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61,005 resultsShowing papers similar to Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach
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.
Adverse Outcomes Pathways (AOPs)
This review examines the concept of adverse outcome pathways (AOPs) and their application to environmental health risk assessment, including the prediction of microplastic toxicity through data mining approaches. Researchers found that AOPs can support the reduction of animal testing by identifying data gaps and guiding the development of in silico and in vitro tests for toxicity prediction.
Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives
Researchers applied integrative toxicogenomics to develop adverse outcome pathway networks for plastic additives that leach into the environment during plastic degradation. The study suggests that this approach can help systematically assess the health risks of chemical additives released from plastics across atmospheric, terrestrial, and aquatic ecosystems.
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.
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.
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.
A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations
Researchers developed a machine learning method combined with biological pathway analysis to predict the toxic effects of chemical mixtures, including PFAS (forever chemicals), at different doses. The approach was validated with lab experiments and accurately identified how chemical mixtures cause harm at the cellular level. While focused on PFAS rather than microplastics, this tool is relevant because microplastics often carry chemical mixtures, and better methods to assess combined toxicity are needed to understand real-world health risks.
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.
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.
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.
Exploring the prognostic implications of PET microplastic degradation products in colorectal cancer: insights from an integrated computational analysis on glucocorticoid pathway–mediated mechanisms
Combining network toxicology, machine learning, and molecular docking, this study found that PET plastic degradation products ethylene glycol and terephthalic acid may influence colorectal cancer prognosis through 43 shared genes linked to TNF/IL-17 signaling and glucocorticoid-mediated metabolic pathways.
Toxicogenomic analysis of the carcinogenic potential of plastic additives
Researchers performed a toxicogenomic analysis of 2,712 plastic additives across chemical databases, finding that over 150 have known carcinogenicity while roughly 90% lack any cancer-related safety data, and that both carcinogenic and unstudied additives share biological pathways involving DNA damage and immune disruption.
Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms
Researchers used integrated machine learning and bioinformatic analysis to identify key molecular markers and pathways associated with microplastic-induced biological effects, generating mechanistic hypotheses for further experimental validation.
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.
Machine learning-driven QSAR models for predicting the mixture toxicity of nanoparticles
Researchers used machine learning to predict how toxic different mixtures of metal nanoparticles are to bacteria. Their models outperformed traditional methods at predicting combined toxicity effects. While focused on engineered nanoparticles rather than microplastics, the computational approach could be adapted to predict health risks from the complex mixtures of nano-sized pollutants people encounter.
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.
Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models
Scientists used advanced machine learning models to predict how microplastics interact with and absorb organic pollutants in water. The results showed that microplastics with certain chemical properties attract more toxic compounds, which matters because contaminated microplastics in waterways can concentrate harmful chemicals that may eventually reach humans through drinking water and seafood.
Metabolomics‑driven, data‑augmented machine learning for predicting toxicity of microplastic mixtures
Scientists developed a computer model that can predict how harmful mixtures of microplastics (tiny plastic particles) might be to our cells without testing each combination individually. The model works by analyzing how these plastic particles change the way cells produce energy, which helps explain why microplastics can be toxic. This breakthrough could help researchers quickly assess health risks from the complex mix of microplastics we're exposed to in real life through food, water, and air.
Unraveling the ecotoxicity of micro(nano)plastics loaded with environmental pollutants using ensemble machine learning.
Researchers developed an ensemble machine learning algorithm to predict the ecotoxicity of micro(nano)plastics loaded with environmental pollutants, addressing a key knowledge gap where most studies examine plastic particles alone. The model revealed that co-pollutant loading substantially amplifies toxicity and that particle characteristics govern outcomes.
The use of artificial neural networks in modelling migration pollutants from the degradation of microplastics
Researchers used artificial neural networks to model the emission of additives from degrading microplastics, finding that machine learning could predict migration patterns from the vast range of polymer types, chemical structures, and environmental conditions involved. This approach could reduce the need for extensive laboratory testing by identifying high-risk scenarios for further investigation.
Human Endocrine-Disrupting Effects of Phthalate Esters through Adverse Outcome Pathways: A Comprehensive Mechanism Analysis
Researchers used machine learning techniques to build adverse outcome pathways for understanding how phthalate esters, commonly used as plasticizers in plastics, may disrupt the human endocrine system. The study identified key molecular features and biological events that influence endocrine disruption effects, providing a framework for assessing the potential health impacts of phthalate exposure.
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.
Integrative network toxicology and molecular docking preliminarily explore the potential role of polystyrene microplastics in childhood obesity
Researchers used computational methods including network toxicology, machine learning, and molecular docking to explore how polystyrene microplastics might contribute to childhood obesity. They identified 40 overlapping genes between obesity-related and microplastic-affected pathways, concentrated in lipid metabolism and insulin signaling. The study suggests that polystyrene microplastics may act as environmental triggers capable of disrupting metabolic balance by interacting with key regulatory genes.
In silico insights into microplastic additive toxicity: Risks of pulmonary fibrosis and endocrine disruption
Researchers used computational modeling to investigate how five common microplastic additives, including phthalates and flame retardants, interact with proteins involved in lung fibrosis and endocrine function. Molecular docking revealed that these additives bind strongly to fibrotic markers like TGF-beta and to hormone receptors, suggesting potential mechanisms for tissue damage and hormonal disruption. The study highlights the need for further investigation into the health risks posed by chemical additives leaching from microplastics.