Liver toxicity is a leading systemic toxicity of drugs and chemicals demanding more human-relevant, high throughput, cost effective in vitro solutions. In addition to contributing to animal welfare, in vitro techniques facilitate exploring and understanding the molecular mechanisms underlying toxicity. New ‘omics technologies can provide comprehensive information on the toxicological mode of action of compounds, as well as quantitative information about the multi-parametric metabolic response of cellular systems in normal and patho-physiological conditions. Here, we combined mass-spectroscopy metabolomics with an in vitro liver toxicity model. Metabolite profiles of HepG2 cells treated with 35 test substances resulted in 1114 cell supernatants and 3556 intracellular samples analyzed by metabolomics. Control samples showed relative standard deviations of about 10–15%, while the technical replicates were at 5–10%. Importantly, this procedure revealed concentration–response effects and patterns of metabolome changes that are consistent for different liver toxicity mechanisms (liver enzyme induction/inhibition, liver toxicity and peroxisome proliferation). Our findings provide evidence that identifying organ toxicity can be achieved in a robust, reliable, human-relevant system, representing a non-animal alternative for systemic toxicology.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-017-2079-6) contains supplementary material, which is available to authorized users.

New technologies, such as metabolomics, can address chemical grouping and read across from a biological perspective. In a virtual case study, we selected MCPP as target substance and MCPA and 2,4-DP as source substances with the goal to waive a 90-day study with MCPP. In order to develop a convincing case to show how biological data can substantiate read across, we used metabolomics on blood samples from the 28-day studies to show the qualitative and quantitative similarity of the substances. The 28-day metabolome evaluation of source substances and the target substance indicate liver and kidneys as target organs. 2,4-DP was identified as the best source substance. Using the information of the 90-day 2,4-DP study, we predicted MCPP's toxicity profile at 2500 ppm: reduced food consumption and body weight gain, liver and kidney weight increases with clinical-pathology changes and a moderate red blood cell parameter reduction. NOEL prediction for MCPP was below that of 2,4-DP (<500 ppm), and similar to that of MCPA (≥150 ppm). Qualitatively, these predictions are comparable to the results of the real MCPP 90-day study in rats (reduced food consumption and body weight gain, weight increases and clinical-pathology changes in liver and kidneys, reduced red blood cells values). Quantitatively, the predicted NOAEL (150 ppm) is similar to the actual study (NOEL = 75 ppm, NOAEL ≤ 500 ppm). Thus, the 90-day rat toxicity study of MCPP could have been waived and substituted by the 90-day results of 2,4-DP by using metabolome data of 28 day studies.

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The gut microbiome is vital to the health and development of an organism, specifically in determining the host response to a chemical (drug) administration. To understand this, we investigated the effects of six antibiotic (AB) treatments (Streptomycin sulfate, Roxithromycin, Sparfloxacin, Vancomycin, Clindamycin and Lincomycin hydrochloride) and diet restriction (–20%) on the gut microbiota in 28-day oral toxicity studies on Wistar rats. The fecal microbiota was determined using 16S rDNA marker gene sequencing. AB-class specific alterations were observed in the bacterial composition, whereas restriction in diet caused no observable difference. These changes associated well with the changes in the LC–MS/MS- and GC–MS-based metabolome profiles, particularly of feces and to a lesser extent of plasma. Particularly strong and AB-specific metabolic alterations were observed for bile acids in both plasma and feces matrices. Although AB-group-specific plasma metabolome changes were observed, weaker associations between fecal and plasma metabolome suggest a profound barrier between them. Numerous correlations between the bacterial families and the fecal metabolites were established, providing a holistic overview of the gut microbial functionality. Strong correlations were observed between microbiota and bile acids, lipids and fatty acids, amino acids and related metabolites. These microbiome–metabolome correlations promote understanding of the functionality of the microbiome for its host.

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