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Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency.

ABSTRACT: The potential of metabolites to contribute to drug-drug interactions (DDIs) is not well defined. The aim of this study was to determine the quantitative role of circulating metabolites in inhibitory DDIs in vivo. The area under the plasma concentration-time curve (AUC) data related to at least one circulating metabolite was available for 71% of the 102 inhibitor drugs identified. Of the 80 metabolites characterized at steady state, 78% had AUCs >10% of that of the parent drug. A comparison of the inhibitor concentration/inhibition constant ([I]/K(i)) ratios of metabolites and the respective parent drugs showed that 17 of the 21 (80%) reversible inhibitors studied had metabolites that were likely to contribute to in vivo DDIs, with some metabolites predicted to have inhibitory effects greater than those of the parent drug. The in vivo drug interaction risks associated with amiodarone, bupropion, and sertraline could be identified from in vitro data only, when data pertaining to metabolites were included in the predictions. In conclusion, cytochrome P450 (CYP) inhibitors often have circulating metabolites that contribute to clinically observed CYP inhibition.

SUBMITTER: Yeung CK

PROVIDER: S-EPMC3474849 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency.

Yeung C K CK Fujioka Y Y Hachad H H Levy R H RH Isoherranen N N

Clinical pharmacology and therapeutics 20101201 1

The potential of metabolites to contribute to drug-drug interactions (DDIs) is not well defined. The aim of this study was to determine the quantitative role of circulating metabolites in inhibitory DDIs in vivo. The area under the plasma concentration-time curve (AUC) data related to at least one circulating metabolite was available for 71% of the 102 inhibitor drugs identified. Of the 80 metabolites characterized at steady state, 78% had AUCs >10% of that of the parent drug. A comparison of th ...[more]

PMID: 21124313

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Project description: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.

Dataset Information

Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency.

Publications

Are circulating metabolites important in drug-drug interactions?: Quantitative analysis of risk prediction and inhibitory potency.

Similar Datasets

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