COMP 354 |
| In today's drug discovery process, the early consideration of ADME properties is aimed at a reduction of drug candidate drop out rate in later clinical development stages. Apart from in vitro testing, in silico methods are evaluated as complementary screening tools for compounds with unfavorable ADME attributes. Especially members of the cytochrome P450 (P450) enzyme superfamily - e.g. P450 1A2, P450 2C9, P450 2C19, P450 2D6, and P450 3A4 - contribute to xenobiotic metabolism, and compound interaction with one of these enzymes is therefore critically evaluated. In this study, 3D pharmacophore modeling and screening techniques are applied to the prediction of ADME characteristics of small molecules. This binding-mode specific approach is quite different from other physico-chemical property-based models and bears the advantage of being able to qualitatively evaluate the results by considering the 3D interaction between the ligand and the metabolic target. Although this approach is suitable for virtual screening by its high computational efficiency, it still delivers characteristic affinity information of a potential drug candidate towards a certain metabolic binding behavior. Both structure-based and ligand-based models for prominent drug-metabolizing members of the P450 family were generated using the software packages LigandScout and Catalyst identifying essential chemical features for substrate and inhibitor activity for all five P450 enzymes investigated. From all the generated pharmacophores, a collection of 11 pharmacophores for substrates and inhibitors was selected, and we suggest using this set of pharmacophores as in silico P450 profiling tool for early ADME estimation in lead structure identification and optimization. |
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ADME and Physical Property Prediction
9:00 AM-12:15 PM, Wednesday, 13 September 2006 Moscone Center -- Room 228/230, Oral
Division of Computers in Chemistry |