| الوصف: |
Animal health risk assessment of chemicals aims to protect species from adverse health effects as a result of chemical exposure and ultimately also aims to protect human health. Chemical hazards in feed can come from various sources, including feed additives, process and storage-derived contaminants, natural and environmental contaminants, as well as pesticides. Since chemicalspecific in vivo data from animal studies is scarce, the application of new approach methodologies (NAMs) has become more common to address data gaps. An important application of NAMs is the use of in vitro metabolism data in chemical evaluations to fill data gaps with respect to potential differences in metabolic profiles between various animal species. Another important tool is the so called physiologically-based kinetic models (PBK) for animal species, which have shown to play a crucial role in the development of next-generation risk assessment approaches such as the prediction of plasma or target organ concentrations in various animal species. Over the last few years, the European food safety Authority (EFSA) together with academic and national authorities, have been developing modelling tools for human health, animal health and ecological risk assessment. These include generic kinetic, QIVIVE and PBK models for various species, including farm animal species (cattle, pig, sheep, chicken). These models have been published open-access, assessed and validated using relevant case studies following the principles laid out in the OECD guidance on using PBK models (OECD331, 2021). In addition, the models have been implemented in the TKPlate graphical interface to further support their implementation in food and feed safety. Here, applications of the PBK models are shown, focusing specifically on the prediction of blood and organ concentrations compared to experimental data. For future applications, species-specific and chemical-specific kinetic data should be provided by the user for PBK modelling purposes. Further work is needed to reduce ... |