Package: httk 2.4.0
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:
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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')) |
Bug tracker:https://github.com/usepa/comptox-expocast-httk/issues
- 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
Last updated 2 months agofrom:c6d7afdbe3. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 16 2024 |
R-4.5-win-x86_64 | OK | Nov 16 2024 |
R-4.5-linux-x86_64 | OK | Nov 16 2024 |
R-4.4-win-x86_64 | OK | Nov 16 2024 |
R-4.4-mac-x86_64 | OK | Nov 16 2024 |
R-4.4-mac-aarch64 | OK | Nov 16 2024 |
R-4.3-win-x86_64 | OK | Nov 16 2024 |
R-4.3-mac-x86_64 | OK | Nov 16 2024 |
R-4.3-mac-aarch64 | OK | Nov 16 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
Rendered fromV1_IntroToHTTK.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2024-04-09
Started: 2023-02-21
Introduction to In Vitro-In Vivo Extrapolation (IVIVE) with R Package httk
Rendered fromV2_IntrotoIVIVE.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2024-09-17
Started: 2023-02-21
Pearce et al. (2017): Updated v79i04.R Examples
Rendered fromVa_Pearce2017.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2024-09-17
Started: 2023-02-21
Ring et al. (2017) HTTK-Pop: Generating subpopulations
Rendered fromVb_Ring2017.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2024-04-09
Started: 2023-02-21
Frank et al. (2018): Neuronal Network IVIVE
Rendered fromVd_Frank2019.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11
Pearce et al. (2017): Evaluation of Tissue Partitioning
Rendered fromVc_Pearce2017.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11
Wambaugh et al. (2018): Evaluating In Vitro-In Vivo Extrapolation
Rendered fromVe_Wambaugh2018.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11
Honda et al. (2019): Updated Armitage et al. (2014) Model
Rendered fromVf_Honda2019.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11
Wambaugh et al. (2019): Uncertainty Monte Carlo
Rendered fromVg_Wambaugh2019.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11
Linakis et al. (2020): High Throughput Inhalation Model
Rendered fromVh_Linakis2020.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2024-09-17
Started: 2023-12-11
Kapraun et al. (2022): Generic Human Gestational Model
Rendered fromVi_Kapraun2022.Rmd
usingknitr::rmarkdown
on Nov 16 2024.Last update: 2023-12-11
Started: 2023-12-11