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>).

Version: 2.3.1
Depends: R (≥ 2.10)
Imports: deSolve, msm, data.table, survey, mvtnorm, truncnorm, stats, graphics, utils, magrittr, purrr, methods, Rdpack, ggplot2
Suggests: knitr, rmarkdown, R.rsp, gplots, scales, EnvStats, MASS, RColorBrewer, stringr, reshape, viridis, gmodels, colorspace, cowplot, ggrepel, dplyr, forcats, smatr, gridExtra, readxl, ks
Published: 2024-03-27
DOI: 10.32614/CRAN.package.httk
Author: John Wambaugh ORCID iD [aut, cre], Sarah Davidson-Fritz ORCID iD [aut], Robert Pearce ORCID iD [aut], Caroline Ring ORCID iD [aut], Greg Honda ORCID iD [aut], Mark Sfeir [aut], Matt Linakis ORCID iD [aut], Dustin Kapraun ORCID iD [aut], Nathan Pollesch ORCID iD [ctb], Miyuki Breen ORCID iD [ctb], Shannon Bell ORCID iD [ctb], Xiaoqing Chang ORCID iD [ctb], Todor Antonijevic ORCID iD [ctb], Jimena Davis [ctb], Elaina Kenyon ORCID iD [ctb], Katie Paul Friedman ORCID iD [ctb], James Sluka ORCID iD [ctb], Noelle Sinski [ctb], Nisha Sipes ORCID iD [ctb], Barbara Wetmore ORCID iD [ctb], Lily Whipple [ctb], Woodrow Setzer ORCID iD [ctb]
Maintainer: John Wambaugh <wambaugh.john at>
License: GPL-3
Copyright: This package is primarily developed by employees of the U.S. Federal government as part of their official duties and is therefore public domain.
NeedsCompilation: yes
Citation: httk citation info
Materials: README NEWS
CRAN checks: httk results


Reference manual: httk.pdf
Vignettes: 1) Introduction to HTTK
2) Introduction to IVIVE
a) Pearce (2017): HTTK Basics
b) Ring (2017) HTTK-Pop: Generating subpopulations
c) Pearce (2017): Evaluation of Tissue Partitioning
c) Frank (2018): Neuronal Network IVIVE
d) Wambaugh (2018): Evaluating In Vitro-In Vivo Extrapolation
e) Honda (2019): Updated Armitage et al. (2014) Model
f) Wambaugh (2019): Uncertainty Monte Carlo
g) Linakis (2020): High Throughput Inhalation Model
h) Kapraun (2022): Human Gestational Model


Package source: httk_2.3.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): httk_2.3.1.tgz, r-oldrel (arm64): httk_2.3.1.tgz, r-release (x86_64): httk_2.3.1.tgz, r-oldrel (x86_64): httk_2.3.1.tgz
Old sources: httk archive

Reverse dependencies:

Reverse suggests: pksensi


Please use the canonical form to link to this page.