CRAN/E | httk

httk

High-Throughput Toxicokinetics

Installation

About

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).

Citation httk citation info
www.epa.gov/chemical-research/rapid-chemical-exposure-and-dose-research
Copyright This package is primarily developed by employees of the U.S. Federal government as part of their official duties and is therefore public domain.
Bug report File report

Key Metrics

Version 2.3.0
R ≥ 2.10
Published 2023-12-08 112 days ago
Needs compilation? yes
License GPL-3
CRAN checks httk results

Downloads

Yesterday 90 0%
Last 7 days 99 -27%
Last 30 days 640 -18%
Last 90 days 2.304 -0%
Last 365 days 8.411 -23%

Maintainer

Maintainer

John Wambaugh

wambaugh.john@epa.gov

Authors

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

Nathan Pollesch

ctb

Miyuki Breen

ctb

Shannon Bell

ctb

Xiaoqing Chang

ctb

Todor Antonijevic

ctb

Jimena Davis

ctb

Elaina Kenyon

ctb

James Sluka

ctb

Noelle Sinski

ctb

Nisha Sipes

ctb

Barbara Wetmore

ctb

Lily Whipple

ctb

Woodrow Setzer

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

1) Introduction to HTTK
2) Introduction to IVIVE
a) Pearce (2017): HTTK Basics
b) Ring (2017) HTTK-Pop: Generating subpopulations
c) Pearce (2017): Evaluation of Tissue Partitioning
c) Frank (2018): Neuronal Network IVIVE
d) Wambaugh (2018): Evaluating In Vitro-In Vivo Extrapolation
e) Honda (2019): Updated Armitage et al. (2014) Model
f) Wambaugh (2019): Uncertainty Monte Carlo
g) Linakis (2020): High Throughput Inhalation Model
h) Kapraun (2022): Human Gestational Model

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

httk archive

Depends

R ≥ 2.10

Imports

deSolve
msm
data.table
survey
mvtnorm
truncnorm
stats
graphics
utils
magrittr
purrr
methods
Rdpack
ggplot2

Suggests

knitr
rmarkdown
R.rsp
gplots
scales
EnvStats
MASS
RColorBrewer
TeachingDemos
stringr
reshape
viridis
gmodels
colorspace
cowplot
ggrepel
dplyr
forcats
smatr
gridExtra
readxl
ks

Reverse Suggests

pksensi