Bayesian inference for whole-body physiologically-based toxicokinetic modeling of PCDD/F and PCB transfer to milk and tissues of high-yielding cows
Year: 2026
Type: article
Abstract: 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.
Source: Computational Toxicology
Institutions: Federal Institute for Risk Assessment, Max Rubner Institut
Language: English
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,870
OpenAlex ID: W7148698793