A high-throughput metabolomics in vitro platform for the characterization of hepatotoxicity.

Cell-based metabolomics provides multiparametric physiologically relevant readouts that can be highly advantageous for improved, biologically based decision making in early stages of compound development. Here, we present the development of a 96-well plate LC-MS/MS-based targeted metabolomics screening platform for the classification of liver toxicity modes of action (MoAs) in HepG2 cells. Different parameters of the workflow (cell seeding density, passage number, cytotoxicity testing, sample preparation, metabolite extraction, analytical method, and data processing) were optimized and standardized to increase the efficiency of the testing platform. The applicability of the system was tested with seven substances known to be representative of three different liver toxicity MoAs (peroxisome proliferation, liver enzyme induction, and liver enzyme inhibition). Five concentrations per substance, aimed at covering the complete dose-response curve, were analyzed and 221 uniquely identified metabolites were measured, annotated, and allocated in 12 different metabolite classes such as amino acids, carbohydrates, energy metabolism, nucleobases, vitamins and cofactors, and diverse lipid classes. Multivariate and univariate analyses showed a dose response of the metabolic effects, a clear differentiation between liver toxicity MoAs and resulted in the identification of metabolite patterns specific for each MoA. Key metabolites indicative of both general and mechanistic specific hepatotoxicity were identified. The method presented here offers a multiparametric, mechanistic-based, and cost-effective hepatotoxicity screening that provides MoA classification and sheds light into the pathways involved in the toxicological mechanism. This assay can be implemented as a reliable compound screening platform for improved safety assessment in early compound development pipelines.

Prediction of liver toxicity and mode of action using metabolomics in vitro in HepG2 cells.

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.

Use of in vitro metabolomics in NRK cells to help predicting nephrotoxicity and differentiating the MoA of nephrotoxicants.

We describe a strategy using an in vitro metabolomics assay with tubular rat NRK-52E cells to investigate the Modes of Action (MoAs) of nephrotoxic compounds. Chemicals were selected according to their MoAs based on literature information: acetaminophen, 4-aminophenol and S-(trichlorovinyl-)L-cysteine (TCVC), (covalent protein binding); gentamycin, vancomycin, polymycin B and CdCl2 (lysosomal overload) and tenofovir and cidofovir (mitochondrial DNA-interaction). After treatment and harvesting of the cells, intracellular endogenous metabolites were quantified relative to vehicle control. Metabolite patterns were evaluated in a purely data-driven pattern generation process excluding published information. This strategy confirmed the assignment of the chemicals to the respective MoA except for TCVC and CdCl2. Finally, TCVC was defined as unidentified and CdCl2 was reclassified to the MoA "covalent protein binding". Hierarchical cluster analysis of 58 distinct metabolites from the patterns enabled a clear visual separation of chemicals in each MoA. The assay reproducibility was very good and metabolic responses were consistent. These results support the use of metabolome analysis in NRK-52E cells as a suitable tool for understanding and investigating the MoA of nephrotoxicants. This assay could enable the early identification of nephrotoxic compounds and finally reduce animal testing.