This commentary finds that short-term in vivo studies using omics enhance risk assessment, suggesting better regulatory alignment is needed.
Toxicology is shifting toward predictive, mechanism-based approaches that support quicker, more human-relevant risk assessments and reduce reliance on animal testing. Central to this shift are short-term in vivo studies enriched with omics endpoints which provide early molecular indicators of toxicity. These data enable the derivation of molecular points of departures (mPODs) and other biologically anchored metrics that can inform potency ranking, hazard identification, and risk assessment. This commentary summarizes insights from the 2025 Society of Toxicology (SOT) session of the same name and highlights the importance of aligning technical advances with regulatory needs. Next Generation Risk Assessment (NGRA) is a safety evaluation approach that incorporates emerging tools such as in vitro methods, computational models, and omics data to inform decision-making for human health or the environment, while aiming to reduce dependence on traditional animal testing. NGRA frameworks, while potentially generic in principle, must be tailored to the specific regulatory requirements and exposure contexts of different product sectors, including pharmaceuticals, industrial chemicals, agrochemicals, and cosmetics. Short-term mechanistic animal studies serve as a bridge between traditional long-term animal testing and new approach methodologies (NAMs). From a technical standpoint, the generation, analysis, and interpretation of omics data have matured considerably, bringing regulatory acceptance within reach. Remaining challenges include standardizing bioinformatics pipelines, building confidence through validation against apical endpoints, and expanding training. Addressing these gaps through collaborative science and flexible regulatory frameworks will be key to realizing the full potential of omics-enabled hazard profiles to support NGRA.
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Mitchell et al. (2025) studied this question.
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