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Updated: Aug 11, 2025
Facilitating Drug Discovery: An Automated High-content Inflammation Assay in Zebrafish
Published on: July 16, 2012
Pathway network-based quantitative modeling of the time-dependent and dose-response anti-inflammatory effect of
Fuda Xie1, Mingxiang Xie2, Yibing Yang2
1Research Center of Integrative Medicine, School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China; Department of Anatomical and Cellular Pathology, State Key Laboratory of Translational Oncology, Institute of Digestive Disease, State Key Laboratory of Digestive Disease, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, 999077, China.
Ethnopharmacological Relevance:
Traditional Chinese medicine (TCM) has extensive healing effects on inflammatory diseases with few side effects. Reduning injection (RDNI), a TCM prescription composed of Lonicera japonica Thunb., Gardenia jasminoides Ellis. and Artemisia annua L., is wildly used for treating inflammatory diseases. However, the mechanism of action of RDNI, like most TCM prescriptions, is unclear due to the complexity of relationships between components and their curative effects.
Aim Of The Study:
To develop a universal systems pharmacology protocol for mechanism modeling of TCM and apply it to reveal the real-time anti-inflammatory effect of Reduning Injection.
Materials And Methods:
Combined with database mining and references, a regulatory mechanism network of inflammation was constructed. A quantitative model was established afterwards by integrating pharmacokinetic data and two network parameters, namely Network Efficiency and Network Flux. The time-dependent and dose-response relationship of RDNI on the regulation of inflammation was then quantitatively evaluated. ELISA tests were performed to verify the reliability of the model.
Results:
Three cytokines, namely IL-1β, IL-6 and TNF-α were screened out to be markers for evaluation of the anti-inflammatory effect of RDNI. An HPLC method for the simultaneous determination of 10 RDNI compounds in SD rat plasma was established and then applied to pharmacokinetic studies. Based on compound activity and pharmacokinetic data, the time-dependent effect of RDNI were quantitatively predicted by the pathway network-based modeling procedure.
Conclusions:
The quantitative model established in this work was successfully applied to predict a TCM prescription's real-time dynamic healing effect after administration. It is qualified to provide novel insights into the time-dependent and dose-effect relationship of drugs in an intricate biological system and new strategies for investigating the detailed molecular mechanisms of TCM.
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