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In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids
Citation:
Chang, D., Rocky Goldsmith, R. Tornero-Velez, C. Tan, Chris Grulke, E. Ulrich, A. Lindstrom, M. Pasquinelli, J. Rabinowitz, AND C. Dary. In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids. Chapter 16, Parameters for Pesticide QSAR and PBPK Models for Human Risk Assessment. American Chemical Society, Washington, DC, 1099:245-269, (2012).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL′s) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s 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.
Description:
In silico methods are invaluable tools to researchers seeking to understand and predict metabolic processes within PBPK models. Even though these methods have been successfully utilized to predict and quantify metabolic processes, there are many challenges involved. Stereochemical processes are a particular challenge since they require computational methods that can elucidate 3D structures and their inherent conformational dependence within a biological context. Developed methods to estimate stereoselective metabolic hydrolysis in mammals are presented to aid PBPK modelers in determining qualitative as well as quantitative relationships among the chiral pyrethroid pesticides. We illustrate a case example of rat serum carboxylesterase (rsCE)-mediated hydrolysis of 27 pyrethroid stereisomers elucidated through a proposed three-step in silico workflow. The methodology involves (i) a pharmacophore structural qualifier/filter to determine whether or not a particular stereoisomer is indeed a viable substrate, and (ii) a mechanism-specific quantitative structure activity relationship (QSAR) to predict metabolic rate constants. Our strategy extends the utility of pharmacophore filters in the reduction of misclassification of mechanistically competent substrates, while strengthening the utility of QSAR models within PK/PD model development.
URLs/Downloads:
CHANG-IN SILICO STRATEGIES_FINAL FINAL.PDF (PDF, NA pp, 697.657 KB, about PDF)American Chemical Society
