Pharmaceutical Design and Simulation (PhDS) Laboratory, School of Pharmaceutical Sciences, Universiti Sains Malaysia, 11800 Minden, Pulau Pinang, Malaysia.
Eur J Med Chem. 2011 Jun;46(6):2513-29. doi: 10.1016/j.ejmech.2011.03.040. Epub 2011 Mar 25.
Peroxisome Proliferator-Activated Receptor γ (PPARγ) activators have drawn great recent attention in the clinical management of type 2 diabetes mellitus, prompting several attempts to discover and optimize new PPARγ activators. With this in mind, we explored the pharmacophoric space of PPARγ using seven diverse sets of activators. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing self-consistent and predictive quantitative structure-activity relationship (QSAR) (r2(71)=0.80, F=270.3, r2LOO=0.73, r2PRESS against 17 external test inhibitors=0.67). Three orthogonal pharmacophores emerged in the QSAR equation and were validated by receiver operating characteristic (ROC) curves analysis. The models were then used to screen the national cancer institute (NCI) list of compounds. The highest-ranking hits were tested in vitro. The most potent hits illustrated EC50 values of 15 and 224 nM.
过氧化物酶体增殖物激活受体 γ (PPARγ) 激动剂在 2 型糖尿病的临床治疗中受到了极大的关注，这促使人们尝试发现和优化新的 PPARγ 激动剂。考虑到这一点，我们使用了七组不同的激动剂来探索 PPARγ 的药效基团空间。随后，我们使用遗传算法和多元线性回归分析选择了最佳的药效基团模型和二维物理化学描述符组合，以建立能够进行自我一致和预测性定量构效关系 (QSAR) 的模型 (r2(71)=0.80，F=270.3，r2LOO=0.73，r2PRESS 对 17 个外部测试抑制剂=0.67)。QSAR 方程中出现了三个正交药效基团，并通过接收者操作特性 (ROC) 曲线分析进行了验证。然后，我们使用这些模型对国家癌症研究所 (NCI) 的化合物列表进行了筛选。排名最高的命中化合物进行了体外测试。最有效的命中化合物的 EC50 值分别为 15 和 224 nM。
