Generic kinetic and kinetic‐dynamic modelling in human subgroups of the population and animal species to support transparency in food and feed safety: Case studies

Abstract The present report describes the work performed in the EFSA‐project ‘Data collection, update and further development of biologically‐based models for humans and animal species to support transparency in food and feed safety’. Here, Focus is given to case studies for food and feed chemicals to predict kinetic parameters and profiles using generic and substance‐specific physiologically‐based kinetic (PBK) models for humans, including human subgroups, laboratory animal species, farm animals and a kinetic‐dynamic model in salmon. For humans, five case studies were conducted to compare kinetic predictions using the human generic PBK 6‐compartment COSMOS/TKPlatewith i) in vivo data from human clinical or biomonitoring studies, ii) substance‐specific model predictions using molecules relevant to food safety. Another five case studies assessed the impact of physiological variability (including pregnancy, renal excretion, metabolism variability, or ontogeny) and their impact on biomarkers of exposure. Case studies on laboratory and farm animals focused on theophylline, caffeine, cannabinoids, alkaloids and mycotoxins using the generic 11/12 PBK compartment models integrated in EFSA's TKPlate to assess predicted and experimental parameters i.e. plasma concentrations, excretion via milk or eggs. Overall, predictions from the human generic and substance‐specific PBK models for parameters of chronic exposure were similar and robust compared to the available experimental data. For test species and farm animals, model predictions from the generic TKPlate PBK models also performed well and were mostly within 2‐fold compared to available experimental in vivo data. In addition, 3D molecular modelling case studies were also conducted to investigate transport of chemicals (ochratoxin A, perfluoroalkyls) and cytochrome P450 metabolism (ochratoxin A, safrole and other alkenylbenzenes) as a useful tool to generate metabolism information at the molecular level. Conclusions and recommendations for future work are formulated to further develop generic PBK models for parent compounds and metabolites and further guidance to use and parameterise these models in next generation risk assessment.