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Design, Synthesis and Evaluation of Thiazole Analogues as α-Glucosidase Inhibitors: A QSAR-Guided Study

Oct 2026 · Journal of Biomedical and Pharmaceutical Research · Vol 15, pp. 1-14 · 0 citations

TL;DR

Computer-aided drug-design methods are used to investigate thiazole and benzothiazole analogues as potential α-glucosidase inhibitors, followed by synthesis and in vitro evaluation of selected derivatives, identifying preliminary structure–activity patterns for further optimization.

Abstract

α-Glucosidase inhibition is an established approach for reducing postprandial glucose excursions in type 2 diabetes. This study used computer-aided drug-design methods to investigate thiazole and benzothiazole analogues as potential α-glucosidase inhibitors, followed by synthesis and in vitro evaluation of selected derivatives. A dataset of 46 inhibitors was assembled from previously reported series, and reported IC₅₀ values were converted to pIC₅₀ values for quantitative structure–activity relationship (QSAR) modelling. Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), hologram QSAR (HQSAR), pharmacophore modelling and molecular docking were used to examine structure–activity trends and prioritize candidate structures. Based on the computational workflow, 102 compounds were designed and a subset was selected for synthesis. Thirteen derivatives (MP1–MP13) were reported as synthesized and evaluated in an in vitro α-glucosidase inhibition assay, with acarbose as the reference inhibitor. The reported IC₅₀ values ranged from 294.55 to 623.5 µg/mL for the synthesized compounds, compared with 110.9 µg/mL for acarbose. MP2 and MP13 showed the lowest IC₅₀ values among the synthesized derivatives. These results identify preliminary structure–activity patterns for further optimization; however, the synthesized compounds were less potent than acarbose in the reported assay and require additional validation. Keywords: α-glucosidase; thiazole; benzothiazole; QSAR; CoMFA; CoMSIA; HQSAR; molecular docking; pharmacophore modelling.

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