Al-Sha'er Mahmoud A, Almazari Inas S, Taha Mutasem O
Faculty of Pharmacy, Zarqa University, Zarqa, Jordan.
Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, Amman, Jordan.
J Mol Recognit. 2017 Jun;30(6). doi: 10.1002/jmr.2604. Epub 2016 Dec 23.
Inhibitor kappa-B kinase-beta (IKK-β) controls the activation of nuclear transcription factor kappa-B and has been linked to inflammation and cancer. Therefore, inhibitors of this kinase should have potent anti-inflammatory and anticancer properties. Accordingly, we explored the pharmacophoric space of 218 IKK-β inhibitors to identify high-quality binding models. Subsequently, genetic algorithm-based quantitative structure activity relationship (QSAR) analysis was employed to select the best possible combination of pharmacophoric models and physicochemical descriptors that explain bioactivity variation among training compounds. Three successful pharmacophores emerged in 2 optimal QSAR equations (r = 0.733, r = 0.52, F1 = 65.62, r against 43 test inhibitors = 0.63 and r = 0.683, r = 0.52, F2 = 72.66, r against 43 test inhibitors = 0.65). Two pharmacophores were merged in a single binding model. Receiver operating characteristic curve validation proved the excellent qualities of this model. The merged pharmacophore and the associated QSAR equations were applied to screen the National Cancer Institute list of compounds. Ten hits were found to exhibit potent anti-IKK-β bioactivity, out of which, one illustrates IC50 of 11.0nM.
抑制蛋白κB激酶β（IKK-β）控制核转录因子κB的激活，并且与炎症和癌症相关。因此，这种激酶的抑制剂应具有强大的抗炎和抗癌特性。相应地，我们探索了218种IKK-β抑制剂的药效团空间，以识别高质量的结合模型。随后，采用基于遗传算法的定量构效关系（QSAR）分析来选择药效团模型和物理化学描述符的最佳可能组合，以解释训练化合物之间的生物活性差异。在2个最佳QSAR方程中出现了3个成功的药效团（r = 0.733，r = 0.52，F1 = 65.62，与43种测试抑制剂的r = 0.63以及r = 0.683，r = 0.52，F2 = 72.66，与43种测试抑制剂的r = 0.65）。将两个药效团合并到一个单一的结合模型中。受试者工作特征曲线验证证明了该模型的优异质量。将合并的药效团和相关的QSAR方程应用于筛选美国国立癌症研究所的化合物列表。发现有10种化合物表现出强大的抗IKK-β生物活性，其中一种的IC50为11.0nM。
