AOP4EUpest: mapping of pesticides in adverse outcome pathways using a text mining tool

Abstract

Motivation Exposure to pesticides may lead to adverse health effects in human populations, in particular vulnerable groups. The main long-term health concerns are neurodevelopmental disorders, carcinogenicity as well as endocrine disruption possibly leading to reproductive and metabolic disorders. Adverse Outcome Pathways (AOP) consist in linear representations of mechanistic perturbations at different levels of the biological organization. Although AOPs are chemical-agnostic, they can provide a better understanding of the Mode of Action of pesticides and can support a rational identification of effect markers. Results With the increasing amount of scientific literature and the development of biological databases, investigation of putative links between pesticides, from various chemical groups, and AOPs using the biological events present in the AOP-Wiki database is now feasible. To identify co-occurrence between a specific pesticide and a biological event in scientific abstracts from the PubMed database, we used an updated version of the artificial intelligence-based AOP-helpFinder tool. This allowed us to decipher multiple links between the studied substances and molecular initiating events (MIE), key events (KE) and adverse outcomes (AO). These results were collected, structured and presented in a web application named AOP4EUpest that can support regulatory assessment of the prioritized pesticides, and trigger new epidemiological and experimental studies. Availability and implementation http://www.biomedicale.parisdescartes.fr/aop4EUpest/home.php Supplementary information Supplementary data and information are available at Bioinformatics online and on GitHub https://github.com/jornod/aophelpfinder2 .

Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome

“…One way could be to adapt existing tools such as AOP-helpFinder, which uses artificial intelligence and text mining to automatically screen the literature, to compile more data (42,43). For example, this tool was successfully applied to a set of pesticides by automatically identifying among the 32 million abstracts present in the PubMed database, links between pesticides and events (44). Another way would be to gather information from publicly available databases, which should become more accessible within the next few years following the interoperability of the data based on the goals established by Findable, Accessible, Interoperable, and Reusable (FAIR) data principles (45).…”

Evidenced-Based Approaches to Support the Development of Endocrine-Mediated Adverse Outcome Pathways: Challenges and Opportunities

“…Specificity and precision have the advantage of making a KER more searchable. Notably, an artificial intelligence based approach using text mining and graph theory, named AOP-helpFinder, allows to automatically identify and extract specific AOP-related terms and evidence (aop-helpfinder.u-paris-sciences.fr) (Jornod et al, 2020;Jornod et al, 2021;Zgheib et al, 2021). In "Rugard et al, 2020", AOP-helpFinder results pointed out linkages between Bisphenol-F and relevant MIEs [i.e., "PPARc inhibition" (Peroxisome proliferator-activated receptor γ)] often associated with liver steatosis and lipid accumulation (Yang et al, 2010).…”

AOP-helpFinder webserver: a tool for comprehensive analysis of the literature to support adverse outcome pathways development

“…For example, Limtox provides a biomedical search for adverse hepatobiliary reactions ( Cañada et al , 2017 ). Recently, the AOP-helpFinder tool, based on TM and graph theory was proposed to identify stressor-KE relationships by examining large collections of scientific abstracts, and was applied to bisphenol A substituents and pesticides ( Carvaillo et al , 2019 ; Jornod et al , 2020 ; Rugard et al , 2020 ).…”