Hajalsiddig Tawassl T H, Osman Abu Baker M, Saeed Ahmed E M
Department of Chemistry, College of Science, Sudan University of Science and Technology, Khartoum, Sudan.
Department of Chemistry, College of Science & Arts (Suratabidha), King Khalid University, Abha, Saudi Arabia.
ACS Omega. 2020 Jul 24;5(30):18662-18674. doi: 10.1021/acsomega.0c01323. eCollection 2020 Aug 4.
Epidermal growth factor receptor (EGFR) kinase has been commonly associated with cancers such as lung, ovarian, hormone-refractory prostate, metastatic colorectal, glioblastoma, pancreatic, and breast cancers. A series of 1-pyrazole-1-carbothioamide derivatives and their EGFR inhibitory activities were subjected to two-dimensional (2D) quantitative structure-activity relationship (2D-QSAR) studies. The 2D-QSAR models were constructed based on a forward selection of partial least-squares (PLS) and stepwise multiple linear regression (SW-MLR) methods validated by leave-one-out (LOO) and external test set prediction approaches. The stepwise multiple linear regression (SW-MLR) method presented an encouraging result as compared to other methods. The results of the study indicated that the activity of 1-pyrazole-1-carbothioamide derivatives as an EGFR kinase inhibitor was more influenced by adjacency distance matrix descriptors. The models were improved after outlier removal through the applicability domain. Based on the resultant models, 11 new compounds with high potency were designed as EGFR kinase inhibitors. Molecular docking studies were performed for designing compounds, and they were compared with erlotinib as a reference to predict their interactions in the active site and identify structural features necessary for producing biological activities.
表皮生长因子受体（EGFR）激酶通常与肺癌、卵巢癌、激素难治性前列腺癌、转移性结直肠癌、胶质母细胞瘤、胰腺癌和乳腺癌等癌症相关。对一系列1-吡唑-1-碳硫酰胺衍生物及其EGFR抑制活性进行了二维（2D）定量构效关系（2D-QSAR）研究。基于偏最小二乘法（PLS）的正向选择和逐步多元线性回归（SW-MLR）方法构建2D-QSAR模型，并通过留一法（LOO）和外部测试集预测方法进行验证。与其他方法相比，逐步多元线性回归（SW-MLR）方法给出了令人鼓舞的结果。研究结果表明，1-吡唑-1-碳硫酰胺衍生物作为EGFR激酶抑制剂的活性受邻接距离矩阵描述符的影响更大。通过适用域去除异常值后，模型得到了改进。基于所得模型，设计了11种高效的新型化合物作为EGFR激酶抑制剂。对设计的化合物进行了分子对接研究，并与作为参考的厄洛替尼进行比较，以预测它们在活性位点的相互作用，并确定产生生物活性所需的结构特征。
