An integrated network pharmacology and molecular modelling study of phytoconstituents targeting Alzheimer's disease

Saumya Khanna, Divakar Selvaraj, Mehak Tyagi, Devadharshini, Saravanan Jayaram
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Abstract

The present study involves the use of combined network pharmacology and molecular modelling approach for identifying important phytoconstituents that could modulate the functions of multiple therapeutic targets in Alzheimer’s disease. A list of botanicals reported in the literature for their efficacy in Alzheimer’s disease, the phytochemicals present in the botanicals were identified with the help of network pharmacology approach. The pharmacokinetic properties like blood brain barrier penetration and Lipinski’s rule of five for the selected phytoconstituents were analyzed. The major targets involved in the pathogenesis of Alzheimer’s disease were collected from the DisGeNET database. The selected proteins were subjected to topological analysis using Cytoscape software to identify the important targets in the network. The top 7 phytoconstituents and 5 proteins were subjected to molecular docking, MM-GBSA and molecular dynamics studies. A total of 15 plants and 1443 phytoconstituents were identified through a literature survey and from several databases. The pharmacokinetics study revealed that 7 phytoconstituents - glycyrrhisoflavone, eugenol, ferulic acid, methyl jasmonate, geranyl formate, formononetin, and elemicin- exhibited favourable pharmacokinetic properties. Five targets, HMOX1, CNR1, STAT3, HDAC2, and MAOB were found to be important in the network of 3300 proteins based on degree centrality and betweenness centrality. Among the seven phytoconstituents, glycyrrhisoflavone exhibited good dock scores and free energy value. Based on this, the stability of glycyrrhisoflavone with the five selected targets were analyzed using molecular dynamics study. Glycyrrhisoflavone showed good stability with most of the selected therapeutic targets. The current study reveals that the selected phytoconstituents i.e glycyrrhisoflavone, eugenol, ferulic acid, methyl jasmonate, geranyl formate, formononetin, and elemicin could serve as good lead molecules in treatment and management of Alzheimer’s disease through modulation of multiple targets.

针对阿尔茨海默病的植物成分的网络药理学和分子模型综合研究
本研究采用网络药理学和分子建模相结合的方法,以确定可调节阿尔茨海默病多个治疗靶点功能的重要植物成分。在网络药理学方法的帮助下,确定了文献中报道的对阿尔茨海默病有疗效的植物药清单以及植物药中的植物化学物质。分析了所选植物成分的药代动力学特性,如脑血屏障渗透性和利宾斯基五法则。从 DisGeNET 数据库中收集了涉及阿尔茨海默病发病机制的主要靶标。利用 Cytoscape 软件对所选蛋白质进行拓扑分析,以确定网络中的重要靶标。对排名前 7 位的植物成分和 5 个蛋白质进行了分子对接、MM-GBSA 和分子动力学研究。通过文献调查和多个数据库,共鉴定出 15 种植物和 1443 种植物成分。药代动力学研究显示,7 种植物成分--甘草次黄酮、丁香酚、阿魏酸、茉莉酸甲酯、甲酸香叶酯、甲芒柄花素和榄香素--表现出良好的药代动力学特性。根据度中心性和间度中心性,发现 HMOX1、CNR1、STAT3、HDAC2 和 MAOB 这五个靶点在由 3300 个蛋白质组成的网络中非常重要。在这七种植物成分中,甘草黄酮表现出良好的对接得分和自由能值。在此基础上,利用分子动力学研究分析了甘草黄酮与五个选定靶标的稳定性。甘草黄酮与大多数选定的治疗靶点都表现出良好的稳定性。目前的研究表明,所选的植物成分(即甘草黄酮、丁香酚、阿魏酸、茉莉酸甲酯、甲酸香叶酯、甲芒柄花素和榄香素)可作为良好的先导分子,通过调节多个靶点来治疗和控制阿尔茨海默病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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