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

Prediction of Quality Markers of Yinhua Miyanling Tablets Based on Fingerprinting, Chemical Pattern Recognition and Network Pharmacology

关键词:  银花泌炎灵片  指纹图谱  化学模式识别  质量评价  网络药理学  质量标志物

Key words:  Yinhua Miyanling tablets  fingerprints  chemical pattern recognition  quality evaluation  network pharmacology  quality marker

摘要:

目的 基于指纹图谱和网络药理学方法分析预测银花泌炎灵片中潜在的质量标志物(quality marker，Q-Marker)。方法 建立13批银花泌炎灵片的HPLC指纹图谱，采用“中药色谱指纹图谱相似度评价系统”进行相似度分析，指认共有峰并对其进行归属。运用化学计量学方法，结合SPSS 26.0和SIMCA 14.1软件进行聚类分析、主成分分析以及正交偏最小二乘判别分析对银花泌炎灵片指纹图谱进行研究，筛选出造成差异的主要标志性成分。运用网络药理学筛选并分析银花泌炎灵片的作用靶点和通路，构建“药物-成分-靶点-通路”网络图，预测银花泌炎灵片Q-Marker及核心靶点。结果 建立了银花泌炎灵片的HPLC指纹图谱，确定了绿原酸、芒果苷、野黄芩素、木犀草素、槲皮素等27个共有峰，化学模式识别分析筛选出5个成分作为银花泌炎灵片的差异性标志物。通过网络药理学筛选出5个活性成分、20个核心靶点和20条关键通路，结果显示5个活性成分均可作为潜在Q-Marker。结论 该方法稳定、准确可行，筛选出5个可作为银花泌炎灵片潜在Q-Marker的化学成分，为全方面控制银花泌炎灵片质量提供参考，同时也为进一步研究银花泌炎灵片的作用机制奠定基础。

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.