Computational Models for Predicting Interactions with Cytochrome p450 Enzyme

摘要: Cytochrome p450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. As only 6 isoenzymes responsible for approximately 90 % known oxidative drug metabolism, a number frequently prescribed drugs share the CYP-mediated metabolic pathways. Competing single enzyme by co-administered therapeutic agents can substantially alter plasma concentration and clearance agents. Furthermore, many to inhibit certain which they not substrates for. Because some drug-drug interactions could cause serious adverse events leading costly failure development, early detection potential is highly desirable. The ultimate goal be able predict CYP specificity novel compound from its chemical structure. Current computational modeling approaches, such as two-dimensional three-dimensional quantitative structure-activity relationship (QSAR), pharmacophore mapping machine learning methods have resulted statistically valid predictions. Homology models been often combined with 3D-QSAR impose additional steric restrictions and/or identify interaction site on proteins. This article summarizes available models, methods, key findings CYP1A2, 2A6, 2C9, 2D6 3A4 isoenzymes.