Raghuvir Ramakant Pissurlenkar

Goa College of Pharmacy, India

Title: Targeting Amylase and Glycosidase: Exhaustive Computational Study for Potential Anti-Diabetic Candidates

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Biography

Raghuvir Pissurlenkar is an Associate Professor at Goa College of Pharmacy, Goa, India.  He has a rich experience of teaching and research in Organic and Medicinal Chemistry.  Dr. Pissurlenkar has his expertise in molecular modeling for drug design using structure-based and ligand-based methods along with molecular simulations of natural and synthetic polymers for the estimation of free reneges for binding and solvation. Dr. Pissurlenkar has graduate from Goa College of Pharmacy under Goa University  affiliation. where he has joined as an Associate Professor. He has his Masters and PhD from Bombay College of Pharmacy, affiliated to the Mumbai University. Dr.  Pissurlenkar has published his research work extensively in peer-reviewed journals with high impact factor.  He’s looking for collaborations for drug design research where he can share his expertise.

Abstract

Diabetes is a major health issue that has reached alarming levels with nearly half a billion people affected worldwide. It is a serious and long-term medical condition with a major impact on the lives and well-being of individuals, families, and societies at large. Diabetes is amongst the top 10 diseases responsible for the death amongst adults with an expected rise to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. The carbohydrates get absorbed into the body upon hydrolysis by human pancreatic ?-amylase and other intestinal enzymes like human ?-glucosidase. The ?-amylase and ?-glucosidase are well validated therapeutic targets in the treatment of Type II diabetes (T2DM) that play a vital role in modulating the blood glucose level after a meal. Herein, we report novel and diverse molecules identified as potential candidates, predicted to have affinity for ?-amylase and ?-glucosidase. These molecules have been identified via hierarchical multistep docking of small molecules database with the estimated binding free energies. A Glide XP Score cutoff ?8.00 kcal/mol was implemented to filter out non potential molecules from the database. Four molecules viz. AMYRD- 202101, AMY-RD-202104, AMY-RD-202113, and AMYRD-202134 have been identified after an exhaustive computational study involving the evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. The in-depth analysis of protein–ligand interactions was performed using a 100ns molecular dynamics (MD) simulation to establish the dynamic stability. Furthermore MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinity of these molecules for ?-amylase and ?-glucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.