基于指纹图谱和网络药理学的健脾安胎颗粒质量标志物预测与分析

任宁; 陈赟; 杨越悦; 何昱; 曾义英; 章勤

doi:10.13748/j.cnki.issn1007-7693.20242137

基于指纹图谱和网络药理学的健脾安胎颗粒质量标志物预测与分析

Prediction and Analysis of Quality Markers for Jianpi Antai Granules Based on Fingerprint Chromatography and Network Pharmacology

摘要

摘要:
目的通过高效液相色谱(high performance liquid chromatography，HPLC)对健脾安胎颗粒进行多成分指纹图谱分析，并结合网络药理学预测其潜在的质量标志物，为健脾安胎颗粒的全面质量控制提供科学依据。
方法采用HPLC对健脾安胎颗粒进行指纹图谱分析，预测其潜在质量标志物。同时，通过网络药理学方法预测健脾安胎颗粒治疗自然流产的作用靶点，采用分子对接技术评估质量标志物与核心靶点的结合亲和力。
结果成功建立了健脾安胎颗粒的HPLC指纹图谱，共识别出7个主要色谱峰作为质量标志物。网络药理学分析预测出128个作用靶点，涉及PI3K-Akt/Ras/MAPK等重要信号通路。分子对接结果显示，质量标志物与关键靶点具有良好的结合亲和力，其中金丝桃苷与AKT1的结合能最低为−10.6 kcal·mol⁻¹。
结论所建立的HPLC指纹图谱稳定性、重复性好，可为健脾安胎颗粒的质量评价提供参考。通过网络药理学成功预测并验证了健脾安胎颗粒的潜在质量标志物及其作用机制，为进一步的作用机制研究提供了科学依据。

Abstract:
OBJECTIVE To provide a scientific basis for comprehensive quality control of Jianpi Antai granules through a multi-component fingerprint analysis of Jianpi Antai granules using high performance liquid chromatography(HPLC) and predict potential quality marker(Q-marker) through network pharmacology.
METHODS HPLC was employed to analyze the fingerprint of Jianpi Antai granules and predict its potential Q-markers. Concurrently, network pharmacology methods were utilized to predict the therapeutic targets of Jianpi Antai granules for spontaneous abortion, and molecular docking techniques were applied to evaluate the binding affinity between Q-markers and core targets.
RESULTS An HPLC fingerprint of Jianpi Antai granules was successfully established, identifying 7 major chromatographic peaks as Q-markers. Network pharmacology analysis predicted 128 therapeutic targets, involving crucial signaling pathways such as PI3K-Akt/Ras/MAPK. Molecular docking results revealed good binding affinity between Q-markers and key targets, with the lowest binding energy of −10.6 kcal·mol⁻¹ between hyperin and AKT1.
CONCLUSION The established HPLC fingerprint exhibits good stability and reproducibility, providing a reference for quality evaluation of Jianpi Antai granules. The potential Q-markers and their mechanisms of action are successfully predicted and validated through network pharmacology, providing a scientific basis for further research on their mechanisms of action.

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