Dataset Information

Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.

ABSTRACT: Fenebrutinib is a CYP3A substrate and time-dependent inhibitor, as well as a BCRP and OATP1B transporter inhibitor in vitro. Physiologically-based pharmacokinetic (PBPK) modeling strategies with the ultimate goal of understanding complex drug-drug interactions (DDIs) and proposing doses for untested scenarios were developed. The consistency in the results of two independent approaches, PBPK simulation and endogenous biomarker measurement, supported that the observed transporter DDI is primarily due to fenebrutinib inhibition of intestinal BCRP, rather than hepatic OATP1B. A mechanistic-absorption model accounting for the effects of excipient complexation with fenebrutinib was used to rationalize the unexpected observation of itraconazole-fenebrutinib DDI (maximum plasma concentration (C_max ) decreased, and area under the curve (AUC) increased). The totality of the evidence from sensitivity analysis and clinical and nonclinical data suggested that fenebrutinib is likely a sensitive CYP3A substrate. This advanced PBPK application allowed the use of model-informed approach to facilitate the development of concomitant medication recommendations for fenebrutinib without requiring additional clinical DDI studies.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7306618 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.

Chen Yuan Y Ma Fang F Jones Nicholas S NS Yoshida Kenta K Chiang Po-Chang PC Durk Matthew R MR Wright Matthew R MR Jin Jin Yan JY Chinn Leslie W LW

CPT: pharmacometrics & systems pharmacology 20200529 6

Fenebrutinib is a CYP3A substrate and time-dependent inhibitor, as well as a BCRP and OATP1B transporter inhibitor in vitro. Physiologically-based pharmacokinetic (PBPK) modeling strategies with the ultimate goal of understanding complex drug-drug interactions (DDIs) and proposing doses for untested scenarios were developed. The consistency in the results of two independent approaches, PBPK simulation and endogenous biomarker measurement, supported that the observed transporter DDI is primarily ...[more]

PMID: 32383787

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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

Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.

Publications

Physiologically-Based Pharmacokinetic Model-Informed Drug Development for Fenebrutinib: Understanding Complex Drug-Drug Interactions.

Similar Datasets

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