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Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng.
Liu, Chunlu; Xu, Furong; Zuo, Zhitian; Wang, Yuanzhong.
Afiliación
  • Liu C; Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan, P. R. China.
  • Xu F; College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, P. R. China.
  • Zuo Z; College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, Yunnan, P. R. China.
  • Wang Y; Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, Yunnan, P. R. China.
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 AND

METHODS:

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.
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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

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
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