基于数据挖掘和网络药理学研究中药免疫增强剂的作用机制及用药规律

信立媛; 郑亮

doi:10.14148/j.issn.1672-0482.2024.0174

基于数据挖掘和网络药理学研究中药免疫增强剂的作用机制及用药规律

doi: 10.14148/j.issn.1672-0482.2024.0174

信立媛^1,,
郑亮^{1, 2, ,}

1.
南京中医药大学针灸推拿学院·养生康复学院, 江苏南京 210023
2.
南京中医药大学第二附属医院, 江苏南京 210017

基金项目:

第四批省名老中医药专家传承工作室建设项目苏中医科教〔2021〕7号

详细信息

作者简介:
信立媛, 女, 硕士研究生, E-mail: xinliyuan1998@163.com

通讯作者:
郑亮, 男, 教授, 主任中医师, 博士生导师, 主要从事中医药防治脾胃系疾病的研究, E-mail: 13915919898@126.com

中图分类号: R212
计量
- 文章访问数: 122
- HTML全文浏览量: 6
- PDF下载量: 21
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 网络出版日期: 2024-02-26
- 发布日期: 2024-02-10

Study on the Medication Rule and Action Mechanism of Traditional Chinese Medicine Immune Enhancer Based on Data Mining and Network Pharmacology

XIN Liyuan^1
,,
ZHENG Liang^{1, 2
, ,}

1.
School of Acupuncture and Tuina, School of Health and Rehabilitation, Nanjing University of Chinese Medicine, Nanjing 210023, China
2.
The Second Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210017, China

摘要

摘要: 目的采用数据挖掘的方法探究复方专利处方中用于增强免疫力的中药配伍规律, 并运用网络药理学、分子对接技术分析其中核心药物的潜在作用机制。方法挖掘国家知识产权局中药增强免疫力的复方专利数据, 进行中药频次、关联规则以及复杂网络分析, 并筛选出核心药物。利用TCMSP平台筛选核心药物活性成分并进行靶点预测, 于疾病数据库OMIM、DrugBank、GeneCards、Uniprot筛选免疫力相关靶点, 获取核心中药与免疫力的交集靶点; 对交集靶点进行蛋白互作(PPI)分析和基因本体(GO)功能及京都基因与基因组百科全书(KEGG)通路富集分析。使用分子对接技术对药物活性成分、关键靶点进行验证。结果分析中药复方专利得出5味核心药物: 黄芪、人参、枸杞子、茯苓、当归, 有效活性成分对应靶点214个, 1 784个免疫力相关靶点, 交集靶点55个, 关键作用靶点有RELA、TNF、TP53、IL-6。GO分析结果显示主要调控对细菌起源分子的反应、对脂多糖的反应、对生物刺激的反应等。KEGG通路160条, 主要涉及脂质与动脉粥样硬化、南美洲锥虫病、TNF信号通路、癌症通路等多条信号通路。分子对接结果显示, 槲皮素、山柰酚能较好地与RELA、TNF、TP53、IL-6进行结合。结论专利文献中增强免疫力的中药以补虚药为主, 主要有效成分为槲皮素、山柰酚等化合物, 以RELA、TNF、TP53、IL-6为关键靶点, 通过脂质和动脉粥样硬化、南美洲锥虫病、TNF信号通路、癌症通路等多条通路发挥增强免疫力的作用。
- 中药 /
- 复方专利 /
- 增强免疫力 /
- 数据挖掘 /
- 网络药理学 /
- 分子对接
Abstract: OBJECTIVE To explore the compatibility rules of traditional Chinese medicines used to enhance immunity in compound patent prescriptions using data mining method, and analyze the potential mechanism of action of core drugs by network pharmacology and molecular docking technology. METHODS The patent data of the immunity enhancing compound of traditional Chinese medicine from the State Intellectual Property Office was mined, the frequency of traditional Chinese medicine, association rules and complex network were analyzed, and core drugs were screened. The TCMSP platform was used to screen the active ingredients of core drugs and predict the targets. Immune-related targets were screened from disease databases OMIM, DrugBank, GeneCards and Uniprot to obtain the intersection targets of core Chinese medicine and immunity. Protein interaction (PPI) analysis and gene ontology (GO) function and pathway enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed for intersection targets. Molecular docking technology was used to verify the active ingredients and key targets of the drug. RESULTS According to the analysis of Chinese herbal compound patents, five core drugs were obtained: Astragalus, ginseng, Wolfberry, poria, Angelica. The effective active ingredients corresponding to 214 targets, 1 784 immune-related targets, 55 intersection targets, the key action targets are RELA, TNF, TP53, IL-6. The results of GO analysis showed that the main regulation aspects were the response to bacteria origin molecules, the response to lipopolysaccharide, and the response to biological stimuli. There were 160 KEGG pathways, mainly involving lipid and atherosclerosis, Chagas disease, TNF signaling pathway, cancer pathway and other signaling pathways. The molecular docking results showed that quercetin and kaempferol could bind to RELA, TNF, TP53 and IL-6. CONCLUSION The traditional Chinese medicines used in the patent literature to enhance immunity are mainly deficiency tonic drugs, and the main effective components are quercetin, kaempferol and other compounds. RELA, TNF, TP53 and IL-6 are the key targets, which play the role of enhancing immunity through multiple pathways such as lipid, atherosclerosis, Chagas disease, TNF signaling pathway and cancer pathway.
- Chinese medicine /
- compound patent /
- immunity enhancement /
- data mining /
- network pharmacology /
- molecular docking

HTML全文

图 1 相关专利年度公布情况

Figure 1. Annual announcement of relevant patents

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图 2 高频中药性味归经图

Figure 2. High-frequency distribution graph of properties and flavors of traditional Chinese medicine

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图 3 常用药物复杂网络分析

Figure 3. Complex network analysis of commonly used drugs

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图 4 核心药物成分与免疫力靶点Venn图

Figure 4. Venn diagram of core drug components and immune targets

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图 5 免疫力相关核心中药成分靶点网络关系图

Figure 5. Network diagram of immune-related core traditional Chinese medicine component targets

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图 6 PPI网络图

Figure 6. PPI network diagram

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图 7 GO功能富集分析柱状图

Figure 7. GO functional enrichment analysis histogram

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图 8 KEGG通路富集分析气泡图

Figure 8. KEGG pathway enrichment analysis bubble chart

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图 9 分子对接图

注：A. RELA-槲皮素; B. TNF-山柰酚; C. TP53-槲皮素; D. IL-6-槲皮素

Figure 9. Molecular docking diagram

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表 1 相关专利中的高频药物(≥20)

Table 1. High-frequency drugs (≥20 times) in relevant patents

序号	药物	频数	序号	药物	频数
1	黄芪	116	17	肉苁蓉	30
2	人参	93	18	淫羊藿	30
3	枸杞子	92	19	酸枣仁	29
4	茯苓	75	20	五味子	29
5	当归	66	21	冬虫夏草	27
6	山药	61	22	葛根	26
7	甘草	58	23	熟地黄	26
8	灵芝	57	24	麦冬	25
9	大枣	50	25	何首乌	24
10	白术	48	26	山楂	24
11	党参	47	27	薏苡仁	24
12	西洋参	41	28	桑椹	22
13	丹参	37	29	杜仲	21
14	石斛	36	30	鹿茸	21
15	红景天	33	31	陈皮	20
16	黄精	32	32	龙眼肉	20

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表 2 中药关联规则分析

Table 2. Analysis of traditional Chinese medicine association rules

序号	后项	前项	实例数	支持度/%	置信度/%
1	当归	黄芪	115	36.62	35.65
2	枸杞子	黄芪	115	36.62	38.26
3	枸杞子	人参	92	29.30	38.04
4	黄芪	人参	92	29.30	41.30
5	大枣	枸杞子	91	28.98	36.26
6	人参	枸杞子	91	28.98	38.46
7	黄芪	枸杞子	91	28.98	48.35
8	当归	茯苓	75	23.89	36.00
9	枸杞子	茯苓	75	23.89	38.67
10	黄芪	茯苓	75	23.89	40.00
11	茯苓	当归	65	20.70	41.54
12	人参	当归	65	20.70	40.00
13	枸杞子	当归	65	20.70	47.69
14	黄芪	当归	65	20.70	63.08

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表 3 药物活性成分的网络节点信息

Table 3. Network node information of active pharmaceutical components

编号	MOL ID	化学成分	化学成分中文名	度值	对应中药
B	MOL000098	Quercetin	槲皮素	46	黄芪、枸杞子
A	MOL000422	Kaempferol	山柰酚	17	黄芪、人参
RS5	MOL005344	Ginsenoside Rh2	人参皂苷Rh2	8	人参
HQ2	MOL000354	Isorhamnetin	异鼠李素	7	黄芪
HQ4	MOL000378	7-O-Methylisomucronulatol	7-O-甲基-异微凸剑叶莎醇	6	黄芪
HQ7	MOL000392	Formononetin	刺芒柄花素	6	黄芪
C	MOL000358	beta-Sitosterol	β-谷甾醇	6	当归、人参
HQ8	MOL000417	Calycosin	毛蕊异黄酮	4	黄芪
GQZ1	MOL008400	Glycitein	黄豆黄素	4	枸杞子
RS1	MOL005384	Suchilactone	苏齐内酯	3	人参
RS4	MOL005321	Frutinone A	灌木远志酮A	3	人参
HQ1	MOL000239	Jaranol	华良姜素	2	黄芪
HQ3	MOL000371	3,9-Di-O-methylnissolin	3,9-二-O-甲基尼森香豌豆紫檀酚	2	黄芪
HQ5	MOL000380	(6aR, 11aR)-9,10-dimethoxy-6a, 11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol	黄芪紫檀烷苷	2	黄芪
HQ6	MOL000387	Bifendate	联苯双酯	2	黄芪
RS2	MOL000787	Fumarine	原鸦片碱	2	人参
RS3	MOL003648	Inermin	马卡因	2	人参
RS6	MOL005356	Girinimbin	吉九里香碱	2	人参

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表 4 活性成分与靶点受体蛋白的结合结果

Table 4. Binding results of active components and target receptor proteins

基因	Uniprot ID号	化合物	化合物中文名	结合能/(kcal·mol^-1)
RELA	P05412	Quercetin Kaempferol Isorhamnetin	槲皮素山柰酚异鼠李素	-9.20 -9.10 -8.80
TNF	P28482	Kaempferol Quercetin Ginsenoside Rh2	山柰酚槲皮素人参皂苷Rh2	-9.00 -8.60 -8.30
TP53	P04637	Quercetin	槲皮素	-6.10
IL-6	P05231	Quercetin	槲皮素	-7.10

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