Prediction of Alpha-Glucosidase Inhibition Activity for the Management of Type 2 Diabetes Using the Prediction of Activity Spectra of Substances Software

Ms. Neha Sharma


Background: Alpha-glucosidase inhibition was found to be an effective strategy in the management of type 2 diabetes mellitus, a disorder of multiple factors mainly defined by abnormality in either or both insulin secretion or its required function in the body. Objective: The core purpose of the study was to predict the active moieties from a pool of pharmacologically important phytoconstituents for alpha-glucosidase inhibition property using the prediction of activity spectra of substances (PASS) software. Methods: PASS is valuable software which is used in this study for the prediction of alpha-glucosidase inhibition activity of different selected constituents. Canonical Simplified Molecular-Input Line-Entry System is used in the prediction of the activity which is obtained from the PubChem website. The predicted activity was compared with the marketed standard drug, acarbose. Results: It was found that among the screened compounds, rutin, isoquercitrin, and hyperoside are having highest probable activity (Pa) value of 0.858, 0.842, and 0.842, respectively. These phytoconstituents showed less predicted activity against alpha-glucosidase inhibition, as compared to acarbose with Pa of 0.958. Conclusion: Rutin, isoquercitrin, and hyperoside showed good Pa against alpha-glucosidase inhibition, and these phytoconstituents can be further investigated for the same activity using in vitro and in vivo techniques and hence might become future drugs as alpha-glucosidase inhibitors.

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