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.

Application of high throughput in vitro metabolomics for hepatotoxicity mode of action characterization and mechanistic-anchored point of departure derivation: a case study with nitrofurantoin.

Omics techniques have been increasingly recognized as promising tools for Next Generation Risk Assessment. Targeted metabolomics offer the advantage of providing readily interpretable mechanistic information about perturbed biological pathways. In this study, a high-throughput LC-MS/MS-based broad targeted metabolomics system was applied to study nitrofurantoin metabolic dynamics over time and concentration and to provide a mechanistic-anchored approach for point of departure (PoD) derivation. Upon nitrofurantoin exposure at five concentrations (7.5 µM, 15 µM, 20 µM, 30 µM and 120 µM) and four time points (3, 6, 24 and 48 h), the intracellular metabolome of HepG2 cells was evaluated. In total, 256 uniquely identified metabolites were measured, annotated, and allocated in 13 different metabolite classes. Principal component analysis (PCA) and univariate statistical analysis showed clear metabolome-based time and concentration effects. Mechanistic information evidenced the differential activation of cellular pathways indicative of early adaptive and hepatotoxic response. At low concentrations, effects were seen mainly in the energy and lipid metabolism, in the mid concentration range, the activation of the antioxidant cellular response was evidenced by increased levels of glutathione (GSH) and metabolites from the de novo GSH synthesis pathway. At the highest concentrations, the depletion of GSH, together with alternations reflective of mitochondrial impairments, were indicative of a hepatotoxic response. Finally, a metabolomics-based PoD was derived by multivariate PCA using the whole set of measured metabolites. This approach allows using the entire dataset and derive PoD that can be mechanistically anchored to established key events. Our results show the suitability of high throughput targeted metabolomics to investigate mechanisms of hepatoxicity and derive point of departures that can be linked to existing adverse outcome pathways and contribute to the development of new ones.