Modeling and informatics in designing anti-diabetic agents
Abstract
Diabetes mellitus is a chronic metabolic disorder, characterized by glucose overproduction and glucose underutilization. Current therapy for T2DM includes drugs, like metformin, glitazones, sulphonyl ureas, etc. Extensive research has been carried out world wide on molecular targets for T2DM like PPARgamma, PTP1B, DPP-IV, GSK-3, cannabinoid receptor, fructose-bisphosphatases, beta3 adrenoceptor, etc. in the development of newer anti-diabetic agents. These therapeutic targets are quite important and most of them are suitable for in silico analysis. Hence, many molecular modeling and informatics studies like, molecular docking, pharmacophore mapping, 3D-QSAR, virtual screening, quantum chemical studies, and pharmacoinformatics like bioinformatics and chemoinformatics studies have been performed on the drugs/leads/targets associated with T2DM. Several of these in silico efforts are exemplary studies; the methodologies adopted in these studies can be emulated in many other therapeutic areas. A review of the rational approaches reported in designing anti-diabetic agents is presented in this article.
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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