https://doi.org/10.1016/j.aquatox.2020.105616

Grimard, C., Mangold-Döring, A., Schmitz, M., Alharbi, H., Jones, P. D., Giesy, J. P., Hecker, M., & Brinkmann, M. (2020). In vitro-in vivo and cross-life stage extrapolation of uptake and biotransformation of benzo [a] pyrene in the fathead minnow (Pimephales promelas). Aquatic Toxicology, 228, 105616. https://doi.org/10.1016/j.aquatox.2020.105616

Understanding internal dose metrics is integral to adequately assess effects environmental contaminants might have on aquatic wildlife, including fish. In silico toxicokinetic (TK) models are a leading approach for quantifying internal exposure metrics for fishes; however, they often do not adequately consider chemicals that are actively biotransformed and have not been validated against early-life stages (ELS) that are often considered the most sensitive to the exposure to contaminants. To address these uncertainties, TK models were parameterized for the rapidly biotransformed chemical benzo[a]pyrene (B[a]P) in embryo-larval and adult life stages of fathead minnows. Biotransformation of B[a]P was determined through measurements of in vitro clearance. Using in vitro-in vivo extrapolation, in vitro clearance was integrated into a multi-compartment TK model for adult fish and a one-compartment model for ELS. Model predictions were validated using measurements of B[a]P metabolites from in vivo flow-through exposures to graded concentrations of water-borne B[a]P. Significantly greater amounts of B[a]P metabolites were observed with exposure to greater concentrations of parent compound in both life stages. However, when assessing biotransformation capacity, no differences in phase I or phase II biotransformation were observed with greater exposures to B[a]P. Results of modelling suggested that biotransformation of B[a]P can be successfully implemented into in silico models to accurately predict life stage-specific abundances of B[a]P metabolites in either whole-body larvae or the bile of adult fish. Models developed increase the scope of applications in which TK models can be used to support environmental risk assessments.

Omic

https://doi.org/10.1016/j.aquatox.2020.105616