Ligand-based design of a potent and selective inhibitor of cytochrome P450 2C19.
ABSTRACT A series of omeprazole-based analogues was synthesized and assessed for inhibitory activity against CYP2C19. The data was used to build a CYP2C19 inhibition pharmacophore model for the series. The model was employed to design additional analogues with inhibitory potency against CYP2C19. Upon identifying inhibitors of CYP2C19, ligand-based design shifted to attenuating the rapid clearance observed for many of the inhibitors. While most analogues underwent metabolism on their aliphatic side chain, metabolite identification indicated that for analogues such as compound 30 which contain a heterocycle adjacent to the sulfur moiety, metabolism primarily occurred on the benzimidazole moiety. Compound 30 exhibited improved metabolic stability (Cl(int) = 12.4 mL/min/nmol) and was selective in regard to inhibition of CYP2C19-catalyzed (S)-mephenytoin hydroxylation in human liver microsomes. Finally, representative compounds were docked into a homology model of CYP2C19 in an effort to understand the enzyme-ligand interactions that may lead to favorable inhibition or metabolism properties.
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ABSTRACT: Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (kcat, Km, and kcat/Km), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (kcat and Km) were more consistent with experimental values than those for catalytic efficiency (kcat/Km). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.Bioorganic & medicinal chemistry 04/2013; · 2.82 Impact Factor