Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 30, 2019

Drug-drug interaction (DDI) assessments of ruxolitinib, a dual substrate of CYP3A4 and CYP2C9, using a verified physiologically based pharmacokinetic (PBPK) model to support regulatory submissions

  • Kenichi Umehara EMAIL logo , Felix Huth , Yi Jin , Hilmar Schiller , Vassilios Aslanis , Tycho Heimbach and Handan He

Abstract

Ruxolitinib is mainly metabolized by cytochrome P450 (CYP) enzymes CYP3A4 and CYP2C9 followed by minor contributions of other hepatic CYP enzymes in vitro. A physiologically based pharmacokinetic (PBPK) model was established to evaluate the changes in the ruxolitinib systemic exposures with co-administration of CYP3A4 and CYP2C9 perpetrators. The fractions metabolized in the liver via oxidation by CYP enzymes (fm,CYP3A4 = 0.75, fm,CYP2C9 = 0.19, and fm,CYPothers = 0.06) for an initial ruxolitinib model based on in vitro data were optimized (0.43, 0.56, and 0.01, respectively) using the observed exposure changes of ruxolitinib (10 mg) with co-administered ketoconazole (200 mg). The reduced amount of fm,CYP3A4 was distributed to fm,CYP2C9. For the initial ruxolitinib model with co-administration of ketoconazole, the area under the curve (AUC) increase of 2.60-fold was over-estimated compared with the respective observation (1.91-fold). With the optimized fm values, the predicted AUC ratio was 1.82. The estimated AUC ratios of ruxolitinib by co-administration of the moderate CYP3A4 inhibitor erythromycin (500 mg) and the strong CYP3A4 inducer rifampicin (600 mg) were within a 20% error compared with the clinically observed values. The PBPK modeling results may provide information on the labeling, i.e. supporting a dose reduction by half for co-administration of strong CYP3A4 inhibitors. Furthermore, an AUC increase of ruxolitinib in the absence or presence of the dual CYP3A4 and CYP2C9 inhibitor fluconazole (100–400 mg) was prospectively estimated to be 1.94- to 4.31-fold. Fluconazole simulation results were used as a basis for ruxolitinib dose adjustment when co-administering perpetrator drugs. A ruxolitinib PBPK model with optimized fm,CYP3A4 and fm,CYP2C9 was established to evaluate victim DDI risks. The previous minimal PBPK model was supported by the FDA for the dose reduction strategy, halving the dose with the concomitant use of strong CYP3A4 inhibitors and dual inhibitors on CYP2C9 and CYP3A4, such as fluconazole at ≤200 mg. Fluconazole simulation results were used as supportive evidence in discussions with the FDA and EMA about ruxolitinib dose adjustment when co-administering perpetrator drugs. Thus, this study demonstrated that PBPK modeling can support characterizing DDI liabilities to inform the drug label and might help reduce the number of clinical DDI studies by simulations of untested scenarios, when a robust PBPK model is established.

Acknowledgments

The authors wish to acknowledge Kim Crewe and Karen Rowland Yeo at Simcyp Limited (A Certara Company, Blades Enterprise Centre, John Street, S2 4SU Sheffield, UK) for their great support for verifying the fluconazole PBPK model as a CYP2C9 inhibitor.

  1. Author contributions: Participated in research designs: Umehara, Huth, Jin, and Schiller. Conducted experiments: Umehara, Huth, Jin, and Schiller. Performed data analysis: Umehara, Huth, Jin, and Schiller. Wrote or contributed to the writing of the manuscript: Umehara, Huth, Jin, Schiller, Aslanis, Heimbach, and He. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Ethical approval: This PBPK study does not require ethics approval; for the cited clinical studies ethical approval has been granted.

  3. Research funding: None declared.

  4. Employment or leadership: None declared.

  5. Honorarium: None declared.

  6. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

[1] Valentino L, Pierre J. JAK/STAT signal transduction: regulators and implication in hematological malignancies. Biochem Pharmacol 2006;71:713–21.10.1016/j.bcp.2005.12.017Search in Google Scholar PubMed

[2] Verstovsek S, Kantarjian H, Mesa RA, Pardanani AD, Cortes-Franco J, Thomas DA, et al. Safety and efficacy of INCB018424, a JAK1 and JAK2 inhibitor, in myelofibrosis. N Engl J Med 2010;363:1117–27.10.1056/NEJMoa1002028Search in Google Scholar PubMed PubMed Central

[3] Shi JG, Chen X, Emm T, Scherle PA, McGee RF, Lo Y, et al. The effect of CYP3A4 inhibition or induction on the pharmacokinetics and pharmacodynamics of orally administered ruxolitinib (INCB018424 phosphate) in healthy volunteers. J Clin Pharmacol 2012;52:809–18.10.1177/0091270011405663Search in Google Scholar PubMed

[4] US Food and Drug Administration. Drugs@FDA: FDA approved drug products. Available from: http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm. Accessed: 12 September 2018.Search in Google Scholar

[5] European Medicines Agency (EMA) (2016) Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) model and simulation. Available from: http://www.ema.europa.eu/ema/. Accessed: 12 September 2018.Search in Google Scholar

[6] US Food and Drug Administration. Drugs@FDA: FDA approved drug products. Guidance for industry: physiologically based pharmacokinetic analysis. Available from: http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm. Accessed: 12 September 2018.Search in Google Scholar

[7] US Food and Drug Administration. Drugs@FDA: FDA approved drug products: drug interaction studies – study design, data analysis, implications for dosing, and labeling recommendations. Available from: http://www.accessdata.fda.gov/scripts/cder/drugsatfda/index.cfm. Accessed: 12 September 2018.Search in Google Scholar

[8] Sato M, Ochiai Y, Kijima S, Nagai N, Ando Y, Shikano M, et al. Quantitative modeling and simulation in PMDA: a Japanese regulatory perspective. CPT Pharmacometrics Syst Pharmacol 2017;6:413–5.10.1002/psp4.12203Search in Google Scholar PubMed PubMed Central

[9] Black DJ, Kunze KL, Wienkers LC, Gidal BE, Seaton TL, McDonnell ND, et al. WARFARIN-FLUCONAZOLE II: a metabolically based drug interaction: in vivo studies. Drug Metab Dispos 1996;24:422–8.Search in Google Scholar

[10] Wagner C, Pan Y, Hsu V, Sinha V, Zhao P. Predicting the effect of CYP3A inducers on the pharmacokinetics of substrate drugs using physiologically based pharmacokinetic (PBPK) modeling: an analysis of PBPK submissions to the US FDA. Clin Pharmacokinet 2016;55:475–83.10.1007/s40262-015-0330-ySearch in Google Scholar PubMed

[11] Proctor NJ, Tucker GT, Rostami-Hodjegan A. Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors. Xenobiotica 2004;34:151–78.10.1080/00498250310001646353Search in Google Scholar PubMed

[12] Shi JG, Chen X, McGee RF, Landman RR, Emm T, Lo Y, et al. The pharmacokinetics, pharmacodynamics, and safety of orally dosed INCB018424 phosphate in healthy volunteers. J Clin Pharmacol 2011;51:1644–54.10.1177/0091270010389469Search in Google Scholar PubMed

[13] Rowland Yeo K, Rostami-Hodjegan A, Tucker GT. Abundance of cytochromes P450 in human liver: a meta-analysis. Br J Clin Pharmacol 2004;57:687–8.Search in Google Scholar

[14] Rodgers T, Rowland M. Mechanistic approaches to volume of distribution predictions: understanding the processes. Pharm Res 2007;24:918–33.10.1007/s11095-006-9210-3Search in Google Scholar PubMed

[15] Yang J, Jamei M, Yeo KR, Tucker GT, Rostami-Hodjegan A. Prediction of intestinal first-pass drug metabolism. Curr Drug Metab 2007;8:676–84.10.2174/138920007782109733Search in Google Scholar PubMed

[16] Camenisch G, Umehara K. Predicting human hepatic clearance from in vitro drug metabolism and transport data: a scientific and pharmaceutical perspective for assessing drug-drug interactions. Biopharm Drug Dispos 2012;33:179–94.10.1002/bdd.1784Search in Google Scholar PubMed

[17] Kunze KL, Wienkers LC, Thummel KE, Trager WF. Warfarin-fluconazole I: inhibition of the human cytochrome P450-dependent metabolism of warfarin by fluconazole: in vitro studies. Drug Metab Dispos 1996;24:414–21.Search in Google Scholar

[18] Neal JM, Kunze KL, Levy RH, O’Reilly RA, Trager WF. Ki iv, an in vivo parameter for predicting the magnitude of a drug interaction arising from competitive enzyme inhibition. Drug Metab Dispos 2003;31:1043–48.10.1124/dmd.31.8.1043Search in Google Scholar PubMed

[19] Bavisotto LM, Ellis DJ, Milner PG, Combs DL, Irwin I, Canafax DM. Tecarfarin, a novel vitamin K reductase antagonist, is not affected by CYP2C9 and CYP3A4 inhibition following concomitant administration of fluconazole in healthy participants. J Clin Pharmacol 2011;51:561–74.10.1177/0091270010370588Search in Google Scholar PubMed

[20] Lazar JD, Wilner KD. Drug interactions with fluconazole. Rev Infect Dis 1990;12:327–33.10.1093/clinids/12.Supplement_3.S327Search in Google Scholar

[21] Blum RA, Wilton JH, Hilligoss DM, Gardner MJ, Henry EB, Harrison NJ, et al. Effect of fluconazole on the disposition of phenytoin. Clin Pharmacol Ther 1991;49:420–5.10.1038/clpt.1991.49Search in Google Scholar PubMed

[22] Shon JH, Yoon YR, Kim KA, Lim YC, Lee KY, Park JY, et al. Effects of CYP2C19 and CYP2C9 genetic polymorphisms on the disposition of and blood glucose lowering response to tolbutamide in humans. Pharmacogenetics 2002;12:111–9.10.1097/00008571-200203000-00005Search in Google Scholar PubMed

[23] Guest EJ, Aarons L, Houston JB, Rostami-Hodjegan A, Galetin A. Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions. Drug Metab Dispos 2011;39:170–3.10.1124/dmd.110.036103Search in Google Scholar PubMed

[24] Camenisch G, Riede J, Kunze A, Huwyler J, Poller B, Umehara K. The extended clearance model and its use for the interpretation of hepatobiliary elimination data. ADMET & DMPK 2015;3:1–14.10.5599/admet.3.1.144Search in Google Scholar

[25] Obach RS, Reed-Hagen AE. Measurement of Michaelis constants for cytochrome P450-mediated biotransformation reactions using a substrate depletion approach. Drug Metab Dispos 2002;30:831–7.10.1124/dmd.30.7.831Search in Google Scholar PubMed

[26] Shi JG, Fraczkiewicz G, Williams WV, Yeleswaram S. Predicting drug-drug interactions involving multiple mechanisms using physiologically based pharmacokinetic modeling: a case study with ruxolitinib. Clin Pharmacol Ther 2015;97:177–85.10.1002/cpt.30Search in Google Scholar PubMed

[27] Kirby BJ, Collier AC, Kharasch ED, Dixit V, Desai P, Whittington D, et al. Complex drug interactions of HIV protease inhibitors 2: in vivo induction and in vitro to in vivo correlation of induction of cytochrome P450 1A2, 2B6, and 2C9 by ritonavir or nelfinavir. Drug Metab Dispos 2011;39:2329–37.10.1124/dmd.111.038646Search in Google Scholar PubMed PubMed Central

[28] Vormfelde SV, Brockmöller J, Bauer S, Herchenhein P, Kuon J, Meineke I, et al. Relative impact of genotype and enzyme induction on the metabolic capacity of CYP2C9 in healthy volunteers. Clin Pharmacol Ther 2009;86:54–61.10.1038/clpt.2009.40Search in Google Scholar PubMed

[29] Cheeti S, Budha NR, Rajan S, Dresser MJ, Jin JY. A physiologically based pharmacokinetic (PBPK) approach to evaluate pharmacokinetics in patients with cancer. Biopharm Drug Dispos 2013;34:141–54.10.1002/bdd.1830Search in Google Scholar PubMed


Supplementary Material

The online version of this article offers supplementary material (DOI: https://doi.org/10.1515/dmpt-2018-0042).


Received: 2018-12-14
Accepted: 2019-03-26
Published Online: 2019-05-30

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 3.2.2023 from https://www.degruyter.com/document/doi/10.1515/dmpt-2018-0042/html
Scroll Up Arrow