load_pradeep2020

This function returns an updated version of chem.physical_and_invitro.data
that includes data predicted with Support Vector Machine and Random Forest 
models developed and presented in Pradeep et al. 2020, included in
pradeep2020.

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>).

John Wambaugh

httk

High-Throughput Toxicokinetics

Sarah Davidson

Robert Pearce

Caroline Ring

Greg Honda

Mark Sfeir

Matt Linakis

Dustin Kapraun

Miyuki Breen

Shannon Bell

Xiaoqing Chang

Todor Antonijevic

Jimena Davis

James Sluka

Nisha Sipes

Barbara Wetmore

Woodrow Setzer

load_pradeep2020 function

<dl><dt>overwrite</dt>
<dd>Only matters if load.image=FALSE. If overwrite=TRUE then
existing data in chem.physical_and_invitro.data will be replaced by any
data/predictions in Pradeep et al. (2020) that is for the same chemical and
property. If overwrite=FALSE (DEFAULT) then new data for the same chemical
and property are ignored. Funbound.plasma values of 0 (below limit of
detection) are overwritten either way.</dd>
<dt>target.env</dt>
<dd>The environment where the new
chem.physical_and_invitro.data is loaded. Defaults to global environment.</dd></dl>

Arguments

Author

Load data from Pradeep et al. 2020. — load_pradeep2020

<dl>

<dt>overwrite</dt>
<dd>Only matters if load.image=FALSE. If overwrite=TRUE then
existing data in chem.physical_and_invitro.data will be replaced by any
data/predictions in Pradeep et al. (2020) that is for the same chemical and
property. If overwrite=FALSE (DEFAULT) then new data for the same chemical
and property are ignored. Funbound.plasma values of 0 (below limit of
detection) are overwritten either way.</dd>


<dt>target.env</dt>
<dd>The environment where the new
chem.physical_and_invitro.data is loaded. Defaults to global environment.</dd>

</dl>

Load data from Pradeep et al. 2020.

load_pradeep2020: Load data from Pradeep et al. 2020.

Description

Usage

Value

Arguments

Author

References

Examples