Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates

Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates.

We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site.

We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively.

We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.