Archives of Toxicology

, Volume 92, Issue 10, pp 3007–3029 | Cite as

In vitro assessment of hepatotoxicity by metabolomics: a review

  • Matthias CuykxEmail author
  • Robim M. Rodrigues
  • Kris Laukens
  • Tamara Vanhaecke
  • Adrian CovaciEmail author
Review Article


Omics technologies, and in particular metabolomics, have received an increasing attention during the assessment of hepatotoxicity in vitro. However, at present, a consensus on good metabolomics practices has yet to be reached. Therefore, in this review, a range of experimental approaches, applied methodologies, and data processing workflows are compared and critically evaluated. Experimental designs among the studies are similar, reporting the use of primary hepatocytes or hepatic cell lines as the most frequently used cell sources. Experiments are usually conducted in short time-frames (< 48 h) at sub-toxic dosages. Applied sample preparations are protein precipitation or Bligh-and-Dyer extraction. Most analytical platforms rely on chromatographic separations with mass spectrometric detection using high-resolution instruments. Untargeted metabolomics was typically used to allow the simultaneous detection of several classes of the metabolome, including endogenous metabolites that are not initially linked to toxicity. This non-biased detection platform is a valuable tool for generating hypothesis-based mechanistic research. The most frequently reported metabolites that are altered under toxicological impulses are alanine, lactate, and proline, which are often correlated. Other unspecific biomarkers of hepatotoxicity in vitro are the down-regulation of choline, glutathione, and 3-phospho-glycerate. Disruptions on the Krebs cycle are associated with increased glutamate, tryptophan, and valine. Phospholipid alterations are described in steatosis, lipo-apoptosis, and oxidative stress. Although there is a growing trend towards quality control, data analysis procedures do often not follow good contemporary metabolomics practices, which include feature filtering, false-discovery rate correction, and reporting the confidence of metabolite annotation. The currently annotated biomarkers can be used to identify hepatotoxicity in general and provide, to a certain extent, a tool for mechanistic distinction.


Metabolomics In vitro Liver Hepatotoxicity Drug-induced liver injury (DILI) 









Replace/reduce/refine-principle for animal testing


Amino acids


Accurate mass


Adenosine monophosphate


Analysis of variance


Adverse outcome pathway


Adenosine triphosphate


Bisphenol A


Capillary electrophoresis


Diacyl glycerol


Drug-induced liver injury




Deoxyribosyl nucleic acids




False-discovery rate


Fourier-transform ion cyclotron resonance mass spectrometry


Gas chromatography


Reduced glutathione


Oxidised glutathione




Hierarchical clustering analysis


Hepatitis C virus


Hydrophilic liquid interaction chromatography


High resolution


Inhibitory concentration


Ion pairing


Liquid chromatography


Linear ion trap mass spectrometer






Molecular initiating event


Mode of action


Mass spectrometry


Metabolomics Standards Initiative


3-(4,5-Dimethylthiazol-2-yl)- 2,5-diphenyltetrazolium bromide


Missing values


Mann–Whitney U


Not applicable


Not reported


Nicotinamide adenine dinucleotide phosphate


Nucleic magnetic resonance


No observed adverse effect level


Neutral red uptake


Poly-aromatic hydrocarbons


Phosphate buffer saline


Phosphatidyl choline


Principal component analysis


Phosphatidyl ethanolamine


Phospho-enol pyruvate




Partial least-squares analysis


Probabilistic quotient normalisation


Quadrupole mass spectrometer


Quality control


Quantum mechanical total line shape fitting


Ribosyl nucleic acids


Reactive oxygen species


Reversed phase


s-Adenosyl methionine


Tricarboxylic acid cycle




Triacyl glycerol


Time-of-flight mass spectrometer


Ultra-performance liquid chromatography


Variable importance of projection



Matthias Cuykx and Dr. Robim M. Rodrigues have been funded by individual fellowships of Research Foundation Flanders (FWO, fellowships 12H2216N and 11Z3318N). Further funding has been available from the Vrije Universiteit Brussel (VUB, Brussels, Belgium) and the University of Antwerp (UA, Antwerp, Belgium).

Compliance with ethical standards

Conflict of interest

The authors declare to have no conflicts of interests.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Toxicological CentreUniversity of AntwerpWilrijkBelgium
  2. 2.Department of In Vitro Toxicology and Dermato-CosmetologyVrije Universiteit BrusselJetteBelgium
  3. 3.Department of Mathematics and Computer ScienceUniversity of AntwerpAntwerpBelgium
  4. 4.Biomedical Informatics Network Antwerpen (Biomina)University of AntwerpAntwerpBelgium

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