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: | 2.7.4 |
| Depends: | R (≥ 2.10) |
| Imports: | deSolve,msm,data.table,survey,mvtnorm,truncnorm, stats, graphics, utils,magrittr,purrr, methods,Rdpack (≥ 2.3),ggplot2,dplyr |
| Suggests: | knitr,rmarkdown,gplots,scales,EnvStats,MASS,RColorBrewer,stringr,reshape,viridis,gmodels,colorspace,cowplot,ggrepel,forcats,smatr,gridExtra,readxl,ks,testthat,ggpubr,tidyverse |
| Published: | 2025-12-09 |
| 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], Kimberly Truong [aut], Colin Thomson [aut], Meredith Scherer [aut], Annabel Meade [aut], Celia Schacht [aut], Leonie Lautz [aut], Todor Antonijevic [ctb], Miyuki Breen [ctb], Shannon Bell [ctb], Xiaoqing Chang [ctb], Jimena Davis [ctb], Elaina Kenyon [ctb], Gilberto Padilla Mercado [ctb], Katie Paul Friedman [ctb], Nathan Pollesch [ctb], Noelle Sinski [ctb], Nisha Sipes [ctb], James Sluka [ctb], Caroline Stevens [ctb], Barbara Wetmore [ctb], Lily Whipple [ctb], Woodrow Setzer [ctb] |
| Maintainer: | John Wambaugh <wambaugh.research at gmail.com> |
| BugReports: | https://github.com/USEPA/CompTox-ExpoCast-httk/issues |
| License: | MIT + fileLICENSE |
| NeedsCompilation: | yes |
| Citation: | httk citation info |
| Materials: | README,NEWS |
| CRAN checks: | httk results |
Documentation:
| Reference manual: | httk.html ,httk.pdf |
| Vignettes: | 1) Introduction to HTTK (source,R code) 2) Introduction to IVIVE (source,R code) |
Downloads:
| Package source: | httk_2.7.4.tar.gz |
| Windows binaries: | r-devel:httk_2.7.4.zip, r-release:httk_2.7.4.zip, r-oldrel:httk_2.7.4.zip |
| macOS binaries: | r-release (arm64):httk_2.7.4.tgz, r-oldrel (arm64):httk_2.7.4.tgz, r-release (x86_64):httk_2.7.4.tgz, r-oldrel (x86_64):httk_2.7.4.tgz |
| Old sources: | httk archive |
Reverse dependencies:
| Reverse imports: | httkexamples,invivoPKfit |
| Reverse suggests: | GeoTox,pksensi |
Linking:
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