目的 建立清宁散HPLC指纹图谱检测方法，并对其质量标志物（quality markers，Q-Marker）进行预测分析，为清宁散质量控制提供参考依据。方法 基于指纹图谱和网络药理学方法，通过响应面优化法确定清宁散基准样品的最佳制备工艺；采用HPLC法建立清宁散指纹图谱，对共有峰进行归属和指认；基于可行性和可追溯性进行活性成分筛选，通过网络药理学构建“成分-靶点-通路”网络，分析清宁散潜在的Q-Marker。结果 确定了清宁散中桑白皮、赤茯苓、甘草的最佳炮制工艺，清宁散HPLC指纹图谱共标定了39个共有峰，通过对照品色谱及质谱，指认出18个共有成分，分别为1号峰异鼠李素、2号峰甘草苷、3号峰芥子碱硫氰酸盐、4号峰β-胡萝卜苷、10号峰松苓新酸、15号峰去氢土莫酸、16号峰甘草酸铵、19号峰槲皮素、20号峰绿原酸、21号峰甘草查耳酮A、22号峰桑黄酮G、24号峰京尼平苷酸、26号峰桑皮苷A、29号峰桑辛素、31号峰槲皮素-3-O-β-D-葡萄糖-7-O-β-D-龙胆双糖苷、34号峰毛蕊花糖苷、35号峰芹菜素、38号峰茯苓新酸A，相似度均大于0.93；经网络药理学分析，筛选出桑皮苷A、桑辛素、槲皮素-3-O-β-D-葡萄糖-7-O-β-D-龙胆双糖苷、茯苓新酸A、京尼平苷酸、毛蕊花糖苷、甘草苷、甘草酸铵8个活性成分，进行“成分-靶点-通路”网络构建分析，富集的通路包括T细胞受体信号通路、白细胞介素-17（interleukin-17，IL-17）信号通路、磷脂酰肌醇-3-激酶-蛋白激酶B（phosphatidylinositol-3-hydroxykinase-protein kinase B，PI3K-Akt）信号通路等，根据连接度分析，初步预测8个活性成分可能通过调节相关信号通路达到清肺止咳的作用。结论 响应面优化结合指纹图谱和网络药理学分析预测清宁散治疗疾病的Q-Marker，所建立的方法操作简捷，结果准确，稳定性好，可用于清宁散的质量控制评价，也为进一步研究清宁散的作用机制提供参考。

Objective To establish a fingerprint detection method for Qingning Powder (清宁散) which quality markers (Q-Marker) were predicted and analyzed to provide a reference for the quality control of Qingning Powder. Methods Based on fingerprint and network pharmacology methods, the optimal preparation process for Qingning Powder substance benchmark was determined through response surface optimization method; HPLC was used to establish the fingerprint of Qingning Powder to attribute and identify the shared peaks; Conducting a comprehensive screening of active ingredients based on feasibility and traceability, and constructing a network of “compound-target-pathway” through network pharmacology to analyze the potential Q-Marker of Qingning Powder. Results The optimal processing technology of Sangbaipi (Mori Cortex), Chifuling (Rubra Poria) and Gancao (Glycyrrhizae Radix et Rhizoma) in Qingning Powder was determined. In the HPLC fingerprint of Qingning Powder, a total of 39 common peaks were calibrated, with 18 common components of peaks 1—4, 10, 15, 16, 19—22, 24, 26, 29, 31, 34, 35, 38 were identified by reference substance chromatography and mass spectrometry, respectively, named isorhamnetin, liquiritin, sinapine thiocyanate, β-daucosterol, dehydrotrametenolic acid, dehydrotumulosic acid, glycyrrhizic acid ammonium salt, quercetin, chlorogenic acid, licorice chalcone A, kuwanon G, genipinic acid, mulberroside A, morusin, quercetin-3-O-β-D-glucose-7-O-β-D-gentiobiosiden, verbascoside, apigenin and poricoic acid A, all of which exhibit similarities exceeding 0.93. Utilizing network pharmacology analysis, eight active ingredients including mulberroside A, morusin, quercetin-3-O-β-D-glucose-7-O-β-D-gentiobiosiden, poricoic acid A, geniposidic acid, verbascoside, liquiritin, and glycyrrhizic acid ammonium salt were screened out to conduct “component-target-pathway” network construction analysis, the enriched pathways include T cell receptor signaling pathway, interleukin-17 (IL-17) signaling pathway, phosphatidylinositol-3-hydroxykinase-protein kinase B (PI3K-Akt) signaling pathway, etc. Based on connectivity analysis, it was tentatively predicted that these eight active ingredients may play a role in clearing the lungs and relieving cough by modulating related signaling pathways. Conclusion Utilizing response surface optimization in conjunction with fingerprint and network pharmacology analysis to predict the quality markers of Qingning Powder in the context of disease treatment. This established method, characterized by its simplicity of operation, result accuracy, and robust stability, could be used for quality control evaluation of Qingning Powder, and also offers a valuable reference for the investigation of Qingning Powder’s mechanism of action.

R283.6

国家自然科学基金项目（82373843）；国家自然科学基金项目（81602967）；陕西省中医药管理局中医药传承创新暨“秦药”开发重点科学研究项目（2021-02-220-020）；陕西省中医药管理局中医药“双链融合”中青年科研创新团队立项项目（2022-SLRH-YQ-009）；陕西省中医药管理局秦创原中药创新研发项目（2022-QCYZH-013）；陕西省中医药管理局秦创原中药创新研发项目（2022-QCYZH-032）；陕西省中医药管理局秦创原中药创新研发项目（2022-QCYZH-033）；陕西省科学技术厅重点产业创新链（群）项目（2023-ZDLNY-17）；陕西省教育厅服务地方专项-产业化培育项目（23JC011）；西安市科技局西安市科技计划项目（22GXFW0006）