Dataset Information

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

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 (Cmax) area under the concentration-time curve (AUC) values and Cmax 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 Cmax 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.

SUBMITTER: Xue C

PROVIDER: S-EPMC5874814 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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

Xue Caifu C Zhang Xunjie X Cai Weimin W

Pharmaceutics 20171221 1

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" ...[more]

PMID: 29267251

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Project description:Eliglustat is a glucosylceramide synthase inhibitor indicated as a long-term substrate reduction therapy for adults with type 1 Gaucher disease, a lysosomal rare disease. It is primarily metabolized by cytochrome P450 2D6 (CYP2D6), and variants in the gene encoding this enzyme are important determinants of eliglustat pharmacokinetics (PK) and drug-drug interactions (DDIs). The existing drug label addresses the DDIs to some extent but has omitted scenarios where both metabolizing CYPs (2D6 and 3A4) are mildly or moderately inhibited. The objectives of this study were (i) to develop and validate an eliglustat physiologically-based pharmacokinetic (PBPK) model with and without drug interactions, (ii) to simulate untested DDI scenarios, and (iii) to explore potential dosing flexibility using lower dose strength of eliglustat (commercially not available). PK data from healthy adults receiving eliglustat with or without interacting drugs were obtained from literature and used for the PBPK model development and validation. The model-predicted single-dose and steady-state maximum concentration (Cmax ) and area under the concentration-time curve (AUC) of eliglustat were within 50-150% of the observed values when eliglustat was administered alone or coadministered with ketoconazole or paroxetine. Then as model-based simulations, we illustrated eliglustat exposure as a victim of interaction when coadministered with fluvoxamine following the US Food and Drug Administration (FDA) dosing recommendations. Second, we showed that with lower eliglustat doses (21 mg, 42 mg once daily) the exposure in participants of intermediate and poor metabolizer phenotypes was within the outlined safety margin (Cmax <250 ng/mL) when eliglustat was administered with ketoconazole, where the current recommendation is a contraindication of coadministration (84 mg). The present study demonstrated that patients with CYP2D6 deficiency may benefit from lower doses of eliglustat.

Dataset Information

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

Publications

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

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