从细胞扰动到概率风险评估。

ALTEX Pub Date : 2025-01-01 Epub Date: 2025-05-26 DOI:10.14573/altex.2501291
Alexandra Maertens, Breanne Kincaid, Eric Bridgeford, Celine Brochot, Arthur de Carvalho E Silva, Jean-Lou C M Dorne, Liesbet Geris, Trine Husøy, Nicole Kleinstreuer, Luiz C M Ladeira, Alistair Middleton, Joe Reynolds, Blanca Rodriguez, Erwin L Roggen, Giulia Russo, Kris Thayer, Thomas Hartung
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引用次数: 0

摘要

化学品风险评估正在从传统的确定性方法演变为采用概率方法,其中危害表现的风险被理解为取决于暴露、个体因素和随机过程的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,最近又由大规模人工智能模型推动。这些创新使人们能够更细致地了解化学危害,捕捉生物反应的复杂性和种群内的可变性。然而,每种技术都有其自身的不确定性,影响着对危险概率的估计。这种转变解决了过度简化危险评估的点估计和阈值的局限性,允许将动态可变性和不确定性指标整合到风险模型中。通过利用现代技术和广泛的毒理学数据,概率方法提供了化学品安全的全面评估。本文总结了2023年举行的一次研讨会,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,特别关注了生物扰动作为危害估计的基础。毒理学风险评估的未来取决于这些概率模型的成功整合,从而有希望进行更准确和全面的危害评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From cellular perturbation to probabilistic risk assessments.

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.

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