Bayesian inference for whole-body physiologically-based toxicokinetic modeling of PCDD/F and PCB transfer to milk and tissues of high-yielding cows

• Predicts content-time profiles for the transfer of 29 congeners in tissues and milk. • Provides credible intervals for each prediction, communicating uncertainty. • Accounts for changing adipose tissue size and milk yield throughout lactation. • Modular Bayesian inference handles data imbalance and avoids overparameterization. Polychlorinated dibenzo- p -dioxins (PCDDs) and dibenzofurans (PCDFs), commonly known as “dioxins”, and polychlorinated biphenyls (PCBs) are persistent, environmentally ubiquitous lipophilic contaminants. Most human exposure comes from animal products, such as milk and meat. In this study, a whole-body physiologically-based toxicokinetic (wbPBTK) model is presented for the quantitative prediction of the transfer of these contaminants to milk and multiple tissues of high-yielding dairy cows. The model is parametrized using a comprehensive in vivo study in which cows were orally exposed to 35 dioxin and PCB congeners. To describe the transfer to cow tissues, the wbPBTK model incorporates dynamic changes in the physiological characteristics across lactation and comprises 11 compartments, including blood, adipose tissues, liver and udder. Congener-specific parameters were derived from the data based on a novel modular Bayesian inference approach designed to handle the numerical challenges of this experimental dataset, such as the imbalance between numerous milk measurements and comparatively few tissue measurements. A final model was derived for 29 congeners, showing at least reasonable performance in describing milk data for 28, adipose tissue data during lactation for 27, blood data for 21, and organ tissue data at slaughter for 21 congeners. It includes an estimation of the prediction uncertainties using credible intervals. A code implementation for German Holstein cows is included, which may be modified for other breeds, ages or milk yields. The model is also available for interactive calculation in the ConTrans.bfr.bund.de platform tool to support exposure assessment and risk management for dioxins and PCBs in dairy production.

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Publication Details

Journal
Computational Toxicology
Published
2026-04-03
DOI
https://doi.org/10.1016/j.comtox.2026.100412
Primary Topic
Toxic Organic Pollutants Impact
Type
article
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article

Bayesian inference for whole-body physiologically-based toxicokinetic modeling of PCDD/F and PCB transfer to milk and tissues of high-yielding cows

Jorge Numata, Jan-Louis Moenning, Julika Lamp, Robert Pieper et al.
Computational Toxicology
Toxic Organic Pollutants Impact
article

Bayesian inference for whole-body physiologically-based toxicokinetic modeling of PCDD/F and PCB transfer to milk and tissues of high-yielding cows

Jorge Numata, Jan-Louis Moenning, Julika Lamp, Robert Pieper, Torsten Krause
article en

Abstract

• Predicts content-time profiles for the transfer of 29 congeners in tissues and milk. • Provides credible intervals for each prediction, communicating uncertainty. • Accounts for changing adipose tissue size and milk yield throughout lactation. • Modular Bayesian inference handles data imbalance and avoids overparameterization. Polychlorinated dibenzo- p -dioxins (PCDDs) and dibenzofurans (PCDFs), commonly known as “dioxins”, and polychlorinated biphenyls (PCBs) are persistent, environmentally ubiquitous lipophilic contaminants. Most human exposure comes from animal products, such as milk and meat. In this study, a whole-body physiologically-based toxicokinetic (wbPBTK) model is presented for the quantitative prediction of the transfer of these contaminants to milk and multiple tissues of high-yielding dairy cows. The model is parametrized using a comprehensive in vivo study in which cows were orally exposed to 35 dioxin and PCB congeners. To describe the transfer to cow tissues, the wbPBTK model incorporates dynamic changes in the physiological characteristics across lactation and comprises 11 compartments, including blood, adipose tissues, liver and udder. Congener-specific parameters were derived from the data based on a novel modular Bayesian inference approach designed to handle the numerical challenges of this experimental dataset, such as the imbalance between numerous milk measurements and comparatively few tissue measurements. A final model was derived for 29 congeners, showing at least reasonable performance in describing milk data for 28, adipose tissue data during lactation for 27, blood data for 21, and organ tissue data at slaughter for 21 congeners. It includes an estimation of the prediction uncertainties using credible intervals. A code implementation for German Holstein cows is included, which may be modified for other breeds, ages or milk yields. The model is also available for interactive calculation in the ConTrans.bfr.bund.de platform tool to support exposure assessment and risk management for dioxins and PCBs in dairy production.

Computational ToxicologyVol. 38
Federal Institute for Risk Assessment (DE), Max Rubner Institut (DE)
Zero hunger
Openalex Percentile: Top 29%
Toxic Organic Pollutants Impact
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