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Article(id=1196886704490590601, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1196886663541600644, articleNumber=1001-2494(2024)22-2140-08, orderNo=null, doi=10.11669/cpj.2024.22.006, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1715875200000, receivedDateStr=2024-05-17, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1763289627430, onlineDateStr=2025-11-16, pubDate=1732204800000, pubDateStr=2024-11-22, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1763289627430, onlineIssueDateStr=2025-11-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1763289627430, creator=13701087609, updateTime=1763289627430, updator=13701087609, issue=Issue{id=1196886663541600644, tenantId=1146029695717560320, journalId=1190317699101192196, year='2024', volume='59', issue='22', pageStart='2099', pageEnd='2196', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1763289617666, creator=13701087609, updateTime=1763292152892, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1196897297100488858, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1196886663541600644, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1196897297104683163, tenantId=1146029695717560320, journalId=1190317699101192196, issueId=1196886663541600644, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2140, endPage=2147, ext={EN=ArticleExt(id=1196886704759026059, articleId=1196886704490590601, tenantId=1146029695717560320, journalId=1190317699101192196, language=EN, title=Quality Markers of Suanzaoren Decoction Based on Chemical Components in Vivo and Network Pharmacology, columnId=null, journalTitle=Chinese Pharmaceutical Journal, columnName=null, runingTitle=null, highlight=null, articleAbstract=

OBJECTIVE To predict the potential quality marker (Q-Marker) components of Suanzaoren Decoction based on the prototype chemical composition of in vivo by using network pharmacology. METHODS Firstly, the plasma samples and urine samples of rats were collected within 4 h and 24 h after intragastric administration of Suanzaoren Decoction, respectively. Then, based on the chemical components found in vivo, the key action targets and key pathway were obtained by network pharmacology, and the network map of "component-target-pathway" was carried out to predict the quality markers of Suanzaoren Decoction against insomnia, anxiety and depression. RESULTS A total of 20 prototype compounds were detected in rats. The 23 core targets were predicted and 14 prototype components associated with pharmacodynamic efficacy were screened through network pharmacology, the 14 components including jujuboside A, enoxolone, ferulic acid, magnoflorine, liquiritigenin, isoliquiritigenin, poricoic acid A, spinosin, coclaurine, timosaponin BII, glycyrrhizic acid, jujuboside B, liquiritin and senkyunolide A played multiple targets and multiple pathways to regulate insomnia, anxiety, depression. CONCLUSION It is predicted that the 14 chemical components are considered as potential quality markers of Suanzaoren Decoction by analyzing prototype chemical composition in vivo combined with network pharmacology. The results can provide reference for the quality control of Suanzaoren Decoction, and lay a foundation for elucidating the mechanism of its pharmacodynamic substances.

, correspAuthors=Minyan LIU, Zhiwei LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Jing LI, Weiyan CHEN, Song LI, Shaohua ZHAO, Minyan LIU, Zhiwei LI), CN=ArticleExt(id=1196887003850650262, articleId=1196886704490590601, tenantId=1146029695717560320, journalId=1190317699101192196, language=CN, title=基于体内化学成分及网络药理学的经典名方酸枣仁汤质量标志物预测分析, columnId=1190352405612040510, journalTitle=中国药学杂志, columnName=论著, runingTitle=null, highlight=null, articleAbstract=

目的 研究酸枣仁汤进入体内的原型化学成分,利用网络药理学进行质量标志物(quality markers,Q-Marker)的预测分析,为经典名方酸枣仁汤的质量控制提供参考依据。方法 首先大鼠灌胃给予酸枣仁汤,于给药后采集4 h的血样和24 h的尿样,检测血和尿中的原型化合物;然后基于进入体内的化学成分,利用网络药理学获得关键作用靶点及关键通路,绘制“成分-靶点-通路”网络图,以预测酸枣仁汤抗失眠、焦虑、抑郁的质量标志物。结果 在大鼠体内共检测到20种原型化合物;通过网络药理学预测核心靶点23个,进入体内的14种原型成分酸枣仁皂苷A、甘草次酸、阿魏酸、木兰花碱、甘草素、异甘草素、茯苓酸A、斯皮诺素、乌药碱、知母皂苷BⅡ、甘草酸、酸枣仁皂苷B、甘草苷、洋川芎内酯A与之相关联,发挥多靶点调控失眠、焦虑、抑郁等通路的作用。。结论 通过分析体内原型化学成分结合网络药理学预测的14种化学成分可作为酸枣仁汤的潜在质量标志物,为酸枣仁汤的质量控制提供参考,同时也为阐明其药效物质的作用机制奠定基础。

, correspAuthors=刘敏彦, 李志伟, authorNote=null, correspAuthorsNote=
* 李志伟,男,博士,副教授,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)81668391;
刘敏彦,女,博士,主任中药师,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)66617323
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李静,女,硕士研究生 研究方向:药物分析及药效物质基础研究

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李静,女,硕士研究生 研究方向:药物分析及药效物质基础研究

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李静,女,硕士研究生 研究方向:药物分析及药效物质基础研究

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journalId=1190317699101192196, articleId=1196886704490590601, language=EN, label=Tab.1, caption=

Indentification of the chemical components in vivo after oral Suanzaoren Decoction

, figureFileSmall=null, figureFileBig=null, tableContent=
Peak
number		 Compound Molecular
formula		 tR
/min		 Monitoring
ions		 m/z Error
×10-6		 MS2 Source
1 Neomangiferin C25H28O16 6.79 [M-H]- 583.130 5 -2.6 493.093 9,421.077,331.041 1,301.030 7,271.018 7 Plasma/urine
2 Mangiferin C19H18O11 8.55 [M-H]- 421.077 6 -0.1 331.043 5,301.034 3,259.027 3,258.015 2 Plasma/urine
3 Coclaurine C17H19NO3 8.67 [M+H]+ 286.143 8 -0.6 269.118 4,237.094 6,209.097 4,175.079 3,107.050 9 Plasma/urine
4 Vicenin-2 C27H30O15 9.12 [M-H]- 593.151 2 0.7 417.124 3,255.069,175.026 8,113.026 6 Plasma
5 Magnoflorine C20H24NO4 9.21 [M]+ 342.170 0 -1.7 299.133 1,297.116 1,282.092 3,265.089 4,237.094 5,222.070 8,191.088 1 Plasma/urine
6 Ferulic acid C10H10O4 12.00 [M-H]- 193.050 6 -3.1 193.051 5,134.030 4 Plasma
7 Spinosin C28H32O15 12.33 [M-H]- 607.166 8 0.7 321.043,292.035 4,236.048 2,117.037 1 Plasma/urine
8 Vitexin C21H20O10 12.65 [M-H]- 431.098 4 -0.5 255.064 3,175.022 9,135.008 1,119.049 7 Plasma/urine
9 Liquiritin C21H22O9 12.73 [M-H]- 417.119 1 -0.4 255.064 8,135.007 4,119.048 4 Plasma/urine
10 6‴-Feruloylspinosin C38H40O18 18.12 [M-H]- 783.214 2 -1.6 427.115 8,292.040 2,191.074 2 Plasma
11 Isoliquiritin apioside C26H30O13 19.40 [M-H]- 549.161 4 -3.5 255.064 3,135.009,119.050 3 Plasma
12 Timosaponin B Ⅱ C45H76O19 20.58 [M-H]- 919.490 8 0.8 757.433 8,595.378,433.323 7 Plasma
13 Liquiritigenin C15H12O4 20.41 [M-H]- 255.066 3 0.1 135.006 7,119.048 8,91.018 8 Plasma/urine
14 Jujuboside A C58H94O26 26.39 [M-H]- 1 205.596 1 2.6 1 073.559 4,911.510 5,749.444 1,603.397 9 Plasma
15 Isoliquiritigenin C15H12O4 26.59 [M-H]- 255.066 3 -0.9 135.007 3,119.049 5,91.019 1 Plasma/urine
16 Jujuboside B C52H84O21 27.06 [M-H]- 1 043.543 2 1 911.498,749.443 5,603.384 9 Plasma
17 Glycyrrhizic acid C42H62O16 27.09 [M-H]- 821.396 5 0 627.343 1,583.371 8,511.359 7,469.320 9,351.061 Plasma
18 Senkyunolide A C12H16O2 29.17 [M+H]+ 193.122 3 0.6 175.11,147.116 2,137.005 87,105.069 1 Urine
19 Poricoic acid A C31H46O5 32.86 [M-H]- 497.327 3 -2.7 423.287 6,363.266 3,347.235 2,213.125 9,211.146 1 Plasma
20 Enoxolone C30H46O4 33.46 [M-H]- 469.332 3 -3.8 425.341 3,409.308 4,355.259 1,229.229 2 Plasma
), ArticleFig(id=1197125033844850981, tenantId=1146029695717560320, journalId=1190317699101192196, articleId=1196886704490590601, language=CN, label=表1, caption=

酸枣仁汤体内原型化学成分的鉴定

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Peak
number		 Compound Molecular
formula		 tR
/min		 Monitoring
ions		 m/z Error
×10-6		 MS2 Source
1 Neomangiferin C25H28O16 6.79 [M-H]- 583.130 5 -2.6 493.093 9,421.077,331.041 1,301.030 7,271.018 7 Plasma/urine
2 Mangiferin C19H18O11 8.55 [M-H]- 421.077 6 -0.1 331.043 5,301.034 3,259.027 3,258.015 2 Plasma/urine
3 Coclaurine C17H19NO3 8.67 [M+H]+ 286.143 8 -0.6 269.118 4,237.094 6,209.097 4,175.079 3,107.050 9 Plasma/urine
4 Vicenin-2 C27H30O15 9.12 [M-H]- 593.151 2 0.7 417.124 3,255.069,175.026 8,113.026 6 Plasma
5 Magnoflorine C20H24NO4 9.21 [M]+ 342.170 0 -1.7 299.133 1,297.116 1,282.092 3,265.089 4,237.094 5,222.070 8,191.088 1 Plasma/urine
6 Ferulic acid C10H10O4 12.00 [M-H]- 193.050 6 -3.1 193.051 5,134.030 4 Plasma
7 Spinosin C28H32O15 12.33 [M-H]- 607.166 8 0.7 321.043,292.035 4,236.048 2,117.037 1 Plasma/urine
8 Vitexin C21H20O10 12.65 [M-H]- 431.098 4 -0.5 255.064 3,175.022 9,135.008 1,119.049 7 Plasma/urine
9 Liquiritin C21H22O9 12.73 [M-H]- 417.119 1 -0.4 255.064 8,135.007 4,119.048 4 Plasma/urine
10 6‴-Feruloylspinosin C38H40O18 18.12 [M-H]- 783.214 2 -1.6 427.115 8,292.040 2,191.074 2 Plasma
11 Isoliquiritin apioside C26H30O13 19.40 [M-H]- 549.161 4 -3.5 255.064 3,135.009,119.050 3 Plasma
12 Timosaponin B Ⅱ C45H76O19 20.58 [M-H]- 919.490 8 0.8 757.433 8,595.378,433.323 7 Plasma
13 Liquiritigenin C15H12O4 20.41 [M-H]- 255.066 3 0.1 135.006 7,119.048 8,91.018 8 Plasma/urine
14 Jujuboside A C58H94O26 26.39 [M-H]- 1 205.596 1 2.6 1 073.559 4,911.510 5,749.444 1,603.397 9 Plasma
15 Isoliquiritigenin C15H12O4 26.59 [M-H]- 255.066 3 -0.9 135.007 3,119.049 5,91.019 1 Plasma/urine
16 Jujuboside B C52H84O21 27.06 [M-H]- 1 043.543 2 1 911.498,749.443 5,603.384 9 Plasma
17 Glycyrrhizic acid C42H62O16 27.09 [M-H]- 821.396 5 0 627.343 1,583.371 8,511.359 7,469.320 9,351.061 Plasma
18 Senkyunolide A C12H16O2 29.17 [M+H]+ 193.122 3 0.6 175.11,147.116 2,137.005 87,105.069 1 Urine
19 Poricoic acid A C31H46O5 32.86 [M-H]- 497.327 3 -2.7 423.287 6,363.266 3,347.235 2,213.125 9,211.146 1 Plasma
20 Enoxolone C30H46O4 33.46 [M-H]- 469.332 3 -3.8 425.341 3,409.308 4,355.259 1,229.229 2 Plasma
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基于体内化学成分及网络药理学的经典名方酸枣仁汤质量标志物预测分析
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李静 1 , 陈伟燕 2 , 李松 3 , 赵韶华 3 , 刘敏彦 2, * , 李志伟 1, *
中国药学杂志 | 论著 2024,59(22): 2140-2147
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中国药学杂志 | 论著 2024, 59(22): 2140-2147
基于体内化学成分及网络药理学的经典名方酸枣仁汤质量标志物预测分析
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李静1, 陈伟燕2, 李松3, 赵韶华3, 刘敏彦2, *, 李志伟1, *
作者信息
  • 1 河北科技大学,石家庄 050018
  • 2 石家庄学院, 河北省藏药质量技术创新中心,石家庄 050035
  • 3 石家庄以岭药业股份有限公司, 石家庄 050035

    • 李静,女,硕士研究生 研究方向:药物分析及药效物质基础研究

通讯作者:

* 李志伟,男,博士,副教授,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)81668391;
刘敏彦,女,博士,主任中药师,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)66617323
Quality Markers of Suanzaoren Decoction Based on Chemical Components in Vivo and Network Pharmacology
Jing LI1, Weiyan CHEN2, Song LI3, Shaohua ZHAO3, Minyan LIU2, *, Zhiwei LI1, *
Affiliations
  • 1 Hebei University of Science & Technology, Shijiazhuang 050018, China
  • 2 Heibei Tibetan Medicine Quality and Technology Innovation Center, Shijiazhuang University, Shijiazhuang 050035, China
  • 3 Shijiazhuang Yiling Pharmaceutical Co., Ltd., Shijiazhuang 050035, China
出版时间: 2024-11-22 doi: 10.11669/cpj.2024.22.006
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摘要
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目的 研究酸枣仁汤进入体内的原型化学成分,利用网络药理学进行质量标志物(quality markers,Q-Marker)的预测分析,为经典名方酸枣仁汤的质量控制提供参考依据。方法 首先大鼠灌胃给予酸枣仁汤,于给药后采集4 h的血样和24 h的尿样,检测血和尿中的原型化合物;然后基于进入体内的化学成分,利用网络药理学获得关键作用靶点及关键通路,绘制“成分-靶点-通路”网络图,以预测酸枣仁汤抗失眠、焦虑、抑郁的质量标志物。结果 在大鼠体内共检测到20种原型化合物;通过网络药理学预测核心靶点23个,进入体内的14种原型成分酸枣仁皂苷A、甘草次酸、阿魏酸、木兰花碱、甘草素、异甘草素、茯苓酸A、斯皮诺素、乌药碱、知母皂苷BⅡ、甘草酸、酸枣仁皂苷B、甘草苷、洋川芎内酯A与之相关联,发挥多靶点调控失眠、焦虑、抑郁等通路的作用。。结论 通过分析体内原型化学成分结合网络药理学预测的14种化学成分可作为酸枣仁汤的潜在质量标志物,为酸枣仁汤的质量控制提供参考,同时也为阐明其药效物质的作用机制奠定基础。

酸枣仁汤  /  体内化学成分  /  高效液相色谱-四极杆飞行时间串联质谱法  /  网络药理学  /  质量标志物

OBJECTIVE To predict the potential quality marker (Q-Marker) components of Suanzaoren Decoction based on the prototype chemical composition of in vivo by using network pharmacology. METHODS Firstly, the plasma samples and urine samples of rats were collected within 4 h and 24 h after intragastric administration of Suanzaoren Decoction, respectively. Then, based on the chemical components found in vivo, the key action targets and key pathway were obtained by network pharmacology, and the network map of "component-target-pathway" was carried out to predict the quality markers of Suanzaoren Decoction against insomnia, anxiety and depression. RESULTS A total of 20 prototype compounds were detected in rats. The 23 core targets were predicted and 14 prototype components associated with pharmacodynamic efficacy were screened through network pharmacology, the 14 components including jujuboside A, enoxolone, ferulic acid, magnoflorine, liquiritigenin, isoliquiritigenin, poricoic acid A, spinosin, coclaurine, timosaponin BII, glycyrrhizic acid, jujuboside B, liquiritin and senkyunolide A played multiple targets and multiple pathways to regulate insomnia, anxiety, depression. CONCLUSION It is predicted that the 14 chemical components are considered as potential quality markers of Suanzaoren Decoction by analyzing prototype chemical composition in vivo combined with network pharmacology. The results can provide reference for the quality control of Suanzaoren Decoction, and lay a foundation for elucidating the mechanism of its pharmacodynamic substances.

Suanzaoren Decoction  /  chemical component in vivo  /  high performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry  /  network pharmacology  /  Q-Marker
李静, 陈伟燕, 李松, 赵韶华, 刘敏彦, 李志伟. 基于体内化学成分及网络药理学的经典名方酸枣仁汤质量标志物预测分析. 中国药学杂志, 2024 , 59 (22) : 2140 -2147 . DOI: 10.11669/cpj.2024.22.006
Jing LI, Weiyan CHEN, Song LI, Shaohua ZHAO, Minyan LIU, Zhiwei LI. Quality Markers of Suanzaoren Decoction Based on Chemical Components in Vivo and Network Pharmacology[J]. Chinese Pharmaceutical Journal, 2024 , 59 (22) : 2140 -2147 . DOI: 10.11669/cpj.2024.22.006
正文
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睡眠障碍、焦虑障碍和抑郁障碍是临床上常见的神经系统疾病,严重危害患者的身心健康。近年来,随着生活节奏的加快,越来越多的人被失眠困扰,久而久之,出现焦虑、抑郁等症状。而抑郁症是一种发病率高、治愈率低、复发率高的疾病,据报道,到2030年将成为全球致残率最高的疾病之一[1]。酸枣仁汤首次记载于东汉著名医家张仲景的《金匮要略·血痹虚劳病脉证并治》,处方由炒酸枣仁、知母、川芎、茯苓、甘草5味中药组成,处方中使用酸枣仁补血养肝,清心安神,为方中君药;茯苓与知母为方中臣药,具有滋阴清热、宁心安神的功效;川芎为佐药,具有活血行气的功效;甘草为使药,可调和诸药,与方中各药共同发挥养血安神、清热除烦的作用[2]。现代药理研究表明,酸枣仁汤具有镇静催眠、抗抑郁、抗焦虑等药理作用[3-5]。中药化学成分复杂,具有多成分、多途径、多靶点的作用特点,目前仍缺乏足够有效的科学表达体系和质量控制体系,酸枣仁汤是有着上百种化学成分的复方中药,发挥药效的关键性成分不清楚,导致其质量标准难以控制。
2006年,刘昌孝院士提出中药质量标志物(quality marker,Q-Marker)的概念[6],这一概念的提出,将中药所含的化学成分与中药的有效性、安全性进行了关联,有利于构建中药全过程质量控制和质量溯源体系,成为中药质量研究的热点,极大地推动了中药质量控制研究的发展。另外,网络药理学的发展,推动了“成分-靶标-通路-疾病”的关联性,又为中药质量标志物的发现提供了科学的方法,被广泛应用于预测和分析有效化合物[7]。因此本研究拟通过体内原型化学成分的分析检测,结合网络药理学方法,将酸枣仁汤的体内原型成分与靶标通路相结合,预测酸枣仁汤的潜在质量标志物,以期为酸枣仁汤的后续质量控制提供参考。
1 材料
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1.1 仪器与试剂
超高效液相色谱系统(Agilent 1290 Infinity,配有二元泵、柱温箱和自动进样器,美国Agilent公司);质谱仪(SCEIX Triple-TOFTM 5600+System,美国AB SCIEX公司);数据采集软件:Analyst TF 1.7 software(美国AB SCEIX公司);数据处理软件系统:Peakview 2.2 software(美国AB SCEIX公司);色谱柱(Waters ACQUITY UPLC BEH C18,2.1 mm×100 mm,1.7 μm,美国Waters公司);冷冻干燥机(FD5-3,GOLD-SIM公司);超声波清洗器(KQ-300VDB,昆山市超声仪器有限公司);电子分析天平(XS204,瑞士METTLER TOLEDO公司)。
乙腈、甲酸为色谱纯,甲醇为分析纯,水为屈臣氏纯净水,其他试剂均为分析纯。
1.2 药材与对照品
炒酸枣仁、知母、川芎、茯苓、甘草饮片购自安国华仁中药饮片有限公司,所有饮片经河北医科大学黄芸老师鉴定,均符合《中国药典》2020年版一部各品种项下规定,酸枣仁汤冻干粉(自制)。
木兰花碱(批号:112090-202201)、酸枣仁皂苷A(批号:110734-201914)、酸枣仁皂苷B(批号:110814-201809)、芒果苷(批号:111607-201704)、阿魏酸(批号:110773-201915)、斯皮诺素(批号:111869-202005)、甘草苷(批号:111610-201908)、牡荆素(批号:111687-202306)、甘草酸(批号:110731-202122)、甘草次酸(批号:110723-202316)、知母皂苷 BⅡ(批号:111839-202107)(中国食品药品检定研究院);新芒果苷(批号:MUST-22071315)、6‴-阿魏酰斯皮诺素(批号:MUST-22101301)、甘草素(批号:MUST-22092613)、异甘草素(批号:MUST-22081317)、茯苓酸A(批号:MUST-23080810)(成都曼思特生物科技有限公司);洋川芎内酯A(批号:wkq22051803)、乌药碱(批号:wkq22070810)、维采宁Ⅱ(批号:wkq23020704)(四川维克奇生物有限公司);芹糖异甘草苷(批号:18121104)(成都普菲德生物技术有限公司)。
SD大鼠,雄性, 180~200 g,北京华阜康生物科技股份有限公司,许可证编号SCXK(京)2019-0008。
2 方法
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2.1 体内原型化学成分研究
2.1.1 酸枣仁汤的制备
炒酸枣仁药材69 g,粗碎,加水1 600 mL,煮至1 200 mL,再加入茯苓27.6 g、知母27.6 g、川芎27.6 g、甘草13.8 g,煮至600 mL,过滤,浓缩,冻干,得冻干粉。
2.1.2 分组与给药
将大鼠随机分为2组,空白组和给药组,每组3只,分别饲养于代谢笼中,自由饮水,温度20~26 ℃,湿度40%~70%。于给药前禁食12 h。给药组灌胃给予酸枣仁汤,按大鼠体质量给药,每千克体质量给20 g生药,空白组灌胃等体积的水,分别于0.5、1、2、4 h采集血浆样品,同时采集0~24 h的尿液,-80 ℃保存。
2.1.3 生物样品预处理
①血浆样本的预处理:将4个时间点的血浆样本混合均匀,精密吸取100 μL,加入预冷的甲醇300 μL,涡旋5 min,于13 000 r·min-1离心10 min,取上清液,用氮吹仪吹干,再用体积分数50%乙腈水100 μL复溶,涡旋1 min,13 000 r·min-1离心10 min,取上清液进样检测。②尿样本的预处理:将5 mL大鼠尿样加在固相萃取小柱上(固相萃取小柱先用3 mL甲醇和3 mL水预洗),先用水3 mL洗涤,再用甲醇1 mL洗脱,收集甲醇洗脱液,洗脱液定容至1 mL,13 000 r·min-1离心10 min,取上清液进样检测。
2.1.4 对照品溶液的制备
①对照品溶液1的制备:精密称定斯皮诺素、酸枣仁皂苷A、酸枣仁皂苷B、木兰花碱、乌药碱、维采宁Ⅱ、6‴-阿魏酰斯皮诺素、新芒果苷、芒果苷、甘草苷、芹糖异甘草苷、甘草素、甘草酸、甘草次酸适量,加甲醇制成每1 mL约含1~10 μg的溶液。②对照品溶液2的制备:精密称定知母皂苷BⅡ、异甘草素、牡荆素、茯苓酸A、阿魏酸、洋川芎内酯A适量,加甲醇制成每1 mL约含1~10 μg的溶液。
2.1.5 色谱条件
色谱柱:Waters ACQUITY UPLC BEH C18柱(2.1 mm×100 mm,1.7 μm);以体积分数0.1%甲酸水为流动相A,乙腈为流动相B,梯度洗脱(0~2 min,95%A;2~7 min,95%~85%A;7~14 min,85%~83%A;14~18 min,83%~74%A;18~22 min,74%~73%A;22~25 min,73%~55%A;25~28 min,55%~45%A;28~35 min,45%~5%A;35~40 min,5%A;40~41 min,5%~95%A;41~47 min,95%A);流速:0.2 mL·min-1;柱温:30 ℃;进样体积:5 μL。
2.1.6 质谱条件
电喷雾离子(ESI)源,正、负离子模式均扫描;正离子化电压:5.5 kV,负离子化电压:-4.5 kV;涡轮喷雾温度:550 ℃;去簇电压(declustering potential,DP):100 eV/-100 eV;碰撞能量 (collision energy,CE):50 V/-50 V;扩展碰撞能量(collision energy spread,CES):20 eV;选择信息依赖性(IDA)扫描模式,选择响应值大于100 cps,子离子扫描范围m/z 100~1 500。
3 结果
收起
3.1 体内原型化学成分的鉴定
前期对酸枣仁汤的化学成分进行了分析鉴定,其中收集到53个化合物的对照品,通常认为入血成分或入血后以原型或代谢产物排出体外的成分,可能是药物发挥作用的药效物质,因此本研究以53个具有对照品的化合物为基础对酸枣仁汤的入血原型成分及通过尿液排出的原型成分进行分析检测。以一级全扫描获得的精确相对分子质量和信息依赖采集(IDA)触发的二级质谱中的碎片离子信息为基础,利用Peakview软件针对性地对53种化学成分进行初筛(根据保留时间、相对分子质量误差、同位素组成以及软件综合评分等级),而后将初筛目标物的二级碎片与对照品的二级碎片裂解规律进行逐一比对核实,结果在血浆中共检测到19种原型成分,在尿液中检测到10种原型成分,共计20个原型化学成分,结果见表1和图1~5。
3.2 网络药理学预测分析
3.2.1 酸枣仁汤活性成分和疾病靶点交集靶点的获取
将进入体内的20个化合物,利用Swiss Target Prediction数据库和BATMAN数据库获得潜在作用靶点共451个;通过OMIM、TTD、Drugbank、DisgeNET和GeneCards数据库,以“Depression”“Insomnia”和“Anxiety disorder”为关键词,搜索与抗抑郁、失眠和焦虑相关的靶点,取并集获得靶点4 071个;利用VENNY软件将成分靶点与疾病靶点进行交集得到共有靶点295个,见图6。
3.2.2 关键靶点的获取
将295个共有靶点导入STRING数据库构建PPI网络,选择物种为“Homo sapiens”,获得PPI网络图。将分析得到的TVS格式文件导入Cytoscape 3.7.1软件,根据连接度(degree)、介度(betweenness)和紧密度(closeness)3个重要拓扑参数的中位数(degree>69、betweenness>823和closeness>0.53)进行核心靶点的筛选,经筛选得到共计23个核心靶点。按度值进行排序为:ALB、IL6、TNF、SRC、IL1B、TP53、EGFR、ESR1、PPARG、STAT3、PTGS2、CTNNB1、MAPK3、JUN、HSP90AA1、IFNG、CXCR4、GSK3B、NR3C1、ACE、APP、JAK2、CYP3A4,见图7。
3.2.3 生物信息学分析
①京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes,KEGG)通路分析:将23个关键靶点输入DAVID 平台,进行基因本体(gene ontology,GO)分析和KEGG通路富集分析,设置属性为“Homo sapiens”。并将KEGG 通路分析中P<0.01 的前20条通过微生信平台进行可视化分析(图8)。分析结果显示,酸枣仁汤治疗失眠、抑郁、焦虑主要与脂质和动脉粥样硬化通路、人类巨细胞病毒感染、癌症信号通路、白细胞介素-17信号通路、Th17细胞分化、化学致癌-受体激活、EGFR酪氨酸激酶抑制剂耐药性等通路相关。②GO分析:GO 分析包括生物过程(biological process,BP)、细胞定位(cellular component,CC)和分子功能(molecular function,MF)。以P<0.05 为筛选条件,失眠、抑郁、焦虑涉及BP、CC、MF分别为220、35、44个;P<0.01被认为显著富集,以P<0.01 为筛选条件,失眠、抑郁、焦虑涉及BP、CC、MF通路分别为124、21、28个。分别选取P值前10位的进行GO分析(图9)。分析结果显示,在BP方面主要包括对外源刺激的反应、一氧化氮生物合成过程的正调控、基因表达的负调控、细胞凋亡负调节等有关;与CC相关的靶点显著富集于大分子复合体、膜筏、转录因子复合体、细胞质、谷氨酸能突触等。在MF方面主要包括酶结合、蛋白磷酸酶结合、雌激素受体结合、一氧化氮合酶调节活性等。这些结果表明,酸枣仁汤活性成分对抑郁、失眠和焦虑的影响涉及多种途径和多个靶点,通过多通路协同发挥作用。
3.2.4 “成分-靶点-通路”网络的构建
将筛选得到的23个核心靶点反推得到相关联的化合物共14种,根据度值进行排序为:酸枣仁皂苷A、甘草次酸、阿魏酸、木兰花碱、甘草素、异甘草素、茯苓酸A、斯皮诺素、乌药碱、知母皂苷BⅡ、甘草酸、酸枣仁皂苷B、甘草苷和洋川芎内酯A。将筛选得到的14种化合物、23个核心靶点与前20条信号通路导入Cytoscape软件中,构建“成分-靶点-通路”相关联的多维网络图(图10)。该网络共有 57个节点,217条边,深黄色圆形代表体内成分,深绿色三角形代表关键通路,浅粉色方形代表关键靶点。
4 讨论
收起
经典名方酸枣仁汤由炒酸枣仁、知母、川芎、茯苓、甘草组成,化学成分复杂,药理作用广泛。本研究以进入体内的原型化学成分为研究对象,以抗抑郁、抗焦虑、改善失眠为治疗疾病,通过网络药理学获得与之相关的靶点,通过靶点、通路及成分的关联分析,共得到14个药效相关的化合物,其中酸枣仁皂苷A、酸枣仁皂苷B、木兰花碱、乌药碱和斯皮诺素来源于酸枣仁;茯苓酸A和知母皂苷BⅡ分别来源于臣药茯苓和知母;洋川芎内酯A和阿魏酸来源于佐药川芎;甘草次酸、甘草酸、甘草苷、甘草素和异甘草素来源于使药甘草。
酸枣仁为方中君药,其主要含有皂苷类、黄酮类、生物碱等成分,其中皂苷类成分以酸枣仁皂苷A和酸枣仁皂苷B为代表性成分。文献报道酸枣仁皂苷A和B是酸枣仁中皂苷的主要成分,具有镇静安神、改善睡眠、神经保护、改善记忆、心脏保护、抗氧化、抗炎及抗肿瘤等广泛的药理效应[8-9]。木兰花碱和乌药碱为酸枣仁中生物碱的代表性成分,文献报道,酸枣仁中抗抑郁的主要成分为其生物碱成分[10];斯皮诺素为酸枣仁中的黄酮类成分,是酸枣仁发挥镇静催眠作用的主要药效物质[11-12]。
茯苓酸A是一种存在于茯苓中的五环三萜类化合物,具有抗肿瘤、抗炎、抗氧化、镇静等多种药理作用[13-14]。知母皂苷BⅡ为知母中甾体皂苷类成分,具有抗抑郁的作用,能显著减少小鼠不动时间,能够增强5-羟色胺酸(5-HTP)诱导的甩头行为,可增加脑内5-羟色胺(5-HT)和多巴胺(DA)的含量[15-16]。
川芎中酚酸类成分对缺氧神经细胞有显著的保护作用,阿魏酸为其代表性成分,文献报道阿魏酸与抗血小板聚集活性关联性最大,可促进血管舒张[17]。洋川芎内酯A是一种苯酞类物质,是主要的抗氧化损伤活性成分,主要通过蛋白磷酸化酶2A/α突触核蛋白(PP2A/α-syn)途径对皮质酮(corticosterone, Cort)诱导的神经元凋亡发挥保护作用[18]。
甘草酸和甘草次酸是存在于甘草的根和根茎中的一种天然三萜皂苷和皂苷元,具有多种药理功能,甘草酸是一种天然的高迁移率族蛋白1(HMGB1)抑制剂,通过与HMGB1结合进而阻断HMGB1与其受体的相互作用从而发挥抗炎活性,能通过影响病毒的吸附、侵入和复制等过程发挥广谱抗病毒生物活性[19-20];甘草次酸通过抑制磷酸甘油酸激酶1(PGK1)介导的糖酵解途径进而抑制氧化低密度脂蛋白(ox-LDL)诱导的血管内皮细胞凋亡,能够抑制p38丝裂原活化蛋白激酶(p38 MAPK)信号通路促进脂多糖(lipopolysaccharide, LPS)诱导的小胶质细胞M2极化[21-22]。甘草素、异甘草素和甘草苷为甘草中黄酮类化合物,具有抗抑郁、抗氧化、抗炎、抗病毒、抗菌等多种药理活性[23-27]。
上述分析表明,网络药理学预测的药效相关质量标志物与文献报道的研究结果基本一致。因此,酸枣仁皂苷A、甘草次酸等14个成分可作为酸枣仁汤潜在的质量标志物,用于评价和控制酸枣仁汤的质量。
基金
收起
  • 河北省自然科学基金项目(H2022106048)
  • 河北省中央引导地方科技发展资金项目(236Z2502G)
  • 石家庄学院博士科研启动基金项目(22BS001)
文献
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2024年第59卷第22期
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doi: 10.11669/cpj.2024.22.006
  • 接收时间：2024-05-17
  • 首发时间：2025-11-16
  • 出版时间：2024-11-22
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  • 收稿日期：2024-05-17
基金
河北省自然科学基金项目(H2022106048)
河北省中央引导地方科技发展资金项目(236Z2502G)
石家庄学院博士科研启动基金项目(22BS001)
作者信息
    1 河北科技大学,石家庄 050018
    2 石家庄学院, 河北省藏药质量技术创新中心,石家庄 050035
    3 石家庄以岭药业股份有限公司, 石家庄 050035

通讯作者:

* 李志伟,男,博士,副教授,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)81668391;
刘敏彦,女,博士,主任中药师,硕士生导师 研究方向:药物分析及药效物质基础研究 Tel:(0311)66617323
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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