Effect of amiodarone on the plasma levels of metoprolol

Werner, Dierk; Wuttke, H; Fromm, Martin F.; Schaefer, Simon; Eschenhagen, Thomas; Brune, Kay; Daniel, Werner G.; Werner, Ulrike

doi:10.1016/j.amjcard.2004.07.125

Cited by 37 publications

(18 citation statements)

References 14 publications

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“…For an inhibitor, D 50 may be calculated if IX is known for a single-dose level. For an inducer, D 50 and IX max may be calculated if IX is known for two-dose levels. Hence, the AUC ratios for the victim drug may be calculated for different dose levels of interacting drugs.…”

Section: Discussionmentioning

confidence: 99%

Impact of Genetic Polymorphism on Drug-Drug Interactions Mediated by Cytochromes: A General Approach

Tod

Nkoud-Mongo

Gueyffier

2013

AAPS J

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Abstract. Currently, quantitative prediction of the impact of genetic polymorphism and drug-drug interactions mediated by cytochromes, based on in vivo data, is made by two separate methods and restricted to a single cytochrome. We propose a unified approach for describing the combined impact of drug-drug interactions and genetic polymorphism on drug exposure. It relies on in vivo data and uses the following three characteristic parameters: one for the victim drug, one for the interacting drug, and another for the genotype. These parameters are known for a wide range of drugs and genotypes. The metrics of interest are the ratio of victim drug area under the curve (AUC) in patients with genetic variants taking both drugs, to the AUC in patients with either variant or wild-type genotype taking the victim drug alone. The approach was evaluated by external validation, comparing predicted and observed AUC ratios found in the literature. Data were found for 22 substrates, 30 interacting drugs, and 38 substrate-interacting drug couples. The mean prediction error of AUC ratios was 0.02, and the mean prediction absolute error was 0.38 and 1.34, respectively. The model may be used to predict the variations in exposure resulting from a number of drug-drug-genotype combinations. The proposed approach will help (1) to identify comedications and population at risk, (2) to adapt dosing regimens, and (3) to prioritize the clinical pharmacokinetic studies to be done.

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Section: Discussionmentioning

confidence: 99%

Impact of Genetic Polymorphism on Drug-Drug Interactions Mediated by Cytochromes: A General Approach

Tod

Nkoud-Mongo

Gueyffier

2013

AAPS J

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“…Four specific P450 enzymes are implicated in the majority of these metabolism-dependent in vivo DDIs: CYP1A2 (lidocaine and theophylline), CYP2C9 (S-warfarin), CYP2D6 (metoprolol and flecainide), and CYP3A4 (cyclosporine A and simvastatin) (Soto et al, 1990;Nicolau et al, 1992;Chitwood et al, 1993;Funck-Brentano et al, 1994;Ha et al, 1996;Trujillo and Nolan, 2000;Orlando et al, 2004;Werner et al, 2004;Becquemont et al, 2007;Thi et al, 2009). AMIO, itself, appears to be a fairly weak in vitro inhibitor of these enzymes (Kobayashi et al, 1998;Ohyama et al, 2000), which raises the possibility that inhibitory metabolites play a more direct role than the parent drug.…”

Section: Introductionmentioning

confidence: 99%

P450-Based Drug-Drug Interactions of Amiodarone and its Metabolites: Diversity of Inhibitory Mechanisms

McDonald

Rettie

2015

Drug Metab Dispos

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In this study, IC 50 shift and time-dependent inhibition (TDI) experiments were carried out to measure the ability of amiodarone (AMIO), and its circulating human metabolites, to reversibly and irreversibly inhibit CYP1A2, CYP2C9, CYP2D6, and CYP3A4 activities in human liver microsomes. The [I] u /K i,u values were calculated and used to predict in vivo AMIO drug-drug interactions (DDIs) for pharmaceuticals metabolized by these four enzymes. Based on these values, the minor metabolite N,N-didesethylamiodarone (DDEA) is predicted to be the major cause of DDIs with xenobiotics primarily metabolized by CYP1A2, CYP2C9, or CYP3A4, while AMIO and its N-monodesethylamiodarone (MDEA) derivative are the most likely cause of interactions involving inhibition of CYP2D6 metabolism. AMIO drug interactions predicted from the reversible inhibition of the four P450 activities were found to be in good agreement with the magnitude of reported clinical DDIs with lidocaine, warfarin, metoprolol, and simvastatin. The TDI experiments showed DDEA to be a potent inactivator of CYP1A2 (K I = 0.46 mM, k inact = 0.030 minute 21 ), while MDEA was a moderate inactivator of both CYP2D6 (K I = 2.7 mM, k inact = 0.018 minute 21) and CYP3A4 (K I = 2.6 mM, k inact = 0.016 minute 21). For DDEA and MDEA, mechanism-based inactivation appears to occur through formation of a metabolic intermediate complex.Additional metabolic studies strongly suggest that CYP3A4 is the primary microsomal enzyme involved in the metabolism of AMIO to both MDEA and DDEA. In summary, these studies demonstrate both the diversity of inhibitory mechanisms with AMIO and the need to consider metabolites as the culprit in inhibitory P450-based DDIs.

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“…AMIO has documented clinical DDIs with comedications, including cyclosporine A, simvastatin, dextromethorphan, metoprolol, and warfarin. It was reported that multiple doses of AMIO (200-400 mg) increased the exposure of simvastatin by 1.7-fold, dextromethorphan by 1.3-to 2-fold, and warfarin by 1.2-to 2-fold (Heimark et al, 1992;Werner et al, 2004;Becquemont et al, 2007). These clinically observed DDIs were not expected based on in vitro competitive inhibition data from AMIO itself (K i .…”

Section: Introductionmentioning

confidence: 68%

“…The DDI simulations were conducted using the dose regimen described in the clinical DDI study reports (O'Reilly et al, 1987;Heimark et al, 1992;Werner et al, 2004;Becquemont et al, 2007). In brief, the interactions between AMIO [300 mg, every day (QD) for 13 days or 200 mg twice a day (BID) for 14 days] and warfarin (0.75 mg/kg, day 4), AMIO (1200 mg, QD for 6 days) and metoprolol (119 mg, QD, day 6), and AMIO (400 mg, QD for 4 days) and simvastatin (40 mg, day 4) were simulated with 10 trials each containing 5, 10, and 12 subjects, respectively.…”

Section: Simulation Of Pk and Ddimentioning

confidence: 99%

Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions Involving Inhibitory Metabolite: A Case Study of Amiodarone

Chen¹,

Mao²,

Hop³

2014

Drug Metab Dispos

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Evaluation of drug-drug interaction (DDI) involving circulating inhibitory metabolites of perpetrator drugs has recently drawn more attention from regulatory agencies and pharmaceutical companies. Here, using amiodarone (AMIO) as an example, we demonstrate the use of physiologically based pharmacokinetic (PBPK) modeling to assess how a potential inhibitory metabolite can contribute to clinically significant DDIs. Amiodarone was reported to increase the exposure of simvastatin, dextromethorphan, and warfarin by 1.2-to 2-fold, which was not expected based on its weak inhibition observed in vitro. The major circulating metabolite, mono-desethyl-amiodarone (MDEA), was later identified to have a more potent inhibitory effect. Using a combined "bottom-up" and "top-down" approach, a PBPK model was built to successfully simulate the pharmacokinetic profile of AMIO and MDEA, particularly their accumulation in plasma and liver after a long-term treatment. The clinical AMIO DDIs were predicted using the verified PBPK model with incorporation of cytochrome P450 inhibition from both AMIO and MDEA. The closest prediction was obtained for CYP3A (simvastatin) DDI when the competitive inhibition from both AMIO and MDEA was considered, for CYP2D6 (dextromethorphan) DDI when the competitive inhibition from AMIO and the competitive plus time-dependent inhibition from MDEA were incorporated, and for CYP2C9 (warfarin) DDI when the competitive plus time-dependent inhibition from AMIO and the competitive inhibition from MDEA were considered. The PBPK model with the ability to simulate DDI by considering dynamic change and accumulation of inhibitor (parent and metabolite) concentration in plasma and liver provides advantages in understanding the possible mechanism of clinical DDIs involving inhibitory metabolites.

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Effect of amiodarone on the plasma levels of metoprolol

Cited by 37 publications

References 14 publications

Impact of Genetic Polymorphism on Drug-Drug Interactions Mediated by Cytochromes: A General Approach

Impact of Genetic Polymorphism on Drug-Drug Interactions Mediated by Cytochromes: A General Approach

P450-Based Drug-Drug Interactions of Amiodarone and its Metabolites: Diversity of Inhibitory Mechanisms

Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions Involving Inhibitory Metabolite: A Case Study of Amiodarone

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