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Article(id=1322926905376252249, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322926740955341011, articleNumber=null, orderNo=null, doi=10.13193/j.issn.1673-7717.2025.10.044, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339953035, onlineDateStr=2026-10-30, pubDate=1760025600000, pubDateStr=2025-10-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339953035, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339953035, creator=13701087609, updateTime=1793339953035, updator=13701087609, issue=Issue{id=1322926740955341011, tenantId=1146029695717560320, journalId=1255847919539208197, year='2025', volume='43', issue='10', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='1760025600000', pubDateStr='2025-10-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793339913834, creator='13701087609', updateTime=1793340753123, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322930261259846420, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322926740955341011, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322930261259846421, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322926740955341011, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=232, endPage=236, ext={EN=ArticleExt(id=1322926905573384538, articleId=1322926905376252249, tenantId=1146029695717560320, journalId=1255847919539208197, language=EN, title=Predictive Analysis of Quality Markers of Baizhi（Angelicae Dahuricae Radix）Based on Fingerprint and Network Pharmacology, columnId=1256183365644968140, journalTitle=Chinese Archives of Traditional Chinese Medicine, columnName=Platform of Provincial Project, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore and analyze the potential quality markers（Q-markers）of Baizhi（Angelicae Dahuricae Radix）combined with fingerprint and network pharmacology methods based on the primary principles of Q-markers.

Methods

The similarity evaluation and common peaks identification were performed by establishing fingerprints of Baizhi（Angelicae Dahuricae Radix），and then the“active ingredient-target-pathway”network was constructed to predict the key targets and core components of Baizhi（Angelicae Dahuricae Radix）through the network pharmacology.Meanwhile，the integrative analysis of Q-markers of Baizhi（Angelicae Dahuricae Radix）from the perspectives of chemical composition validity，measurability and so forth based on current literatures.

Results

Ten batches of fingerprints of Baizhi（Angelicae Dahuricae Radix）were established，10 common peaks were identified，and 4 peaks（byakangelicol，imperatorin，phellopterin，isoimperatorin）were identified by reference substances.In accordance with network analysis，the above four coumarins were its main bioactive compounds with high connectivity.Byakangelicol，imperatorin，phellopterin and isoimperatorin could be further clarified as the potential Q-markers of Baizhi（Angelicae Dahuricae Radix）according to the Q-marker concept.

Conclusion

The preliminary prediction and analysis of the Qmarkers of Baizhi（Angelicae Dahuricae Radix）provides a theoretical basis for further study on pharmacodynamics and pharmacological mechanism，which can militate in favor of whole quality control system of Baizhi（Angelicae Dahuricae Radix）.

, authors=Baojun ZHANG, Yanlong HONG, Lei ZHANG, Jianying WANG, authorsList=Baojun ZHANG, Yanlong HONG, Lei ZHANG, Jianying WANG, authorCompany=null, correspAuthors=Jianying WANG, 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, fund=null), CN=ArticleExt(id=1322926910812070248, articleId=1322926905376252249, tenantId=1146029695717560320, journalId=1255847919539208197, language=CN, title=基于指纹图谱和网络药理学的白芷质量标志物预测分析, columnId=1256183369033965799, journalTitle=中华中医药学刊, columnName=省级项目平台, runingTitle=null, highlight=null, articleAbstract=
目的

基于中药质量标志物（quality marker，Q-marker）核心原则，通过建立指纹图谱结合网络药理学对白芷质量标志物进行初步预测分析。

方法

通过白芷药材指纹图谱，进行相似度评价及共有峰指认；运用网络药理学构建“活性成分-靶点-通路”网络，筛选预测白芷关键靶点及核心成分；基于现有文献，从有效性、可测性等角度对白芷Qmarker进行整合分析。

结果

建立了10批白芷药材的指纹图谱，标定了10个共有峰，通过对照品指认了4个色谱峰，分别为白当归脑、欧前胡素、珊瑚菜素、异欧前胡素；网络药理学分析结果表明这些香豆素类成分具有较高的连接度，是发挥主要活性的重要物质基础；结合Q-marker理念，进一步明确了白当归脑、欧前胡素、珊瑚菜素、异欧前胡素可作为白芷的潜在质量标志物。

结论

白芷Q-marker的预测分析，为后续白芷药效学及药理机制的深入研究奠定了理论基础，提供了科学参考，有助于白芷质量控制体系的建立和完善。

, authors=张宝军, 洪燕龙, 张磊, 王健英, authorsList=张宝军, 洪燕龙, 张磊, 王健英, authorCompany=null, correspAuthors=王健英, authorNote=

张宝军（1990-），男，甘肃天水人，实验师，硕士，研究方向：中药物质基础研究。

, correspAuthorsNote=
王健英（1983-），女，上海人，副研究员，硕士，研究方向：中药药效及健康产品研发。E-mail：。
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张宝军（1990-），男，甘肃天水人，实验师，硕士，研究方向：中药物质基础研究。

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张宝军（1990-），男，甘肃天水人，实验师，硕士，研究方向：中药物质基础研究。

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注：5.byakangelicol，8.imperatorin，9.phellopterin，10.isoimperatorin，a.prangenin，b.prangenidin，c.cnidilin。

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白芷药材来源信息

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基于指纹图谱和网络药理学的白芷质量标志物预测分析
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张宝军 , 洪燕龙 , 张磊 , 王健英
中华中医药学刊 | 省级项目平台 2025,43(10): 232-236
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中华中医药学刊 |省级项目平台 2025 , 43 (10) : 232 -236
基于指纹图谱和网络药理学的白芷质量标志物预测分析
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张宝军, 洪燕龙, 张磊, 王健英
作者信息
  • 上海中医药大学，上海中医健康服务协同创新中心，上海 201203
通讯作者:
王健英（1983-），女，上海人，副研究员，硕士，研究方向：中药药效及健康产品研发。E-mail：。
作者简介:

张宝军（1990-），男，甘肃天水人，实验师，硕士，研究方向：中药物质基础研究。

Predictive Analysis of Quality Markers of Baizhi（Angelicae Dahuricae Radix）Based on Fingerprint and Network Pharmacology
Baojun ZHANG, Yanlong HONG, Lei ZHANG, Jianying WANG
Affiliations
  • Shanghai Innovation Center of TCM Health Service，Shanghai University of Traditional Chinese Medicine，Shanghai201203，China
出版时间: 2025-10-10 doi: 10.13193/j.issn.1673-7717.2025.10.044
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摘要
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目的

基于中药质量标志物（quality marker，Q-marker）核心原则，通过建立指纹图谱结合网络药理学对白芷质量标志物进行初步预测分析。

方法

通过白芷药材指纹图谱，进行相似度评价及共有峰指认；运用网络药理学构建“活性成分-靶点-通路”网络，筛选预测白芷关键靶点及核心成分；基于现有文献，从有效性、可测性等角度对白芷Qmarker进行整合分析。

结果

建立了10批白芷药材的指纹图谱，标定了10个共有峰，通过对照品指认了4个色谱峰，分别为白当归脑、欧前胡素、珊瑚菜素、异欧前胡素；网络药理学分析结果表明这些香豆素类成分具有较高的连接度，是发挥主要活性的重要物质基础；结合Q-marker理念，进一步明确了白当归脑、欧前胡素、珊瑚菜素、异欧前胡素可作为白芷的潜在质量标志物。

结论

白芷Q-marker的预测分析，为后续白芷药效学及药理机制的深入研究奠定了理论基础，提供了科学参考，有助于白芷质量控制体系的建立和完善。

白芷  /  质量标志物  /  指纹图谱  /  网络药理学
Objective

To explore and analyze the potential quality markers（Q-markers）of Baizhi（Angelicae Dahuricae Radix）combined with fingerprint and network pharmacology methods based on the primary principles of Q-markers.

Methods

The similarity evaluation and common peaks identification were performed by establishing fingerprints of Baizhi（Angelicae Dahuricae Radix），and then the“active ingredient-target-pathway”network was constructed to predict the key targets and core components of Baizhi（Angelicae Dahuricae Radix）through the network pharmacology.Meanwhile，the integrative analysis of Q-markers of Baizhi（Angelicae Dahuricae Radix）from the perspectives of chemical composition validity，measurability and so forth based on current literatures.

Results

Ten batches of fingerprints of Baizhi（Angelicae Dahuricae Radix）were established，10 common peaks were identified，and 4 peaks（byakangelicol，imperatorin，phellopterin，isoimperatorin）were identified by reference substances.In accordance with network analysis，the above four coumarins were its main bioactive compounds with high connectivity.Byakangelicol，imperatorin，phellopterin and isoimperatorin could be further clarified as the potential Q-markers of Baizhi（Angelicae Dahuricae Radix）according to the Q-marker concept.

Conclusion

The preliminary prediction and analysis of the Qmarkers of Baizhi（Angelicae Dahuricae Radix）provides a theoretical basis for further study on pharmacodynamics and pharmacological mechanism，which can militate in favor of whole quality control system of Baizhi（Angelicae Dahuricae Radix）.

Baizhi（Angelicae Dahuricae Radix）  /  quality marker  /  fingerprint  /  network pharmacology
张宝军, 洪燕龙, 张磊, 王健英. 基于指纹图谱和网络药理学的白芷质量标志物预测分析. 中华中医药学刊, 2025 , 43 (10) : 232 -236 . DOI: 10.13193/j.issn.1673-7717.2025.10.044
Baojun ZHANG, Yanlong HONG, Lei ZHANG, Jianying WANG. Predictive Analysis of Quality Markers of Baizhi（Angelicae Dahuricae Radix）Based on Fingerprint and Network Pharmacology[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (10) : 232 -236 . DOI: 10.13193/j.issn.1673-7717.2025.10.044
正文
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白芷为伞形科植物白芷Angelica dahurica（Fisch.ex Hoffm.）Benth.et Hook.f.或杭白芷Angelica dahurica（Fisch.ex Hoffm.）Benth.et Hook.f.var.formosana（Boiss.）Shan et Yuan的干燥根，具有解表散寒，祛风止痛，宣通鼻窍，燥湿止带，消肿排脓的作用[1]，主要分布在我国浙江、四川、河南、河北等地区[2]。现代药理研究表明，白芷富含挥发油类、香豆素类、多糖类、黄酮类、生物碱类及氨基酸类等多种化学成分[3]，具有解热镇痛、抗炎、抗肿瘤、降血糖、调节中枢神经、抗氧化、美白等多种药理作用[4]，作为一种中医临床常用药材及药食同源物质[5]，白芷已广泛应用于中药制剂、香料、保健食品、化妆品等领域，尤其在治疗鼻炎、头痛、皮肤美白等方面显示出广阔的应用前景[6]。
中药指纹图谱作为一种有效的质量控制方法，能够全面反映中药所含的化学信息，体现出中药“整体性”和“复杂性”的特点，目前已广泛应用于中药质量控制、真伪鉴别和谱效关系研究等方面[7-8]。网络药理学通过构建“成分-靶点-通路”网络，可整体系统地反映出中药多成分、多作用靶点、多作用途径的特点，是一种阐述作用规律，解析中药复杂体系的重要分析策略[9]。中药质量标志物将中药有效性、安全性相关联，具有整体、多元质控的特点，能够反映出中药全过程质量控制中物质基础特有、差异、动态变化、质量传递、溯源性的属性特征[10]。随着药效及药理学的持续深入研究，白芷资源的开发利用呈现出广阔的应用前景，但白芷质量因各地方炮制工艺标准的不统一，表现出一定的种间质量差异性，目前《中华人民共和国药典》中仅以欧前胡素含量为标准的白芷质量控制方法，难以反映出白芷药材的整体质量特点，在一定程度上不能够系统全面地评价白芷的药材质量，因此更为系统完善的品质、质量评价俨然成为了白芷研究开发中急需解决的首要问题。本研究基于Q-marker核心理念，结合指纹图谱、网络药理学筛选预测白芷的潜在质量标志物，为进一步完善白芷药材的质量控制标准提供研究基础。
1　材料
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1.1　仪器
Agilent 1260 InfinityⅡ高效液相色谱仪（安捷伦科技有限公司）；SQPQUINTIX125D-1CN电子天平［赛多利科学仪器（北京）有限公司］；SK7210HP型超声波清洗器（上海科导超声仪器有限公司，350W）；H1850医用离心机（湖南湘仪实验室仪器开发有限公司）。
1.2　试药
对照品白当归脑（byakangelicol，批号：22010421）、欧前胡素（imperatorin，批号：21090231）、珊瑚菜素（phellopterin，批号：21120721）、异欧前胡素（isoimperatorin，批号：21092231）均购自上海同田生物科技有限公司，质量分数均大于98.0%；甲醇［色谱纯，赛默飞世尔科技（中国）有限公司］；甲醇（分析纯，国药集团化学试剂有限公司）；娃哈哈水（杭州娃哈哈集团有限公司）。共收集来源于安徽省、四川省、浙江省3个产地10批白芷饮片，详情见表1，经上海中医药大学张红梅副教授鉴定为伞形科植物白芷Angelica dahurica（Fisch，ex Hoffm.）Benth.et Hook.f.或杭白芷Angelica dahurica（Fisch.ex Hoffm.）Benth.et Hook.f.var.formosana（Boiss.）Shan et Yuan的干燥根，样品留存于上海中医药大学中医健康服务协同创新中心。
2　方法与结果
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2.1　白芷指纹图谱定性分析
2.1.1　色谱条件
采用安捷伦InfinityLab Poroshell 120 ECC18色谱柱（4.6 mm ×250 mm，2.7μm）；流动相为甲醇-水（55∶45）等度洗脱，46 min；流速：1.0 mL·min-1，检测波长300 nm；柱温30℃；进样量10μL。
2.1.2　对照品溶液的制备
精密称取prangenin、byakangelicol、imperatorin、cnidilin、phellopterin、isoimperatorin、prangenidin对照品适量，加甲醇配制成质量浓度分别为0.067、0.128、0.151、0.195、0.135、0.132、0.165 mg·mL-1的混合对照品溶液。
2.1.3　供试品溶液的制备
精密称定约0.4 g样品粉末（过3号筛），置50 mL量瓶中，加甲醇45 mL，称定质量，超声处理（功率350 W，频率53 kHz）1 h，取出，放冷，再称定质量，加甲醇补重，摇匀，离心，经0.22μm微孔滤膜滤过，取续滤液，即得供试品溶液。
2.1.4　方法学考察
精密度试验：白芷样品（S1）溶液，重复进样6次，记录其色谱图，以imperatorin为参照峰，计算结果显示各共有峰相对保留时间相对标准偏差（relative standard deviation，RSD）<0.25%，相对峰面积RSD<1.98%，表明仪器精密度良好。
重复性试验：平行制备6份白芷样品（S1）供试品溶液，进样分析记录其色谱图，以imperatorin为参照峰，计算结果显示各共有峰相对保留时间RSD<0.17%，相对峰面积RSD<2.94%，表明该方法的重复性良好。
稳定性试验：白芷样品（S1）溶液，分别在0、2、4、8、12、24 h进样分析记录其色谱图，以imperatorin为参照峰，计算结果显示各共有峰相对保留时间RSD<0.43%，相对峰面积RSD<1.02%，表明白芷供试品溶液在24 h内的稳定性良好。
2.1.5　白芷指纹图谱的建立及共有峰标定
10批次白芷药材供试品溶液依次进样测定，记录其色谱图并导入至中药指纹图谱相似度评价系统（2012年版）软件中，以S1号样品的图谱作为参照图谱，采用中位数法，时间窗设为0.3，进行相似度评价。结果共标定了10个共有峰，指纹图谱叠加图，见插页ⅩⅩⅪ图1。相似度结果表明白芷药材指纹图谱相似度范围在0.959～0.999，S1～S10样品相似度均大于0.900，分别为0.959、0.997、0.998、0.962、0.988、0.992、0.987、0.986、0.996、0.999，表明这3个产地白芷药材质量稳定均一。通过对照品溶液色谱图比对，指认了4个共有峰，分别为byakangelicol（5号峰）、imperatorin（8号峰）、phellopterin（9号峰）、isoimperatorin（10号峰），指纹图谱共有模式图及混合对照品色谱图，见图2。
2.2　基于成分-靶点-通路的网络药理学分析
2.2.1　候选化合物靶点预测
通过中药系统药理学数据库与分析平台（traditional Chinese medicine systems pharmacology database and analysis platform，TCMSP，https://www.tcmsp-e.com/）选择口服生物利用度（oral bioavailability，OB）≥30%，类药性（drug-like，DL）≥0.18的12个香豆素类成分作为候选化合物，其中8个化合物byakangelicol、oxyimperatorin、isoimperatorin、imperatorin、prangenidin、alloisoimperatorin、cnidilin、phellopterin通过Swiss target prediction平台（http://www.swisstargetprediction.ch/）收集其活性靶点，4个未检索到活性靶点的化合物sen-byakangelicol，oxypeucedanin hydrate，pabuleno、neobyakangelicol，选取其在TCMSP数据库中的靶点，合并12个化合物相关靶点，去重共得到248个相关靶点蛋白。
2.2.2　蛋白质作用（protein-protein interaction，PPI）网络的构建
将248个靶点蛋白导入在线STRING 11.5数据库平台（https://cn.string-db.org/）分析，物种设置为“Homo sapiens”，最高置信度蛋白交互参数评分值 >0.9，去掉网络中离散的节点，将获得的PPI数据导入Cytoscape 3.9.1软件中，通过“Network Analyzer”功能进行靶点拓扑属性分析，其中度值最高的HSP90AA1能与28个蛋白发生相互作用，PPI网络图，见插页ⅩⅩⅪ图3。
2.2.3　基因本体功能富集分析
利用DAVID数据库（https://david.ncifcrf.gov/）对248个作用靶点进行基因本体功能（Gene Ontology，GO）富集分析。物种设置为“Homo sapiens”，“Gene Ontology”项下，以P<0.05、FDR<0.05进行筛选，得到GO条目，包含160个生物过程（biological process，BP），主要涉及蛋白质磷酸化、药物反应、炎症反应、凋亡过程的负调控、脂多糖反应等过程。34个细胞组成（cellular component，CC）主要富集在细胞质膜、胞质溶胶、细胞质膜的整体组成、胞质核周区、神经元胞体等区域。56个分子功能（molecular function，MF）条目，主要富集在三磷酸腺苷结合、蛋白质丝氨酸/苏氨酸激酶活性、蛋白激酶活性、酶结合、激酶等功能。分别选取BP、CC、MF根据P值排名前10的条目，见插页ⅩⅩⅫ图4。
2.2.4　KEGG通路富集分析
以P<0.05、FDR <0.05作为筛选条件，对上述248个作用靶点进行京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes，KEGG）通路富集分析，共得到95个条目，主要涉及神经活性配体-受体相互作用、肿瘤通路、磷脂酰肌醇3-激酶-丝氨酸/苏氨酸激酶信号通路、钙信号通路、环磷酸腺苷信号通路等信号通路。选取排名前20的通路进行KEGG通路富集分析气泡图绘制，如插页ⅩⅩⅩⅢ图5所示，圆点越大表示富集的基因数越多；P值越小表示圆点颜色越接近蓝色，P值越大表示圆点颜色越接近黄色。
2.2.5　“活性成分-靶点-通路”网络构建及分析
将KEGG通路富集分析得到的排名前10通路及涉及的120个靶点，和靶点对应的12个成分导入Cytoscape 3.9.1软件中，构建白芷“活性成分-靶点-通路”网络，见插页ⅩⅩⅩⅢ图6。以连接度（degree）值为参考，化合物prangenin（degree＝51）、imperatorin（degree＝48）、cnidilin（degree＝45）、byakangelicol（degree＝44）、phellopterin（degree＝38）、prangenidin（degree＝23）、isoimperatorin（degree＝13）在网络中具有较高的连接度，表明这些成分可能是白芷发挥药效的潜在活性物质；以大于2倍中位数的degree值（degree中位数值为4）进行筛选，得到10个核心关键靶点，分别为PIK3CG（degree＝16）、PIK3CA（degree＝15）、PIK3CB（degree＝14）、PIK3CD（degree＝14）、JAK1（degree＝10）、GABRB3（degree＝10）、MAP2K1（degree＝10）、RELA（degree＝9）、HSP90AA1（degree＝9）、MAPK8（degree＝9）预示着这些靶点可能是白芷香豆素类成分作用于神经活性配体-受体相互作用、肿瘤、PI3K-AKT信号通路及其他信号通路发挥治疗作用的关键靶点。
2.3　白芷Q-marker的预测分析
刘昌孝等[11]提出中药质量标志物概念，密切关注中药有效性、物质基础、质量控制标志性成分，促进了中药全过程质量控制体系的建立和完善，目前已广泛应用于药材、饮片、制剂全过程质量控制等方面。因此，本研究基于Q-marker核心原则，从传统药性、植物亲缘性及化学成分特有性、质量传递与溯源性、成分可测性等方面探讨预测分析了白芷的质量标志物。
2.3.1　基于传统药性的白芷Q-marker预测分析
白芷为伞形科植物白芷或杭白芷的干燥根，味辛，性温，归胃、大肠、肺经，具有解表散寒，祛风止痛，宣通鼻窍，燥湿止带，消肿排脓的功效[1]，其主要成分欧前胡素，异欧前胡素等香豆素类成分具有良好的舒张血管作用[12-13]，这与辛味能行、能散的性味功能相对应，同时基于分子对接虚拟筛选出的花椒毒酚、水合氧化前胡素、白当归脑、补骨脂素、花椒毒素、佛手柑内酯、氧化前胡素、欧前胡素、异欧前胡素、异栓翅芹醇等香豆素成分可能是白芷抗炎镇痛活性的重要物质基础[14-15]，呈现出与白芷祛风止痛传统药性相一致的生物效应，这进一步科学解释了辛善走窜通窍之性而达开通疏泄之目的功能[16]。研究表明欧前胡素、水合氧化前胡素、花椒毒酚、佛手酚、5-羟基-8-甲氧基补骨脂素等香豆素成分具有良好的抗氧化活性[17]，基于谱效关系分析，筛选出异欧前胡素，白当归脑等香豆素成分具有显著的抗氧化作用[18]，液相色谱-核磁共振波谱法-质谱联用（liquid chromatography-nuclear magnetic resonance spectroscopymass spectrometry，LC-MMR-MS）快速鉴定了白芷中呋喃香豆素欧前胡素，珊瑚菜素可通过抑制一氧化氮活性从而发挥抗炎作用[19]，综上，这些香豆素类成分与白芷的药物效应相对应，是白芷发挥药效的主要物质基础，可考虑将香豆素类成分作为白芷Q-marker的重要选择依据。
2.3.2　基于植物亲缘性及化学成分特有性探讨白芷Qmarker
白芷为伞形科当归属植物，我国伞形科药用植物有65属，262种，其中当归属23种，主要分布于西北、华北、东北、西南等地[20]。白芷的化学成分丰富，其中挥发油类及香豆素类是其主要的有效活性成分，目前已分离得到121种挥发油类成分，主要为烯烃类、烷烃类、有机酸类、酯类、醇类等五大类化合物[4，21]，受生长环境、提取分析方法的影响，不同品种的白芷挥发油在组成和含量上往往存在较大差异[22]。研究表明，香豆素类是白芷中含量最高，最主要的活性成分，总香豆素含量为0.533%[23-24]，已分离鉴定153种，其结构类型以呋喃香豆素为主[4]，目前白芷中以欧前胡素为代表的线性呋喃香豆素类化合物的生物合成机制研究鲜有报道，刘洋[25]通过生物信息学分析，发现阿魏酰辅酶A-原羟化酶、羟基佛手柑内酯-O-甲基转移酶可能参与了川白芷中香豆素类合成代谢，并在苯丙烷代谢途径下游发挥着关键作用。白芷产地众多，不同产地间质量参差不齐，研究表明通过建立不同产地白芷UPLC指纹图谱结合系统聚类分析、主成分分析及正交偏最小二乘法判别分析方法化学模式识别技术，可筛选出白芷质量差异性的标志性成分：水合氧化前胡素、欧前胡素、珊瑚菜素、佛手柑内酯、氧化前胡素、异欧前胡素、白当归素等[26-27]。通过近红外光谱结合修剪随机森林算法可实现不同产地白芷的快速无损鉴别[28]，基于欧前胡素、白当归脑、氧化前胡素的定量1 H核磁共振法用于白芷的质量控制[29]，GAO F Y等[30]通过指纹图谱结合化学计量学分析建立“质量标志物靶向筛选策略”，并成功应用于白芷样品的质量评价。GAO H等[31]基于化学成分动态变化及网络药理学分析，阐明了白芷不同部位化学成分的积累和相互转化规律，筛选预测出欧前胡素、氧化前胡素、补骨脂素、佛手内酯等香豆素类成分可作为白芷潜在的质量标志物。综上所述，可考虑将香豆素类成分作为白芷Q-marker的重要选择参考。
2.3.3　基于质量传递与溯源性探讨白芷Q-marker
2020版《中华人民共和国药典》以白芷晒干或低温干燥的根作为入药部位，生白芷饮片炮制工艺因各地方标准不统一，在一定程度上造成了不同品种间的质量差异，其中干燥方式对白芷成分及含量的影响较大[32]。WU Q H等[33]通过液相色谱-串联质谱法（liquid chromatography-tandem mass spectrometry，LC-MS/MS）靶向代谢组学分析，探讨了冷冻干燥及烘箱干燥过程中白芷代谢产物的变化的规律，鉴定了193种关键的差异代谢物，适度加热可以促进苯丙氨酸途径，导致香豆素含量增加，同时嘌呤和嘧啶途径的激活可明显上调大多数初级和次级代谢产物含量。刘培等[34]探索了不同干燥方法对杭白芷中香豆素及挥发油类成分的影响，结果表明经带皮石灰掩埋干燥处理后的白芷，香豆素及挥发油类成分含量综合评分最高。白芷原药材经硫磺熏蒸加工养护后，在提高其治虫防霉，色泽保鲜、药材干燥效果的同时也造成其活性成分的改变，基于高分离度快速液相色谱法结合化学计量学分析，筛选出氧化前胡素、欧前胡素可作用白芷硫熏前后的重要差异标志物[35]，以欧前胡素为质控指标，通过近红外光谱技术可用于硫熏白芷的定性鉴别和定量研究[36]，进一步的研究表明经硫熏处理后，白芷主要活性成分呋喃香豆素如异欧前胡素、欧前胡素、氧化前胡素含量明显下降，可分别转化为佛手酚、花椒毒酚、水合氧化前胡素，这些成分可作为鉴别白芷硫磺熏蒸的差异标志物[37-38]。整合代谢组学和毒理学策略分析，发现硫熏后白芷香豆素类成分可转化为亲水的硫代产物，从而降低白芷细胞毒性，但不影响其抗炎活性[39]。通过吹扫捕集/二维气相色谱-质谱法分析，发现硫熏后白芷中的挥发性成分含量明显降低[40]。张翠英等[41]研究发现硫熏和发汗干燥对禹白芷中挥发性成分有较大影响。通过对白芷酒炙前后化学成分分析[42]，在弱酸或受热条件下，白当归脑、氧化前胡素可发生醚键开环反应分别转化为白当归素、水合氧化前胡素，使原有成分含量降低。可见《中华人民共和国药典》中仅以欧前胡素含量为标准的白芷质量控制方法，还需进一步完善，综上，香豆素类成分在炮制过程中表现出一定的传递性，可作为白芷Q-marker的潜在成分。
2.3.4　基于成分可测性探讨白芷Q-marker
通过指纹图谱指认了4个共有峰，结合网络药理学分析显示，化合物prangenin、imperatorin、cnidilin、byakangelicol、phellopterin、prangenidin、isoimperatorin具有较高的degree值，其中prangenin、cnidilin、prangenidin在白芷样品中含量较低未检出（见图2），根据Qmarker成分可测性的原则，可考虑将imperatorin、byakangelicol、phellopterin、isoimperatorin作为评价白芷质量的标志物成分。
3　讨论
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“活性成分-靶点-通路”网络分析显示，PIK3CG（degree＝16）、PIK3CA（degree＝15）、PIK3CB（degree＝14）、PIK3CD（degree＝14）、JAK1（degree＝10）等基因可能是白芷中香豆素类成分通过神经活性配体-受体相互作用、肿瘤及PI3K-AKT信号通路发挥治疗作用的关键靶点，其中多个磷脂酰肌醇-3激酶（PI3K）家族成员在网络中具有较高的degree值，提示其可能参与调控了多个生物学过程并发挥着重要作用。研究表明，欧前胡素具有广泛的药理活性，可通过调节PI3K/AKT/NF-κB、Nrf2/HO-1/ROS/MAPK、mTOR、AKT/GSK3β/β-catenin、NO等信号通路发挥抗炎、镇痛、抗肿瘤、抗过敏、改善骨质疏松、影响心血管等药理作用[43]。作为欧前胡素的同分异构体，异欧前胡素在药理活性上与其具有一定的相似性，但药效强弱不同，两者相比，欧前胡素表现出更强的镇痛及扩张血管作用[44]。白当归脑作为一种有效的呼吸道炎症治疗药物，具有良好的应用前景，可选择性地抑制COX-2的表达和活性，从而剂量依赖性调控A549细胞中PGE2的释放[45]。这些香豆素类成分是白芷发挥药效的主要物质基础，与白芷辛、温，解表散寒，祛风止痛，宣通鼻窍，燥湿止带，消肿排脓的传统药性相一致。虽然这4种香豆素类成分（白当归脑、欧前胡素、珊瑚菜素、异欧前胡素）在大多数伞形科植物如当归、佛手等中也有分布，并不能作为白芷的特异性成分，但其含量在白芷中较为丰富，同时这些成分在白芷饮片的炮制过程中体现出一定的传递性。综上所述，基于Q-marker核心原则，结合指纹图谱及网络药理学分析，从传统药性功效、生源途径及成分可测性等方面，可进一步确定将欧前胡素、白当归脑、珊瑚菜素、异欧前胡素等成分作为白芷的潜在质量标志物。
中药具有物种，化合物种类、数量、含量多样性的特点，优良的质量控制及评价体系是保障其功效稳定性和应用安全性的基础[46]。中药白芷具有悠久的用药历史和广阔的开发应用前景，本研究基于Q-marker核心原则，通过建立指纹图谱结合网络药理学筛选预测了白芷潜在的质量标志物，为后续白芷药效研究开展及药理作用机制的深入研究奠定了理论基础，提供了科学参考，有助于白芷质量控制体系的建立和完善，从而在整体上提高质量控制水平。
基金
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  • 上海市教育委员会上海中医药慢性病防治与健康服务省部共建协同创新中心项目(2021科技02-37)
  • 上海中医药大学预算内项目(2021LK088)
文献
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doi: 10.13193/j.issn.1673-7717.2025.10.044
  • 首发时间：2026-10-30
  • 出版时间：2025-10-10
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上海市教育委员会上海中医药慢性病防治与健康服务省部共建协同创新中心项目(2021科技02-37)
上海中医药大学预算内项目(2021LK088)
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    上海中医药大学，上海中医健康服务协同创新中心，上海 201203

通讯作者:

王健英（1983-），女，上海人，副研究员，硕士，研究方向：中药药效及健康产品研发。E-mail：。
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https://castjournals.cast.org.cn/joweb/zhzyyxk/CN/10.13193/j.issn.1673-7717.2025.10.044
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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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