Extending the high-throughput toxicokinetics R package to include brain and adipose compartments
Chemical risk assessment has traditionally been performed based on in vivo studies. These studies have limited throughput, consequently leading to limited toxicity information for the majority of chemicals in industrial and commercial use. In addition, the translation from animal to human effects can be challenging. To increase human-relevant toxicity knowledge, high-throughput in vitro approaches have been developed. However, one challenge in employing these assays for hazard and risk assessment is the translation of in vitro bioactive concentrations to in vivo doses and internal tissue concentrations. To improve decision-making in chemical risk assessment, as well as drug discovery for humans, the U.S. Environmental Protection Agency has developed a simple physiologically based toxicokinetic (PBTK) model as part of the High-throughput Toxicokinetics open-source R package (httk ). In this study, the PBTK model was extended to include compartments for the brain and adipose tissues. The model incorporates a blood–brain barrier to control chemical penetration and disposition, which is essential for predicting neurotoxicity. The integration of an adipose tissue compartment further enables the prediction of bioaccumulation, particularly for lipophilic chemicals. The model’s performance was evaluated by comparing predicted chemical concentrations against experimental data from both oral and intravenous routes. This PBTK model extends the applicability of httk to allow for assessing potential neurotoxicity and chronic exposure to lipophilic chemicals and better evaluate chemical pharmacokinetics. Moreover, this PBTK model can act as a tool for optimizing dosages and other study parameters to reduce and refine the design of in vivo studies.
Authors
- Aswani Unnikrishnan (ORCID: https://orcid.org/0000-0001-9285-8714)
- John F. Wambaugh (ORCID: https://orcid.org/0000-0002-4024-534X)
- Xiaoqing Chang (ORCID: https://orcid.org/0000-0003-0752-1848)
- James Patrick Sluka (ORCID: https://orcid.org/0000-0002-5901-1404)
- Matthew W. Linakis (ORCID: https://orcid.org/0000-0003-0526-2395)
- Helena T. Högberg (ORCID: https://orcid.org/0000-0001-8034-6818)
- Nicole Kleinstreuer (ORCID: https://orcid.org/0000-0002-7914-3682)
- Anna Kreutz (ORCID: https://orcid.org/0000-0002-7787-0683)
- Dingsheng Li
Institutions
- University of Nevada, Reno (US)
- National Institute of Environmental Health Sciences (US)
- UL Research Institutes (US)
- Applied Research Associates (United States) (US)
- Indiana University (US)
Publication Details
- Journal
- Frontiers in Pharmacology
- Published
- 2026-10-28
- DOI
- https://doi.org/10.3389/fphar.2026.1895108
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00