Cytochrome p450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. As only 6 isoenzymes are responsible for ∼90% of known oxidative drug metabolism, a number of frequently prescribed drugs share the CYP-mediated metabolic pathways. Competing for a single enzyme by the co-administered therapeutic agents can substantially alter the plasma concentration and clearance of the agents. Furthermore, many drugs are known to inhibit certain p450 enzymes which they are not substrates for. Because some drug-drug interactions could cause serious adverse events leading to a costly failure of drug development, early detection of potential drug-drug interactions is highly desirable. The ultimate goal is to be able to predict the CYP specificity and the interactions for a novel compound from its chemical structure. Current computational modeling approaches, such as two-dimensional and three-dimensional quantitative structure-activity relationship (QSAR), pharmacophore mapping and machine learning methods have resulted in statistically valid predictions. Homology models have been often combined with 3D-QSAR models to impose additional steric restrictions and/or to identify the interaction site on the proteins. This article summarizes the available models, methods, and key findings for CYP1A2, 2A6, 2C9, 2D6 and 3A4 isoenzymes.
Keywords: 3D-QSAR, Pharmacophore modeling, CYP1A subfamily, support vector machine (SVM), neural network