In Silico Searching for Alternative Lead Compounds to Treat Type 2 Diabetes through a QSAR and Molecular Dynamics Study

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic beta-cells. An advantage of therapies that target FFA1 is their reduced risk of hypoglycemia relative to common type 2 diabetes treatments. In this work, quantitative structure-activity relationship (QSAR) approach was used to construct models to identify possible FFA1 agonists by applying four different machine-learning algorithms. The best model (M2) meets the Tropsha's test requirements and has the statistics parameters R-2 = 0.843, Q(CV)(2) = 0.785, and Q(ext)(2) = 0.855. Also, coverage of 100% of the test set based on the applicability domain analysis was obtained. Furthermore, a deep analysis based on the ADME predictions, molecular docking, and molecular dynamics simulations was performed. The lipophilicity and the residue interactions were used as relevant criteria for selecting a candidate from the screening of the DiaNat and DrugBank databases. Finally, the FDA-approved drugs bilastine, bromfenac, and fenofibric acid are suggested as potential and lead FFA1 agonists.

Automated summary

In this study, a best model was constructed using machine-learning algorithms to identify FFA1 agonists. Deep analysis based on ADME predictions and molecular simulations suggested bilastine, bromfenac, and fenofibric acid as potential FFA1 agonists.