Metabolomics should have particular value in regulatory toxicology as the measurement of the molecular phenotype is the closest to traditional apical endpoints, whilst offering mechanistic insights into the biological perturbations. Despite this, the application of untargeted metabolomics for point-of-departure (POD) derivation via benchmark concentration (BMC) modelling is still relatively unexplored. In this study, a high-throughput workflow was applied to derive PODs associated with a chemical exposure by measuring the intracellular metabolome of the HepaRG cell line following treatment with model toxicants, each at seven concentrations and five sampling time points. The study explored three approaches to derive PODs using BMC modelling applied to the metabolomics datasets: the 1st rank-ordered unannotated feature, the 1st rank-ordered putatively annotated feature, and 25th rank-ordered feature, demonstrating that for three out of four chemical datasets, these approaches led to relatively consistent BMC values, varying less than 10-fold. By using the 1st rank-ordered unannotated feature it was possible to investigate temporal trends in the datasets, which were shown to be chemical-specific. A possible integration of metabolomics-driven POD derivation with the liver steatosis adverse outcome pathway was also demonstrated.

MALINOWSKA Julia M.;  PALOSAARI Taina;  SUND Jukka;  CARPI Donatella;  WEBER Ralf;  LLOYD Gavin;  WHELAN Maurice;  VIANT Mark; 

2023-03-13

SPRINGER HEIDELBERG

JRC131118

0340-5761 (online),   

https://link.springer.com/article/10.1007/s00204-022-03439-3,    https://publications.jrc.ec.europa.eu/repository/handle/JRC131118,   

10.1007/s00204-022-03439-3 (online),