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3D-QSAR Docking ADME/Tox studies on Flavone analogs reveal anticancer activity through Tankyrase inhibition

机译:对黄酮类似物的3D-QSAR对接ADME / Tox研究表明通过Tankyrase抑制作用具有抗癌活性

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摘要

Flavones are known as an inhibitor of tankyrase, a potential drug target of cancer. We here expedited the use of different computational approaches and presented a fast, easy, cost-effective and high throughput screening method to identify flavones analogs as potential tankyrase inhibitors. For this, we developed a field point based (3D-QSAR) quantitative structure-activity relationship model. The developed model showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 (0.89) and q2 (0.67). This model may help to explain SAR data and illustrated the key descriptors which were firmly related with the anticancer activity. Using the QSAR model a dataset of 8000 flavonoids were evaluated to classify the bioactivity, which resulted in the identification of 1480 compounds with the IC50 value of less than 5 µM. Further, these compounds were scrutinized through molecular docking and ADMET risk assessment. Total of 25 compounds identified which further analyzed for drug-likeness, oral bioavailability, synthetic accessibility, lead-likeness, and alerts for PAINS & Brenk. Besides, metabolites of screened compounds were also analyzed for pharmacokinetics compliance. Finally, compounds F2, F3, F8, F11, F13, F20, F21 and F25 with predicted activity (IC50) of 1.59, 1, 0.62, 0.79, 3.98, 0.79, 0.63 and 0.64, respectively were find as top hit leads. This study is offering the first example of a computationally-driven tool for prioritization and discovery of novel flavone scaffold for tankyrase receptor affinity with high therapeutic windows.
机译:黄酮类化合物是坦科酶的抑制剂,坦科酶是一种潜在的癌症靶标。在这里,我们加快了不同计算方法的使用,并提出了一种快速,简便,具有成本效益和高通量的筛选方法,以鉴定黄酮类似物是否为潜在的tankyrase抑制剂。为此,我们开发了基于场点的(3D-QSAR)定量构效关系模型。所开发的模型具有标准的统计参数r 2 (0.89)和q 2 (0.67)表示的可接受的预测和描述能力。该模型可能有助于解释SAR数据,并说明与抗癌活性密切相关的关键描述符。使用QSAR模型评估了8000个类黄酮的数据集,以对生物活性进行分类,从而鉴定出1480种化合物,IC50值小于5 µM。此外,通过分子对接和ADMET风险评估对这些化合物进行了审查。总共鉴定出25种化合物,进一步分析了它们的药物相似性,口服生物利用度,合成可及性,铅相似性以及PAINS和Brenk的警报。此外,还分析了筛选化合物的代谢物的药代动力学依从性。最后,发现化合物F2,F3,F8,F11,F13,F20,F21和F25的预测活性(IC50)分别为1.59、1、0.62、0.79、3.98、0.79、0.63和0.64。这项研究提供了一个计算驱动工具的第一个示例,该工具可对具有高治疗窗的坦科酶受体亲和力的新型黄酮支架进行优先级排序和发现。

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