Package: httk 2.7.3
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:
httk_2.7.3.tar.gz
httk_2.7.3.zip(r-4.7)httk_2.7.3.zip(r-4.6)httk_2.7.3.zip(r-4.5)
httk_2.7.3.tgz(r-4.6-x86_64)httk_2.7.3.tgz(r-4.6-arm64)httk_2.7.3.tgz(r-4.5-x86_64)httk_2.7.3.tgz(r-4.5-arm64)
httk_2.7.3.tar.gz(r-4.6-arm64)httk_2.7.3.tar.gz(r-4.6-x86_64)
httk_2.7.3.tgz(r-4.5-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
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
- 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.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
- dawson2023 - Machine Learning PFAS Half-Life Predictions from Dawson et al. 2023
- Dimitrijevic.IVD - Dimitrijevic et al. (2022)In Vitro Cellular and Nominal Concentration
- EPA.ref - Reference for EPA Physico-Chemical 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
- Frank2018invivo - Literature In Vivo Data on Doses Causing Neurological Effects
- 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
- invitro.assay.params - ToxCast In Vitro Assay Descriptors
- 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.
- Obach2008 - Published Pharmacokinetic Parameters from Obach et al. 2008
- onlyp - NHANES Exposure Data
- pc.data - Partition Coefficient Data
- pearce2017regression - Pearce et al. 2017 data
- pfas.clearance - Interspecies In vivo Clearance Data for PFAS
- 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
- Scherer2025.IVD - Literature Measurements of In Vitro Cellular and Nominal Concentration
- 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.
- Tables.Rdata.stamp - A timestamp of table creation
- thyroid.ac50s - ToxCast thyroid-related bioactivity data
- tissue.data - Tissue composition and species-specific physiology parameters
- truong25.seem3 - SEEM3 Example Data for Truong et al. 2025
- 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
- Wetmore2012 - Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012.
- wfl - WHO weight-for-length charts
Last updated from:6453ef44e5. Checks:11 WARNING, 1 ERROR, 1 OK. Indexed: yes.
The latest version of this package failed to build. Look at thebuild logs for more information.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 353 | ||
| linux-devel-x86_64 | WARNING | 406 | ||
| source / vignettes | ERROR | 528 | ||
| linux-release-arm64 | WARNING | 327 | ||
| linux-release-x86_64 | WARNING | 380 | ||
| macos-release-arm64 | WARNING | 309 | ||
| macos-release-x86_64 | WARNING | 504 | ||
| macos-oldrel-arm64 | WARNING | 259 | ||
| macos-oldrel-x86_64 | WARNING | 456 | ||
| windows-devel | WARNING | 396 | ||
| windows-release | WARNING | 378 | ||
| windows-oldrel | WARNING | 353 | ||
| wasm-release | OK | 260 |
Exports:add_chemtableage_draw_smoothapply_clint_adjustmentapply_fup_adjustmentarmitage_estimate_sareaarmitage_evalavailable_rblood2plasmabenchmark_httkblood_mass_correctblood_weightbody_surface_areacalc_analytic_csscalc_analytic_css_3comp2calc_analytic_css_sumclearancescalc_clearance_fraccalc_csscalc_dermal_equivcalc_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_id_checkCAS.checksumckd_epi_eqconvert_solve_xconvert_unitscreate_mc_samplesdtxsid_id_checkestimate_gfrestimate_gfr_pedexport_pbtk_jarnacexport_pbtk_sbmlget_2023pfasinfoget_caco2get_chem_idget_cheminfoget_fbioget_gfr_categoryget_input_param_timeseriesget_invitroPK_paramget_lit_cheminfoget_lit_cssget_lit_oral_equivget_physchem_paramget_rblood2plasmaget_weight_classget_wetmore_cheminfoget_wetmore_cssget_wetmore_oral_equivhonda.ivivehttk_chem_subsethttkpop_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.toxcastkramer_evallist_modelsload_dawson2021load_honda2023load_honda2025load_pradeep2020load_sipes2017lump_tissuesmonte_carloparameterize_1compparameterize_1tri_pbtkparameterize_3compparameterize_3comp2parameterize_armitageparameterize_dermal_pbtkparameterize_fetal_pbtkparameterize_gas_pbtkparameterize_IVDparameterize_kramerparameterize_pbtkparameterize_pfas1compparameterize_schmittparameterize_steadystateparameterize_sumclearancesparameterize_sumclearancespfaspredict_partitioning_schmittr_left_censored_normreset_httkrfunrmed0non0u95solve_1compsolve_1comp_lifestagesolve_1tri_pbtksolve_3compsolve_3comp_lifestagesolve_3comp2solve_dermal_pbtksolve_fetal_pbtksolve_full_pregnancysolve_gas_pbtksolve_modelsolve_pbtksolve_pbtk_lifestage
Dependencies:clicpp11data.tableDBIdeSolvedplyrexpmfarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMatrixminqamitoolsmsmmvtnormnumDerivpillarpkgconfigpurrrR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrlangS7scalessurveysurvivaltibbletidyselecttruncnormutf8vctrsviridisLitewithr
