一种基于时序代谢组学筛选中药复方中活性成分的方法
By combining temporal metabolomics and network pharmacology, the active ingredients in Xiandi Qianggu tablets were screened out, and a dynamic correlation network was constructed. This solved the problem of the lack of temporal action mechanism of active ingredients in traditional Chinese medicine compound preparations, and achieved effective treatment of osteoporosis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHANGCHUN UNIV OF CHINESE MEDICINE
- Filing Date
- 2023-04-06
- Publication Date
- 2026-07-17
AI Technical Summary
In the existing technology, there is insufficient research on the temporal mechanism of action of active ingredients in traditional Chinese medicine compound formulas, making it difficult to screen effective compound groups through precise metabolomics analysis. Furthermore, the metabolite database is incomplete, making it impossible to systematically analyze the dynamic changes of traditional Chinese medicine compound formulas in the disease evolution process.
Using a time-series metabolomics-based approach, we separated and extracted the decoction of traditional Chinese medicine compound, membrane-separated components, and water-extracted and alcohol-precipitated components. These were then used to intervene in cell or animal disease models. Combined with network pharmacology analysis, the results were integrated through protein-protein interaction technology to screen for potential active components and effector targets. Molecular docking technology was then used to verify effective compounds or groups of compounds.
A "targeted component-effect target-temporal metabolism" network was constructed to elucidate the multi-pathway and temporal regulatory mechanism of bone formation by the active ingredient group of Xiandi Qianggu tablets. Effective active ingredients were screened out, and their efficacy in osteoporosis drugs was verified, providing a new approach for screening active ingredients of traditional Chinese medicine.
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Figure CN116386759B_ABST