Centre of Drug Discovery, State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China.
J Mol Model. 2012 Jun;18(6):2795-804. doi: 10.1007/s00894-011-1303-1. Epub 2011 Nov 27.
Sodium-dependent glucose co-transporter 2 (SGLT2) plays a pivotal role in maintaining glucose equilibrium in the human body, emerging as one of the most promising targets for the treatment of diabetes mellitus type 2. Pharmacophore models of SGLT2 inhibitors have been generated with a training set of 25 SGLT2 inhibitors using Discovery Studio V2.1. The best hypothesis (Hypo1(SGLT2)) contains one hydrogen bond donor, five excluded volumes, one ring aromatic and three hydrophobic features, and has a correlation coefficient of 0.955, cost difference of 68.76, RMSD of 0.85. This model was validated by test set, Fischer randomization test and decoy set methods. The specificity of Hypo1(SGLT2) was evaluated. The pharmacophore features of Hypo1(SGLT2) were different from the best pharmacophore model (Hypo1(SGLT1)) of SGLT1 inhibitors we developed. Moreover, Hypo1(SGLT2) could effectively distinguish selective inhibitors of SGLT2 from those of SGLT1. These results indicate that a highly predictive and specific pharmacophore model of SGLT2 inhibitors has been successfully obtained. Then Hypo1(SGLT2) was used as a 3D query to screen databases including NCI and Maybridge for identifying new inhibitors of SGLT2. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. And several compounds selected from the top ranked hits have been suggested for further experimental assay studies.
钠依赖型葡萄糖共转运蛋白 2(SGLT2)在维持人体葡萄糖平衡中发挥着关键作用,成为治疗 2 型糖尿病最有前途的靶点之一。使用 Discovery Studio V2.1 用 25 种 SGLT2 抑制剂的训练集生成 SGLT2 抑制剂的药效团模型。最佳假设(Hypo1(SGLT2))包含一个氢键供体、五个排除体积、一个环芳基和三个疏水性特征,相关系数为 0.955,成本差异为 68.76,RMSD 为 0.85。该模型通过测试集、Fischer 随机化测试和诱饵集方法进行了验证。评估了 Hypo1(SGLT2)的特异性。Hypo1(SGLT2)的药效团特征与我们开发的 SGLT1 抑制剂的最佳药效团模型(Hypo1(SGLT1))不同。此外,Hypo1(SGLT2)可以有效地将 SGLT2 的选择性抑制剂与 SGLT1 的抑制剂区分开来。这些结果表明,已经成功获得了 SGLT2 抑制剂的高预测性和特异性药效团模型。然后,Hypo1(SGLT2)被用作 3D 查询来筛选包括 NCI 和 Maybridge 在内的数据库,以鉴定 SGLT2 的新抑制剂。随后,对命中化合物进行 Lipinski 的五规则过滤。从排名靠前的命中化合物中选择了几种化合物,建议进行进一步的实验测定研究。