Jan-Georg Wojtyniak (1,2), Simeon Rüdesheim (1), Roman Tremmel (2), Matthias Schwab (2,3,4) and Thorsten Lehr
(1) Clinical Pharmacy, Saarland University, Saarbrücken, Germany, (2) Dr. Margarete Fischer-Bosch-Institut für Klinische Pharmakologie, Stuttgart, Germany, (3) Department of Clinical Pharmacology, University Hospital Tübingen, Tübingen, Germany, (4) Department of Pharmacy and Biochemistry, University Tübingen, Tübingen, Germany
Introduction: Metoprolol is on the World Health Organization’s “List of Essential Medicines” and the most frequently prescribed β1-receptor blocker in Germany[1,2]. However, altered metoprolol exposure can occur due to concomitant use of metoprolol with CYP2D6 inhibitors or inducers (drug-drug interaction (DDI)) or due to CYP2D6 polymorphisms (drug-gene interaction (DGI))[3,4]. Following, this can reduce therapy efficacy or lead to severe adverse drug events (ADE) like hypotension, bradycardia and cardiorespiratory arrest[3]. To overcome this, physiologically-based pharmacokinetic (PBPK) modelling can be applied as a valuable tool for quantifying DDI and DGI effects and following, for the development of dose recommendations under different DDI/DGI scenarios[5].
Objectives:
- Development of PBPK models for metoprolol given as racemate, the enantiomers R-metoprolol and S-metoprolol, its metabolite α-hydroxymetoprolol and paroxetine as a CYP2D6 inhibitor
- Subsequently, prediction of both, DDI and DGI effects
- Development of metoprolol dose recommendations for various DDI/DGI situations
Methods: PBPK model development was performed with PK-Sim® and MoBi® (version 7.3.0) as part of the Open Systems Pharmacology Suite[6]. Data for model development were extracted from literature, including physicochemical parameters and plasma concentration-time profiles for all compounds and for various CYP2D6 genotypes. Data were separated in internal and external data for model development and evaluation, respectively. After individual development of a metoprolol and a paroxetine model they were coupled to predict the DDI effects of concomitant use. Finally, the models were used for dose optimization. For this purpose, exposure of 200 mg metoprolol q.d. as the area under the plasma concentration-time curve (AUC) at stead-state was simulated as reference value. Afterwards, exposures were simulated for different CYP2D6 metabolizers with and without concomitant administration of 40 mg paroxetine q.d. at steady-state adapting the dose stepwise until matching exposure compared to placebo was reached.
Results: The final model was capable to describe all plasma concentration time profiles satisfactorily with a mean AUC ratio predicted versus observed of 0.87, 0.93, 0.96, 1.0, 1.2 for all R-metoprolol, S-metoprolol, racemic metoprolol, α -hydroxymetoprolol and paroxetine profiles, respectively. Based on CYP2D6 activity scores CYP2D6 poor- (PM), extensive- (EM) and ultrarapid-metabolizer (UM) profiles could be predicted successfully with mean DGI ratios (predicted AUC ratio PM or UM to EM versus observed AUC ratio PM or UM to EM) of 1.18 for PM and 1.22 for UM. Furthermore, an effective coupling enabled the forecast of DDI effects of concomitant use of paroxetine with metoprolol. The DDI mean ratio (predicted AUC ratio metoprolol + paroxetine to metoprolol versus observed AUC ratio metoprolol + paroxetine to metoprolol) was 1.42 and hence, within the common acceptance criterion of a twofold deviation. Dose optimization results were in agreement with existing guidelines for metoprolol dose adaption under various DGI conditions. For example, according to the Dutch Pharmacogenetics Working Group (DPWG) guideline metoprolol dose for UMs should be increased up to 250% whereas the dose for PMs should be reduced by 75%[7]. For the same scenario, model simulations resulted in an increase up to 200% for UMs and a dose reduction of 75% for PMs. Apart from this, model simulations revealed that metoprolol dose should be reduced for all genotypes by 75% if metoprolol is given together with paroxetine.
Conclusion: A functional metoprolol PBPK model to evaluate the pharmacokinetic effects due to CYP2D6 DDIs and DGIs was developed. Overall the model holds the potential for a more personalized medicine by reducing the risk of ADEs or therapy failure.
Funding: Supported by the Robert Bosch Stiftung (Stuttgart, Germany) and the European Commission Horizon 2020 UPGx grant 668353
References:
[1] World Health Organization. WHO Model List of Essential Medicines. 20th list (2017).
[2] Schwabe U et al. Arzneiverordnungs-Report 2018: Aktuelle Daten, Kosten, Trends und Kommentare (2018).
[3] Bahar A M et al. The impact of CYP2D6 mediated drug–drug interaction: a systematic review on a combination of metoprolol andparoxetine/fluoxetine. Br J Clin Pharmacol (2018) 84(12):2704-2715.
[4] Blake C M et al. A meta-analysis of CYP2D6 metabolizer phenotype and metoprolol pharmacokinetics. Clin Pharmacol Ther (2013) 94(3):394-399.
[5] U.S. Food and Drug Administration. Clinical Drug Interaction Studies – Study Design, Data Analysis, Implications for Dosing, and Labeling Recommendations. Draft Guidance for Industry. (2017)
[6] Eissing T et al. 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.
[7] Swen J J et al. Pharmacogenetics: From bench to byte an update of guidelines. Clin Pharmacol Ther (2011) 89(5): 662-673.
Reference: PAGE 28 (2019) Abstr 8815 [www.page-meeting.org/?abstract=8815]
Poster: Drug/Disease Modelling - Absorption & PBPK