The probable future of toxicology – probabilistic risk assessment
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Abstract
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 artificial intelligence (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.
Plain language summary
This workshop report discusses the future of toxicology and how probabilistic risk assessment can help address uncertainties in assessing chemical risks. Experts emphasize the importance of quantitative assessment in toxicology and the need for a deeper understanding of how chemicals affect our health. By incorporating probabilistic risk assessment, we can better evaluate the potential risks posed by chemicals and make more informed decisions to protect human health and the environment. Embracing new technologies like artificial intelligence and natural language processing can enhance data analysis and improve the accuracy of risk assessments in toxicology.
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