Strategic development of benzimidazole-derived glycosidase modulators for antihyperglycemic intervention: a comprehensive medicinal study integrating molecular docking, biological evaluation, in vivo validation and pharmacokinetic assessment.

Abstract

Current in silico drug discovery workflows largely rely on ligand-based screening and structure-based molecular docking to identify enzyme inhibitors, often overlooking dynamic biological contexts that influence drug efficacy and stability. Integrating multiscale computational modeling with experimental validation at early stages of lead identification can significantly improve predictive accuracy and reduce attrition rates. In this study, we applied an integrated computational-biological framework to identify benzimidazole-based thiophene-derived bis-Schiff base derivatives as potential antidiabetic agents targeting α-amylase and α-glucosidase. Lead compounds were prioritized through molecular docking, pharmacophore modeling, density functional theory (DFT) analysis, quantitative structure-activity relationship (QSAR) modeling, and molecular dynamics (MD) simulations to evaluate binding affinity, electronic features, and protein-ligand stability. The computational predictions were corroborated by in vitro enzyme inhibition assays, identifying compound 1 as the most potent inhibitor with superior activity compared to acarbose. Furthermore, in vivo wound-healing evaluation in rabbit models demonstrated enhanced tissue regeneration and accelerated healing, indicating biological relevance beyond enzymatic inhibition. In silico ADMET analysis predicted favorable pharmacokinetic and low-toxicity profiles for the lead compound. Although demonstrated for α-amylase and α-glucosidase inhibition, the proposed integrative framework is readily adaptable to other enzyme-driven therapeutic discovery pipelines.