Use of In Vitro and Predictive In Silico Models to Study the Inhibition of Cytochrome P4503A by Stilbenes.

CYP3A4 is recognized as the main enzyme involved in the metabolism of drugs and xenobiotics in the human body and its inhibition may lead to undesirable consequences. Stilbenes, including resveratrol, belong to a group of dietary health-promoting compounds that also act as inhibitors of CYP3A4. The aim of this study was to examine the use of computer modeling of enzyme-ligand interactions to analyze and predict the inhibition of structurally related compounds. To this end, an aldehyde group was attached to resveratrol and the interactions of CYP3A4 with resveratrol, its aldehyde analogue (RA) and a known synthetic inhibitor were studied and compared in two biological models. Specifically, the metabolism of testosterone was examined in a human intestine cell line (Caco-2/TC7) and in rat liver microsomes (RLM). The results demonstrated a weak inhibitory effect of RA on CYP3A4, as compared to resveratrol itself, in both biological models. Human CYP3A4 was more susceptible to inhibition than the commonly used model isozyme from rat. Modeling of the binding site of CYP3A4 revealed a combination of three types of interactions: hydrophobic interactions, electrostatic interactions and hydrogen bonds. A docking simulation revealed that the RA lacked an important binding feature, as compared to resveratrol, and that that difference may be responsible for its lower level of affinity for CYP3A4. Software analysis of binding affinity may serve as a predictive tool for designing new therapeutic compounds in terms of inhibition of CYP3A4 and help to reveal the biochemical nature of the interactions of dietary compounds, herbal compounds and drugs whose metabolism is mediated by this enzyme.

Aldehyde recognition and discrimination by mammalian odorant receptors via functional group-specific hydration chemistry.

The mammalian odorant receptors (ORs) form a chemical-detecting interface between the atmosphere and the nervous system. This large gene family is composed of hundreds of membrane proteins predicted to form as many unique small molecule binding niches within their G-protein coupled receptor (GPCR) framework, but very little is known about the molecular recognition strategies they use to bind and discriminate between small molecule odorants. Using rationally designed synthetic analogs of a typical aliphatic aldehyde, we report evidence that among the ORs showing specificity for the aldehyde functional group, a significant percentage detect the aldehyde through its ability to react with water to form a 1,1-geminal (gem)-diol. Evidence is presented indicating that the rat OR-I7, an often-studied and modeled OR known to require the aldehyde function of octanal for activation, is likely one of the gem-diol activated receptors. A homology model based on an activated GPCR X-ray structure provides a structural hypothesis for activation of OR-I7 by the gem-diol of octanal.

Epigallocatechin gallate induces upregulation of the two-component VraSR system by evoking a cell wall stress response in Staphylococcus aureus.

We previously found that a short exposure of Staphylococcus aureus to subinhibitory (SI) doses of epigallocatechin gallate (EGCG) results in increased cell wall thickness, adaptation, and enhanced tolerance to cell-wall-targeted antibiotics. In this study, the response to EGCG of sigB and vraSR transcription factor mutants was characterized. We show that in contrast to the results observed for wild-type (WT) strains, an S. aureus 315 vraSR null mutant exposed to SI doses of EGCG did not exhibit increased tolerance to EGCG and oxacillin. A diminished increase in tolerance to ampicillin (from 16-fold to 4-fold) and no change in the magnitude of resistance to vancomycin were observed. Preexposure to EGCG enhanced the tolerance of wild-type and sigB null mutant cells to lysostaphin, but this enhancement was much weaker in the vraSR null mutant. Marked upregulation (about 60-fold) of vraR and upregulation of the peptidoglycan biosynthesis-associated genes murA, murF, and pbp2 (2-, 5-, and 6-fold, respectively) in response to SI doses of EGCG were determined by quantitative reverse transcription-PCR (qRT-PCR). EGCG also induced the promoter of sas016 (encoding a cell wall stress protein of unknown function which is not induced in vraSR null mutants) in a concentration-dependent manner, showing kinetics comparable to those of cell-wall-targeting antibiotics. Taken together, our results suggest that the two-component VraSR system is involved in modulating the cell response to SI doses of EGCG.

An integrated in silico 3D model-driven discovery of a novel, potent, and selective amidosulfonamide 5-HT1A agonist (PRX-00023) for the treatment of anxiety and depression.

We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized. In this paper we detail the entire discovery process, which started with modeling the 3D structure of 5-HT1A using the PREDICT methodology, and then performing in silico screening on that structure leading to the discovery of a 1 nM lead compound (8). The lead compound was optimized following a strategy devised based on in silico 3D models and realized through an in silico-driven optimization process, rapidly overcoming selectivity issues (affinity to 5-HT1A vs alpha1-adrenergic receptor) and potential cardiovascular issues (hERG binding), leading to a clinical compound. Finally we report key in vivo preclinical and Phase I clinical data for 20m tolerability, pharmacokinetics, and pharmacodynamics and show that these favorable results are a direct outcome of the properties that were ascribed to the compound during the rational structure-based discovery process. We believe that this is one of the first examples for a Phase III drug candidate that was discovered and optimized, from start to finish, using in silico model-based methods as the primary tool.

PREDICT modeling and in-silico screening for G-protein coupled receptors.

G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex. In this paper we present a comprehensive analysis of three PREDICT models for the dopamine D2, neurokinin NK1, and neuropeptide Y Y1 receptors. A shorter discussion of the CCR3 receptor model is also included. All models were found to be in good agreement with a large body of experimental data. The quality of the PREDICT models, at least for drug discovery purposes, was evaluated by their successful utilization in in-silico screening. Virtual screening using all three PREDICT models yielded enrichment factors 9-fold to 44-fold better than random screening. Namely, the PREDICT models can be used to identify active small-molecule ligands embedded in large compound libraries with an efficiency comparable to that obtained using crystal structures for non-GPCR targets.

G protein-coupled receptors: in silico drug discovery in 3D.

The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets. More than 100,000 compounds were typically screened in silico for each target, leading to a selection of <100 "virtual hit" compounds to be tested in the lab. In vitro binding assays of the selected compounds confirm high hit rates, of 12-21% (full dose-response curves, Ki < 5 microM). In most cases, the best hit was a novel compound (New Chemical Entity) in the 1- to 100-nM range, with very promising pharmacological properties, as measured by a variety of in vitro and in vivo assays. These assays validated the quality of the hits as lead compounds for drug discovery. The results demonstrate the usefulness and robustness of ab initio in silico 3D models and of in silico screening for GPCR drug discovery.