Developments in toxicokinetic modeling of chemical residues in food

Toxicokinetic modeling is critical for accurately assessing human health risks associated with chemical residues ingested through food. This chapter highlights recent advancements in toxicokinetic modeling, emphasizing physiologically based toxicokinetic (PBTK) models that predict internal doses more realistically. Such models incorporate detailed physiological parameters, enzyme activities, and organ-specific dynamics across different life stages, from infancy to adulthood. Innovations include generic PBTK models adaptable to numerous chemicals and sophisticated computational tools, such as quantitative structure-activity relationship (QSAR) models, machine learning, and Bayesian inference, which significantly enhance predictive power and address population variability. The chapter describes the shift toward next-generation risk assessment (NGRA), utilizing high-throughput in vitro data and in vitro-to-in vivo extrapolation (IVIVE) methods to minimize reliance on animal testing. Incorporating these advanced toxicokinetic modeling approaches into regulatory practices provides robust, human-centric risk evaluations, enabling more effective public health protection measures against foodborne chemical exposure.