Pharmacophore- based virtual screening, 3D- QSAR, molecular docking approach for identification of potential dipeptidyl peptidase IV inhibitors

Abstract

Pharmacophore modeling, molecular docking, and in silico ADME studies have been carried out to determine the binding mode and drug likeliness profile of Pyrrolidine derivatives as Dipeptidyl peptidase IV inhibitors. A five point pharmacophore model (AAADH_1) was generated using 96 compounds with IC₅₀ values ranging from 1.8 to 13000 nM. A statistically significant 3D-QSAR model was generated from the pharmacophore hypothesis. The model had a high correlation coefficient (R² - 0.92), cross validation coefficient (Q² - 0.776) and F value (F - 144) at 6 component Partial least square factor. Pearson r of 0.7124 indicated greater degree of confidence on the model. The accuracy and predictability of the generated model were checked by Enrichment factor, Receiver operating characteristic curves, area under curve, Boltzmann-enhanced discrimination of Receiver operating characteristic and the Robust initial enhancement. To identify novel and potent Dipeptidyl peptidase IV inhibitors, virtual screening was performed using the ligand and database screening. Considering the potent hit molecules on the basis of pharmacophore virtual screening, we have designed new molecules and further subjected to see the interaction with protein. The catalytic domain of Dipeptidyl peptidase IV enzyme in complex with Vildagliptin (PDB Code: 6B1E) was obtained from protein data bank with resolution 1.77 A°. Compound 75 showed the better binding (dock score -7.966) with protein than standard drug vildagliptin (Dock Score -6.554). The hits obtained on virtual screening of the database have provided new chemical starting points for design and development of novel Dipeptidyl peptidase IV inhibitory agents.

Communicated by Ramaswamy H. Sarma.

Keywords:

• Dipeptidyl peptidase IV inhibitors
• pharmacophore modeling
• 3D QSAR
• virtual screening
• docking
• ADME
• pyrrolidine derivatives

Acknowledgements

Authors are thankful to Dr. Mahesh T. Chhabria, principal L. M.college of pharmacy and Mr. Vinod Devaraji for providing software facility.

Disclosure statement

This research did not receive any specific grant from funding agencies in the public, commercial or not for profit sectors.