Bayesian toxicokinetic modeling of subcellular partitioning in grass carp exposed to copper nanoparticles and its implication for detoxification

Understanding the accumulation mechanisms and particle-specific effects of nanoparticles in aquatic organisms is crucial for assessing ecotoxicological risks of nanomaterials. The aim of this study is to evaluate the influence of copper nanoparticles (CuNPs) particle size (40-60 nm and 60-80 nm) on the subcellular partitioning kinetics of Cu in grass carp (Ctenopharyngodon idellus) tissues, utilizing a Bayesian two-compartment toxicokinetic (TK) model. Fish were exposed to these CuNPs at concentrations of 0.03, 0.1, and 0.3 µg mL^{- 1} for 10 days. Bayesian Markov chain Monte Carlo (MCMC) simulation enabled stochastic estimation of uptake (k_uA), detoxification (k_d), and elimination (k_e) rate constants, along with uncertainty quantification. Results showed that smaller CuNP particle sizes affect greater Cu accumulation in the liver and kidney at low to moderate concentrations than at high concentrations, likely due to particle aggregation at higher exposure level. Bayesian TK model revealed tissue-specific kinetic profiles, larger CuNP exhibited faster uptake, while smaller particle enhanced higher detoxification rates, especially in the liver and intestine. These results showed how particle size and concentration affect Cu subcellular fate through dynamic detoxification mechanisms. In conclusion, Bayesian MCMC subcellular partitioning TK model advances the understanding of particle size effects in aquatic nanotoxicology, emphasizing the importance of considering particle characteristics and exposure levels in assessing biological responses and environmental risks of exposure to CuNPs in aquatic ecosystems.