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Drug Metabolism and Disposition Fast Forward
First published on March 15, 2006; DOI: 10.1124/dmd.105.008631

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Received for publication December 2, 2005.
Revised March 10, 2006.
Accepted for publication March 10, 2006.

Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions

Diansong Zhou 1*, Lovisa Afzelius 1, Scott W Grimm 1, Tommy B Andersson 1, Randy J Zauhar 2, Ismael Zamora 3

1 AstraZeneca Pharmaceuticals 2 The University of Sciences in Philadelphia 3 Lead Molecular Design

* Address correspondence to: E-mail: diansong.zhou{at}astrazeneca.com

Abstract

Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic lability. This information can now be obtained from in silico predictions and therefore a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging. In this study, metabolite identification using MetaSite® and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and 4-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite® and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor-enabled achieved a similar prediction success (78%) using the MetaSite® method. The docking method had a relatively lower prediction success (~57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite® methodology is automated, rapid, and has relatively accurate predictions compared to the docking methodology used in this study.


Key words: computational models, CYP3A, cytochrome P450 catalyzed oxidations


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