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

Abstract

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.

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

“…Not surprisingly, since kinetic inhibition parameters can often differ considerably depending on the literature source, there are some significant differences in the K i , K I , and k inact values the authors used to generate their predictions compared with those reported here. However, it is of particular interest that, considering only AMIO and MDEA inhibition parameters, the model of Chen et al, (2015) appears to substantially underpredict the wellknown warfarin-AMIO DDI. This discrepancy is in line with our contention that the minor AMIO metabolite, DDEA, is the primary culprit in AMIO DDIs involving CYP2C9.…”

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification

“…For example, more complex absorption models such as advanced dissolution, absorption, and metabolism (ADAM) models (Jamei et al, 2009b) and advanced compartmental absorption and transit (ACAT) models (Agoram et al, 2001) have been developed that enable the use of PBPK modeling for the simulation of food effects (Shono et al, 2009;Turner et al, 2012;Heimbach et al, 2013;Xia et al, 2013b;Patel et al, 2014;Zhang et al, 2014), the impact of drug properties on absorption kinetics (Kambayashi et al, 2013;Parrott et al, 2014), and intestinal interactions (Fenneteau et al, 2010). The development of sophisticated models that allow for the simulation of multiple inhibitors or inducers, relevant metabolites, and multiple mechanisms of interaction have permitted the prediction of complex DDIs involving enzymes, transporters, and multiple interaction mechanisms Reki c et al, 2011;Varma et al, 2012Varma et al, , 2013Dhuria et al, 2013;Gertz et al, 2013Gertz et al, , 2014Guo et al, 2013;Kudo et al, 2013;Siccardi et al, 2013;Wang et al, 2013a;Sager et al, 2014;Chen et al, 2015;Shi et al, 2015). Furthermore, the mechanistic understanding of ADME changes that occur in different age groups or disease states has improved, and consequently PBPK modeling has been used to simulate drug disposition in special populations including hepatic (Johnson et al, 2014) and renal impairment populations (Li et al, 2012;Zhao et al, 2012a;Lu et al, 2014;Sayama et al, 2014), children (Leong et al, 2012), and pregnant women (Andrew et al, 2008;Gaohua et al, 2012;…”

A Physiologically Based Pharmacokinetic Model of Amiodarone and its Metabolite Desethylamiodarone in Rats: Pooled Analysis of Published Data

“…The pony PBPK model [58] consisted of limited number of tissue compartments, including blood, liver, kidney, fat, rapidly perfused tissues, and slowly perfused tissues. Although the human PBPK model [59] included more tissues, it may not provide insight into the kinetic behaviors of AMD in tissues since the predicted concentration-time profiles in most tissues cannot be evaluated due to the lack of observed data. Apparently, gaining insight into the kinetic behaviors of a drug in more organs is highly desirable, particularly in therapeutic target organs and those to which the compound may be potentially harmful.…”

“…One model [58] described the disposition of AMD and DEA in ponies and another one [59] was developed in humans. The pony PBPK model [58] consisted of limited number of tissue compartments, including blood, liver, kidney, fat, rapidly perfused tissues, and slowly perfused tissues.…”