We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Development of AOP relevant to microplastics based on toxicity mechanisms of chemical additives using ToxCast™ and deep learning models combined approach
AI summary Read the abstract
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.
Various additives are used in plastic products to improve the properties and the durability of the plastics. Their possible elution from the plastics when plastics are fragmented into micro- and nano-size in the environment is suspected to one of the major contributors to environmental and human toxicity of microplastics. In this context, to better understand the hazardous effect of microplastics, the toxicity of chemical additives was investigated. Fifty most common chemicals presented in plastics were selected as target additives. Their toxicity was systematically identified using apical and molecular toxicity databases, such as ChemIDplus and ToxCast™. Among the vast ToxCast assays, those having intended gene targets were selected for identification of the mechanism of toxicity of plastic additives. Deep learning artificial neural network models were further developed based on the ToxCast assays for the chemicals not tested in the ToxCast program. Using both the ToxCast database and deep learning models, active chemicals on each ToxCast assays were identified. Through correlation analysis between molecular targets from ToxCast and mammalian toxicity results from ChemIDplus, we identified the fifteen most relevant mechanisms of toxicity for the understanding mechanism of toxicity of plastic additives. They are neurotoxicity, inflammation, lipid metabolism, and cancer pathways. Based on these, along with, previously conducted systemic review on the mechanism of toxicity of microplastics, here we have proposed potential adverse outcome pathways (AOPs) relevant to microplastics pollution. This study also suggests in vivo and in vitro toxicity database and deep learning model combined approach is appropriate to provide insight into the toxicity mechanism of the broad range of environmental chemicals, such as plastic additives.
More Papers Like This
Adverse Outcomes Pathways (AOPs)
AI summary Read the abstract
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.
Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Strict Leave-One-Polymer-Out Validation
AI summary Read the abstract
Scientists used AI to predict how toxic different types of microplastics are to human lung cells, based on their chemical makeup. This matters because not all plastics are equally harmful, and this approach could help researchers flag risky plastics faster, without needing to test every single one in the lab.
The toxicological impact of PET-MPs exposure on atherosclerosis: insights from network toxicology, molecular docking, and machine learning
AI summary Read the abstract
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.
Leveraging integrative toxicogenomic approach towards development of stressor-centric adverse outcome pathway networks for plastic additives
AI summary Read the abstract
Using a toxicogenomics framework, researchers catalogued 6,470 plastic additives and traced molecular pathways connecting 1,287 of them to adverse health outcomes across 27 disease categories. This approach, known as adverse outcome pathway network mapping, reveals how plastic additives may contribute to a broad range of conditions from endocrine disruption to cardiovascular disease. The findings underscore the need to regulate plastic additives not just individually but as a class based on shared mechanisms of harm.
Predicting the toxicity of microplastic particles through machine learning models
AI summary Read the abstract
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.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.