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.
[Show abstract][Hide abstract] ABSTRACT: One of the major reasons for late-stage failure of drug candidates is due to problems uncovered in pharmacokinetics during clinical trials. There is now a general consensus for earlier consideration of these effects in the drug discovery process. Computer-aided design technology provides us with tools to develop predictive models for such pharmacokinetic properties. Among these tools, we focus on pharmacophore modeling techniques in this article. Pharmacophore models that are reported for various cytochrome P450 (CYP) enzymes are reviewed for the isoenzymes CYP1A2, 2B6, 2C9, 2C19, 2D6, 2E1, and 3A4. In addition pharmacophore models for related metabolic processes through CYP19 (aromatase), CYP51 (14α-lanosterol demethylase), PXR (pregnane X-receptor), and finally for human intrinsic clearance are also reviewed. The models reported by various scientists are schematically represented in the figures in order to visually demonstrate their similarities and differences. The different models developed by different researchers or sometimes even by the same research group for different sets of ligands, provide a clear picture of the challenges in coming up with a single model with good predictive values. The main reason for this challenge is due to the relatively large size of the active sites and flexibility of the CYP isoenzymes, resulting with multiple binding sites. We propose development of multiple-diverse pharmacophore models for each binding mode (as opposed to a single predictive model for each CYP isoenzyme). After scoring and prioritization of the models, we propose the use of a battery of pharmacophore models for each CYP isoenzyme binding mode to computationally obtain a P450 interaction profile for drug candidates early in the drug development cycle, when decisions on their fate can be made before incurring cost of synthesis and testing.
Current topics in medicinal chemistry 05/2013; 13(11). DOI:10.2174/15680266113139990037 · 3.40 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: An efficient and facile Au(I)/Ag(I)-catalyzed cascade method has been developed for one-pot synthesis of the complex polycyclic heterocycles benzo[4,5]imidazo[1,2-c]pyrrolo[1,2-a]quinazolinone derivatives through treatment of the substituted 2-(1H-benzo[d]imidazol-2-yl)anilines with 4-pentynoic acid or 5-hexynoic acid. The strategy features Au(I)/Ag(I)-catalyzed one-pot cascade process involving in the formation of three new C-N bonds in high yields, and with broad substrate scope.
The Journal of Organic Chemistry 04/2013; 78(9). DOI:10.1021/jo400228g · 4.72 Impact Factor
[Show abstract][Hide abstract] 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.
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