Advertisement

Quantitative Biology

, Volume 7, Issue 1, pp 30–41 | Cite as

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

  • Wen Yang
  • Xia Wang
  • Kenan Li
  • Yuanru Liu
  • Ying LiuEmail author
  • Rui WangEmail author
  • Honglin LiEmail author
Research Article
  • 147 Downloads

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.

Keywords

arachidonic acid metabolic network computational model anti-inflammation natural product 

Notes

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.

Supplementary material

40484_2018_163_MOESM1_ESM.pdf (577 kb)
S1 Ordinary differential equations (ODEs) of in AA metabolic pathway

References

  1. 1.
    Libby, P. (2007) Inflammatory mechanisms: the molecular basis of inflammation and disease. Nutr. Rev., 65, S140–S146CrossRefGoogle Scholar
  2. 2.
    Davies, P., Bailey, P. J., Goldenberg, M. M. and Ford-Hutchinson, A. W. (1984) The role of arachidonic acid oxygenation products in pain and inflammation. Annu. Rev. Immunol., 2, 335–357CrossRefGoogle Scholar
  3. 3.
    Marx, J. (2004) Cancer research: inflammation and cancer: the link grows stronger. Science, 306, 966–968CrossRefGoogle Scholar
  4. 4.
    Needleman, P., Truk, J., Jakschik, B. A., Morrison, A. R. and Lefkowith, J. B. (1986) Arachidonic acid metabolism. Annu. Rev. Biochem., 55, 69–102CrossRefGoogle Scholar
  5. 5.
    Kühn, H. and O’Donnell, V. B. (2006) Inflammation and immune regulation by 12/15-lipoxygenases. Prog. Lipid Res., 45, 334–356CrossRefGoogle Scholar
  6. 6.
    Harvey, R. J., Depner, U. B., Wässle, H., Ahmadi, S., Heindl, C., Reinold, H., Smart, T. G., Harvey, K., Schütz, B., Abo-Salem, O. M., et al. (2004) GlyR α3: an essential target for spinal PGE2-mediated inflammatory pain sensitization. Science, 304, 884–887CrossRefGoogle Scholar
  7. 7.
    Guay, J., Bateman, K., Gordon, R., Mancini, J. and Riendeau, D. (2004) Carrageenan-induced paw edema in rat elicits a predominant prostaglandin E2 (PGE2) response in the central nervous system associated with the induction of microsomal PGE2 synthase-1. J. Biol. Chem., 279, 24866–24872CrossRefGoogle Scholar
  8. 8.
    Nakanishi, M. and Rosenberg, D. W. (2013) Multifaceted roles of PGE2 in inflammation and cancer. Semin. Immunopathol., 35, 123–137CrossRefGoogle Scholar
  9. 9.
    Smith, J. B., Araki, H. and Lefer, A. M. (1980) Thromboxane A2, prostacyclin and aspirin: effects on vascular tone and platelet aggregation. Circulation, 62, V19–V25CrossRefGoogle Scholar
  10. 10.
    Honn, K. V., Cicone, B. and Skoff, A. (1981) Prostacyclin: a potent antimetastatic agent. Science, 212, 1270–1272CrossRefGoogle Scholar
  11. 11.
    Scher, J. U. and Pillinger, M. H. (2005) 15d-PGJ2: the antiinflammatory prostaglandin? Clin. Immunol., 114, 100–109CrossRefGoogle Scholar
  12. 12.
    Palmblad, J., Malmsten, C. L., Udén, A. M., Rådmark, O., Engstedt, L. and Samuelsson, B. (1981) Leukotriene B4 is a potent and stereospecific stimulator of neutrophil chemotaxis and adherence. Blood, 58, 658–661Google Scholar
  13. 13.
    Csoma, Z., Kharitonov, S. A., Balint, B., Bush, A., Wilson, N. M. and Barnes, P. J. (2002) Increased leukotrienes in exhaled breath condensate in childhood asthma. Am. J. Respir. Crit. Care Med., 166, 1345–1349CrossRefGoogle Scholar
  14. 14.
    Peters-Golden, M., Gleason, M. M. and Togias, A. (2006) Cysteinyl leukotrienes: multi-functional mediators in allergic rhinitis. Clin. Exp. Allergy, 36, 689–703CrossRefGoogle Scholar
  15. 15.
    Sozzani, S., Zhou, D., Locati, M., Bernasconi, S., Luini, W., Mantovani, A. and O’Flaherty, J. T. (1996) Stimulating properties of 5-oxo-eicosanoids for human monocytes: synergism with monocyte chemotactic protein-1 and-3. J. Immunol., 157, 4664–4671Google Scholar
  16. 16.
    Rainsford, K. D. (1999) Profile and mechanisms of gastrointestinal and other side effects of nonsteroidal anti-inflammatory drugs (NSAIDs). Am. J. Med., 107, 27–35CrossRefGoogle Scholar
  17. 17.
    Psaty, B. M. and Furberg, C. D. (2005) COX-2 inhibitors–lessons in drug safety. N. Engl. J. Med., 352, 1133–1135CrossRefGoogle Scholar
  18. 18.
    Singh, D. (2004) Merck withdraws arthritis drug worldwide. BMJ, 329, 816.2Google Scholar
  19. 19.
    Berger, W., De Chandt, M. T. and Cairns, C. B. (2007) Zileuton: clinical implications of 5-Lipoxygenase inhibition in severe airway disease. Int. J. Clin. Pract., 61, 663–676CrossRefGoogle Scholar
  20. 20.
    Pergola, C. and Werz, O. (2010) 5-Lipoxygenase inhibitors: a review of recent developments and patents. Expert Opin. Ther. Pat., 20, 355–375CrossRefGoogle Scholar
  21. 21.
    Bertolini, A., Ottani, A. and Sandrini, M. (2001) Dual acting antiinflammatory drugs: a reappraisal. Pharmacol. Res., 44, 437–450CrossRefGoogle Scholar
  22. 22.
    Kitano, H. (2007) A robustness-based approach to systemsoriented drug design. Nat. Rev. Drug Discov., 6, 202–210CrossRefGoogle Scholar
  23. 23.
    Yang, K., Ma, W., Liang, H., Ouyang, Q., Tang, C. and Lai, L. (2007) Dynamic simulations on the arachidonic acid metabolic network. PLOS Comput. Biol., 3, e55CrossRefGoogle Scholar
  24. 24.
    Meng, H., Liu, Y. and Lai, L. (2015) Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation. Acc. Chem. Res., 48, 2242–2250CrossRefGoogle Scholar
  25. 25.
    Su, Y. Q., Zhang, W. D., Zhang, C., Liu, R. H. and Shen, Y. H. (2008) A new caffeic ester from Incarvillea mairei var. granditlora (Wehrhahn) Grierson. Chin. Chem. Lett., 19, 829–831CrossRefGoogle Scholar
  26. 26.
    Li, L., Zeng, H., Liu, F., Zhang, J., Yue, R., Lu, W., Yuan, X., Dai, W., Yuan, H., Sun, Q., et al. (2012) Target identification and validation of (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate, an anti-inflammatory natural product. Eur. J. Inflamm., 10, 297–309CrossRefGoogle Scholar
  27. 27.
    Buczynski, M. W., Stephens, D. L., Bowers-Gentry, R. C., Grkovich, A., Deems, R. A. and Dennis, E. A. (2007) TLR-4 and sustained calcium agonists synergistically produce eicosanoids independent of protein synthesis in RAW264.7 cells. J. Biol. Chem., 282, 22834–22847CrossRefGoogle Scholar
  28. 28.
    Leslie, C. C. (2015) Cytosolic phospholipase A2: physiological function and role in disease. J. Lipid Res., 56, 1386–1402CrossRefGoogle Scholar
  29. 29.
    Christmas, P., Weber, B. M., McKee, M., Brown, D. and Soberman, R. J. (2002) Membrane localization and topology of leukotriene C4 synthase. J. Biol. Chem., 277, 28902–28908CrossRefGoogle Scholar
  30. 30.
    Funk, C. D. (2001) Prostaglandins and leukotrienes: advances in eicosanoid biology. Science, 294, 1871–1875CrossRefGoogle Scholar
  31. 31.
    Honda, Z., Nakamura, M., Miki, I., Minami, M., Watanabe, T., Seyama, Y., Okado, H., Toh, H., Ito, K., Miyamoto, T., et al. (1991) Cloning by functional expression of platelet-activating factor receptor from guinea-pig lung. Nature, 349, 342–346CrossRefGoogle Scholar
  32. 32.
    Horton, J. K., Williams, A. S., Smith-Phillips, Z., Martin, R. C. and O’Beirne, G. (1999) Intracellular measurement of prostaglandin E2: effect of anti-inflammatory drugs on cyclooxygenase activity and prostanoid expression. Anal. Biochem., 271, 18–28CrossRefGoogle Scholar
  33. 33.
    Kramer, R. M., Roberts, E. F., Um, S. L., Börsch-Haubold, A. G., Watson, S. P., Fisher, M. J. and Jakubowski, J. A. (1996) p38 mitogen-activated protein kinase phosphorylates cytosolic phospholipase A2 (cPLA2) in thrombin-stimulated platelets. Evidence that proline-directed phosphorylation is not required for mobilization of arachidonic acid by cPLA2. J. Biol. Chem., 271, 27723–27729Google Scholar
  34. 34.
    Kozawa, O., Tokuda, H., Matsuno, H. and Uematsu, T. (1999) Involvement of p38 mitogen-activated protein kinase in basic fibroblast growth factor-induced interleukin-6 synthesis in osteoblasts. J. Cell. Biochem., 74, 479–485CrossRefGoogle Scholar
  35. 35.
    Tokuda, H., Kozawa, O. and Uematsu, T. (2000) Basic fibroblast growth factor stimulates vascular endothelial growth factor release in osteoblasts: divergent regulation by p42/p44 mitogen-activated protein kinase and p38 mitogen-activated protein kinase. J. Bone Miner. Res., 15, 2371–2379CrossRefGoogle Scholar
  36. 36.
    Shen, J.-N., Xu, L.-X., Shan, L., Zhang, W.-D., Li, H.-L. and Wang, R. (2015) Neuroprotection of (+)-2-(1-hydroxyl-4-oxocyclohexyl) ethyl caffeate against hydrogen peroxide and lipopolysaccharide induced injury via modulating arachidonic acid network and p38-MAPK signaling. Curr. Alzheimer Res., 12, 892–902CrossRefGoogle Scholar
  37. 37.
    Kanehisa, M., Goto, S., Hattori, M., Aoki-Kinoshita, K. F., Itoh, M., Kawashima, S., Katayama, T., Araki, M. and Hirakawa, M. (2006) From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res., 34, D354–D357Google Scholar
  38. 38.
    Schomburg, I., Chang, A., Ebeling, C., Gremse, M., Heldt, C., Huhn, G. and Schomburg, D. (2004) BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res., 32, D431–D433CrossRefGoogle Scholar
  39. 39.
    Yang, K., Bai, H., Ouyang, Q., Lai, L. and Tang, C. (2008) Finding multiple target optimal intervention in disease-related molecular network. Mol. Syst. Biol., 4, 228CrossRefGoogle Scholar
  40. 40.
    Csermely, P., Korcsmáros, T., Kiss, H. J., London, G. and Nussinov, R. (2013) Structure and dynamics of molecular networks: a novel paradigm of drug discovery. A comprehensive review. Pharmacol. Ther., 138, 333–408CrossRefGoogle Scholar
  41. 41.
    Rossi, A., Pergola, C., Koeberle, A., Hoffmann, M., Dehm, F., Bramanti, P., Cuzzocrea, S., Werz, O. and Sautebin, L. (2010) The 5-lipoxygenase inhibitor, zileuton, suppresses prostaglandin biosynthesis by inhibition of arachidonic acid release in macrophages. Br. J. Pharmacol., 161, 555–570CrossRefGoogle Scholar
  42. 42.
    Chan, M. M.-Y., Moore, A. R. (2010) Resolution of inflammation in murine autoimmune arthritis is disrupted by cyclooxygenase-2 inhibition and restored by prostaglandin E(2)-mediated lipoxin A(4) Production. J. Immunol., 184, 6418–6426CrossRefGoogle Scholar
  43. 43.
    Rajakariar, R., Yaqoob, M. M. and Gilroy, D. W. (2006) COX-2 in inflammation and resolution. Mol. Interv., 6, 199–207CrossRefGoogle Scholar
  44. 44.
    Seibert, K., Zhang, Y., Leahy, K., Hauser, S., Masferrer, J., Perkins, W., Lee, L. and Isakson, P. (1994) Pharmacological and biochemical demonstration of the role of cyclooxygenase 2 in inflammation and pain. Proc. Natl. Acad. Sci. USA, 91, 12013–12017CrossRefGoogle Scholar
  45. 45.
    Samuelsson, B., Dahlén, S. E., Lindgren, J. A., Rouzer, C. A. and Serhan, C. N. (1987) Leukotrienes and lipoxins: structures, biosynthesis, and biological effects. Science, 237, 1171–1176CrossRefGoogle Scholar
  46. 46.
    Dinarello, C. A. (2000) Proinflammatory cytokines. Chest, 118, 503–508CrossRefGoogle Scholar
  47. 47.
    Pettus, B. J., Bielawska, A., Spiegel, S., Roddy, P., Hannun, Y. A. and Chalfant, C. E. (2003) Ceramide kinase mediates cytokineand calcium ionophore-induced arachidonic acid release. J. Biol. Chem., 278, 38206–38213CrossRefGoogle Scholar
  48. 48.
    Piomelli, D. (1993) Arachidonic acid in cell signaling. Curr. Opin. Cell Biol., 5, 274–280CrossRefGoogle Scholar
  49. 49.
    De Micheli, G. and Benini, L. (2006) Networks on Chips: Technology and Tools. Academic Press.Google Scholar
  50. 50.
    Benini, L., De Micheli, G. (2002) Networks on chips: a new SoC paradigm. Computer, 35, 70–78CrossRefGoogle Scholar
  51. 51.
    Hopkins, A. L. (2008) Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol., 4, 682–690CrossRefGoogle Scholar
  52. 52.
    Wang, X., Terfve, C., Rose, J. C. and Markowetz, F. (2011) HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens. Bioinformatics, 27, 879–880CrossRefGoogle Scholar
  53. 53.
    Zhao, S. and Iyengar, R. (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu. Rev. Pharmacol. Toxicol., 52, 505–521CrossRefGoogle Scholar
  54. 54.
    Walpole, J., Papin, J. A. and Peirce, S. M. (2013) Multiscale computational models of complex biological systems. Annu. Rev. Biomed. Eng., 15, 137–154CrossRefGoogle Scholar
  55. 55.
    Gupta, S., Maurya, M. R., Stephens, D. L., Dennis, E. A. and Subramaniam, S. (2009) An integrated model of eicosanoid metabolism and signaling based on lipidomics flux analysis. Biophys. J., 96, 4542–4551CrossRefGoogle Scholar
  56. 56.
    Kihara, Y., Gupta, S., Maurya, M. R., Armando, A., Shah, I., Quehenberger, O., Glass, C. K., Dennis, E. A. and Subramaniam, S. (2014) Modeling of eicosanoid fluxes reveals functional coupling between cyclooxygenases and terminal synthases. Biophys. J., 106, 966–975CrossRefGoogle Scholar
  57. 57.
    Yang, K., Ma, W., Liang, H., Ouyang, Q., Tang, C. and Lai, L. (2007) Dynamic simulations on the arachidonic acid metabolic network. PLOS Comput. Biol., 3, e55CrossRefGoogle Scholar
  58. 58.
    Yang, K., Bai, H., Ouyang, Q., Lai, L. and Tang, C. (2008) Finding multiple target optimal intervention in disease-related molecular network. Mol. Syst. Biol., 4, 228CrossRefGoogle Scholar
  59. 59.
    Fajmut, A., Schäfer, D., Brumen, M., Dobovišek, A., Antić, N. and Emeršič, T. (2015) Dynamic model of eicosanoid production with special reference to non-steroidal anti-inflammatory drug-triggered hypersensitivity. IET Syst. Biol., 9, 204–215CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of New Drug Design, School of PharmacyEast China University of Science and TechnologyShanghaiChina
  2. 2.BNLMS, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, College of Chemistry and Molecular EngineeringPeking UniversityBeijingChina

Personalised recommendations