httk: High-Throughput Toxicokinetics

Generic models and chemical-specific data for simulation and statistical analysis of chemical toxicokinetics ("TK") as described by Pearce et al. (2017) <doi:10.18637/jss.v079.i04>. Chemical-specific in vitro data have been obtained from relatively high-throughput experiments. Both physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models can be parameterized with the data provided for thousands of chemicals, multiple exposure routes, and various species. The models consist of systems of ordinary differential equations which are solved using compiled (C-based) code for speed. A Monte Carlo sampler is included, which allows for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty. 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 in vitro-in vivo extrapolation ("IVIVE") of high-throughput screening data (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Package details

AuthorJohn Wambaugh [aut, cre] (<>), Sarah Davidson [aut] (<>), Robert Pearce [aut] (<>), Caroline Ring [aut] (<>), Greg Honda [aut] (<>), Mark Sfeir [aut], Matt Linakis [aut] (<>), Dustin Kapraun [aut] (<>), Miyuki Breen [ctb] (<>), Shannon Bell [ctb] (<>), Xiaoqing Chang [ctb] (<>), Todor Antonijevic [ctb] (<>), Jimena Davis [ctb], James Sluka [ctb] (<>), Nisha Sipes [ctb] (<>), Barbara Wetmore [ctb] (<>), Woodrow Setzer [ctb] (<>)
MaintainerJohn Wambaugh <>
Package repositoryView on CRAN
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httk documentation built on March 26, 2022, 9:05 a.m.