目的 基于多元统计分析和网络药理学分析预测炒酸枣仁饮片潜在的质量标志物（Q-Marker）。方法 利用超高效液相色谱-四极杆-静电场轨道阱质谱联用（ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap mass spectrometry，UHPLC-Q-Orbitrap-MS）解析生、炒酸枣仁饮片的主要成分，运用多元统计分析结合VIP>1和P<0.05筛选出炮制前后潜在的差异化学成分。结合网络药理学筛选核心靶点构建“成分-靶点-通路”网络关系，预测炒酸枣仁潜在的Q-Marker，以潜在Q-Marker为指标对市售样品进行含量测定，验证Q-Marker的科学性。结果 生、炒酸枣仁共鉴定54个化学成分，包括斯皮诺素、当药黄素等黄酮类成分，酸枣仁皂苷A、酸枣仁皂苷B等皂苷类成分，以及木兰花碱、乌药碱等生物碱类成分；结合多元统计分析共找到木兰花碱、6′′′-阿魏酰斯皮诺素、酸枣仁皂苷A、白桦脂酸等28个差异性成分；经网络药理学分析筛选出包括17个差异成分的Q-Marker库，分别为酸枣仁皂苷A、酸枣仁皂苷B、美洲茶酸、白桦脂酸、木兰花碱、乌药碱、去甲荷叶碱、巴婆碱、斯皮诺素、当药黄素、维采宁II、6′′′-对-香豆酰斯皮诺素、6′′′-阿魏酰斯皮诺素、芹菜素、桦木酮酸、牡荆素、异牡荆素；结合课题组前期研究及市售样品测定结果，最终确定木兰花碱、乌药碱、斯皮诺素、6′′′-阿魏酰斯皮诺素、酸枣仁皂苷A和B作为炒酸枣仁的Q-Marker。结论 分析预测的Q-Marker为炒酸枣仁的整体质量控制及临床应用提供科学依据，同时也为其功效关联物质的研究及作用机制的阐释奠定基础。

Objective To explore the potential Q-Markers of fried Suanzaoren (Ziziphi Spinosae Semen, ZSS) (FZSS) based on multivariate statistical analysis and network pharmacology. Methods Ultra performance liquid chromatography-quadrupole-electrostatic field orbital trap mass spectrometry (UHPLC-Q-Orbitrap-MS) was used to analyze the main components in ZSS and FZSS, and the potential differential components between ZSS and FZSS were screened out by using multivariate statistical analysis combined with VIP > 1 and P < 0.05. Further, the main action targets were analyzed to construct the "component-target-pathway" network relationship through network pharmacology, and predict the potential Q-Markers of FZSS. The scientificity of potential Q-Markers was verified by the content determination of the potential Q-Markers in commercial samples. Results A total of 54 chemical components, including flavonoid (such as spinosin and swertisin), saponin (jujuboside A and jujuboside B, etc.) and alkaloid components (magnoflorine, coclaurine, etc.), were identified from ZSS and FZSS, and 28 differential components, such as magnoflorine, 6′′′-feruloylspinosin, jujuboside A, and betulinic acid, were found through multivariate statistical analysis. A potential Q-Marker component library contained 17 potential bioactive components (including jujuboside A, jujuboside B, ceanothic acid, betulinic acid, magnoflorine, coclaurine, nornuciferine, asimilobine, spinosin, swertisin, vicenin II, 6′′′-p-coumaroylspinosin, 6′′′-feruloylspinosin, apigenin, betulonic acid, vitexin, isovitexin) was screened by network pharmacology. Magnoflorine, coclaurine, spinosin, 6′′′-feruloylspinosin, jujuboside A, and jujuboside B were finally identified as the Q-Markers of FZSS combined with the preliminary research of our research group and the results of the commercial samples. Conclusion The Q-Marker predicted in this study can provide a reference for the whole quality control and clinical application of FZSS. Meanwhile, it can also provide the basis for the further research on the efficacy-substance relation and mechanism of FZSS.

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国家自然科学基金项目（81603251）；国家自然科学基金项目（81603289）；山西省科技厅重点研发计划项目（201803D31087）；山西省教育厅高等学校青年科研人员培育计划（20）；山西省教育厅高等学校科技创新项目（2020L0422）；2019年山西省教育厅优秀研究生创新项目（2019SY515）；山西中医药大学科技创新团队项目（2018-TD-009）；山西中医药大学2021年科技创新能力培育计划（2021PY-QN-07）