Skip to main content

Advertisement

Log in

Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein

  • Original Paper
  • Published:
Journal of Pharmacokinetics and Pharmacodynamics Aims and scope Submit manuscript

Abstract

Inflammation underlies many diseases and is an undesired effect of several therapy modalities. Biomathematical modeling can help unravel the complex inflammatory processes and the mechanisms triggering their emergence. We developed a model for induction of C-reactive protein (CRP), a clinically reliable marker of inflammation, by interleukin (IL)-11, an approved cytokine for treatment of chemotherapy-induced thrombocytopenia. Due to paucity of information on the mechanisms underlying inflammation-induced CRP dynamics, our model was developed by systematically evaluating several models for their ability to retrieve variable CRP profiles observed in IL-11-treated breast cancer patients. The preliminary semi-mechanistic models were designed by non-linear mixed-effects modeling, and were evaluated by various performance criteria, which test goodness-of-fit, parsimony and uniqueness. The best-performing model, a robust population model with minimal inter-individual variability, uncovers new aspects of inflammation dynamics. It shows that CRP clearance is a nonlinear self-controlled process, indicating an adaptive anti-inflammatory reaction in humans. The model also reveals a dual IL-11 effect on CRP elevation, whereby the drug has not only a potent immediate influence on CRP incline, but also a long-term influence inducing elevated CRP levels for several months. Consistent with this, model simulations suggest that periodic IL-11 therapy may result in prolonged low-grade (chronic) inflammation post treatment. Future application of the model can therefore help design improved IL-11 regimens with minimized long-term CRP toxicity. Our study illuminates the dynamics of inflammation and its control, and provides a prototype for progressive modeling of complex biological processes in the medical realm and beyond.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Mantovani A, Allavena P, Sica A, Balkwill F (2008) Cancer-related inflammation. Nature 454(7203):436–444. doi:10.1038/nature07205

    Article  PubMed  CAS  Google Scholar 

  2. Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A (2009) Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis 30(7):1073–1081. doi:10.1093/carcin/bgp127

    Article  PubMed  CAS  Google Scholar 

  3. Abou-Raya A, Abou-Raya S (2006) Inflammation: a pivotal link between autoimmune diseases and atherosclerosis. Autoimmun Rev 5(5):331–337. doi:10.1016/j.autrev.2005.12.006

    Article  PubMed  CAS  Google Scholar 

  4. Davies M (2014) New modalities of cancer treatment for NSCLC: focus on immunotherapy. Cancer Manag Res 6:63–75. doi:10.2147/CMAR.S57550

    Article  PubMed  PubMed Central  Google Scholar 

  5. Pellegrini M, Mak TW, Ohashi PS (2010) Fighting cancers from within: augmenting tumor immunity with cytokine therapy. Trends Pharmacol Sci 31(8):356–363. doi:10.1016/j.tips.2010.05.003

    Article  PubMed  CAS  Google Scholar 

  6. Xu XJ, Tang YM (2014) Cytokine release syndrome in cancer immunotherapy with chimeric antigen receptor engineered T cells. Cancer Lett 343(2):172–178. doi:10.1016/j.canlet.2013.10.004

    Article  PubMed  CAS  Google Scholar 

  7. Vodovotz Y, Csete M, Bartels J, Chang S, An G (2008) Translational systems biology of inflammation. PLoS Comput Biol 4(4):e1000014. doi:10.1371/journal.pcbi.1000014

    Article  PubMed  PubMed Central  Google Scholar 

  8. Agur Z, Elishmereni M, Kheifetz Y (2013) Personalizing oncology treatments in solid cancer diseases by predicting drug efficacy, side-effects, and improved therapy: mathematics, statistics and their integration. WIREs Systems Biology and Medicine In Press

  9. Radosavljevic V, Ristovski K, Obradovic Z (2013) A data-driven acute inflammation therapy. BMC Med Genomics 6(3):1–9. doi:10.1186/1755-8794-6-S3-S7

    Google Scholar 

  10. Machavaram KK, Almond LM, Rostami-Hodjegan A, Gardner I, Jamei M, Tay S, Wong S, Joshi A, Kenny JR (2013) A physiologically based pharmacokinetic modeling approach to predict disease-drug interactions: suppression of CYP3A by IL-6. Clin Pharmacol Ther 94(2):260–268. doi:10.1038/clpt.2013.79

    Article  PubMed  CAS  Google Scholar 

  11. Reynolds A, Rubin J, Clermont G, Day J, Vodovotz Y, Bard Ermentrout G (2006) A reduced mathematical model of the acute inflammatory response: I. Derivation of model and analysis of anti-inflammation. J Theor Biol 242(1):220–236. doi:10.1016/j.jtbi.2006.02.016

    Article  PubMed  CAS  Google Scholar 

  12. Vodovotz Y, Clermont G, Chow C, An G (2004) Mathematical models of the acute inflammatory response. Curr Opin Crit Care 10(5):383–390. doi:10.1097/00075198-200410000-00014

    Article  PubMed  Google Scholar 

  13. Lauffenburger DA, Kennedy CR (1981) Analysis of a lumped model for tissue inflammation dynamics. Math Biosci 53(3–4):189–221. doi:10.1016/0025-5564(81)90018-3

    Article  PubMed  CAS  Google Scholar 

  14. Schwertschlag US, Trepicchio WL, Dykstra KH, Keith JC, Turner KJ, Dorner AJ (1999) Hematopoietic, immunomodulatory and epithelial effects of interleukin-11. Leukemia 13(9):1307–1315

    Article  PubMed  CAS  Google Scholar 

  15. Bhatia M, Davenport V, Cairo MS (2007) The role of interleukin-11 to prevent chemotherapy-induced thrombocytopenia in patients with solid tumors, lymphoma, acute myeloid leukemia and bone marrow failure syndromes. Leuk Lymphoma 48(1):9–15. doi:10.1080/10428190600909115

    Article  PubMed  CAS  Google Scholar 

  16. Vadhan-Raj S (2009) Management of chemotherapy-induced thrombocytopenia: current status of thrombopoietic agents. Semin Hematol 46(1 Suppl 2):S26–S32. doi:10.1053/j.seminhematol.2008.12.007

    Article  PubMed  CAS  Google Scholar 

  17. Gordon MS, McCaskill-Stevens WJ, Battiato LA, Loewy J, Loesch D, Breeden E, Hoffman R, Beach KJ, Kuca B, Kaye J, Sledge GW Jr (1996) A phase I trial of recombinant human interleukin-11 (neumega rhIL-11 growth factor) in women with breast cancer receiving chemotherapy. Blood 87(9):3615–3624

    PubMed  CAS  Google Scholar 

  18. Smith JW 2nd (2000) Tolerability and side-effect profile of rhIL-11. Oncology (Williston Park) 14(9 Suppl 8):41–47

    Google Scholar 

  19. Wu S, Zhang Y, Xu L, Dai Y, Teng Y, Ma S, Ho SH, Kim JM, Yu SS, Kim S, Song S (2012) Multicenter, randomized study of genetically modified recombinant human interleukin-11 to prevent chemotherapy-induced thrombocytopenia in cancer patients receiving chemotherapy. Support Care Cancer 20(8):1875–1884. doi:10.1007/s00520-011-1290-x

    Article  PubMed  Google Scholar 

  20. Kurzrock R (2005) Thrombopoietic factors in chronic bone marrow failure states: the platelet problem revisited. Clin Cancer Res 11(4):1361–1367. doi:10.1158/1078-0432.CCR-04-1094

    Article  PubMed  CAS  Google Scholar 

  21. Cotreau MM, Stonis L, Strahs A, Schwertschlag US (2004) A multiple-dose, safety, tolerability, pharmacokinetics and pharmacodynamic study of oral recombinant human interleukin-11 (oprelvekin). Biopharm Drug Dispos 25(7):291–296. doi:10.1002/bdd.415

    Article  PubMed  CAS  Google Scholar 

  22. Ellis M, Hedstrom U, Frampton C, Alizadeh H, Kristensen J, Shammas FV, al-Ramadi BK (2006) Modulation of the systemic inflammatory response by recombinant human interleukin-11: a prospective randomized placebo controlled clinical study in patients with hematological malignancy. Clin Immunol 120(2):129–137. doi:10.1016/j.clim.2006.03.003

    Article  PubMed  CAS  Google Scholar 

  23. Coventry BJ, Ashdown ML, Quinn MA, Markovic SN, Yatomi-Clarke SL, Robinson AP (2009) CRP identifies homeostatic immune oscillations in cancer patients: a potential treatment targeting tool? J Transl Med 7:102. doi:10.1186/1479-5876-7-102

    Article  PubMed  PubMed Central  Google Scholar 

  24. Eklund CM (2009) Proinflammatory cytokines in CRP baseline regulation. Adv Clin Chem 48:111–136

    Article  PubMed  CAS  Google Scholar 

  25. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM (2001) C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 286(3):327–334

    Article  PubMed  CAS  Google Scholar 

  26. Yousuf O, Mohanty BD, Martin SS, Joshi PH, Blaha MJ, Nasir K, Blumenthal RS, Budoff MJ (2013) High-sensitivity C-reactive protein and cardiovascular disease: a resolute belief or an elusive link? J Am Coll Cardiol 62(5):397–408. doi:10.1016/j.jacc.2013.05.016

    Article  PubMed  CAS  Google Scholar 

  27. Danesh J, Wheeler JG, Hirschfield GM, Eda S, Eiriksdottir G, Rumley A, Lowe GD, Pepys MB, Gudnason V (2004) C-reactive protein and other circulating markers of inflammation in the prediction of coronary heart disease. N Engl J Med 350(14):1387–1397

    Article  PubMed  CAS  Google Scholar 

  28. Ridker PM (2003) Cardiology patient page. C-reactive protein: a simple test to help predict risk of heart attack and stroke. Circulation 108(12):e81–e85. doi:10.1161/01.CIR.0000093381.57779.67

    Article  PubMed  CAS  Google Scholar 

  29. de Martino M, Klatte T, Seemann C, Waldert M, Haitel A, Schatzl G, Remzi M, Weibl P (2013) Validation of serum C-reactive protein (CRP) as an independent prognostic factor for disease-free survival in patients with localised renal cell carcinoma (RCC). BJU Int 111(8):E348–E353. doi:10.1111/bju.12067

    Article  PubMed  Google Scholar 

  30. Allin KH, Nordestgaard BG (2011) Elevated C-reactive protein in the diagnosis, prognosis, and cause of cancer. Crit Rev Clin Lab Sci 48(4):155–170. doi:10.3109/10408363.2011.599831

    Article  PubMed  CAS  Google Scholar 

  31. Heikkila K, Ebrahim S, Rumley A, Lowe G, Lawlor DA (2007) Associations of circulating C-reactive protein and interleukin-6 with survival in women with and without cancer: findings from the British Women’s Heart and Health Study. Cancer Epidemiol Biomarkers Prev 16(6):1155–1159. doi:10.1158/1055-9965.EPI-07-0093

    Article  PubMed  Google Scholar 

  32. Steffens S, Kohler A, Rudolph R, Eggers H, Seidel C, Janssen M, Wegener G, Schrader M, Kuczyk MA, Schrader AJ (2012) Validation of CRP as prognostic marker for renal cell carcinoma in a large series of patients. BMC Cancer 12:399. doi:10.1186/1471-2407-12-399

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  33. Han Y, Mao F, Wu Y, Fu X, Zhu X, Zhou S, Zhang W, Sun Q, Zhao Y (2011) Prognostic role of C-reactive protein in breast cancer: a systematic review and meta-analysis. Int J Biol Markers 26(4):209–215. doi:10.5301/JBM.2011.8872

    PubMed  CAS  Google Scholar 

  34. Mazhar D, Ngan S (2006) C-reactive protein and colorectal cancer. QJM 99(8):555–559. doi:10.1093/qjmed/hcl056

    Article  PubMed  CAS  Google Scholar 

  35. Zhou B, Liu J, Wang ZM, Xi T (2012) C-reactive protein, interleukin 6 and lung cancer risk: a meta-analysis. PLoS ONE 7(8):e43075. doi:10.1371/journal.pone.0043075

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  36. Shimura T, Kitagawa M, Yamada T, Ebi M, Mizoshita T, Tanida S, Kataoka H, Kamiya T, Joh T (2012) C-reactive protein is a potential prognostic factor for metastatic gastric cancer. Anticancer Res 32(2):491–496

    PubMed  CAS  Google Scholar 

  37. Aoyama K, Uchida T, Takanuki F, Usui T, Watanabe T, Higuchi S, Toyoki T, Mizoguchi H (1997) Pharmacokinetics of recombinant human interleukin-11 (rhIL-11) in healthy male subjects. Br J Clin Pharmacol 43(6):571–578

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Akaike H (1973) Information theory as an extension of the maximum likelihood principle. 2nd International Symposium on Information Theory. Akademiai Kiado, Budapest

  39. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Automat Contr 19(6):716–723. doi:10.1109/tac.1974.1100705

    Article  Google Scholar 

  40. Forster M, Sober E (1994) How to tell when simpler, more unified, or less ad hoc theories will provide more accurate predictions. Br J Philos Sci 45(1):1–35. doi:10.1093/bjps/45.1.1

    Article  Google Scholar 

  41. Davidian M, Giltinan DM (1995) Nonlinear models for repeated measurement data. Monographs on Statistics and Applied Probability, vol 62. Chapman and Hall, London

    Google Scholar 

  42. Davidian M, Giltinan DM (2003) Nonlinear models for repeated measurements: an overview and update. J Agric Biol Environ Stat 8:387–419. doi:10.1198/1085711032697

  43. Delyon B, Lavielle M, Moulines E (1999) Convergence of a stochastic approximation version of the EM algorithm. Ann Statist 27(1):94–128

    Article  Google Scholar 

  44. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Statist Soc Ser B 39(1):1–38

    Google Scholar 

  45. Kuhn E, Lavielle M (2004) Coupling a stochastic approximation version of EM with an MCMC procedure. ESAIM: Probab Stat 8:115–131. doi:10.1051/ps:2004007

    Article  Google Scholar 

  46. Kuhn E, Lavielle M (2005) Maximum likelihood estimation in nonlinear mixed effects models. Comput Stat Data Anal 49(4):1020–1038. doi:10.1016/j.csda.2004.07.002

    Article  Google Scholar 

  47. Faller D, Klingmüller U, Timmer J (2003) Simulation methods for optimal experimental design in systems biology. Simulation 79(12):717–725

    Article  Google Scholar 

  48. Louis TA (1982) Finding the observed information matrix when using the EM algorithm. J Roy Statist Soc Ser B 44(2):226–233

    Google Scholar 

  49. Stuart J, Whicher JT (1988) Tests for detecting and monitoring the acute phase response. Arch Dis Child 63(2):115–117

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  50. Catsburg C, Gunter MJ, Chen C, Cote ML, Kabat GC, Nassir R, Tinker L, Wactawski-Wende J, Page DL, Rohan TE (2014) Insulin, estrogen, inflammatory markers, and risk of benign proliferative breast disease. Cancer Res 74(12):3248–3258. doi:10.1158/0008-5472.CAN-13-3514

    Article  PubMed  CAS  Google Scholar 

  51. Putoczki T, Ernst M (2010) More than a sidekick: the IL-6 family cytokine IL-11 links inflammation to cancer. J Leukoc Biol 88(6):1109–1117. doi:10.1189/jlb.0410226

    Article  PubMed  CAS  Google Scholar 

  52. Gurfein BT, Zhang Y, Lopez CB, Argaw AT, Zameer A, Moran TM, John GR (2009) IL-11 regulates autoimmune demyelination. J immunol 183(7):4229–4240. doi:10.4049/jimmunol.0900622

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  53. Hermann JA, Hall MA, Maini RN, Feldmann M, Brennan FM (1998) Important immunoregulatory role of interleukin-11 in the inflammatory process in rheumatoid arthritis. Arthritis Rheum 41(8):1388–1397. doi:10.1002/1529-0131(199808)41:8<1388:AID-ART7>3.0.CO;2-F

    Article  PubMed  CAS  Google Scholar 

  54. Walmsley M, Butler DM, Marinova-Mutafchieva L, Feldmann M (1998) An anti-inflammatory role for interleukin-11 in established murine collagen-induced arthritis. Immunology 95(1):31–37

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  55. Trepicchio WL, Ozawa M, Walters IB, Kikuchi T, Gilleaudeau P, Bliss JL, Schwertschlag U, Dorner AJ, Krueger JG (1999) Interleukin-11 therapy selectively downregulates type I cytokine proinflammatory pathways in psoriasis lesions. J Clin Investig 104(11):1527–1537. doi:10.1172/JCI6910

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  56. Bozza M, Bliss JL, Dorner AJ, Trepicchio WL (2001) Interleukin-11 modulates Th1/Th2 cytokine production from activated CD4 + T cells. J Interferon Cytokine Res 21(1):21–30. doi:10.1089/107999001459123

    Article  PubMed  CAS  Google Scholar 

  57. Kapina MA, Shepelkova GS, Avdeenko VG, Guseva AN, Kondratieva TK, Evstifeev VV, Apt AS (2011) Interleukin-11 drives early lung inflammation during Mycobacterium tuberculosis infection in genetically susceptible mice. PLoS ONE 6(7):e21878. doi:10.1371/journal.pone.0021878

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  58. Wong PK, Campbell IK, Robb L, Wicks IP (2005) Endogenous IL-11 is pro-inflammatory in acute methylated bovine serum albumin/interleukin-1-induced (mBSA/IL-1)arthritis. Cytokine 29(2):72–76. doi:10.1016/j.cyto.2004.09.011

    Article  PubMed  CAS  Google Scholar 

  59. Vial T, Descotes J (1995) Clinical toxicity of cytokines used as haemopoietic growth factors. Drug Saf 13(6):371–406

    Article  PubMed  CAS  Google Scholar 

  60. van Leeuwen MA, van Rijswijk MH, Sluiter WJ, van Riel PL, Kuper IH, van de Putte LB, Pepys MB, Limburg PC (1997) Individual relationship between progression of radiological damage and the acute phase response in early rheumatoid arthritis. Towards development of a decision support system. J Rheumatol 24(1):20–27

    PubMed  Google Scholar 

  61. Wick MC, Lindblad S, Klareskog L, Van Vollenhoven RF (2004) Relationship between inflammation and joint destruction in early rheumatoid arthritis: a mathematical description. Ann Rheum Dis 63(7):848–852. doi:10.1136/ard.2003.01517263/7/848

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  62. Liu B, Zhang J, Tan PY, Hsu D, Blom AM, Leong B, Sethi S, Ho B, Ding JL, Thiagarajan PS (2011) A computational and experimental study of the regulatory mechanisms of the complement system. PLoS Comput Biol 7(1):e1001059. doi:10.1371/journal.pcbi.1001059

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  63. Bauer R, Guzy S, Ng C (2007) A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples. AAPS J 9(1):E60–E83. doi:10.1208/aapsj0901007

    Article  PubMed  PubMed Central  Google Scholar 

  64. Friberg LE, Henningsson A, Maas H, Nguyen L, Karlsson MO (2002) Model of chemotherapy-induced myelosuppression with parameter consistency across drugs. J Clin Oncol 20(24):4713–4721

    Article  PubMed  Google Scholar 

  65. Friberg LE, Karlsson MO (2003) Mechanistic models for myelosuppression. Invest New Drugs 21(2):183–194

    Article  PubMed  CAS  Google Scholar 

  66. Karlsson MO, Molnar V, Freijs A, Nygren P, Bergh J, Larsson R (1999) Pharmacokinetic models for the saturable distribution of paclitaxel. Drug Metab Dispos 27(10):1220–1223

    PubMed  CAS  Google Scholar 

  67. Quartino AL, Friberg LE, Karlsson MO (2012) A simultaneous analysis of the time-course of leukocytes and neutrophils following docetaxel administration using a semi-mechanistic myelosuppression model. Invest New Drugs 30(2):833–845. doi:10.1007/s10637-010-9603-3

    Article  PubMed  CAS  Google Scholar 

  68. Dartois C, Brendel K, Comets E, Laffont CM, Laveille C, Tranchand B, Mentre F, Lemenuel-Diot A, Girard P (2007) Overview of model-building strategies in population PK/PD analyses: 2002–2004 literature survey. Br J Clin Pharmacol 64(5):603–612. doi:10.1111/j.1365-2125.2007.02975.x

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  69. Skomorovski K, Harpak H, Ianovski A, Vardi M, Visser TP, Hartong SC, van Vliet HH, Wagemaker G, Agur Z (2003) New TPO treatment schedules of increased safety and efficacy: pre-clinical validation of a thrombopoiesis simulation model. Br J Haematol 123(4):683–691

    Article  PubMed  Google Scholar 

  70. Vainas O, Ariad S, Amir O, Mermershtain W, Vainstein V, Kleiman M, Inbar O, Ben-Av R, Mukherjee A, Chan S, Agur Z (2012) Personalising docetaxel and G-CSF schedules in cancer patients by a clinically validated computational model. Br J Cancer 107(5):814–822. doi:10.1038/bjc.2012.316

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  71. Vainstein V, Ginosar Y, Shoham M, Ranmar DO, Ianovski A, Agur Z (2005) The complex effect of granulocyte colony-stimulating factor on human granulopoiesis analyzed by a new physiologically-based mathematical model. J Theor Biol 234(3):311–327

    Article  PubMed  CAS  Google Scholar 

  72. Gorelik B, Ziv I, Shohat R, Wick M, Hankins WD, Sidransky D, Agur Z (2008) Efficacy of weekly docetaxel and bevacizumab in mesenchymal chondrosarcoma: a new theranostic method combining xenografted biopsies with a mathematical model. Cancer Res 68(21):9033–9040. doi:10.1158/0008-5472.can-08-1723

    Article  PubMed  CAS  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors thank Dr. Marina Kleiman, Prof. Gerard Wagemaker and Yuri Kogan for helpful discussions. This work was supported by the Chai Foundation.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zvia Agur.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 472 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kheifetz, Y., Elishmereni, M. & Agur, Z. Complex pattern of interleukin-11-induced inflammation revealed by mathematically modeling the dynamics of C-reactive protein. J Pharmacokinet Pharmacodyn 41, 479–491 (2014). https://doi.org/10.1007/s10928-014-9383-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10928-014-9383-z

Keywords

Navigation