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首页> 外文期刊>Environmental Science & Technology >Application of Bayesian Population Physiologically Based Pharmacokinetic (PBPK) Modeling and Markov Chain Monte Carlo Simulations to Pesticide Kinetics Studies in Protected Marine Mammals: DDT, DDE, and DDD in Harbor Porpoises
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Application of Bayesian Population Physiologically Based Pharmacokinetic (PBPK) Modeling and Markov Chain Monte Carlo Simulations to Pesticide Kinetics Studies in Protected Marine Mammals: DDT, DDE, and DDD in Harbor Porpoises

机译：贝叶斯人口生理学上的药代动力学（PBPK）模型和马尔可夫链蒙特卡罗模拟法在港口海豚体内DDT，DDE和DDD农药动力学研究中的应用

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摘要

Physiologically based pharmacokinetic (PBPK) modeling in marine mammals is a challenge because of the lack of parameter information and the ban on exposure experiments. To minimize uncertainty and variability, parameter estimation methods are required for the development of reliable PBPK models. The present study is the first to develop PBPK models for the lifetime bioaccumulation of p,p'-DDT, p,p'-DDE, and p,p'-DDD in harbor porpoises. In addition, this study is also the first to apply the Bayesian approach executed with Markov chain Monte Carlo simulations using two data sets of harbor porpoises from the Black and North Seas. Parameters from the literature were used as priors for the first "model update" using the Black Sea data set, the resulting posterior parameters were then used as priors for the second "model update" using the North Sea data set. As such, PBPK models with parameters specific for harbor porpoises could be strengthened with more robust probability distributions. As the science and biomonitoring effort progress in this area, more data sets will become available to further strengthen and update the parameters in the PBPK models for harbor porpoises as a species anywhere in the world. Further, such an approach could very well be extended to other protected marine mammals.
机译：由于缺乏参数信息和禁止暴露实验，在海洋哺乳动物中基于生理学的药代动力学（PBPK）建模是一个挑战。为了使不确定性和可变性最小化，需要使用参数估计方法来开发可靠的PBPK模型。本研究是第一个开发PBPK模型用于港口海豚体内p，p'-DDT，p，p'-DDE和p，p'-DDD终生生物积累的模型。此外，这项研究也是第一个应用马尔可夫链蒙特卡罗模拟执行的贝叶斯方法，该方法使用了来自黑海和北海的两个海豚的数据集。来自文献的参数用作使用Black Sea数据集的第一个“模型更新”的先验，然后将所得后验参数用作使用North Sea数据集的第二个“模型更新”的先验。这样，可以通过更可靠的概率分布来增强具有特定于港口海豚的参数的PBPK模型。随着该领域科学和生物监测工作的进展，将有更多的数据集可用于进一步加强和更新PBPK模型中用于世界各地的海豚的物种的参数。此外，这种方法很可能会扩展到其他受保护的海洋哺乳动物。

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  • 来源
    《Environmental Science & Technology》 |2013年第9期|4365-4374|共10页
  • 作者

    Liesbeth Weijs; Raymond S. H. Yang; Krishna Das; Adrian Covaci; Ronny Blust;

  • 作者单位

    Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium,Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;

    Quantitative and Computational Toxicology Group, Department of Environmental and Radiological Health Sciences, Colorado State University, 1680 Campus Delivery, Fort Collins, 80523, Colorado, United States;

    Laboratory for Oceanology-MARE Center, University of Liege, 4000 Liege, Belgium;

    Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium;

    Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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