Predicting the Effect of Cytochrome P450 Inhibitors on Substrate Drugs: Analysis of Physiologically Based Pharmacokinetic Modeling Submissions to the US Food and Drug Administration
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Background and Objective
The US Food and Drug Administration (FDA) has seen a recent increase in the application of physiologically based pharmacokinetic (PBPK) modeling towards assessing the potential of drug–drug interactions (DDI) in clinically relevant scenarios. To continue our assessment of such approaches, we evaluated the predictive performance of PBPK modeling in predicting cytochrome P450 (CYP)-mediated DDI.
This evaluation was based on 15 substrate PBPK models submitted by nine sponsors between 2009 and 2013. For these 15 models, a total of 26 DDI studies (cases) with various CYP inhibitors were available. Sponsors developed the PBPK models, reportedly without considering clinical DDI data. Inhibitor models were either developed by sponsors or provided by PBPK software developers and applied with minimal or no modification. The metric for assessing predictive performance of the sponsors’ PBPK approach was the R predicted/observed value (R predicted/observed = [predicted mean exposure ratio]/[observed mean exposure ratio], with the exposure ratio defined as [C max (maximum plasma concentration) or AUC (area under the plasma concentration–time curve) in the presence of CYP inhibition]/[C max or AUC in the absence of CYP inhibition]).
In 81 % (21/26) and 77 % (20/26) of cases, respectively, the R predicted/observed values for AUC and C max ratios were within a pre-defined threshold of 1.25-fold of the observed data. For all cases, the R predicted/observed values for AUC and C max were within a 2-fold range.
These results suggest that, based on the submissions to the FDA to date, there is a high degree of concordance between PBPK-predicted and observed effects of CYP inhibition, especially CYP3A-based, on the exposure of drug substrates.
KeywordsPredictive Performance PBPK Model Inhibitor Model Strong CYP3A Inhibitor PBPK Approach
The authors sincerely acknowledge Drs. Shiew Mei Huang and Issam Zineh (Office of Clinical Pharmacology, FDA) for their valuable comments and advice during the preparation of this manuscript.
This project was supported in part by an appointment to the ORISE Research Participation Program at the Center for Drug Evaluation and Research (CDER) administered by the Oak Ridge Institute for Science and Education through an agreement between the US Department of Energy and CDER.
This project was supported in part by the Commissioner Fellowship Program from the FDA Office of Commissioner, Office of Chief Scientist, and Office of Scientific Professional Development.
All authors have no conflicts of interest that are directly relevant to the content of this manuscript.
The contents of this manuscript do not reflect the view or policies of the FDA or its staff. No official support or endorsement by the FDA is intended or should be inferred.
Sponsors used commercially available software platforms (GastroPlus™, PK-Sim®, and Simcyp®). The FDA does not recommend any specific software for PBPK predictions and expects the sponsors to be solely responsible for selecting appropriate PBPK modeling tools to address drug development questions.
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