Skip to main content

Advertisement

Log in

Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates

  • Research Paper
  • Published:
Pharmaceutical Research Aims and scope Submit manuscript

Abstract

Purpose

For decades, inflammation has been considered a cause of pharmacokinetic variability, mainly in relation to the inhibitory effect of pro-inflammatory cytokines on the expression level and activity of cytochrome P450 (CYP). In vitro and clinical studies have shown that two major CYPs, CYP2C19 and CYP3A4, are both impaired. The objective of the present study was to quantify the impact of the inflammatory response on the activity of both CYPs in order to predict the pharmacokinetic profile of their substrates according to systemic C-reactive protein (CRP).

Methods

The relationships between CRP concentration and both CYPs activities were estimated and validated using clinical data first on midazolam then on voriconazole. Finally, clinical data on omeprazole were used to validate the findings. For each substrate, a physiologically based pharmacokinetics model was built using a bottom-up approach, and the relationships between CRP level and CYP activities were estimated by a top-down approach. After incorporating the respective relationships, we compared the predictions and observed drug concentrations.

Results

Changes in pharmacokinetic profiles and parameters induced by inflammation seem to be captured accurately by the models.

Conclusions

These findings suggest that the pharmacokinetics of CYP2C19 and CYP3A4 substrates can be predicted depending on the CRP concentration.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Stanke-Labesque F, Gautier-Veyret E, Chhun S, Guilhaumou R. French Society of P, therapeutics. Inflammation is a major regulator of drug metabolizing enzymes and transporters: consequences for the personalization of drug treatment. Pharmacol Ther. 2020:107627.

  2. Klein M. Role of inflammatory cytokine signaling in the regulation of detoxifying functiuns in human hepatocytes and liver. University, Stuttgart; 2014.

  3. Martin F, Santolaria F, Batista N, Milena A, Gonzalez-Reimers E, Brito MJ, et al. Cytokine levels (IL-6 and IFN-gamma), acute phase response and nutritional status as prognostic factors in lung cancer. Cytokine. 1999;11(1):80–6.

    Article  CAS  Google Scholar 

  4. Robak T, Gladalska A, Stepien H, Robak E. Serum levels of interleukin-6 type cytokines and soluble interleukin-6 receptor in patients with rheumatoid arthritis. Mediat Inflamm. 1998;7(5):347–53.

    Article  CAS  Google Scholar 

  5. Klein M, Thomas M, Hofmann U, Seehofer D, Damm G, Zanger UM. A systematic comparison of the impact of inflammatory signaling on absorption, distribution, metabolism, and excretion gene expression and activity in primary human hepatocytes and HepaRG cells. Drug Metab Disposition: Biol Fate Chem. 2015;43(2):273–83.

    Article  Google Scholar 

  6. Dickmann LJ, Patel SK, Rock DA, Wienkers LC, Slatter JG. Effects of interleukin-6 (IL-6) and an anti-IL-6 monoclonal antibody on drug-metabolizing enzymes in human hepatocyte culture. Drug Metab Dispos. 2011;39(8):1415–22.

    Article  CAS  Google Scholar 

  7. Simon F, Garcia J, Guyot L, Guitton J, Vilchez G, Bardel C, et al. Impact of Interleukin-6 on drug-metabolizing enzymes and transporters in intestinal cells. AAPS J. 2019;22(1):16.

    Article  Google Scholar 

  8. Lee EB, Daskalakis N, Xu C, Paccaly A, Miller B, Fleischmann R, et al. Disease-drug interaction of Sarilumab and simvastatin in patients with rheumatoid arthritis. Clin Pharmacokinet. 2017;56(6):607–15.

    Article  CAS  Google Scholar 

  9. Schmitt C, Kuhn B, Zhang X, Kivitz AJ, Grange S. Disease-drug-drug interaction involving tocilizumab and simvastatin in patients with rheumatoid arthritis. Clin Pharmacol Ther. 2011;89(5):735–40.

    Article  CAS  Google Scholar 

  10. van Wanrooy MJ, Span LF, Rodgers MG, van den Heuvel ER, Uges DR, van der Werf TS, et al. Inflammation is associated with voriconazole trough concentrations. Antimicrob Agents Chemother. 2014;58(12):7098–101.

    Article  Google Scholar 

  11. Gautier-Veyret E, Bailly S, Fonrose X, Tonini J, Chevalier S, Thiebaut-Bertrand A, et al. Pharmacogenetics may influence the impact of inflammation on voriconazole trough concentrations. Pharmacogenomics. 2017;18(12):1119–23.

    Article  CAS  Google Scholar 

  12. Jiang X, Zhuang Y, Xu Z, Wang W, Zhou H. Development of a physiologically based pharmacokinetic model to predict disease-mediated therapeutic protein-drug interactions: modulation of multiple cytochrome P450 enzymes by Interleukin-6. AAPS J. 2016;18(3):767–76.

    Article  CAS  Google Scholar 

  13. Machavaram KK, Almond LM, Rostami-Hodjegan A, Gardner I, Jamei M, Tay S, et al. A physiologically based pharmacokinetic modeling approach to predict disease-drug interactions: suppression of CYP3A by IL-6. Clin Pharmacol Ther. 2013;94(2):260–8.

    Article  CAS  Google Scholar 

  14. Kraynov E, Martin SW, Hurst S, Fahmi OA, Dowty M, Cronenberger C, et al. How current understanding of clearance mechanisms and pharmacodynamics of therapeutic proteins can be applied for evaluation of their drug-drug interaction potential. Drug Metab Dispos. 2011;39(10):1779–83.

    Article  CAS  Google Scholar 

  15. Veringa A, Ter Avest M, Span LF, van den Heuvel ER, Touw DJ, Zijlstra JG, et al. Voriconazole metabolism is influenced by severe inflammation: a prospective study. J Antimicrob Chemother. 2017;72(1):261–7.

    Article  CAS  Google Scholar 

  16. Zhuang Y, Vries DE, de Xu Z, Marciniak SJ, Jr . Chen D, Leon F, et al. Evaluation of disease-mediated therapeutic protein-drug interactions between an anti-interleukin-6 monoclonal antibody (sirukumab) and cytochrome P450 activities in a phase 1 study in patients with rheumatoid arthritis using a cocktail approach. J Clin Pharmacol. 2015;55(12):1386–94.

    Article  CAS  Google Scholar 

  17. Peters SA. Evaluation of a generic physiologically based pharmacokinetic model for lineshape analysis. Clin Pharmacokinet. 2008;47(4):261–75.

    Article  CAS  Google Scholar 

  18. WebPlotDigitizer. Version 4.2; 2019.

  19. Gautier-Veyret E, Truffot A, Bailly S, Fonrose X, Thiebaut-Bertrand A, Tonini J, et al. Inflammation is a potential risk factor of voriconazole overdose in hematological patients. Fundam Clin Pharmacol. 2019;33(2):232–8.

    CAS  PubMed  Google Scholar 

  20. Vet NJ, Brussee JM, de Hoog M, Mooij MG, Verlaat CW, Jerchel IS, et al. Inflammation and organ failure severely affect midazolam clearance in critically ill children. Am J Respir Crit Care Med. 2016;194(1):58–66.

    Article  CAS  Google Scholar 

  21. Hanke N, Frechen S, Moj D, Britz H, Eissing T, Wendl T, et al. PBPK models for CYP3A4 and P-gp DDI prediction: a modeling network of rifampicin, Itraconazole, clarithromycin, midazolam, Alfentanil, and digoxin. CPT Pharmacometrics Syst Pharmacol. 2018;7(10):647–59.

    Article  CAS  Google Scholar 

  22. Michael C, Bierbach U, Frenzel K, Lange T, Basara N, Niederwieser D, et al. Voriconazole pharmacokinetics and safety in immunocompromised children compared to adult patients. Antimicrob Agents Chemother. 2010;54(8):3225–32.

    Article  CAS  Google Scholar 

  23. Hohmann N, Kocheise F, Carls A, Burhenne J, Weiss J, Haefeli WE, et al. Dose-dependent bioavailability and CYP3A inhibition contribute to non-linear pharmacokinetics of Voriconazole. Clin Pharmacokinet. 2016;55(12):1535–45.

    Article  CAS  Google Scholar 

  24. Dolton MJ, Mikus G, Weiss J, Ray JE, McLachlan AJ. Understanding variability with voriconazole using a population pharmacokinetic approach: implications for optimal dosing. J Antimicrob Chemother. 2014;69(6):1633–41.

    Article  CAS  Google Scholar 

  25. GENOPHAR II WORKING GROUP. DDI-predictor [cited 5 Jan 2020].

  26. Damle B, Varma MV, Wood N. Pharmacokinetics of voriconazole administered concomitantly with fluconazole and population-based simulation for sequential use. Antimicrob Agents Chemother. 2011;55(11):5172–7.

    Article  CAS  Google Scholar 

  27. Goutelle S, Bourguignon L, Bleyzac N, Berry J, Clavel-Grabit F, Tod M. In vivo quantitative prediction of the effect of gene polymorphisms and drug interactions on drug exposure for CYP2C19 substrates. AAPS J. 2013;15(2):415–26.

    Article  CAS  Google Scholar 

  28. Strom CM, Goos D, Crossley B, Zhang K, Buller-Burkle A, Jarvis M, et al. Testing for variants in CYP2C19: population frequencies and testing experience in a clinical laboratory. Genet Med. 2012;14(1):95–100.

    Article  CAS  Google Scholar 

  29. Martis S, Peter I, Hulot JS, Kornreich R, Desnick RJ, Scott SA. Multi-ethnic distribution of clinically relevant CYP2C genotypes and haplotypes. Pharmacogenomics J. 2013;13(4):369–77.

    Article  CAS  Google Scholar 

  30. CHMP. VFEND, INN-voriconazole.

  31. Jin H, Wang T, Falcione BA, Olsen KM, Chen K, Tang H, et al. Trough concentration of voriconazole and its relationship with efficacy and safety: a systematic review and meta-analysis. J Antimicrob Chemother. 2016;71(7):1772–85.

    Article  CAS  Google Scholar 

  32. McArdle PA, McMillan DC, Sattar N, Wallace AM, Underwood MA. The relationship between interleukin-6 and C-reactive protein in patients with benign and malignant prostate disease. Br J Cancer. 2004;91(10):1755–7.

    Article  CAS  Google Scholar 

  33. Leser HG, Gross V, Scheibenbogen C, Heinisch A, Salm R, Lausen M, et al. Elevation of serum interleukin-6 concentration precedes acute-phase response and reflects severity in acute pancreatitis. Gastroenterology. 1991;101(3):782–5.

    Article  CAS  Google Scholar 

  34. Xu Y, Hijazi Y, Wolf A, Wu B, Sun YN, Zhu M. Physiologically based pharmacokinetic model to assess the influence of Blinatumomab-mediated cytokine elevations on cytochrome P450 enzyme activity. CPT Pharmacometrics Syst Pharmacol. 2015;4(9):507–15.

    Article  CAS  Google Scholar 

  35. Machavaram KK, Endo-Tsukude C, Terao K, Gill KL, Hatley OJ, Gardner I, et al. Simulating the impact of elevated levels of Interleukin-6 on the pharmacokinetics of various CYP450 substrates in patients with Neuromyelitis Optica or Neuromyelitis Optica Spectrum disorders in different ethnic populations. AAPS J. 2019;21(3):42.

    Article  Google Scholar 

  36. Dickmann LJ, Patel SK, Wienkers LC, Slatter JG. Effects of interleukin 1β (IL-1β) and IL-1β/interleukin 6 (IL-6) combinations on drug metabolizing enzymes in human hepatocyte culture. Curr Drug Metab. 2012;13(7):930–7.

    Article  CAS  Google Scholar 

  37. Febvre-James M, Bruyere A, Le Vee M, Fardel O. The JAK1/2 inhibitor Ruxolitinib reverses Interleukin-6-mediated suppression of drug-detoxifying proteins in cultured human hepatocytes. Drug Metab Dispos. 2018;46(2):131–40.

    Article  CAS  Google Scholar 

  38. Zubiaur P, Kneller LA, Ochoa D, Mejía G, Saiz-Rodríguez M, Borobia AM, et al. Evaluation of Voriconazole CYP2C19 phenotype-guided dose adjustments by physiologically based pharmacokinetic modeling. Clin Pharmacokinet. 2020.

  39. Le Vee M, Lecureur V, Stieger B, Fardel O. Regulation of drug transporter expression in human hepatocytes exposed to the proinflammatory cytokines tumor necrosis factor-alpha or interleukin-6. Drug Metab Dispos. 2009;37(3):685–93.

    Article  Google Scholar 

  40. Don BR, Kaysen G. Serum albumin: relationship to inflammation and nutrition. Semin Dial. 2004;17(6):432–7.

    Article  Google Scholar 

  41. Coutant DE, Hall SD. Disease-drug interactions in inflammatory states via effects on CYP-mediated drug clearance. J Clin Pharmacol. 2018;58(7):849–63.

    Article  CAS  Google Scholar 

  42. Benet LZ, Hoener BA. Changes in plasma protein binding have little clinical relevance. Clin Pharmacol Ther. 2002;71(3):115–21.

    Article  CAS  Google Scholar 

  43. Li X, Frechen S, Moj D, Lehr T, Taubert M, Hsin CH et al. A physiologically based pharmacokinetic model of Voriconazole integrating time-dependent inhibition of CYP3A4, genetic polymorphisms of CYP2C19 and predictions of drug-drug interactions. Clin Pharmacokinet 2019.

  44. Qi F, Zhu L, Li N, Ge T, Xu G, Liao S. Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole. Int J Antimicrob Agents. 2017;49(4):403–9.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Florian Simon.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

ESM 1

(DOCX 42 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Simon, F., Gautier-Veyret, E., Truffot, A. et al. Modeling Approach to Predict the Impact of Inflammation on the Pharmacokinetics of CYP2C19 and CYP3A4 Substrates. Pharm Res 38, 415–428 (2021). https://doi.org/10.1007/s11095-021-03019-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11095-021-03019-7

KEY WORDS

Navigation