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[一种基于高效液相色谱指纹图谱智能预测中药制剂质量的新模式]

[A novel pattern for predicting the quality of Chinese herba preparation intelligently by high performance liquid chromatographic formula fingerprints].

作者信息

Gong Dandan, Dong Jiajun, Sun Guoxiang, Zhang Jing, Zhang Yujing, Sun Wanyang

机构信息

College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.

Institute of Traditional Chinese Medicine & Natural Products, College of Pharmacy, Jinan University, Guangzhou 510632, China.

出版信息

Se Pu. 2017 Jun 8;35(6):643-649. doi: 10.3724/SP.J.1123.2016.12041.

DOI:10.3724/SP.J.1123.2016.12041
PMID:29048792
Abstract

In order to build the fusion models, the high performance liquid chromatographic (HPLC) fingerprints of scutellariae radix (SR), rhei radix et rhizoma (RRR), coptidis rhizoma (CR) and their synthesizing fingerprints were developed in this study. After exploring the consistency between the fingerprints of compound synthesizing fingerprints (CSF) and the sample, the quality of traditional Chinese medicine preparation was predicted intelligently using CSF. HPLC coupled with diode array detector was used to obtain chromatograms of SR, RRR, CR and Yi Qing Tablet (YQT) samples at 268 nm. Meanwhile, the quality of CSF and the 15 batches of YQT samples was evaluated by systematically quantified fingerprint method (SQFM) qualitatively and quantitatively. The chromatograms showed that CSF covered the main fingerprints' information of each herb and the 55 common peaks of CSF covered the main information of the 50 common peaks in YQT sample. The evaluation results showed that among the 15 batches of YQT samples, only YQT-S01 was grade 5 and the others were all above grade 3. Most of the CSFs were grade 2 or grade 1 except CSF-2 which was grade 6. The fingerprints of Chinese herba preparation could be replaced by CSF to achieve a novel pattern for predicting the quality of TCM preparation intelligently by studying the relationship between the standard fingerprints and the CSF, and simultaneously developing first-class evaluation software.

摘要

为构建融合模型,本研究建立了黄芩(SR)、大黄(RRR)、黄连(CR)的高效液相色谱(HPLC)指纹图谱及其合成指纹图谱。在探究复方合成指纹图谱(CSF)与样品指纹图谱的一致性后,利用CSF对中药制剂质量进行智能预测。采用HPLC结合二极管阵列检测器在268 nm处获取SR、RRR、CR及一清片(YQT)样品的色谱图。同时,采用系统定量指纹图谱法(SQFM)对CSF及15批YQT样品的质量进行定性和定量评价。色谱图显示,CSF涵盖了各药材的主要指纹图谱信息,CSF的55个共有峰涵盖了YQT样品中50个共有峰的主要信息。评价结果表明,15批YQT样品中,只有YQT - S01为5级,其他均在3级以上。除CSF - 2为6级外,大多数CSF为2级或1级。通过研究标准指纹图谱与CSF之间的关系,用CSF替代中药制剂指纹图谱,实现智能预测中药制剂质量的新模式,同时开发一流的评价软件。

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引用本文的文献

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Se Pu. 2022 Jan;40(1):48-56. doi: 10.3724/SP.J.1123.2021.02031.
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A strategy for qualitative and quantitative profiling of glycyrrhiza extract and discovery of potential markers by fingerprint-activity relationship modeling.甘草提取物的定性定量分析策略及指纹-活性关系模型发现潜在标志物。
Sci Rep. 2019 Feb 4;9(1):1309. doi: 10.1038/s41598-019-38601-y.