Prediction of usage and mechanism of Traditional Chinese Medicine in treating pulmonary tuberculosis

doi:10.3969/j.issn.1000-6621.2020.10.018

Abstract

Abstract:

Objective To explore the usage of Traditional Chinese Medicine (TCM) in treating pulmonary tuberculosis and the mechanism of frequently used TCM. Methods The relevant Chinese literatures were collected from CNKI database and Wanfang Med Online with the keywords of “pulmonary tuberculosis” and “traditional Chinese medicine”; the foreign literature was collected from PubMed database with the keywords of “tuberculosis” and “traditional Chinese medicine”; the published time was limited from January 1, 1998 to December 31, 2018. A total of 512 related articles were retrieved (405 Chinese literatures, 107 foreign literatures), and 363 articles were included (352 Chinese literatures, 11 foreign literatures) finally. Rapidminer software was used to extracted the information, to explore the key TCM and the common combination for treating tuberculosis, the usage and combination of drugs were analyzed. BATMAN-TCM database was used to obtain the potential active ingredients and targets of frequently used TCM, and the DisGeNET database, Therapeutic Target Database (TTD) and Gene Cards database were used to predict and screen the targets of key TCM for the treatment of tuberculosis, and the drug targets and disease targets were matched. DAVID database was used to analyze the GO (Gene Ontology) biological function and Kyoto Encyclopedia of Genes and Genomes (KEGG). Results Tonic (663 times), activating blood circulation (403 times) and heat-clearing medicines (325 times) were mainly used to treat pulmonary tuberculosis. The most frequently used medicines were liquorice (122 times), chiretta (102 times), rehmanniae praeparatum (99 times) and paeoniae alba (74 times). The most frequently used two-drug pair compatibility were liquorice-chiretta (70 times) and liquorice-rehmanniae praeparatum (52 times). The liquorice-chiretta-rehmanniae praeparatum was the most frequently used three-drug pair (37 times), and liquorice-chiretta-rehmanniae praeparatum-paeoniae alba was used most as four-drug pair (24 times). When rehmanniae praeparatum compatible with paeoniae alba, the usage rate of chiretta was 95.0%. A total of screened 92 key targets against tuberculosis of 4 frequently used TCM (liquorice, chiretta, rehmanniae praeparatum, paeoniae alba) were found, which may play the role of anti-tuberculosis through cancer pathways (14 targets), leukocyte trans-endothelial migration (13 targets), natural killer cell mediated cytotoxicity (10 targets), and ErbB signaling pathway (10 targets). Conclusion The treatment of tuberculosis mainly use consists of tonic, activating blood circulation and heat-clearing medicines. Liquorice, chiretta, rehmanniae praeparatum and paeoniae alba could be used in clinical treatment. The mechanism of these 4 frequently used TCM in treating tuberculosis may be act through cancer pathway, leukocyte trans-endothelial migration pathway, natural killer cell mediated cytotoxicity pathway and ErbB signaling pathway.

Key words: Tuberculosis, pulmonary, Drugs, Chinese herbal, Drug therapy, combination, Review literature as topic, Pharmacologic actions

LI Ting-ting, LIU Huan-qing, DU Qiao-ying. Prediction of usage and mechanism of Traditional Chinese Medicine in treating pulmonary tuberculosis[J]. Chinese Journal of Antituberculosis, 2020, 42(10): 1115-1120. doi: 10.3969/j.issn.1000-6621.2020.10.018

Figures/Tables 5

References 24

[1]	景凤英. 活血化瘀法治疗肺结核初探. 临床肺科杂志, 2012,17(9):1709-1710. doi: 10.3969/j.issn.1009-6663.2012.09.088.
[2]	石广灿. 生脉饮治疗气阴两虚型肺结核并多汗症临床研究. 中医学报, 2014,29(11):1561-1562. doi: 10.16368/j.issn.1674-8999.2014.11.029.
[3]	周建民, 王胜圣, 田洋, 等. 中药联合化疗治疗初治继发性肺结核701例临床观察. 中医杂志, 2013,54(12):1017-1020. doi: 10.13288/j.11-2166/r.2013.12.028.
[4]	李志明, 俞珊, 王涛. 柴竹石膏汤对29例反复发热结核病并发其他感染患者的疗效分析. 中国防痨杂志, 2020,42(2):121-125. doi: 10.3969/j.issn.1000-6621.2020.02.008.
[5]	Van Poucke S, Zhang Z, Schmitz M, et al. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform. PLoS One, 2016,11(1):e0145791. doi: 10.1371/journal.pone.0145791. doi: 10.1371/journal.pone.0145791 URL pmid: 26731286
[6]	Liu Z, Guo F, Wang Y, et al. BATMAN-TCM: a bioinformatics analysis tool for molecular mechanism of traditional Chinese medicine. Sci Rep, 2016,6:21146. doi: 10.1038/srep21146. doi: 10.1038/srep21146 URL pmid: 26879404
[7]	钟赣生. 中药学. 9版. 北京: 中国中医药出版社, 2012: 1-482.
[8]	吕婷, 姜友好. 文本挖掘在生物医学领域中的应用及其系统工具. 中华医学图书情报杂志, 2010,19(4):56-64. doi: 10.3969/j.issn.1671-3982.2010.04.018.
[9]	王丽颖, 郑光, 赵学尧, 等. 基于文本挖掘的高血压病中医辨证用药情况分析. 世界中西医结合杂志, 2018,13(4):462-465, 470. doi: 10.13935/j.cnki.sjzx.180405.
[10]	孙惠力, 宋高峰, 戈娜, 等. 基于数据挖掘探讨中医辨治特发性膜性肾病的证治规律. 江西中医药, 2017,48(6):12-15.
[11]	王胜圣, 周杰, 张彦峰, 等. 肺结核中医证候规律研究. 世界中西医结合杂志, 2014,9(5):498-500.
[12]	景凤英. 活血化瘀法治疗肺结核初探. 临床肺科杂志, 2012,17(9):1709-1710. doi: 10.3969/j.issn.1009-6663.2012.09.088.
[13]	伍明俊. 扶正汤在肺结核化疗中对78例毒副反应症状的临床观察. 贵阳中医学院学报, 2003,25(2):15-16. doi: 10.3969/j.issn.1002-1108.2003.02.010.
[14]	龚居中. 红炉点雪. 上海: 上海科学技术出版社, 1959: 1.
[15]	黄洁, 周建英. 生甘草协同抗痨药物治疗肺结核66例临床观察. 浙江中医杂志, 2009,44(8):582-583.
[16]	宁炼, 陈长勋, 金若敏, 等. 当归补血汤促进造血功能的成分及其作用的研究. 中国中药杂志, 2002,27(1):50-53. doi: 10.3321/j.issn:1001-5302.2002.01.019.
[17]	李伟霞. 当归-川芎药对功效物质与配伍作用机理研究. 南京: 南京中医药大学, 2014.
[18]	陈士铎. 本草秘录. 太原: 科学教育出版社, 1986: 42.
[19]	郭晓燕, 张惠勇, 耿佩华, 等. 基于现代文献的肺结核中医证候及证候要素分布规律研究. 辽宁中医杂志, 2012,39(1):68-70. doi: 10.13192/j.ljtcm.2012.01.72.guoxy.020.
[20]	凌彦博, 梁艳, 王小美, 等. 中药复方牛贝消核提取物治疗结核病相关靶标的研究. 中国防痨杂志, 2016,38(1):17-22. doi: 10.3969/j.issn.1000-6621.2016.01.006.
[21]	Lodoen MB, Lanier LL. Natural killer cells as an initial defense against pathogens. Curr Opin Immunol, 2006,18(4):391-398. doi: 10.1016/j.coi.2006.05.002. doi: 10.1016/j.coi.2006.05.002 URL pmid: 16765573
[22]	Flynn JL, Chan J, Triebold KJ, et al. An essential role for interferon gamma in resistance to Mycobacterium tuberculosis infection. J Exp Med, 1993,178(6):2249-2254. doi: 10.1084/jem.178.6.2249. doi: 10.1084/jem.178.6.2249 URL pmid: 7504064
[23]	Feng CG, Kaviratne M, Rothfuchs AG, et al. NK cell-derived IFN-gamma differentially regulates innate resistance and neutrophil response in T cell-deficient hosts infected with Mycobacterium tuberculosis. J Immunol, 2006,177(10):7086-7093. doi: 10.4049/jimmunol.177.10.7086. doi: 10.4049/jimmunol.177.10.7086 URL pmid: 17082625
[24]	Sogi KM, Lien KA, Johnson JR, et al. The tyrosine kinase inhibitor gefitinib restricts Mycobacterium tuberculosis growth through increased lysosomal biogenesis and modulation of cytokine signaling. ACS Infect Dis, 2017,3(8):564-574. doi: 10.1021/acsinfecdis.7b00046. doi: 10.1021/acsinfecdis.7b00046 URL pmid: 28537707

序号	用药规律	置信度(%)
1	生地,白芍→当归	95.0
2	党参,白芍→当归	94.7
3	丹参,白芍→当归	94.7
4	没药→乳香	90.6
5	甘草,白芍→当归	90.3
6	当归,白芍→甘草	84.8
7	五味子→甘草	83.3
8	陈皮→甘草	83.3
9	甘草,赤芍→当归	81.8
10	白芍→甘草	81.6

类别	靶点数(个)	部分靶点名称
核心中药作用靶点	430	ABAT、ABCA1、ABCB1、ABCB11、ABCC2、ABCC8、ABCC9、ABCG1、ABL1、ABL2、ACADSB、ACHE、ACSL1、ACSL3、ACSL4、ACSS1、ACSS2、ACVR1、ACVR1B、ACVRL1、ADCY1、ADH1A、ADH1B、AKT1、ALDH1A1、ALDH1A2、BBOX1、BCHE、BCL2、BGLAP、CACNA1C、CACNA1D、CACNA1S……
肺结核相关作用靶点	2482	AADAC、ABCA1、ABCA3、ABCA4、ABCB1、ABCB11、ABCB7、ABCB8、BAD、BAG6、BAIAP2-DT、BAK1、BAMBI、BANK1、BAP1、BATF2、CALCR、CALM1、CALM2、CALM3、CALML3、CALML4、CALML5、ABCC2、FAAH、FABP1、FABP4、FADD、FADS1、FADS2……
两者共同靶点	92	ABCA1、ABCB1、ABCB11、ABCC2、ABL1、ABL2、ADK、AGTR1、AGTR2、AKT1、ALDH2、ALDH5A1、ALK、ALOX5、ANXA1、APAF1、ARAF、ASNS、BCHE、BCL2、BGLAP、CAT、CDK15、CES1、CFTR、CYBA、CYBB、CYP17A1、CYP19A1、CYP27B1、ENPP1、ESD、F2、FADS1、FADS2、GGT1、GLO1、GPX1、GSTM1、GSTP1、GSTT1、GSTZ1、HMGCR、HPGDS、HTR3A、ITGAL、ITGB2、JUN、MTHFR、MTR、MTRR、MTTP、NCF1、NCF2、NCF4、NQO1、NR0B1、NR1I2、NR3C1……