Laura Maria Fuhr (1), Fatima Zahra Marok (1) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, 66123 Saarbruecken
Introduction: Grapefruit juice is listed by the FDA as strong inhibitor of cytochrome P450 (CYP) 3A4 [1] which metabolizes up to 50% of marketed drugs. The inhibition is mainly limited to the gastrointestinal tract [2] and can be attributed to furanocoumarin ingredients, that inhibit CYP3A4 irreversibly. Especially orally administered drugs with a high CYP3A4 related first pass metabolism bare a high risk of interaction [3]. For example, pronounced increases in felodipine (up to 3.3-fold [2,4,5]) and simvastatin (up to 16-fold [6–8]) area under the plasma concentration-time curve (AUC) can be observed after grapefruit intake. As the inhibition is irreversible, moderate inhibitory effects are still observed up to 24h after grapefruit consumption [9]. Physiologically-based pharmacokinetic (PBPK) modeling can be helpful to predict the effect of grapefruit and to assess the potential risk for patients regarding established and new compounds.
Objectives:
- To develop a grapefruit model, represented by whole-body PBPK models of bergamottin and 6,7-dihydroxybergamottin as main CYP3A4 inhibitors
- To describe and predict grapefruit-drug interactions (DGIs) with various CYP3A4 substrates, namely felodipine, midazolam, alprazolam, triazolam, itraconazole, carbamazepine, alfentanil and simvastatin.
Methods: The PBPK model was developed with PK-Sim® and MoBi® (Version 9.1, Open Systems Pharmacology [10]). An extensive literature research was conducted to identify the main CYP3A4 inhibitors in grapefruit and to collect information on their pharmacokinetics, inhibitory potency as well as plasma concentration measurements for model development. Additionally, plasma concentration-time profiles of victim drugs without and with grapefruit juice intake were extracted from clinical studies. Based on the research, PBPK models of bergamottin and 6,7-dihydroxybergamottin were developed. The models were coupled with a PBPK model of felodipine, to refine model parameters and to evaluate the CYP3A4 inhibition. Subsequently, the PBPK models were applied to predict GDIs with further CYP3A4 victim drugs for which PBPK models were previously established [11–15]. To assess model performance, predicted victim drug plasma concentration-time profiles without and with grapefruit consumption and predicted GDI AUClast ratios and GDI Cmax ratios were compared to corresponding observed data and geometric mean fold errors (GMFEs) of all GDI AUClast and Cmax ratios were calculated.
Results: A total of 37 clinical studies, providing 87 GDI plasma concentration-time profiles, was used for the establishment of the grapefruit PBPK model. Grapefruit is represented by PBPK models of bergamottin and 6,7-dihydroxybergamottin. Intake of grapefruit juice was simulated by oral administration of modeled ingredients, while the ingested doses were calculated based on the concentration in grapefruit juice and the juice volume. Both PBPK models apply (1) an unspecific clearance process and (2) mechanism-based inhibition of CYP3A4. The clearance values were estimated, while the mechanism-based inhibition was parametrized using literature values. Interactions with grapefruit juice were predicted (1) for a broad range of CYP3A4 substrates, (2) after oral or intravenous administrations, (3) for single or frequent intake of grapefruit juice, (4) as single or double strength preparation and (5) for different time intervals between grapefruit and victim drug administration. Overall, the grapefruit model shows a good performance in GDI predictions as indicated by GMFEs of 1.31 (1.00 – 3.62) and 1.31 (1.00 – 2.16) for predicted DDI AUClast and Cmax ratios.
Conclusion: A grapefruit PBPK model was successfully developed. Although it is known that the grapefruit effect cannot be attributed to single ingredients but to the overall combination of ingredients, the establishment of PBPK models for bergamottin and 6,7-dihydroxybergamottin was sufficient to describe the grapefruit-effect for several CYP3A4 substrates. The model can be applied to predict the effect of grapefruit juice on CYP3A4 victim drugs and draws the attention to the potential risks of grapefruit consumption in drug treatment.
Funding: The project has received support from the project “Open-source modeling framework for automated quality control and management of complex life science systems models” (OSMOSES), which is funded by the German Federal Ministry of Education and Research (BMBF, grant ID: 031L0161C).
References:
[1] U.S. Food and Drug Administration. Table of Substrates, Inhibitors and Inducers https://www.fda.gov/drugs/drug-interactions-labeling/drug-development-and-drug-interactions-table-substrates-inhibitors-and-inducers. Accessed 2022 Mar 01
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[10] Open Systems Pharmacology. www.open-systems-pharmacology.org. Accessed: 2022 Apr 01.
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[12] Fuhr LM, Marok FZ, Hanke N, et al. Pharmacokinetics of the CYP3A4 and CYP2B6 inducer carbamazepine and its drug-drug interaction potential: a physiologically based pharmacokinetic modeling approach. Pharmaceutics. 2021;13(2):1–21.
[13] Wojtyniak J, Selzer D, Schwab M, et al. Physiologically based precision dosing approach for drug‐drug‐gene interactions: a simvastatin network analysis. Clin Pharmacol Ther. 2020;cpt.2111.
[14] Alprazolam PBPK model. https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/tree/master/Alprazolam. Accessed: 2022 Apr 1].
[15] Triazolam PBPK mode. https://github.com/Open-Systems-Pharmacology/OSP-PBPK-Model-Library/tree/master/Triazolam. Accessed: 2022 Apr 1
Reference: PAGE 30 (2022) Abstr 10147 [www.page-meeting.org/?abstract=10147]
Poster: Drug/Disease Modelling - Absorption & PBPK