Toxicokinetic modeling of the transfer of polychlorinated biphenyls (PCBs) and polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) into milk of high-yielding cows during negative and positive energy balance
Jan-Louis Moenning , Julika Lamp , Karin Knappstein , Joachim Molkentin , Andreas Susenbeth , Karl-Heinz Schwind , Sven Dänicke , Peter Fürst , Hans Schenkel , Robert Pieper , Torsten Krause , Jorge Numata
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引用次数: 0
Abstract
A toxicokinetic modeling approach was used to study the transfer of 7 polychlorinated dibenzo-p-dioxins (PCDDs), 10 dibenzofurans (PCDFs), 12 dioxin-like polychlorinated biphenyls (dl-PCB) and 3 non-dioxin like (ndl) PCBs in dairy cows. The model describes the concentration–time profile of each congener in milk and blood of high-yielding dairy cows. It was parametrized using an in-house transfer study with 3 cows exposed to a defined synthetic congener mixture for two dosing periods, as well as 3 control cows to account for background exposure. The first dosing was administered during negative energy balance (NEB) after calving, and the second during positive energy balance (PEB) in late lactation. Results include extrapolated steady-state transfer rates and elimination half-lives, many of which have never been reported before. Transfer rates (TRs) were significantly higher during the NEB by a median of 27%, likely due to an increase in non-milk elimination during PEB. The difference draws attention to the influence of the metabolic state of food-producing animals in risk assessment. Comparison of the TRs derived here with those reported in the literature showed that they were, in median, 43% higher in the NEB phase and 16% higher in the PEB phase probably because we report TRs in steady-state unlike most literature sources.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs