Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng.
Phytochem Anal
; 34(7): 772-787, 2023 Oct.
Article
en En
| MEDLINE
| ID: mdl-36479744
ABSTRACT
INTRODUCTION:
Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted.OBJECTIVES:
The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. MATERIALS ANDMETHODS:
The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level).RESULTS:
Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) range is 0.301-1.519.CONCLUSION:
This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Saponinas
/
Panax notoginseng
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Qualitative_research
/
Risk_factors_studies
Idioma:
En
Revista:
Phytochem Anal
Asunto de la revista:
BOTANICA
/
QUIMICA
Año:
2023
Tipo del documento:
Article