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基于网络药理学的臭牡丹属植物天然产物虚拟筛选以鉴定新型抗癌疗法。

Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics.

作者信息

Gogoi Barbi, Gogoi Dhrubajyoti, Silla Yumnam, Kakoti Bibhuti Bhushan, Bhau Brijmohan Singh

机构信息

Plant Genomic Laboratory, Medicinal Aromatic & Economic Plants (MAEP) Group, Biological Sciences & Technology Division (BSTD), CSIR-North East Institute of Science and Technology, Jorhat-785006, Assam, India.

DBT-BIF, Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh, Assam, India.

出版信息

Mol Biosyst. 2017 Jan 31;13(2):406-416. doi: 10.1039/c6mb00807k.

Abstract

Plant-derived natural products (NPs) play a vital role in the discovery of new drug molecules and these are used for development of novel therapeutic drugs for a specific disease target. Literature review suggests that natural products possess strong inhibitory efficacy against various types of cancer cells. Clerodendrum indicum and Clerodendrum serratum are reported to have anticancer activity; therefore a study was carried out to identify selective anticancer agents from these plants species. In this report, we employed a docking weighted network pharmacological approach to understand the multi-therapeutics potentiality of C. indicum and C. serratum against various types of cancer. A library of 53 natural products derived from these plants was compiled from the literature and three dimensional space analyses were performed in order to establish the drug-likeness of the NPs library. Further, an NPs-cancer network was built based on docking. We predicted five compounds, namely apigenin 7-glucoside, hispidulin, scutellarein-7-O-beta-d-glucuronate, acteoside and verbascoside, to be potential binding therapeutics for cancer target proteins. Apigenin 7-glucoside and hispidulin were found to have maximum binding interactions (relationship) with 17 cancer drug targets in terms of docking weighted network pharmacological analysis. Hence, we used an integrative approach obtained from network pharmacology for identifying combinatorial drug actions against the cancer targets. We believe that our present study may provide important clues for finding novel drug inhibitors for cancer.

摘要

植物源天然产物在新药分子的发现中起着至关重要的作用,这些天然产物被用于开发针对特定疾病靶点的新型治疗药物。文献综述表明,天然产物对各种类型的癌细胞具有强大的抑制功效。据报道,臭牡丹和大青具有抗癌活性;因此,开展了一项研究以从这些植物物种中鉴定出选择性抗癌剂。在本报告中,我们采用对接加权网络药理学方法来了解臭牡丹和大青对各种类型癌症的多治疗潜力。从文献中汇编了一个由这两种植物衍生的53种天然产物的文库,并进行了三维空间分析,以确定该天然产物文库的类药性。此外,基于对接构建了一个天然产物-癌症网络。我们预测了5种化合物,即芹菜素7-葡萄糖苷、圣草酚、黄芩素-7-O-β-D-葡萄糖醛酸、毛蕊花糖苷和异毛蕊花糖苷,它们是癌症靶蛋白的潜在结合治疗剂。根据对接加权网络药理学分析,发现芹菜素7-葡萄糖苷和圣草酚与17种癌症药物靶点具有最大的结合相互作用(关系)。因此,我们使用从网络药理学获得的综合方法来确定针对癌症靶点的联合药物作用。我们相信,我们目前的研究可能为寻找新型癌症药物抑制剂提供重要线索。

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