Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

Caifu Xue; Xunjie Zhang; Weimin Cai

doi:10.3390/pharmaceutics10010001

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

Pharmaceutics. 2017 Dec 21;10(1):1. doi: 10.3390/pharmaceutics10010001.

Authors

Caifu Xue¹, Xunjie Zhang², Weimin Cai³

Affiliations

¹ Department of Clinical Pharmacy and Pharmaceutical Management, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China. 15111030045@fudan.edu.cn.
² Department of Clinical Pharmacy and Pharmaceutical Management, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China. 12307120290@fudan.edu.cn.
³ Department of Clinical Pharmacy and Pharmaceutical Management, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, China. weimincai@fudan.edu.cn.

Abstract

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C_max) area under the concentration-time curve (AUC) values and C_max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C_max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Keywords: bupropion; drug-drug interactions; erythrohydrobupropion; hydroxybupropion; inhibitory metabolites; physiologically based pharmacokinetic model; threohydrobupropion.