Physiologically-based pharmacokinetic (PBPK) modeling of alfentanil as a CYP3A4 victim drug
Nina Hanke (1), Sebastian Frechen (2), Thomas Eissing (2), Thomas Wendl (2) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Bayer AG, Systems Pharmacology & Medicine, Leverkusen, Germany
Objectives: 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. Alfentanil is a sensitive CYP3A4 substrate (≥ 10-fold increase of AUC with strong inhibitors) and recommended by the FDA for the assessment of the DDI potential of investigational new drugs [1]. Our objective was to establish a full body PBPK model of alfentanil and to demonstrate its ability to predict the rifampicin-alfentanil DDI.
Methods: PBPK models of alfentanil and rifampicin were built in PK-Sim® modeling software (Version 6.3.2) as part of the Open Systems Pharmacology Suite [2, 3]. Alfentanil drug-dependent parameters as well as plasma and urine concentration-time profiles of various clinical studies (dosing range 15-50 µg/kg as intravenous and 60-75 µg/kg as oral application) were obtained from literature and used to establish a model accurately describing and predicting observed clinical data. The alfentanil model was then coupled to a previously established rifampicin model [4], clinical DDI studies were predicted and the results were compared to published observed data.
Results: Model development was accomplished with data of 3 clinical studies as an internal dataset; model evaluation was performed with an external dataset of 4 different trials. The newly developed alfentanil model applies metabolism by CYP3A4. The passive glomerular filtration rate was reduced to a fraction of 0.06 to recover the low urinary excretion of approximately 0.3% as unchanged drug [5]. Although in clinical use alfentanil is administered solely in intravenous form, some DDI studies published plasma concentrations of alfentanil after oral application. Colonic absorption was disabled in our model, as late absorption was not consistent with the reported concentration time-profiles after oral administration.
Simulation of 12 different DDI scenarios with the coupled models generates alfentanil plasma concentration-time profiles during rifampicin treatment that are in very good agreement with observed data. Predicted AUC ratios (AUC with rifampicin /AUC without) show a low fold bias of 1.19 (geometric mean fold absolute deviation, range 1.01-1.84, n=12).
Conclusions: We provide a full body PBPK model of alfentanil as a tool for the drug development process for dynamic evaluation of the DDI potential of investigational drugs that are CYP3A4 inducers or inhibitors.
References:
[1] U.S. Department of Health and Human Services, Food and Drug Administration Center for Drug Evaluation and Research (CDER). Drug interaction studies - Study design, data analysis, implications for dosing, and labeling recommendations. (2012).
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[3] www.open-systems-pharmacology.org.
[4] Hanke N, Frechen S, Britz H, Moj D, Kanacher T, Eissing T, Wendl T, Lehr T. Physiologically-based pharmacokinetic modeling of rifampin drug-drug interactions with midazolam and digoxin. PAGE 25 (2016) Abstr 5929.
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