基于数据挖掘和网络药理学探讨治疗抑郁症的中药复方专利用药规律及机制  

Study on medication rules and mechanisms of patented traditional Chinese medicine compound formulas for depression treatment based on data mining and network pharmacology

作　　者：李学杰 吴小宇 刘鑫[1] 曲道炜 高健美 李海波 LI Xuejie;WU Xiaoyu;LIU Xin;QU Daowei;GAO Jianmei;LI Haibo(Graduate School,Liaoning University of Traditional Chinese Medicine,Shenyang,Liaoning 110847,China;School of Pharmacy,Zunyi Medical University,Zunyi,Guizhou 563000,China)

机构地区：[1]辽宁中医药大学研究生学院,辽宁沈阳110847 [2]遵义医科大学药学院,贵州遵义563000

出　　处：《上海中医药杂志》2024年第6期31-39,共9页Shanghai Journal of Traditional Chinese Medicine

基　　金：辽宁省科学技术计划项目(2022JH2/101300058)。

摘　　要：目的基于数据挖掘、网络药理学方法,分析探讨治疗抑郁症的中药复方专利用药规律以及核心药物的分子作用机制。方法通过中国专利公布公告网检索获得治疗抑郁症的中药复方,提取中药名称、药味、药性、归经等信息,借助Microsoft Excel 2016、SPSS Modeler 18.0、Cytoscape 3.9.1、中医传承计算平台V3.5软件,对中药进行用药频次、性味归经、关联规则分析,并筛选出核心药物。在数据挖掘基础上,借助中药系统药理数据库与分析平台(TCMSP)获取核心药物的活性成分并预测其作用靶点;通过DisGeNET、GeneCards数据库收集抑郁症靶点,并利用EVenn在线平台获得核心药物治疗抑郁症的潜在靶点;通过STRING 11.5数据库分析蛋白-蛋白相互作用(PPI)及关键靶点网络;借助Cytoscape 3.9.1软件构建核心药物-成分-潜在靶点-通路网络;利用R软件包org.Hs.eg.db(version 3.1.0)和ClusterProfiler(version 3.14.3)对潜在靶点进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析。结果①本研究共收集303首中药复方、涉及中药663味,其中使用频次≥30次的中药有26味,共出现1469次,占总频次的39.93%。②本研究涉及的663味中药的药性以温、寒、平为主,药味以甘、苦、辛为主,归经以肝、心、脾、肺、肾、胃为主。③关联规则显示,中药复方治疗抑郁症常用药物组合有15组,涉及中药有当归、柴胡、甘草、郁金、白芍、茯苓等。④网络药理学分析结果显示,中药复方核心药物当归、柴胡、甘草、郁金、酸枣仁、白芍、茯苓的主要活性成分可能是植物甾醇、茯苓酸D、胡萝卜苷、芍药吉酮和当药黄素,这些成分可能作用于非受体酪氨酸蛋白激酶(SRC)、V-Rel网状内皮增生病毒癌基因同源物A(RELA)、蛋白激酶B(AKT)1等关键靶点,并通过调控白介素-17(IL^(-1)7)、C型凝集素受体(CLRs)、肿瘤坏死因子(TNF)、磷脂酰肌醇3激酶(PI3K)/AKT等Objective To investigate the medication rules of patented traditional Chinese medicine(TCM)compound formulas and molecular mechanisms of core drugs for treating depression using data mining and network pharmacology approaches.Methods Patented traditional Chinese medicine compound formulas for depression treatment were retrieved from China National Intellect Property Administration(CNIPA).Information such as the names of the traditional Chinese herbal medicines(TCHMs),their flavors,properties,and meridian classifications were extracted.With Microsoft Excel 2016,SPSS Modeler 18.0,Cytoscape 3.9.1,and the Traditional Chinese Medicine Inheritance Computing Platform(TCMICP)V3.5,we analyzed medication frequency,properties,flavors,meridian classifications,and association rules,and identified core drugs.Additionally,we used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)to obtain the active ingredients of core drugs and predict their targets.Depression-related targets were collected from DisGeNET and GeneCards databases,and potential targets for core drugs in treating depression were obtained using the EVenn.Protein-protein interaction(PPI)networks and key target networks were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING)11.5 database.A network of core drugs-components-potential targets-pathways was constructed using Cytoscape 3.9.1.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses of potential targets were conducted using the R software packages org.Hs.eg.db(version 3.1.0)and ClusterProfiler(version 3.14.3).Results①A total of 303 traditional Chinese medicine compound formulas were collected,involving 663 different TCHMs.Among them,26 TCHMs were used with a frequency of≥30,totaling 1469 times,accounting for 39.93%of the total frequency.②The properties of the 663 TCHMs were mainly warm,cold and neutral,while the tastes were predominantly sweet,bitter,and pungent.Regarding meridian classific

关 键 词：抑郁症 中药 方剂 专利处方 用药规律 数据挖掘 网络药理学 

分 类 号：R277.7[医药卫生—中医学]