R Package "httk"

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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) measurements. 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) for a recent review.

This repository is for reporting bugs and contributing enhancements. Installable files, documentation, and other information can be obtained from


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). 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) and propagating parameter uncertainty (Wambaugh et al., 2019). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017). 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).

Getting Started

For an introduction to R, see Irizarry (2022) "Introduction to Data Science":

For an introduction to toxicokinetics, with examples in "httk", see Ring (2021) in the "TAME Toolkit":



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)


Load the HTTK data, models, and functions



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( = "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(package = httk)
vignette(package = "httk")


Principal Investigator

John Wambaugh []

Lead Software Engineer

Sarah Davidson []

Model Authors and Function Developers

Robert Pearce, Caroline Ring [], Greg Honda [], Mark Sfeir, Matt Linakis [], and Dustin Kapraun []

Bug-Fixes Vignette edits, and Parameter Values

Miyuki Breen [], Shannon Bell [], Xiaoqing Chang [], Todor Antonijevic [], Jimena Davis, Elaina Kenyon [] James Sluka [], Nisha Sipes [], and Barbara Wetmore []

Statistical Expertise

Woodrow Setzer []


License: GPL-3

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httk documentation built on June 22, 2024, 6:48 p.m.