IN SILICO EVALUATION OF FLAVONOIDS FROM ACALYPHA INDICA L. AS Α-GLUCOSIDASE INHIBITORS FOR THE TREATMENT OF DIABETES MELLITUS
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Abstract
Type 2 diabetes mellitus continues to pose a significant global health burden, highlighting the urgent need for safer and more effective therapeutic agents with minimal adverse effects. One promising strategy involves targeting α-glucosidase, a key enzyme in postprandial glucose regulation, using bioactive compounds derived from medicinal plants. Acalypha indica, a tropical species widely used in traditional medicine, contains diverse flavonoids with potential antidiabetic activity; however, their molecular mechanisms remain insufficiently explored. This study employed an in silico approach to systematically evaluate the α-glucosidase inhibitory potential of five flavonoids from A. indica—mauritanin, repandusinic acid, hesperetin, glucogalin, and acaindinin. Molecular docking analysis using AutoDock revealed that all compounds exhibited favorable binding affinities toward the α-glucosidase enzyme (PDB ID: 5NN8), indicating spontaneous interactions at the active site. Among them, mauritanin demonstrated the highest binding affinity (–10.3 kcal/mol), forming multiple stabilizing hydrogen bonds with critical catalytic residues, including ASP69, HIS279, and GLU411. Notably, both mauritanin and repandusinic acid showed stronger binding interactions compared to the standard inhibitor acarbose, suggesting superior inhibitory potential. Further interaction analysis using LigPlot+ and Discovery Studio Visualizer confirmed stable ligand–enzyme complex formation, while ADMET predictions using SwissADME indicated favorable pharmacokinetic properties, including good oral bioavailability, low toxicity risk, and absence of major cytochrome P450 inhibition. Overall, these findings identify mauritanin and repandusinic acid as promising lead compounds for α-glucosidase inhibition and support the therapeutic potential of A. indica as a natural source for antidiabetic drug development. This study provides a strong computational foundation for future experimental validation and drug design efforts.
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