目的 基于指纹图谱和网络药理学分析南柴胡Bupleurum scorzonerifolium中潜在的质量标志物（quality marker，Q-Marker）并测定其含量。方法 运用高效液相色谱法建立南柴胡药材指纹图谱，确认共有峰并进行指认，再运用网络药理学方法构建“活性成分-靶点-通路”网络，预测Q-Marker，并测定其含量。结果 建立了12批南柴胡药材指纹图谱，确认了13个共有峰，通过柴胡皂苷对照品指认5个色谱峰，分别为柴胡皂苷a、柴胡皂苷b₂、柴胡皂苷c、柴胡皂苷d、柴胡皂苷f；经网络药理学确认以上5种成分为活性成分，可作用于14个核心靶点、20条关键通路发挥抗炎、抗抑郁、抗肿瘤作用，初步预测柴胡皂苷a、柴胡皂苷b₂、柴胡皂苷c、柴胡皂苷d、柴胡皂苷f为南柴胡潜在的Q-Marker，南柴胡药材中其总质量分数不低于0.10%。结论 南柴胡Q-Marker预测分析为药材质量评价提供参考，为阐明其药效物质基础的作用机制奠定基础。

Objective To predict potential quality marker (Q-Marker) of Bupleurum scorzonerifolium based on fingerprint detection and network pharmacology analysis, and determine the contents of Q-Marker. Method Fingerprint of B. scorzonerifolium was established by HPLC, common peaks were confirmed and identified. “Active component-target-pathway” network was constructed by network pharmacology method. Q-Marker were predicted and their contents were determined. Result Fingerprints of 12 batches of B. scorzonerifolium were established, 13 common peaks were identified, and 5 peaks (saikosaponin a, saikosaponin b₂, saikosaponin c, saikosaponin d, and saikosaponin f) were identified by saikosaponin reference substances. According to network pharmacology, above five components were identified as active components of B. scorzonerifolium, which showed anti-inflammatory, antidepressant, and anti-tumor effects through acting on 14 core targets and 20 key pathways. Saikosaponin a, saikosaponin b₂, saikosaponin c, saikosaponin d, and saikosaponin f were preliminarily predicted as potential Q-Marker of B. scorzonerifolium, total content of which were not less than 0.10%. Conclusion Prediction and analysis of Q-Marker of B. scorzonerifolium can provide references for quality evaluation of medicinal materials and lay a foundation for elucidation of action mechanism of pharmacodynamic substance basis.

R284.1;285.5

哈尔滨商业大学青年创新人才支持计划项目（2020CX11）