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Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction - poster
Lu, J., M. Goldsmith, Chris Grulke, J. Leonard, E. Hypes, M. Fair, R. Tornero-Velez, AND C. Tan. Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction - poster. ASCCT Annual Meeting, EPA RTP Campus, Durham (RTP), NC, October 01 - 02, 2015.
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Building new physiologically based pharmacokinetic (PBPK) models requires a lot data, such as the chemical-specific parameters and in vivo pharmacokinetic data. Previously-developed, well-parameterized, and thoroughly-vetted models can be great resource for supporting the construction of models pertaining to new chemicals. Thus, a PBPK knowledgebase containing existing PBPK-related articles was compiled. From the analysis of 2,039 PBPK-related articles, 307 unique chemicals were identified in 795 publications. Keywords related to species, gender, developmental stages, and organs were analyzed from these 795 articles. These articles together with the chemical names, species and other indexes were included in the PBPK knowledgebase. In addition, a correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on their pharmacokinetic-relevant molecular descriptors. Two case studies were conducted to demonstrate the utility of the knowledgebase. In these case studies, all chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, chemicals from the upper and lower portion of the ranking list were selected to represent exact matches, close analogues, or non-analogues of the target chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals were used for new model construction and predictions. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities.