Related Experiment Video
Updated: Jul 24, 2026

Functional Evaluation of Biological Neurotoxins in Networked Cultures of Stem Cell-derived Central Nervous System Neurons
Published on: February 5, 2015
Validation of counter propagation neural network models for predictive toxicology according to the OECD principles: a
M Vracko1, V Bandelj, P Barbieri
1European Chemical Beaureau, Institute for Health and Consumer Protection, European Commission Joint Research Centre, 21020 Ispra, Italy. marjan.vracko@ki.si
Abstract:
The OECD has proposed five principles for validation of QSAR models used for regulatory purposes. Here we present a case study investigating how these principles can be applied to models based on Kohonen and counter propagation neural networks. The study is based on a counter propagation network model that has been built using toxicity data in fish fathead minnow for 541 compounds. The study demonstrates that most, if not all, of the OECD criteria may be met when modeling using this neural network approach.
More Related Videos
09:01A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
Published on: March 14, 2019
05:47In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
Published on: August 28, 2019
Related Concept Videos
Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Pharmacodynamic Models: Overview
Impact of Pharmacokinetic–Pharmacodynamic Models: Regulatory Decisions
Drug Toxicity: Overview
Toxicokinetics: Overview
Toxicity Testing in Animals