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Clinical Pharmacokinetics

, Volume 54, Issue 1, pp 117–127 | Cite as

Predicting the Effect of Cytochrome P450 Inhibitors on Substrate Drugs: Analysis of Physiologically Based Pharmacokinetic Modeling Submissions to the US Food and Drug Administration

  • Christian Wagner
  • Yuzhuo Pan
  • Vicky Hsu
  • Joseph A. Grillo
  • Lei Zhang
  • Kellie S. Reynolds
  • Vikram Sinha
  • Ping ZhaoEmail author
Original Research Article

Abstract

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.

Methods

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]).

Results

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.

Conclusion

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.

Keywords

Predictive Performance PBPK Model Inhibitor Model Strong CYP3A Inhibitor PBPK Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

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.

Disclaimer

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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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2014

Authors and Affiliations

  • Christian Wagner
    • 1
  • Yuzhuo Pan
    • 2
  • Vicky Hsu
    • 1
  • Joseph A. Grillo
    • 1
  • Lei Zhang
    • 1
  • Kellie S. Reynolds
    • 1
  • Vikram Sinha
    • 1
  • Ping Zhao
    • 1
    Email author
  1. 1.Office of Clinical Pharmacology, Office of Translational SciencesCenter for Drug Evaluation and Research, US Food and Drug AdministrationSilver SpringUSA
  2. 2.Office of Generic DrugsCenter for Drug Evaluation and Research, US Food and Drug AdministrationSilver SpringUSA

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