Evaluation of models for predicting drug–drug interactions due to induction

Abstract

Importance of the field: Drug–drug interactions caused by induction of metabolizing enzymes, particularly CYP3A, can impact the efficacy and safety of co-administered drugs. It is, therefore, important to understand a new compound's potential for enzyme induction and to understand how to use the induction data generated in vitro to predict potential for drug–drug interactions in vivo.

Areas covered in this review: Recent advances in methods for using in vitro data to predict potential for CYP3A induction in vivo are reviewed.

What the reader will gain: The reader will gain a comprehensive understanding of the advantages and disadvantages of various prediction methods for induction and be able to directly compare different methods using a common in vitro data set.

Take home message: The various methods for predicting clinical CYP3A induction from in vitro induction data all have demonstrated utility; it is the authors' opinion that the correlation-based approaches offer as good or better predictivity and have simpler input requirements than more complex approaches. Of the different correlation approaches, the relatively simple unbound C_max/EC₅₀ or AUC/EC₅₀ approaches are the simplest and yet show the best correlation to the observed clinical data. While the approaches discussed herein represent an improvement in our understanding of the predictive value of in vitro induction data, it is important to recognize that there is still room for improvement in quantitative prediction of magnitude of drug interactions due to induction.

Keywords::

• CYP3A
• drug–drug interactions
• in vitro–in vivo correlation
• in vitro–in vivo extrapolation
• induction

Acknowledgements

The authors thank S Obach for his comments on the manuscript. The authors also thank S Boldt and M Kish for providing the in vitro induction data.

Notes

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