IDENTIFICATION OF NOVEL PPAR$\gamma$ MODULATORS / PARTIAL AGONISTS THROUGH VIRTUAL SCREENING WORKFLOW

Abstract

Thiazolidinedione’s (TZDs) being insulin sensitizer’s act as agonists of PPAR$\gamma $ used in the treatment of type 2 diabetes but suffered with serious side effects. After understanding the trans-activation mechanism of PPAR receptors and in order to overcome these side effects a new path has been led to new approaches like, PPAR-$\alpha $/$\gamma $ dual agonists, PPAR-$\delta $/$\gamma $ dual agonists, PPAR-pan agonists, selective PPAR-$\gamma $ modulators (SPPAR$\gamma $Ms) / partial agonists. Among them SPPAR$\gamma $Ms) / partial agonists attracted due to their selectivity and expression in the selective tissue. The present study aims at identifying novel SPPAR$\gamma $Ms) / partial agonists by using VS workflow and molecular docking. Virtual screening workflow is fallowed which consists of several steps like (a) Ligand based anti-pharmacophore screening(b) Ligand based Pharmacophore screening (c) ADME /Toxicity analysis and (d) Molecular Docking. Out of 21,818 molecules subjected to anti pharmacophore model, 4936 molecules qualify for the next step i.e., pharmacophore model screening. Out of these molecules only 12 molecules showed Qfit > 70. Therefore, these molecules were further subjected to ADME /TOX filter step in which 7 molecules passed the step. Further these molecules subjected to docking studies. In the docking studies based on the typical binding modes of the standard partial agonist (INT131) 5 molecules were found to have good binding mode required for a typical partial agonist. The Virtual screening workflow used in the study identifies 5 molecules as partial agonists.