Integrating High-Throughput Toxicity Testing with Mechanistic and Kinetic Modeling for Food Chemical Safety Assessment

Abstract Food chemical safety assessment is increasingly challenged by diverse exposure scenarios arising from environmental contamination, food processing, food-contact materials (FCMs), and emerging food systems. These challenges highlight the need for approaches with greater scalability, mechanistic resolution, and human relevance. This review examines how high-throughput toxicity testing (HTT), integrated with mechanistic interpretation and kinetic modeling, can contribute to next-generation risk assessment (NGRA) of food-relevant chemicals. We summarize advances in omics-based toxicology, phenotypic profiling, adverse outcome pathway (AOP) frameworks, benchmark dose (BMD) modeling, and physiologically based kinetic modeling (PBK) coupled with quantitative in vitro to in vivo extrapolation (QIVIVE). Case studies involving contaminants, additives, food-contact material migrants, nanomaterials, and novel foods illustrate how HTT can identify early molecular, cellular, and phenotypic perturbations and provide mechanistic evidence for hazard prioritization. AOP frameworks provide mechanistic context for interpreting HTT-derived bioactivity signals, while BMD modeling and PBK-QIVIVE enable quantitative translation toward human-relevant dose metrics. However, regulatory application remains limited by insufficient quantitative AOPs, dose–response uncertainty, mixture complexity, and data standardization challenges. Coupling HTT with mechanistic and kinetic modeling provides an integrative decision support framework to advance more human-relevant, transparent, and adaptive food chemical safety assessment.