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

Arch Toxicol. 2019 Nov;93(11):3067-3098. doi: 10.1007/s00204-019-02585-5. Epub 2019 Oct 5.


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.

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

Publication types

  • Review

MeSH terms

  • Animal Testing Alternatives / methods*
  • Animals
  • Cell Line, Tumor
  • Chemical and Drug Induced Liver Injury* / etiology
  • Chemical and Drug Induced Liver Injury* / metabolism
  • Chemical and Drug Induced Liver Injury* / pathology
  • Computational Biology
  • Gene Expression Profiling
  • Hepatocytes / drug effects
  • Hepatocytes / metabolism
  • Hepatocytes / pathology
  • Humans
  • In Vitro Techniques
  • Lab-On-A-Chip Devices
  • Liver / drug effects*
  • Liver / metabolism
  • Liver / pathology
  • Spheroids, Cellular / drug effects
  • Spheroids, Cellular / metabolism
  • Spheroids, Cellular / pathology
  • Stem Cells / drug effects
  • Stem Cells / metabolism
  • Stem Cells / pathology
  • Transcriptome / drug effects*