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Arch Toxicol. 2019 Oct 5. doi: 10.1007/s00204-019-02585-5. [Epub ahead of print]

The application of omics-based human liver platforms for investigating the mechanism of drug-induced hepatotoxicity in vitro.

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Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
Vlaamse Instelling voor Technologisch Onderzoek (VITO) NV, Health, Boeretang 200, 2400, Mol, Belgium.
Department of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands.
Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.


Drug-induced liver injury (DILI) complicates safety assessment for new drugs and poses major threats to both patient health and drug development in the pharmaceutical industry. A number of human liver cell-based in vitro models combined with toxicogenomics methods have been developed as an alternative to animal testing for studying human DILI mechanisms. In this review, we discuss the in vitro human liver systems and their applications in omics-based drug-induced hepatotoxicity studies. We furthermore present bioinformatic approaches that are useful for analyzing toxicogenomic data generated from these models and discuss their current and potential contributions to the understanding of mechanisms of DILI. Human pluripotent stem cells, carrying donor-specific genetic information, hold great potential for advancing the study of individual-specific toxicological responses. When co-cultured with other liver-derived non-parenchymal cells in a microfluidic device, the resulting dynamic platform enables us to study immune-mediated drug hypersensitivity and accelerates personalized drug toxicology studies. A flexible microfluidic platform would also support the assembly of a more advanced organs-on-a-chip device, further bridging gap between in vitro and in vivo conditions. The standard transcriptomic analysis of these cell systems can be complemented with causality-inferring approaches to improve the understanding of DILI mechanisms. These approaches involve statistical techniques capable of elucidating regulatory interactions in parts of these mechanisms. The use of more elaborated human liver models, in harmony with causality-inferring bioinformatic approaches will pave the way for establishing a powerful methodology to systematically assess DILI mechanisms across a wide range of conditions.


Bayesian networks; Bioinformatics; Drug-induced hepatotoxicity; Graphical Gaussian networks; In vitro human liver model; Omics approaches


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