目的:桥连网络药理学和特征图谱技术,预测天葵子质量标志物,为天葵子全面的质量控制体系建立提供参考。方法:文献及成分-靶点-通路网络可视化研究,定位并预测天葵子干预疾病的潜在质量标志物;采用Agilent Eclipse Plus C18 (250 mm×4.6 mm, 5 μm)色谱柱,柱温30 ℃,以乙腈(A)-含15%乙腈的0.1%磷酸水溶液(每100 mL含SDS 0.2 g)(B)为流动相,梯度洗脱(0~5 min,0%A;5~10 min,0%A→27%A;10~33 min,27%A→29%A;33~38 min,29%A→37%A;38~55 min,37%A→38%A;55~72 min,38%A),流速0.8 mL·min-1,检测波长为260 nm (0~6 min)、220 nm(6~10 min)、260 nm(10~29 min)、220 nm(29~34 min)、260 nm(34~37 min)、220 nm(37~72 min);对收集的38批天葵子药材进行HPLC特征图谱定性定量分析,运用OPLS-DA筛选出造成组间差异的主要标志性成分;根据质量标志物的有效性、特有性及可测性预测天葵子潜在的Q-marker。结果:锁定了天葵子质量标志物的主要来源范围,网络药理学预测了其干预疾病的3个潜在质量标志物;成功建立了7个共有峰的特征图谱,标定了4个特征峰,其中3个差异性代谢产物,且格列风内酯、木兰花碱和小檗红碱质量浓度分别在2.22~44.4、14.2~284、3.8~76 μg·mL-1范围内均与峰面积积分值呈良好的线性关系,平均加样回收率分别为98.8%、98.8%和99.6%,RSD分别为0.64%、1.7%和1.9%;38批样品中格列风内酯、木兰花碱和小檗红碱的含量范围分别为0.046~0.757、0.391~2.796、0.077~0.572 mg·g-1。根据质量标志物的有效性、特有性及可测性,预测天葵子中格列风内酯和小檗红碱2个成分为天葵子的Q-marker成分。结论:本研究预测了天葵子的Q-marker成分,同时建立了简便、准确、可靠的天葵子特征图谱定性定量方法,为天葵子全面质量控制体系的建立提供了参考,研究结果将有利于市场上天葵子药材质量评价。
Objective: To establish a comprehensive quality control system for Semiaquilegiae Radix. To predict the quality markers of Semiaquilegiae Radix by a bridged network pharmacology and feature map technology. Methods: Based on literature research and network pharmacology research, potential quality biomarkers of Semiaquilegiae Radix were predicted. Qualitative and quantitative analysis of HPLC characteristic chromatogram was carried out on 38 batches of Semiaquilegiae Radix. Agilent Eclipse Plus C18 chromatographic column (250 mm×4.6 mm, 5 μm) was used. The mobile phase was acetonitrile(A) and a 0.1% phosphoric acid aqueous solution containing 15% acetonitrile(containing 0.2 g of SDS per 100 mL)(B), with gradient elution(0-5 min, 0%A; 5-10 min, 0%A→27%A; 10-33 min, 27%A→29%A; 33-38 min, 29%A→37%A; 38-55 min, 37%A→38%A; 55-72 min, 38%A) at flow rate of 0.8 mL·min-1. The column temperature was 30 ℃, and the detection wavelengths were 260 nm for 0-6 min, 220 nm for 6-10 min, 260 nm for 10-29 min, 220 nm for 29-34 min, 260 nm for 34-37 min, and 220 nm for 37-72 min. OPLS-DA was used to screen the main marker components causing the difference between groups. According to the effectiveness, specificity and testability of quality markers, the potential Q-marker of Semiaquilegiae Radix was predicted. Results: The main source range of quality markers of Semiaquilegiae Radix was locked, and three potential quality markers for disease intervention were predicted by network pharmacology. The characteristic chromatogram with 7 common peaks was successfully established, and 3 differential metabolites were screened. Contents of glifepristone, magnoflorine, and berberine were between 2.22 and 44.4, respectively μg·mL-1, 14.2-284 μg·mL-1 and 3.8-76 μg·mL-1. There was a good linear relationship between the concentration range and the peak area integral value in each of them, with average sample recovery rates of 98.8%, 98.8%, and 99.6%, and RSD of 0.64%, 1.7%, and 1.9%, respectively. The content ranges of glifepristone, magnoflorine, and berberine in 38 batches of samples were 0.046-0.757 mg·g-1, 0.391-2.796 mg·g-1, and 0.077-0.572 mg·g-1. According to the effectiveness, specificity and testability of quality markers, it was predicted that griffonilide and berberrubine were Q-marker components of Semiaquilegiae Radix. Conclusion: This study predicts the Q-marker composition of Semiaquilegiae Radix, and establishes a simple, accurate and reliable qualitative and quantitative method for characteristic chromatogram of Semiaquilegiae Radix, which provides a reference for the establishment of Semiaquilegiae Radix total quality control system. The research results will be conducive to the quality evaluation of Semiaquilegiae Radix in the market.
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