Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Corradi, Marie; Haan, Alyanne M. de; Staumont, Bernard; Piersma, Aldert H.; Geris, Liesbet; Pieters, Raymond; Krul, Cyrille; Teunis, Marc

doi:10.1016/j.bbiosy.2022.100061

Cited by 8 publications

(2 citation statements)

References 37 publications

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“…AI text mining of enormous scientific literature can accelerate evidence gathering. For instance, natural language processing methods developed by Corradi et al ( 2022 ) could extract factual toxicological findings and relationships from PubMed abstracts and full texts. Further, AI techniques can unlock legacy toxicity data trapped as scan PDF reports via optical character recognition and document layout analysis (Palm et al 2019).…”

Section: Ai To Enable the Ontox Project Goalsmentioning

confidence: 99%

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer,

Hartung

2024

Arch Toxicol

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The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has the potential to transform chemical safety evaluation. Toxicology has evolved from an empirical science focused on observing apical outcomes of chemical exposure, to a data-rich field ripe for AI integration. The volume, variety and velocity of toxicological data from legacy studies, literature, high-throughput assays, sensor technologies and omics approaches create opportunities but also complexities that AI can help address. In particular, machine learning is well suited to handle and integrate large, heterogeneous datasets that are both structured and unstructured—a key challenge in modern toxicology. AI methods like deep neural networks, large language models, and natural language processing have successfully predicted toxicity endpoints, analyzed high-throughput data, extracted facts from literature, and generated synthetic data. Beyond automating data capture, analysis, and prediction, AI techniques show promise for accelerating quantitative risk assessment by providing probabilistic outputs to capture uncertainties. AI also enables explanation methods to unravel mechanisms and increase trust in modeled predictions. However, issues like model interpretability, data biases, and transparency currently limit regulatory endorsement of AI. Multidisciplinary collaboration is needed to ensure development of interpretable, robust, and human-centered AI systems. Rather than just automating human tasks at scale, transformative AI can catalyze innovation in how evidence is gathered, data are generated, hypotheses are formed and tested, and tasks are performed to usher new paradigms in chemical safety assessment. Used judiciously, AI has immense potential to advance toxicology into a more predictive, mechanism-based, and evidence-integrated scientific discipline to better safeguard human and environmental wellbeing across diverse populations.

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Section: Ai To Enable the Ontox Project Goalsmentioning

confidence: 99%

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Kleinstreuer,

Hartung

2024

Arch Toxicol

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“…Exciting AI applications in toxicology include: -Natural language processing of scientific literature and reports to extract toxicity data for modeling (Corradi et al, 2022). -Computer vision analysis of digital images from cell assays to quantify morphological changes and cytotoxicity.…”

Section: Avoiding Regrettable Substitutionsmentioning

confidence: 99%

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

2024

ALTEX

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Green toxicology is marching chemistry into the 21 st century. This emerging framework will transform how chemical safety is evaluated by incorporating evaluation of the hazards, exposures, and risks associated with chemicals into early product development in a way that minimizes adverse impacts on human and environmental health. The goal is to minimize toxic threats across entire supply chains through smarter designs and policies. Traditional animal testing methods are replaced by faster, cutting-edge innovations like organs-on-chips and artificial intelligence predictive models that are also more cost-effective. Core principles of green toxicology include utilizing alternative test methods, applying the precautionary principle, considering lifetime impacts, and emphasizing risk prevention over reaction. This paper provides an overview of these foundational concepts and describes current initiatives and future opportunities to advance the adoption of green toxicology approaches. Challenges and limitations are also discussed. Green shoots are emerging with governments offering carrots like the European Green Deal to nudge industry. Noteworthy, animal rights and environmental groups have different ideas about the needs for testing and their consequences for animal use. Green toxicology represents the way forward to support both these societal needs with sufficient throughput and human relevance for hazard information and minimal animal suffering. Green toxicology thus sets the stage to synergize human health and ecological values. Overall, the integration of green chemistry and toxicology has potential to profoundly shift how chemical risks are evaluated and managed to achieve safety goals in a more ethical, ecologically-conscious manner.

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Chemical-induced liver cancer: an adverse outcome pathway perspective

Sanz-Serrano,

Callewaert,

De Boever

et al. 2024

Expert Opinion on Drug Safety

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Natural language processing in toxicology: Delineating adverse outcome pathways and guiding the application of new approach methodologies

Cited by 8 publications

References 37 publications

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Artificial intelligence (AI)—it’s the end of the tox as we know it (and I feel fine)*

Alternative methods go green! Green toxicology as a sustainable approach for assessing chemical safety and designing safer chemicals

Chemical-induced liver cancer: an adverse outcome pathway perspective

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