通过定量构效关系和分子动力学研究在计算机上寻找治疗2型糖尿病的替代先导化合物

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

作者信息

Cabrera Nicolás, Cuesta Sebastián A, Mora José R, Calle Luis, Márquez Edgar A, Kaunas Roland, Paz José Luis

机构信息

Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.

Department of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.

出版信息

Pharmaceutics. 2022 Jan 19;14(2):232. doi: 10.3390/pharmaceutics14020232.

Free fatty acid receptor 1 (FFA1) stimulates insulin secretion in pancreatic β-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 = 0.843, Q = 0.785, and Q = 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.

游离脂肪酸受体1（FFA1）可刺激胰腺β细胞分泌胰岛素。相对于常见的2型糖尿病治疗方法，靶向FFA1的疗法的一个优势在于其低血糖风险较低。在本研究中，采用定量构效关系（QSAR）方法，通过应用四种不同的机器学习算法构建模型，以识别可能的FFA1激动剂。最佳模型（M2）符合特罗普沙检验要求，其统计参数为R = 0.843、Q = 0.785和Q = 0.855。此外，基于适用域分析获得了对测试集100%的覆盖率。此外，还基于药物代谢动力学/药物效应动力学（ADME）预测、分子对接和分子动力学模拟进行了深入分析。亲脂性和残基相互作用被用作从DiaNat和DrugBank数据库筛选中选择候选物的相关标准。最后，美国食品药品监督管理局（FDA）批准的药物比拉斯汀、溴芬酸和非诺贝特酸被建议作为潜在的先导FFA1激动剂。