Computer-Aided Drug Design and Molecular Dynamics Simulation of Novel Thiazolidinedione Derivatives as PPAR? Agonists for Type 2 Diabetes Management: An In Silico Approach with Pharmacophore Modelling and ADMET Prediction

Abstract

Type 2 diabetes mellitus (T2DM), affecting an estimated 101 million people in India as of 2019 — the second highest national prevalence globally —demands continuous innovation in antidiabetic drug discovery to address thelimitations of currently available therapeutic agents, including the fluidretention and cardiovascular risk concerns associated with the approvedthiazolidinedione class PPAR? agonists rosiglitazone and pioglitazone.Peroxisome proliferator-activated receptor gamma (PPAR?) remains avalidated and mechanistically attractive antidiabetic target given its centralrole in adipogenesis, glucose homeostasis, and insulin sensitisation, but theclinical translation of full PPAR? agonists has been limited by on-targetcardiovascular adverse effects attributable to full agonism at the adipogenictranscriptional programme. This computer-aided drug design (CADD) studydesigned six novel thiazolidinedione (TZD) derivatives based on pharmacophore modelling of known partial PPAR? agonists, conductedmolecular docking at the PPAR? ligand-binding domain (LBD) usingAutoDock Vina with the PPAR?-rosiglitazone co-crystal structure (PDB:2PRG), performed 50-nanosecond molecular dynamics simulation of the leadcompound TZD-4 in complex with PPAR?-LBD to assess binding stability, andpredicted ADMET properties using SwissADME and pkCSM platforms. TZD-4exhibited the highest predicted binding affinity (?11.68 kcal/mol versus ?9.28 kcal/mol for rosiglitazone), with molecular dynamics simulation confirming stable binding over 50 ns (mean RMSD 1.84 Å), and a predicted ADMET profile consistent with oral drug-likeness and reduced cardiovascular liability relative to rosiglitazone. These computational findings provide a rationally designed lead TZD scaffold for synthesis and in vitro biological validation.  KEYWORDS: Computer-aided drug design, PPAR?, Thiazolidinedione, Type2 diabetes, Molecular docking, Molecular dynamics, ADMET, Drugdiscovery, Pharmacophore, Antidiabetic