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QSAR and docking studies on xanthone derivatives for anticancer activity targeting DNA topoisomerase IIα

机译:蒽酮衍生物对DNA拓扑异构酶IIα的抗癌活性的QSAR和对接研究

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Abstract: Due to the high mortality rate in India, the identification of novel molecules is important in the development of novel and potent anticancer drugs. Xanthones are natural constituents of plants in the families Bonnetiaceae and Clusiaceae, and comprise oxygenated heterocycles with a variety of biological activities along with an anticancer effect. To explore the anticancer compounds from xanthone derivatives, a quantitative structure activity relationship (QSAR) model was developed by the multiple linear regression method. The structure–activity relationship represented by the QSAR model yielded a high activity–descriptors relationship accuracy (84%) referred by regression coefficient (r2=0.84) and a high activity prediction accuracy (82%). Five molecular descriptors – dielectric energy, group count (hydroxyl), LogP (the logarithm of the partition coefficient between n-octanol and water), shape index basic (order 3), and the solvent-accessible surface area – were significantly correlated with anticancer activity. Using this QSAR model, a set of virtually designed xanthone derivatives was screened out. A molecular docking study was also carried out to predict the molecular interaction between proposed compounds and deoxyribonucleic acid (DNA) topoisomerase IIα. The pharmacokinetics parameters, such as absorption, distribution, metabolism, excretion, and toxicity, were also calculated, and later an appraisal of synthetic accessibility of organic compounds was carried out. The strategy used in this study may provide understanding in designing novel DNA topoisomerase IIα inhibitors, as well as for other cancer targets.
机译:摘要:由于印度的高死亡率,新型分子的鉴定对于新型有效抗癌药物的开发非常重要。氧杂蒽酮是Bonnetiaceae和Clusiaceae家族中植物的天然成分,并且包含具有多种生物活性以及抗癌作用的氧化杂环。为了探索黄嘌呤衍生物的抗癌化合物,采用多元线性回归方法建立了定量构效关系(QSAR)模型。由QSAR模型表示的构效关系产生了高的活动描述符关系准确度(84%)(由回归系数(r2 = 0.84)表示)和高的活动预测准确度(82%)。五个分子描述符-介电能,基团数(羟基),LogP(正辛醇和水之间的分配系数的对数),形状指数碱性(3阶)和溶剂可及的表面积-与抗癌性显着相关活动。使用该QSAR模型,筛选出一组虚拟设计的蒽酮衍生物。还进行了分子对接研究,以预测拟议化合物与脱氧核糖核酸(DNA)拓扑异构酶IIα之间的分子相互作用。还计算了吸收,分布,代谢,排泄和毒性等药代动力学参数,随后对有机化合物的合成可及性进行了评估。这项研究中使用的策略可以为设计新型DNA拓扑异构酶IIα抑制剂以及其他癌症靶标提供理解。

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