httk.performance

<p>This table records the historical performance and other metrics 
of the R package "httk" as
profiled with the function <code>benchmark_httk</code>. There is a row for
each version and a column for each benchmark or metric. This table is used
to generate graphs comparing the current version to past performance in order
to help identify unintended degradtion of package capabilities.</p>

datasets

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>).

John Wambaugh

httk

High-Throughput Toxicokinetics

Sarah Davidson-Fritz

Robert Pearce

Caroline Ring

Greg Honda

Mark Sfeir

Matt Linakis

Dustin Kapraun

Nathan Pollesch

Miyuki Breen

Shannon Bell

Xiaoqing Chang

Todor Antonijevic

Jimena Davis

Elaina Kenyon

Katie Paul Friedman

Celia Schacht

Meredith Scherer

James Sluka

Noelle Sinski

Nisha Sipes

Barbara Wetmore

Lily Whipple

Woodrow Setzer

httk.performance function

<p>An object of class <code>data.frame</code> with 25 rows and 18 columns.</p>

Format

Historical Performance of R Package httk — httk.performance

Historical Performance of R Package httk

Column Name	Description
Version	The release of httk (major.minor.patch)
N.steadystate	The number of chemicals for which Css can be predicted for the steady-state model
calc_analytic.units	The ratio of the output of `calc_analytic_css` in mg/L to uM multiplied by 1000/MW (should be 1)
calc_mc.units	The ratio of the output of `calc_mc_css` in mg/L to uM multiplied by 1000/MW (should be 1)
solve_pbtk.units	The ratio of a Cplasma value from `solve_pbtk` in mg/L to uM multiplied by 1000/MW (should be 1)
RMSLE.Wetmore	Root mean squared log10 error between Css predictions from httk and published values from Wetmore papers (both 95th percentile)
N.Wetmore	Number of chemicals used in RMSLE evaluation
RMSLE.noMC	RMSLE between 95th percentile Css prediction and median prediction
N.noMC	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCss	RMSLE for predictions of in vivo measured Css
N.InVivoCss	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoAUC	RMSLE for predictions of in vivo measured AUCs
N.InVivoAUC	Number of chemicals used in RMSLE evaluation
RMSLE.InVivoCmax	RMSLE for predictions of in vivo measured Cmax
N.InVivoCmax	Number of chemicals used in RMSLE evaluation
RMSLE.TissuePC	RMSLE for predicted tissue:plasma partition coefficients
N.TissuePC	Number of chemicals used in RMSLE evaluation
Notes	Why benchmarks/metrics may have changed

httk.performance: Historical Performance of R Package httk

Description

Usage

Arguments

Format

Details

References

See Also