基于数据挖掘和网络药理学探讨中医药治疗糖尿病牙周炎的组方规律及作用机制  

Exploring the Prescription Rules and Mechanisms of Traditional Chinese Medicine in the Treatment of Diabetic Periodontitis Based on Data Mining and Network Pharmacology

作　　者：李惠菁 郜然然 刘敏[3] 魏静 何翔 吴也可[4] LI Huijing;GAO Ranran;LIU Min;WEI Jing;HE Xiang;WU Yeke(College of Clinical Medicine,Chengdu University of Traditional Chinese Medicine,Chengdu 610072 Sichuan,China;Department of Gynecology,Henan Provincial People’s Hospital,Zhengzhou 450000 Henan,China;Department of Gynecology,Hospital of Chengdu University of Traditional Chinese Medicine,Chengdu 610072 Sichuan,China;Department of Stomatology,Hospital of Chengdu University of Traditional Chinese Medicine,Chengdu 610072 Sichuan,China)

机构地区：[1]成都中医药大学临床医学院,四川成都610072 [2]河南省人民医院妇科,河南郑州450000 [3]成都中医药大学附属医院妇科,四川成都610072 [4]成都中医药大学附属医院口腔科,四川成都610072

出　　处：《中药新药与临床药理》2024年第10期1600-1610,共11页Traditional Chinese Drug Research and Clinical Pharmacology

基　　金：国家自然科学基金项目(81973684);四川省自然科学基金项目(23NSFSC2574);成都中医药大学杏林学者青年进阶人才专项(QJJJ2023005)。

摘　　要：目的挖掘中医药治疗糖尿病牙周炎(diabetic periodontitis,DP)的组方规律,并探讨核心药物组合的作用机制。方法以中国知网、万方数据库、维普中文期刊、中国生物医学文献服务系统收录的中医药治疗DP文献为资料来源,建立DP处方信息数据库,运用Excel 2021、SPSS Modeler 18.0和SPSS Statistics 26.0,对纳入中药进行频次、功效分类、性味归经统计,并进行关联规则分析和聚类分析,筛选出核心药物组合;通过中药系统药理学数据库与分析平台(TCMSP)、HERB获取核心药物组合的活性成分,预测药物作用靶点;运用GeneCards预测疾病相关靶点;运用Venny平台得到疾病与药物的交集靶点;运用Cytoscape建立“活性成分-靶点”网络,筛选出关键成分;以STRING平台数据为基础,利用Cytoscape软件构建蛋白质-蛋白质相互作用网络,筛选核心靶点;通过DAVID数据库对交集靶点进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)富集分析;采用AutoDockVina对关键成分与核心靶点进行分子对接。结果共纳入中医药治疗DP文献36篇,提取方剂50首,涉及药物100味;最常见的药物有泽泻、熟地黄、黄芪等,使用最多的药物类别为补虚药,药性以寒、温为主,药味以甘、苦味为主,归经以肾、肝经居多,系统聚类分析可归为6大类;筛选出牡丹皮-山茱萸-熟地黄为核心药物组合,涉及18个活性成分、164个作用靶点,与DP有104个交集靶点;槲皮素、豆甾醇、山奈酚、β-谷甾醇、四氢鸭脚木碱、谷甾醇等为核心成分,AKT1、IL-6、TNF、IL-1B、PTGS2、JUN、TP53、ESR1、MMP9等为关键靶点;GO分析得到生物过程3724项、细胞组成228项、分子功能404项;KEGG分析显示主要涉及235条信号通路;分子对接显示关键靶点与核心成分具有良好亲和力。结论DP的中医治法主要以补虚为主,兼以清热利湿、活血化瘀、益气养阴,核心药物组合牡丹皮-山茱萸-熟地黄可通过Objective To explore the prescription rules of traditional Chinese medicine(TCM)in the treatment of diabetes periodontitis(DP)and the acting mechanisms of core drug combination.Methods Based on the relevant literature retrieved from the CNKI,Wanfang,VIP and Sinomed,a DP prescription database was established.Excel 2021,SPSS Modeler 18.0 and SPSS Statistics 26.0 were used to conduct the statistics of the frequency,efficacy classifications,properties,flavors,and meridian tropism of the included drugs.Association rule analysis and cluster analysis were performed to screen out the core drug combinations.The active components and action targets of core drug combinations were obtained through TCMSP and HERB.The DP related disease targets were predicted using GeneCards.The Venny platform was used to obtain the intersection of disease targets and drug targets.Key components were screened by Cytoscape to establish an“active component-target”network.Based on STRING platform data,PPI network was constructed by Cytoscape to screen core targets.GO functional annotation and KEGG signaling pathway enrichment analysis were carried out for the intersection targets by DAVID.AutoDockVina was applied for molecular docking between core targets and key components.Results A total of 36 articles were included,and 50 prescriptions involving 100 Chinese herbal medicines were extracted.Alismatis Rhizoma,Rehmanniae Radix Praeparata and Astragali Radix were the most common drugs.The most used drug category was deficiency-nourishing drugs.The properties of the herbs were mainly cold and warm,the major flavors were sweet and bitter,and the main meridian tropisms were kidney and liver.Six categories were classified by clustering analysis.Moutan Cortes-Corni Fructus-Rehmanniae Radix Praeparata was screened out as the core drug combination involving 18 active components,164 drug action targets and 104 intersection of DP targets and drug combination targets.Quercetin,stigmasterol,kaempferol,β-sitosterol,tetrahydroalstonine,and sitosterol were

关 键 词：糖尿病牙周炎 组方规律 补虚 牡丹皮-山茱萸-熟地黄 作用机制 数据挖掘 网络药理学 分子对接 

分 类 号：R285.6[医药卫生—中药学] R285.5[医药卫生—中医学]