Application of in vitro new approach methodologies data to chemical risk assessment: current status and perspectives toward next generation risk assessment

The rapid transition toward animal-free chemical safety evaluation has positioned in vitro new approach methodologies (NAMs) as central components of next-generation risk assessment (NGRA). Advances in complex in vitro systems, high-content phenotypic profiling, multi-omics technologies, and AI-assisted analytics have greatly expanded the capacity to characterize human-relevant biological responses. However, despite their scientific promise, the translation of NAM-derived information into regulatory decision-making remains challenging in general. Key bottlenecks include incomplete alignment with apical regulatory endpoints, limited toxicokinetic context in conventional in vitro systems, and substantial variability across assays, data structures, and analytical pipelines. This review aims to summarize the current state of in vitro NAM technologies, evaluate the major barriers limiting their regulatory application, and discuss emerging frameworks that enable their integration into NGRA. To strengthen regulatory relevance, increasing efforts focus on integrating mechanistic NAM outputs into adverse outcome pathway (AOP) frameworks and applying high-throughput toxicokinetic (HTTK) modeling to support in vitro –to– in vivo extrapolation (IVIVE). Early NGRA case studies show that NAM-based points of departure can, in some instances, approximate or bracket traditional in vivo thresholds, although results remain heterogeneous across chemical classes and endpoint domains. Going forward, progress in NAM-based risk assessment will depend not only on advancements in assay technologies but also on decision frameworks capable of effectively incorporating existing NAM evidence. Tiered and evidence-integrated approaches will be essential, particularly in light of the varied NAM data availability across chemicals. Strengthening the iterative exchange between NAM application and method development will help guide future improvements and support a more transparent, adaptive, and human-relevant assessment paradigm.