Package: httk 2.4.0

John Wambaugh

httk: High-Throughput Toxicokinetics

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]

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httk.pdf |httk.html✨
httk/json (API)
NEWS

# Install 'httk' in R:
install.packages('httk', repos = c('https://usepa.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/usepa/comptox-expocast-httk/issues

Datasets:
  • EPA.ref - Reference for EPA Physico-Chemical Data
  • Frank2018invivo - Literature In Vivo Data on Doses Causing Neurological Effects
  • Obach2008 - Published Pharmacokinetic Parameters from Obach et al. 2008
  • Tables.Rdata.stamp - A timestamp of table creation
  • Wetmore2012 - Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012.
  • armitage_input - Armitage et al.
  • aylward2014 - Aylward et al. 2014
  • bmiage - CDC BMI-for-age charts
  • chem.invivo.PK.aggregate.data - Parameter Estimates from Wambaugh et al.
  • chem.invivo.PK.data - Published toxicokinetic time course measurements
  • chem.invivo.PK.summary.data - Summary of published toxicokinetic time course experiments
  • chem.physical_and_invitro.data - Physico-chemical properties and in vitro measurements for toxicokinetics
  • concentration_data_Linakis2020 - Concentration data involved in Linakis 2020 vignette analysis.
  • dawson2021 - Dawson et al. 2021 data
  • example.seem - 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
  • example.toxcast - 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
  • fetalpcs - Fetal Partition Coefficients
  • hct_h - KDE bandwidths for residual variability in hematocrit
  • honda2023.data - Measured Caco-2 Apical-Basal Permeability Data
  • honda2023.qspr - Predicted Caco-2 Apical-Basal Permeabilities
  • howgate - Howgate 2006
  • httk.performance - Historical Performance of R Package httk
  • hw_H - KDE bandwidth for residual variability in height/weight
  • johnson - Johnson 2006
  • kapraun2019 - Kapraun et al. 2019 data
  • mcnally_dt - Reference tissue masses and flows from tables in McNally et al. 2014.
  • mecdt - Pre-processed NHANES data.
  • metabolism_data_Linakis2020 - Metabolism data involved in Linakis 2020 vignette analysis.
  • onlyp - NHANES Exposure Data
  • pc.data - Partition Coefficient Data
  • pearce2017regression - Pearce et al. 2017 data
  • pharma - DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data
  • physiology.data - Species-specific physiology parameters
  • pksim.pcs - Partition Coefficients from PK-Sim
  • pradeep2020 - Pradeep et al. 2020
  • pregnonpregaucs - AUCs for Pregnant and Non-Pregnant Women
  • scr_h - KDE bandwidths for residual variability in serum creatinine
  • sipes2017 - Sipes et al. 2017 data
  • supptab1_Linakis2020 - Supplementary output from Linakis 2020 vignette analysis.
  • supptab2_Linakis2020 - More supplementary output from Linakis 2020 vignette analysis.
  • tissue.data - Tissue composition and species-specific physiology parameters
  • wambaugh2019 - In vitro Toxicokinetic Data from Wambaugh et al.
  • wambaugh2019.nhanes - NHANES Chemical Intake Rates for chemicals in Wambaugh et al.
  • wambaugh2019.raw - Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al.
  • wambaugh2019.seem3 - ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates
  • wambaugh2019.tox21 - Tox21 2015 Active Hit Calls
  • wang2018 - 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.
  • well_param - Microtiter Plate Well Descriptions for Armitage et al. (2014) Model
  • wfl - WHO weight-for-length charts

On CRAN:

comptoxord

108 exports 26 stars 3.02 score 46 dependencies 3 mentions 278 scripts 710 downloads

Last updated 11 hours agofrom:c6d7afdbe3. Checks:OK: 9. Indexed: yes.

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Doc / VignettesOKSep 17 2024
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R-4.3-mac-aarch64OKSep 17 2024

Exports:add_chemtableage_draw_smoothapply_clint_adjustmentapply_fup_adjustmentarmitage_estimate_sareaarmitage_evalavailable_rblood2plasmabenchmark_httkblood_mass_correctblood_weightbody_surface_areacalc_analytic_csscalc_csscalc_dowcalc_elimination_ratecalc_fabs.oralcalc_fbio.oralcalc_fetal_physcalc_fgut.oralcalc_fup_correctioncalc_half_lifecalc_hep_bioavailabilitycalc_hep_clearancecalc_hep_fucalc_hepatic_clearancecalc_ionizationcalc_kaircalc_krbc2pucalc_macalc_maternal_bwcalc_mc_csscalc_mc_oral_equivcalc_mc_tkcalc_rblood2plasmacalc_statscalc_tkstatscalc_total_clearancecalc_vdistCAS.checksumckd_epi_eqconvert_solve_xconvert_unitscreate_mc_samplesestimate_gfrestimate_gfr_pedexport_pbtk_jarnacexport_pbtk_sbmlget_caco2get_chem_idget_cheminfoget_fbioget_gfr_categoryget_invitroPK_paramget_lit_cheminfoget_lit_cssget_lit_oral_equivget_physchem_paramget_rblood2plasmaget_weight_classget_wetmore_cheminfoget_wetmore_cssget_wetmore_oral_equivhonda.ivivehttkpop_biotophys_defaulthttkpop_direct_resamplehttkpop_direct_resample_innerhttkpop_generatehttkpop_mchttkpop_virtual_indivin.listinvitro_mcis_in_inclusiveis.expocastis.httkis.nhanesis.nhanes.blood.analyteis.nhanes.blood.parentis.nhanes.serum.analyteis.nhanes.serum.parentis.nhanes.urine.analyteis.nhanes.urine.parentis.pharmais.tox21is.toxcastload_dawson2021load_honda2023load_pradeep2020load_sipes2017lump_tissuesmonte_carloparameterize_1compparameterize_3compparameterize_fetal_pbtkparameterize_gas_pbtkparameterize_pbtkparameterize_schmittparameterize_steadystatepredict_partitioning_schmittr_left_censored_normreset_httkrfunrmed0non0u95solve_1compsolve_3compsolve_fetal_pbtksolve_gas_pbtksolve_modelsolve_pbtk

Dependencies:clicolorspacedata.tableDBIdeSolveexpmfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvminqamitoolsmsmmunsellmvtnormnlmenumDerivpillarpkgconfigpurrrR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrlangscalessurveysurvivaltibbletruncnormutf8vctrsviridisLitewithr

Introduction to HTTK

Miyuki Breen, Anna Kreutz, and John Wambaugh

Rendered fromV1_IntroToHTTK.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2024-04-09
Started: 2023-02-21

Introduction to In Vitro-In Vivo Extrapolation (IVIVE) with R Package httk

John Wambaugh and Elaina Kenyon

Rendered fromV2_IntrotoIVIVE.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2024-09-17
Started: 2023-02-21

Pearce et al. (2017): Updated v79i04.R Examples

Robert G Pearce, R Woodrow Setzer, Cory L Strope, Nisha S Sipes, and John F Wambaugh

Rendered fromVa_Pearce2017.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2024-09-17
Started: 2023-02-21

Ring et al. (2017) HTTK-Pop: Generating subpopulations

Caroline Ring

Rendered fromVb_Ring2017.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2024-04-09
Started: 2023-02-21

Frank et al. (2018): Neuronal Network IVIVE

John F. Wambaugh

Rendered fromVd_Frank2019.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Pearce et al. (2017): Evaluation of Tissue Partitioning

Robert Pearce

Rendered fromVc_Pearce2017.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Wambaugh et al. (2018): Evaluating In Vitro-In Vivo Extrapolation

John Wambaugh

Rendered fromVe_Wambaugh2018.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Honda et al. (2019): Updated Armitage et al. (2014) Model

Gregory S. Honda

Rendered fromVf_Honda2019.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Wambaugh et al. (2019): Uncertainty Monte Carlo

John F. Wambaugh

Rendered fromVg_Wambaugh2019.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Linakis et al. (2020): High Throughput Inhalation Model

Matt Linakis

Rendered fromVh_Linakis2020.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2024-09-17
Started: 2023-12-11

Kapraun et al. (2022): Generic Human Gestational Model

Dustin F. Kapraun, Mark Sfeir, Robert Pearce, Sarah Davidson, Annie Lumen, Andre Dallmann, Richard Judson, and John F. Wambaugh

Rendered fromVi_Kapraun2022.Rmdusingknitr::rmarkdownon Sep 17 2024.

Last update: 2023-12-11
Started: 2023-12-11

Readme and manuals

Help Manual

Help pageTopics
Add a table of chemical information for use in making httk predictions.add_chemtable
Draws ages from a smoothed distribution for a given gender/race combinationage_draw_smooth
Correct the measured intrinsive hepatic clearance for fraction freeapply_clint_adjustment
Correct the measured fraction unbound in plasma for lipid bindingapply_fup_adjustment
Estimate well surface areaarmitage_estimate_sarea
Evaluate the updated Armitage modelarmitage_eval
Armitage et al. (2014) Model Inputs from Honda et al. (2019)armitage_input
Add a parameter value to the chem.physical_and_invitro.data tableaugment.table
Find the best available ratio of the blood to plasma concentration constant.available_rblood2plasma
Aylward et al. 2014Aylward2014 aylward2014
Assess the current performance of httk relative to historical benchmarksbenchmark_httk
Find average blood masses by age.blood_mass_correct
Predict blood mass.blood_weight
CDC BMI-for-age chartsbmiage
Predict body surface area.body_surface_area
Predict bone massbone_mass_age
Predict brain mass.brain_mass
Calculate the analytic steady state plasma concentration.calc_analytic_css
Calculate the analytic steady state concentration for the one compartment model.calc_analytic_css_1comp
Calculate the analytic steady state concentration for model 3compartmentcalc_analytic_css_3comp
Calculate the analytic steady state concentration for the three compartment steady-state modelcalc_analytic_css_3compss
Calculate the analytic steady state plasma concentration for model pbtk.calc_analytic_css_pbtk
Find the steady state concentration and the day it is reached.calc_css
Calculate the distribution coefficientcalc_dow
Calculate the elimination rate for a one compartment modelcalc_elimination_rate
Functions for calculating the bioavaialble fractions from oral dosescalc_fabs.oral calc_fbio.oral calc_fgut.oral calc_kgutabs calc_peff
Calculate maternal-fetal physiological parameterscalc_fetal_phys
Calculate the correction for lipid binding in plasma binding assaycalc_fup_correction
Calculates the half-life for a one compartment model.calc_half_life
Calculate first pass heaptic metabolismcalc_hep_bioavailability
Calculate the hepatic clearance.calc_hep_clearance
Calculate the free chemical in the hepaitic clearance assaycalc_hep_fu
Calculate the hepatic clearance (deprecated).calc_hepatic_clearance
Calculate the ionization.calc_ionization
Calculate air:matrix partition coefficientscalc_kair
Back-calculates the Red Blood Cell to Unbound Plasma Partition Coefficientcalc_krbc2pu
Calculate the membrane affinitycalc_ma
Calculate maternal body weightcalc_maternal_bw
Distribution of chemical steady state concentration with uncertainty and variabilitycalc_mc_css
Calculate Monte Carlo Oral Equivalent Dosecalc_mc_oral_equiv
Conduct multiple TK simulations using Monte Carlocalc_mc_tk
Calculate the constant ratio of the blood concentration to the plasma concentration.calc_rblood2plasma
Calculate toxicokinetic summary statistics (deprecated).calc_stats
Calculate toxicokinetic summary statistics.calc_tkstats
Calculate the total plasma clearance.calc_total_clearance
Calculate the volume of distribution for a one compartment model.calc_vdist
Test the check digit of a CAS number to confirm validityCAS.checksum
Check for sufficient model parameterscheck_model
Parameter Estimates from Wambaugh et al. (2018)chem.invivo.PK.aggregate.data
Published toxicokinetic time course measurementschem.invivo.PK.data
Summary of published toxicokinetic time course experimentschem.invivo.PK.summary.data
Physico-chemical properties and in vitro measurements for toxicokineticschem.physical_and_invitro.data
CKD-EPI equation for GFR.ckd_epi_eq
Concentration data involved in Linakis 2020 vignette analysis.concentration_data_Linakis2020
convert_solve_xconvert_solve_x
convert_unitsconvert_units
Create a table of parameter values for Monte Carlocreate_mc_samples
Dawson et al. 2021 dataDawson2021 dawson2021
Reference for EPA Physico-Chemical DataEPA.ref
Predict GFR.estimate_gfr
Predict GFR in children.estimate_gfr_ped
Generate hematocrit values for a virtual populationestimate_hematocrit
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.RDataexample.seem
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=62bb560ee4b07abf29f88fefexample.toxcast
Export model to jarnac.export_pbtk_jarnac
Export model to sbml.export_pbtk_sbml
Fetal Partition CoefficientsfetalPCs fetalpcs
Literature In Vivo Data on Doses Causing Neurological EffectsFrank2018invivo
Generate demographic parameters for a virtual populationgen_age_height_weight
Generate heights and weights for a virtual population.gen_height_weight
Generate serum creatinine values for a virtual population.gen_serum_creatinine
Retrieve in vitro measured Caco-2 membrane permeabilitget_caco2
Retrieve chemical identity from HTTK packageget_chem_id
Retrieve chemical information available from HTTK packageget_cheminfo
Retrieve and parse intrinsic hepatic clearanceget_clint
Retrieve or calculate fraction of chemical absorbed from the gutget_fbio
Retrieve and parse fraction unbound in plasmaget_fup
Categorize kidney function by GFR.get_gfr_category
Retrieve species-specific in vitro data from chem.physical_and_invitro.data tableget_invitroPK_param
Get literature Chemical Information.get_lit_cheminfo
Get literature Cssget_lit_css
Get Literature Oral Equivalent Doseget_lit_oral_equiv
Get physico-chemical parameters from chem.physical_and_invitro.data tableget_physchem_param
Get ratio of the blood concentration to the plasma concentration.get_rblood2plasma
Assign weight class (underweight, normal, overweight, obese)get_weight_class
Get literature Chemical Information. (deprecated).get_wetmore_cheminfo
Get literature Css (deprecated).get_wetmore_css
Get Literature Oral Equivalent Dose (deprecated).get_wetmore_oral_equiv
KDE bandwidths for residual variability in hematocrithct_h
Predict hematocrit in infants under 1 year old.hematocrit_infants
Return the assumptions used in Honda et al. 2019honda.ivive
Measured Caco-2 Apical-Basal Permeability Datahonda2023.data
Predicted Caco-2 Apical-Basal Permeabilitieshonda2023.qspr
Howgate 2006howgate
Historical Performance of R Package httkhttk.performance
httkpop: Virtual population generator for HTTK.httkpop
Convert HTTK-Pop-generated parameters to HTTK physiological parametershttkpop_biotophys_default
Generate a virtual population by directly resampling the NHANES data.httkpop_direct_resample
Inner loop function called by 'httkpop_direct_resample'.httkpop_direct_resample_inner
Generate a virtual population for PBTKhttkpop_generate
httk-pop: Correlated human physiological parameter Monte Carlohttkpop_mc
Generate a virtual population by the virtual individuals method.httkpop_virtual_indiv
KDE bandwidth for residual variability in height/weighthw_H
Convenience Boolean (yes/no) functions to identify chemical membership in several key lists.in.list is.expocast is.nhanes is.nhanes.blood.analyte is.nhanes.blood.parent is.nhanes.serum.analyte is.nhanes.serum.parent is.nhanes.urine.analyte is.nhanes.urine.parent is.pharma is.tox21 is.toxcast
Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability.invitro_mc
Checks whether a value, or all values in a vector, is within inclusive limitsis_in_inclusive
Convenience Boolean (yes/no) function to identify chemical membership and treatment within the httk project.is.httk
Johnson 2006johnson
Kapraun et al. 2019 dataKapraun2019 kapraun2019
Predict kidney mass for childrenkidney_mass_children
Predict liver mass for childrenliver_mass_children
Load CLint and Fup QSPR predictions from Dawson et al. 2021.load_dawson2021
Load Caco2 QSPR predictions from Honda et al. 2023load_honda2023
Load CLint and Fup QSPR predictions predictions from Pradeep et al. 2020.load_pradeep2020
Load CLint and Fup QSPR predictions from Sipes et al 2017.load_sipes2017
Lump tissue parameters into model compartmentslump_tissues
Predict lung mass for childrenlung_mass_children
Reference tissue masses and flows from tables in McNally et al. 2014.mcnally_dt
Pre-processed NHANES data.mecdt
Metabolism data involved in Linakis 2020 vignette analysis.metabolism_data_Linakis2020
Monte Carlo for toxicokinetic model parametersmonte_carlo
Published Pharmacokinetic Parameters from Obach et al. 2008Obach2008
NHANES Exposure Dataonlyp
Predict pancreas mass for childrenpancreas_mass_children
Parameters for a one compartment (empirical) toxicokinetic modelparameterize_1comp
Parameters for a three-compartment toxicokinetic model (dynamic)parameterize_3comp
Parameterize_fetal_PBTKparameterize_fetal_pbtk
Parameters for a generic gas inhalation physiologically-based toxicokinetic modelparameterize_gas_pbtk
Parameters for a generic physiologically-based toxicokinetic modelparameterize_pbtk
Parameters for Schmitt's (2008) Tissue Partition Coefficient Methodparameterize_schmitt
Parameters for a three-compartment toxicokinetic model at steady-stateparameterize_steadystate
Partition Coefficient Datapc.data
Pearce et al. 2017 dataPearce2017Regression pearce2017regression
DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Datapharma
Species-specific physiology parametersphysiology.data
Partition Coefficients from PK-Simpksim.pcs
Pradeep et al. 2020Pradeep2020 pradeep2020
Predict partition coefficients using the method from Schmitt (2008).predict_partitioning_schmitt
AUCs for Pregnant and Non-Pregnant Womenpregnonpregaucs
Propagates uncertainty and variability in in vitro HTTK data into one compartment model parameterspropagate_invitrouv_1comp
Propagates uncertainty and variability in in vitro HTTK data into three compartment model parameterspropagate_invitrouv_3comp
Propagates uncertainty and variability in in vitro HTTK data into PBPK model parameterspropagate_invitrouv_pbtk
Returns draws from a normal distribution with a lower censoring limit of lod (limit of detection)r_left_censored_norm
Reset HTTK to Default Data Tablesreset_httk
Randomly draws from a one-dimensional KDErfun
Draw random numbers with LOD median but non-zero upper 95th percentilermed0non0u95
Scale mg/kg body weight doses according to body weight and unitsscale_dosing
KDE bandwidths for residual variability in serum creatininescr_h
set_httk_precisionset_httk_precision
Sipes et al. 2017 dataSipes2017 sipes2017
Predict skeletal muscle massskeletal_muscle_mass
Predict skeletal muscle mass for childrenskeletal_muscle_mass_children
Predict skin massskin_mass_bosgra
Solve one compartment TK modelsolve_1comp
Solve_3compsolve_3comp
Solve_fetal_PBTKsolve_fetal_pbtk
solve_gas_pbtksolve_gas_pbtk
Solve_modelsolve_model
Solve_PBTKsolve_pbtk
Predict spleen mass for childrenspleen_mass_children
Supplementary output from Linakis 2020 vignette analysis.supptab1_Linakis2020
More supplementary output from Linakis 2020 vignette analysis.supptab2_Linakis2020
A timestamp of table creationTables.Rdata.stamp
Given a data.table describing a virtual population by the NHANES quantities, generates HTTK physiological parameters for each individual.tissue_masses_flows
Allometric scaling.tissue_scale
Tissue composition and species-specific physiology parameterstissue.data
in vitro Toxicokinetic Data from Wambaugh et al. (2019)wambaugh2019
NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019)wambaugh2019.nhanes
Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019)wambaugh2019.raw
ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rateswambaugh2019.seem3
Tox21 2015 Active Hit Calls (EPA)wambaugh2019.tox21
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.Wang2018 wang2018
Microtiter Plate Well Descriptions for Armitage et al. (2014) Modelwell_param
Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012.Wetmore2012
WHO weight-for-length chartswfl