基于网络药理学和指纹图谱的黄柏质量标志物预测分析

Predictive analysis of Phellodendri Chinensis Cortex quality markers based on network pharmacology and fingerprint

目的为确定质量标志物(quality marker,Q-Marker)体现药材共性的重要度提供数学评价方法,结合网络药理学和指纹图谱方法,并基于Q-Marker "五原则",对中药黄柏从化学成分有效性以及可测性等角度进行Q-Marker预测分析。方法利用现有文献对黄柏药材Q-Marker的来源进行整合,运用网络药理学方法构建"成分-靶点-通路"网络并预测黄柏药材中的核心成分。结合指纹图谱对10批黄柏药材进行定性和定量研究,进一步确定黄柏药材中潜在的Q-Marker,并利用多元统计分析对结果进行验证。结果通过文献查阅确定生物碱、黄酮类以及酚酸类为黄柏Q-Marker主要来源。网络药理学结果表明生物碱类和黄酮类具有高连接度,是其主要活性成分。同时建立了 10批黄柏药材指纹图谱,并指认其中8个共有成分,分别为新绿原酸、黄柏碱、木兰花碱、4-O-阿魏酰奎宁酸、盐酸药根碱、绿原酸、盐酸巴马汀、盐酸小檗碱。结合网络药理学活性和成分可测性以及Q-Marker理念,初步预测黄柏碱、4-O-阿魏酰奎宁酸、盐酸小檗碱可作为黄柏药材潜在的Q-Marker,最后利用数理统计方法进一步验证了 Q-Marker体现药材共性的重要度。结论利用网络药理学进行初步预测,同时建立指纹图谱结合数理统计的黄柏药材质量控制方法,为黄柏药材的Q-Marker的研究提供参考。

Objective To predict and analyze the potential quality markers(Q-Markers) of Huangbo(Phellodendri Chinensis Cortex) based on the "five principles" of Q-Markers combined with fingerprint and network pharmacology methods from the perspectives of chemical composition validity and measurability and so on, so as to provide a mathematical evaluation method for determining the importance of Q-Marker to reflect the commonness of medicinal materials. Methods The sources of Phellodendri Chinensis Cortex Q-Marker were integrated by existing literature, and a "component-target-pathway" network was constructed by using network pharmacology and the core components of Phellodendri Chinensis Cortex were predicted, and the qualitative and quantitative research of 10 batches of Phellodendri Chinensis Cortex was carried out to further determine the potential Q-Marker, and the results were verified by using multivariate statistical analysis. Results Literature studies had determined that alkaloids, flavonoids and phenolic acids were the main source of Phellodendri Chinensis Cortex Q-Marker. The results of network pharmacology showed that alkaloids and flavonoids were its main active ingredients with high connectivity. At the same time, 10 batches of Phellodendri Chinensis Cortex fingerprints were established, and eight common components, neochlorogenic acid, phellodendrine, magnoflorine, 4-O-feruloylquinic acid, jatrorrhizine hydrochloride, chlorogenic acid, palmatine chloride, berberine hydrochloride, were identified. Combining network pharmacological activity and component measurability as well as Q-Marker concept, it is preliminary predicted that phellodendrine, 4-O-feruloylquinic acid, and berberine hydrochloride could be the potential Q-Markers of Phellodendri Chinensis Cortex. Finally, mathematical statistics methods were used to further verify the importance of Q-Marker reflecting the commonness of medicinal materials. Conclusion Using network pharmacology to make preliminary predictions, and establish a method for quality control of Phellodendri Chinensis Cortex with fingerprint and mathematical statistics, which provides a reference for the research on the quality control and mechanism of Phellodendri Chinensis Cortex.