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: |
2.4.0 |
| 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: |
2024-09-05 |
| DOI: |
10.32614/CRAN.package.httk |
| Author: |
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] |
| Maintainer: |
John Wambaugh <wambaugh.john at epa.gov> |
| BugReports: |
https://github.com/USEPA/CompTox-ExpoCast-httk/issues |
| 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. |
| URL: |
https://www.epa.gov/chemical-research/rapid-chemical-exposure-and-dose-research |
| NeedsCompilation: |
yes |
| Citation: |
httk citation info |
| Materials: |
README NEWS |
| CRAN checks: |
httk results |