Computational Toxicology for Nutraceutical Safety Assessment: Bridging Rapid Innovation with Reliable Risk Evaluation

The rapid expansion of the global nutraceutical industry has heightened the urgency for rigorous yet efficient safety assessment of food-derived bioactives. Conventional toxicological methods, while indispensable, are often costly, time-consuming, and insufficient to keep pace with the rapid product innovation in this sector. In this context, in silico models for predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), alongside broader computational toxicology tools, are increasingly recognized as scalable and cost-effective strategies for early-stage safety evaluation of nutraceuticals. Techniques such as quantitative structure–activity relationship (QSAR) modeling, molecular docking, and physiologically based pharmacokinetic (PBPK) simulation enable early prioritization of candidate compounds, forecasting of potential enzyme- or receptor-mediated interactions, and prediction of bioactive metabolite profiles, prior to costly experimental or clinical evaluation. Yet, their application in the nutraceutical domain presents unique challenges. Food matrices are chemically complex, bioactive data remain sparse and unevenly curated, and long-term, low-dose effects are difficult to capture with existing computational frameworks. These limitations constrain predictive accuracy and raise questions about general applicability across diverse classes of compounds. Therefore, this review critically examines both the opportunities and limitations of computational safety assessment for nutraceuticals. It synthesizes representative case studies, evaluates current methodological limitations, and discusses the evolving regulatory landscape alongside emerging integrative frameworks such as New Approach Methodologies (NAMs) that may guide the future of nutraceutical safety assessment.