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Current Signal Transduction Therapy

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ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

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Research Article

Utilization of Computational Tools for the Discovery of Schiff Base-based 1, 3, 4-thiadiazole Scaffold as SGLT2 Inhibitors

Author(s): Shivani Sharma, Amit Mittal* and Navneet Khurana

Volume 18, Issue 3, 2023

Published on: 10 October, 2023

Article ID: e101023221993 Pages: 19

DOI: 10.2174/0115743624247062230926110428

Price: $65

Abstract

Background: High or abnormal blood sugar levels are the hallmark of diabetes mellitus (DM), a metabolic disorder that will be one of the major causes of mortality in 2021. SGLT2 inhibitors have recently shown beneficial effects in the treatment of diabetes by reducing hyperglycemia and glucosuria.

Objective: Molecular docking and ADMET studies of Schiff base- based 1, 3, 4-thiadiazole scaffold as SGLT2 inhibitors.

Methods: Chem draw Ultra 16.0 software was used to draw the structures of newly designed molecules of Schiff base-based 1, 3, 4-thiadiazole, which were then translated into 3D structures. For the molecular docking study, AutoDock Vina 1.5.6 software was employed. Lazar in silico and Swiss ADME predictors were used to calculate in silico ADMET characteristics.

Results: We have designed 111 novel Schiff base-based 1, 3, 4-thiadiazole derivatives as SGLT2 inhibitors. A total of 10 compounds from the thiadiazole series were found to have higher binding affinity to the SGLT2 protein than dapagliflozin. SSS 56 had the best docking scores and binding affinities, with -10.4 Kcal/mol, respectively. In silico ADMET parameters demonstrated that the best binding compounds were found to be non-carcinogenic with LogP = 2.53-4.02.

Conclusion: Novel Schiff base-based 1, 3, 4-thiadiazole were designed and binding affinity was assessed against SGLT2 protein, which resulted in a new lead molecule with a maximal binding affinity and estimated to be noncarcinogenic with an optimal partition coefficient (iLogP = 2.53- 4.02).

Graphical Abstract

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