Exploration of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one Derivatives As JAK Inhibitors Using Various in Silico Techniques

Overview

Journal In Silico Pharmacol

Date 2017 Nov 1

PMID 29085766

Citations 1

Authors

Radhakrishnan S Jisha

Lilly Aswathy

Vijay H Masand

Jayant M Gajbhiye

Indira G Shibi

Affiliations

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Abstract

This study focuses on understanding the structural features of 3,6-dihydroimidazo(4,5-d)pyrrolo(2,3-b)pyridin-2(1H)-one (dpp) derivatives to computationally identify new JAK inhibiting compounds. For the purpose, a novel virtual screening strategy, with 2D and 3D-QSAR (CoMFA and CoMSIA), data mining, pharmacophore modeling, ADMET prediction, multi-targeted protein-based docking and inverse QSAR, was employed. The 2D-QSAR equations developed for the JAK3, JAK2 and JAK1 involved five physicochemical descriptors. These descriptors correlate with the anti-RA activity with R values for JAK3, JAK2 and JAK1 are 0.9811, 0.8620 and 0.9740, respectively. The 3D-QSAR studies such as CoMFA and CoMSIA carried out through PLS analysis of the training set of JAK3, JAK2 and JAK1, gave Q values as 0.369, 0.476 and 0.490; [Formula: see text] values as 0.863, 0.684 and 0.724 and, F values as 23.098, 28.139 and 31.438, respectively. The contour maps produced by the CoMFA and CoMSIA models were used to understand the importance of hydrogen bond donor, acceptor, hydrophobic, steric and electrostatic interactions. The molecular docking studies of these selected compounds with various JAK proteins were carried out and the protein-ligand interactions were also studied. The study concluded that dpp15(s) is a highly potent JAK inhibitor with a very good predicted IC value.

Citing Articles

Design of novel amyloid β aggregation inhibitors using QSAR, pharmacophore modeling, molecular docking and ADME prediction.

Aswathy L, Jisha R, Masand V, Gajbhiye J, Shibi I In Silico Pharmacol. 2019; 6(1):12.

PMID: 30607325 PMC: 6314802. DOI: 10.1007/s40203-018-0049-1.

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