基于化学成分动态变化和网络药理学预测 的抗氧化 Q 标志物。
The Prediction of Antioxidant Q-Markers for Based on the Dynamics Change in Chemical Compositions and Network Pharmacology.
机构信息
State Key Laboratory of Aridland Crop Science, College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China.
出版信息
Molecules. 2023 Jul 6;28(13):5248. doi: 10.3390/molecules28135248.
OBJECTIVE
To clarify the accumulation and mutual transformation patterns of the chemical components in () and predict the quality markers (Q-Markers) of its antioxidant activity.
METHOD
The types of and content changes in the chemical components in various parts of during different periods were analyzed by using gas chromatography-mass spectrometry technology (GC-MS). The antioxidant effect of the Q-Markers was predicted using network pharmacological networks, and molecular docking was used to verify the biological activity of the Q-Markers.
RESULT
The differences in the content changes in the coumarin compounds in different parts were found by using GC-MS technology, with the relative content being the best in the root, followed by the leaves, and the least in the stems. The common components were used as potential Q-Markers for a network pharmacology analysis. The component-target-pathway-disease network was constructed. In the molecular docking, the Q-Markers had a good binding ability with the core target, reflecting better biological activity.
CONCLUSIONS
The accumulation and mutual transformation patterns of the chemical components in different parts of were clarified. The predicted Q-Markers lay a material foundation for the establishment of quality standards and a quality evaluation.
目的
阐明（）中化学成分的积累和相互转化模式，并预测其抗氧化活性的质量标志物（Q-Markers）。
方法
采用气相色谱-质谱联用技术（GC-MS）分析不同时期不同部位（）中化学成分的类型和含量变化。利用网络药理学网络预测 Q-Markers 的抗氧化作用，并进行分子对接验证 Q-Markers 的生物活性。
结果
通过 GC-MS 技术发现不同部位香豆素类化合物含量变化的差异，其中根中相对含量最好，其次是叶，茎中最少。将常见成分作为潜在的 Q-Markers 进行网络药理学分析，构建成分-靶标-通路-疾病网络。在分子对接中，Q-Markers 与核心靶标具有良好的结合能力，反映出更好的生物活性。
结论
阐明了不同部位（）中化学成分的积累和相互转化模式。预测的 Q-Markers 为建立质量标准和质量评价奠定了物质基础。
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