A virtual screening filter for identification of cytochrome P450 2C9 (CYP2C9) inhibitors

Abstract

Cytochrome P450 2C9 (CYP2C9) is one of the most important phase 1 metabolizing enzymes in humans for therapeutically relevant pharmaceuticals. Any new compound inhibiting this membrane-associated protein would notably affect the metabolism of physiologically important molecules and drugs, resulting in clinically significant drug-drug interactions. In search for computational tools to identify potential CYP2C9 inhibitors early in discovery, we present here the construction of filters based on 1100 structurally diverse molecules tested for CYP2C9 inhibition under identical conditions. Their chemical structures were encoded using various 2D descriptors including Three-Point Pharmacophoric (3PP) fingerprints, followed by generation of statistical models using Support Vector Machines (SVM) and Partial Least Squares (PLS). This consistently led to significant and predictive models for regression and classification of CYP2C9 inhibitors. Their predictive ability was underscored by successfully applying them to different test sets of 238 diverse and 344 GPCR-targeted compounds. Even more important for early drug discovery is the ability of these models to correctly discriminate CYP2C9 inhibitors from inactive molecules. These models collectively are able to identify true CYP2C9 inhibitors with relatively low rates of false positives. The 3PP-based filter also allows visualizing important substructures and functional groups, which are linked to protein-ligand interactions for CYP2C9, as illustrated for selected structure-activity series. The application of these models to the substrate S-warfarin, recently co-crystallized with CYP2C9, revealed that the important substructures are indeed involved in the interaction with the CYP2C9 binding site. For example, the model correctly indicated aromatic stacking interactions with Phe114 and Phe476 as well as a hydrogen bond with Phe100. Hence, these models consistently provide guidelines for reducing CYP2C9 inhibition in novel candidate molecules. © 2007 Wiley-VCH Verlag GmbH & Co. KGaA.