基于数据挖掘、网络药理和分子对接分析中药组方治疗梅尼埃病的作用机制  

Analyzing mechanism of traditional Chinese medicine formulas in treatment of Meniere’s disease based on data mining,network pharmacology and molecular docking

作　　者：王胜男 孔智谦 周智慧 何念财 黄海燕[1] WANG Shengnan;KONG Zhiqian;ZHOU Zhihui;HE Niancai;HUANG Haiyan(Dongguan Hospital,Guangzhou University of Chinese Medicine,Dongguan 523000,China;Guangzhou University of Traditional Chinese Medicine,Guangzhou 510405,China)

机构地区：[1]广州中医药大学东莞医院,广东东莞523000 [2]广州中医药大学,广东广州510405

出　　处：《中草药》2025年第6期2066-2078,共13页Chinese Traditional and Herbal Drugs

基　　金：东莞市社会科技发展项目(202050715002245);东莞市社会科技发展项目(20211800900132)。

摘　　要：目的基于“无痰不作眩”的理论,分析中药组方治疗梅尼埃病的用药规律及作用机制。方法通过计算机检索中国知网(CNKI)、中国学术期刊数据库(万方)、中文期刊数据库(维普)和中国生物医学文献数据库(CBM),根据筛选标准获取文献,对中药频次、性味、归经进行挖掘,并通过SPSS Modeler 18.0和SPSS Statistics 25.0进行关联规则分析和聚类分析,得到核心方;通过中药系统药理学数据库(Traditional Chinese Medicine Systems Pharmacology Database,TCMSP)检索活性成分和作用靶点,通过GeneCards、OMIM和CTD数据库检索疾病靶点,使用Veeny 2.1.0在线平台获取药物与梅尼埃病的交集靶点,利用String数据库搭建蛋白质相互作用(protein-protein interaction,PPI)网络,通过Cytoscape 3.7.0软件建立“药物-活性成分-交集靶点-疾病”可视化网络图,使用DAVID数据平台进行基因本体(gene ontology,GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)富集分析,并使用AutoDockVina1.1.2、PyMol 2.3.0和DiscoveryStudio软件进行分子对接及可视化处理。根据分子对接的结果进行实验验证。结果共纳入处方130个,中药123味,性平、温,味甘、苦,归肝、脾经的药物最多,通过关联规则得到半夏→茯苓、半夏→白术等药物组合,聚类分析得到核心方半夏、茯苓、白术、泽泻、陈皮。筛选出活性成分49个,疾病靶点8342个,交集靶点61个,核心靶点有雌激素受体1(estrogen receptor 1,ESR1)、肿瘤蛋白53(tumor protein 53,TP53)、缺氧诱导因子1α(hypoxia inducible factor 1 alpha,HIF1A)等,核心成分有黄芩素、β-谷甾醇、卡维丁等,主要作用于环磷酸腺苷(cyclic adenosine monophosphate,cAMP)、磷脂酰肌醇-3-羟激酶(phosphatidylinositol-3-hydroxykinase,PI3K)/蛋白激酶B(protein kinase B,Akt)等信号通路。分子对接发现主要成分与靶点对接活性良好,HIF1A与卡维丁的结合能最低。验证实验发现,黄Objective To analyze the medication pattern and mechanism of traditional Chinese medicine prescription for the treatment of Meniere’s disease based on the theory of“no phlegm,no vertigo”.Methods China National Knowledge Network(CNKI),China Academic Journal Database(Wanfang),Chinese Journal Database(VIP),and China Biomedical Literature Database(CBM)were searched using a computer program to find pertinent content that met predetermined screening criteria.The frequen cy,flavor,and attribution of Chinese drugs were investigated.Using SPSS Modeler 18.0 and SPSS Statistics 25.0,cluster analysis and association rule analysis were utilized to identify the key formulas.The remedial targets and vital elements came from the Traditional Chinese Medicine Systems Pharmacology Database(TCMSP),while sickness targets were taken from the GeneCards,OMIM,and CTD databases.A protein-protein interaction(PPI)network was developed using the String data structure,and intersection targets were obtained using the Veeny 2.1.0 information platform.On the DAVID data platform,enrichment analysis was carried out using the gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)algorithms.Finally,PyMol 2.3.0,Discovery Studio,and Auto Dock Vina 1.1.2 were used for molecular docking and visualization,based on the previous findings.Results A total of 130 prescriptions were included,123 traditional Chinese medicines with warm,supple qualities,bitter and sweet tastes,and connections to the liver and spleen meridians.Using association criteria,Banxia(Pinelliae Rhizoma)→Fuling(Poria),Pinelliae Rhizoma→Baizhu(Atractylodis Macrocephalae Rhizoma),and other pharmaceutical combinations were discovered.The core formulations included Pinelliae Rhizoma,Poria,Atractylodis Macrocephalae Rhizoma,Zexie(Alismatis Rhizoma),and Chenpi(Citri Reticulatae Pericarpium),were identified via cluster analysis.A total of 49 active components,8342 disease targets,and 61 intersecting targets were found in the study.The core targets include estrogen receptor

关 键 词：无痰不作眩 梅尼埃病 数据挖掘 网络药理学 分子对接 黄芩素 卡维丁 

分 类 号：R285[医药卫生—中药学]