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基于聚类分析的不同批次清开灵注射液HPLC指纹图谱研究 被引量:3

Study on the HPLC Fingerprint of Qingkailing Injection Based on Cluster Analysis
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摘要 目的:建立清开灵注射液的HPLC指纹图谱,并对样品进行聚类,为清开灵注射液高发和低发不良反应批次的区分提供参考依据。方法:采用HPLC梯度洗脱法测定清开灵注射液的指纹图谱,色谱柱为Tnature C18(250 mm×4.6 mm,5μm),乙腈为流动相A,0.2%甲酸溶液为流动相B,梯度洗脱;流速:1.0 ml·min^-1;检测波长:235 nm,柱温:35℃,进样量:20μl。结果:10批样清开灵注射液的指纹图谱标示出18个共有峰,并确证其中3个峰分别为咖啡酸、尿苷、鸟苷。10批药材样品可聚为2类,S2、S4、S5、S6、S7、S9、S10聚为第Ⅰ类,S1、S4、S8聚为第Ⅱ类。结论:将聚类分析与指纹图谱数据结合起来进行识别,该方法简便,重现性好,可为不同清开灵注射液高发和低发不良反应批次质量鉴定提供科学依据。 Objective: To establish an HPLC fingerprint of Qingkailing injection,and conduct cluster analysis to provide reference for the batch differentiation of Qingkailing injection with high-or low-incidence adverse reactions.Methods: The separation was performed on a Tnature C18 (250 mm × 4.6 mm,5 μm) column eluted with the mobile phase consisting of 0.2% formic acid solution and acetonitrile in gradient mode at a flow rate of 0.1 ml·min^-1,the column temperature was 35 ℃,the detection wavelength was set at 235 nm,and the sample size was 20 μl.Results: There were 18 common peaks in the fingerprints of 10 batches of Qingkailing injection,and among them,3 marker peaks were identified as caffeic acid,uridine and guanosine.Totally 10 batches of samples could be clustered into 2 categories,namely S2,S4,S5,S6,S7,S9 and S10 were clustered into category Ⅰ,and S1,S4 and S8 were clustered into category Ⅱ.Conclusion: Fingerprint data with cluster analysis can provide a credible method for the differentiation of highor low-incidence adverse reactions of Qingkailing injection.
作者 赵丹 朱乃亮 孙桂波 马国需 胡美赓 许旭东 Zhao Dan;Zhu Nailiang;Sun Guibo;Ma Guoxu;Hu Meigeng;Xu Xudong(Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine,Ministry of Education,Institute of Medicinal Plant Development,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100193,China)
出处 《中国药师》 CAS 2019年第8期1441-1444,共4页 China Pharmacist
基金 国家科技重大专项“重大新药创制”项目(编号:2015ZX09501004-001-003)
关键词 清开灵注射液 指纹图谱 聚类分析 质量标准 Qingkailing injection Fingerprint Cluster analysis Quality standard
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