Prediction Analysis of Quality Markers of Rhododendron molle G.Don Based on Chemical Pattern Recog-nition and Network Pharmacology
OBJECTIVE To screen the quality markers(Q-marker)of Rhododendron molle based on HPLC-ELSD and network pharmacology,compare the contents of quality markers in different parts,different flowering periods and different producing areas of R.molle,and explore the potential medicinal value of R.molle and speculate its possible mechanism of action.METHODS HPLC-ELSD was used to establish the fingerprint of R.molle,and hierarchical clustering analysis(HCA),principal component analysis(PCA)and orthogonal partial least squares discriminant analysis(OPLS-DA)were carried out.The network diagram of'component-target-pathway-pharmacological effect-efficacy'was constructed by network pharmacology method,and the disease prediction was carried out.The quality markers of R.molle were screened and quantitatively analyzed.RESULTS In this study,the fingerprints of 11 batches of Rhododendron molle were established,and total of 10 common peaks were calibrated.Four differential components were obtained after analysis.It was predicted that rhodojaponin-Ⅲ,rhodojaponin-Ⅱ,hyperoside and quercitrin were candidate components of quality markers of R.molle and network pharmacology analysis was performed.It involved 84 target proteins,including ATK1,TNF,INS,etc.,acting on 159 signaling pathways,and had potential therapeutic effects on tumors,autoimmune diseases,and cardio-vascular diseases.The results of component content determination showed that the flowering of Rhododendron molle might have better curative effect,and the S10 origin was the best.CONCLUSION The quality markers of R.molle are predicted by chemical pattern recognition,fingerprint and network pharmacology.It is proved that R.molle could treat diseases through multiple targets and multiple pathways,which provides a reference for improving the establishment of quality control standards of R.molle and further exploring the mechanism of disease treatment.
万方数据
维普
