Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors.

Phosphodiesterase 4 (PDE4), is a hydrolytic enzyme, is proposed as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B selective inhibitors are desirable to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. To achieve this goal, ligand based pharmacophore modeling and molecular docking approach is employed. Pharmacophore hypotheses for PDE4B and PDE4D are generated using HypoGen algorithm. The best PDE4B pharmacophore hypothesis (Hypo1_PDE4B) consist of one hydrogen-bond acceptor and two ring aromatic features, whereas PDE4D pharmacophore hypothesis (Hypo1_PDE4D) consist of one hydrogen-bond acceptor, one hydrophobic aliphatic, and two ring aromatic features. The validated pharmacophore hypotheses are used in virtual screening to identify selective PDE4B inhibitors. The hits were screened for their estimated activity, FitValue, and quantitative estimation of drug likeness. After molecular docking analysis, ten hits were purchased for in vitro analysis. Out of these, six hits have shown potent and selective inhibitory activity against PDE4B with IC50 values ranging from 2 to 378nM.

Biological evaluation and structural insights for design of subtype-selective peroxisome proliferator activated receptor-α (PPAR-α) agonists.

Peroxisome proliferator activated receptors-α (PPAR-α) control the expression of several genes involved in diseases like diabetes, hyperlipidaemia, and inflammatory disorders. Herein, we report the biological evaluation of recently identified hits from pharmacophore based virtual screening. The most potent hits, ZINC17167211, ZINC06472206 and ZINC08438472 showed EC50 values of 0.16, 1.1 and 12.1nM in PPAR-α agonist assay, respectively. Further, comparative docking and molecular dynamics analysis of selective PPAR-α agonists revealed that Thr279, Ala333, Lys358 and Met325 residues play an important role in the selective PPAR-α agonistic activity. The insights from docking and molecular dynamic studies will serve as a guideline for the development of potent and selective PPAR-α agonists.

Identification of dual Acetyl-CoA carboxylases 1 and 2 inhibitors by pharmacophore based virtual screening and molecular docking approach.

Acetyl-CoA carboxylase (ACC) is a crucial metabolic enzyme that plays a vital role in obesity-induced type 2 diabetes and fatty acid metabolism. To identify dual inhibitors of Acetyl-CoA carboxylase1 and Acetyl-CoA carboxylase2, a pharmacophore modelling approach has been employed. The best HypoGen pharmacophore model for ACC2 inhibitors (Hypo1_ACC2) consists of one hydrogen bond acceptor, one hydrophobic aliphatic and one hydrophobic aromatic feature, whereas the best pharmacophore (Hypo1_ACC1) for ACC1 consists of one additional hydrogen-bond donor (HBD) features. The best pharmacophore hypotheses were validated by various methods such as test set, decoy set and Cat-Scramble methodology. The validated pharmacophore models were used to screen several small-molecule databases, including Specs, NCI, ChemDiv and Natural product databases to identify the potential dual ACC inhibitors. The virtual hits were then subjected to several filters such as estimated [Formula: see text] value, quantitative estimation of drug-likeness and molecular docking analysis. Finally, three novel compounds with diverse scaffolds were selected as potential starting points for the design of novel dual ACC inhibitors.