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Toxicity Testing in Animals

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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Related Experiment Video

Updated: May 30, 2026

Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity (AIDN): A Translational Neuroscience Approach

Use of a Piglet Model for the Study of Anesthetic-induced Developmental Neurotoxicity (AIDN): A Translational Neuroscience Approach

Published on: June 11, 2017

Modeling developmental processes in animals: applications in neurodevelopmental toxicology.

Elaine M Faustman¹, Julia Gohlke, Nancy L Judd

¹Center for Child Environmental Health Risks Research, Seattle, WA, USA; Institute for Risk Analysis and Risk Communication, Seattle, WA, USA; Department of Environmental and Occupational Health Sciences, University of Washington, 4225 Roosevelt Way NE, #100, Seattle, WA, USA.

Environmental Toxicology and Pharmacology

|July 26, 2011

Summary

Biologically based models assess neurotoxicity by integrating kinetic and dynamic processes. These models help understand ethanol and methylmercury

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Area of Science:

Neuroscience
Toxicology
Computational Biology

Background:

Neurodevelopmental toxicity assessment requires understanding complex kinetic and dynamic processes.
Existing models often lack mechanistic detail for accurate neurotoxicity predictions.

Purpose of the Study:

To develop and apply biologically based dose-response models for evaluating neurodevelopmental toxicants.
To investigate the mechanisms of ethanol and methylmercury neurodevelopmental toxicity.
To integrate toxicokinetic and toxicodynamic data for improved risk assessment.

Main Methods:

Construction of computational models for normal midbrain and neocortex development.
Extension of models to simulate the effects of ethanol and methylmercury.
Utilizing mechanistic data, including gene expression, to support model development and validation.

Main Results:

Models successfully simulated ethanol's effects on neurogenesis and apoptosis.
Methylmercury's toxicokinetic and toxicodynamic relationships were successfully linked.
Mechanistic data on gene expression informed and improved model predictions.

Conclusions:

Biologically based dose-response models are crucial for assessing neurodevelopmental toxicants.
Integrating qualitative and quantitative kinetic and dynamic factors enhances understanding of toxic impacts.
Modeling approaches provide a robust framework for testing hypotheses on neurotoxicant modes of action.