Nina Hanke (1), Sebastian Frechen (2), Hannah Britz (1), Daniel Moj (1), Tobias Kanacher (2), Thomas Eissing (2), Thomas Wendl (2) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Bayer Technology Services GmbH, Systems Pharmacology CV, Leverkusen, Germany
Objectives: Physiologically-based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict the magnitude of drug-drug interactions (DDIs) and may even offer an alternative to dedicated clinical studies. Rifampin is an established potent inducer of multiple drug metabolizing enzymes and transporters. Therefore, the FDA recommends rifampin as inducer for the assessment of the DDI potential of investigational new drugs[1]. Our objective was to build and evaluate PBPK models of rifampin and digoxin to predict the DDIs of rifampin with midazolam (CYP3A4 substrate) and digoxin (P-gp substrate).
Methods: PBPK models of rifampin and digoxin were built in PK-Sim® modeling software (Version 6.0.3)[2]. Drug-dependent parameters as well as plasma-, urine- and bile concentration-time profiles of various clinical studies (broad dosing range, intravenous (iv) and oral (po) application, single- and multiple-dosing) were obtained from literature and used to establish models accurately describing and predicting observed clinical study data. Processes mediating the induction and simultaneous competitive inhibition of CYP3A4 and P-gp were integrated into the rifampin model[3-6]. Finally, the rifampin model was coupled to the digoxin model and a previously developed midazolam model[7].
Results: Our new rifampin model applies transport processes (P-gp and OATP1B1), metabolism by arylacetamide deacetylase (AADAC) and glomerular filtration. Auto-induction of P-gp and AADAC by rifampin was taken into account. The newly developed digoxin model features P-gp transport in various organs including gut, liver and kidney. Implementation of target binding (Na+/K+-ATPase) was crucial to accurately describe published plasma concentrations after iv and po administration of digoxin. Simulation of the DDIs with the coupled models generates midazolam and digoxin plasma concentration-time profiles during rifampin treatment that are in good agreement with observed data. Predicted AUC ratios (AUC with rifampin /AUC without) show an acceptable fold bias of 1.39 (geometric mean fold absolute deviation, range 1.00-1.96, N=5) for midazolam and of 1.04 (range 1.02-1.07, N=4) for digoxin.
Conclusion: We provide PBPK models of rifampin and digoxin as tools for the drug development process to evaluate the DDI potential of investigational drugs that are CYP3A4 or P-gp substrates (coupling to the rifampin model) or P-gp inducers or inhibitors (coupling to the digoxin model).
References: [1] Drug Interaction Studies – Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations. 2012. U.S. Department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER).
[2] Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, Gaub T, Goerlitz L, Jaeger J, Loosen R, Ludewig B, Meyer M, Niederalt C, Sevestre M, Siegmund H, Solodenko J, Thelen K, Telle U, Weiss W, Wendl T, Willmann S, Lippert J. A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol (2011) 2: 4.
[3] Templeton IE, Houston JB, Galetin A. Predictive utility of in vitro rifampin induction data generated in fresh and cryopreserved human hepatocytes, Fa2N-4, and HepaRG cells. Drug Metab Dispos (2011) 39(10): 1921-9.
[4] Greiner B, Eichelbaum M, Fritz P, Kreichgauer HP, von Richter O, Zundler J, Kroemer HK. The role of intestinal P-glycoprotein in the interaction of digoxin and rifampin. J Clin Invest (1999) 104(2): 147-53.
[5] Kajosaari LI, Laitila J, Neuvonen PJ, Backman JT. Metabolism of repaglinide by CYP2C8 and CYP3A4 in vitro: effect of fibrates and rifampicin. Basic Clin Pharmacol Toxicol (2005) 97(4): 249-56.
[6] Reitman ML, Chu X, Cai X, Yabut J, Venkatasubramanian R, Zajic S, Stone JA, Ding Y, Witter R, Gibson C, Roupe K, Evers R, Wagner JA, Stoch A. Rifampin’s acute inhibitory and chronic inductive drug interactions: experimental and model-based approaches to drug-drug interaction trial design. Clin Pharmacol Ther (2011) 89(2): 234-42.
[7] Wendl T, Frechen S, Teutonico D, Hermes HE, Pilari S, Eissing T. A whole-body physiologically-based pharmacokinetic (PBPK) model for itraconazole and its metabolite to predict dynamic drug-drug-interactions. PAGE 24 (2015) Abstr 3517.
Reference: PAGE 25 (2016) Abstr 5929 [www.page-meeting.org/?abstract=5929]
Poster: Drug/Disease modeling - Absorption & PBPK