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

Autor:	Corradi MPF; Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht 3584 CS, the Netherlands., de Haan AM; Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht 3584 CS, the Netherlands., Staumont B; Biomechanics Research Unit, GIGA In Silico Medicine, University of Liège, Avenue de l'Hôpital 11, Liège 4000, Belgium., Piersma AH; Centre for Health Protection of the Dutch National Institute for Public Health and the Environment (RIVM), Heidelberglaan 8, Utrecht 3584 CS, the Netherlands., Geris L; Biomechanics Research Unit, GIGA In Silico Medicine, University of Liège, Avenue de l'Hôpital 11, Liège 4000, Belgium., Pieters RHH; Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht 3584 CS, the Netherlands., Krul CAM; Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht 3584 CS, the Netherlands., Teunis MAT; Innovative Testing in Life Sciences and Chemistry, University of Applied Sciences Utrecht, Heidelberglaan 7, Utrecht 3584 CS, the Netherlands.
Jazyk:	angličtina
Zdroj:	Biomaterials and biosystems [Biomater Biosyst] 2022 Jul 28; Vol. 7, pp. 100061. Date of Electronic Publication: 2022 Jul 28 (Print Publication: 2022).
DOI:	10.1016/j.bbiosy.2022.100061
Abstrakt:	Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiating event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and information, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advancement of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Authors.)
Databáze:	MEDLINE
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