AIMC Topic: Adverse Outcome Pathways

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Hepatic toxicity prediction of bisphenol analogs by machine learning strategy.

The Science of the total environment May 21, 2024
Toxicological studies have demonstrated the hepatic toxicity of several bisphenol analogs (BPs), a prevalent type of endocrine disruptor. The development of Adverse Outcome Pathway (AOP) has substantially contributed to the rapid risk assessment for ...
Molecular Docking Simulation Liver Machine Learning Risk Assessment Endocrine Disruptors Adverse Outcome Pathways Phenols Benzhydryl Compounds Humans
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Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders.

Alternatives to laboratory animals : ATLA Jan 18, 2024
The first Stakeholder Network Meeting of the EU Horizon 2020-funded ONTOX project was held on 13-14 March 2023, in Brussels, Belgium. The discussion centred around identifying specific challenges, barriers and drivers in relation to the implementatio...
Artificial Intelligence Animals Humans Belgium Adverse Outcome Pathways Risk Assessment Toxicity Tests
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Control list of high-priority chemicals based on 5-HT-RI functionality and the human health interference effects selective CNN-GRU deep learning model.

The Science of the total environment Jan 4, 2024
The antidepressant drug known as 5-HT reuptake inhibitor (5-HT-RI) was commonly detected in biological tissues and result in significant adverse health effects. Homology modeling was used to characterize the functionalities (efficacy and resistance),...
Drug-Related Side Effects and Adverse Reactions Deep Learning Humans Serotonin Adverse Outcome Pathways Biological Transport Reproducibility of Results
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Making the Case for Quantum Mechanics in Predictive Toxicology─Nearly 100 Years Too Late?

Chemical research in toxicology Sep 7, 2023
The use of quantum mechanics (QM) has long been the norm to study covalent-binding phenomena in chemistry and biochemistry. The pharmaceutical industry leverages QM models explicitly in covalent drug discovery and implicitly to characterize short-ran...
Adverse Outcome Pathways Artificial Intelligence Drug Discovery Drug Industry Quantitative Structure-Activity Relationship
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Derivation, characterisation and analysis of an adverse outcome pathway network for human hepatotoxicity.

Toxicology Jul 10, 2021
Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study...
Adverse Outcome Pathways Computer Simulation Chemical and Drug Induced Liver Injury Risk Assessment Humans Oxidative Stress Cell Death Neural Networks, Computer Mitochondria, Liver Treatment Outcome Fatty Acids
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Development of Adverse Outcome Pathway for PPARγ Antagonism Leading to Pulmonary Fibrosis and Chemical Selection for Its Validation: ToxCast Database and a Deep Learning Artificial Neural Network Model-Based Approach.

Chemical research in toxicology Jun 7, 2019
Exposure to certain chemicals such as disinfectants through inhalation is suspected to be involved in the development of pulmonary fibrosis, a lung disease in which lung tissue becomes damaged and scarred. Pulmonary fibrosis is known to be regulated ...
Deep Learning Adverse Outcome Pathways Neural Networks, Computer Humans Pulmonary Fibrosis Toxicity Tests Databases, Factual PPAR gamma
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Machine Learning and Artificial Intelligence in Toxicological Sciences.

Toxicological sciences : an official journal of the Society of Toxicology Aug 25, 2022
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different ...
Quantitative Structure-Activity Relationship Adverse Outcome Pathways High-Throughput Screening Assays Artificial Intelligence Machine Learning Animals
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[AI-based QSAR Modeling for Prediction of Active Compounds in MIE/AOP].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2020
Toxicity testing is critical for new drug and chemical development process. A clinical study, experimental animal models, and in vitro study are performed to evaluate the safety of a new drug. The limitations of these methods include extensive time f...
Animal Testing Alternatives Computer Simulation Adverse Outcome Pathways Animals Artificial Intelligence Quantitative Structure-Activity Relationship Drug-Related Side Effects and Adverse Reactions Models, Animal Toxicity Tests
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