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Physiologically Based Pharmacokinetic (PBPK) models are often developed using animal data and applied to predict chemical movement and concentration in humans. However, differences in physiology and exposure routes between animal experiments and human exposures may impact the predictions and interpretation of PBPK model results. Data are needed to parameterize PBPK models, potentially requiring chemical-specific adjustment to model inputs. Since data may be limited for a chemical or exposure of interest, in silico approaches such as chemical structure-based modeling can help fill the gaps. This case study assesses generalized, open-source PBPK models for interspecies kinetic extrapolation of nicotine using both in vivo data from a rat oral gavage study and in silico predictions. Nicotine is used as a data-rich example chemical because PK data are available from different exposure routes in both humans and animals. Rat nicotine plasma data were obtained after oral gavage dosing of nicotine (up to 8 mg/kg/day over 7 days) and used to develop human nicotine models for both oral ingestion and buccal (mouth tissue) absorption. As an open-source buccal tissue absorption model was not available, we mimicked a buccal exposure by modifying the open-source model for the intravenous exposure route. An 8 mg/kg/day human dose using oral ingestion and buccal absorption resulted in an approximately 2- and 4-fold higher predicted maximum plasma concentration (Cmax) than an 8 mg/kg/day gavage exposure in rats, respectively, highlighting the impact of species and exposure route on model predictions. This study demonstrates that a generalized and open-source PBPK model can extrapolate the plasma kinetic profiles of nicotine between species using in vivo and in silico data after accounting for differences in exposure routes. In silico-informed model parameterizations provided similar results to rat in vivo-based parameterizations, highlighting the potential use of in silico approaches when data are limited.

New approach methodologies (NAMs) for toxicological applications such as in vitro assays and in silico models generate data that can be useful for assessing potential health impacts of chemicals. The National Toxicology Program’s (NTP’s) Integrated Chemical Environment (ICE; https://ice.ntp.niehs.nih.gov/ ) provides user-friendly access to NAM data and tools to explore and contextualize chemical bioactivity and molecular properties. ICE contains curated in vivo and in vitro toxicity testing data and experimental physicochemical property data gathered from different literature sources. ICE also contains computationally generated toxicity data and physicochemical parameter predictions. ICE provides interactive computational tools that characterize, analyze, and predict bioactivity for user-defined chemicals. ICE Search allows users to select and merge data sets for lists of chemicals and mixtures, yielding summary-level information, curated reference data, and bioactivity details mapped to mechanistic targets and modes of action. With the Curve Surfer tool, the user can explore concentration–response relationships of curated high-throughput screening assays. The Physiologically Based Pharmacokinetics (PBPK) tool predicts tissue-level concentrations resulting from in vivo doses, while the In Vitro–In Vivo Extrapolation (IVIVE) tool translates in vitro activity concentrations to equivalent in vivo dose estimates. The Chemical Characterization tool displays distributions of physicochemical properties, bioactivity- and structure-based projections, and consumer product use information. Chemical Quest, the newest ICE tool, allows users to search for structurally similar chemicals to a target chemical or substructure from within the extensive ICE database. Retrieved information on target chemicals and those with similar structures can then be used to query other ICE tools and datasets, greatly expanding data available to address the user’s question. ICE links to other NTP and U.S. Environmental Protection Agency data sources, expanding ICE’s capacity to examine chemicals based on physicochemical properties, bioactivity, and product use categories.