HTTK logo

R Package “httk”

CRAN_Status_Badge Monthly Downloads

This R package provides data and models for prediction toxicokinetics (chemical absorption, distribution, metabolism, and excretion by the body). The models are design to be parameterized with chemical-specific in vitro (animal free) measurments. The predictions can be used for traditional dosimetry as well as in vivo-in vitro extrapolation (IVIVE) of in vitro bioactivity testing data (for example, ToxCast). See Breen et al.  (2021, https://doi.org/10.1080/17425255.2021.1935867) for a recent review.

This repository is for reporting bugs and contributing enhancements. Installable files, documentation, and other information can be obtained from https://cran.r-project.org/package=httk.

Description

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) (https://doi.org/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 https://doi.org/10.1016/j.envint.2017.06.004) and propagating parameter uncertainty (Wambaugh et al., 2019 https://doi.org/10.1093/toxsci/kfz205). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 https://doi.org/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 https://doi.org/10.1093/toxsci/kfv171).

Getting Started

For an introduction to R, see Irizarry (2022) “Introduction to Data Science”: https://rafalab.github.io/dsbook/getting-started.html

For an introduction to toxicokinetics, with examples in “httk”, see Ring (2021) in the “TAME Toolkit”: https://uncsrp.github.io/Data-Analysis-Training-Modules/toxicokinetic-modeling.html

Dependencies

install.packages("X")

Or, if using RStudio, look for ‘Install Packages’ under ‘Tools’ tab. * Note that R does not recognize fancy versions of quotation marks ‘,\(~\)’,\(~\)“, or\(~\)”. If you are cutting and pasting from software like Word or Outlook you may need to replace the quotation marks that curve toward each other with ones typed by the keyboard.

Installing R package “httk”

Adapted from Breen et al. (2021) https://doi.org/10.1080/17425255.2021.1935867

install.packages("httk")

Load the HTTK data, models, and functions

library(httk)
packageVersion("httk")

Examples

get_cheminfo()
get_cheminfo(info = "all", median.only=TRUE)
"80-05-7" %in% get_cheminfo()
subset(get_cheminfo(info = "all"), Compound %in% c("A","B","C"))
calc_mc_oral_equiv(0.1,chem.cas = "34256-82-1",species = "human")
calc_mc_oral_equiv(0.1,chem.cas = "99-71-8", species = "human")
calc_tkstats(chem.cas = "34256-82-1",species = "rat")
calc_tkstats(chem.cas = "962-58-3", species = "rat")
solve_pbtk(chem.name = "bisphenol a", plots = TRUE)
my_data <- subset(get_cheminfo(info = "all"), Compound %in% c("A","B","C"))
write.csv(my_data, file = "my_data.csv")

User Notes

Help

help(httk)
help(package = httk)
vignette(package = "httk")
vignette("IntroToHTTK")

Authors

Principal Investigator

John Wambaugh [wambaugh.john@epa.gov]

Lead Software Engineer

Sarah Davidson [Davidson.Sarah.E@epa.gov]

Model Authors and Function Developers

Robert Pearce, Caroline Ring [Ring.Caroline@epa.gov], Greg Honda [honda.gregory@epa.gov], Mark Sfeir, Matt Linakis [MLINAKIS@ramboll.com], and Dustin Kapraun [kapraun.dustin@epa.gov]

Bug-Fixes and Parameter Values

Miyuki Breen [breen.miyuki@epa.gov], Shannon Bell [Shannon.bell@inotivco.com], Xiaoqing Chang [Xiaoqing.chang@inotivco.com], Todor Antonijevic [tantonijevic@toxstrategies.com], Jimena Davis, James Sluka [jsluka@indiana.edu],
Nisha Sipes [sipes.nisha@epa.gov], and Barbara Wetmore [wetmore.barbara@epa.gov]

Statistical Expertise

Woodrow Setzer [setzer.woodrow@epa.gov]

License

License: GPL-3 https://www.gnu.org/licenses/gpl-3.0.en.html