ArticleCurrent drug discovery technologies2024
In Search of Novel SGLT2 Inhibitors by High-throughput Virtual Screening.
Article in Current drug discovery technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
10 authors.
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Abstract
backgroundType 2 diabetes mellitus constitutes approximately 90% of all reported forms of diabetes mellitus. Insulin resistance characterizes this manifestation of diabetes. The prevalence of this condition is commonly observed in patients aged 45 and above; however, there is an emerging pattern of younger cohorts receiving diagnoses primarily attributed to lifestyle-related variables, including obesity, sedentary behavior, and poor dietary choices. The enzyme SGLT2 exerts a negative regulatory effect on insulin signaling pathways, resulting in the development of insulin resistance and subsequent elevation of blood glucose levels. The maintenance of glucose homeostasis relies on the proper functioning of insulin signaling pathways, while disruptions in insulin signaling can contribute to the development of type 2 diabetes.
objectiveOur study aimed to identify novel SGLT2 inhibitors by high-throughput virtual Screening.
methodsWe screened the May bridge Hit Discover database to identify potent hits followed by druglikeness, synthetic accessibility, PAINS alert, toxicity estimation, ADME assessment, and consensus molecular docking.
resultsThe screening process led to the identification of three molecules that demonstrated significant binding affinity, favorable drug-like properties, effective ADME, and minimal toxicity.
conclusionThe identified molecules could manage T2DM effectively by inhibiting SGLT2, providing a promising avenue for future therapeutic strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.