AIMC Topic: Toxicology

Clear Filters Showing 21 to 30 of 33 articles

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

Chemical research in toxicology
Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of ti...

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...

Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.

Scientific reports
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA's Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs wer...

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.

Expert review of gastroenterology & hepatology
Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can b...

Surface enhanced Raman spectroscopy (SERS) as a method for the toxicological analysis of synthetic cannabinoids.

Talanta
Synthetic cannabinoids (K2, spice) present problems in forensic investigations because standard presumptive methods, such as immunoassays, are insufficiently specific for the wide range of potential target compounds. This issue can lead to problems w...

An in silico expert system for the identification of eye irritants.

SAR and QSAR in environmental research
This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient c...

Comparing answers of artificial intelligence systems and clinical toxicologists to questions about poisoning: Can their answers be distinguished?

Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias
OBJECTIVE: To present questions about poisoning to 4 artificial intelligence (AI) systems and 4 clinical toxicologists and determine whether readers can identify the source of the answers. To evaluate and compare text quality and level of knowledge f...

E-validation - Unleashing AI for validation.

ALTEX
The validation of new approach methods (NAMs) in toxicology faces significant challenges, including the integration of diverse data, selection of appropriate reference chemicals, and lengthy, resource-intensive consensus processes. This article propo...

A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro.

Toxicological sciences : an official journal of the Society of Toxicology
The failure to predict kidney toxicity of new chemical entities early in the development process before they reach humans remains a critical issue. Here, we used primary human kidney cells and applied a systems biology approach that combines multidim...