Abstract
Cytotoxicity testing remains a cornerstone of modern toxicology, providing critical insight into how chemicals and drugs affect cell viability and function. Classical colorimetric assays such as MTT, LDH release, and neutral red uptake established the methodological basis of in vitro toxicology and continue to serve as regulatory benchmarks. However, their limited mechanistic depth and physiological relevance have prompted the field to evolve towards more predictive and human-centred approaches. Recent advances in high-content imaging, flow cytometry, and real-time impedance analysis have transformed cytotoxicity testing into a multiparametric discipline capable of detecting adaptive and sub-lethal cellular responses. Parallel progress in computational toxicology has introduced in silico models-QSAR, machine learning, and physiologically based pharmacokinetic (PBPK) modelling-that enable quantitative in vitro-in vivo extrapolation (QIVIVE). The integration of these computational tools with 3D organoids, organ-on-chip systems, and stem cell-based models allows for cross-validation between predictive simulations and experimental evidence, enhancing mechanistic interpretation and translational accuracy. Together, these developments underpin New Approach Methodologies (NAMs) and Integrated Approaches to Testing and Assessment (IATA), marking the transition from descriptive assays to predictive, mechanism-anchored frameworks that bridge in silico prediction with in vitro and in vivo validation-advancing both biomedical research and regulatory toxicology.