结构洞察苯基喹啉类似物作为微管蛋白聚合抑制剂:在硅方法

Prerna Chourasia, Vivek Asati, Shivangi Agarwal, Mitali Mishra, Varsha Kashaw, R. Das, S. Kashaw
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引用次数: 0

摘要

癌症是增长最快的流行病之一,每年影响数百万人。市场上有一些抗癌药物,但它们会产生令人讨厌的副作用。目前,以秋水仙碱结合位点为靶点的微管蛋白抑制剂因其结构简单、药代动力学良好、副作用少而被认为是一个重要的靶点。不同的研究人员进行了许多研究,以发现一种新的靶向秋水仙碱结合位点的高安全性和效力的微管蛋白抑制剂。在本研究中,我们使用不同的药物设计工具对从文献中获得的48种苯乙烯喹啉类似物进行了计算分析。药效团作图研究是为了确定生物活性所必需的重要药效特征。通过基于原子的3D-QSAR(三维定量构效关系)分析,了解不同原子对模型开发的贡献。生成的模型对训练集和测试集的决定系数具有统计显著性。根据R2(0.8624)和Q2(0.6707)值选择最佳QSAR模型。模型的等高线图分析揭示了微管蛋白抑制所必需的化学特征。对强效苯基喹啉类似物9VII-f(46)进行了对接研究,其SP对接得分最高(-5.494)。ADME(吸收、分布、代谢和排泄)分析为新设计化合物的可药性提供了有价值的信息。
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Structural insights into styrylquinolines analogs as tubulin polymerization inhibitors: In silico Approach
: Cancer is one of the fastest-growing epidemics that affect millions yearly. A handful of anticancer drugs are available on the market, but they produce undesirable side effects. Currently, tubulin inhibitors targeting the colchicine binding site are considered an important target due to their structural simplicity and favorable pharmacokinetics with fewer side effects. Different researchers conducted many studies to discover a novel tubulin inhibitor targeting the colchicine binding site with high safety and potency. In the present study, we performed computational analysis of 48 styrylquinolines analogs obtained from literature using different drug designing tools. The pharmacophore mapping study was conducted to identify the important pharmacophoric features essential for biological activity. Atom-based 3D-QSAR (3-dimensional quantitative structure-activity relationship) analysis was carried out to know the contribution of different atoms to model development. The generated model showed a statistically significant coefficient of determinations for the training and test sets. The best QSAR model was selected based on R2 (0.8624) and Q2 (0.6707) values. Contour plot analysis of the developed model unveiled the chemical features necessary for tubulin inhibition. A docking study was performed on potent styrylquinoline analog 9VII-f(46), which shows the highest SP docking scores (-5.494). ADME (Absorption, distribution, metabolism, and excretion) analysis provides valuable information about the drugability of newly designed compounds.
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