In an effort to discover novel selective monoamine oxidase (MAO) B inhibitors with favorable physicochemical and pharmacokinetic profiles, 7-[(m-halogeno)benzyloxy]coumarins bearing properly selected polar substituents at position 4 were designed, synthesized, and evaluated as MAO inhibitors. Several compounds with MAO-B inhibitory activity in the nanomolar range and excellent MAO-B selectivity (selectivity index SI > 400) were identified. Structure-affinity relationships and docking simulations provided valuable insights into the enzyme-inhibitor binding interactions at position 4, which has been poorly explored. Furthermore, computational and experimental studies led to the identification and biopharmacological characterization of 7-[(3-chlorobenzyl)oxy]-4-[(methylamino)methyl]-2H-chromen-2-one methanesulfonate 22b (NW-1772) as an in vitro and in vivo potent and selective MAO-B inhibitor, with rapid blood-brain barrier penetration, short-acting and reversible inhibitory activity, slight inhibition of selected cytochrome P450s, and low in vitro toxicity. On the basis of this preliminary preclinical profile, inhibitor 22b might be viewed as a promising clinical candidate for the treatment of neurodegenerative diseases.
A total of 142 matrix metalloproteinase (MMP) X-ray crystallographic structures were retrieved from the Protein Data Bank (PDB) and analyzed by an automated and efficient routine, developed in-house, with a series of bioinformatic tools. Highly informative heat maps and hierarchical clusterograms provided a reliable and comprehensive representation of the relationships existing among MMPs, enlarging and complementing the current knowledge in the field. Multiple sequence and structural alignments permitted better location and display of key MMP motifs and quantification of the residue consensus at each amino acid position in the most critical binding subsites of MMPs. The MMP active site consensus sequences, the C-alpha root-mean-square deviation (RMSd) analysis of diverse enzymatic subsites, and the examination of the chemical nature, binding topologies, and zinc binding groups (ZBGs) of ligands extracted from crystallographic complexes provided useful insights on the structural arrangements of the most potent MMP inhibitors.
A novel approach was developed to rationally interface structure- and ligand-based drug design through the rescoring of docking poses and automated generation of molecular alignments for 3D quantitative structure-activity relationship investigations. The procedure was driven by a genetic algorithm optimizing the value of a novel fitness function, accounting simultaneously for best regressions among binding-energy docking scores and affinities and for minimal geometric deviations from properly established crystal-based binding geometry. The GRID/CPCA method, as implemented in GOLPE, was used to feature molecular determinants of ligand binding affinity for each molecular alignment. In addition, unlike standard procedures, a novel multipoint equation was adopted to predict the binding affinity of ligands in the prediction set. Selectivity was investigated through square plots reporting experimental versus recalculated binding affinities on the targets under examination. The application of our approach to the modeling of affinity data of a large series of 3-amidinophenylalanine inhibitors of thrombin, trypsin, and factor Xa generated easily interpretable and independent models with robust statistics. As a further validation study, our approach was successfully applied to a series of 3,4,7-substituted coumarins, acting as selective MAO-B inhibitors.
A multiobjective optimization algorithm was proposed for the automated integration of structure- and ligand-based molecular design. Driven by a genetic algorithm, the herein proposed approach enabled the detection of a number of trade-off QSAR models accounting simultaneously for two independent objectives. The first was biased toward best regressions among docking scores and biological affinities; the second minimized the atom displacements from a properly established crystal-based binding topology. Based on the concept of dominance, 3D QSAR equivalent models profiled the Pareto frontier and were, thus, designated as nondominated solutions of the search space. K-means clustering was, then, operated to select a representative subset of the available trade-off models. These were effectively subjected to GRID/GOLPE analyses for quantitatively featuring molecular determinants of ligand binding affinity. More specifically, it was demonstrated that a) diverse binding conformations occurred on the basis of the ligand ability to profitably contact different part of protein binding site; b) enzyme selectivity was better approached and interpreted by combining diverse equivalent models; and c) trade-off models were successful and even better than docking virtual screening, in retrieving at high sensitivity active hits from a large pool of chemically similar decoys. The approach was tested on a large series, very well-known to QSAR practitioners, of 3-amidinophenylalanine inhibitors of thrombin and trypsin, two serine proteases having rather different biological actions despite a high sequence similarity.
Preliminary evidence in an animal model, that is, primary cultures of rat microglia cells, suggested that some antiretroviral drugs (ARVs), namely darunavir, atazanavir, efavirenz, and nevirapine, increase NO production through a mechanism involving the inhibition of arginase (ARG) activity. This study was conceived to investigate the effects of ARVs on ARG activity in a human experimental model. We compared CHME-5 human microglial immortalized cells under basal conditions with cells exposed to either IL-4, a mix of inflammatory cytokines, or both stimuli given together. We also tested the effects of ARVs on CHME-5 cell lysates after exposure to the above stimuli. Moreover, the interaction between the ARVs and ARG was investigated via computational chemistry. We found that ARVs consistently inhibit ARG activity both in intact and lysed cells. In docking studies, darunavir and atazanavir showed similar scores compared with both L-arginine and the ARG antagonist nor-NOHA. Efavirenz and nevirapine, which are less potent in inhibiting ARG in the biochemical assay, also had lower scores. In conclusion, the present findings in a human model support the notion that ARG pathway can present a new, additional molecular target for different ARVs in HIV treatments.
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