Maertens Alexandra, Kincaid Breanne, Bridgeford Eric, Brochot Celine, de Carvalho E Silva Arthur, Dorne Jean-Lou C M, Geris Liesbet, Husøy Trine, Kleinstreuer Nicole, Ladeira Luiz C M, Middleton Alistair, Reynolds Joe, Rodriguez Blanca, Roggen Erwin L, Russo Giulia, Thayer Kris, Hartung Thomas
Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health and Whiting School of Engineering, Baltimore, MD, USA.
Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
ALTEX. 2025;42(3):413-434. doi: 10.14573/altex.2501291. Epub 2025 May 26.
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.
化学风险评估正在从传统的确定性方法发展到采用概率方法,在概率方法中,危害表现的风险被理解为根据暴露、个体因素和随机过程或多或少可能发生的事件。这是由人类干细胞、复杂组织工程、高性能计算和化学信息学的进步推动的,最近大规模人工智能模型也起到了促进作用。这些创新使人们能够更细致入微地理解化学危害,把握生物反应的复杂性以及人群中的变异性。然而,每项技术都有其自身的不确定性,会影响危害概率的估计。这种转变解决了点估计和阈值的局限性,这些局限性过度简化了危害评估,使得能够将动力学变异性和不确定性指标纳入风险模型。通过利用现代技术和广泛的毒理学数据,概率方法提供了对化学安全性的全面评估。本文总结了2023年举办的一次研讨会,并讨论了技术和数据驱动的推动因素,以及在实施过程中面临的挑战,特别关注作为危害估计基础的生物学扰动。毒理学风险评估的未来在于这些概率模型的成功整合,有望实现更准确、更全面的危害评估。