Physiologically-based pharmacokinetic (PBPK) modeling in predicting metabolic drug–drug interactions (mDDIs) is routinely used in drug development. Currently, the US FDA endorses the use of PBPK to potentially support dosing recommendations for investigational drugs as enzyme substrates of mDDIs, and to inform a lack of mDDIs for investigational drugs as enzyme modulators.
We systematically evaluated the performance of PBPK modeling in predicting mDDIs published in the literature. Models developed to assess both investigational drugs as enzyme substrates (Groups 1 and 2, as being inhibited and induced, respectively) or enzyme modulators (Groups 3 and 4, as inhibitors and inducers, respectively) were evaluated. Predicted ratios of the area under the curve (AUCRs) and/or maximum plasma concentration (CmaxRs) with and without comedication were compared with the observed ratios.
For Groups 1, 2, 3, and 4, 62, 50, 44, and 43% of model-predicted AUCRs, respectively, were within a predefined threshold of 1.25-fold of observed values (0.8–1.25x). When the threshold was widened to twofold, the values increased to 100, 80, 81, and 86% (0.5–2.0x). For Groups 3 and 4, prediction for mDDI liability (the existence or lack of mDDIs) using PBPK appears to be satisfactory.
Our analysis supports the FDA’s current recommendations on the use of PBPK to predict mDDIs.
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Funding for this paper was made possible, in part, by the US FDA through the Medical Countermeasures Initiative. In addition, Dr. Chia-Hsiang Hsueh was supported in part by appointments to the Research Participation Program at the Center for Drug Evaluation and Research, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and the FDA. Views expressed in this paper do not necessarily reflect the official policies or endorsements of the FDA, Gilead Sciences, Inc., and Bill and Melinda Gates Foundation.
Conflicts of interest
Chia-Hsiang Hsueh, Vicky Hsu, Yuzhuo Pan, and Ping Zhao declare no conflicts of interest.
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Hsueh, CH., Hsu, V., Pan, Y. et al. Predictive Performance of Physiologically-Based Pharmacokinetic Models in Predicting Drug–Drug Interactions Involving Enzyme Modulation. Clin Pharmacokinet 57, 1337–1346 (2018). https://doi.org/10.1007/s40262-018-0635-8