The AAPS Journal

, 21:78 | Cite as

Guidance for Rifampin and Midazolam Dosing Protocols To Study Intestinal and Hepatic Cytochrome P450 (CYP) 3A4 Induction and De-induction

  • Asha J Kapetas
  • Michael J Sorich
  • A David Rodrigues
  • Andrew RowlandEmail author
Research Article


Cytochrome P450 3A4 (CYP3A4) catalyses the metabolism of > 30% of clinically used small molecule drugs. Induction of CYP3A4 is often associated with clinically important metabolic drug–drug interactions (DDIs). To collate published data regarding induction of CYP3A4 expression by rifampin and identify an optimal protocol to study DDIs using physiologically based pharmacokinetic (PBPK) modelling. The University of Washington Drug Interaction Database was searched for published data regarding induction of CYP3A4 by rifampin. A verified PBPK model was used to define the optimal dose, duration, timing and route of administration of rifampin and midazolam to assess induction of intestinal and hepatic CYP3A4 by rifampin. Sensitivity analysis was performed to evaluate the impact of participant characteristics including sex, race and age. The maximal induction of intestinal CYP3A4 (9.5-fold) was almost double that of hepatic CYP3A4 (5.5-fold). Maximal induction of intestinal and hepatic CYP3A4 was achieved in > 90% of participants within 5 and 10 days, respectively. Intestinal CYP3A4 expression returned to baseline in > 90% of participants within 7 days of rifampin cessation, whereas induction of hepatic CYP3A4 persisted for greater than 7 days in > 50% of participants. There was a significant difference in magnitude, but not time course, of CYP3A4 induction between males and females. Age and race did not significantly affect either the magnitude or time course of CYP3A4 induction. Maximal induction of intestinal CYP3A4 is achieved faster than hepatic CYP3A4. To assess maximal hepatic CYP3A4 induction, oral rifampin (600 mg daily) should be dosed for > 10 days.

Key Words

CYP3A4 induction physiologically based pharmacokinetic modelling rifampin study protocol 



This work was supported by a grant from the National Health and Medical Research Council of Australia [Grant ID 1100179].

Supplementary material

12248_2019_341_MOESM1_ESM.docx (109 kb)
ESM 1 (DOCX 109 kb)


  1. 1.
    Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138(1):103–41.CrossRefGoogle Scholar
  2. 2.
    Lamba JK, Lin YS, Schuetz EG, Thummel KE. Genetic contribution to variable human CYP3A-mediated metabolism. Adv Drug Deliv Rev. 2002;54(10):1271–94.CrossRefGoogle Scholar
  3. 3.
    Paine MF, Hart HL, Ludington SS, Haining RL, Rettie AE, Zeldin DC. The human intestinal cytochrome P450 “pie”. Drug Metab Dispos. 2006;34(5):880–6.CrossRefGoogle Scholar
  4. 4.
    Drozdzik M, Busch D, Lapczuk J, Muller J, Ostrowski M, Kurzawski M, et al. Protein abundance of clinically relevant drug-metabolizing enzymes in the human liver and intestine: a comparative analysis in paired tissue specimens. Clin Pharmacol Ther. 2018;104(3):515–24.CrossRefGoogle Scholar
  5. 5.
    Takahashi M, Washio T, Suzuki N, Igeta K, Yamashita S. Investigation of the intestinal permeability and first-pass metabolism of drugs in cynomolgus monkeys using single-pass intestinal perfusion. Biol Pharm Bull. 2010;33(1):111–6.CrossRefGoogle Scholar
  6. 6.
    Wilkinson GR. Drug metabolism and variability among patients in drug response. N Engl J Med. 2005;352(21):2211–21.CrossRefGoogle Scholar
  7. 7.
    Yan Z, Caldwell GW. Metabolism profiling, and cytochrome P450 inhibition & induction in drug discovery. Curr Top Med Chem. 2001;1(5):403–25.CrossRefGoogle Scholar
  8. 8.
    Chu V, Einolf HJ, Evers R, Kumar G, Moore D, Ripp S, et al. In vitro and in vivo induction of cytochrome P450. Drug Metab Dispos. 2009;37(7):1339–54.CrossRefGoogle Scholar
  9. 9.
    Guillouzo A, Corlu A, Aninat C, Glaise D, Morel F, Guguen-Guillouzo C. The human hepatoma HepaRG cells: a highly differentiated model for studies of liver metabolism and toxicity of xenobiotics. Chem Biol Interact. 2007;168(1):66–73.CrossRefGoogle Scholar
  10. 10.
    Gerets HHJ, Tilmant K, Gerin B, Chanteux H, Depelchin BO, Dhalluin S, et al. Characterization of primary human hepatocytes, HepG2 cells, and HepaRG cells at the mRNA level and CYP activity in response to inducers and their predictivity for the detection of human hepatotoxins. Cell Biol Toxicol. 2012;28(2):69–87.CrossRefGoogle Scholar
  11. 11.
    Rowland A, Ruanglertboon W, van Dyk M, Wijayakumara D, Wood L, Meech R, et al. Plasma extracellular nanovesicle (exosome) derived biomarkers for ADME pathways: a novel approach to characterise variability in drug exposure. Br J Clin Pharmacol. 2018.Google Scholar
  12. 12.
    Bibi Z. Role of cytochrome P450 in drug interactions. Nutr Metab. 2008;5(1):27.CrossRefGoogle Scholar
  13. 13.
    Kenny JR, Ramsden D, Buckley DB, Dallas S, Fung C, Mohutsky M, et al. Considerations from the Innovation and Quality Induction Working Group in response to drug-drug interaction guidances from regulatory agencies: focus on CYP3A4 mRNA in vitro response thresholds, variability, and clinical relevance. Drug Metab Dispos. 2018;46(9):1285–303.CrossRefGoogle Scholar
  14. 14.
    FDA. Clinical drug interaction studies: study design, data analysis, and clinical implications guidance for Industry Center for Drug Evaluation and Research; 2017.Google Scholar
  15. 15.
    EMA. Guideline on the investigation of drug interactions In: (CHMP) CfHMP, editor. 2015.Google Scholar
  16. 16.
    Bolton AE, Peng B, Hubert M, Krebs-Brown A, Capdeville R, Keller U, et al. Effect of rifampicin on the pharmacokinetics of imatinib mesylate (Gleevec, STI571) in healthy subjects. Cancer Chemother Pharmacol. 2004;53(2):102–6.CrossRefGoogle Scholar
  17. 17.
    Pithavala YK, Tortorici M, Toh M, Garrett M, Hee B, Kuruganti U, et al. Effect of rifampin on the pharmacokinetics of axitinib (AG-013736) in Japanese and Caucasian healthy volunteers. Cancer Chemother Pharmacol. 2009;65(3):563.CrossRefGoogle Scholar
  18. 18.
    Bello C, Houk B, Sherman L, Sarapa N, Smeraglia J, Huang X, et al. The effect of rifampin on the pharmacokinetics of sunitinib malate (SU11248) in Caucasian and Japanese populations. Eur J Cancer (Oxford, England : 1990). 2005;3(2):430.Google Scholar
  19. 19.
    Yamazaki S, Johnson TR, Smith BJ. Prediction of drug-drug interactions with crizotinib as the CYP3A substrate using a physiologically based pharmacokinetic model. Drug Metab Dispos. 2015;43(10):1417–29.CrossRefGoogle Scholar
  20. 20.
    Ohnhaus EE, Breckenridge AM, Park BK. Urinary excretion of 6β-hydroxycortisol and the time course measurement of enzyme induction in man. Eur J Clin Pharmacol. 1989;36(1):39–46.CrossRefGoogle Scholar
  21. 21.
    van Dyk M, Marshall J, Sorich M, Wood L, Rowland A. Assessment of inter-racial variability in CYP3A4 activity and inducibility among healthy adult males. Eur J Clin Pharmacol. 2018;74:913–20. Scholar
  22. 22.
    Haas CE, Brazeau D, Cloen D, Booker BM, Frerichs V, Zaranek C, et al. Cytochrome P450 mRNA expression in peripheral blood lymphocytes as a predictor of enzyme induction. Eur J Clin Pharmacol. 2005;61(8):583–93.CrossRefGoogle Scholar
  23. 23.
    Hamilton M, Wolf JL, Drolet DW, Fettner SH, Rakhit AK, Witt K, et al. The effect of rifampicin, a prototypical CYP3A4 inducer, on erlotinib pharmacokinetics in healthy subjects. Cancer Chemother Pharmacol. 2014;73(3):613–21.CrossRefGoogle Scholar
  24. 24.
    Gashaw I, Kirchheiner J, Goldammer M, Bauer S, Seidemann J, Zoller K, et al. Cytochrome p450 3A4 ribonucleic acid induction by rifampin in peripheral blood mononuclear cells. Clin Pharmacol Ther. 2003;74(5):448–57.CrossRefGoogle Scholar
  25. 25.
    Ged C, Rouillon J, Pichard L, Combalbert J, Bressot N, Bories P, et al. The increase in urinary excretion of 6 beta-hydroxycortisol as a marker of human hepatic cytochrome P450IIIA induction. Br J Clin Pharmacol. 1989;28(4):373–87.CrossRefGoogle Scholar
  26. 26.
    Lau WC, Gurbel PA, Watkins PB, Neer CJ, Hopp AS, Carville DGM, et al. Contribution of hepatic cytochrome P450 3A4 metabolic activity to the phenomenon of clopidogrel resistance. Circulation. 2004;109(2):166–71.CrossRefGoogle Scholar
  27. 27.
    Yamashita F, Sasa Y, Yoshida S, Hisaka A, Asai Y, Kitano H, et al. Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data. PLoS One. 2013;8(9):e70330.CrossRefGoogle Scholar
  28. 28.
    Baneyx G, Parrott N, Meille C, Iliadis A, Lave T. Physiologically based pharmacokinetic modeling of CYP3A4 induction by rifampicin in human: influence of time between substrate and inducer administration. Eur J Pharm Sci. 2014;56:1–15.CrossRefGoogle Scholar
  29. 29.
    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.CrossRefGoogle Scholar
  30. 30.
    Jamei M, Marciniak S, Feng K, Barnett A, Tucker G, Rostami-Hodjegan A. The Simcyp® population-based ADME simulator. Expert Opin Drug Metab Toxicol. 2009;5(2):211–23.CrossRefGoogle Scholar
  31. 31.
    Rowland Yeo K, Jamei M, Yang J, Tucker GT, Rostami-Hodjegan A. Physiologically based mechanistic modelling to predict complex drug-drug interactions involving simultaneous competitive and time-dependent enzyme inhibition by parent compound and its metabolite in both liver and gut—the effect of diltiazem on the time-course of exposure to triazolam. Eur J Pharm Sci. 2010;39(5):298–309.CrossRefGoogle Scholar
  32. 32.
    Hanna D, Romero K, Schito M. Advancing tuberculosis drug regimen development through innovative quantitative translational pharmacology methods and approaches. Int J Infect Dis. 2017;56:208–11.CrossRefGoogle Scholar
  33. 33.
    Howgate EM, Rowland Yeo K, Proctor NJ, Tucker GT, Rostami-Hodjegan A. Prediction of in vivo drug clearance from in vitro data. I: impact of inter-individual variability. Xenobiotica. 2006;36(6):473–97.CrossRefGoogle Scholar
  34. 34.
    Polasek TM, Polak S, Doogue MP, Rostami-Hodjegan A, Miners JO. Assessment of inter-individual variability in predicted phenytoin clearance. Eur J Clin Pharmacol. 2009;65(12):1203–10.CrossRefGoogle Scholar
  35. 35.
    Wattanachai N, Polasek TM, Heath TM, Uchaipichat V, Tassaneeyakul W, Tassaneeyakul W, et al. In vitro-in vivo extrapolation of CYP2C8-catalyzed paclitaxel 6alpha-hydroxylation: effects of albumin on in vitro kinetic parameters and assessment of interindividual variability in predicted clearance. Eur J Clin Pharmacol. 2011;67(8):815–24.CrossRefGoogle Scholar
  36. 36.
    Almond LM, Mukadam S, Gardner I, Okialda K, Wong S, Hatley O, et al. Prediction of drug-drug interactions arising from CYP3A induction using a physiologically based dynamic model. Drug Metab Dispos. 2016;44(6):821–32.CrossRefGoogle Scholar
  37. 37.
    Bjorkhem-Bergman L, Backstrom T, Nylen H, Ronquist-Nii Y, Bredberg E, Andersson TB, et al. Comparison of endogenous 4beta-hydroxycholesterol with midazolam as markers for CYP3A4 induction by rifampicin. Drug Metab Dispos. 2013;41(8):1488–93.CrossRefGoogle Scholar
  38. 38.
    van Dyk M, Marshall JC, Sorich MJ, Wood LS, Rowland A. Assessment of inter-racial variability in CYP3A4 activity and inducibility among healthy adult males of Caucasian and South Asian ancestries. Eur J Clin Pharmacol. 2018;74(7):913–20.CrossRefGoogle Scholar
  39. 39.
    Soldin OP, Chung SH, Mattison DR. Sex differences in drug disposition. J Biomed Biotechnol. 2011;2011:187103.CrossRefGoogle Scholar
  40. 40.
    Wolbold R, Klein K, Burk O, Nussler AK, Neuhaus P, Eichelbaum M, et al. Sex is a major determinant of CYP3A4 expression in human liver. Hepatology. 2003;38(4):978–88.CrossRefGoogle Scholar
  41. 41.
    Prueksaritanont T, Chu X, Gibson C, Cui D, Yee KL, Ballard J, et al. Drug-drug interaction studies: regulatory guidance and an industry perspective. AAPS J. 2013;15(3):629–45.CrossRefGoogle Scholar
  42. 42.
    FDA. Guidance for industry. Drug interaction studies—study design, data analysis, implications for dosing, and labeling recommendations. 2012.Google Scholar
  43. 43.
    Darwich AS, Aslam U, Ashcroft DM, Rostami-Hodjegan A. Meta-analysis of the turnover of intestinal epithelia in preclinical animal species and humans. Drug Metab Dispos. 2014;42(12):2016–22.CrossRefGoogle Scholar
  44. 44.
    Neuhoff S, Tucker GT. Was 4beta-hydroxycholesterol ever going to be a useful marker of CYP3A4 activity? Br J Clin Pharmacol. 2018;84(7):1620–1.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  • Asha J Kapetas
    • 1
  • Michael J Sorich
    • 1
  • A David Rodrigues
    • 2
  • Andrew Rowland
    • 1
    Email author
  1. 1.College of Medicine and Public HealthFlinders UniversityAdelaideAustralia
  2. 2.ADME Sciences, Medicine DesignPfizer Inc.GrotonUSA

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