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基于标准制剂控制模式和定量指纹图谱评价复方甘草片的质量一致性
引用本文:闫慧,孙国祥,迟晗笑,张晶,孙万阳,侯志飞,李显林,蒲道俊,陈振鸿.基于标准制剂控制模式和定量指纹图谱评价复方甘草片的质量一致性[J].色谱,2019,37(11):1200-1208.
作者姓名:闫慧  孙国祥  迟晗笑  张晶  孙万阳  侯志飞  李显林  蒲道俊  陈振鸿
作者单位:1.沈阳药科大学药学院, 辽宁 沈阳 110016;2.暨南大学中药和天然药物研究所, 广东 广州 510632;3.河北化工医药职业技术学院, 河北 石家庄 050026;4.国药集团工业有限公司, 北京 101300;5.西南药业股份有限公司, 重庆 400038;6.新疆富沃药业有限公司, 新疆 沙雅 842200
基金项目:国家自然科学基金(81573586).
摘    要:通过建立复方甘草片标准制剂(SP)控制模式和定量高效液相色谱指纹图谱,结合5个质量标志物的精准定量评价了9个厂家共145批复方甘草片质量一致性。首先建立了复方甘草片标准制剂的标准指纹图谱(SP-RFP),然后以SP-RFP作为评价标准,采用系统指纹定量法对145批复方甘草片进行整体定性和整体定量评价。用双标校正法校正定量指纹图谱的系统误差,结果表明所检样品质量均合格。此外,在统一化色谱条件下测定各原料药和模拟样品,对制剂指纹进行归属相关度和准确度评判,得到原料指纹与制剂指纹的相关性,从而实现智能预测制剂或原料药质量和阻止低劣原料入药。同时用紫外全指纹溶出度法测定5个厂家的复方甘草片的溶出度曲线,用以评价制剂工艺的合理性。以上方法可行且准确度高,实现了对复方甘草片质量和工艺的一致性评价。该文为中药质量一致性评价提供了基础评价模式和基本操作思路以及具体实例。

关 键 词:复方甘草片标准制剂  双标校正法  定量指纹图谱  系统指纹定量法  统一化色谱条件  定量指纹智能预测质量  
收稿时间:2019-06-15

Quality consistency evaluation of Fufanggancao tablets based on the control mode of standard preparation and quantitative fingerprint
YAN Hui,SUN Guoxiang,CHI Hanxiao,ZHANG Jing,SUN Wanyang,HOU Zhifei,LI Xianlin,PU Daojun,CHEN Zhenhong.Quality consistency evaluation of Fufanggancao tablets based on the control mode of standard preparation and quantitative fingerprint[J].Chinese Journal of Chromatography,2019,37(11):1200-1208.
Authors:YAN Hui  SUN Guoxiang  CHI Hanxiao  ZHANG Jing  SUN Wanyang  HOU Zhifei  LI Xianlin  PU Daojun  CHEN Zhenhong
Institution:1. College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China;2. Institute of Traditional Chinese Medicine & Natural Products, Jinan University, Guangzhou 510632, China;3. Hebei Chemical and Pharmaceutical College, Shijiazhuang 050026, China;4. China National Pharmaceutical Industry Co., Ltd., Beijing 101300, China;5. Southwest Pharmaceutical Co., Ltd., Chongqing 400038, China;6. Xinjiang Fuwo Pharmaceutical Co., Ltd., Shaya 842200, China
Abstract:The control mode of standard preparation (SP) and quantitative high performance liquid chromatography (HPLC) fingerprint of Fufanggancao tablets (FFGCTs) combined with the quantification of five markers were successfully developed and applied to the precise and accurate assessment of the quality consistency of 145 FFGCTs from nine manufacturers. The profiling was determined by reversed-phase HPLC at 220 nm wavelength, where the reference fingerprint (RFP) of the FFGCTs reserved as standard preparation was established. Subsequently, the SP-RFP was considered as the evaluation standard, and a systematic quantitative fingerprint method was applied to the integrative quality discrimination of 145 batches of FFGCTs, from both qualitative and quantitative perspectives. Besides, the chromatographic systematic error of quantitative fingerprints was corrected by the double marker calibration method. The results showed that the qualities of the FFGCTs from the nine manufacturers were completely qualified. Moreover, all raw herb fingerprints and the simulated sample were determined by using the combined chromatographic conditions applied to the FFGCTs, which helped identify the source of the profiling peaks in the preparation and establish the correlation between the raw herb fingerprints and the preparation fingerprints. This eventually aided the intelligent prediction of the quality of the preparation or raw herbs and effective prevention of the inputs of inferior raw materials. In addition, we employed the ultraviolet full fingerprint dissolution method to differentiate the FFGCTs from five manufacturer dissolution, in which the rationality of the preparation process was evaluated. The method is feasible and accurate, and it can be applied to evaluate the quality and process technology consistency of FFGCTs. This paper provides a fundamental standard preparation evaluation mode and the basic operation procedure for the quality consistency assessment of traditional Chinese medicine.
Keywords:standard preparation (SP) of Fufanggancao tablets  double marker calibration method  quantitative fingerprint  systematic quantitative fingerprint method  unified chromatographic condition  quantitative fingerprint predicting quality intelligently  
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