摘要
目的建立牛黄解毒丸HPLC定量指纹图谱和组方定量指纹图谱。方法以22批牛黄解毒丸建立的标准指纹图谱为评价基准,用SQFM法评价两类不同药量的4种预测模式。以物理混样和化学混样的HPLC定量指纹图谱来验证混合煎煮与物理混样的药效物质总差异。结果用SQFM法评价两类不同药量的4种HPLC指纹图谱预测模式的平均值为是S_m=0.928,P_m=104.8%;物理混样和化学混样的平均结果是S_m=0.953,P_m=100.6%。以上结果表明能够通过单味药HPLC定量指纹图谱对中成药质量进行组方智能预测。结论通过合理校正单味药称样量,基于HPLC定量指纹图谱和组方定量指纹图谱能实现中药制剂质量的定量预测,结果误差在5%以内。利用组方指纹图谱能实现对中药制剂产品质量的智能预测,这为中药临床提供了一种智能化的质量预测新模式。
Objective To establish the quantitative HPLC fingerprints of Niuhuang Jiedu pills （NHJDP） and their quantitative compound synthesis fingerprints. Methods Based on the referential fingerprints synthesized from 22 batches of NHJDPs as the assessment standard, the quality grades of the different prediction modes including 2 contents for the 4 Chinese drugs were evaluated by systematic quantified fingerprint method （SQFM）. It was used to verify the total material difference between the decocting method and the direct mixing method HPLC fingerprints from physically mixed and chemically mixed samples. Results The average values of the different prediction modes including 2 contents （4 modes） showed that S,,, = 0.928 and Pm = 104.8%, when assessed by SQFM. The means of the physically mixed and chemically mixed samples were S,,, = 0.953 and P,,, = 100.6%, respectively. We could accurately predict the quality of traditional Chinese medicine （TCM） by the quantitative HPLC fingerprints of each herb. Conclusion By reasonable correction of single herb weight, quantitative HPLC fingerprints and quantitative compound synthesis fingerprints can be used in the quantitative prediction of TCM, with the P,,, error of prediction no more than 5%. It is a new intelligent mode to forecast the clinical application of TCM by the compound synthesis profiles to predict accurately.
出处
《中南药学》
CAS
2017年第4期399-403,共5页
Central South Pharmacy
基金
国家自然科学基金资助项目(No.90612002
No.81573586)
关键词
牛黄解毒丸
组方指纹图谱
智能预测模式
物理混样指纹图谱
化学混样指纹图谱
单位质量药味色谱指纹
组方量药味色谱指纹
Niuhuang Jiedu pill
compound synthesis fingerprint： intelligent prediction mode： physically mixed fingerprint
chemically mixed fingerprint
chromatographic fingerprints fiom unit content
chromatographic fingerprint from prescription content
