Suaifan Ghadeer, Shehadeh Mayadah, Al-Ijel Hebah, Al-Jamal Khuloud T, Taha Mutasem
Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan.
Med Chem. 2016;12(4):371-93. doi: 10.2174/1573406411666151002130609.
Neuronal Nitric Oxide synthase (nNOS) is an attractive challenging target for the treatment of various neurodegenerative disorders. To date, several structure-based studies were conducted to search novel selective nNOS inhibitors.
Discovery of novel nNOS lead scaffolds through the integration of ligand-based threedimensional (3D) pharmacophore (s) with quantitative structure-activity relationship model.
The pharmacophoric space of ten structurally diverse sets acquired from 145 previously reported nNOS inhibitors was scrutinize to fabricate representative pharmacophores. Afterwards, genetic algorithm together with multiple linear regression analysis was applied to find out an optimal pharmacophoric models and 2D physicochemical descriptors able to produce optimal predictive QSAR equation (r(2) 116 =0.76, F = 353, r(2) LOO = 0.69, r(2) PRESS against 29 external test ligands =0.51). A minimum of three binding modes between ligands and nNOS binding pocket rationalized by the emergence of three pharmacophoric models in the QSAR equation were illustrated. The QSAR-selected pharmacophores were validated by receiver operating characteristic curves analysis and afterward invested as a tool for screening national cancer institute (NCI) database.
Low micro molar novel nNOS inhibitors were revealed.
Two structurally diverse compounds 148 and 153 demonstrated new scaffolds toward the discovery of potent nNOS inhibitors.
神经元型一氧化氮合酶（nNOS）是治疗各种神经退行性疾病的一个有吸引力且具有挑战性的靶点。迄今为止，已经进行了几项基于结构的研究以寻找新型选择性nNOS抑制剂。
通过将基于配体的三维（3D）药效团与定量构效关系模型相结合，发现新型nNOS先导骨架。
对从145种先前报道的nNOS抑制剂中获得的十个结构多样的数据集的药效团空间进行仔细研究，以构建代表性药效团。然后，应用遗传算法和多元线性回归分析来找出能够产生最佳预测QSAR方程（r(2) 116 = 0.76，F = 353，r(2) LOO = 0.69，r(2) PRESS针对29个外部测试配体 = 0.51）的最佳药效团模型和二维物理化学描述符。通过QSAR方程中出现的三种药效团模型，阐述了配体与nNOS结合口袋之间至少三种结合模式。通过受试者工作特征曲线分析对QSAR选择的药效团进行验证，然后将其用作筛选美国国立癌症研究所（NCI）数据库的工具。
发现了低微摩尔浓度的新型nNOS抑制剂。
两种结构不同的化合物148和153展示了用于发现强效nNOS抑制剂的新骨架。
