Prediction of Quality Markers of Yinhua Miyanling Tablets Based on Fingerprinting, Chemical Pattern Recognition and Network Pharmacology
10.13748/j.cnki.issn1007-7693.20230140
- VernacularTitle:基于指纹图谱、化学模式识别及网络药理学预测银花泌炎灵片质量标志物
- Author:
Zhenzhou WANG
1
;
Rui LIU
2
;
Sheng LI
3
;
Jizhong ZHU
3
;
Pingya LI
4
Author Information
1. Changchun University of Chinese Medicine, Changchun 130117, China；Jilin Huakang Pharmaceutical Co., Ltd., Dunhua 133700, China；Jilin University, Changchun 130117, China
2. Changchun University of Chinese Medicine, Changchun 130117, China
3. Jilin Huakang Pharmaceutical Co., Ltd., Dunhua 133700, China
4. Jilin University, Changchun 130117, China
- Publication Type:Journal Article
- Keywords:
Yinhua Miyanling tablets ； fingerprints ； chemical pattern recognition； quality evaluation ；network pharmacology； quality marker
- From:
Chinese Journal of Modern Applied Pharmacy
2024;41(1):97-105
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE :To predict potential quality markers(Q-markers) in Yinhua Miyanling tablets based on fingerprinting and network pharmacology methods.
METHODS
HPLC fingerprints of 13 batches of Yinhua Miyanling tablets were established, and the similarity analysis was carried out using the "Chromatographic Fingerprint Evaluation System for Traditional Chinese Medicine" to identify the common peaks and attribute them. The fingerprints of Yinhua Miyanling tablets were investigated using chemometrics, cluster analysis, principal component analysis and orthogonal partial least squares discriminant analysis in combination with SPSS 26.0 and SIMCA 14.1 software to identify the major signature components responsible for the differences. The network pharmacology was used to screen and analyze the targets and pathways of Yinhua Miyanling tablets, construct a "drug-component-target-pathway" network diagram, and predict the Q-Marker and core targets of Yinhua Miyanling tablets.
RESULTS
HPLC fingerprint of Yinhua Miyanling tablets was established, and 27 common peaks including chlorogenic acid, mangostin, wild baicalin, lignocerin and quercetin were identified. Chemical pattern recognition analysis screened five components as differential markers for Yinhua Miyanling tablets. Five active ingredients, 20 core targets and 20 key pathways were screened by network pharmacology, showing that all five active ingredients could be used as potential Q-Markers.
CONCLUSION
The method is stable, accurate and feasible for screening five chemical components as potential Q-Markers for Yinhua Miyanling tablets. It provides a reference for the overall control of the quality of Yinhua Miyanling tablets, and also lays the foundation for further research on the mechanism of action of Yinhua Miyanling tablets.
