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

IF 3.1 Q2 TOXICOLOGY Computational Toxicology Pub Date : 2023-11-01 DOI:10.1016/j.comtox.2023.100290
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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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.

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多氯联苯(PCB)和多氯二苯并对二恶英及二苯并呋喃(PCDD/Fs)在能量负平衡和能量正平衡期间向高产奶牛牛奶中转移的毒物动力学模型
毒物动力学建模方法用于研究奶牛体内 7 种多氯二苯并对二恶英 (PCDD)、10 种二苯并呋喃 (PCDF)、12 种二恶英类多氯联苯 (dl-PCB) 和 3 种非二恶英类多氯联苯 (ndl) 的转移。该模型描述了高产奶牛牛奶和血液中每种同系物的浓度-时间曲线。通过一项内部转移研究对该模型进行了参数化,3 头奶牛在两个给药期暴露于确定的合成同系物混合物,3 头对照奶牛则暴露于背景暴露。第一次给药在产犊后的负能量平衡(NEB)期间进行,第二次在泌乳后期的正能量平衡(PEB)期间进行。研究结果包括推断的稳态转移率和消除半衰期,其中许多数据以前从未报道过。NEB期间的转移率(TRs)明显较高,中位数为27%,这可能是由于PEB期间非乳排除量增加所致。这一差异引起了人们对食用动物新陈代谢状态在风险评估中的影响的关注。将此处得出的TRs与文献中报告的TRs进行比较后发现,它们的中位数在NEB阶段高出43%,在PEB阶段高出16%,这可能是因为我们报告的是稳态TRs,与大多数文献来源不同。
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来源期刊
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
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
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