Abstract

COMPUTATIONAL INSIGHTS INTO SULFONYLUREA CONTAINING ANTIDIABETIC DRUGS TARGETING PPAR-γ: A COMPREHENSIVE REVIEW

Shubham N. Karle* and Dr. Megha T. Salve

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Abstract

Diabetes mellitus is a global health concern characterized by impaired insulin function or production, leading to elevated blood glucose levels. SU-containing antidiabetic drugs have long been utilized in the management of type 2 diabetes mellitus. Their mechanism of action primarily involves the stimulation of insulin secretion from pancreatic β-cells. In recent years, the role of PPAR-γ in glucose and lipid metabolism has gained significant attention. This comprehensive review explores the molecular interactions between SU drugs and PPAR-γ through computational methods, shedding light on their potential significance in diabetes treatment. This comprehensive review provides a detailed exploration of Binding affinity to the receptor, Toxicity, Pharmacokinetic, Pharmacodynamic properties such as BBB permeation, HIA, P-glycoprotein substrate, cytochrome P450 isoform inhibition, Log Kp, and bioavailability ranking etc. of SU-based antidiabetic drugs and their ability to produce agonist effect on PPAR isoforms (PPAR-γ) by using various in silico evaluation software’s like Molinspiration, ADMETlab 2.0, ProTox-II, 1-Click Docking, ChemSketch, SwissADME etc. A major focus of this review is the utilization of computational techniques and tools to rationalize the design of PPAR-γ agonists. Utilizing in silico techniques, potential agonistic effects on PPAR- γ have been uncovered, focusing on the examination of six commercially available anti-diabetic agents. The study pinpointed PPAR- γ a protein targets (1i7i). Further investigation is advised to explore the potential of these marketed anti-diabetic agents to evolve into a safe and scientifically substantiated multi-target drug for diabetes treatments.

Keywords: Computer-aided method, Sulfonylurea-containing antidiabetic drugs, Pharmacokinetics, Peroxisome Proliferator-Activated Receptor-? (1i7i), Toxicity Profile.