目的 基于指纹图谱和网络药理学方法，分析预测加味四逆散（Jiawei Sini Powder，JSP）（颗粒）潜在的质量标志物（quality markers，Q-Marker），并对Q-Marker成分进行含量测定，为其质量控制提供参考。方法 采用HPLC指纹图谱结合化学计量学分析JSP（颗粒）潜在Q-Marker。通过网络药理学构建物质-效应网络，进一步分析预测JSP（颗粒）药效关联的Q-Marker，并建立对预测得到的标志性成分进行含量测定的HPLC方法。结果 建立了JSP（颗粒）的HPLC指纹图谱，标记28个共有峰，对其进行峰归属，其中1、9、11号峰来自白芍，26、27号峰来自枳实，13、28号峰来自炙甘草，2、4、10、12、16、24号峰来自代代花，15、17、22号峰来自半枝莲，25号峰来自白芍和炙甘草共有，3、5～8、14、18～21、23号峰来自枳实和代代花共有，并指认了其中的9个共有峰，分别为芍药内酯苷、芍药苷、甘草苷、野黄芩苷、柚皮苷、橙皮苷、新橙皮苷、柚皮素、甘草酸铵。相似度评价显示，10批JSP（颗粒）样品的相似度为0.954～1.000。主成分分析（principal component analysis，PCA）显示前4个主成分的累积方差贡献率为95.003%，正交偏最小二乘-判别分析（orthogonal partial least squares-discriminant analysis，OPLS-DA）显示有12个成分变量重要性投影（variable importance projection，VIP）值较大。采用网络药理学的方法分析得出芍药内酯苷、芍药苷、柚皮苷、橙皮苷、新橙皮苷5个成分可能为JSP（颗粒）潜在的Q-Marker。同时测定以上5个成分的含量，方法学考察结果良好，平均加样回收率为92.52%～96.48%，RSD为1.3%～2.6%。10批样品中芍药内酯苷、芍药苷、柚皮苷、橙皮苷、新橙皮苷的质量分数分别为0.107 6～0.203 6、0.475 9～1.204 6、2.310 9～5.223 8、0.410 3～0.796 8、4.311 9～8.343 3 mg/g。结论 建立了JSP（颗粒）的HPLC指纹图谱，并结合网络药理学预测出5个Q-Marker成分及建立其定量测定方法，为JSP（颗粒）研制成古代经典名方中药复方制剂提供质量控制依据。

Objective To analyze and predict potential quality markers (Q-Marker) of Jiawei Sini Powder (JSP, 加味四逆散) (Granules), and determine the content of Q-Marker components based on fingerprint and network pharmacology methods, providing reference for quality control. Methods Firstly, the potential Q-Marker of JSP (Granules) were analyzed by fingerprint and chemometrics. On this basis, a substance effect network was constructed through network pharmacology to further analyze and predict the Q-Marker associated with the efficacy of JSP (Granules), and an HPLC method was established to determine the content of the predicted landmark components. Results An HPLC fingerprint of JSP (Granules) was established, identifying 28 common peaks, and assigning them to different peaks. Among them, peaks 1, 9, and 11 come from Baishao (Paeoniae Radix Alba, PRA), peaks 26, and 27 come from Zhishi (Aurantii Fructus Immaturus, AFI), peaks 13, and 28 come from Zhigancao (Glycyrrhizae Radix et Rhizoma Praeparata cum Melle, GRRPM), peaks 2, 4, 10, 12, 16, and 24 come from Daidaihua (Citrus aurantium var. amara), peaks 15, 17, and 22 come from Banzhilian (Scutellariae Barbatae Herba), peaks 25 come from both PRA and GRRPM, peaks 3, 5-8, 14, 18-21, and 23 come from both AFI and C. aurantium var. amara. Nine common peaks were identified, including albiflorin, paeoniflorin, glycyrrhizin, scutellarin, naringin, and hesperidin, neohesperidin, naringin, ammonium glycyrrhetate. The similarity evaluation showed that the similarity of 10 batches of JSP (Granules) samples ranges from 0.954-1.000. Principal component analysis (PCA) showed that the cumulative variance contribution rate of the first four principal components was 95.003%, while orthogonal partial least squares-discriminant analysis (OPLS-DA) showed that 12 components had higher variable importance projection values. On this basis, the network pharmacology method was used to analyze and conclude that albiflorin, paeoniflorin, naringin, hesperidin and neohesperidin may be the potential Q-Marker of JSP (Granules). The content of the above five components was determined simultaneously, and the methodological investigation results were good. The average sample recovery rate was 92.52%-96.48%, and the RSD was 1.3%-2.6%. The mass fractions of albiflorin, paeoniflorin, naringin, hesperidin, and neohesperidin in 10 batches of samples were 0.107 6-0.203 6, 0.475 9-1.204 6, 2.310 9-5.223 8, 0.410 3-0.796 8, 4.311 9-8.343 3 mg/g. Conclusion This study established an HPLC fingerprint of JSP (Granules) and combined it with network pharmacology to predict five Q-marker components and establish a quantitative determination method, providing a quality control basis for the development of JSP (Granules) into ancient classic Chinese medicine compound formulations.

R283.6

中国东盟传统药物研究国际合作联合实验室建设（二期）资助项目（AD19110165）；广西中医药大学2022年研究生教育创新计划项目（YCSZ2022004）；2020年度广西中药药效研究重点实验室运行补助项目（20-065-38）