS[1:5])
{
  this.index <- httk.table$CAS==this.cas
  if (is.nhanes(this.cas)) httk.table[this.index,"NHANES"] <- "Y"
  if (is.tox21(this.cas)) httk.table[this.index,"Tox21"] <- "Y"
  if (is.toxcast(this.cas)) httk.table[this.index,"ToxCast"] <- "Y"
  if (is.expocast(this.cas)) httk.table[this.index,"ExpoCast"] <- "Y"
  if (is.httk(this.cas,model="PBTK")) httk.table[this.index,"PBTK"] <- "Y"
  if (is.httk(this.cas,species="rat")) httk.table[this.index,"Rat"] <- "Y"
}

Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability.

Description

Given a CAS in the HTTK data set, a virtual population from HTTK-Pop, some user specifications on the assumed distributions of Funbound.plasma and Clint, draw "individual" values of Funbound.plasma and Clint from those distributions. The methodology for this function was developed and described by Wambaugh et al. (2019) (doi:10.1093/toxsci/kfz205).

Usage

invitro_mc(
  parameters.dt = NULL,
  samples,
  fup.meas.mc = TRUE,
  fup.pop.mc = TRUE,
  clint.meas.mc = TRUE,
  clint.pop.mc = TRUE,
  fup.meas.cv = 0.4,
  clint.meas.cv = 0.3,
  fup.pop.cv = 0.3,
  clint.pop.cv = 0.3,
  caco2.meas.sd = 0.3,
  caco2.pop.sd = 0.3,
  Caco2.Fgut = TRUE,
  Caco2.Fabs = TRUE,
  keepit100 = FALSE,
  poormetab = TRUE,
  fup.lod = 0.01,
  fup.censored.dist = FALSE,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  clint.pvalue.threshold = 0.05,
  minimum.Funbound.plasma = 1e-04
)

Arguments

parameters.dt

A data table of physiological and chemical-specific parameters
samples

The number of samples to draw.
fup.meas.mc

Logical – should we perform measurment (uncertainty) Monte Carlo for Funbound.plasma values (Default TRUE). If FALSE, the user may choose to provide columns for "unadjusted.Funbound.plasma" or "fup.mean" from their own methods.
fup.pop.mc

Logical – should we perform population (variability) Monte Carlo for Funbound.plasma values (Default TRUE)
clint.meas.mc

Logical – should we perform measurment (uncertainty) Monte Carlo for Clint values (Default TRUE)
clint.pop.mc

Logical – should we perform population (variability) Monte Carlo for Clint values (Default TRUE)
fup.meas.cv

Coefficient of variation of distribution of measured Funbound.plasma values.
clint.meas.cv

Coefficient of variation of distribution of measured Clint values.
fup.pop.cv

Coefficient of variation of distribution of population Funbound.plasma values.
clint.pop.cv

Coefficient of variation of distribution of population Clint values.
caco2.meas.sd

Standard deviation of the measured oral absorption - numeric value (Default 0.3).
caco2.pop.sd

Standard deviation of the population level oral absorption - numeric value (Default 0.3).
Caco2.Fgut

= TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut.
Caco2.Fabs

= TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs.
keepit100

= TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings.
poormetab

Logical. Whether to include poor metabolizers in the Clint distribution or not.
fup.lod

The average limit of detection for Funbound.plasma, below which distribution will be censored if fup.censored.dist is TRUE. Default 0.01.
fup.censored.dist

Logical. Whether to draw Funbound.plasma from a censored distribution or not.
adjusted.Funbound.plasma

Uses the Pearce et al. (2017) lipid binding adjustment for Funbound.plasma when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
parameters

A list of chemical-specific model parameters containing at least Funbound.plasma, Clint, and Fhep.assay.correction.

Details

The Monte Carlo methods used here were recently updated and described by Breen et al. (2022).

Value

A data.table with three columns: Funbound.plasma and Clint, containing the sampled values, and Fhep.assay.correction, containing the value for fraction unbound in hepatocyte assay.

Author(s)

Caroline Ring and John Wambaugh

References

Breen M, Wambaugh JF, Bernstein A, Sfeir M, Ring CL (2022). “Simulating toxicokinetic variability to identify susceptible and highly exposed populations.” Journal of Exposure Science & Environmental Epidemiology, 32(6), 855–863.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Wambaugh JF, Wetmore BA, Ring CL, Nicolas CI, Pearce RG, Honda GS, Dinallo R, Angus D, Gilbert J, Sierra T, others (2019). “Assessing toxicokinetic uncertainty and variability in risk prioritization.” Toxicological Sciences, 172(2), 235–251.

Examples

#Simply generate a virtual population of 100 individuals,
#using the direct-resampling method
set.seed(42)

# Pull mean chemical=specific values:
chem.props <- parameterize_pbtk(chem.name="bisphenolb")

# Convert to data.table with one row per sample:
parameters.dt <- monte_carlo(chem.props,samples=100)

# Use httk-pop to generate a population:
pop <- httkpop_generate(method='direct resampling', nsamp=100)

# Overwrite parameters specified by httk-pop:
parameters.dt[,names(pop):=pop]

# Vary in vitro parameters:
parameters.dt <- invitro_mc(parameters.dt,samples=100)

Checks whether a value, or all values in a vector, is within inclusive limits

Description

Checks whether a value, or all values in a vector, is within inclusive limits

Usage

is_in_inclusive(x, lims)

Arguments

x

A numeric value, or vector of values.
lims

A two-element vector of (min, max) values for the inclusive limits. If x is a vector, lims may also be a two-column matrix with nrow=length(x) where the first column is lower limits and the second column is upper limits. If x is a vector and lims is a two-element vector, then each element of x will be checked against the same limits. If x is a vector and lims is a matrix, then each element of x will be checked against the limits given by the corresponding row of lims.

Value

A logical vector the same length as x, indicating whether each element of x is within the inclusive limits given by lims.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Convenience Boolean (yes/no) function to identify chemical membership and treatment within the httk project.

Description

Allows easy identification of whether or not a chemical CAS is included in various aspects of the httk research project (by model type and species of interest). While it is our intent to keep these lists up-to-date, the information here is only for convenience and should not be considered definitive.

Usage

is.httk(chem.cas, species = "Human", model = "3compartmentss")

Arguments

chem.cas

The Chemical Abstracts Service Resgistry Number (CAS-RN) corresponding to the chemical of interest.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
model

Model used in calculation, 'pbtk' for the multiple compartment model, '1compartment' for the one compartment model, '3compartment' for three compartment model, '3compartmentss' for the three compartment model without partition coefficients, or 'schmitt' for chemicals with logP and fraction unbound (used in predict_partitioning_schmitt).

Details

Tox21: Toxicology in the 21st Century (Tox21) is a U.S. federal High Throughput Screening (HTS) collaboration among EPA, NIH, including National Center for Advancing Translational Sciences and the National Toxicology Program at the National Institute of Environmental Health Sciences, and the Food and Drug Administration. (Bucher et al., 2008)

ToxCast: The Toxicity Forecaster (ToxCast) is a HTS screening project led by the U.S. EPA to perform additional testing of a subset of Tox21 chemicals. (Judson et al. 2010)

ExpoCast: ExpoCast (Exposure Forecaster) is an U.S. EPA research project to generate tenetative exposure estimates (e.g., mg/kg BW/day) for thousands of chemicals that have little other information using models and informatics. (Wambaugh et al. 2014)

NHANES: The U.S. Centers for Disease Control (CDC) National Health and Nutrition Examination Survery (NHANES) is an on-going survey to characterize the health and biometrics (e.g., weight, height) of the U.S. population. One set of measurments includes the quantification of xenobiotic chemicals in various samples (blood, serum, urine) of the thousands of surveyed individuals. (CDC, 2014)

Value

logical

A Boolean (1/0) value that is TRUE if the chemical is included in the httk project with a given modeling scheme (PBTK) and a given species

Author(s)

John Wambaugh

References

Bucher, J. R. (2008). Guest Editorial: NTP: New Initiatives, New Alignment. Environ Health Perspect 116(1).

Judson, R. S., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M. and Dix, D. J. (2010). In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project. Environmental Health Perspectives 118(4), 485-492.

Wambaugh, J. F., Wang, A., Dionisio, K. L., Frame, A., Egeghy, P., Judson, R. and Setzer, R. W. (2014). High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals. Environmental Science & Technology, 10.1021/es503583j.

CDC (2014). National Health and Nutrition Examination Survey. Available at: https://www.cdc.gov/nchs/nhanes.htm.

See Also

in.list for determining chemical membership in several other key lists

Examples

httk.table <- get_cheminfo(info=c("CAS","Compound"))
httk.table[,"Rat"] <- ""
httk.table[,"NHANES"] <- ""
httk.table[,"Tox21"] <- ""
httk.table[,"ToxCast"] <- ""
httk.table[,"ExpoCast"] <- ""
httk.table[,"PBTK"] <- ""
# To make this example run quickly, this loop is only over the first five 
# chemicals. To build a table with all available chemicals use:
# for (this.cas in httk.table$CAS)
for (this.cas in httk.table$CAS[1:5])
{
  this.index <- httk.table$CAS==this.cas
  if (is.nhanes(this.cas)) httk.table[this.index,"NHANES"] <- "Y"
  if (is.tox21(this.cas)) httk.table[this.index,"Tox21"] <- "Y"
  if (is.toxcast(this.cas)) httk.table[this.index,"ToxCast"] <- "Y"
  if (is.expocast(this.cas)) httk.table[this.index,"ExpoCast"] <- "Y"
  if (is.httk(this.cas,model="PBTK")) httk.table[this.index,"PBTK"] <- "Y"
  if (is.httk(this.cas,species="rat")) httk.table[this.index,"Rat"] <- "Y"
}

Johnson 2006

Description

This data set is only used in Vignette 5.

Usage

johnson

Format

A data.table containing 60 rows and 11 columns.

Author(s)

Caroline Ring

References

Johnson, Trevor N., Amin Rostami-Hodjegan, and Geoffrey T. Tucker. "Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children." Clinical pharmacokinetics 45.9 (2006): 931-956.

Kapraun et al. 2019 data

Description

A list object containing time-varying parameters for the human maternal-fetal HTTK model. List elements contain scalar coefficients for the polynomial, logistic, Gompertz, and other functions of time describing blood flow rates, tissue volumes, hematocrits, and other anatomical/physiological quantities that change in the human mother and her fetus during pregnancy and gestation.

Usage

kapraun2019

Format

list

Author(s)

Dustin F. Kapraun

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Kapraun DF, Wambaugh JF, Setzer RW, Judson RS (2019). “Empirical models for anatomical and physiological changes in a human mother and fetus during pregnancy and gestation.” PLOS ONE, 14(5), 1-56. doi:10.1371/journal.pone.0215906.

Predict kidney mass for children

Description

For individuals under age 18, predict kidney mass from weight, height, and gender. using equations from Ogiu et al. 1997

Usage

kidney_mass_children(weight, height, gender)

Arguments

weight

Vector of weights in kg.
height

Vector of heights in cm.
gender

Vector of genders (either 'Male' or 'Female').

Value

A vector of kidney masses in kg.

Author(s)

Caroline Ring

References

Ogiu, Nobuko, et al. "A statistical analysis of the internal organ weights of normal Japanese people." Health physics 72.3 (1997): 368-383.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict liver mass for children

Description

For individuals under 18, predict the liver mass from height, weight, and gender, using equations from Ogiu et al. 1997

Usage

liver_mass_children(height, weight, gender)

Arguments

height

Vector of heights in cm.
weight

Vector of weights in kg.
gender

Vector of genders (either 'Male' or 'Female').

Value

A vector of liver masses in kg.

Author(s)

Caroline Ring

References

Ogiu, Nobuko, et al. "A statistical analysis of the internal organ weights of normal Japanese people." Health physics 72.3 (1997): 368-383.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Load CLint and Fup QSPR predictions from Dawson et al. 2021.

Description

This function returns an updated version of chem.physical_and_invitro.data that includes Clint and Fup predictions from the Random Forest quantitative structure-property relationship (QSPR) models developed and presented in Dawson et al. 2021, included in table dawson2021.

Usage

load_dawson2021(overwrite = FALSE, exclude_oad = TRUE, target.env = .GlobalEnv)

Arguments

overwrite

Only matters if load.image=FALSE. If overwrite=TRUE then existing data in chem.physical_and_invitro.data will be replaced by any predictions in Dawson et al. (2021) that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way.
exclude_oad

Include the chemicals only within the applicability domain. If exclude_oad=TRUE (DEFAULT) chemicals outside the applicability domain do not have their predicted values loaded.
target.env

The environment where the new chem.physical_and_invitro.data is loaded. Defaults to global environment.

Details

Because Clint and Fup are the only measurements required for many HTTK models, changing the number of chemicals for which a value is available will change the number of chemicals which are listed with the get_cheminfo command. Use the command reset_httk to return to the initial (measured only) chem.physical_and_invitro.data (for all parameters).

Value

data.frame

An updated version of chem.physical_and_invitro.data.

Author(s)

Sarah E. Davidson

References

Dawson DE, Ingle BL, Phillips KA, Nichols JW, Wambaugh JF, Tornero-Velez R (2021). “Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors.” Environmental Science & Technology, 55(9), 6505-6517. doi:10.1021/acs.est.0c06117, PMID: 33856768, https://doi.org/10.1021/acs.est.0c06117.

See Also

reset_httk

get_cheminfo

Examples

# Count how many chemicals for which HTTK is available without the QSPR:
num.chems <- length(get_cheminfo())
print(num.chems)

# For chemicals with Dawson et al. (2021) Clint and Fup QSPR predictions, 
# add them to our chemical information wherever measured values are 
# unavailable:
load_dawson2021()
# For chemicals with Dawson et al. (2021) QSPR predictions, add them to
# our chemical information -- overwriting measured values where we had them:
load_dawson2021(overwrite=TRUE)

# Let's see how many chemicals we have now with the Dawson et al. (2021) 
# predictions loaded:
length(get_cheminfo()) 

# Now let us reset the chemical data to the initial version:
reset_httk()

# We should be back to our original number:
num.chems == length(get_cheminfo())

Load Caco2 QSPR predictions from Honda et al. 2023

Description

This function returns an updated version of chem.physical_and_invitro.data that includes Caco2 Pab predictions from the Random Forest quantitative structure-property relationship (QSPR) models developed and presented in Honda et al. 2023, included in table honda2023.qspr.

Usage

load_honda2023(overwrite = FALSE, exclude_oad = TRUE, target.env = .GlobalEnv)

Arguments

overwrite

Only matters if load.image=FALSE. If overwrite=TRUE then existing data in chem.physical_and_invitro.data will be replaced by any prediction in Honda et al. (2023) that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored.
exclude_oad

Include the chemicals only within the applicability domain. If exclude_oad=TRUE (DEFAULT) chemicals outside the applicability domain do not have their predicted values loaded.
target.env

The environment where the new chem.physical_and_invitro.data is loaded. Defaults to global environment.

Details

Note that because Pab is not required for most HTTK models, changing the number of chemicals for which a value is available will not change the number of chemicals which are listed with the get_cheminfo command. Use the command reset_httk to return to the initial (measured only) chem.physical_and_invitro.data (for all parameters).

Value

data.frame

An updated version of chem.physical_and_invitro.data.

Author(s)

John Wambaugh

See Also

reset_httk

get_cheminfo

Examples

# For chemicals with Honda et al. (2023) Caco2 Pab QSPR predictions, 
# add them to our chemical information wherever measured values are 
# unavailable:
load_honda2023()

# Or, for chemicals with Honda et al. (2023) QSPR predictions, add them to
# our chemical information but overwrite measured values where we had them:
load_honda2023(overwrite=TRUE) 

# Now let us reset the chemical data to the initial version:
reset_httk()

Load CLint and Fup QSPR predictions predictions from Pradeep et al. 2020.

Description

This function returns an updated version of chem.physical_and_invitro.data that includes quantitative structure-property relationship (QSPR) predictions from Support Vector Machine and Random Forest models developed and presented in Pradeep et al. 2020, included in pradeep2020.

Usage

load_pradeep2020(overwrite = FALSE, target.env = .GlobalEnv)

Arguments

overwrite

Only matters if load.image=FALSE. If overwrite=TRUE then existing data in chem.physical_and_invitro.data will be replaced by any predictions in Pradeep et al. (2020) that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way.
target.env

The environment where the new chem.physical_and_invitro.data is loaded. Defaults to global environment.

Details

Because Clint and Fup are the only measurements required for many HTTK models, changing the number of chemicals for which a value is available will change the number of chemicals which are listed with the get_cheminfo command. Use the command reset_httk to return to the initial (measured only) chem.physical_and_invitro.data (for all parameters).

Value

data.frame

An updated version of chem.physical_and_invitro.data.

Author(s)

Sarah E. Davidson

References

Pradeep P, Patlewicz G, Pearce R, Wambaugh J, Wetmore B, Judson R (2020). “Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment.” Computational Toxicology, 16, 100136. ISSN 2468-1113, doi:10.1016/j.comtox.2020.100136, https://doi.org/https://doi.org/10.1016/j.comtox.2020.100136.

See Also

reset_httk

get_cheminfo

Examples

# Count how many chemicals for which HTTK is available without the QSPR:
num.chems <- length(get_cheminfo())
print(num.chems)

# For chemicals with Pradeep et al. (2020) Clint and Fup QSPR predictions, 
# add them to our chemical information wherever measured values are 
# unavailable:
load_pradeep2020()

# Or, for chemicals with Pradeep et al. (2020) QSPR predictions, add them to
# our chemical information but overwrite measured values where we had them:
load_pradeep2020(overwrite=TRUE) 

# Let's see how many chemicals we have now with the Pradeep et al. (2020)
# predictions data loaded:
length(get_cheminfo())

# Now let us reset the chemical data to the initial version:
reset_httk()

# We should be back to our original number:
num.chems == length(get_cheminfo())

Load CLint and Fup QSPR predictions from Sipes et al 2017.

Description

This function returns an updated version of chem.physical_and_invitro.data that includes quantitative structure-property relationship (QSPR) predictions from Simulations Plus' ADMET predictor as used in Sipes et al. 2017, included in sipes2017.

Usage

load_sipes2017(overwrite = FALSE, target.env = .GlobalEnv)

Arguments

overwrite

Only matters if load.image=FALSE. If overwrite=TRUE then existing data in chem.physical_and_invitro.data will be replaced by any predictions in Sipes et al. (2017) that is for the same chemical and property. If overwrite=FALSE (DEFAULT) then new data for the same chemical and property are ignored. Funbound.plasma values of 0 (below limit of detection) are overwritten either way.
target.env

The environment where the new chem.physical_and_invitro.data is loaded. Defaults to global environment.

Details

Because Clint and Fup are the only measurements required for many HTTK models, changing the number of chemicals for which a value is available will change the number of chemicals which are listed with the get_cheminfo command. Use the command reset_httk to return to the initial (measured only) chem.physical_and_invitro.data (for all parameters).

Value

data.frame

An updated version of chem.physical_and_invitro.data.

Author(s)

Robert Pearce and John Wambaugh

References

Sipes, Nisha S., et al. "An intuitive approach for predicting potential human health risk with the Tox21 10k library." Environmental Science & Technology 51.18 (2017): 10786-10796.

See Also

reset_httk

get_cheminfo

Examples

# Count how many chemicals for which HTTK is available without the QSPR:
num.chems <- length(get_cheminfo())
print(num.chems)

# For chemicals with Sipes et al. (2017) Clint and Fup QSPR predictions, 
# add them to our chemical information wherever measured values are 
# unavailable:
load_sipes2017()

# Here's a chemical we didn't have before (this one is a good test since the 
# logP is nearly 10 and it probably wouldn't work in vitro):
calc_css(chem.cas="26040-51-7")

# Let's see how many chemicals we have now with the Sipes et al. (2017) 
# predictions data loaded:
length(get_cheminfo())

# Now let us reset the chemical data to the initial version:
reset_httk()

# We should be back to our original number:
num.chems == length(get_cheminfo())

Lump tissue parameters into model compartments

Description

This function takes the tissue:plasma partition coefficients from predict_partitioning_schmitt along with the tissue-specific volumes and flows and aggregates (or "lumps") them according to the needed scheme of toxicokinetic model tissue comparments.

predict_partitioning_schmitt makes tissue-specific predictions drawing from those tissues described in tissue.data. Since different physiologically-based toxicokinetic (PBTK) models use diffeent schemes for rganizing the tissues of the body into differing compartments (for example, "rapidly perfused tissues"), this function lumps tissues into compartments as specified by the argument 'tissuelist'. Aggregate flows, volumes, and partition coefficients are provided for the lumped tissue compartments. Flows and volumes are summed while partition coefficients is calculated using averaging weighted by species-specific tissue volumes.

The name of each entry in 'tissuelist' is its own compartment. The modelinfo_MODEL.R file corresponding to the model specified by argument 'model' includes both a 'tissuelist' describing to the model's compartmentallumping schme as well as a vector of 'tissuenames' specifying all tissues to be lumped into those compartments.

Alternatively the 'tissuelist' and 'tissuenames' can also be manually specified for alternate lumping schemes not necessarily related to a pre-made httk model. For example, tissuelist<-list(Rapid=c("Brain","Kidney")).

The tissues contained in 'tissuenames' that are unused in 'tissuelist' are aggregated into a single compartment termed "rest".

NOTE: The partition coefficients of lumped compartments vary according to individual and species differences since the volumes of the consitutent tissues may vary.

Usage

lump_tissues(
  Ktissue2pu.in,
  parameters = NULL,
  tissuelist = NULL,
  species = "Human",
  tissue.vols = NULL,
  tissue.flows = NULL,
  tissuenames = NULL,
  model = "pbtk",
  suppress.messages = FALSE
)

Arguments

Ktissue2pu.in

List of partition coefficients from predict_partitioning_schmitt. The tissues named in this list are lumped into the compartments specified by tissuelist unless they are not present the specified model's associated alltissues.
parameters

A list of physiological parameters including flows and volumes for tissues named in Ktissue2pu.in
tissuelist

Manually specifies compartment names and tissues, which override the standard compartment names and tissues that are usually specified in a model's associated modelinfo file. Remaining tissues in the model's associated alltissues listing are lumped in the rest of the body.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
tissue.vols

A list of volumes for tissues in tissuelist.
tissue.flows

A list of flows for tissues in tissuelist.
tissuenames

A list of tissue names in tissuenames.
model

Specify which model (and therefore which tissues) are being considered.
suppress.messages

Whether or not the output message is suppressed.

Value

Krbc2pu

Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.
Krest2pu

Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.
Vrestc

Volume of the rest of the body per kg body weight, L/kg BW.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
Qtotal.liverf

Fraction of cardiac output flowing to the gut and liver, i.e. out of the liver.
Qgutf

Fraction of cardiac output flowing to the gut.
Qkidneyf

Fraction of cardiac output flowing to the kidneys.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

See Also

predict_partitioning_schmitt

tissue.data

Examples

pcs <- predict_partitioning_schmitt(chem.name='bisphenola')
 tissuelist <- list(
   liver=c("liver"),
   rapid=c("lung","kidney","muscle","brain"),
   fat=c("adipose"),
   slow=c('bone'))
 lump_tissues(pcs,tissuelist=tissuelist)

Predict lung mass for children

Description

For individuals under 18, predict the liver mass from height, weight, and gender, using equations from Ogiu et al. 1997

Usage

lung_mass_children(height, weight, gender)

Arguments

height

Vector of heights in cm.
weight

Vector of weights in kg.
gender

Vector of genders (either 'Male' or 'Female').

Value

A vector of lung masses in kg.

Author(s)

Caroline Ring

References

Ogiu, Nobuko, et al. "A statistical analysis of the internal organ weights of normal Japanese people." Health physics 72.3 (1997): 368-383.

Price, Paul S., et al. "Modeling interindividual variation in physiological factors used in PBPK models of humans." Critical reviews in toxicology 33.5 (2003): 469-503.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Reference tissue masses and flows from tables in McNally et al. 2014.

Description

Reference tissue masses, flows, and residual variance distributions from Tables 1, 4, and 5 of McNally et al. 2014.

Usage

mcnally_dt

Format

A data.table with variables:

tissue

Body tissue

gender

Gender: Male or Female

mass_ref

Reference mass in kg, from Reference Man

mass_cv

Coefficient of variation for mass

mass_dist

Distribution for mass: Normal or Log-normal

flow_ref

Reference flow in L/h, from Reference Man

flow_cv

Coefficient of variation for flow (all normally distributed)

height_ref

Reference heights (by gender)

CO_ref

Reference cardiac output by gender

flow_frac

Fraction of CO flowing to each tissue: flow_ref/CO_ref

Author(s)

Caroline Ring

Source

McNally K, Cotton R, Hogg A, Loizou G. "PopGen: A virtual human population generator." Toxicology 315, 70-85, 2004.

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Pre-processed NHANES data.

Description

NHANES data on demographics, anthropometrics, and some laboratory measures, cleaned and combined into a single data set.

Usage

mecdt

Format

A data.table with 23620 rows and 12 variables.

seqn

NHANES unique identifier for individual respondents.

sddsrvyr

NHANES two-year cycle: one of "NHANES 2013-2014", "NHANES 2015-2016", "NHANES 2017-2018".

riagendr

Gender: "Male" or "Female"

ridreth1

Race/ethnicity category: one of "Mexican American", "Non-Hispanic White", "Non-Hispanic Black", "Other", "Other Hispanic".

ridexagm

Age in months at the time of examination (if not recorded by NHANES, it was imputed based on age at the time of screening)

ridexagy

Age in years at the time of examination (if not recorded by NHANES, it was imputed based on age at the time of screening)

bmxwt

Weight in kg

lbxscr

Serum creatinine, mg/dL

lbxhct

Hematocrit, percent by volume of blood composed of red blood cells

wtmec6yr

6-year sample weights for combining 3 cycles, computed by dividing 2-year sample weights by 3.

bmxhtlenavg

Average of height and recumbent length if both were measured; if only one was measured, takes value of the one that was measured.

weight_class

One of Underweight, Normal, Overweight, or Obese. Assigned using methods in get_weight_class.

Author(s)

Caroline Ring

Source

https://wwwn.cdc.gov/nchs/nhanes/Default.aspx

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Metabolism data involved in Linakis 2020 vignette analysis.

Description

Metabolism data involved in Linakis 2020 vignette analysis.

Usage

metabolism_data_Linakis2020

Format

A data.frame containing x rows and y columns.

Author(s)

Matt Linakis

Source

Matt Linakis

Monte Carlo for toxicokinetic model parameters

Description

This function performs basic, uncorrelated Monte Carlo to simulate uncertainty and/or variability for parameters of toxicokinetic models. Parameters can be varied according to either a normal distribution that is truncated at zero (using argument cv.params) or from a normal distribution that is censored for values less than the limit of detection (censored.params). Coefficient of variation (cv) and limit of of detectin can be specified separately for each parameter.

Usage

monte_carlo(
  parameters,
  cv.params = NULL,
  censored.params = NULL,
  samples = 1000,
  suppress.messages = TRUE
)

Arguments

parameters

These parameters that are also listed in either cv.params or censored.params are sampled using Monte Carlo.
cv.params

The parameters listed in cv.params are sampled from a normal distribution that is truncated at zero. This argument should be a list of coefficients of variation (cv) for the normal distribution. Each entry in the list is named for a parameter in "parameters". New values are sampled with mean equal to the value in "parameters" and standard deviation equal to the mean times the cv.
censored.params

The parameters listed in censored.params are sampled from a normal distribution that is censored for values less than the limit of detection (specified separately for each parameter). This argument should be a list of sub-lists. Each sublist is named for a parameter in "params" and contains two elements: "cv" (coefficient of variation) and "LOD" (limit of detection), below which parameter values are censored. New values are sampled with mean equal to the value in "params" and standard deviation equal to the mean times the cv. Censored values are sampled on a uniform distribution between 0 and the limit of detection.
samples

This argument is the number of samples to be generated for calculating quantiles.
suppress.messages

Whether or not the output message is suppressed.

Value

A data.table with a row for each individual in the sample and a column for each parater in the model.

Author(s)

John Wambaugh

References

Pearce, Robert G., et al. "Httk: R package for high-throughput toxicokinetics." Journal of statistical software 79.4 (2017): 1.

Examples

#Example based on Pearce et al. (2017):

# Set up means:
params <- parameterize_pbtk(chem.name="zoxamide")
# Nothing changes:
monte_carlo(params)

vary.params <- NULL
for (this.param in names(params)[!(names(params) %in%
  c("Funbound.plasma", "pKa_Donor", "pKa_Accept" )) &
  !is.na(as.numeric(params))]) vary.params[this.param] <- 0.2
# Most everything varies with CV of 0.2:
monte_carlo(
  parameters=params, 
  cv.params = vary.params)

censored.params <- list(Funbound.plasma = list(cv = 0.2, lod = 0.01))
# Fup is censored below 0.01:
monte_carlo(
  parameters=params, 
  cv.params = vary.params,
  censored.params = censored.params)

Published Pharmacokinetic Parameters from Obach et al. 2008

Description

This data set is used in Vignette 4 for steady state concentration.

Usage

Obach2008

Format

A data.frame containing 670 rows and 8 columns.

References

Obach, R. Scott, Franco Lombardo, and Nigel J. Waters. "Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 670 drug compounds." Drug Metabolism and Disposition 36.7 (2008): 1385-1405.

NHANES Exposure Data

Description

This data set is only used in Vignette 6.

Usage

onlyp

Format

A data.table containing 1060 rows and 5 columns.

Author(s)

Caroline Ring

References

Wambaugh, John F., et al. "High throughput heuristics for prioritizing human exposure to environmental chemicals." Environmental science & technology 48.21 (2014): 12760-12767.

Predict pancreas mass for children

Description

For individuals under 18, predict the pancreas mass from height, weight, and gender, using equations from Ogiu et al.

Usage

pancreas_mass_children(height, weight, gender)

Arguments

height

Vector of heights in cm.
weight

Vector of weights in kg.
gender

Vector of genders (either 'Male' or 'Female').

Value

A vector of pancreas masses in kg.

Author(s)

Caroline Ring

References

Ogiu, Nobuko, et al. "A statistical analysis of the internal organ weights of normal Japanese people." Health physics 72.3 (1997): 368-383.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Parameters for a one compartment (empirical) toxicokinetic model

Description

This function initializes the parameters needed in the function solve_1comp. Volume of distribution is estimated by using a modified Schmitt (2008) method to predict tissue particition coefficients (Pearce et al., 2017) and then lumping the compartments weighted by tissue volume:

Usage

parameterize_1comp(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  well.stirred.correction = TRUE,
  suppress.messages = FALSE,
  clint.pvalue.threshold = 0.05,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = list()
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing rat values with human values if true.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts volume of distribution) when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
regression

Whether or not to use the regressions in calculating partition coefficients in volume of distribution calculation.
restrictive.clearance

In calculating elimination rate and hepatic bioavailability, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
well.stirred.correction

Uses correction in calculation of hepatic clearance for well-stirred model if TRUE. This assumes clearance relative to amount unbound in whole blood instead of plasma, but converted to use with plasma concentration.
suppress.messages

Whether or not to suppress messages.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-value greater than the threshold are set to zero.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.

Details

V_d,steady-state = Sum over all tissues (K_i * V_i) + V_plasma

where K_i is the tissue:unbound plasma concentration partition coefficient for tissue i.

Value

Vdist

Volume of distribution, units of L/kg BW.
Fabsgut

Fraction of the oral dose absorbed and surviving gut metabolism, i.e. the fraction of the dose that enters the gutlumen.
kelim

Elimination rate, units of 1/h.
hematocrit

Percent volume of red blood cells in the blood.
Fabsgut

Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gutlumen.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
kelim

Elimination rate, units of 1/h.
hematocrit

Percent volume of red blood cells in the blood.
kgutabs

Rate chemical is absorbed, 1/h.
million.cells.per.gliver

Millions cells per gram of liver tissue.
MW

Molecular Weight, g/mol.
Rblood2plasma

The ratio of the concentration of the chemical in the blood to the concentration in the plasma. Not used in calculations but included for the conversion of plasma outputs.
hepatic.bioavailability

Fraction of dose remaining after first pass clearance, calculated from the corrected well-stirred model.
BW

Body Weight, kg.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

See Also

solve_1comp

calc_analytic_css_1comp

calc_vdist

parameterize_steadystate

apply_clint_adjustment

tissue.data

physiology.data

Examples

parameters <- parameterize_1comp(chem.name='Bisphenol-A',species='Rat')
 parameters <- parameterize_1comp(chem.cas='80-05-7',
                                  restrictive.clearance=FALSE,
                                  species='rabbit',
                                  default.to.human=TRUE)
 out <- solve_1comp(parameters=parameters,days=1)

Parameters for a three-compartment toxicokinetic model (dynamic)

Description

This function generates the chemical- and species-specific parameters needed for model '3compartment', for example solve_3comp. A call is masde to parameterize_pbtk to use Schmitt (2008)'s method as modified by Pearce et al. (2017) to predict partition coefficients based on descriptions in tissue.data. Organ volumes and flows are retrieved from table physiology.data.

Usage

parameterize_3comp(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  force.human.clint.fup = FALSE,
  clint.pvalue.threshold = 0.05,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  regression = TRUE,
  suppress.messages = FALSE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = NULL
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing animal values with human values if true.
force.human.clint.fup

Forces use of human values for hepatic intrinsic clearance and fraction of unbound plasma if true.
clint.pvalue.threshold

Hepatic clearances with clearance assays having p-values greater than the threshold are set to zero.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
regression

Whether or not to use the regressions in calculating partition coefficients.
suppress.messages

Whether or not the output message is suppressed.
restrictive.clearance

In calculating hepatic.bioavailability, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.

Value

BW

Body Weight, kg.
Clmetabolismc

Hepatic Clearance, L/h/kg BW.
Fabsgut

Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gutlumen.
Funbound.plasma

Fraction of plasma that is not bound.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
hematocrit

Percent volume of red blood cells in the blood.
Kgut2pu

Ratio of concentration of chemical in gut tissue to unbound concentration in plasma.
Kliver2pu

Ratio of concentration of chemical in liver tissue to unbound concentration in plasma.
Krbc2pu

Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.
Krest2pu

Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.
million.cells.per.gliver

Millions cells per gram of liver tissue.
MW

Molecular Weight, g/mol.
Qcardiacc

Cardiac Output, L/h/kg BW^3/4.
Qgfrc

Glomerular Filtration Rate, L/h/kg BW^3/4, volume of fluid filtered from kidney and excreted.
Qgutf

Fraction of cardiac output flowing to the gut.
Qliverf

Fraction of cardiac output flowing to the liver.
Rblood2plasma

The ratio of the concentration of the chemical in the blood to the concentration in the plasma.
Vgutc

Volume of the gut per kg body weight, L/kg BW.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
Vrestc

Volume of the rest of the body per kg body weight, L/kg BW.

Author(s)

Robert Pearce and John Wambaugh

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

See Also

solve_3comp

calc_analytic_css_3comp

parameterize_pbtk

apply_clint_adjustment

tissue.data

physiology.data

Examples

parameters <- parameterize_3comp(chem.name='Bisphenol-A',species='Rat')
 parameters <- parameterize_3comp(chem.cas='80-05-7',
                                  species='rabbit',default.to.human=TRUE)
 out <- solve_3comp(parameters=parameters,plots=TRUE)

Parameterize_fetal_PBTK

Description

This function initializes the parameters needed in the functions solve_fetal_pbtk by calling solve_pbtk and adding additional parameters.

Usage

parameterize_fetal_pbtk(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  fetal_fup_adjustment = TRUE,
  return.kapraun2019 = TRUE,
  suppress.messages = FALSE,
  ...
)

Arguments

chem.cas

Either the chemical name or the CAS number must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human"). Currently only a narrow human model is supported.
fetal_fup_adjustment

Logical indicator of whether to use an adjusted estimate for fetal fup based on the fetal:maternal plasma protein binding ratios presented in McNamara and Alcorn's 2002 study "Protein Binding Predictions in Infants." Defaults to TRUE.
return.kapraun2019

If TRUE (default) the empirical parameters for the Kapraun et al. (2019) maternal-fetal growth parameters are provided.
suppress.messages

Whether or not the output message is suppressed.
...

Arguments passed to parameterize_pbtk.

Value

pre_pregnant_BW

Body Weight before pregnancy, kg.
Clmetabolismc

Hepatic Clearance, L/h/kg BW.
Fabsgut

Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gutlumen.
Funbound.plasma

Fraction of plasma that is not bound.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
hematocrit

Percent volume of red blood cells in the blood.
Kgut2pu

Ratio of concentration of chemical in gut tissue to unbound concentration in plasma.
kgutabs

Rate that chemical enters the gut from gutlumen, 1/h.
Kkidney2pu

Ratio of concentration of chemical in kidney tissue to unbound concentration in plasma.
Kliver2pu

Ratio of concentration of chemical in liver tissue to unbound concentration in plasma.
Klung2pu

Ratio of concentration of chemical in lung tissue to unbound concentration in plasma.
Krbc2pu

Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.
Krest2pu

Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.
million.cells.per.gliver

Millions cells per gram of liver tissue.
MW

Molecular Weight, g/mol.
Qgfrc

Glomerular Filtration Rate, L/h/kg BW^3/4, volume of fluid filtered from kidney and excreted.
Rblood2plasma

The ratio of the concentration of the chemical in the blood to the concentration in the plasma from available_rblood2plasma.
Vgutc

Volume of the gut per kg body weight, L/kg BW.
Vkidneyc

Volume of the kidneys per kg body weight, L/kg BW.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
Vlungc

Volume of the lungs per kg body weight, L/kg BW.
Vthyroidc

Volume of the thyroid per kg body weight, L/kg BW.
Kfgut2pu

Ratio of concentration of chemical in fetal gut tissue to unbound concentration in plasma.
Kfkidney2pu

Ratio of concentration of chemical in fetal kidney tissue to unbound concentration in plasma.
Kfliver2pu

Ratio of concentration of chemical in fetal liver tissue to unbound concentration in plasma.
Kflung2pu

Ratio of concentration of chemical in fetal lung tissue to unbound concentration in plasma.
Kfrest2pu

Ratio of concentration of chemical in fetal rest of body tissue to unbound concentration in plasma.
Kfbrain2pu

Ratio of concentration of chemical in fetal brain tissue to unbound concentration in plasma.
Kthyroid2pu

Ratio of concentration of chemical in fetal thyroid tissue to unbound concentration in plasma.
Kfthyroid2pu

Ratio of concentration of chemical in fetal thyroid tissue to unbound concentration in plasma.
Kplacenta2pu

Ratio of concentration of chemical in placental tissue to unbound concentration in maternal plasma.
Kfplacenta2pu

Ratio of concentration of chemical in placental tissue to unbound concentration in fetal plasma.

Author(s)

Robert Pearce, Mark Sfeir, John Wambaugh, and Dustin Kapraun

Mark Sfeir, Dustin Kapraun, John Wambaugh

References

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

McNamara PJ, Alcorn J. Protein binding predictions in infants. AAPS PharmSci. 2002;4(1):E4. doi: 10.1208/ps040104. PMID: 12049488.

See Also

solve_fetal_pbtk

parameterize_pbtk

predict_partitioning_schmitt

apply_clint_adjustment

tissue.data

physiology.data

kapraun2019

Examples

parameters <- parameterize_fetal_pbtk(chem.cas='80-05-7')

 parameters <- parameterize_fetal_pbtk(chem.name='Bisphenol-A',species='Rat')

Parameters for a generic gas inhalation physiologically-based toxicokinetic model

Description

This function initializes the parameters needed for the model 'gas_pbtk', for example solve_gas_pbtk. Chemical- and species-specific model parameters are generated. These include tissue:plasma partition coefficients via Schmitt (2008)'s method as modified by Pearce et al. (2017). Organ volumes and flows are retrieved from table physiology.data). This model was first described by Linakis et al. (2020).

Usage

parameterize_gas_pbtk(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  tissuelist = list(liver = c("liver"), kidney = c("kidney"), lung = c("lung"), gut =
    c("gut")),
  force.human.clint.fup = FALSE,
  clint.pvalue.threshold = 0.05,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  regression = TRUE,
  vmax = 0,
  km = 1,
  exercise = FALSE,
  fR = 12,
  VT = 0.75,
  VD = 0.15,
  suppress.messages = FALSE,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = NULL,
  class.exclude = TRUE,
  ...
)

Arguments

chem.cas

Either the chemical name or the CAS number must be specified.
chem.name

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
tissuelist

Specifies compartment names and tissues groupings. Remaining tissues in tissue.data are lumped in the rest of the body. However, solve_pbtk only works with the default parameters.
force.human.clint.fup

Forces use of human values for hepatic intrinsic clearance and fraction of unbound plasma if true.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
regression

Whether or not to use the regressions in calculating partition coefficients.
vmax

Michaelis-Menten vmax value in reactions/min
km

Michaelis-Menten concentration of half-maximal reaction velocity in desired output concentration units.
exercise

Logical indicator of whether to simulate an exercise-induced heightened respiration rate
fR

Respiratory frequency (breaths/minute), used especially to adjust breathing rate in the case of exercise. This parameter, along with VT and VD (below) gives another option for calculating Qalv (Alveolar ventilation) in case pulmonary ventilation rate is not known
VT

Tidal volume (L), to be modulated especially as part of simulating the state of exercise
VD

Anatomical dead space (L), to be modulated especially as part of simulating the state of exercise
suppress.messages

Whether or not the output messages are suppressed.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
...

Other parameters

Value

BW

Body Weight, kg.
Clint

Hepatic intrinsic clearance, uL/min/10^6 cells
Clint.dist

Distribution of hepatic intrinsic clearance values (median, lower 95th, upper 95th, p value)
Clmetabolismc

Hepatic Clearance, L/h/kg BW.
Fabsgut

Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gut lumen.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
Funbound.plasma

Fraction of chemical unbound to plasma.
Funbound.plasma.adjustment

Fraction unbound to plasma adjusted as described in Pearce et al. 2017
Funbound.plasma.dist

Distribution of fraction unbound to plasma (median, lower 95th, upper 95th)
hematocrit

Percent volume of red blood cells in the blood.
Kblood2air

Ratio of concentration of chemical in blood to air
Kgut2pu

Ratio of concentration of chemical in gut tissue to unbound concentration in plasma.
kgutabs

Rate that chemical enters the gut from gutlumen, 1/h.
Kkidney2pu

Ratio of concentration of chemical in kidney tissue to unbound concentration in plasma.
Kliver2pu

Ratio of concentration of chemical in liver tissue to unbound concentration in plasma.
Klung2pu

Ratio of concentration of chemical in lung tissue to unbound concentration in plasma.
km

Michaelis-Menten concentration of half-maximal activity
Kmuc2air

Mucus to air partition coefficient
Krbc2pu

Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.
Krest2pu

Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.
kUrtc

Unscaled upper respiratory tract uptake parameter (L/h/kg^0.75)
liver.density

Density of liver in g/mL
MA

phospholipid:water distribution coefficient, membrane affinity
million.cells.per.gliver

Millions cells per gram of liver tissue.
MW

Molecular Weight, g/mol.
pKa_Accept

compound H association equilibrium constant(s)
pKa_Donor

compound H dissociation equilibirum constant(s)
Pow

octanol:water partition coefficient (not log transformed)
Qalvc

Unscaled alveolar ventilation rate (L/h/kg^0.75)
Qcardiacc

Cardiac Output, L/h/kg BW^3/4.
Qgfrc

Glomerular Filtration Rate, L/h/kg BW^0.75, volume of fluid filtered from kidney and excreted.
Qgutf

Fraction of cardiac output flowing to the gut.
Qkidneyf

Fraction of cardiac output flowing to the kidneys.
Qliverf

Fraction of cardiac output flowing to the liver.
Qlungf

Fraction of cardiac output flowing to lung tissue.
Qrestf

Fraction of blood flow to rest of body
Rblood2plasma

The ratio of the concentration of the chemical in the blood to the concentration in the plasma from available_rblood2plasma.
Vartc

Volume of the arteries per kg body weight, L/kg BW.
Vgutc

Volume of the gut per kg body weight, L/kg BW.
Vkidneyc

Volume of the kidneys per kg body weight, L/kg BW.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
Vlungc

Volume of the lungs per kg body weight, L/kg BW.
vmax

Michaelis-Menten maximum reaction velocity (1/min)
Vmucc

Unscaled mucosal volume (L/kg BW^0.75
Vrestc

Volume of the rest of the body per kg body weight, L/kg BW.
Vvenc

Volume of the veins per kg body weight, L/kg BW.

Author(s)

Matt Linakis, Robert Pearce, John Wambaugh

References

Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, Gearhart JM, Wambaugh JF (2020). “Development and evaluation of a high-throughput inhalation model for organic chemicals.” Journal of exposure science & environmental epidemiology, 30(5), 866–877.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

See Also

solve_gas_pbtk

apply_clint_adjustment

predict_partitioning_schmitt

available_rblood2plasma

calc_kair

tissue.data

physiology.data

get_clint

get_fup

get_physchem_param

Examples

parameters <- parameterize_gas_pbtk(chem.cas='129-00-0')


parameters <- parameterize_gas_pbtk(chem.name='pyrene',species='Rat')

parameterize_gas_pbtk(chem.cas = '56-23-5')

parameters <- parameterize_gas_pbtk(chem.name='Carbon tetrachloride',species='Rat')

# Change the tissue lumping:
compartments <- list(liver=c("liver"),fast=c("heart","brain","muscle","kidney"),
                      lung=c("lung"),gut=c("gut"),slow=c("bone"))
parameterize_gas_pbtk(chem.name="Bisphenol a",species="Rat",default.to.human=TRUE,
                   tissuelist=compartments)

Parameters for a generic physiologically-based toxicokinetic model

Description

Generate a chemical- and species-specific set of PBPK model parameters. Parameters include tissue:plasma partition coefficients, organ volumes, and flows for the tissue lumping scheme specified by argument tissuelist. Tissure:(fraction unbound in) plasma partitition coefficients are predicted via Schmitt (2008)'s method as modified by Pearce et al. (2017) using predict_partitioning_schmitt. Organ volumes and flows are retrieved from table physiology.data. Tissues must be described in table tissue.data.

Usage

parameterize_pbtk(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  default.to.human = FALSE,
  tissuelist = list(liver = c("liver"), kidney = c("kidney"), lung = c("lung"), gut =
    c("gut")),
  force.human.clint.fup = FALSE,
  clint.pvalue.threshold = 0.05,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  regression = TRUE,
  suppress.messages = FALSE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  class.exclude = TRUE,
  million.cells.per.gliver = 110,
  liver.density = 1.05,
  kgutabs = NA,
  Caco2.options = NULL
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
tissuelist

Specifies compartment names and tissues groupings. Remaining tissues in tissue.data are lumped in the rest of the body. However, solve_pbtk only works with the default parameters.
force.human.clint.fup

Forces use of human values for hepatic intrinsic clearance and fraction of unbound plasma if true.
clint.pvalue.threshold

Hepatic clearance for chemicals where the in vitro clearance assay result has a p-values greater than the threshold are set to zero.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
regression

Whether or not to use the regressions in calculating partition coefficients.
suppress.messages

Whether or not the output message is suppressed.
restrictive.clearance

In calculating hepatic.bioavailability, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

fupf_{up} is not allowed to drop below this value (default is 0.0001).
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
million.cells.per.gliver

Hepatocellularity (defaults to 110 10^6 cells/g-liver, from Carlile et al. (1997))
liver.density

Liver density (defaults to 1.05 g/mL from International Commission on Radiological Protection (1975))
kgutabs

Oral absorption rate from gut (determined from Peff)
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.

Details

By default, this function initializes the parameters needed in the functions solve_pbtk, calc_css, and others using the httk default generic PBTK model (for oral and intravenous dosing only).

The default PBTK model includes an explicit first pass of the chemical through the liver before it becomes available to systemic blood. We model systemic oral bioavailability as Fbio=Fabs*Fgut*Fhep. Only if Fbio has been measured in vivo and is found in table chem.physical_and_invitro.data then we set Fabs*Fgut to the measured value divided by Fhep where Fhep is estimated from in vitro TK data using calc_hep_bioavailability. If Caco2 membrane permeability data or predictions are available Fabs is estimated using calc_fabs.oral. Intrinsic hepatic metabolism is used to very roughly estimate Fgut using calc_fgut.oral.

Value

BW

Body Weight, kg.
Clmetabolismc

Hepatic Clearance, L/h/kg BW.
Fabsgut

Fraction of the oral dose absorbed, i.e. the fraction of the dose that enters the gutlumen.
Funbound.plasma

Fraction of plasma that is not bound.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
hematocrit

Percent volume of red blood cells in the blood.
Kgut2pu

Ratio of concentration of chemical in gut tissue to unbound concentration in plasma.
kgutabs

Rate that chemical enters the gut from gutlumen, 1/h.
Kkidney2pu

Ratio of concentration of chemical in kidney tissue to unbound concentration in plasma.
Kliver2pu

Ratio of concentration of chemical in liver tissue to unbound concentration in plasma.
Klung2pu

Ratio of concentration of chemical in lung tissue to unbound concentration in plasma.
Krbc2pu

Ratio of concentration of chemical in red blood cells to unbound concentration in plasma.
Krest2pu

Ratio of concentration of chemical in rest of body tissue to unbound concentration in plasma.
million.cells.per.gliver

Millions cells per gram of liver tissue.
MW

Molecular Weight, g/mol.
Qcardiacc

Cardiac Output, L/h/kg BW^3/4.
Qgfrc

Glomerular Filtration Rate, L/h/kg BW^3/4, volume of fluid filtered from kidney and excreted.
Qgutf

Fraction of cardiac output flowing to the gut.
Qkidneyf

Fraction of cardiac output flowing to the kidneys.
Qliverf

Fraction of cardiac output flowing to the liver.
Rblood2plasma

The ratio of the concentration of the chemical in the blood to the concentration in the plasma from available_rblood2plasma.
Vartc

Volume of the arteries per kg body weight, L/kg BW.
Vgutc

Volume of the gut per kg body weight, L/kg BW.
Vkidneyc

Volume of the kidneys per kg body weight, L/kg BW.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
Vlungc

Volume of the lungs per kg body weight, L/kg BW.
Vrestc

Volume of the rest of the body per kg body weight, L/kg BW.
Vvenc

Volume of the veins per kg body weight, L/kg BW.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

International Commission on Radiological Protection. Report of the task group on reference man. Vol. 23. Pergamon, Oxford. 1975.

See Also

solve_pbtk

calc_analytic_css_pbtk

predict_partitioning_schmitt

apply_clint_adjustment

tissue.data

physiology.data

Examples

parameters <- parameterize_pbtk(chem.cas='80-05-7')

 parameters <- parameterize_pbtk(chem.name='Bisphenol-A',species='Rat')

 # Change the tissue lumping (note, these model parameters will not work with our current solver):
 compartments <- list(liver=c("liver"),fast=c("heart","brain","muscle","kidney"),
                      lung=c("lung"),gut=c("gut"),slow=c("bone"))
 parameterize_pbtk(chem.name="Bisphenol a",species="Rat",default.to.human=TRUE,
                   tissuelist=compartments)

Parameters for Schmitt's (2008) Tissue Partition Coefficient Method

Description

This function provides the necessary parameters to run predict_partitioning_schmitt, excluding the data in table tissue.data. The model is based on the Schmitt (2008) method for predicting tissue:plasma partition coefficients as modified by Pearce et al. (2017). The modifications include approaches adapted from Peyret et al. (2010).

Usage

parameterize_schmitt(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  parameters = NULL,
  species = "Human",
  default.to.human = FALSE,
  force.human.fup = FALSE,
  adjusted.Funbound.plasma = TRUE,
  suppress.messages = FALSE,
  class.exclude = TRUE,
  minimum.Funbound.plasma = 1e-04
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – if parameters is not specified then the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemcial and physiological description parameters needed to run the Schmitt et al. (2008) model
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
default.to.human

Substitutes missing fraction of unbound plasma with human values if true.
force.human.fup

Returns human fraction of unbound plasma in calculation for rats if true. When species is specified as rabbit, dog, or mouse, the human unbound fraction is substituted.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
suppress.messages

Whether or not the output message is suppressed.
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).

Value

Funbound.plasma

Unbound fraction in plasma, adjusted for lipid binding according to Pearce et al. (2017)
unadjusted.Funbound.plasma

measured unbound fraction in plasma (0.005 if below limit of detection)
Pow

octanol:water partition coefficient (not log transformed)
pKa_Donor

compound H dissociation equilibrium constant(s)
pKa_Accept

compound H association equilibrium constant(s)
MA

phospholipid:water distribution coefficient, membrane affinity
Fprotein.plasma

protein fraction in plasma
plasma.pH

pH of the plasma

Author(s)

Robert Pearce and John Wambaugh

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Schmitt W (2008). “Corrigendum to:'General approach for the calculation of tissue to plasma partition coefficients'[Toxicology in Vitro 22 (2008) 457–467].” Toxicology in Vitro, 22(6), 1666.

Pearce RG, Setzer RW, Davis JL, Wambaugh JF (2017). “Evaluation and calibration of high-throughput predictions of chemical distribution to tissues.” Journal of pharmacokinetics and pharmacodynamics, 44, 549–565.

Peyret T, Poulin P, Krishnan K (2010). “A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals.” Toxicology and applied pharmacology, 249(3), 197–207.

See Also

predict_partitioning_schmitt

tissue.data

calc_ma

apply_fup_adjustment

Examples

parameterize_schmitt(chem.name='bisphenola')

Parameters for a three-compartment toxicokinetic model at steady-state

Description

This function initializes the parameters needed in the functions calc_mc_css, calc_mc_oral_equiv, and calc_analytic_css for the three compartment steady state model ('3compartmentss') as used in Rotroff et al. (2010), Wetmore et al. (2012), Wetmore et al. (2015), and elsewhere. By assuming that enough time has passed to reach steady-state, we eliminate the need for tissue-specific parititon coefficients because we assume all tissues have come to equilibrium with the unbound concentration in plasma. However, we still use chemical properties to predict the blood:plasma ratio for estimating first-pass hepatic metabolism for oral exposures.

Usage

parameterize_steadystate(
  chem.cas = NULL,
  chem.name = NULL,
  dtxsid = NULL,
  species = "Human",
  clint.pvalue.threshold = 0.05,
  default.to.human = FALSE,
  class.exclude = TRUE,
  force.human.clint.fup = FALSE,
  adjusted.Funbound.plasma = TRUE,
  adjusted.Clint = TRUE,
  restrictive.clearance = TRUE,
  fup.lod.default = 0.005,
  suppress.messages = FALSE,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = NULL,
  ...
)

Arguments

chem.cas

Chemical Abstract Services Registry Number (CAS-RN) – the chemical must be identified by either CAS, name, or DTXISD
chem.name

Chemical name (spaces and capitalization ignored) – the chemical must be identified by either CAS, name, or DTXISD
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) – the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
clint.pvalue.threshold

Hepatic clearances with clearance assays having p-values greater than the threshold are set to zero.
default.to.human

Substitutes missing species-specific values with human values if TRUE (default is FALSE).
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
force.human.clint.fup

Uses human hepatic intrinsic clearance and fraction of unbound plasma in calculation of partition coefficients for rats if true.
adjusted.Funbound.plasma

Uses Pearce et al. (2017) lipid binding adjustment for Funbound.plasma (which impacts partition coefficients) when set to TRUE (Default).
adjusted.Clint

Uses Kilford et al. (2008) hepatocyte incubation binding adjustment for Clint when set to TRUE (Default).
restrictive.clearance

In calculating hepatic.bioavailability, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
fup.lod.default

Default value used for fraction of unbound plasma for chemicals where measured value was below the limit of detection. Default value is 0.0005.
suppress.messages

Whether or not the output message is suppressed.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Other parameters

Details

We model systemic oral bioavailability as Fbio=Fabs*Fgut*Fhep. Fhep is estimated from in vitro TK data using calc_hep_bioavailability. If Fbio has been measured in vivo and is found in table chem.physical_and_invitro.data then we set Fabs*Fgut to the measured value divided by Fhep Otherwise, if Caco2 membrane permeability data or predictions are available Fabs is estimated using calc_fabs.oral. Intrinsic hepatic metabolism is used to very roughly estimate Fgut using calc_fgut.oral.

Value

Clint

Hepatic Intrinsic Clearance, uL/min/10^6 cells.
Fabsgut

Fraction of the oral dose absorbed and surviving gut metabolism, that is, the fraction of the dose that enters the gutlumen.
Funbound.plasma

Fraction of plasma that is not bound.
Qtotal.liverc

Flow rate of blood exiting the liver, L/h/kg BW^3/4.
Qgfrc

Glomerular Filtration Rate, L/h/kg BW^3/4, volume of fluid filtered from kidney and excreted.
BW

Body Weight, kg
MW

Molecular Weight, g/mol
million.cells.per.gliver

Millions cells per gram of liver tissue.
Vliverc

Volume of the liver per kg body weight, L/kg BW.
liver.density

Liver tissue density, kg/L.
Fhep.assay.correction

The fraction of chemical unbound in hepatocyte assay using the method of Kilford et al. (2008)
hepatic.bioavailability

Fraction of dose remaining after first pass clearance, calculated from the corrected well-stirred model.

Author(s)

John Wambaugh and Greg Honda

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Kilford PJ, Gertz M, Houston JB, Galetin A (2008). “Hepatocellular binding of drugs: correction for unbound fraction in hepatocyte incubations using microsomal binding or drug lipophilicity data.” Drug Metabolism and Disposition, 36(7), 1194–1197.

See Also

calc_analytic_css_3compss

apply_clint_adjustment

tissue.data

physiology.data

Examples

parameters <- parameterize_steadystate(chem.name='Bisphenol-A',species='Rat')
 parameters <- parameterize_steadystate(chem.cas='80-05-7')

Partition Coefficient Data

Description

Measured rat in vivo partition coefficients and data for predicting them.

Usage

pc.data

Format

A data.frame.

Author(s)

Jimena Davis and Robert Pearce

References

Schmitt, W., General approach for the calculation of tissue to plasma partition coefficients. Toxicology in Vitro, 2008. 22(2): p. 457-467.

Schmitt, W., Corrigendum to:"General approach for the calculation of tissue to plasma partition coefficients"[Toxicology in Vitro 22 (2008) 457-467]. Toxicology in Vitro, 2008. 22(6): p. 1666.

Poulin, P. and F.P. Theil, A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically based pharmacokinetic models in drug discovery. Journal of pharmaceutical sciences, 2000. 89(1): p. 16-35.

Rodgers, T. and M. Rowland, Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. Journal of pharmaceutical sciences, 2006. 95(6): p. 1238-1257.

Rodgers, T., D. Leahy, and M. Rowland, Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. Journal of pharmaceutical sciences, 2005. 94(6): p. 1259-1276.

Rodgers, T., D. Leahy, and M. Rowland, Tissue distribution of basic drugs: Accounting for enantiomeric, compound and regional differences amongst beta-blocking drugs in rat. Journal of pharmaceutical sciences, 2005. 94(6): p. 1237-1248.

Gueorguieva, I., et al., Development of a whole body physiologically based model to characterise the pharmacokinetics of benzodiazepines. 1: Estimation of rat tissue-plasma partition ratios. Journal of pharmacokinetics and pharmacodynamics, 2004. 31(4): p. 269-298.

Poulin, P., K. Schoenlein, and F.P. Theil, Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. Journal of pharmaceutical sciences, 2001. 90(4): p. 436-447.

Bjorkman, S., Prediction of the volume of distribution of a drug: which tissue-plasma partition coefficients are needed? Journal of pharmacy and pharmacology, 2002. 54(9): p. 1237-1245.

Yun, Y. and A. Edginton, Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters. Xenobiotica, 2013. 43(10): p. 839-852.

Uchimura, T., et al., Prediction of human blood-to-plasma drug concentration ratio. Biopharmaceutics & drug disposition, 2010. 31(5-6): p. 286-297.

Pearce et al. 2017 data

Description

This table includes the adjusted and unadjusted regression parameter estimates for the chemical-specifc plasma protein unbound fraction (fup) in 12 different tissue types.

Usage

pearce2017regression

Format

data.frame

Details

Predictions were made with regression models, as reported in Pearce et al. (2017).

Author(s)

Robert G. Pearce

Source

Pearce et al. 2017 Regression Models

References

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

See Also

predict_partitioning_schmitt

DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data

Description

SWISSPHARMA is a list of pharmaceuticals with consumption data from Switzerland, France, Germany and the USA, used for a suspect screening/exposure modelling approach described in Singer et al 2016, DOI: 10.1021/acs.est.5b03332. The original data is available on the NORMAN Suspect List Exchange.

Usage

pharma

Format

An object of class matrix (inherits from array) with 14 rows and 954 columns.

Source

https://comptox.epa.gov/dashboard/chemical_lists/swisspharma

References

Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.

Species-specific physiology parameters

Description

This data set contains values from Davies and Morris (1993) necessary to paramaterize a toxicokinetic model for human, mouse, rat, dog, or rabbit. The temperature for each species are taken from Reece (2015), Jordon (1995), and Stammers (1926). Mean residence time for the small intestine is from Grandoni et al. (2019). Human small intestine radius is from Yu et al. (1999). Rat small intestine radius is from Griffin and O'Driscoll (2008).

Usage

physiology.data

Format

A data.frame containing 18 rows and 7 columns.

Author(s)

John Wambaugh and Nisha Sipes

References

Davies B, Morris T (1993). “Physiological parameters in laboratory animals and humans.” Pharmaceutical research, 10(7), 1093–1095.

Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP (1997). “Physiological parameter values for physiologically based pharmacokinetic models.” Toxicology and industrial health, 13(4), 407–484.

Birnbaum L, Brown R, Bischoff K, Foran J, Blancato J, Clewell H, Dedrick R (1994). “Physiological parameter values for PBPK models.” International Life Sciences Institute, Risk Science Institute, Washington, DC.

Reece WO (2015). “14 Body Temperature and Its Regulation.” Dukes' physiology of domestic animals, 149.

Stammers AD (1926). “The blood count and body temperature in normal rats.” The Journal of Physiology, 61(3), 329.

Jordan D (1995). “Temperature regulation in laboratory rodents.” Journal of anatomy, 186(Pt 1), 228.

Grandoni S, Cesari N, Brogin G, Puccini P, Magni P (2019). “Building in-house PBPK modelling tools for oral drug administration from literature information.” ADMET and DMPK, 7(1), 4–21.

Griffin B, O’Driscoll C (2008). “Models of the small intestine.” Drug Absorption Studies: In Situ, In Vitro and In Silico Models, 34–76.

Partition Coefficients from PK-Sim

Description

Dallmann et al. (2018) made use of PK-Sim to predict chemical- and tissue- specific partition coefficients. The methods include both the default PK-Sim approach and PK-Sim Standard and Rodgers & Rowland (2006).

Usage

pksim.pcs

Format

data.frame

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Dallmann A, Ince I, Coboeken K, Eissing T, Hempel G (2018). “A physiologically based pharmacokinetic model for pregnant women to predict the pharmacokinetics of drugs metabolized via several enzymatic pathways.” Clinical pharmacokinetics, 57(6), 749–768.

Pradeep et al. 2020

Description

This table includes Support Vector Machine and Random Forest model predicted values for unbound fraction plasma protein (fup) and intrinsic hepatic clearance (clint) values for a subset of chemicals in the Tox21 library (see https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21).

Usage

pradeep2020

Format

data.frame

Details

Prediction were made with Support Vector Machine and Random Forest models, as reported in Pradeep et al. (2020).

References

Pradeep P, Patlewicz G, Pearce R, Wambaugh J, Wetmore B, Judson R (2020). “Using chemical structure information to develop predictive models for in vitro toxicokinetic parameters to inform high-throughput risk-assessment.” Computational Toxicology, 16, 100136. ISSN 2468-1113, doi:10.1016/j.comtox.2020.100136, https://doi.org/https://doi.org/10.1016/j.comtox.2020.100136.

See Also

load_pradeep2020

Predict partition coefficients using the method from Schmitt (2008).

Description

This function implements the method from Schmitt (2008) for predicting the tissue to unbound plasma partition coefficients for the tissues contained in the tissue.data table. The method has been modified by Pearce et al. (2017) based on an evaluation using in vivo measured partition coefficients.

To understand this method, it is important to recognize that in a given media the fraction unbound in that media is inverse of the media:water partition coefficient. In Schmitt's model, each tissue is composed of cells and interstitium, with each cell consisting of neutral lipid, neutral phospholipid, water, protein, and acidic phospholipid. Each tissue cell is defined as the sum of separate compartments for each constituent, all of which partition with a shared water compartment. The partitioning between the cell components and cell water is compound specific and determined by log Pow (in neutral lipid partitioning), membrane affinity (phospholipid and protein partitioning), and pKa (neutral lipid and acidic phospholipid partitioning). For a given compound the partitioning into each component is identical across tissues. Thus the differences among tissues are driven by their composition, that is, the varying volumes of components such as neutral lipid. However, pH differences across tissues also determine small differences in partitioning between cell and plasma water. The fup is used as the plasma water to total plasma partition coefficient and to approximate the partitioning between interstitial protein and water.

A regression is used to predict membrane affinity when measured values are not available (calc_ma). The regressions for correcting each tissue are performed on tissue plasma partition coefficients (Ktissue2pu * Funbound.plasma) calculated with the corrected Funbound.plasma value and divided by this value to get Ktissue2pu. Thus the regressions should be used with the corrected Funbound.plasma.

A separate regression is used when adjusted.Funbound.plasma is FALSE.

The red blood cell regression can be used but is not by default because of the span of the data used for evaluation, reducing confidence in the regression for higher and lower predicted values.

Human tissue volumes are used for species other than Rat.

Usage

predict_partitioning_schmitt(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  species = "Human",
  model = "pbtk",
  default.to.human = FALSE,
  parameters = NULL,
  alpha = 0.001,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  regression.list = c("brain", "adipose", "gut", "heart", "kidney", "liver", "lung",
    "muscle", "skin", "spleen", "bone"),
  tissues = NULL,
  minimum.Funbound.plasma = 1e-04,
  suppress.messages = FALSE
)

Arguments

chem.name

Either the chemical name or the CAS number must be specified.
chem.cas

Either the chemical name or the CAS number must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
model

Model for which partition coefficients are neeeded (for example, "pbtk", "3compartment")
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
parameters

Chemical parameters from parameterize_schmitt overrides chem.name, dtxsid, and chem.cas.
alpha

Ratio of Distribution coefficient D of totally charged species and that of the neutral form
adjusted.Funbound.plasma

Whether or not to use Funbound.plasma adjustment.
regression

Whether or not to use the regressions. Regressions are used by default.
regression.list

Tissues to use regressions on.
tissues

Vector of desired partition coefficients. Returns all by default.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
suppress.messages

Whether or not the output message is suppressed.

Value

Returns tissue to unbound plasma partition coefficients for each tissue.

Author(s)

Robert Pearce

References

Schmitt, Walter. "General approach for the calculation of tissue to plasma partition coefficients." Toxicology in Vitro 22.2 (2008): 457-467.

Birnbaum, L., et al. "Physiological parameter values for PBPK models." International Life Sciences Institute, Risk Science Institute, Washington, DC (1994).

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Yun, Y. E., and A. N. Edginton. "Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters." Xenobiotica 43.10 (2013): 839-852.

See Also

parameterize_schmitt

tissue.data

calc_ma

Examples

predict_partitioning_schmitt(chem.name='ibuprofen',regression=FALSE)

AUCs for Pregnant and Non-Pregnant Women

Description

Dallmann et al. (2018) includes compiled literature descriptions of toxicokinetic summary statistics, including time-integrated plasma concentrations (area under the curve or AUC) for drugs administered to a sample of subjects including both pregnant and non-pregnant women. The circumstances of the dosing varied slightly between drugs and are summarized in the table.

Usage

pregnonpregaucs

Format

data.frame

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Dallmann A, Ince I, Coboeken K, Eissing T, Hempel G (2018). “A physiologically based pharmacokinetic model for pregnant women to predict the pharmacokinetics of drugs metabolized via several enzymatic pathways.” Clinical pharmacokinetics, 57(6), 749–768.

Propagates uncertainty and variability in in vitro HTTK data into one compartment model parameters

Description

Propagates uncertainty and variability in in vitro HTTK data into one compartment model parameters

Usage

propagate_invitrouv_1comp(parameters.dt, ...)

Arguments

parameters.dt

The data table of parameters being used by the Monte Carlo sampler
...

Additional arguments passed to calc_elimination_rate

Value

A data.table whose columns are the parameters of the HTTK model specified in model.

Author(s)

John Wambaugh

Propagates uncertainty and variability in in vitro HTTK data into three compartment model parameters

Description

Propagates uncertainty and variability in in vitro HTTK data into three compartment model parameters

Usage

propagate_invitrouv_3comp(parameters.dt, ...)

Arguments

parameters.dt

The data table of parameters being used by the Monte Carlo sampler
...

Additional arguments passed to calc_hep_clearance

Value

A data.table whose columns are the parameters of the HTTK model specified in model.

Author(s)

John Wambaugh

Propagates uncertainty and variability in in vitro HTTK data into PBPK model parameters

Description

Propagates uncertainty and variability in in vitro HTTK data into PBPK model parameters

Usage

propagate_invitrouv_pbtk(parameters.dt, ...)

Arguments

parameters.dt

The data table of parameters being used by the Monte Carlo sampler
...

Additional arguments passed to calc_hep_clearance

Value

A data.table whose columns are the parameters of the HTTK model specified in model.

Author(s)

John Wambaugh

Returns draws from a normal distribution with a lower censoring limit of lod (limit of detection)

Description

Returns draws from a normal distribution with a lower censoring limit of lod (limit of detection)

Usage

r_left_censored_norm(n, mean = 0, sd = 1, lod = 0.005, lower = 0, upper = 1)

Arguments

n

Number of samples to take
mean

Mean of censored distribution. Default 0.
sd

Standard deviation of censored distribution. Default 1.
lod

Bound below which to censor. Default 0.005.
lower

Lower bound on censored distribution. Default 0.
upper

Upper bound on censored distribution. Default 1.

Value

A vector of samples from the specified censored distribution.

Reset HTTK to Default Data Tables

Description

This function returns an updated version of chem.physical_and_invitro.data that includes data predicted with Simulations Plus' ADMET predictor that was used in Sipes et al. 2017, included in admet.data.

Usage

reset_httk(target.env = .GlobalEnv)

Arguments

target.env

The environment where the new chem.physical_and_invitro.data is loaded. Defaults to global environment.

Value

data.frame

The package default version of chem.physical_and_invitro.data.

Author(s)

John Wambaugh

Examples

chem.physical_and_invitro.data <- load_sipes2017()
reset_httk()

Randomly draws from a one-dimensional KDE

Description

Randomly draws from a one-dimensional KDE

Usage

rfun(n, fhat)

Arguments

n

Number of samples to draw
fhat

A list with elements x, w, and h (h is the KDE bandwidth).

Value

A vector of n samples from the KDE fhat

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Draw random numbers with LOD median but non-zero upper 95th percentile

Description

This function draws N random numbers from a distribution that approximates a median that is equal to the limit of detection (LOD, value x.LOD) but has an upper 95th percentile (x.u95) that is above x.LOD. We make the assumption that values above x.u95 are uniformly distributed between x.u95 and x.u95 + (x.u95 - x.LOD)

Usage

rmed0non0u95(n, x.u95, x.min = 0, x.LOD = 0.005)

Arguments

n

Number of samples to draw
x.u95

The upper limit on the 95th confidence/credible intervale (this is the 97.5 percentile)
x.min

The minimum allowed value (defaults to 0)
x.LOD

The limit of detection (defaults to 0.005)

Value

A vector of N samples where the 50th and 97.5th quantiles approximate x.LOD and x.u95 respectively

Author(s)

John Wambaugh

References

Breen M, Wambaugh JF, Bernstein A, Sfeir M, Ring CL (2022). “Simulating toxicokinetic variability to identify susceptible and highly exposed populations.” Journal of Exposure Science & Environmental Epidemiology, 32(6), 855–863.

Examples

Fup.95 <- 0.02
N <- 1000

set.seed(1235)
Fup.vec <- rmed0non0u95(n=N, x.u95=Fup.95)
quantile(Fup.vec,c(0.5,0.975))

quantile(rmed0non0u95(200,x.u95=0.05,x.min=10^-4,x.LOD=0.01),c(0.5,0.975))
hist(rmed0non0u95(1000,x.u95=0.05,x.min=10^-4,x.LOD=0.01))

quantile(rmed0non0u95(200,x.u95=0.005,x.min=10^-4,x.LOD=0.01),c(0.5,0.975))
hist(rmed0non0u95(1000,x.u95=0.005,x.min=10^-4,x.LOD=0.01))

Scale mg/kg body weight doses according to body weight and units

Description

This function transforms the dose (in mg/kg) into the appropriate units. It handles single doses, matrices of doses, or daily repeated doses at varying intervals. Gut absorption is also factored in through the parameter Fabsgut, and scaling is currently avoided in the inhalation exposure case with a scale factor of 1

Usage

scale_dosing(
  dosing,
  parameters,
  route,
  input.units = NULL,
  output.units = "uM",
  vol = NULL,
  state = "liquid"
)

Arguments

dosing

List of dosing metrics used in simulation, which must include the general entries with names "initial.dose", "doses.per.day", "daily.dose", and "dosing.matrix". The "dosing.matrix" is used for more precise dose regimen specification, and is a matrix consisting of two columns or rows named "time" and "dose" containing the time and amount, in mg/kg BW, of each dose. The minimal usage case involves all entries but "initial.dose" set to NULL in value.
parameters

Chemical parameters from parameterize_pbtk function, overrides chem.name and chem.cas.
route

String specification of route of exposure for simulation: "oral", "iv", "inhalation", ...
input.units

Units of the dose values being scaled. (Default is NULL.) Currently supported units "mg/L", "ug/L","ug/mL", "uM", "umol/L", "ug/dL", "ug/g", "nmol/L", "nM", and "ppmw" (supported input.units subject to change).
output.units

Desired units (either "mg/L", "mg", "umol", or default "uM").
vol

Volume for the target tissue of interest. NOTE: Volume should not be in units of per BW, i.e. "kg".
state

Chemical state of matter (gas or default liquid).

Value

A list of numeric values for doses converted to output.units, potentially (depending on argument dosing) including:

initial.dose

The first dose given
dosing.matrix

A 2xN matrix where the first column is dose time and the second is dose amount for N doses
daily.dose

The total cumulative daily dose

Author(s)

John Wambaugh and Sarah E. Davidson

KDE bandwidths for residual variability in serum creatinine

Description

Bandwidths used for a one-dimensional kernel density estimation of the distribution of residual errors around smoothing spline fits of serum creatinine vs. age for NHANES respondents in each of ten combinations of sex and race/ethnicity categories.

Usage

scr_h

Format

A named list with 10 elements, each a numeric value. Each list element corresponds to, and is named for, one combination of NHANES sex categories (Male and Female) and NHANES race/ethnicity categories (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other).

Details

Each matrix is the standard deviation for a normal distribution: this is the bandwidth to be used for a kernel density estimation (KDE) (using a normal kernel) of the distribution of residual errors around smoothing spline fits of serum creatinine vs. age for NHANES respondents in the specified sex and race/ethnicity category. Optimal bandwidths were pre-calculated by doing the smoothing spline fits, getting the residuals, then calling kde on the residuals (which calls hpi to compute the plug-in bandwidth).

Used by HTTK-Pop only in "virtual individuals" mode (i.e. httkpop_generate with method = "v"), in gen_serum_creatinine.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

set_httk_precision

Description

Although the ODE solver and other functions return very precise numbers, we cannot (or at least do not spend enough computing time to) be sure of the precioion to an arbitrary level. This function both limits the number of signficant figures reported and truncates the numerical precision.

Usage

set_httk_precision(in.num, sig.fig = 4, num.prec = 9)

Arguments

in.num

The numeric variable (or assembly of numerics) to be processed.
sig.fig

The number of significant figures reported. Defaults to 4.
num.prec

The precision maintained, digits below 10^num.prec are dropped. Defaults to 9.

Value

numeric values

Author(s)

John Wambaugh

Sipes et al. 2017 data

Description

This table includes in silico predicted chemical-specifc plasma protein unbound fraction (fup) and intrinsic hepatic clearance values for the entire Tox21 library (see https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21). Predictions were made with Simulations Plus ADMET predictor, as reported in Sipes et al. (2017).

Usage

sipes2017

Format

data.frame

Author(s)

Nisha Sipes

Source

ADMET, Simulations Plus

References

Sipes NS, Wambaugh JF, Pearce R, Auerbach SS, Wetmore BA, Hsieh J, Shapiro AJ, Svoboda D, DeVito MJ, Ferguson SS (2017). “An intuitive approach for predicting potential human health risk with the Tox21 10k library.” Environmental science & technology, 51(18), 10786–10796.

See Also

load_sipes2017

Predict skeletal muscle mass

Description

Predict skeletal muscle mass from age, height, and gender.

Usage

skeletal_muscle_mass(smm, age_years, height, gender)

Arguments

smm

Vector of allometrically-scaled skeletal muscle masses.
age_years

Vector of ages in years.
height

Vector of heights in cm.
gender

Vector of genders, either 'Male' or 'Female.'

Details

For individuals over age 18, use allometrically-scaled muscle mass with an age-based scaling factor, to account for loss of muscle mass with age (Janssen et al. 2000). For individuals under age 18, use skeletal_muscle_mass_children.

Value

Vector of skeletal muscle masses in kg.

Author(s)

Caroline Ring

References

Janssen, Ian, et al. "Skeletal muscle mass and distribution in 468 men and women aged 18-88 yer." Journal of Applied Physiology 89.1 (2000): 81-88

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

See Also

skeletal_muscle_mass_children

Predict skeletal muscle mass for children

Description

For individuals under age 18, predict skeletal muscle mass from gender and age, using a nonlinear equation from Webber and Barr (2012)

Usage

skeletal_muscle_mass_children(gender, age_years)

Arguments

gender

Vector of genders (either 'Male' or 'Female').
age_years

Vector of ages in years.

Value

Vector of skeletal muscle masses in kg.

Author(s)

Caroline Ring

References

Webber, Colin E., and Ronald D. Barr. "Age-and gender-dependent values of skeletal muscle mass in healthy children and adolescents." Journal of cachexia, sarcopenia and muscle 3.1 (2012): 25-29.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Predict skin mass

Description

Using equation from Bosgra et al. 2012, predict skin mass from body surface area.

Usage

skin_mass_bosgra(BSA)

Arguments

BSA

Vector of body surface areas in cm^2.

Value

Vector of skin masses in kg.

Author(s)

Caroline Ring

References

Bosgra, Sieto, et al. "An improved model to predict physiologically based model parameters and their inter-individual variability from anthropometry." Critical reviews in toxicology 42.9 (2012): 751-767.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Solve one compartment TK model

Description

This function solves for the amount or concentration of a chemical in plasma for a one compartment model as a function of time based on the dose and dosing frequency. The model describes blood concentrations in a single compartment. The volume of distribution depends on the physical volume of each tissue and the predicted chemical partitioning into those volumes. Plasma concentration in compartment x is given by Cplasma=CbloodRb2pC_{plasma} = \frac{C_{blood}}{R_{b2p}} for a tissue independent value of Rb2pR_{b2p}.

Usage

solve_1comp(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  times = NULL,
  parameters = NULL,
  days = 10,
  tsteps = 4,
  daily.dose = NULL,
  dose = NULL,
  doses.per.day = NULL,
  initial.values = NULL,
  plots = FALSE,
  suppress.messages = FALSE,
  species = "Human",
  iv.dose = FALSE,
  input.units = "mg/kg",
  output.units = NULL,
  default.to.human = FALSE,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  dosing.matrix = NULL,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  monitor.vars = NULL,
  Caco2.options = list(),
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
times

Optional time sequence for specified number of days.
parameters

Chemical parameters from parameterize_1comp function, overrides chem.name and chem.cas.
days

Length of the simulation.
tsteps

The number time steps per hour.
daily.dose

Total daily dose, default is mg/kg BW.
dose

Amount of a single dose, default is mg/kg BW.
doses.per.day

Number of doses per day.
initial.values

Vector containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to output.units. Defaults are zero.
plots

Plots all outputs if true.
suppress.messages

Whether or not the output message is suppressed.
species

Species desired (either "Rat", "Rabbit", "Dog", or default "Human").
iv.dose

Simulates a single i.v. dose if true.
input.units

Input units of interest assigned to dosing, defaults to "mg/kg" BW.
output.units

A named vector of output units expected for the model results. Default, NULL, returns model results in units specified in the 'modelinfo' file. See table below for details.
default.to.human

Substitutes missing rat values with human values if true.
recalc.blood2plasma

Whether or not to recalculate the blood:plasma chemical concentrationr ratio
recalc.clearance

Whether or not to recalculate the elimination rate.
dosing.matrix

Vector of dosing times or a matrix consisting of two columns or rows named "dose" and "time" containing the time and amount, in mg/kg BW by default, of each dose.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with volume of distribution calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients in volume of distribution calculation.
restrictive.clearance

In calculating elimination rate, protein binding is not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
monitor.vars

Which variables are returned as a function of time. Defaults value of NULL provides "Agutlumen", "Ccompartment", "Ametabolized", "AUC"
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
...

Additional arguments passed to the integrator (deSolve).

Details

Note that the timescales for the model parameters have units of hours while the model output is in days.

Default value of NULL for doses.per.day solves for a single dose.

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitutes human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

AUC is area under plasma concentration curve.

Model Figure Figure: One Compartment Model Schematic

Value

A matrix with a column for time(in days) and a column for the compartment and the area under the curve (concentration only).

Author(s)

Robert Pearce

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

solve_model

parameterize_1comp

calc_analytic_css_1comp

Examples

solve_1comp(chem.name='Bisphenol-A', days=1)


# By storing the model parameters in a vector first, you can potentially
# edit them before using the model:
params <- parameterize_1comp(chem.cas="80-05-7")
solve_1comp(parameters=params, days=1)

head(solve_1comp(chem.name="Terbufos", daily.dose=NULL, dose=1, days=1))
head(solve_1comp(chem.name="Terbufos", daily.dose=NULL,
                 dose=1,days=1, iv.dose=TRUE))

# A dose matrix specifies times and magnitudes of doses:
dm <- matrix(c(0,1,2,5,5,5),nrow=3)
colnames(dm) <- c("time","dose")
solve_1comp(chem.name="Methenamine", dosing.matrix=dm,
            days=2.5, dose=NULL,daily.dose=NULL)

solve_1comp(chem.name="Besonprodil", daily.dose=1, dose=NULL,
            days=2.5, doses.per.day=4)

Solve_3comp

Description

This function solves for the amounts or concentrations of a chemical in the blood of three different compartments representing the body. The volumes of the three compartments are chemical specific, determined from the true tissue volumes multipled by the partition coefficients:

Vpv=VgutV_{pv} = V_{gut}

Vliv=Kliv∗fupRb:pVliverV_{liv} = \frac{K_{liv}*f_{up}}{R_{b:p}}V_{liver}

Vsc=Ksc∗fupRb:pVrestV_{sc} = \frac{K_{sc}*f_{up}}{R_{b:p}}V_{rest}

where "pv" is the portal vein, "liv" is the liver, and "sc" is the systemic compartment; V_gut, V_liver, and V_rest are physiological tissue volumes; K_x are chemical- and tissue-specific equlibrium partition coefficients between tissue and free chemcial concentration in plasma; f_up is the chemical-specific fraction unbound in plasma; and R_b:p is the chemical specific ratio of concentrations in blood:plasma. The blood concentrations evolve according to:

dCpvdt=1Vpv(kabsAsi+QpvCsc−QpvCpv)\frac{d C_{pv}}{dt} = \frac{1}{V_{pv}}\left(k_{abs}A_{si} + Q_{pv}C_{sc}-Q_{pv}C_{pv}\right)

dClivdt=1Vliv(QpvCpv+QhaCsc−(Qpv+Qha)Cliv−1Rb:pClhCliv)\frac{d C_{liv}}{dt} = \frac{1}{V_{liv}}\left(Q_{pv}C_{pv} + Q_{ha}C_{sc}-\left(Q_{pv} + Q{ha}\right)C_{liv}-\frac{1}{R_{b:p}}Cl_{h}C_{liv}\right)

dCscdt=1Vsc((Qpv+Qha)Cliv−(Qpv+Qha)Csc−fupRb:p∗QGFR∗Csc)\frac{d C_{sc}}{dt} = \frac{1}{V_{sc}}\left(\left(Q_{pv} + Q_{ha}\right)C_{liv} - \left(Q_{pv} + Q_{ha}\right)C_{sc} - \frac{f_{up}}{R_{b:p}}*Q_{GFR}*C_{sc}\right)

where "ha" is the hepatic artery, Q's are flows, "GFR" is the glomerular filtration rate in the kidney, clearance (scaled up from intrinsic clearance, which does not depend on flow). Plasma concentration in compartment x is given by Cx,plasma=CxRb2pC_{x,plasma} = \frac{C_{x}}{R_{b2p}} for a tissue independent value of Rb2pR_{b2p}.

Usage

solve_3comp(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  times = NULL,
  parameters = NULL,
  days = 10,
  tsteps = 4,
  daily.dose = NULL,
  dose = NULL,
  doses.per.day = NULL,
  initial.values = NULL,
  plots = FALSE,
  suppress.messages = FALSE,
  species = "Human",
  iv.dose = FALSE,
  input.units = "mg/kg",
  output.units = NULL,
  default.to.human = FALSE,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  clint.pvalue.threshold = 0.05,
  dosing.matrix = NULL,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = list(),
  monitor.vars = NULL,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's 'DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
times

Optional time sequence for specified number of days. The dosing sequence begins at the beginning of times.
parameters

Chemical parameters from parameterize_3comp function, overrides chem.name and chem.cas.
days

Length of the simulation.
tsteps

The number time steps per hour.
daily.dose

Total daily dose, mg/kg BW.
dose

Amount of a single dose, mg/kg BW.
doses.per.day

Number of doses per day.
initial.values

Vector containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to output.units. Defaults are zero.
plots

Plots all outputs if true.
suppress.messages

Whether or not the output message is suppressed.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
iv.dose

Simulates a single i.v. dose if true.
input.units

Input units of interest assigned to dosing, defaults to mg/kg BW
output.units

A named vector of output units expected for the model results. Default, NULL, returns model results in units specified in the 'modelinfo' file. See table below for details.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters, calculated with hematocrit, Funbound.plasma, and Krbc2pu.
recalc.clearance

Recalculates the the hepatic clearance (Clmetabolism) with new million.cells.per.gliver parameter.
clint.pvalue.threshold

Hepatic clearances with clearance assays having p-values greater than the threshold are set to zero.
dosing.matrix

Vector of dosing times or a matrix consisting of two columns or rows named "dose" and "time" containing the time and amount, in mg/kg BW, of each dose.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
monitor.vars

Which variables are returned as a function of time. Defaults value of NULL provides "Cliver", "Csyscomp", "Atubules", "Ametabolized", "AUC"
...

Additional arguments passed to the integrator (deSolve).

Details

Note that the timescales for the model parameters have units of hours while the model output is in days.

Default of NULL for doses.per.day solves for a single dose.

The compartments used in this model are the gutlumen, gut, liver, and rest-of-body, with the plasma related to the concentration in the blood in the systemic compartment by the blood:plasma ratio.

Model Figure Figure: Three Compartment Model Schematic

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitues human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

A matrix of class deSolve with a column for time(in days) and each compartment, the plasma concentration, area under the curve, and a row for each time point.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

solve_model

parameterize_3comp

calc_analytic_css_3comp

Examples

solve_3comp(chem.name='Bisphenol-A', 
            doses.per.day=2, 
            daily.dose=.5,
            days=1,
            tsteps=2)


# By storing the model parameters in a vector first, you can potentially
# edit them before using the model:
params <-parameterize_3comp(chem.cas="80-05-7")
solve_3comp(parameters=params, days=1)

head(solve_3comp(chem.name="Terbufos", daily.dose=NULL, dose=1, days=1))
head(solve_3comp(chem.name="Terbufos", daily.dose=NULL, dose=1, 
                 days=1, iv.dose=TRUE))

# A dose matrix specifies times and magnitudes of doses:
dm <- matrix(c(0,1,2,5,5,5),nrow=3)
colnames(dm) <- c("time","dose")
solve_3comp(chem.name="Methenamine", dosing.matrix=dm,
            dose=NULL, daily.dose=NULL,
            days=2.5)

solve_3comp(chem.name="Besonprodil",
            daily.dose=1, dose=NULL,
            days=2.5, doses.per.day=4)

Solve_fetal_PBTK

Description

This function solves for the amounts or concentrations in uM of a chemical in different tissues of a maternofetal system as functions of time based on the dose and dosing frequency. In this PBTK formulation. CtissueC_{tissue} is the concentration in tissue at time t. Since the perfusion limited partition coefficients describe instantaneous equilibrium between the tissue and the free fraction in plasma, the whole plasma concentration is Ctissue,plasma=1fup∗Ktissue2fup∗CtissueC_{tissue,plasma} = \frac{1}{f_{up}*K_{tissue2fup}}*C_{tissue}. Note that we use a single, constant value of fupf_{up} across all tissues. Corespondingly the free plasma concentration is modeled as Ctissue,freeplasma=1Ktissue2fup∗CtissueC_{tissue,free plasma} = \frac{1}{K_{tissue2fup}}*C_tissue. The amount of blood flowing from tissue x is QtissueQ_{tissue} (L/h) at a concentration Cx,blood=Rb2pfup∗Ktissue2fup∗CtissueC_{x,blood} = \frac{R_{b2p}}{f_{up}*K_{tissue2fup}}*C_{tissue}, where we use a single Rb2pR_{b2p} value throughout the body. Metabolic clearance is modelled as being from the total plasma concentration here, though it is restricted to the free fraction in calc_hep_clearance by default. Renal clearance via glomerulsr filtration is from the free plasma concentration. The maternal compartments used in this model are the gut lumen, gut, liver, venous blood, arterial blood, lung, adipose tissue, kidney, thyroid, and rest of body. A placenta is modeled as a joint organ shared by mother and fetus, through which chemical exchange can occur with the fetus. Fetal compartments include arterial blood, venous blood, kidney, thyroid, liver, lung, gut, brain, and rest of body. The extra compartments include the amounts or concentrations metabolized by the liver and excreted by the kidneys through the tubules. AUC is the area under the curve of the plasma concentration.

Usage

solve_fetal_pbtk(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  times = seq(13 * 7, 40 * 7, 1),
  parameters = NULL,
  days = NULL,
  species = "human",
  tsteps = 4,
  dose = NULL,
  dosing.matrix = NULL,
  daily.dose = NULL,
  doses.per.day = NULL,
  initial.values = NULL,
  plots = FALSE,
  suppress.messages = FALSE,
  iv.dose = FALSE,
  input.units = "mg/kg",
  output.units = NULL,
  default.to.human = FALSE,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  monitor.vars = NULL,
  atol = 1e-08,
  rtol = 1e-08,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
times

Optional time sequence in days. Dosing sequence begins at the beginning of times. Default is from 13th week of pregnancy to 40th due to data constraints.
parameters

Chemical parameters from parameterize_fetal_pbtk function, overrides chem.name and chem.cas.
days

Length of the simulation.
species

Included for compatibility with other functions, but the model will not run for non-human species (default "Human").
tsteps

The number time steps per hour. Default of 4.
dose

Amount of a single, initial oral dose in mg/kg BW.
dosing.matrix

A matrix of either one column (or row) with a set of dosing times or with two columns (or rows) correspondingly named "dose" and "time" containing the time and amount, in mg/kg BW, of each dose.
daily.dose

Total daily dose, mg/kg BW.
doses.per.day

Number of doses per day.
initial.values

Vector containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to compartment.units. Defaults are zero.
plots

Plots all outputs if true.
suppress.messages

Whether or not the output message is suppressed.
iv.dose

Simulates a single i.v. dose if true.
input.units

Input units of interest assigned to dosing, defaults to mg/kg BW
output.units

A named vector of output units expected for the model results. Default, NULL, returns model results in units specified in the 'modelinfo' file. See table below for details.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters, calculated with hematocrit, Funbound.plasma, and Krbc2pu.
recalc.clearance

Recalculates the the hepatic clearance (Clmetabolism) with new million.cells.per.gliver parameter.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
monitor.vars

Which variables to track by default
atol

Absolute tolerance used by integrator (deSolve) to determine numerical precision– defaults to 1e-8.
rtol

Relative tolerance used by integrator (deSolve) to determine numerical precision – defaults to 1e-8.
...

Additional arguments passed to the integrator.

Details

The stage of pregnancy simulated here begins by default at the 13th week due to a relative lack of data to support parameterization prior, in line with the recommendations of Kapraun et al. 2019 ("Empirical models for anatomical and physiological..."), and ends at the 40th week of pregnancy.

Note that the model parameters have units of hours while the model output is in days. Dose is in mg, not scaled for body weight.

Default NULL value for doses.per.day solves for a single dose.

This gestational model is only parameterized for humans.

Value

A matrix of class deSolve with a column for time(in days), each compartment, the area under the curve, and plasma concentration and a row for each time point.

Author(s)

John Wambaugh, Mark Sfeir, and Dustin Kapraun

See Also

solve_model

parameterize_fetal_pbtk

Examples

out = solve_fetal_pbtk(chem.name = 'bisphenol a', daily.dose = 1,
doses.per.day = 3)

# With adjustement to fraction unbound plasma for fetus:
fetal_parms_fup_adjusted <- 
  parameterize_fetal_pbtk(chem.name = "triclosan")
head(solve_fetal_pbtk(parameters = fetal_parms_fup_adjusted))
 
# Without adjustement to fraction unbound plasma for fetus:
fetal_parms_fup_unadjusted <-  
  parameterize_fetal_pbtk(chem.name = "triclosan",
                          fetal_fup_adjustment = FALSE)
head(solve_fetal_pbtk(parameters = fetal_parms_fup_unadjusted))

solve_gas_pbtk

Description

This function solves for the amounts or concentrations of a chemical in different tissues as functions of time as a result of inhalation exposure to an ideal gas. In this PBTK formulation. CtissueC_{tissue} is the concentration in tissue at time t. Since the perfusion limited partition coefficients describe instantaneous equilibrium between the tissue and the free fraction in plasma, the whole plasma concentration is Ctissue,plasma=1fup∗Ktissue2fup∗CtissueC_{tissue,plasma} = \frac{1}{f_{up}*K_{tissue2fup}}*C_{tissue}. Note that we use a single, constant value of fupf_{up} across all tissues. Corespondingly the free plasma concentration is modeled as Ctissue,freeplasma=1Ktissue2fup∗CtissueC_{tissue,free plasma} = \frac{1}{K_{tissue2fup}}*C_tissue. The amount of blood flowing from tissue x is QtissueQ_{tissue} (L/h) at a concentration Cx,blood=Rb2pfup∗Ktissue2fup∗CtissueC_{x,blood} = \frac{R_{b2p}}{f_{up}*K_{tissue2fup}}*C_{tissue}, where we use a single Rb2pR_{b2p} value throughout the body. Metabolic clearance is modelled as being from the total plasma concentration here, though it is restricted to the free fraction in calc_hep_clearance by default. Renal clearance via glomerulsr filtration is from the free plasma concentration.

Usage

solve_gas_pbtk(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  parameters = NULL,
  times = NULL,
  days = 10,
  tsteps = 4,
  daily.dose = NULL,
  doses.per.day = NULL,
  dose = NULL,
  dosing.matrix = NULL,
  forcings = NULL,
  exp.start.time = 0,
  exp.conc = 1,
  period = 24,
  exp.duration = 12,
  initial.values = NULL,
  plots = FALSE,
  suppress.messages = FALSE,
  species = "Human",
  iv.dose = FALSE,
  input.units = "ppmv",
  output.units = NULL,
  default.to.human = FALSE,
  class.exclude = TRUE,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  monitor.vars = NULL,
  vmax = 0,
  km = 1,
  exercise = FALSE,
  fR = 12,
  VT = 0.75,
  VD = 0.15,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
parameters

Chemical parameters from parameterize_gas_pbtk (or other bespoke) function, overrides chem.name and chem.cas.
times

Optional time sequence for specified number of days. Dosing sequence begins at the beginning of times.
days

Length of the simulation.
tsteps

The number of time steps per hour.
daily.dose

Total daily dose
doses.per.day

Number of doses per day.
dose

Amount of a single dose
dosing.matrix

Vector of dosing times or a matrix consisting of two columns or rows named "dose" and "time" containing the time and amount of each dose.
forcings

Manual input of 'forcings' data series argument for ode integrator. If left unspecified, 'forcings' defaults to NULL, and then other input parameters (see exp.start.time, exp.conc, exp.duration, and period) provide the necessary information to assemble a forcings data series.
exp.start.time

Start time in specifying forcing exposure series, default 0.
exp.conc

Specified inhalation exposure concentration for use in assembling "forcings" data series argument for integrator. Defaults to units of ppmv.
period

For use in assembling forcing function data series 'forcings' argument, specified in hours
exp.duration

For use in assembling forcing function data series 'forcings' argument, specified in hours
initial.values

Vector containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to those specified for the model outputs. Default values are zero.
plots

Plots all outputs if true.
suppress.messages

Whether or not the output message is suppressed.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
iv.dose

Simulates a single i.v. dose if true.
input.units

Input units of interest assigned to dosing, including forcings. Defaults to "ppmv" as applied to the default forcings scheme.
output.units

A named vector of output units expected for the model results. Default, NULL, returns model results in units specified in the 'modelinfo' file. See table below for details.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters, calculated with hematocrit, Funbound.plasma, and Krbc2pu.
recalc.clearance

Recalculates the hepatic clearance (Clmetabolism) with new million.cells.per.gliver parameter.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
monitor.vars

Which variables are returned as a function of time. Defaults value of NULL provides "Cgut", "Cliver", "Cven", "Clung", "Cart", "Crest", "Ckidney", "Cplasma", "Calv", "Cendexh", "Cmixexh", "Cmuc", "Atubules", "Ametabolized", "AUC"
vmax

Michaelis-Menten vmax value in reactions/min
km

Michaelis-Menten concentration of half-maximal reaction velocity in desired output concentration units.
exercise

Logical indicator of whether to simulate an exercise-induced heightened respiration rate
fR

Respiratory frequency (breaths/minute), used especially to adjust breathing rate in the case of exercise. This parameter, along with VT and VD (below) gives another option for calculating Qalv (Alveolar ventilation) in case pulmonary ventilation rate is not known
VT

Tidal volume (L), to be modulated especially as part of simulating the state of exercise
VD

Anatomical dead space (L), to be modulated especially as part of simulating the state of exercise
...

Additional arguments passed to the integrator (deSolve). (Note: There are precision differences between M1 Mac and other OS systems for this function due to how long doubles are handled. To replicate results between various OS systems we suggest changing the default method of "lsoda" to "lsode" and also adding the argument mf = 10. See [deSolve::ode()] for further details.)

Details

The default dosing scheme involves a specification of the start time of exposure (exp.start.time), the concentration of gas inhaled (exp.conc), the period of a cycle of exposure and non-exposure (period), the duration of the exposure during that period (exp.duration), and the total days simulated. Together,these arguments determine the "forcings" passed to the ODE integrator. Forcings can also be specified manually, or effectively turned off by setting exposure concentration to zero, if the user prefers to simulate dosing by other means.

The "forcings" object is configured to be passed to the integrator with, at the most, a basic unit conversion among ppmv, mg/L, and uM. No scaling by BW is set to be performed on the forcings series.

Note that the model parameters have units of hours while the model output is in days.

Default NULL value for doses.per.day solves for a single dose.

The compartments used in this model are the gut lumen, gut, liver, kidneys, veins, arteries, lungs, and the rest of the body.

The extra compartments include the amounts or concentrations metabolized by the liver and excreted by the kidneys through the tubules.

AUC is the area under the curve of the plasma concentration.

Model Figure from (Linakis et al. 2020): Figure: Gas PBTK Model Schematic

Model parameters are named according to the following convention:

prefix suffic Meaning units
K Partition coefficient for tissue to free plasma \ tab unitless
V Volume L
Q Flow L/h
k Rate 1/h
c Parameter is proportional to body weight 1 / kg for volumes and 1/kg^(3/4) for flows

When species is specified but chemical-specific in vitro data are not available, the function uses the appropriate physiological data (volumes and flows) but default.to.human = TRUE must be used to substitute human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

A matrix of class deSolve with a column for time(in days), each compartment, the area under the curve, and plasma concentration and a row for each time point.

Author(s)

Matt Linakis, John Wambaugh, Mark Sfeir, Miyuki Breen

References

Linakis MW, Sayre RR, Pearce RG, Sfeir MA, Sipes NS, Pangburn HA, Gearhart JM, Wambaugh JF (2020). “Development and evaluation of a high-throughput inhalation model for organic chemicals.” Journal of exposure science & environmental epidemiology, 30(5), 866–877.

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

solve_model

parameterize_gas_pbtk

Examples

solve_gas_pbtk(chem.name = 'pyrene', exp.conc = 1, period = 24, expduration = 24)

out <- solve_gas_pbtk(chem.name='pyrene',
                      exp.conc = 0, doses.per.day = 2,
                      daily.dose = 3, input.units = "umol",
                      days=2.5, 
                      plots=TRUE, initial.values=c(Aven=20))

out <- solve_gas_pbtk(chem.name = 'pyrene', exp.conc = 3, 
                      period = 24, days=2.5,
                      exp.duration = 6, exercise = TRUE)
                  
params <- parameterize_gas_pbtk(chem.cas="80-05-7")
solve_gas_pbtk(parameters=params, days=2.5)

# Oral dose with exhalation as a route of elimination:
out <- solve_gas_pbtk(chem.name = 'bisphenol a', exp.conc = 0, dose=100,
                      days=2.5, input.units="mg/kg")

# Note that different model compartments for this model have different units 
# and that the final units can be controlled with the output.units argument:
head(solve_gas_pbtk(chem.name="lindane", days=2.5))
# Convert all compartment units to mg/L:
head(solve_gas_pbtk(chem.name="lindane", days=2.5, output.units="mg/L"))
# Convert just the plasma to mg/L:
head(solve_gas_pbtk(chem.name="lindane", days=2.5, 
                    output.units=list(Cplasma="mg/L")))

Solve_model

Description

solve_model is designed to accept systematized metadata (provided by the model.list defined in the modelinfo files) for a given toxicokinetic model, including names of variables, parameterization functions, and key units, and use it along with chemical information to prepare an ode system for numerical solution over time of the amounts or concentrations of chemical in different bodily compartments of a given species (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").

Usage

solve_model(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  times = NULL,
  parameters = NULL,
  model = NULL,
  route = "oral",
  dosing = NULL,
  days = 10,
  tsteps = 4,
  initial.values = NULL,
  initial.value.units = NULL,
  plots = FALSE,
  monitor.vars = NULL,
  suppress.messages = FALSE,
  species = "Human",
  input.units = "mg/kg",
  output.units = NULL,
  method = NULL,
  rtol = 1e-06,
  atol = 1e-06,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  restrictive.clearance = TRUE,
  adjusted.Funbound.plasma = TRUE,
  minimum.Funbound.plasma = 1e-04,
  parameterize.arg.list = list(),
  small.time = 1e-04,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
times

Optional time sequence for specified number of output times (in days) to be returned by the function. The model is solved explicitly at the time sequence specified. Dosing sequence begins at the first time provided.
parameters

List of chemical parameters, as output by parameterize_pbtk function. Overrides chem.name and chem.cas.
model

Specified model to use in simulation: "pbtk", "3compartment", "3compartmentss", "1compartment", "schmitt", ...
route

String specification of route of exposure for simulation: "oral", "iv", "inhalation", ...
dosing

List of dosing metrics used in simulation, which includes the namesake entries of a model's associated dosing.params. In the case of most httk models, these should include "initial.dose", "doses.per.day", "daily.dose", and "dosing.matrix". The "dosing.matrix" is used for more precise dose regimen specification, and is a matrix consisting of two columns or rows named "time" and "dose" containing the time and amount of each dose. If none of the namesake entries of the dosing list is set to a non-NULL value, solve_model uses a default initial dose of 1 mg/kg BW along with the dose type (add/multiply) specified for a given route (for example, add the dose to gut lumen for oral route)
days

Simulated period. Default 10 days.
tsteps

The number of time steps per hour. Default of 4.
initial.values

Vector of numeric values containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to those specified for the model outputs. Default values are zero.
initial.value.units

Vector of character strings containing the units corresponding to 'initial.values' specified for the model outputs. Default is assuming the units match expected compartment units for the model.
plots

Plots all outputs if true.
monitor.vars

Which variables are returned as a function of time. Default values of NULL looks up variables specified in modelinfo_MODEL.R
suppress.messages

Whether or not the output messages are suppressed.
species

Species desired (models have been designed to be parameterized for some subset of the following species: "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
input.units

Input units of interest assigned to dosing. Defaults to mg/kg BW, in line with the default dosing scheme of a one-time dose of 1 mg/kg in which no other dosing parameters are specified.
output.units

Output units of interest for the compiled components. Defaults to NULL, and will provide values in model units if unspecified.
method

Method used by integrator (deSolve).
rtol

Relative tolerance used by integrator (deSolve) to determine numerical precision – defaults to 1e-6.
atol

Absolute tolerance used by integrator (deSolve) to determine
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters, calculated with hematocrit, Funbound.plasma, and Krbc2pu.
recalc.clearance

Recalculates the the hepatic clearance (Clmetabolism) with new million.cells.per.gliver parameter.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset)
parameterize.arg.list

Additional parameterized passed to the model parameterization function.
small.time

A tiny amount of time used to provide predictions on either side of an instaneous event (like an iv injection). This helps ensure that abrupt changes plot well. Defaults to 1e-4.
...

Additional arguments passed to the integrator.

Details

Dosing values with certain acceptable associated input.units (like mg/kg BW) are configured to undergo a unit conversion. All model simulations are intended to run with units as specifed by "compartment.units" in the model.list (as defined by the modelinfo files).

The 'dosing' argument includes all parameters needed to describe exposure in terms of route of administration, frequency, and quantity short of scenarios that require use of a more precise forcing function. If the dosing argument's namesake entries are left NULL, solve_model defaults to a single-time dose of 1 mg/kg BW according to the given dosing route and associated type (either add/multiply, for example we typically add a dose to gut lumen when oral route is specified).

AUC is the area under the curve of the plasma concentration.

Model parameters are named according to the following convention:

prefix suffix Meaning units
K Partition coefficient for tissue to free plasma \ tab unitless
V Volume L
Q Flow L/h
k Rate 1/h
c Parameter is proportional to body weight 1 / kg for volumes and 1/kg^(3/4) for flows

When species is specified but chemical-specific in vitro data are not available, the function uses the appropriate physiological data (volumes and flows) but default.to.human = TRUE must be used to substitute human fraction unbound, partition coefficients, and intrinsic hepatic clearance. (NOTE: The 'default.to.human' specification should be included as part of the arguments listed in 'parameterize.arg.list'.)

For both plotting purposes and helping the numerical equation solver, it is helpful to specify that time points shortly before and after dosing are included. This function automatically add these points, and they are returned to the user unless the times argument is used, in which case only the time points specified by that argument are provided.

Value

A matrix of class deSolve with a column for time(in days), each compartment, the area under the curve, and plasma concentration and a row for each time point.

Author(s)

John Wambaugh, Robert Pearce, Miyuki Breen, Mark Sfeir, and Sarah E. Davidson

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

Examples

# The various "solve_x" functions are wrappers for solve_model:
head(solve_pbtk(chem.name="Terbufos", days=1))

head(solve_model(chem.name="Terbufos",model="pbtk",
                 days=1,
                 dosing=list(
                   initial.dose = 1, # Assume dose is in mg/kg BW/day  
                   doses.per.day=NULL,
                   dosing.matrix = NULL,
                   daily.dose = NULL)))


# A dose matrix specifies times and magnitudes of doses:
dm <- matrix(c(0,1,2,5,5,5),nrow=3)
colnames(dm) <- c("time","dose")

solve_pbtk(chem.name="Methenamine",
           dosing.matrix=dm,
           dose=NULL,
           days=2.5,
           daily.dose=NULL)

solve_model(chem.name="Methenamine",
            model="pbtk",
            days=2.5,
            dosing=list(
              initial.dose =NULL,
              doses.per.day=NULL,
              daily.dose=NULL,
              dosing.matrix=dm))

solve_model(chem.name="Besonprodil",
            model="pbtk",
            days=2.5,
            dosing=list(
              initial.dose=NULL,
              doses.per.day=4,
              daily.dose=1,
              dosing.matrix=NULL))
  
solve_pbtk(chem.name="Besonprodil",
           daily.dose=1,
           dose=NULL,
           doses.per.day=4,
           days=2.5)

Solve_PBTK

Description

This function solves for the amounts or concentrations in uM of a chemical in different tissues as functions of time based on the dose and dosing frequency. In this PBTK formulation. CtissueC_{tissue} is the concentration in tissue at time t. Since the perfusion limited partition coefficients describe instantaneous equilibrium between the tissue and the free fraction in plasma, the whole plasma concentration is Ctissue,plasma=1fup∗Ktissue2fup∗CtissueC_{tissue,plasma} = \frac{1}{f_{up}*K_{tissue2fup}}*C_{tissue}. Note that we use a single, constant value of fupf_{up} across all tissues. Corespondingly the free plasma concentration is modeled as Ctissue,freeplasma=1Ktissue2fup∗CtissueC_{tissue,free plasma} = \frac{1}{K_{tissue2fup}}*C_tissue. The amount of blood flowing from tissue x is QtissueQ_{tissue} (L/h) at a concentration Cx,blood=Rb2pfup∗Ktissue2fup∗CtissueC_{x,blood} = \frac{R_{b2p}}{f_{up}*K_{tissue2fup}}*C_{tissue}, where we use a single Rb2pR_{b2p} value throughout the body. Metabolic clearance is modelled as being from the total plasma concentration here, though it is restricted to the free fraction in calc_hep_clearance by default. Renal clearance via glomerulsr filtration is from the free plasma concentration. The compartments used in this model are the gutlumen, gut, liver, kidneys, veins, arteries, lungs, and the rest of the body. The extra compartments include the amounts or concentrations metabolized by the liver and excreted by the kidneys through the tubules. AUC is the area under the curve of the plasma concentration.

Usage

solve_pbtk(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  times = NULL,
  parameters = NULL,
  days = 10,
  tsteps = 4,
  daily.dose = NULL,
  dose = NULL,
  doses.per.day = NULL,
  initial.values = NULL,
  plots = FALSE,
  suppress.messages = FALSE,
  species = "Human",
  iv.dose = FALSE,
  input.units = "mg/kg",
  output.units = NULL,
  default.to.human = FALSE,
  class.exclude = TRUE,
  recalc.blood2plasma = FALSE,
  recalc.clearance = FALSE,
  dosing.matrix = NULL,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  restrictive.clearance = TRUE,
  minimum.Funbound.plasma = 1e-04,
  Caco2.options = list(),
  monitor.vars = NULL,
  ...
)

Arguments

chem.name

Either the chemical name, CAS number, or the parameters must be specified.
chem.cas

Either the chemical name, CAS number, or the parameters must be specified.
dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs
times

Optional time sequence for specified number of days. Dosing sequence begins at the beginning of times.
parameters

Chemical parameters from parameterize_pbtk function, overrides chem.name and chem.cas.
days

Length of the simulation.
tsteps

The number of time steps per hour.
daily.dose

Total daily dose, defaults to mg/kg BW.
dose

Amount of a single, initial oral dose in mg/kg BW.
doses.per.day

Number of doses per day.
initial.values

Vector containing the initial concentrations or amounts of the chemical in specified tissues with units corresponding to output.units. Defaults are zero.
plots

Plots all outputs if true.
suppress.messages

Whether or not the output message is suppressed.
species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").
iv.dose

Simulates a single i.v. dose if true.
input.units

Input units of interest assigned to dosing, defaults to mg/kg BW
output.units

A named vector of output units expected for the model results. Default, NULL, returns model results in units specified in the 'modelinfo' file. See table below for details.
default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).
class.exclude

Exclude chemical classes identified as outside of domain of applicability by relevant modelinfo_[MODEL] file (default TRUE).
recalc.blood2plasma

Recalculates the ratio of the amount of chemical in the blood to plasma using the input parameters, calculated with hematocrit, Funbound.plasma, and Krbc2pu.
recalc.clearance

Recalculates the the hepatic clearance (Clmetabolism) with new million.cells.per.gliver parameter.
dosing.matrix

Vector of dosing times or a matrix consisting of two columns or rows named "dose" and "time" containing the time and amount, in mg/kg BW, of each dose.
adjusted.Funbound.plasma

Uses adjusted Funbound.plasma when set to TRUE along with partition coefficients calculated with this value.
regression

Whether or not to use the regressions in calculating partition coefficients.
restrictive.clearance

Protein binding not taken into account (set to 1) in liver clearance if FALSE.
minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 – half the lowest measured Fup in our dataset).
Caco2.options

A list of options to use when working with Caco2 apical to basolateral data Caco2.Pab, default is Caco2.options = list(Caco2.Pab.default = 1.6, Caco2.Fabs = TRUE, Caco2.Fgut = TRUE, overwrite.invivo = FALSE, keepit100 = FALSE). Caco2.Pab.default sets the default value for Caco2.Pab if Caco2.Pab is unavailable. Caco2.Fabs = TRUE uses Caco2.Pab to calculate fabs.oral, otherwise fabs.oral = Fabs. Caco2.Fgut = TRUE uses Caco2.Pab to calculate fgut.oral, otherwise fgut.oral = Fgut. overwrite.invivo = TRUE overwrites Fabs and Fgut in vivo values from literature with Caco2 derived values if available. keepit100 = TRUE overwrites Fabs and Fgut with 1 (i.e. 100 percent) regardless of other settings. See get_fbio for further details.
monitor.vars

Which variables are returned as a function of time. The default value of NULL provides "Cgut", "Cliver", "Cven", "Clung", "Cart", "Crest", "Ckidney", "Cplasma", "Atubules", "Ametabolized", and "AUC"
...

Additional arguments passed to the integrator (deSolve).

Details

Note that the model parameters have units of hours while the model output is in days.

Default NULL value for doses.per.day solves for a single dose.

Model Figure Figure: PBTK Model Schematic

When species is specified as rabbit, dog, or mouse, the function uses the appropriate physiological data(volumes and flows) but substitutes human fraction unbound, partition coefficients, and intrinsic hepatic clearance.

Value

A matrix of class deSolve with a column for time(in days), each compartment, the area under the curve, and plasma concentration and a row for each time point.

Author(s)

John Wambaugh and Robert Pearce

References

Pearce RG, Setzer RW, Strope CL, Wambaugh JF, Sipes NS (2017). “Httk: R package for high-throughput toxicokinetics.” Journal of Statistical Software, 79(4), 1.

See Also

solve_model

parameterize_gas_pbtk

calc_analytic_css_pbtk

Examples

# Multiple doses per day:
head(solve_pbtk(
  chem.name='Bisphenol-A',
  daily.dose=.5,
  days=2.5,
  doses.per.day=2,
  tsteps=2))

# Starting with an initial concentration:
out <- solve_pbtk(
  chem.name='bisphenola',
  dose=0,
  days=2.5,
  output.units="mg/L", 
  initial.values=c(Agut=200))

# Working with parameters (rather than having solve_pbtk retrieve them):
params <- parameterize_pbtk(chem.cas="80-05-7")
head(solve_pbtk(parameters=params, days=2.5))
                  
# We can change the parameters given to us by parameterize_pbtk:
params <- parameterize_pbtk(dtxsid="DTXSID4020406", species = "rat")
params["Funbound.plasma"] <- 0.1
out <- solve_pbtk(parameters=params, days=2.5)

# A fifty day simulation:
out <- solve_pbtk(
  chem.name = "Bisphenol A", 
  days = 50, 
  daily.dose=1,
  doses.per.day = 3)
plot.data <- as.data.frame(out)
css <- calc_analytic_css(chem.name = "Bisphenol A")

library("ggplot2")
c.vs.t <- ggplot(plot.data, aes(time, Cplasma)) + 
  geom_line() +
  geom_hline(yintercept = css) + 
  ylab("Plasma Concentration (uM)") +
  xlab("Day") + 
  theme(
    axis.text = element_text(size = 16), 
    axis.title = element_text(size = 16), 
    plot.title = element_text(size = 17)) +
  ggtitle("Bisphenol A")
print(c.vs.t)

Predict spleen mass for children

Description

For individuals under 18, predict the spleen mass from height, weight, and gender, using equations from Ogiu et al. (1997)

Usage

spleen_mass_children(height, weight, gender)

Arguments

height

Vector of heights in cm.
weight

Vector of weights in kg.
gender

Vector of genders (either 'Male' or 'Female').

Value

A vector of spleen masses in kg.

Author(s)

Caroline Ring

References

Ogiu, Nobuko, et al. "A statistical analysis of the internal organ weights of normal Japanese people." Health physics 72.3 (1997): 368-383.

Price, Paul S., et al. "Modeling interindividual variation in physiological factors used in PBPK models of humans." Critical reviews in toxicology 33.5 (2003): 469-503.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Supplementary output from Linakis 2020 vignette analysis.

Description

Supplementary output from Linakis 2020 vignette analysis.

Usage

supptab1_Linakis2020

Format

A data.frame containing x rows and y columns.

Author(s)

Matt Linakis

Source

Matt Linakis

References

DSStox database (https:// www.epa.gov/ncct/dsstox

More supplementary output from Linakis 2020 vignette analysis.

Description

More supplementary output from Linakis 2020 vignette analysis.

Usage

supptab2_Linakis2020

Format

A data.frame containing x rows and y columns.

Author(s)

Matt Linakis

Source

Matt Linakis

References

DSStox database (https:// www.epa.gov/ncct/dsstox

A timestamp of table creation

Description

The Tables.RData file is separately created as part of building a new release of HTTK. This time stamp indicates the script used to build the file and when it was run.

Usage

Tables.Rdata.stamp

Format

An object of class character of length 1.

Author(s)

John Wambaugh

Given a data.table describing a virtual population by the NHANES quantities, generates HTTK physiological parameters for each individual.

Description

Given a data.table describing a virtual population by the NHANES quantities, generates HTTK physiological parameters for each individual.

Usage

tissue_masses_flows(tmf_dt)

Arguments

tmf_dt

A data.table generated by gen_age_height_weight(), containing variables gender, reth, age_months, age_years, weight, and height.

Value

The same data.table, with aditional variables describing tissue masses and flows.

Author(s)

Caroline Ring

References

Barter, Zoe E., et al. "Scaling factors for the extrapolation of in vivo metabolic drug clearance from in vitro data: reaching a consensus on values of human micro-somal protein and hepatocellularity per gram of liver." Current Drug Metabolism 8.1 (2007): 33-45.

Birnbaum, L., et al. "Physiological parameter values for PBPK models." International Life Sciences Institute, Risk Science Institute, Washington, DC (1994).

Geigy Pharmaceuticals, "Scientific Tables", 7th Edition, John Wiley and Sons (1970)

McNally, Kevin, et al. "PopGen: a virtual human population generator." Toxicology 315 (2014): 70-85.

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Allometric scaling.

Description

Allometrically scale a tissue mass or flow based on height^(3/4).

Usage

tissue_scale(height_ref, height_indiv, tissue_mean_ref)

Arguments

height_ref

Reference height in cm.
height_indiv

Individual height in cm.
tissue_mean_ref

Reference tissue mass or flow.

Value

Allometrically scaled tissue mass or flow, in the same units as tissue_mean_ref.

Author(s)

Caroline Ring

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Tissue composition and species-specific physiology parameters

Description

This data set contains values from Schmitt (2008) and Ruark et al. (2014) describing the composition of specific tissues and from Birnbaum et al. (1994) describing volumes of and blood flows to those tissues, allowing parameterization of toxicokinetic models for human, mouse, rat, dog, or rabbit. Tissue volumes were calculated by converting the fractional mass of each tissue with its density (both from ICRP), lumping the remaining tissues into the rest-of-body, excluding the mass of the gastrointestinal contents.

Usage

tissue.data

Format

A data.frame containing 406 rows and 5 columns.

Column Description
Tissue The tissue being described
Species The species being described
Reference The reference for the value reported
variable The aspect of the tissue being characterized
value The value for the variable for the given tissue and species

Details

Many of the parameters were compiled initially in Table 2 of Schmitt (2009). The full list of tissue variables described is:

Variable Description Units
Fcell Cellular fraction of total tissue volume fraction
Fint Interstitial fraction of total tissue volume fraction
FWc Fraction of cell volume that is water fraction
FLc Fraction of cell volume that is lipid fraction
FPc Fraction of cell volume that is protein fraction
Fn_Lc Fraction of cellular lipid tht is neutral lipid fraction
Fn_PLc Fraction of cellular lipid tht is neutral phospholipid fraction
Fa_PLc Fraction of cellular lipid tht is acidic phospholipid fraction
pH Negative logarithm of H+ ion concentration unitless
Density Tissue density g/cm^3
Vol Tissue volume L/kg
Flow Blood flow to tissue mL/min/kg^(3/4)

New tissues can be added to this table to generate their partition coefficients.

Author(s)

John Wambaugh, Robert Pearce, and Nisha Sipes

References

Birnbaum L, Brown R, Bischoff K, Foran J, Blancato J, Clewell H, Dedrick R (1994). “Physiological parameter values for PBPK models.” International Life Sciences Institute, Risk Science Institute, Washington, DC.

Ruark CD, Hack CE, Robinson PJ, Mahle DA, Gearhart JM (2014). “Predicting passive and active tissue: plasma partition coefficients: interindividual and interspecies variability.” Journal of pharmaceutical sciences, 103(7), 2189–2198.

Schmitt W (2008). “General approach for the calculation of tissue to plasma partition coefficients.” Toxicology in vitro, 22(2), 457–467.

Snyder WS (1974). “Report of the task group on reference man.” ICRP publication.

Wambaugh JF, Wetmore BA, Pearce R, Strope C, Goldsmith R, Sluka JP, Sedykh A, Tropsha A, Bosgra S, Shah I, others (2015). “Toxicokinetic triage for environmental chemicals.” Toxicological Sciences, 147(1), 55–67.

See Also

predict_partitioning_schmitt

Examples

# We can add thyroid to the tissue data by making a row containing
# its data, subtracting the volumes and flows from the rest-of-body, 
# and binding the row to tissue.data. Here we assume it contains the same 
# partition coefficient data as the spleen and a tenth of the volume and  
# blood flow:
new.tissue <- subset(tissue.data,Tissue == "spleen")
new.tissue[, "Tissue"] <- "thyroid"
new.tissue[new.tissue$variable %in% c("Vol (L/kg)",
"Flow (mL/min/kg^(3/4))"),"value"] <- new.tissue[new.tissue$variable
%in% c("Vol (L/kg)","Flow (mL/min/kg^(3/4))"),"value"] / 10
tissue.data[tissue.data$Tissue == "rest", "value"] <-
tissue.data[tissue.data$Tissue == "rest", "value"] -
new.tissue[new.tissue$variable %in% c("Vol (L/kg)",
"Flow (mL/min/kg^(3/4))"),"value"]
tissue.data <- rbind(tissue.data, new.tissue)

in vitro Toxicokinetic Data from Wambaugh et al. (2019)

Description

These data are the new HTTK in vitro data for chemicals reported in Wambaugh et al. (2019) They are the processed values used to make the figures in that manuscript. These data summarize the results of Bayesian analysis of the in vitro toxicokinetic experiments conducted by Cyprotex to characterize fraction unbound in the presence of pooled human plasma protein and the intrnsic hepatic clearance of the chemical by pooled human hepatocytes.

Usage

wambaugh2019

Format

A data frame with 496 rows and 17 variables:

Compound

The name of the chemical

CAS

The Chemical Abstracts Service Registry Number

Human.Clint

Median of Bayesian credible interval for intrinsic hepatic clearance (uL/min/million hepatocytes)]

Human.Clint.pValue

Probability that there is no clearance

Human.Funbound.plasma

Median of Bayesian credibl interval for fraction of chemical free in the presence of plasma

pKa_Accept

pH(s) at which hydrogen acceptor sites (if any) are at equilibrium

pKa_Donor

pH(s) at which hydrogne donor sites (if any) are at equilibrium

DSSTox_Substance_Id

Identifier for CompTox Chemical Dashboard

SMILES

Simplified Molecular-Input Line-Entry System structure description

Human.Clint.Low95

Lower 95th percentile of Bayesian credible interval for intrinsic hepatic clearance (uL/min/million hepatocytes)

Human.Clint.High95

Uppper 95th percentile of Bayesian credible interval for intrinsic hepatic clearance (uL/min/million hepatocytes)

Human.Clint.Point

Point estimate of intrinsic hepatic clearance (uL/min/million hepatocytes)

Human.Funbound.plasma.Low95

Lower 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma

Human.Funbound.plasma.High95

Upper 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma

Human.Funbound.plasma.Point

Point estimate of the fraction of chemical free in the presence of plasma

MW

Molecular weight (Daltons)

logP

log base ten of octanol:water partiion coefficient

Author(s)

John Wambaugh

Source

Wambaugh et al. (2019)

References

Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.

NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019)

Description

These data are a subset of the Bayesian inferrences reported by Ring et al. (2017) from the U.S. Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey (NHANES). They reflect the populaton median intake rate (mg/kg body weight/day), with uncertainty.

Usage

wambaugh2019.nhanes

Format

A data frame with 20 rows and 4 variables:

lP

The median of the Bayesian credible interval for median population intake rate (mg/kg bodyweight/day)

lP.min

The lower 95th percentile of the Bayesian credible interval for median population intake rate (mg/kg bodyweight/day)

lP.max

The upper 95th percentile of the Bayesian credible interval for median population intake rate (mg/kg bodyweight/day)

CASRN

The Chemical Abstracts Service Registry Number

Author(s)

John Wambaugh

Source

Wambaugh et al. (2019)

References

Ring CL, Pearce RG, Setzer RW, Wetmore BA, Wambaugh JF (2017). “Identifying populations sensitive to environmental chemicals by simulating toxicokinetic variability.” Environment International, 106, 105–118.

Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.

Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019)

Description

These data are the new HTTK in vitro data for chemicals reported in Wambaugh et al. (2019) They are the output of different Bayesian models evaluated to compare using a single protein concentration vs. the new three concentration titration protocol. These data summarize the results of Bayesian analysis of the in vitro toxicokinetic experiments conducted by Cyprotex to characterize fraction unbound in the presence of pooled human plasma protein and the intrnsic hepatic clearance of the chemical by pooled human hepatocytes. This file includes replicates (diferent CompoundName id's but same chemical')

Usage

wambaugh2019.raw

Format

A data frame with 530 rows and 28 variables:

DTXSID

Identifier for CompTox Chemical Dashboard

Name

The name of the chemical

CAS

The Chemical Abstracts Service Registry Number

CompoundName

Sample name provided by EPA to Cyprotex

Fup.point

Point estimate of the fraction of chemical free in the presence of plasma

Base.Fup.Med

Median of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of 100 physiological plasma protein data only (base model)

Base.Fup.Low

Lower 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of 100 physiological plasma protein data only (base model)

Base.Fup.High

Upper 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of 100 physiological plasma protein data only (base model)

Affinity.Fup.Med

Median of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of protein titration protocol data (affinity model)

Affinity.Fup.Low

Lower 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of protein titration protocol data (affinity model)

Affinity.Fup.High

Upper 95th percentile of Bayesian credible interval for fraction of chemical free in the presence of plasma for analysis of protein titration protocol data (affinity model)

Affinity.Kd.Med

Median of Bayesian credible interval for protein binding affinity from analysis of protein titration protocol data (affinity model)

Affinity.Kd.Low

Lower 95th percentile of Bayesian credible interval for protein binding affinity from analysis of protein titration protocol data (affinity model)

Affinity.Kd.High

Upper 95th percentile of Bayesian credible interval for protein binding affinity from analysis of protein titration protocol data (affinity model)

Decreases.Prob

Probability that the chemical concentration decreased systematiclally during hepatic clearance assay.

Saturates.Prob

Probability that the rate of chemical concentration decrease varied between the 1 and 10 uM hepatic clearance experiments.

Slope.1uM.Median

Estimated slope for chemcial concentration decrease in the 1 uM hepatic clearance assay.

Slope.10uM.Median

Estimated slope for chemcial concentration decrease in the 10 uM hepatic clearance assay.

CLint.1uM.Median

Median of Bayesian credible interval for intrinsic hepatic clearance at 1 uM initital chemical concentration (uL/min/million hepatocytes)]

CLint.1uM.Low95th

Lower 95th percentile of Bayesian credible interval for intrinsic hepatic clearance at 1 uM initital chemical concentration (uL/min/million hepatocytes)

CLint.1uM.High95th

Uppper 95th percentile of Bayesian credible interval for intrinsic hepatic clearance at 1 uM initital chemical concentration(uL/min/million hepatocytes)

CLint.10uM.Median

Median of Bayesian credible interval for intrinsic hepatic clearance at 10 uM initital chemical concentration (uL/min/million hepatocytes)]

CLint.10uM.Low95th

Lower 95th percentile of Bayesian credible interval for intrinsic hepatic clearance at 10 uM initital chemical concentration (uL/min/million hepatocytes)

CLint.10uM.High95th

Uppper 95th percentile of Bayesian credible interval for intrinsic hepatic clearance at 10 uM initital chemical concentration(uL/min/million hepatocytes)

CLint.1uM.Point

Point estimate of intrinsic hepatic clearance (uL/min/million hepatocytes) for 1 uM initial chemical concentration

CLint.10uM.Point

Point estimate of intrinsic hepatic clearance (uL/min/million hepatocytes) for 10 uM initial chemical concentration

Fit

Classification of clearance observed

SMILES

Simplified Molecular-Input Line-Entry System structure description

Author(s)

John Wambaugh

Source

Wambaugh et al. (2019)

References

Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.

ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates

Description

These data are a subset of the Bayesian inferrences reported by Ring et al. (2019) for a consensus model of twelve exposue predictors. The predictors were calibrated based upon their ability to predict intake rates inferred National Health and Nutrition Examination Survey (NHANES). They reflect the populaton median intake rate (mg/kg body weight/day), with uncertainty.

Usage

wambaugh2019.seem3

Format

A data frame with 385 rows and 38 variables:

Author(s)

John Wambaugh

Source

Wambaugh et al. (2019)

References

Ring, Caroline L., et al. "Consensus modeling of median chemical intake for the US population based on predictions of exposure pathways." Environmental science & technology 53.2 (2018): 719-732.

Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.

Tox21 2015 Active Hit Calls (EPA)

Description

The ToxCast and Tox21 research programs employ batteries of high-throughput assays to assess chemical bioactivity in vitro. Not every chemical is tested through every assay. Most assays are conducted in concentration response, and each corresponding assay endpoint is analyzed statistically to determine if there is a concentration-dependent response or "hit" using the ToxCast Pipeline. Most assay endpoint-chemical combinations are non-responsive. Here, only the hits are treated as potential indicators of bioactivity. This bioactivity does not have a direct toxicological interpretation. The October 2015 release (invitrodb_v2) of the ToxCast and Tox21 data were used for this analysis. This object contains just the chemicals in Wambaugh et al. (2019) and only the quantiles across all assays for the ACC.

Usage

wambaugh2019.tox21

Format

A data.table with 401 rows and 6 columns

Author(s)

John Wambaugh

Source

https://gaftp.epa.gov/comptox/High_Throughput_Screening_Data/Previous_Data/ToxCast_Data_Release_Oct_2015/MySQL_Data/

References

Kavlock, Robert, et al. "Update on EPA's ToxCast program: providing high-throughput decision support tools for chemical risk management." Chemical research in toxicology 25.7 (2012): 1287-1302.

Tice, Raymond R., et al. "Improving the human hazard characterization of chemicals: a Tox21 update." Environmental health perspectives 121.7 (2013): 756-765.

Richard, Ann M., et al. "ToxCast chemical landscape: paving the road to 21st century toxicology." Chemical research in toxicology 29.8 (2016): 1225-1251.

Filer, Dayne L., et al. "tcpl: the ToxCast pipeline for high-throughput screening data." Bioinformatics 33.4 (2016): 618-620.

Wambaugh, John F., et al. "Assessing Toxicokinetic Uncertainty and Variability in Risk Prioritization." Toxicological Sciences 172.2 (2019): 235-251.

Wang et al. 2018 Wang et al. (2018) screened the blood of 75 pregnant women for the presence of environmental organic acids (EOAs) and identified mass spectral features corresponding to 453 chemical formulae of which 48 could be mapped to likely structures. Of the 48 with tentative structures the identity of six were confirmed with available chemical standards.

Description

Wang et al. 2018 Wang et al. (2018) screened the blood of 75 pregnant women for the presence of environmental organic acids (EOAs) and identified mass spectral features corresponding to 453 chemical formulae of which 48 could be mapped to likely structures. Of the 48 with tentative structures the identity of six were confirmed with available chemical standards.

Usage

wang2018

Format

data.frame

Source

Kapraun DF, Sfeir M, Pearce RG, Davidson-Fritz SE, Lumen A, Dallmann A, Judson RS, Wambaugh JF (2022). “Evaluation of a rapid, generic human gestational dose model.” Reproductive Toxicology, 113, 172–188.

References

Wang A, Gerona RR, Schwartz JM, Lin T, Sirota M, Morello-Frosch R, Woodruff TJ (2018). “A Suspect Screening Method for Characterizing Multiple Chemical Exposures among a Demographically Diverse Population of Pregnant Women in San Francisco.” Environmental Health Perspectives, 126(7), 077009. doi:10.1289/EHP2920.

Microtiter Plate Well Descriptions for Armitage et al. (2014) Model

Description

Microtiter Plate Well Descriptions for Armitage et al. (2014) model from Honda et al. (2019)

Usage

well_param

Format

A data frame / data table with 11 rows and 8 variables:

sysID

Identifier for each multi-well plate system

well_desc

Well description

well_number

Number of wells on plate

area_bottom

Area of well bottom in mm^2

cell_yield

Number of cells

diam

Diameter of well in mm

v_total

Total volume of well in uL)

v_working

Working volume of well in uL

Author(s)

Greg Honda

Source

https://www.corning.com/catalog/cls/documents/application-notes/CLS-AN-209.pdf

References

Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol. 2014, 48, 9770-9779. dx.doi.org/10.1021/es501955g Honda GS, Pearce RG, Pham LL, Setzer RW, Wetmore BA, Sipes NS, Gilbert J, Franz B, Thomas RS, Wambaugh JF (2019). “Using the concordance of in vitro and in vivo data to evaluate extrapolation assumptions.” PloS one, 14(5), e0217564.

Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012.

Description

This data set overlaps with Wetmore.data and is used only in Vignette 4 for steady state concentration.

Usage

Wetmore2012

Format

A data.frame containing 13 rows and 15 columns.

References

Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A., Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck, K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125 157-174 (2012)

WHO weight-for-length charts

Description

Charts giving weight-for-length percentiles for boys and girls under age 2.

Usage

wfl

Format

a data.table with 262 rows and 4 variables:

Sex

"Male" or "Female"

Length

Recumbent length in cm

P2.3

The 2.3rd percentile weight in kg for the corresponding sex and recumbent length

P97.7

The 97.7th percentile weight in kg for the corresponding sex and recumbent length

Details

For infants under age 2, weight class depends on weight for length percentile. #'

Underweight

<2.3rd percentile

Normal weight

2.3rd-97.7th percentile

Obese

>=97.7th percentile

Source

https://www.cdc.gov/growthcharts/who/boys_weight_head_circumference.htm and https://www.cdc.gov/growthcharts/who/girls_weight_head_circumference.htm