A human liver organoid platform for hepatotoxicity assessment: evaluation using reference compounds.

Authors

Min Jeong Kim, Seo Yoon Choi, Su Min Youn, Tae Sung Kim, Il Ung Oh, Sang Aeh Park, Jae Ho Oh, Myung Jin Son, Jin Hee Lee

Published in

Frontiers in toxicology. Volume 8. Pages 1805474. Epub Jun 15, 2026.

Abstract

Recent advances in organoid technology have enabled the development of human-relevant models. However, their application in toxicity assessments remains limited. Our previous study employed a liver organoid platform to evaluate drug toxicity and subsequently suggested its utility as a model for toxicity assessment. Building on this, we aimed to assess the exploratory response of the liver organoid model using a panel of selected reference compounds, including substances classified according to the United Nations Globally Harmonized System acute toxicity criteria.
Human liver organoids derived from induced pluripotent stem cells were exposed to a panel of reference compounds, and toxicity responses were evaluated using multiple endpoints, including cell viability assays, functional biomarker measurements, imaging, and transcriptomic analysis.
Exposure to the reference compounds resulted in compound-specific changes across multiple endpoints, including cell viability, hepatic functional markers such as albumin secretion, and transcriptomic responses. Transcriptomic analysis further revealed compound-specific changes in gene expression associated with hepatotoxic responses, supporting the observed functional alterations. These findings suggest that liver organoids may capture compound-specific and multi-dimensional toxicity responses, supporting their potential as an in vitro platform for integrative toxicity assessment. However, this study was conducted using a limited set of reference compounds and did not include direct comparisons with established in vitro or in vivo models. In addition, functional metabolic capacity, including CYP activity, was not assessed.
Therefore, the present results should be interpreted as an exploratory evaluation of platform performance rather than evidence of predictive capability. Future studies incorporating greater chemical diversity and functional validation are required to assess the broader applicability of this model.

PMID:
42371511
Bibliographic data and abstract were imported from PubMed on 29 Jun 2026.

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