Literature DB >> 35486324

A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates.

Sumit Arora1,2, Amita Pansari3, Peter J Kilford4, Masoud Jamei3, David B Turner3, Iain Gardner3.   

Abstract

BACKGROUND AND OBJECTIVES: Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir.
METHODS: A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively.
RESULTS: Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (Cmax) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of Cmax and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all Cmax and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for Cmax and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively.
CONCLUSIONS: The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

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Year:  2022        PMID: 35486324     DOI: 10.1007/s13318-022-00765-w

Source DB:  PubMed          Journal:  Eur J Drug Metab Pharmacokinet        ISSN: 0378-7966            Impact factor:   2.441


  40 in total

1.  General solution for diffusion-controlled dissolution of spherical particles. 1. Theory.

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Review 3.  The utility of modeling and simulation in drug development and regulatory review.

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5.  Physiologically Based Pharmacokinetic Modeling in Regulatory Science: An Update From the U.S. Food and Drug Administration's Office of Clinical Pharmacology.

Authors:  Manuela Grimstein; Yuching Yang; Xinyuan Zhang; Joseph Grillo; Shiew-Mei Huang; Issam Zineh; Yaning Wang
Journal:  J Pharm Sci       Date:  2018-10-29       Impact factor: 3.534

6.  Application of the MechPeff model to predict passive effective intestinal permeability in the different regions of the rodent small intestine and colon.

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7.  Dose-response of ritonavir on hepatic CYP3A activity and elvitegravir oral exposure.

Authors:  A A Mathias; S West; J Hui; B P Kearney
Journal:  Clin Pharmacol Ther       Date:  2008-09-24       Impact factor: 6.875

8.  Verification of a physiologically based pharmacokinetic model of ritonavir to estimate drug-drug interaction potential of CYP3A4 substrates.

Authors:  Ken-Ichi Umehara; Felix Huth; Christina S Won; Tycho Heimbach; Handan He
Journal:  Biopharm Drug Dispos       Date:  2018-03       Impact factor: 1.627

9.  Metabolism of the human immunodeficiency virus protease inhibitors indinavir and ritonavir by human intestinal microsomes and expressed cytochrome P4503A4/3A5: mechanism-based inactivation of cytochrome P4503A by ritonavir.

Authors:  T Koudriakova; E Iatsimirskaia; I Utkin; E Gangl; P Vouros; E Storozhuk; D Orza; J Marinina; N Gerber
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10.  Steady-state pharmacokinetic and safety profiles of voriconazole and ritonavir in healthy male subjects.

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  1 in total

Review 1.  Interactions of the protease inhibitor, ritonavir, with common anesthesia drugs.

Authors:  Anders Svedmyr; Henrik Hack; Brian J Anderson
Journal:  Paediatr Anaesth       Date:  2022-07-24       Impact factor: 2.129

  1 in total

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