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Updated: Jul 3, 2026

An All-Human Hepatic Culture System for Drug Development Applications
Published on: October 20, 2023
Humanized mouse models for drug metabolism and drug transport: a systematic review
Ibrahim Z Hassan1, Wihan Pheiffer2, Rose Hayeshi2
1Department of Paraclinical Sciences, Faculty of Veterinary Science, University of Pretoria, Pretoria, South Africa.
Abstract:
Animal models are commonly used for prediction of human pharmacokinetics (PKs); however, due to species differences, there are often challenges in translation of the data to clinical outcomes. Humanized mice in the form of chimeric and transgenic models are increasingly proposed as better tools for translational preclinical studies during drug development. Therefore, the aim of this systematic review was to assess the utility and predictive value of humanized mice for human drug metabolism and transport. The databases searched were PubMed, Scopus, Web of Science, and Google Scholar. The inclusion criteria were studies using healthy mice humanized with drug metabolizing enzyme and/or drug transporter and/or nuclear receptors; in original research studies undertaking experimental prediction of human drug transport/metabolism published in the English language. A total of 78 studies were identified and data extracted included experimental groups and number of animals; species and sex; method used to genetically modify the mice; PK data; drug-drug interaction (DDI) predictions and correlations. Discussions and conclusions relied on descriptive summary of results. Chimeric models predominated within the dataset, particularly in PK and metabolite profiling investigations, whereas transgenic mice were more associated with DDI studies. Both model types showed good predictive value for PK and DDIs while chimeric mice replicated human metabolic pathways, identifying human-specific metabolites absent in conventional rodent models. Therefore, this systematic review demonstrates humanized mouse models for drug metabolism and drug transport as useful tools to support prediction of clinical outcomes while recognizing variability in predictive utility across model types and study contexts.
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