Transfer of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) from oral exposure into cow's milk - part II: toxicokinetic predictive models for risk assessment.

Understanding the transfer of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) as well as polychlorinated biphenyls (PCBs) from oral exposure into cow's milk is not purely an experimental endeavour, as it has produced a large corpus of theoretical work. This work consists of a variety of predictive toxicokinetic models in the realms of health and environmental risk assessment and risk management. Their purpose is to provide mathematical predictive tools to organise and integrate knowledge on the absorption, distribution, metabolism and excretion processes. Toxicokinetic models are based on more than 50 years of transfer studies summarised in part I of this review series. Here in part II, several of these models are described and systematically classified with a focus on their applicability to risk analysis as well as their limitations. This part of the review highlights the opportunities and challenges along the way towards accurate, congener-specific predictive models applicable to changing animal breeds and husbandry conditions.

A probabilistic model for the carry-over of PCDD/Fs from feed to growing pigs.

When food producing animals are contaminated with PCDD/F congeners, information on the contaminant's concentration in the bodies of the animals at time of slaughter is needed for risk management purposes. We have developed a mathematical model for the kinetics of PCDD/Fs in growing pigs in case of contaminated feed fed for a limited duration of time. This model allows the prediction of concentrations in body fat. It considers absorption fractions of PCDD/Fs, clearance by metabolism, dilution by growth and excretion through fecal fat. The model parameters were calibrated by fitting the model to experimental data. On the basis of this toxicokinetic model a probabilistic model has been constructed. The probabilistic model handles the parameters with appropriate probability distributions and Monte-Carlo simulation technique, providing for realistic situations with many animals and a range of contaminations and feeding intervals. We applied the new model to describe the German dioxin incident of winter 2010/2011 and discuss its viability as decision tool. The approach demonstrated here is a showcase how a risk assessment in the case of contaminated feeding can be performed.