Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network

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Abstract

Background

Arachidonic acid (AA) metabolic network is activated in the most inflammatory related diseases, and small-molecular drugs targeting AA network are increasingly available. However, side effects of above mentioned drugs have always been the biggest obstacle. (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate (HOEC), a natural product acted as an inhibitor of 5-lipoxygenase (5-LOX) and 15-LOX in vitro, exhibited weaker therapeutic effect in high dose than that in low dose to collagen induced arthritis (CIA) rats. In this study, we tried to elucidate the potential regulatory mechanism by using quantitative pharmacology.

Methods

First, we generated an experimental data set by monitoring the dynamics of AA metabolites’ concentration in A23187 stimulated and different doses of HOEC co-incubated RAW264.7. Then we constructed a dynamic model of A23187-stimulated AA metabolic model to evaluate how a model-based simulation of AA metabolic data assists to find the most suitable treatment dose by predicting the pharmacodynamics of HOEC.

Results

Compared to the experimental data, the model could simulate the inhibitory effect of HOEC on 5-LOX and 15-LOX, and reproduced the increase of the metabolic flux in the cyclooxygenase (COX) pathway. However, a concomitant, early-stage of stimulation-related decrease of prostaglandins (PGs) production in HOEC incubated RAW264.7 cells was not simulated in the model.

Conclusion

Using the model, we predict that higher dose of HOEC disrupts the flux balance in COX and LOX of the AA network, and increased COX flux can interfere the curative effects of LOX inhibitor on resolution of inflammation which is crucial for the efficient and safe drug design.

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Acknowledgements

The research is supported in part by the National Key Research and Development Program (No. 2016YFA0502304), Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase, No.U1501501), and the National Natural Science Foundation of China (No. 21173076). Honglin Li is also sponsored by National Program for Special Supports of Eminent Professionals and National Program for Support of Top-notch Young Professionals.

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Correspondence to Ying Liu or Rui Wang or Honglin Li.

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Author summary: Arachidonic acid network is a complex system with many pathways to which non-steroidal anti-inflammatroy drugs (NSAIDs) target. However, side effects have always been the disadvantage of these medicines. Using a natural LOX inhibitor HOEC as probe, we established a computational model to simulate the flux regulation of arachidonic acid network after HOEC treatment. Meanwhile, the experimental data of metabolites of different pathways were used to validate and optimize the model. Our study provides an alternitive in NSAIDs design. In addition, it can also guide clinical medication to avoid side effects.

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Yang, W., Wang, X., Li, K. et al. Pharmacodynamics simulation of HOEC by a computational model of arachidonic acid metabolic network. Quant Biol 7, 30–41 (2019). https://doi.org/10.1007/s40484-018-0163-4

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Keywords

  • arachidonic acid
  • metabolic network
  • computational model
  • anti-inflammation
  • natural product