ONTOX Insights #1

Metabolomics is a potent tool for enhancing preclinical drug safety assessment by unveiling biochemical alterations that underlie toxicity mechanisms. It aids in understanding adverse outcome pathways and the impact of environmental exposures on disease development. Increasingly integrated into toxicology studies, metabolomics provides mechanistic insights and early toxicity biomarkers. However, realizing its regulatory potential requires robust reliability demonstrations through quality assurance practices, reference materials, and interlaboratory studies. Metabolomics holds promise for strengthening mechanistic toxicity understanding, improving safety routine screening, and transforming exposure and risk assessment. Future applications in predictive toxicology will be integrated with computational, in vitro, and personalized medicine advancements.

The emerging emphasis on probabilistic risk assessment is driven by limitations in existing methodologies and recent advancements in machine learning tools. AI models enable the prediction of hazards and risks for particular endpoints, along with estimating the uncertainty of the risk assessment outcome and shifting from deterministic to probabilistic approaches. However, this transition demands increased resources and expertise. Challenges still need to be addressed before regulators can fully adopt this paradigm. A recent workshop explored the implementation of AI-based probabilistic hazard assessment, highlighting the transition to probabilistic and dose-dependent hazard outcomes, use of internal thresholds for data-poor substances, user-friendly open-source software, the requirement of heightened toxicologist expertise in interpreting AI models, and transparent communication of uncertainty in risk assessment to the public.

AI's rapid progress is poised to transform chemical safety evaluation in toxicology. With a shift from empirical observation to data-rich integration, machine learning, including deep neural networks and natural language processing, excels at handling diverse toxicological data sources. AI methods successfully predict toxicity endpoints, analyze high-throughput data, and provide probabilistic risk assessments. Despite challenges like interpretability and biases, collaborative efforts can develop trustworthy AI systems to enhance evidence gathering and hypothesis testing, advancing predictive, mechanism-based toxicology for improved human and environmental well-being across diverse populations.

Green toxicology revolutionizes chemical safety by integrating hazard evaluation, exposures and risks early in product development to minimize adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. It replaces traditional animal testing with faster, cost-effective innovations like organs-on-chips and AI predictive models. Core principles include alternative test methods, precautionary principle, considering lifetime impacts, and risk prevention. Despite challenges, green toxicology aligns with societal needs, offering human-relevant hazard information while minimizing animal suffering. Integration with green chemistry has the potential to shift chemical risk management towards more ethical and ecologically conscious practices.

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