{ "_id": "69e0a56ac5259ad4cd91304b", "Package": "httk", "Version": "2.7.3", "Date": "2025-09-09", "Title": "High-Throughput Toxicokinetics", "Description": "Pre-made models that can be rapidly tailored to various\nchemicals and species using chemical-specific in vitro data and\nphysiological information. These tools allow incorporation of\nchemical toxicokinetics (\"TK\") and in vitro-in vivo\nextrapolation (\"IVIVE\") into bioinformatics, as described by\nPearce et al. (2017) ().\nChemical-specific in vitro data characterizing toxicokinetics\nhave been obtained from relatively high-throughput experiments.\nThe chemical-independent (\"generic\") physiologically-based\n(\"PBTK\") and empirical (for example, one compartment) \"TK\"\nmodels included here can be parameterized with in vitro data or\nin silico predictions which are provided for thousands of\nchemicals, multiple exposure routes, and various species. High\nthroughput toxicokinetics (\"HTTK\") is the combination of in\nvitro data and generic models. We establish the expected\naccuracy of HTTK for chemicals without in vivo data through\nstatistical evaluation of HTTK predictions for chemicals where\nin vivo data do exist. The models are systems of ordinary\ndifferential equations that are developed in MCSim and solved\nusing compiled (C-based) code for speed. A Monte Carlo sampler\nis included for simulating human biological variability (Ring\net al., 2017 ) and\npropagating parameter uncertainty (Wambaugh et al., 2019\n). Empirically calibrated methods\nare included for predicting tissue:plasma partition\ncoefficients and volume of distribution (Pearce et al., 2017\n). These functions and data\nprovide a set of tools for using IVIVE to convert\nconcentrations from high-throughput screening experiments (for\nexample, Tox21, ToxCast) to real-world exposures via reverse\ndosimetry (also known as \"RTK\") (Wetmore et al., 2015\n).", "Authors@R": "c(\nperson(\"John\", \"Wambaugh\", email = \"wambaugh.research@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4024-534X\")),\nperson(\"Sarah\", \"Davidson-Fritz\", email = \"davidsonfritz.sarah@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-2891-9380\")),\nperson(\"Robert\", \"Pearce\", role = c(\"aut\"),\ncomment = c(ORCID = \"0000-0003-3168-4049\")),\nperson(\"Caroline\", \"Ring\", email = \"Ring.Caroline@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-0463-1251\")),\nperson(\"Greg\", \"Honda\", email = \"honda.gregory@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0001-7713-9850\")),\nperson(\"Mark\", \"Sfeir\",\nrole = c(\"aut\")),\nperson(\"Matt\", \"Linakis\", email = \"MLINAKIS@ramboll.com\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0003-0526-2395\")),\nperson(\"Dustin\", \"Kapraun\", email = \"kapraun.dustin@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID= \"0000-0001-5570-6383\")),\nperson(\"Kimberly\", \"Truong\", email = \"truong.kimberly@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0003-1716-1006\")),\nperson(\"Colin\", \"Thomson\", email = \"thomson.colin@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0001-5303-6646\")),\nperson(\"Meredith\", \"Scherer\", email=\"Scherer.Meredith@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-9785-7363\")),\nperson(\"Annabel\", \"Meade\", email = \"aemeade7@gmail.com\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-5452-8797\")),\nperson(\"Celia\", \"Schacht\", email=\"Schacht.Celia@epa.gov\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-8131-513X\")),\nperson(\"Leonie\", \"Lautz\", email=\"leonie.lautz@wur.nl\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0003-4648-666X\")),\nperson(\"Todor\", \"Antonijevic\", email=\"tantonijevic@toxstrategies.com\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0002-0248-8412\")),\nperson(\"Miyuki\", \"Breen\", email = \"breen.miyuki@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID= \"0000-0001-8511-4653\")),\nperson(\"Shannon\", \"Bell\", email=\"Shannon.bell@inotivco.com\",\nrole = c(\"ctb\"), comment = c(ORCID= \"0000-0002-5754-6085\")),\nperson(\"Xiaoqing\", \"Chang\", email=\"Xiaoqing.chang@inotivco.com\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0003-0752-1848\")),\nperson(\"Jimena\", \"Davis\", role = c(\"ctb\")),\nperson(\"Elaina\", \"Kenyon\", email=\"kenyon.elaina@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0001-8781-2391\")),\nperson(\"Gilberto\", \"Padilla Mercado\", email=\"PadillaMercado.Gilberto@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0001-5423-1646\")),\nperson(\"Katie\", \"Paul Friedman\", email=\"Paul-Friedman.Katie@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0002-2710-1691\")),\nperson(\"Nathan\",\"Pollesch\",email = \"pollesch.nathan@epa.gov\",\nrole = c(\"ctb\"),comment = c(ORCID = \"0000-0001-8992-9294\")),\nperson(\"Noelle\", \"Sinski\", email = \"Noelle.Sinski@icf.com\",\nrole = c(\"ctb\")),\nperson(\"Nisha\", \"Sipes\", email = \"sipes.nisha@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0003-4203-6426\")),\nperson(\"James\", \"Sluka\", email = \"jsluka@indiana.edu\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0002-5901-1404\")),\nperson(\"Caroline\", \"Stevens\", email = \"stevens.caroline@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0001-7780-2691\")),\nperson(\"Barbara\", \"Wetmore\", email = \"wetmore.barbara@epa.gov\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0002-6878-5348\")),\nperson(\"Lily\", \"Whipple\",role = c(\"ctb\")),\nperson(\"Woodrow\", \"Setzer\",\nrole = c(\"ctb\"), comment = c(ORCID = \"0000-0002-6709-9186\"))\n)", "RdMacros": "Rdpack", "License": "MIT + file LICENSE", "LazyData": "true", "LazyDataCompression": "xz", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.2", "URL": "https://www.epa.gov/chemical-research/rapid-chemical-exposure-and-dose-research", "BugReports": "https://github.com/USEPA/CompTox-ExpoCast-httk/issues", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-04-16 08:46:48 UTC", "User": "root" }, "Author": "John Wambaugh [aut, cre]\n(), Sarah Davidson-Fritz\n[aut] (), Robert Pearce\n[aut] (), Caroline Ring\n[aut] (), Greg Honda\n[aut] (), Mark Sfeir\n[aut], Matt Linakis [aut]\n(), Dustin Kapraun [aut]\n(), Kimberly Truong\n[aut] (), Colin Thomson\n[aut] (), Meredith\nScherer [aut] (),\nAnnabel Meade [aut] (),\nCelia Schacht [aut] (),\nLeonie Lautz [aut] (),\nTodor Antonijevic [ctb]\n(), Miyuki Breen [ctb]\n(), Shannon Bell [ctb]\n(), Xiaoqing Chang [ctb]\n(), Jimena Davis [ctb],\nElaina Kenyon [ctb] (),\nGilberto Padilla Mercado [ctb]\n(), Katie Paul Friedman\n[ctb] (), Nathan\nPollesch [ctb] (),\nNoelle Sinski [ctb], Nisha Sipes [ctb]\n(), James Sluka [ctb]\n(), Caroline Stevens\n[ctb] (), Barbara\nWetmore [ctb] (), Lily\nWhipple [ctb], Woodrow Setzer [ctb]\n()", "Maintainer": "John Wambaugh ", "Copyright": "This package is primarily developed by employees of the U.S.\nFederal government as part of their official duties and is\ntherefore public domain.", "Config/pak/sysreqs": "make", "Repository": "https://usepa.r-universe.dev", "Date/Publication": "2025-09-15 18:42:11 UTC", "RemoteUrl": "https://github.com/usepa/comptox-expocast-httk", "RemoteRef": "HEAD", "RemoteSha": "6453ef44e51679e61a877691aaf028275de2665b", "RemoteSubdir": "httk", "MD5sum": "f9b9fba9e0b49b87ce258e48495f07a8", "_user": "usepa", "_type": "src", "_file": "httk_2.7.3.tar.gz", "_fileid": 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(2022)In Vitro Cellular and Nominal Concentration", "object": "Scherer2025Vignette", "class": [ "data.table", "data.frame" ], "fields": [ "Name", "casrn", "Type", "Arnot_pka", "Arnot_pkb", "log KOW,N", "log KAW,N", "Reported_Ccell_µM", "Predicted_Ccell_µM", "FoA", "FBSf", "v_working", "cell_yield", "sarea", "nomconc", "well_number", "v_total", "Notes" ], "rows": 12, "table": true, "tojson": true }, { "name": "EPA.ref", "title": "Reference for EPA Physico-Chemical Data", "object": "Tables", "class": [ "character" ], "fields": [], "table": false, "tojson": true }, { "name": "example.seem", "title": "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", "object": "introtoivive-seem", "class": [ "data.frame" ], "fields": [ "dsstox_substance_id", "CAS", "Substance_Name", "Pred.SHEDS.Direct", "Pred.SHEDS.Indirect", "Pred.FINE", "Pred.Food.Contact", "Pred.REDS", "Pred.RAIDAR", "Pred.RAIDAR.ICE", "Pred.USETox.Pest", "Pred.USETox.Indust", "Pred.USETox.Res", "Pred.USETox.Diet", "Pred.Production.Volume", "Pred.Stockholm", "delta.Diet.pred", "delta.Res.pred", "delta.Pest.pred", "delta.Indust.pred", "Pred.SHEDS.Direct.log.scale", "Pred.SHEDS.Indirect.log.scale", "Pred.FINE.log.scale", "Pred.Food.Contact.log.scale", "Pred.REDS.log.scale", "Pred.RAIDAR.log.scale", "Pred.RAIDAR.ICE.log.scale", "Pred.USETox.Pest.log.scale", "Pred.USETox.Indust.log.scale", "Pred.USETox.Res.log.scale", "Pred.USETox.Diet.log.scale", "Pred.Production.Volume.log.scale", "Pred.Stockholm.log.scale", "seem3", "seem3.l95", "seem3.u95", "Rank", "Pathway", "AD" ], "rows": 43, "table": true, "tojson": true }, { "name": "example.toxcast", "title": "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", "object": "introtoivive-toxcast", "class": [ "data.frame" ], "fields": [ "chnm", "dsstox_substance_id", "spid", "hitc", "modl", "aeid", "modl_ga", "modl_ac10", "modl_acc" ], "rows": 38531, "table": true, "tojson": true }, { "name": "fetalpcs", "title": "Fetal Partition Coefficients", "object": "Kapraun2022Vignette", "class": [ "data.frame" ], "fields": [ "Compound", "DTXSID", "Tissue", "PC", "Fup", "Rb2p", "HTTK.pred.t2up", "HTTK.pred.nocal.t2up", "HTTK.pred", "HTTK.pred.nocal", "Weijs2013" ], "rows": 87, "table": true, "tojson": true }, { "name": "Frank2018invivo", "title": "Literature In Vivo Data on Doses Causing Neurological Effects", "object": "Frank2018", "class": [ "data.frame" ], "fields": [ "Substance_CASRN", "Chemical", "DSSTox_Substance_Id", "Species", "Exposure", "Dose.Route", "Endpoint", "Reference", "Dose", "Dose.Units", "Route", "Days", 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"Female.subjects", "Age.min", "Age.max", "Median.Clearance..1.h.", "X90..CI.min", "X90..CI.max" ], "rows": 24, "table": true, "tojson": true }, { "name": "httk.performance", "title": "Historical Performance of R Package httk", "object": "Tables", "class": [ "data.frame" ], "fields": [ "Version", "N.steadystate", "calc_analytic.units", "calc_mc.units", "solve_pbtk.units", "RMSLE.Wetmore", "N.Wetmore", "RMSLE.noMC", "N.noMC", "RMSLE.InVivoCss", "N.InVivoCss", "RMSLE.InVivoAUC", "N.InVivoAUC", "RMSLE.InVivoCmax", "N.InVivoCmax", "RMSLE.TissuePC", "N.TissuePC", "Notes" ], "rows": 30, "table": true, "tojson": true }, { "name": "hw_H", "title": "KDE bandwidth for residual variability in height/weight", "object": "httkpop", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "invitro.assay.params", "title": "ToxCast In Vitro Assay Descriptors", "object": "invitro.assay.params", "class": [ "data.table", "data.frame" ], "fields": [ "assay_name", "assay_component_endpoint_name", "aid", "acid", "aeid", "well_number", "cell_growth_mode", "cell_type", "timepoint_hr", "cell_yield", "media_base", "v_total", "v_working", "media_serum", "plate_material", "radius", "diam", "height", "area_bottom", "sarea", "cell_yield_orig", "cell_size_weighted", "FBSf" ], "rows": 1649, "table": true, "tojson": true }, { "name": "johnson", "title": "Johnson 2006", "object": "vignettes", "class": [ "data.table", "data.frame" ], "fields": [ "Compound", "Route", "Total.subjects", "Age.min", "Age.max", "BW.mean", "BW.sd", "Clearance.mean", "Clearance.sd", "Clearance.units", "Race" ], "rows": 60, "table": true, "tojson": true }, { "name": "kapraun2019", "title": "Kapraun et al. 2019 data", "object": "Tables", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "mcnally_dt", "title": "Reference tissue masses and flows from tables in McNally et al. 2014.", "object": "httkpop", "class": [ "data.table", "data.frame" ], "fields": [ 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"CAL_OSHA_PEL", "NIOSH_REL", "ACGIH_2015_TLV", "max.value", "VMAX", "VMAX_U", "KM", "KM_U", "SOURCE_MET", "SPECIES", "CALC_FUP", "EXP_FUP", "HENRYS_LAW_DIMENSIONLESS", "LOG_HENRYS_LAW_DIMENSIONLESS", "CALC_PBA", "EXP_PBA", "CALC_CLINT", "EXP_CLINT", "WTKG", "GLKG", "CHEM_CLASS" ], "rows": 83, "table": true, "tojson": true }, { "name": "Obach2008", "title": "Published Pharmacokinetic Parameters from Obach et al. 2008", "object": "vignettes", "class": [ "data.frame" ], "fields": [ "Name", "CAS", "VDss (L/kg)", "CL (mL/min/kg)", "fu", "MRT (h)", "t1/2 (h)" ], "rows": 670, "table": true, "tojson": true }, { "name": "onlyp", "title": "NHANES Exposure Data", "object": "vignettes", "class": [ "data.table", "data.frame" ], "fields": [ "exposure.median", "exposure.median.95CI.lower", "exposure.median.95CI.upper", "chemcas", "ExpoCast.group" ], "rows": 1060, "table": true, "tojson": true }, { "name": "pc.data", "title": "Partition Coefficient Data", "object": "vignettes", "class": [ "data.frame" ], "fields": [ "CAS", "Drug", "Tissue", "Species", "fu", "A.B.N", "LogP", "Exp_PC" ], "rows": 1221, "table": true, "tojson": true }, { "name": "pearce2017regression", "title": "Pearce et al. 2017 data", "object": "Tables", "class": [ "matrix", "array" ], "fields": [ "adj.fup.intercept", "adj.fup.slope", "fup.intercept", "fup.slope" ], "rows": 12, "table": true, "tojson": true }, { "name": "pfas.clearance", "title": "Interspecies In vivo Clearance Data for PFAS", "object": "Tables", "class": [ "data.frame" ], "fields": [ "DTXSID", "Species", "Sex", "HalfLifeHours", "HlReference", "VdLpkgbw", "VdReference", "Kelimph", "ClLphpkgbw" ], "rows": 74, "table": true, "tojson": true }, { "name": "pharma", "title": "DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data", "object": "wambaugh2019", "class": [ "matrix", "array" ], "fields": {}, "rows": 14, "table": true, "tojson": true }, { "name": "physiology.data", "title": "Species-specific physiology parameters", "object": "Tables", "class": [ "data.frame" ], "fields": [ "Parameter", "Units", "Mouse", "Rat", "Dog", "Human", "Rabbit", "Monkey" ], "rows": 18, "table": true, "tojson": true }, { "name": "pksim.pcs", "title": "Partition Coefficients from PK-Sim", "object": "Kapraun2022Vignette", "class": [ "data.frame" ], "fields": [ "Drug", "DTXSID", "Tissue", "PKSim.pred", "Blood-to-plasma concentration ratio", "Tissue-to-blood partition coefficient", "Method", "HTTK.pred" ], "rows": 72, "table": true, "tojson": true }, { "name": "pradeep2020", "title": "Pradeep et al. 2020", "object": "Tables", "class": [ "data.frame" ], "fields": [ "DTXSID", "CASRN", "pred_clint_rf", "Consensus (SVM,RF)" ], "rows": 8573, "table": true, "tojson": true }, { "name": "pregnonpregaucs", "title": "AUCs for Pregnant and Non-Pregnant Women", "object": "Kapraun2022Vignette", "class": [ "data.frame" ], "fields": [ "Drug", "DTXSID", "PREFERRED_NAME", "CASRN", "AVERAGE_MASS", "Dose", "Dose Units", "Dosing frequency", "Dose.Route", 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"ExperimentalDose_uM", "CellConcentration_uM", "MediaConcentration_uM", "FigureNumber", "Notes", "radius", "height", "sarea", "Vcell_uL", "casrn", "nomconc", "FBSf", "v_working", "cell_yield" ], "rows": 153, "table": true, "tojson": true }, { "name": "scr_h", "title": "KDE bandwidths for residual variability in serum creatinine", "object": "httkpop", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "sipes2017", "title": "Sipes et al. 2017 data", "object": "Tables", "class": [ "data.frame" ], "fields": [ "CAS", "Human.Funbound.plasma", "Human.Clint" ], "rows": 8719, "table": true, "tojson": true }, { "name": "supptab1_Linakis2020", "title": "Supplementary output from Linakis 2020 vignette analysis.", "object": "Linakis2020", "class": [ "data.frame" ], "fields": [ "Chemical", "DTXSID", "CASRN", "CAMEO.Chemical.Class", "Molecular.Weight..g.mol.", "Log.P", "Solubility..mol.L.", "Blood.Air.Partition.Coefficient", "Available.Species.Data", "Vmax..pmol.min.10.6.cells.", "KM..uM.", "Metabolism.Data.Source", "Concentration.Time.Data.Source" ], "rows": 50, "table": true, "tojson": true }, { "name": "supptab2_Linakis2020", "title": "More supplementary output from Linakis 2020 vignette analysis.", "object": "Linakis2020", "class": [ "data.frame" ], "fields": [ "Chemical.Dropped", "Regression.Slope", "Regression.R.2", "Regression.RMSE", "Orthogonal.RMSE", "Percent.Data.Censored", "Cmax.Regression.Slope", "Cmax.Regression.R.2", "Cmax.Orthogonal.RMSE", "AUC.Regression.Slope", "AUC.Regression.R.2", "AUC.Orthogonal.RMSE" ], "rows": 42, "table": true, "tojson": true }, { "name": "Tables.Rdata.stamp", "title": "A timestamp of table creation", "object": "Tables", "class": [ "character" ], "fields": [], "table": false, "tojson": true }, { "name": "thyroid.ac50s", "title": "ToxCast thyroid-related bioactivity data", "object": "Truong2025Vignette", "class": [ "data.table", "data.frame" ], "fields": [ "dsstox_substance_id", "chnm", "DIO1", "DIO2", "DIO3", "IYD", "min_ac50" ], "rows": 120, "table": true, "tojson": true }, { "name": "tissue.data", "title": "Tissue composition and species-specific physiology parameters", "object": "Tables", "class": [ "data.frame" ], "fields": [ "Tissue", "Species", "Reference", "variable", "value" ], "rows": 406, "table": true, "tojson": true }, { "name": "truong25.seem3", "title": "SEEM3 Example Data for Truong et al. 2025", "object": "Truong2025Vignette", "class": [ "data.table", "data.frame" ], "fields": [ "dsstox_substance_id", "CAS", "Substance_Name", "Pred.SHEDS.Direct", "Pred.SHEDS.Indirect", "Pred.FINE", "Pred.Food.Contact", "Pred.REDS", "Pred.RAIDAR", "Pred.RAIDAR.ICE", "Pred.USETox.Pest", "Pred.USETox.Indust", "Pred.USETox.Res", "Pred.USETox.Diet", "Pred.Production.Volume", "Pred.Stockholm", "delta.Diet.pred", "delta.Res.pred", "delta.Pest.pred", "delta.Indust.pred", "Pred.SHEDS.Direct.log.scale", "Pred.SHEDS.Indirect.log.scale", "Pred.FINE.log.scale", "Pred.Food.Contact.log.scale", "Pred.REDS.log.scale", "Pred.RAIDAR.log.scale", "Pred.RAIDAR.ICE.log.scale", "Pred.USETox.Pest.log.scale", "Pred.USETox.Indust.log.scale", "Pred.USETox.Res.log.scale", "Pred.USETox.Diet.log.scale", "Pred.Production.Volume.log.scale", "Pred.Stockholm.log.scale", "seem3", "seem3.l95", "seem3.u95" ], "rows": 103, "table": true, "tojson": true }, { "name": "wambaugh2019", "title": "in vitro Toxicokinetic Data from Wambaugh et al. (2019)", "object": "wambaugh2019", "class": [ "data.frame" ], "fields": [ "Compound", "CAS", "Human.Clint", "Human.Clint.pValue", "Human.Funbound.plasma", "pKa_Accept", "pKa_Donor", "DSSTox_Substance_Id", "SMILES", "Human.Clint.Low95", "Human.Clint.High95", "Human.Clint.Point", "Human.Funbound.plasma.Low95", "Human.Funbound.plasma.High95", "Human.Funbound.plasma.Point", "MW", "logP" ], "rows": 496, "table": true, "tojson": true }, { "name": "wambaugh2019.nhanes", "title": "NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019)", "object": "wambaugh2019", "class": [ "data.frame" ], "fields": [ "lP", "lP.min", "lP.max", "CASRN" ], "rows": 20, "table": true, "tojson": true }, { "name": "wambaugh2019.raw", "title": "Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019)", "object": "wambaugh2019", "class": [ "data.frame" ], "fields": [ "DTXSID", "Name", "CAS", "CompoundName", "Fup.point", "Base.Fup.Med", "Base.Fup.Low", "Base.Fup.High", "Affinity.Fup.Med", "Affinity.Fup.Low", "Affinity.Fup.High", "Affinity.Kd.Med", "Affinity.Kd.Low", "Affinity.Kd.High", "Decreases.Prob", "Saturates.Prob", "Slope.1uM.Median", "Slope.10uM.Median", "CLint.1uM.Median", "CLint.1uM.Low95th", "CLint.1uM.High95th", "CLint.10uM.Median", "CLint.10uM.Low95th", "CLint.10uM.High95th", "CLint.1uM.Point", "CLint.10uM.Point", "Fit", "SMILES" ], "rows": 530, "table": true, "tojson": true }, { "name": "wambaugh2019.seem3", "title": "ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates", "object": "wambaugh2019", "class": [ "data.table", "data.frame" ], "fields": [ "dsstox_substance_id", "CAS", "Substance_Name", "Pred.SHEDS.Direct", "Pred.SHEDS.Indirect", "Pred.FINE", "Pred.Food.Contact", "Pred.REDS", "Pred.RAIDAR", "Pred.RAIDAR.ICE", "Pred.USETox.Pest", "Pred.USETox.Indust", "Pred.USETox.Res", "Pred.USETox.Diet", "Pred.Production.Volume", "Pred.Stockholm", "delta.Diet.pred", "delta.Res.pred", "delta.Pest.pred", "delta.Indust.pred", "Pred.SHEDS.Direct.log.scale", "Pred.SHEDS.Indirect.log.scale", "Pred.FINE.log.scale", "Pred.Food.Contact.log.scale", "Pred.REDS.log.scale", "Pred.RAIDAR.log.scale", "Pred.RAIDAR.ICE.log.scale", "Pred.USETox.Pest.log.scale", "Pred.USETox.Indust.log.scale", "Pred.USETox.Res.log.scale", "Pred.USETox.Diet.log.scale", "Pred.Production.Volume.log.scale", "Pred.Stockholm.log.scale", "seem3", "seem3.l95", "seem3.u95", "Rank", "Pathway" ], "rows": 385, "table": true, "tojson": true }, { "name": "wambaugh2019.tox21", "title": "Tox21 2015 Active Hit Calls (EPA)", "object": "wambaugh2019", "class": [ "data.frame" ], "fields": [ "casn", "med.conc", "q10.conc", "low.conc", "q90.conc", "high.conc" ], "rows": 401, "table": true, "tojson": true }, { "name": "wang2018", "title": "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.", "object": "Kapraun2022Vignette", "class": [ "matrix", "array" ], "fields": {}, "rows": 20, "table": true, "tojson": true }, { "name": "well_param", "title": "Microtiter Plate Well Descriptions for Armitage et al. (2014) Model", "object": "well_param", "class": [ "data.table", "data.frame" ], "fields": [ "well_number", "well_desc", "sysID", "diam", "area_bottom", "v_total", "v_working", "cell_yield" ], "rows": 11, "table": true, "tojson": true }, { "name": "Wetmore2012", "title": "Published toxicokinetic predictions based on in vitro data from Wetmore et al. 2012.", "object": "vignettes", "class": [ "data.frame" ], "fields": [ "Chemical", "CAS", "Literature.Css", "Lit.Css.Low", "Lit.Css.High", "Notes", "Fub..Clint", "Notes.1", "Fub..Clint..Caco.2", "Notes.2", "Fub..Clint2", "Notes.3", "Fub..Clint..Caco.23", "Notes.4", "X" ], "rows": 13, "table": true, "tojson": true }, { "name": "wfl", "title": "WHO weight-for-length charts", "object": "httkpop", "class": [ "data.table", "data.frame" ], "fields": [ "Sex", "Length", "P2.3", "P97.7" ], "rows": 262, "table": true, "tojson": true } ], "_help": [ { "page": "add_chemtable", "title": "Add a table of chemical information for use in making httk predictions.", "topics": [ "add_chemtable" ] }, { "page": "age_draw_smooth", "title": "Draws ages from a smoothed distribution for a given gender/race combination", "topics": [ "age_draw_smooth" ] }, { "page": "apply_clint_adjustment", "title": "Correct the measured intrinsive hepatic clearance for fraction free", "topics": [ "apply_clint_adjustment" ] }, { "page": "apply_fup_adjustment", "title": "Correct the measured fraction unbound in plasma for lipid binding", "topics": [ "apply_fup_adjustment" ] }, { "page": "armitage_estimate_sarea", "title": "Estimate well surface area", "topics": [ "armitage_estimate_sarea" ] }, { "page": "armitage_eval", "title": "Armitage In Vitro Distribution Model", "topics": [ "armitage_eval" ] }, { "page": "augment.table", "title": "Add a parameter value to the chem.physical_and_invitro.data table", "topics": [ "augment.table" ] }, { "page": "available_rblood2plasma", "title": "Find the best available ratio of the blood to plasma concentration constant.", "topics": [ "available_rblood2plasma" ] }, { "page": "aylward2014", "title": "Aylward et al. 2014", "topics": [ "Aylward2014", "aylward2014" ] }, { "page": "benchmark_httk", "title": "Assess the current performance of httk relative to historical benchmarks", "topics": [ "benchmark_httk" ] }, { "page": "blood_mass_correct", "title": "Find average blood masses by age.", "topics": [ "blood_mass_correct" ] }, { "page": "blood_weight", "title": "Predict blood mass.", "topics": [ "blood_weight" ] }, { "page": "bmiage", "title": "CDC BMI-for-age charts", "topics": [ "bmiage" ] }, { "page": "body_surface_area", "title": "Predict body surface area.", "topics": [ "body_surface_area" ] }, { "page": "bone_mass_age", "title": "Predict bone mass", "topics": [ "bone_mass_age" ] }, { "page": "brain_mass", "title": "Predict brain mass.", "topics": [ "brain_mass" ] }, { "page": "calc_analytic_css", "title": "Calculate the analytic steady state plasma concentration.", "topics": [ "calc_analytic_css" ] }, { "page": "calc_analytic_css_1comp", "title": "Calculate the analytic steady state concentration for the one compartment model.", "topics": [ "calc_analytic_css_1comp" ] }, { "page": "calc_analytic_css_3comp", "title": "Calculate the analytic steady state concentration for model 3compartment", "topics": [ "calc_analytic_css_3comp" ] }, { "page": "calc_analytic_css_3comp2", "title": "Calculate the analytic steady state concentration for model 3compartment", "topics": [ "calc_analytic_css_3comp2" ] }, { "page": "calc_analytic_css_3compss", "title": "Calculate the analytic steady state concentration for the three compartment steady-state model", "topics": [ "calc_analytic_css_3compss" ] }, { "page": "calc_analytic_css_pbtk", "title": "Calculate the analytic steady state plasma concentration for model pbtk.", "topics": [ "calc_analytic_css_pbtk" ] }, { "page": "calc_analytic_css_sumclearances", "title": "Calculate the steady state concentration for the sum of clearances steady-state model with exhalation", "topics": [ "calc_analytic_css_sumclearances" ] }, { "page": "calc_clearance_frac", "title": "Calculate the fractional contributions to total clearance", "topics": [ "calc_clearance_frac" ] }, { "page": "calc_css", "title": "Find the steady state concentration and the day it is reached.", "topics": [ "calc_css" ] }, { "page": "calc_dermal_equiv", "title": "Calculate Dermal Equivalent Dose", "topics": [ "calc_dermal_equiv" ] }, { "page": "calc_dow", "title": "Calculate the distribution coefficient", "topics": [ "calc_dow" ] }, { "page": "calc_elimination_rate", "title": "Calculate the elimination rate for a one compartment model", "topics": [ "calc_elimination_rate" ] }, { "page": "calc_fbio.oral", "title": "Functions for calculating the bioavaialble fractions from oral doses", "topics": [ "calc_fabs.oral", "calc_fbio.oral", "calc_fgut.oral", "calc_kgutabs", "calc_peff" ] }, { "page": "calc_fetal_phys", "title": "Calculate maternal-fetal physiological parameters", "topics": [ "calc_fetal_phys" ] }, { "page": "calc_fup_correction", "title": "Calculate the correction for lipid binding in plasma binding assay", "topics": [ "calc_fup_correction" ] }, { "page": "calc_half_life", "title": "Calculates the half-life for a one compartment model.", "topics": [ "calc_half_life" ] }, { "page": "calc_hep_bioavailability", "title": "Calculate first pass heaptic metabolism", "topics": [ "calc_hep_bioavailability" ] }, { "page": "calc_hep_clearance", "title": "Calculate the hepatic clearance.", "topics": [ "calc_hep_clearance" ] }, { "page": "calc_hep_fu", "title": "Calculate the free chemical in the hepaitic clearance assay", "topics": [ "calc_hep_fu" ] }, { "page": "calc_hepatic_clearance", "title": "Calculate the hepatic clearance (deprecated).", "topics": [ "calc_hepatic_clearance" ] }, { "page": "calc_ionization", "title": "Calculate the ionization.", "topics": [ "calc_ionization" ] }, { "page": "calc_kair", "title": "Calculate air:matrix partition coefficients", "topics": [ "calc_kair" ] }, { "page": "calc_krbc2pu", "title": "Back-calculates the Red Blood Cell to Unbound Plasma Partition Coefficient", "topics": [ "calc_krbc2pu" ] }, { "page": "calc_ma", "title": "Calculate the membrane affinity", "topics": [ "calc_ma" ] }, { "page": "calc_maternal_bw", "title": "Calculate maternal body weight", "topics": [ "calc_maternal_bw" ] }, { "page": "calc_mc_css", "title": "Distribution of chemical steady state concentration with uncertainty and variability", "topics": [ "calc_mc_css" ] }, { "page": "calc_mc_oral_equiv", "title": "Calculate Monte Carlo Oral Equivalent Dose", "topics": [ "calc_mc_oral_equiv" ] }, { "page": "calc_mc_tk", "title": "Conduct multiple TK simulations using Monte Carlo", "topics": [ "calc_mc_tk" ] }, { "page": "calc_rblood2plasma", "title": "Calculate the constant ratio of the blood concentration to the plasma concentration.", "topics": [ "calc_rblood2plasma" ] }, { "page": "calc_stats", "title": "Calculate toxicokinetic summary statistics (deprecated).", "topics": [ "calc_stats" ] }, { "page": "calc_tkstats", "title": "Calculate toxicokinetic summary statistics.", "topics": [ "calc_tkstats" ] }, { "page": "calc_total_clearance", "title": "Calculate the total plasma clearance.", "topics": [ "calc_total_clearance" ] }, { "page": "calc_vdist", "title": "Calculate the volume of distribution for a one compartment model.", "topics": [ "calc_vdist" ] }, { "page": "cas_id_check", "title": "CAS number format check function", "topics": [ "cas_id_check" ] }, { "page": "CAS.checksum", "title": "Test the check digit of a CAS number to confirm validity", "topics": [ "CAS.checksum" ] }, { "page": "check_model", "title": "Check for sufficient model parameters", "topics": [ "check_model" ] }, { "page": "chem.invivo.PK.aggregate.data", "title": "Parameter Estimates from Wambaugh et al. (2018)", "topics": [ "chem.invivo.PK.aggregate.data" ] }, { "page": "chem.invivo.PK.summary.data", "title": "Summary of published toxicokinetic time course experiments", "topics": [ "chem.invivo.PK.summary.data" ] }, { "page": "chem.physical_and_invitro.data", "title": "Physico-chemical properties and in vitro measurements for toxicokinetics", "topics": [ "chem.physical_and_invitro.data" ] }, { "page": "ckd_epi_eq", "title": "CKD-EPI equation for GFR.", "topics": [ "ckd_epi_eq" ] }, { "page": "concentration_data_Linakis2020", "title": "Concentration data involved in Linakis 2020 vignette analysis.", "topics": [ "concentration_data_Linakis2020" ] }, { "page": "convert_solve_x", "title": "convert_solve_x", "topics": [ "convert_solve_x" ] }, { "page": "convert_units", "title": "convert_units", "topics": [ "convert_units" ] }, { "page": "create_mc_samples", "title": "Create a table of parameter values for Monte Carlo", "topics": [ "create_mc_samples" ] }, { "page": "dawson2021", "title": "Dawson et al. 2021 data", "topics": [ "Dawson2021", "dawson2021" ] }, { "page": "dawson2023", "title": "Machine Learning PFAS Half-Life Predictions from Dawson et al. 2023", "topics": [ "dawson2023" ] }, { "page": "Dimitrijevic.IVD", "title": "Dimitrijevic et al. (2022)In Vitro Cellular and Nominal Concentration", "topics": [ "Dimitrijevic.IVD" ] }, { "page": "dtxsid_id_check", "title": "DTXSID number format check function", "topics": [ "dtxsid_id_check" ] }, { "page": "EPA.ref", "title": "Reference for EPA Physico-Chemical Data", "topics": [ "EPA.ref" ] }, { "page": "estimate_gfr", "title": "Predict GFR.", "topics": [ "estimate_gfr" ] }, { "page": "estimate_gfr_ped", "title": "Predict GFR in children.", "topics": [ "estimate_gfr_ped" ] }, { "page": "estimate_hematocrit", "title": "Generate hematocrit values for a virtual population", "topics": [ "estimate_hematocrit" ] }, { "page": "example.seem", "title": "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", "topics": [ "example.seem" ] }, { "page": "example.toxcast", "title": "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", "topics": [ "example.toxcast" ] }, { "page": "export_pbtk_jarnac", "title": "Export model to jarnac.", "topics": [ "export_pbtk_jarnac" ] }, { "page": "export_pbtk_sbml", "title": "Export model to sbml.", "topics": [ "export_pbtk_sbml" ] }, { "page": "fetalpcs", "title": "Fetal Partition Coefficients", "topics": [ "fetalPCs", "fetalpcs" ] }, { "page": "Frank2018invivo", "title": "Literature In Vivo Data on Doses Causing Neurological Effects", "topics": [ "Frank2018invivo" ] }, { "page": "gen_age_height_weight", "title": "Generate demographic parameters for a virtual population", "topics": [ "gen_age_height_weight" ] }, { "page": "gen_height_weight", "title": "Generate heights and weights for a virtual population.", "topics": [ "gen_height_weight" ] }, { "page": "gen_serum_creatinine", "title": "Generate serum creatinine values for a virtual population.", "topics": [ "gen_serum_creatinine" ] }, { "page": "get_2023pfasinfo", "title": "Retrieve chemical information on 2023 EPA PFAS Chemicals", "topics": [ "get_2023pfasinfo" ] }, { "page": "get_caco2", "title": "Retrieve in vitro measured Caco-2 membrane permeabilit", "topics": [ "get_caco2" ] }, { "page": "get_chem_id", "title": "Retrieve chemical identity from HTTK package", "topics": [ "get_chem_id" ] }, { "page": "get_cheminfo", "title": "Retrieve chemical information available from HTTK package", "topics": [ "get_cheminfo" ] }, { "page": "get_clint", "title": "Retrieve and parse intrinsic hepatic clearance", "topics": [ "get_clint" ] }, { "page": "get_fbio", "title": "Retrieve or calculate fraction of chemical absorbed from the gut", "topics": [ "get_fbio" ] }, { "page": "get_fup", "title": "Retrieve and parse fraction unbound in plasma", "topics": [ "get_fup" ] }, { "page": "get_gfr_category", "title": "Categorize kidney function by GFR.", "topics": [ "get_gfr_category" ] }, { "page": "get_input_param_timeseries", "title": "Get timeseries containing the change of each of the input parameters.", "topics": [ "get_input_param_timeseries" ] }, { "page": "get_invitroPK_param", "title": "Retrieve species-specific in vitro data from chem.physical_and_invitro.data table", "topics": [ "get_invitroPK_param" ] }, { "page": "get_lit_cheminfo", "title": "Get literature Chemical Information.", "topics": [ "get_lit_cheminfo" ] }, { "page": "get_lit_css", "title": "Get literature Css", "topics": [ "get_lit_css" ] }, { "page": "get_lit_oral_equiv", "title": "Get Literature Oral Equivalent Dose", "topics": [ "get_lit_oral_equiv" ] }, { "page": "get_physchem_param", "title": "Get physico-chemical parameters from chem.physical_and_invitro.data table", "topics": [ "get_physchem_param" ] }, { "page": "get_rblood2plasma", "title": "Get ratio of the blood concentration to the plasma concentration.", "topics": [ "get_rblood2plasma" ] }, { "page": "get_weight_class", "title": "Assign weight class (underweight, normal, overweight, obese)", "topics": [ "get_weight_class" ] }, { "page": "get_wetmore_cheminfo", "title": "Get literature Chemical Information. (deprecated).", "topics": [ "get_wetmore_cheminfo" ] }, { "page": "get_wetmore_css", "title": "Get literature Css (deprecated).", "topics": [ "get_wetmore_css" ] }, { "page": "get_wetmore_oral_equiv", "title": "Get Literature Oral Equivalent Dose (deprecated).", "topics": [ "get_wetmore_oral_equiv" ] }, { "page": "hct_h", "title": "KDE bandwidths for residual variability in hematocrit", "topics": [ "hct_h" ] }, { "page": "hematocrit_infants", "title": "Predict hematocrit in infants under 1 year old.", "topics": [ "hematocrit_infants" ] }, { "page": "honda.ivive", "title": "Return the assumptions used in Honda et al. 2019", "topics": [ "honda.ivive" ] }, { "page": "honda2023.data", "title": "Measured Caco-2 Apical-Basal Permeability Data", "topics": [ "honda2023.data" ] }, { "page": "honda2023.qspr", "title": "Predicted Caco-2 Apical-Basal Permeabilities", "topics": [ "honda2023.qspr" ] }, { "page": "howgate", "title": "Howgate 2006", "topics": [ "howgate" ] }, { "page": "httk_chem_subset", "title": "HTTK data chemical subsetting function", "topics": [ "httk_chem_subset" ] }, { "page": "httk.performance", "title": "Historical Performance of R Package httk", "topics": [ "httk.performance" ] }, { "page": "httkpop", "title": "httkpop: Virtual population generator for HTTK.", "topics": [ "httkpop" ] }, { "page": "httkpop_biotophys_default", "title": "Convert HTTK-Pop-generated parameters to HTTK physiological parameters", "topics": [ "httkpop_biotophys_default" ] }, { "page": "httkpop_direct_resample", "title": "Generate a virtual population by directly resampling the NHANES data.", "topics": [ "httkpop_direct_resample" ] }, { "page": "httkpop_direct_resample_inner", "title": "Inner loop function called by 'httkpop_direct_resample'.", "topics": [ "httkpop_direct_resample_inner" ] }, { "page": "httkpop_generate", "title": "Generate a virtual population for PBTK", "topics": [ "httkpop_generate" ] }, { "page": "httkpop_mc", "title": "httk-pop: Correlated human physiological parameter Monte Carlo", "topics": [ "httkpop_mc" ] }, { "page": "httkpop_virtual_indiv", "title": "Generate a virtual population by the virtual individuals method.", "topics": [ "httkpop_virtual_indiv" ] }, { "page": "hw_H", "title": "KDE bandwidth for residual variability in height/weight", "topics": [ "hw_H" ] }, { "page": "in.list", "title": "Convenience Boolean (yes/no) functions to identify chemical membership in several key lists.", "topics": [ "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" ] }, { "page": "invitro_mc", "title": "Monte Carlo for in vitro toxicokinetic parameters including uncertainty and variability.", "topics": [ "invitro_mc" ] }, { "page": "invitro.assay.params", "title": "ToxCast In Vitro Assay Descriptors", "topics": [ "invitro.assay.params" ] }, { "page": "is_in_inclusive", "title": "Checks whether a value, or all values in a vector, is within inclusive limits", "topics": [ "is_in_inclusive" ] }, { "page": "is.httk", "title": "Convenience Boolean (yes/no) function to identify chemical membership and treatment within the httk project.", "topics": [ "is.httk" ] }, { "page": "johnson", "title": "Johnson 2006", "topics": [ "johnson" ] }, { "page": "kapraun2019", "title": "Kapraun et al. 2019 data", "topics": [ "Kapraun2019", "kapraun2019" ] }, { "page": "kidney_mass_children", "title": "Predict kidney mass for children", "topics": [ "kidney_mass_children" ] }, { "page": "kramer_eval", "title": "Evaluate the Kramer In Vitro Distribution model", "topics": [ "kramer_eval" ] }, { "page": "list_models", "title": "List all available HTTK models", "topics": [ "list_models" ] }, { "page": "liver_mass_children", "title": "Predict liver mass for children", "topics": [ "liver_mass_children" ] }, { "page": "load_dawson2021", "title": "Load CLint and Fup QSPR predictions from Dawson et al. 2021.", "topics": [ "load_dawson2021" ] }, { "page": "load_honda2025", "title": "Load Caco2 pereneability QSPR predictions from Honda et al. 2025", "topics": [ "load_honda2023", "load_honda2025" ] }, { "page": "load_pradeep2020", "title": "Load CLint and Fup QSPR predictions predictions from Pradeep et al. 2020.", "topics": [ "load_pradeep2020" ] }, { "page": "load_sipes2017", "title": "Load CLint and Fup QSPR predictions from Sipes et al 2017.", "topics": [ "load_sipes2017" ] }, { "page": "lump_tissues", "title": "Lump tissue parameters into model compartments", "topics": [ "lump_tissues" ] }, { "page": "lung_mass_children", "title": "Predict lung mass for children", "topics": [ "lung_mass_children" ] }, { "page": "mcnally_dt", "title": "Reference tissue masses and flows from tables in McNally et al. 2014.", "topics": [ "mcnally_dt" ] }, { "page": "mecdt", "title": "Pre-processed NHANES data.", "topics": [ "mecdt" ] }, { "page": "metabolism_data_Linakis2020", "title": "Metabolism data involved in Linakis 2020 vignette analysis.", "topics": [ "metabolism_data_Linakis2020" ] }, { "page": "monte_carlo", "title": "Monte Carlo for toxicokinetic model parameters", "topics": [ "monte_carlo" ] }, { "page": "Obach2008", "title": "Published Pharmacokinetic Parameters from Obach et al. 2008", "topics": [ "Obach2008" ] }, { "page": "onlyp", "title": "NHANES Exposure Data", "topics": [ "onlyp" ] }, { "page": "pancreas_mass_children", "title": "Predict pancreas mass for children", "topics": [ "pancreas_mass_children" ] }, { "page": "parameterize_1comp", "title": "Parameters for a one compartment (empirical) toxicokinetic model", "topics": [ "parameterize_1comp" ] }, { "page": "parameterize_1tri_pbtk", "title": "Parameterize_1tri_PBTK", "topics": [ "parameterize_1tri_pbtk" ] }, { "page": "parameterize_3comp", "title": "Parameters for a three-compartment toxicokinetic model (dynamic)", "topics": [ "parameterize_3comp" ] }, { "page": "parameterize_3comp2", "title": "Parameters for a three-compartment toxicokinetic model (dynamic)", "topics": [ "parameterize_3comp2" ] }, { "page": "parameterize_armitage", "title": "Parameterize Armitage In Vitro Distribution Model", "topics": [ "parameterize_armitage" ] }, { "page": "parameterize_dermal_pbtk", "title": "Parameterizea generic PBTK model with dermal exposure", "topics": [ "parameterize_dermal_pbtk" ] }, { "page": "parameterize_fetal_pbtk", "title": "Parameterize_fetal_PBTK", "topics": [ "parameterize_fetal_pbtk" ] }, { "page": "parameterize_gas_pbtk", "title": "Parameters for a generic gas inhalation physiologically-based toxicokinetic model", "topics": [ "parameterize_gas_pbtk" ] }, { "page": "parameterize_IVD", "title": "Parameterize In Vitro Distribution Models", "topics": [ "parameterize_IVD" ] }, { "page": "parameterize_kramer", "title": "Parameterize Kramer IVD Model", "topics": [ "parameterize_kramer" ] }, { "page": "parameterize_pbtk", "title": 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