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 can be 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. The models are systems of ordinary
differential equations that are 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>).
ggplot2, knitr, rmarkdown, R.rsp, GGally, gplots, scales,
EnvStats, MASS, RColorBrewer, TeachingDemos, classInt, ks,
stringr, reshape, reshape2, viridis, gmodels, colorspace,
cowplot, ggrepel, dplyr, forcats, smatr, gridExtra, testthat

Implements HTTP Request protocaols. The GET, POST, multipart POST request.

HTTP Request protocols. Implements the GET, POST and multipart POST request.

HTTP Request protocols. Implements the GET, POST and
multipart POST request.