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Papers
61,005 resultsShowing papers similar to Adverse Outcomes Pathways (AOPs)
ClearThe Development and Use of Adverse Outcome Pathways in Mechanistic Toxicology
This thesis explored Adverse Outcome Pathways (AOPs) as a structured mechanistic framework for understanding chemical toxicity without traditional animal testing. It covered AOP development, conceptual structure, and practical applications in modern toxicology, including for environmental contaminants like microplastics.
Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach
Researchers used ToxCast high-throughput screening data and deep learning models to build adverse outcome pathway (AOP) models for common plastic additive chemicals, identifying molecular initiating events and potential health endpoints relevant to human and environmental microplastic toxicity.
Adverse outcome pathways and in vitro toxicology strategies for microplastics hazard testing
Researchers proposed using the adverse outcome pathway framework to systematically assess microplastic hazards to human health, identifying mechanistic parallels with other well-characterized stressors that can guide prioritization of in vitro testing strategies for particles of different sizes, shapes, and chemistries.
Quantitative adverse outcome pathway (qAOP) models for toxicity prediction
This review examines the developing concept of quantitative adverse outcome pathways (qAOPs) — computational models linking molecular initiating events to population-level harm — and proposes a framework for their development, validation, and use in chemical risk assessment.
The hepatotoxicity assessment of micro/nanoplastics: A preliminary study to apply the adverse outcome pathways
Researchers reviewed the literature on how micro- and nanoplastics cause liver damage and organized the findings into an Adverse Outcome Pathway framework. They found that plastic particles can trigger oxidative stress, inflammation, and metabolic disruption in the liver, potentially leading to dysfunction. The study provides a structured way to understand the chain of events from plastic particle exposure to liver harm, highlighting potential health risks for humans.
Adverse outcome pathway networks of microplastic ecotoxicity to aquatic organisms: A critical review
Researchers used automated text-mining combined with multi-level ecotoxicological review to construct adverse outcome pathway networks for microplastic toxicity in aquatic organisms. They mapped how microplastics cause harm from initial tissue contact through molecular disturbances to higher-level biological effects in gills, gut, liver, gonads, and brain. The study found strong evidence for early-stage toxic mechanisms but identified critical knowledge gaps in understanding downstream biological consequences.
A computational framework for multi-scale data fusion in assessing the associations between micro- and nanoplastics and human hepatotoxicity
Researchers developed a computational toxicology framework integrating multi-source data and network analysis to map associations between micro- and nanoplastics and hepatotoxicity, identifying key molecular pathways through which MNPs may damage the liver, offering a scalable alternative to traditional in vivo testing.
Potential threat of microplastics to humans: toxicity prediction modeling by small data analysis
Researchers developed a toxicity prediction model for microplastics using small data analysis techniques, enabling the anticipation of varying toxic effects depending on microplastic types and compositions found in nature.
Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
This review evaluates how machine learning and artificial intelligence are being used to predict the toxic effects of nanomaterials, including nanoplastics, on human health and the environment. These computational tools can help screen thousands of materials for potential hazards much faster than traditional lab experiments, though the authors note that better data quality and standardized methods are still needed.
An Adverse Outcome Pathway for food nanomaterial-induced intestinal barrier disruption
Researchers developed an Adverse Outcome Pathway (AOP) linking food nanomaterial ingestion to intestinal barrier disruption, synthesizing existing evidence while identifying critical gaps in standardized testing methods. This framework is directly applicable to nanoplastics, providing a regulatory toxicology tool for assessing how ingested nano-sized plastic particles could damage gut barrier integrity.
Multi-Omics Approach on the Ecotoxicological Assessment of Microplastics
This review examines the application of multi-omics approaches — including genomics, transcriptomics, proteomics, and metabolomics — to the ecotoxicological assessment of microplastics in living organisms. The authors synthesize how these integrated molecular tools are advancing understanding of the mechanistic pathways by which microplastics disrupt biological systems, offering a more comprehensive picture than single-endpoint toxicity studies.
Ecotoxicoproteomic assessment of microplastics and plastic additives in aquatic organisms: A review
This review examines how proteomics — the large-scale study of proteins — is being applied to understand the toxic effects of microplastics and plastic additives on aquatic organisms, and how this data can feed into adverse outcome pathway frameworks for ecological risk assessment.
Microplastic toxicity: mechanisms, assessment methods, and future research directions
This review synthesizes current knowledge on microplastic toxicity mechanisms, integrating physical, chemical, and biological pathways into a unified framework. Researchers examined assessment methods across aquatic organisms, terrestrial species, and human cell models, identifying critical knowledge gaps and recommending standardized approaches for future microplastic toxicity research.
Predicting the toxicity of microplastic particles through machine learning models
Researchers applied machine learning models to predict the toxicity of microplastic particles from their physical and chemical properties, addressing the challenge that microplastics lack the standardized identifiers used for chemical hazard classification. The models successfully predicted toxicity outcomes from particle descriptors, offering a framework for hazard screening of the diverse and complex microplastic contaminant class.
Adverse outcome pathways potentially related to hazard identification of microplastics based on toxicity mechanisms
Microplastic toxicology research has focused primarily on apical endpoints (mortality, growth, reproduction) rather than mechanisms, but this review identifies reactive oxygen species formation as the likely molecular initiating event in adverse outcome pathways, leading to oxidative stress, inflammation, and organ-level damage.
The toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
Researchers used network toxicology, molecular docking, and machine learning to identify how PET microplastics may promote atherosclerosis, narrowing 28 candidate targets to seven key genes and predicting interactions with atherosclerosis-relevant pathways including inflammation and lipid metabolism.
A critical review of microplastics toxicity and potential adverse outcome pathway in human gastrointestinal tract following oral exposure
This review uses an adverse outcome pathway framework to systematically evaluate how microplastics may cause harm in the human digestive system after being swallowed. The analysis found that while microplastics trigger recognized biological stress responses like cell death and inflammation, there are still major gaps in understanding exactly how they initiate damage at the molecular level. The authors emphasize that we need better data on both external exposure from food and water and internal exposure from particles crossing the gut lining to properly assess the health risks.
Predicting the toxicity of microplastic particles through machine learning models
Researchers developed machine learning models to predict microplastic particle toxicity from physical and chemical descriptors, addressing the classification challenge posed by the enormous diversity of particle types that cannot be characterized using conventional chemical hazard methods. The models provided accurate toxicity predictions across diverse microplastic types, offering a practical screening tool for the field.
Science-based evidence on pathways and effects of human exposure to micro- and nanoplastics
This review summarizes current scientific knowledge on how humans are exposed to micro- and nanoplastics through food, water, and air, and what health effects these particles may cause. Researchers highlight significant gaps in understanding the biological fate of plastics once inside the body and the difficulty of accurately measuring real-world exposure levels. The study recommends developing standardized adverse outcome pathways to improve risk assessment for plastic particle exposure.
Integrating aggregate exposure pathway and adverse outcome pathway for micro/nanoplastics: A review on exposure, toxicokinetics, and toxicity studies
This review brings together research on how micro and nanoplastics enter the human body, where they go once inside, and what harm they may cause, using a framework that links exposure pathways to health outcomes. Studies show these tiny particles can be absorbed through the gut, lungs, and skin, and may accumulate in organs like the liver and kidneys. The paper highlights that micro and nanoplastics can trigger inflammation, oxidative stress, and disruption of hormones, though more research is needed to fully understand the long-term health risks.
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics
Researchers used machine learning to predict the toxicity of five common microplastic types on human lung cells, finding that particle size, plastic type, and exposure concentration were the most important factors determining harm. This computational approach could help assess the health risks of different microplastics more efficiently than traditional lab testing alone.
Prediction of the cytotoxicity of micro- and nanoplastics using machine learning combined with literature data mining
Researchers developed a machine learning framework using decision tree ensemble classifiers trained on 1,824 literature-derived data points to predict the cytotoxicity of micro- and nanoplastics based on nine physicochemical and experimental features. The full-feature model achieved 95% accuracy with 86% recall and precision, and feature selection identified six key predictors, providing a tool to guide experimental design and harmonize MNP toxicity research.
Microplastics toxicity: Classification, sources, exposure routes, and experiments
This review summarized the classification, sources, exposure routes, and toxicological effects of microplastics across environmental matrices and biological systems. The authors synthesized evidence from multiple exposure experiments to assess human health risks from ingestion, inhalation, and dermal contact with common plastic polymers.
Toxicity and mechanism analysis of microplastics
This review summarized experimental evidence on the toxicity and mechanisms of action of microplastics across animal models, covering effects from ingestion including organ damage, oxidative stress, and immune disruption. The synthesis aimed to inform risk assessment for environmental and human health impacts of microplastic exposure.