Abdullahi Sagiru Hamza, Uzairu Adamu, Shallangwa Gideon Adamu, Uba Sani, Umar Abdullahi Bello
Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Kaduna State, Nigeria.
J Taibah Univ Med Sci. 2023 Mar 9;18(5):1018-1029. doi: 10.1016/j.jtumed.2023.02.015. eCollection 2023 Oct.
Breast tumor is ranked as the most common tumor type identified among women globally with over 1.7 million cases annually, representing 11.9% of the total number of cancer cases. Approved anti-breast tumor drugs exhibit several side effects and some patients develop resistance during the early treatment stage. This study aimed to use an in-silico approach to identify and design potential therapeutic agents.
Robust 3D-QSAR models were developed using quinazoline-4(3H)-one analogs as EGFR inhibitors. The best model was then selected based on statistical parameters and was subsequently used to design more potent therapeutic agents. Molecular docking simulation was executed using the data set and the designed compounds to identify lead compounds which were further screened by pharmacokinetic profiling by applying SwissADME and pkCSM software.
Internal validations of the best CoMFA and CoMSIA models (R = 0.855 and 0.895; Q = 0.570 and 0.599) passed the threshold values for the establishment of a consistent QSAR model. The constructed models were further validated externally using six compounds as a test set, thus revealing a satisfactory predicted correlation coefficient (R = 0.657 and 0.681). The CoMSIA_SHE models with the best statistical parameters were further subjected to applicability domain checks and only three influentials were detected. These were then utilized to design five novel compounds with activities ranging from 5.62 to 6.03. Molecular docking studies confirmed that compounds 20 to 26, with docking scores ranging from -163.729 to -169.796, represented lead compounds with higher docking scores compared to Gefitinib (-127.495). Furthermore, the designed compounds exhibited better docking scores ranging from -171.379 to -179.138.
Pharmacological studies identified compounds 20, 24 26 and the designed compounds 2, 3, 5 as feasible drug candidates. However, these theoretical findings should now be validated experimentally.
乳腺癌是全球女性中最常见的肿瘤类型,每年有超过170万例病例,占癌症病例总数的11.9%。已获批的抗乳腺癌药物存在多种副作用,且部分患者在治疗早期就会产生耐药性。本研究旨在采用计算机辅助方法来识别和设计潜在的治疗药物。
以喹唑啉-4(3H)-酮类似物作为表皮生长因子受体(EGFR)抑制剂,构建稳健的三维定量构效关系(3D-QSAR)模型。然后根据统计参数选择最佳模型,并随后用于设计更有效的治疗药物。使用数据集和设计的化合物进行分子对接模拟,以识别先导化合物,并通过应用SwissADME和pkCSM软件进行药代动力学分析对其进一步筛选。
最佳比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)模型的内部验证(R = 0.855和0.895;Q = 0.570和0.599)通过了建立一致QSAR模型的阈值。使用六种化合物作为测试集对构建的模型进行外部进一步验证,从而揭示了令人满意的预测相关系数(R = 0.657和0.681)。对具有最佳统计参数的CoMSIA_SHE模型进行适用域检查,仅检测到三个有影响力的因素。然后利用这些因素设计了五种活性范围为5.62至6.03的新型化合物。分子对接研究证实,对接分数范围为-163.729至-169.796的化合物20至26与吉非替尼(-127.495)相比代表具有更高对接分数的先导化合物。此外,设计的化合物表现出更好的对接分数,范围为-171.379至-179.138。
药理学研究确定化合物20、24、26以及设计的化合物2、3、5为可行的候选药物。然而,这些理论发现现在应该通过实验进行验证。