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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Publication Details

Journal
ALTEX
Published
2025-01-01
DOI
https://doi.org/10.14573/altex.2501291
Citations
2
Primary Topic
Statistical Methods in Clinical Trials
Type
article
Field-Weighted Citation Impact
5.26

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article

From cellular perturbation to probabilistic risk assessments

Trine Husøy, Erwin Roggen, Giulia Russo, Kris Thayer et al.
2 citations
ALTEX
Statistical Methods in Clinical Trials
5.26
article

From cellular perturbation to probabilistic risk assessments

Trine Husøy, Erwin Roggen, Giulia Russo, Kris Thayer, Alexandra Maertens, Liesbet Geris, A. Middleton, Blanca Rodríguez, Breanne Kincaid, Céline Brochot, L.C. Maia Ladeira, J.L.C.M. Dorne, Nicole Kleinstreuer, Eric Bridgeford, Thomas Härtung, Joe Reynolds, Alexandre Neves Reis Araújo e Silva
article en
2 citations

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.

ALTEXVol. 42(3)
European Food Safety Authority (IT), Johns Hopkins University (US), University of Konstanz (DE), University of Oxford (GB), KU Leuven (BE)
European Commission, Rijksinstituut voor Volksgezondheid en Milieu
Openalex Percentile: Top 5%
Statistical Methods in Clinical Trials
5.26
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