Building Confidence in Physiologically Based Pharmacokinetic Modeling of CYP3A Induction Mediated by Rifampin: An Industry Perspective.

Autor: Reddy MB; Clinical Pharmacology, Oncology, Pfizer Inc., Boulder, Colorado, USA., Cabalu TD; DMPK, Pharmacokinetics, Dynamics, Metabolism, and Bioanalytics, Merck & Co., Inc., Rahway, New Jersey, USA., de Zwart L; DMPK, Janssen Pharmaceutica NV, A Johnson & Johnson Company, Beerse, Belgium., Ramsden D; DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Boston, Massachusetts, USA., Dowty ME; Pharmacokinetics Dynamics and Metabolism, Pfizer Inc, Cambridge, Massachusetts, USA., Taskar KS; DMPK, Pre-Clinical Sciences, Research Technologies, GSK, Stevenage, UK., Badée J; PK Sciences, Biomedical Research, Novartis, Basel, Switzerland., Bolleddula J; Quantitative Pharmacology, EMD Serono Research & Development Institute, Inc., Billerica, Massachusetts, USA., Boulu L; Modeling and Simulation, Translational Medicine and Early Development, Sanofi, Montpellier, France., Fu Q; Modeling and Simulation, Vertex Pharmaceuticals, Boston, Massachusetts, USA., Kotsuma M; Quantitative Clinical Pharmacology, Daiichi Sankyo Co., Ltd., Tokyo, Japan., Leblanc AF; Quantitative, Translational & ADME Sciences, Development Science, AbbVie, North Chicago, Illinois, USA., Lewis G; DMPK, Pre-Clinical Sciences, Research Technologies, GSK, Stevenage, UK., Liang G; DMPK, Vertex Pharmaceuticals, Boston, Massachusetts, USA., Parrott N; Pharmaceutical Sciences, Roche Pharma Research & Early Development, Roche Innovation Center Basel, Basel, Switzerland., Pilla Reddy V; Global PKPD/Pharmacometrics, Eli Lilly and Company, Bracknell, UK and Indianapolis, Indiana, USA., Prakash C; DMPK and Clinical Pharmacology, Agios, Cambridge, Massachusetts, USA., Shah K; Quantitative Clinical Pharmacology, Takeda Pharmaceuticals International Inc., Cambridge, Massachusetts, USA., Umehara K; Roche Pharmaceutical Research and Early Development, Roche Innovation Center, F. Hoffmann-La Roche Ltd., Basel, Switzerland., Mukherjee D; Quantitative Clinical Pharmacology, Daiichi-Sankyo Inc., Basking Ridge, New Jersey, USA., Rehmel J; Global PKPD/Pharmacometrics, Eli Lilly and Company, Bracknell, UK and Indianapolis, Indiana, USA., Hariparsad N; DMPK, Research and Early Development, Oncology R&D, AstraZeneca, Boston, Massachusetts, USA.
Jazyk: angličtina
Zdroj: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2024 Oct 18. Date of Electronic Publication: 2024 Oct 18.
DOI: 10.1002/cpt.3477
Abstrakt: Physiologically-based pharmacokinetic (PBPK) modeling offers a viable approach to predict induction drug-drug interactions (DDIs) with the potential to streamline or reduce clinical trial burden if predictions can be made with sufficient confidence. In the current work, the ability to predict the effect of rifampin, a well-characterized strong CYP3A4 inducer, on 20 CYP3A probes with publicly available PBPK models (often developed using a workflow with optimization following a strong inhibitor DDI study to gain confidence in fraction metabolized by CYP3A4, f m,CYP3A4 , and fraction available after intestinal metabolism, Fg), was assessed. Substrates with a range of f m,CYP3A4 (0.086-1.0), Fg (0.11-1.0) and hepatic availability (0.09-0.96) were included. Predictions were most often accurate for compounds that are not P-gp substrates or that are P-gp substrates but that have high permeability. Case studies for three challenging DDI predictions (i.e., for eliglustat, tofacitinib, and ribociclib) are presented. Along with parameter sensitivity analysis to understand key parameters impacting DDI simulations, alternative model structures should be considered, for example, a mechanistic absorption model instead of a first-order absorption model might be more appropriate for a P-gp substrate with low permeability. Any mechanisms pertinent to the CYP3A substrate that rifampin might impact (e.g., induction of other enzymes or P-gp) should be considered for inclusion in the model. PBPK modeling was shown to be an effective tool to predict induction DDIs with rifampin for CYP3A substrates with limited mechanistic complications, increasing confidence in the rifampin model. While this analysis focused on rifampin, the learnings may apply to other inducers.
(© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)
Databáze: MEDLINE
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