背景:三七(Burkill)F.H.ChenexC.H.Chow,是一种具有多种功效的著名草药。在这项研究中,对作用机理进行了一系列全面分析,组分含量,原产地识别,并进行了三七的含量预测。
目的:目的是分析药效机制,不同产地三七的含量和组间差异,并确定来源和预测内容。
方法:数据库使用来自四个不同来源的三七样品进行分析,网络药理学(Q标记)和指纹分析[高效液相色谱(HPLC),衰减全反射傅里叶变换红外(ATR-FTIR)和近红外(NIR)]结合数据融合策略(低和特征级)。
结果:四种皂苷被鉴定为Q标记,并通过24个核心靶标对信号通路发挥药理作用。HPLC的定性和定量分析表明,各组之间和不同来源之间存在差异。因此,考虑到治疗疾病的需要,结合网络数据库和网络药理学,通过作用机理和所需的皂苷含量确定适宜的生产区域。低层次数据融合成功识别了不同产地三七的产地,并对其含量进行了预测。偏最小二乘判别分析(PLS-DA)模型各评价指标准确率均为1,t-SNE(t分布随机近邻嵌入)可视化效果良好。偏最小二乘回归(PLSR)模型的决定系数(R2)范围为0.9235-0.9996,交叉验证均方根误差(RMSECV)和预测均方根误差(RMSEP)范围为0.301-1.519。
结论:本研究旨在为三七的质量控制提供足够的理论依据。
BACKGROUND: 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.
OBJECTIVE: 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.
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.
CONCLUSIONS: This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.