Beyond chemicals: Opportunities and challenges of integrating non-chemical stressors in adverse outcome pathways
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Notice bibliographique
Résumé
Adverse outcome pathways (AOPs) were developed to accelerate evidence-based chemical risk assessment by leveraging data from new approach methodologies. Thanks to their stressor-agnostic approach, AOPs were seen as instrumental in other fields. Here, we present AOPs that report non-chemical stressors along with the challenges encountered for their development. Challenges regarding AOPs linked to nanomaterials include non-specific molecular initiating events, limited understanding of nanomaterial biodistribution, and needs for adaptations of in silico modeling and testing systems. Development of AOPs for radiation faces challenges in how to incorporate ionizing events type, dose rate, energy deposition, and how to account for targeting multiple macromolecules. AOPs for COVID-19 required the inclusion of SARS-CoV-2-specific replicative steps to capture the essential events driving the disease. Developing AOPs to evaluate efficacy and toxicity of cell therapies necessitates addressing the cellular nature and the therapeutic function of the stressor. Finally, addressing toxicity of emerging biological stressors like microbial pesticides can learn from COVID-19 AOPs. We further discuss that the adaptations needed to expand AOP applicability beyond chemicals are mainly at the molecular and cellular levels, while downstream key events at tissue or organ level, such as inflammation, are shared by many AOPs initiated by various stressors. In conclusion, although it is challenging to integrate non-chemical stressors within AOPs, this expands opportunities to account for real-world scenarios, to identify vulnerable individuals, and to bridge knowledge on mechanisms of adversity. Plain language summary The adverse outcome pathway (AOP) framework was developed to help predict whether chemicals have toxic effects on humans. Structuring available information in an accessible database can reduce animal testing. AOPs usually capture the path from the interaction of a stressor, usually a chemical, with the human body to an adverse outcome, e.g., a disease symptom. The concept of AOPs has now been expanded to include non-chemical stressors such as nanomaterials, radiation, viruses, cells used to treat patients, and microorganisms employed as pesticides. We discuss how these stressors need to be accommodated within the framework and point out that pathways initiated by these stressors share downstream events like inflammation with chemical stressors. By integrating non-chemical stressors into the framework, real-world scenarios where people may be exposed to different stressor types can be considered, vulnerable individuals can be identified, and knowledge on toxic effects can be compounded.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle