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.

Transferability and reproducibility of exposed air-liquid interface co-culture lung models.

BACKGROUND: The establishment of reliable and robust in vitro models for hazard assessment, a prerequisite for moving away from animal testing, requires the evaluation of model transferability and reproducibility. Lung models that can be exposed via the air, by means of an air-liquid interface (ALI) are promising in vitro models for evaluating the safety of nanomaterials (NMs) after inhalation exposure. We performed an inter-laboratory comparison study to evaluate the transferability and reproducibility of a lung model consisting of the human bronchial cell line Calu-3 as a monoculture and, to increase the physiologic relevance of the model, also as a co-culture with macrophages (either derived from the THP-1 monocyte cell line or from human blood monocytes). The lung model was exposed to NMs using the VITROCELL® Cloud12 system at physiologically relevant dose levels. RESULTS: Overall, the results of the 7 participating laboratories are quite similar. After exposing Calu-3 alone and Calu-3 co-cultures with macrophages, no effects of lipopolysaccharide (LPS), quartz (DQ12) or titanium dioxide (TiO2) NM-105 particles on the cell viability and barrier integrity were detected. LPS exposure induced moderate cytokine release in the Calu-3 monoculture, albeit not statistically significant in most labs. In the co-culture models, most laboratories showed that LPS can significantly induce cytokine release (IL-6, IL-8 and TNF-α). The exposure to quartz and TiO2 particles did not induce a statistically significant increase in cytokine release in both cell models probably due to our relatively low deposited doses, which were inspired by in vivo dose levels. The intra- and inter-laboratory comparison study indicated acceptable interlaboratory variation for cell viability/toxicity (WST-1, LDH) and transepithelial electrical resistance, and relatively high inter-laboratory variation for cytokine production. CONCLUSION: The transferability and reproducibility of a lung co-culture model and its exposure to aerosolized particles at the ALI were evaluated and recommendations were provided for performing inter-laboratory comparison studies. Although the results are promising, optimizations of the lung model (including more sensitive read-outs) and/or selection of higher deposited doses are needed to enhance its predictive value before it may be taken further towards a possible OECD guideline.

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.