Predictability of human exposure by human-CYP3A4-transgenic mouse models: A meta-analysis

David Damoiseaux*, Alfred H. Schinkel, Jos H. Beijnen, Alwin D.R. Huitema, Thomas P.C. Dorlo

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

First-in-human dose predictions are primarily based on no-observed-adverse-effect levels in animal studies. Predictions from these animal models are only as effective as their ability to predict human results. To narrow the gap between human and animals, researchers have, among other things, focused on the replacement of animal cytochrome P450 (CYP) enzymes with their human counterparts (called humanization), especially in mice. Whereas research in humanized mice is extensive, the emphasis has been particularly on qualitative rather than quantitative predictions. Because the CYP3A4 enzyme is most involved in the metabolism of clinically used drugs, most benefit was expected from CYP3A4 models. There are several applications of these mouse models regarding in vivo CYP3A4 functionality, one of which might be their capacity to help improve first-in-human (FIH) dose predictions for CYP3A4-metabolized drugs. To evaluate whether human-CYP3A4-transgenic mouse models are better predictors of human exposure compared to the wild-type mouse model, we performed a meta-analysis comparing both mouse models in their ability to accurately predict human exposure of small-molecule drugs metabolized by CYP3A4. Results showed that, in general, the human-CYP3A4-transgenic mouse model had similar accuracy in the prediction of human exposure compared to the wild-type mouse model, suggesting that there is limited added value in humanization of the mouse Cyp3a enzymes if the primary aim is to acquire more accurate FIH dose predictions. Despite the results of this meta-analysis, corrections for interspecies differences through extension of human-CYP3A4-transgenic mouse models with pharmacokinetic modeling approaches seems a promising contribution to more accurate quantitative predictions of human pharmacokinetics.

Original languageEnglish
Article numbere13668
JournalClinical and Translational Science
Volume17
Issue number1
DOIs
Publication statusPublished - Jan 2024

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