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

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...

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Main Authors: Yuan Chen, Fang Ma, Nicholas S. Jones, Kenta Yoshida, Po‐Chang Chiang, Matthew R. Durk, Matthew R. Wright, Jin Yan Jin, Leslie W. Chinn
Format: Article
Language:English
Published: Wiley 2020-06-01
Series:CPT: Pharmacometrics & Systems Pharmacology
Online Access:https://doi.org/10.1002/psp4.12515
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author Yuan Chen
Fang Ma
Nicholas S. Jones
Kenta Yoshida
Po‐Chang Chiang
Matthew R. Durk
Matthew R. Wright
Jin Yan Jin
Leslie W. Chinn
author_facet Yuan Chen
Fang Ma
Nicholas S. Jones
Kenta Yoshida
Po‐Chang Chiang
Matthew R. Durk
Matthew R. Wright
Jin Yan Jin
Leslie W. Chinn
author_sort Yuan Chen
collection DOAJ
description 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 (Cmax) 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.
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spelling doaj.art-4b03517de89446c89515dc196f2449a52022-12-21T23:44:51ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062020-06-019633234110.1002/psp4.12515Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug InteractionsYuan Chen0Fang Ma1Nicholas S. Jones2Kenta Yoshida3Po‐Chang Chiang4Matthew R. Durk5Matthew R. Wright6Jin Yan Jin7Leslie W. Chinn8Department of Drug Metabolism and Pharmacokinetics Genentech, Inc. South San Francisco California USADepartment of Drug Metabolism and Pharmacokinetics Genentech, Inc. South San Francisco California USADepartment of Clinical Science Genentech, Inc. South San Francisco California USADepartment of Clinical Pharmacology Genentech, Inc. South San Francisco California USADepartment of Pharmaceutical Science Genentech, Inc. South San Francisco California USADepartment of Drug Metabolism and Pharmacokinetics Genentech, Inc. South San Francisco California USADepartment of Drug Metabolism and Pharmacokinetics Genentech, Inc. South San Francisco California USADepartment of Clinical Pharmacology Genentech, Inc. South San Francisco California USADepartment of Clinical Pharmacology Genentech, Inc. South San Francisco California USAFenebrutinib 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 (Cmax) 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.https://doi.org/10.1002/psp4.12515
spellingShingle Yuan Chen
Fang Ma
Nicholas S. Jones
Kenta Yoshida
Po‐Chang Chiang
Matthew R. Durk
Matthew R. Wright
Jin Yan Jin
Leslie W. Chinn
Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
CPT: Pharmacometrics & Systems Pharmacology
title Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
title_full Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
title_fullStr Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
title_full_unstemmed Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
title_short Physiologically‐Based Pharmacokinetic Model‐Informed Drug Development for Fenebrutinib: Understanding Complex Drug‐Drug Interactions
title_sort physiologically based pharmacokinetic model informed drug development for fenebrutinib understanding complex drug drug interactions
url https://doi.org/10.1002/psp4.12515
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