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Toxicity Tests

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Artificial Intelligence for Drug Toxicity and Safety.

Trends in pharmacological sciences
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and preve...

A mode-of-action ontology model for safety evaluation of chemicals: Outcome of a series of workshops on repeated dose toxicity.

Toxicology in vitro : an international journal published in association with BIBRA
Repeated dose toxicity evaluation aims at assessing the occurrence of adverse effects following chronic or repeated exposure to chemicals. Non-animal approaches have gained importance in the last decades because of ethical considerations as well as d...

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
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 ...

Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.

Chemical research in toxicology
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this miss...

G-Networks to Predict the Outcome of Sensing of Toxicity.

Sensors (Basel, Switzerland)
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through ...

Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Toxicology and applied pharmacology
Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mech...

Evaluation of a novel automated water analyzer for continuous monitoring of toxicity and chemical parameters in municipal water supply.

Ecotoxicology and environmental safety
A novel tool, the DAMTA analyzer (Device for Analytical Monitoring and Toxicity Assessment), designed for fully automated toxicity measurements based on luminescent bacteria as well as for concomitant determination of chemical parameters, was develop...

Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity.

Toxicological sciences : an official journal of the Society of Toxicology
Animal-based methods for assessing chemical toxicity are struggling to meet testing demands. In silico approaches, including machine-learning methods, are promising alternatives. Recently, deep neural networks (DNNs) were evaluated and reported to ou...

Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence.

Archives of toxicology
Human lungs are susceptible to the toxicity induced by soluble xenobiotics. However, the direct cellular effects of many pulmonotoxic chemicals are not always clear, and thus, a general in vitro assay for testing pulmonotoxicity applicable to a wide ...

[Construction of a High-precision Chemical Prediction System Using Human ESCs].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
 Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells...