RESUMEN
Respiratory sensitization is a complex immunological process eventually leading to hypersensitivity following re-exposure to the chemical. A frequent consequence is occupational asthma, which may occur after long latency periods. Although chemical-induced respiratory hypersensitivity has been known for decades, there are currently no comprehensive and validated approaches available for the prospective identification of chemicals that induce respiratory sensitization, while the expectations of new approach methodologies (NAMs) are high. A great hope is that due to a better understanding of the molecular key events, new methods can be developed now. However, this is a big challenge due to the different chemical classes to which respiratory sensitizers belong, as well as because of the complexity of the response and the late manifestation of symptoms. In this review article, the current information on respiratory sensitization related processes is summarized by introducing it in the available adverse outcome pathway (AOP) concept. Potentially useful models for prediction are discussed. Knowledge gaps and gaps of regulatory concern are identified.
RESUMEN
Introduction: The Adverse Outcome Pathway (AOP) concept facilitates rapid hazard assessment for human health risks. AOPs are constantly evolving, their number is growing, and they are referenced in the AOP-Wiki database, which is supported by the OECD. Here, we present a study that aims at identifying well-defined biological areas, as well as gaps within the AOP-Wiki for future research needs. It does not intend to provide a systematic and comprehensive summary of the available literature on AOPs but summarizes and maps biological knowledge and diseases represented by the already developed AOPs (with OECD endorsed status or under validation). Methods: Knowledge from the AOP-Wiki database were extracted and prepared for analysis using a multi-step procedure. An automatic mapping of the existing information on AOPs (i.e., genes/proteins and diseases) was performed using bioinformatics tools (i.e., overrepresentation analysis using Gene Ontology and DisGeNET), allowing both the classification of AOPs and the development of AOP networks (AOPN). Results: AOPs related to diseases of the genitourinary system, neoplasms and developmental anomalies are the most frequently investigated on the AOP-Wiki. An evaluation of the three priority cases (i.e., immunotoxicity and non-genotoxic carcinogenesis, endocrine and metabolic disruption, and developmental and adult neurotoxicity) of the EU-funded PARC project (Partnership for the Risk Assessment of Chemicals) are presented. These were used to highlight under- and over-represented adverse outcomes and to identify and prioritize gaps for further research. Discussion: These results contribute to a more comprehensive understanding of the adverse effects associated with the molecular events in AOPs, and aid in refining risk assessment for stressors and mitigation strategies. Moreover, the FAIRness (i.e., data which meets principles of findability, accessibility, interoperability, and reusability (FAIR)) of the AOPs appears to be an important consideration for further development.
RESUMEN
To support the use of alternative methods in regulatory assessment of chemical risks, the concept of adverse outcome pathway (AOP) constitutes an important toxicological tool. AOP represents a structured representation of existing knowledge, linking molecular initiating event (MIE) initiated by a prototypical stressor that leads to a cascade of biological key event (KE) to an adverse outcome (AO). Biological information to develop such AOP is very dispersed in various data sources. To increase the chance of capturing relevant existing data to develop a new AOP, the AOP-helpFinder tool was recently implemented to assist researchers to design new AOP. Here, an updated version of AOP-helpFinder proposes novel functionalities. The main one being the implementation of an automatic screening of the abstracts from the PubMed database to identify and extract event-event associations. In addition, a new scoring system was created to classify the identified co-occurred terms (stressor-event or event-event (which represent key event relationships) to help prioritization and support the weight of evidence approach, allowing a global assessment of the strength and reliability of the AOP. Moreover, to facilitate interpretation of the results, visualization options are also proposed. The AOP-helpFinder source code are fully accessible via GitHub, and searches can be performed via a web interface at http://aop-helpfinder-v2.u-paris-sciences.fr/.
Asunto(s)
Rutas de Resultados Adversos , Medición de Riesgo/métodos , Reproducibilidad de los Resultados , Bases de Datos Factuales , Manejo de DatosRESUMEN
Prenatal exposure to per- and polyfluorinated substances (PFAS) may impair fetal growth. Our knowledge of the underlying mechanisms is incomplete. We used the Adverse Outcome Pathway (AOP)-helpFinder tool to search PubMed for studies published until March 2021 that examined PFAS exposure in relation to birth weight, oxidative stress, hormones/hormone receptors, or growth signaling pathways. Of these 1880 articles, 106 experimental studies remained after abstract screening. One clear finding is that PFAS are associated with oxidative stress in in vivo animal studies and in vitro studies. It appears that PFAS-induced reactive-oxygen species (ROS) generation triggers increased peroxisome proliferator-activated receptor (PPAR)γ expression and activation of growth signaling pathways, leading to hyperdifferentiation of pre-adipocytes. Fewer proliferating pre-adipocytes result in lower adipose tissue weight and in this way may reduce birth weight. PFAS may also impair fetal growth through endocrine effects. Estrogenic effects have been noted in in vivo and in vitro studies. Overall, data suggest thyroid-damaging effects of PFAS affecting thyroid hormones, thyroid hormone gene expression, and histology that are associated in animal studies with decreased body and organ weight. The effects of PFAS on the complex relationships between oxidative stress, endocrine system function, adipogenesis, and fetal growth should be further explored.
RESUMEN
Human exposure to per- and polyfluoroalkyl substances (PFAS) has been associated with numerous adverse health effects, depending on various factors such as the conditions of exposure (dose/concentration, duration, route of exposure, etc.) and characteristics associated with the exposed target (e.g., age, sex, ethnicity, health status, and genetic predisposition). The biological mechanisms by which PFAS might affect systems are largely unknown. To support the risk assessment process, AOP-helpFinder, a new artificial intelligence tool, was used to rapidly and systematically explore all available published information in the PubMed database. The aim was to identify existing associations between PFAS and metabolic health outcomes that may be relevant to support building adverse outcome pathways (AOPs). The collected information was manually organized to investigate linkages between PFAS exposures and metabolic health outcomes, including dyslipidemia, hypertension, insulin resistance, and obesity. Links between PFAS exposure and events from the existing metabolic-related AOPs were also retrieved. In conclusion, by analyzing dispersed information from the literature, we could identify some associations between PFAS exposure and components of existing AOPs. Additionally, we identified some linkages between PFAS exposure and metabolic outcomes for which only sparse information is available or which are not yet present in the AOP-wiki database that could be addressed in future research.
RESUMEN
Adverse outcome pathways (AOPs) are formalized and structured linear concepts that connect one molecular initiating event (MIE) to an adverse outcome (AO) via different key events (KE) through key event relationships (KER). They are mainly used in eco-toxicology toxicology, and regulatory health issues. AOPs must respond to specific guidelines from the Organization for Economic Co-operation and Development (OECD) to weight the evidence between each KE. Breast cancer is the deadliest cancer in women with a poor prognosis in case of metastatic breast cancer. The role of the environments in the formation of metastasis has been suggested. We hypothesized that activation of the AhR (MIE), a xenobiotic receptor, could lead to breast cancer related death (AO), through different KEs, constituting a new AOP. An artificial intelligence tool (AOP-helpfinder), which screens the available literature, was used to collect all existing scientific abstracts to build a novel AOP, using a list of key words. Four hundred and seven abstracts were found containing at least a word from our MIE list and either one word from our AO or KE list. A manual curation retained 113 pertinent articles, which were also screened using PubTator. From these analyses, an AOP was created linking the activation of the AhR to breast cancer related death through decreased apoptosis, inflammation, endothelial cell migration, angiogenesis, and invasion. These KEs promote an increased tumor growth, angiogenesis and migration which leads to breast cancer metastasis and breast cancer related death. The evidence of the proposed AOP was weighted using the tailored Bradford Hill criteria and the OECD guidelines. The confidence in our AOP was considered strong. An in vitro validation must be carried out, but our review proposes a strong relationship between AhR activation and breast cancer-related death with an innovative use of an artificial intelligence literature search.
Asunto(s)
Rutas de Resultados Adversos , Neoplasias de la Mama , Apoptosis , Inteligencia Artificial , Femenino , Humanos , Medición de RiesgoRESUMEN
MOTIVATION: Adverse outcome pathways (AOPs) are a conceptual framework developed to support the use of alternative toxicology approaches in the risk assessment. AOPs are structured linear organizations of existing knowledge illustrating causal pathways from the initial molecular perturbation triggered by various stressors, through key events (KEs) at different levels of biology, to the ultimate health or ecotoxicological adverse outcome. RESULTS: Artificial intelligence can be used to systematically explore available toxicological data that can be parsed in the scientific literature. Recently, a tool called AOP-helpFinder was developed to identify associations between stressors and KEs supporting thus documentation of AOPs. To facilitate the utilization of this advanced bioinformatics tool by the scientific and the regulatory community, a webserver was created. The proposed AOP-helpFinder webserver uses better performing version of the tool which reduces the need for manual curation of the obtained results. As an example, the server was successfully applied to explore relationships of a set of endocrine disruptors with metabolic-related events. The AOP-helpFinder webserver assists in a rapid evaluation of existing knowledge stored in the PubMed database, a global resource of scientific information, to build AOPs and Adverse Outcome Networks supporting the chemical risk assessment. AVAILABILITY AND IMPLEMENTATION: AOP-helpFinder is available at http://aop-helpfinder.u-paris-sciences.fr/index.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Asunto(s)
Rutas de Resultados Adversos , Inteligencia Artificial , Medición de Riesgo/métodos , Bases de Datos Factuales , Manejo de DatosRESUMEN
BACKGROUND: Exposure to endocrine disrupting chemicals (EDCs) represents a critical public health threat. Several adverse health outcomes (e.g., cancers, metabolic and neurocognitive/neurodevelopmental disorders, infertility, immune diseases and allergies) are associated with exposure to EDCs. However, the regulatory tests that are currently employed in the EU to identify EDCs do not assess all of the endocrine pathways. OBJECTIVE: Our objective was to explore the literature, guidelines and databases to identify relevant and reliable test methods which could be used for prioritization and regulatory pre-validation of EDCs in missing and urgent key areas. METHODS: Abstracts of articles referenced in PubMed were automatically screened using an updated version of the AOP-helpFinder text mining approach. Other available sources were manually explored. Exclusion criteria (computational methods, specific tests for estrogen receptors, tests under validation or already validated, methods accepted by regulatory bodies) were applied according to the priorities of the French Public-privatE Platform for the Pre-validation of Endocrine disRuptors (PEPPER) characterisation methods. RESULTS: 226 unique non-validated methods were identified. These experimental methods (in vitro and in vivo) were developed for 30 species using diverse techniques (e.g., reporter gene assays and radioimmunoassays). We retrieved bioassays mainly for the reproductive system, growth/developmental systems, lipogenesis/adipogenicity, thyroid, steroidogenesis, liver metabolism-mediated toxicity, and more specifically for the androgen-, thyroid hormone-, glucocorticoid- and aryl hydrocarbon receptors. CONCLUSION: We identified methods to characterize EDCs which could be relevant for regulatory pre-validation and, ultimately for the efficient prevention of EDC-related severe health outcomes. This integrative approach highlights a successful and complementary strategy which combines computational and manual curation approaches.
Asunto(s)
Disruptores Endocrinos , Inteligencia Artificial , Bioensayo , Disruptores Endocrinos/toxicidad , Sistema Endocrino , Receptores de EstrógenosRESUMEN
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, key events and adverse outcomes. 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 are available at Bioinformatics online.