Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

• Published in: PLoS computational biology 2016-02, Vol.12 (2), p.e1004495-e1004495
• Main Authors: Lu, Jingtao, Goldsmith, Michael-Rock, Grulke, Christopher M, Chang, Daniel T, Brooks, Raina D, Leonard, Jeremy A, Phillips, Martin B, Hypes, Ethan D, Fair, Matthew J, Tornero-Velez, Rogelio, Johnson, Jeffre, Dary, Curtis C, Tan, Yu-Mei
• Format: Article
• Language: English
• Subjects: Animals; Biology and Life Sciences; Chemicals; Computational Biology; Construction; Environmental protection; Humans; Knowledge Bases; Mathematical models; Medicine and Health Sciences; Metabolism; Metabolites; Mice; Models, Biological; Pharmaceutical Preparations - metabolism; Pharmacokinetics; Physical Sciences; Rats; Risk assessment; Software; Swine
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004495

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.