Development of 3D-pharmacophore model followed by successive virtual screening, molecular docking and ADME studies for the design of potent CCR2 antagonists for inflammation-driven diseases

Abstract

In order to elucidate the essential structural features for CC chemokine receptor 2 (CCR2) antagonism, 3D-pharmacophore hypotheses were built based on a set of known compounds from the literature. The hypotheses were developed with the aid of HypoGen module within Discovery Studio 2.5 program. Multiple validation approaches provided the confidence in utilising the predictive pharmacophore models developed in this study. The most predictive pharmacophore model (Hypo1) was found to be statistically significant along with its ability to predict activities of the known CCR2 antagonists in the training and test set with high correlation coefficient. The best model was then used as a 3D search query in the virtual screening of chemical databases including ChemDiv and MiniMaybridge. Lipinski's rule of five and molecular docking studies were applied to the screened hits for retrieving potential lead compounds. Eight hits showed better in silico CCR2-binding affinities than the reported CCR2 antagonists, along with good absorption, distribution, metabolism and excretion profiles. The current 3D-quantitative structure–activity relationship (QSAR) pharmacophore modelling and molecular docking studies attempt to elucidate QSAR for CCR2 antagonism and identify novel potent CCR2 antagonist scaffolds.