Antidiabetic drugs and its pharmacological profile target in human for type 2 diabetes mellitus

With the advent of Covid-19, it is necessary to repurpose new treatment alternatives, either in the form of an effective single dose of these antidiabetic drug agents or an efficacious combination of them for elderly population with or had suffered this condition. Newer medication classes for diabetes have been increasing in use, and their utilization varied across provinces and how the COVID-19 pandemic may have affected these trends. This mini review has the objective of predicting the targets of bioactive small molecules in human using healthcare applied artificial intelligence. These substances in the body are target specific receptors on the surface of cells, and antidiabetic drugs contains moiety´s and functional groups that may act on one or several subtypes of receptors. The tool is a tuned algorithm, with novel data bank in web interface. Information figures of Swiss Target Prediction (STP), which allow predictions on therapy combinations and probable side effects, were reordered. The bibliographic databases were searched to identify existing cost studies, economic evaluations and models, and most of the parameters were derived from the published literatura. Predictions are done using a ligand-based approach, based on the similarity between a query molecule and the known ligands of a large collection of protein targets. The result of this study highlighted crucial recommendations for the use of some of the antidiabetic agent groups in diabetic patients. Our findings provide real world evidence supporting the potential clinical benefits of diabetes drug agents in mitigating side effects and development in patients with T2DM.