基于网络药理学和指纹图谱的远志抗抑郁质量标志物的预测

Predictive of Polygala Tenuifolia quality markers for antidepressant effect based on network pharmacology and fingerprints

目的预测远志药材抗抑郁的质量标志物(Q-marker),并建立其质量评价方法。方法利用网络药理学和分子对接,预测远志抗抑郁的核心成分。结合指纹图谱、化学模式识别方法对远志药材进行定性研究,寻找不同产地远志药材的共性成分,进一步确定远志药材中潜在的Q-marker。结果通过网络药理学分析,预测细叶远志皂苷、3,6'-二芥子酰基蔗糖、瓜子金皂苷ⅩⅩⅠ、远志酮Ⅲ、西伯利亚远志糖A5和西伯利亚远志糖A6等12个成分为远志抗抑郁的核心成分,通过作用于IL2、VEGFA、APP、HSP90AA1、PLCG1等关键靶点,从而调控信号通路,发挥抗抑郁作用。分子对接验证显示关键靶点与核心成分具有较好的结合活性。同时建立20批远志药材的指纹图谱,发现29个共有峰,相似度在0.95以上。综合网络药理学活性和成分的可测性以及Q-marker理念,最终确定3,6'-二芥子酰基蔗糖、远志酮Ⅲ、西伯利亚远志糖A5和西伯利亚远志糖A6为远志药材的Q-marker。结论本研究利用网络药理学进行初步预测,同时建立指纹图谱结合化学模式识别分析加以确定,为远志药材Q-marker的研究提供参考。

Objective To predict the antidepressant quality-markers(Q-markers)of Polygala Tenuifolia,and establish corresponding quality evaluation method.Methods Network pharmacology and molecular docking were used to predict the core components of Polygala Tenuifolia with antidepressant effect.Fingerprints and chemical pattern recognition methods were used to conduct qualitative research on Polygala Tenuifolia,and to find the commonality of ingredients in Polygala Tenuifolia from different origins to further identify the potential Q-markers in Polygala.Results Through network pharmacology analysis,totally 12 core ingredients,such as tenuifolin,3,6'-disinapoylsucrose,polygalasaponinⅩⅩⅠ,polygalaxanthoneⅢ,sibiricose A5 and sibiricose A6 were predicted.The core components regulated signaling pathways and exerted antidepressant effects by acting on the key targets such as IL2,VEGFA,APP,HSP90AA1,and PLCG1.The molecular docking revealed that the core components and targets showed good binding interactions.The fingerprints of 20 batches of Polygala Tenuifolia were established,and 29 common peaks were detected with the similarity above 0.95.Based on the network pharmacology,components and the concept of Q-markers,that 3,6'-disinapoylsucrose,polygalaxanthoneⅢ,sibiricose A5 and sibiricose A6 were preliminarily predicted to be the Q-markers of Polygala Tenuifolia.Conclusion Network pharmacology helps make preliminary predictions,and establish fingerprints combined with chemical pattern recognition to determine the Q-markers of Polygala Tenuifolia.