Integrated in Silico and in Vitro Bioactivity Profiling of Selected South African Plant Constituents Against Carbohydrate-Metabolizing Enzymes for Type 2 Diabetes Therapy

The increasing global burden of Type 2 diabetes mellitus (T2DM) and the side effects of conventional drugs have driven the search for natural therapeutic alternatives. This study investigates the antidiabetic potential of South African essential oils — specifically Artemisia afra, Agathosma betulina, and Cymbopogon citratus — which are traditionally used in diabetes treatment but lack comprehensive scientific validation. Using an integrated research framework: mining the phytochemical metabolites of the plant source from Dr. Duke’s Phytochemical and Ethnobotanical Database and relevant literature; screening of metabolites using molecular docking and molecular dynamics (MD) to identify potential leads; and in vitro enzymatic assays of the oils for α-amylase (AA) and α-glucosidase (AG) inhibition. Computational results identified quercetin-3,7-diglucoside (Q37DG), quercetin-7-O-glucoside (Q7G), and rutin as promising lead compounds. Notably, Q37DG demonstrated favorable inhibitory potential, with ΔG _bind values of -52.73 kcal/mol for AA and -36.60 kcal/mol for AG, exceeding the known inhibitor acarbose (-43.59 and -34.41 kcal/mol, respectively). MD simulations further supported Q37DG’s role in enhancing enzyme stability upon binding, indicating a competitive inhibition mechanism through strong interactions with catalytic residues such as ASP469 (AG) and ASP197 (AA). Genetic Algorithm-based Quantitative Structure-Activity Relationship (GA-QSAR) models predict impressive IC ₅₀ values for Q37DG (3.47 μM for AG and 0.68 μM for AA) compared to acarbose (6.92 and 2.51 μM). Experimental in vitro assays confirmed A. betulina essential oil as the oil with the most activity (IC ₅₀ = 6.02 μg/ml for AA and 6.70 μg/ml for AG). Surprisingly, the lead compounds identified via computational screening are reported constituents of A. betulina , thereby validating the integrated data mining and in silico approach. This work promotes green chemistry by valuing indigenous plant resources and suggests that these leads warrant further investigation for nano-formulation and therapeutic development.