The probable future of toxicology - probabilistic risk assessment

Authors Alexandra Maertens, Eric Antignac, Emilio Benfenati, Denise Bloch, Ellen Fritsche, Sebastian Hoffmann, Joanna Jaworska, George Loizou, Kevin McNally, Przemyslaw Piechota, Edwin Roggen, Marc Teunis, Thomas Hartung
Published in ALTEX
Publication date 2024
Research groups Innovative Testing in Life Sciences and Chemistry
Type Article


Both because of the shortcomings of existing risk assessment methodologies, as well as newly available tools to predict hazard and risk with machine learning approaches, there has been an emerging emphasis on probabilistic risk assessment. Increasingly sophisticated AI models can be applied to a plethora of exposure and hazard data to obtain not only predictions for particular endpoints but also to estimate the uncertainty of the risk assessment outcome. This provides the basis for a shift from deterministic to more probabilistic approaches but comes at the cost of an increased complexity of the process as it requires more resources and human expertise. There are still challenges to overcome before a probabilistic paradigm is fully embraced by regulators. Based on an earlier white paper (Maertens et al., 2022), a workshop discussed the prospects, challenges and path forward for implementing such AI-based probabilistic hazard assessment. Moving forward, we will see the transition from categorized into probabilistic and dose-dependent hazard outcomes, the application of internal thresholds of toxicological concern for data-poor substances, the acknowledgement of user-friendly open-source software, a rise in the expertise of toxicologists required to understand and interpret artificial intelligence models, and the honest communication of uncertainty in risk assessment to the public.

On this publication contributed

  • Marc Teunis | Associate Professor | Research Group Innovative Testing in Life Sciences & Chemistry
    Marc Teunis
    • Associate professor
    • Research group: Innovative Testing in Life Sciences and Chemistry

Language English
Published in ALTEX
Year and volume 41 2
Key words artificial intelligence, chemical hazard, computational toxicology, new approach methodologies (NAMs), risk assessment
Digital Object Identifier 10.14573/altex.2310301
Page range 273-281

Innovative Testing in Life Sciences and Chemistry