From cellular perturbation to probabilistic risk assessments
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Abstract
Chemical risk assessment is evolving from traditional deterministic approaches to embrace probabilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxicological risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.
Plain language summary
Understanding chemical risks is key to public health. Traditional risk assessments rely on fixed safety margins and animal tests, which can miss complex human responses. Probabilistic risk assessment uses advanced tools – human stem cells, organ‑on‑chip systems, and AI – to estimate the likelihood of harm across different scenarios. By modeling individual variability (genetics, exposures) and quantifying uncertainty, it provides nuanced risk estimates rather than binary “safe/unsafe” labels. This approach increases transparency, shows confidence intervals, and reduces animal testing by integrating human‑relevant data. Challenges include defining harm thresholds, integrating diverse datasets, and gaining regulatory acceptance. Workshops like the 2023 CAAT-ONTOX meeting in Italy highlighted how measuring biological perturbations (e.g., molecular or cellular changes) informs probability of adverse outcomes. As technologies and data improve, probabilistic methods promise more realistic, protective chemical safety evaluations that reflect real‑world human diversity.
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