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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: Jun 30, 2026

Micro-dissection of Enamel Organ from Mandibular Incisor of Rats Exposed to Environmental Toxicants

Micro-dissection of Enamel Organ from Mandibular Incisor of Rats Exposed to Environmental Toxicants

Published on: March 29, 2018

Modeling oral rat chronic toxicity.

Paolo Mazzatorta¹, Manuel Dominguez Estevez, Myriam Coulet

¹Department of Quality and Safety, Nestlè Research Center, Vers-Chez-les-Blanc 44, 1000 Lausanne 26, Vaud, Switzerland. paolo-francesco.mazzatorta@rdls.nestle.com

Journal of Chemical Information and Modeling

|September 23, 2008

Summary

This summary is machine-generated.

This study developed a predictive computational model for chronic toxicity, crucial for risk assessment. The model accurately estimates toxicity based on chemical structure, aiding safety evaluations when experimental data is lacking.

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Last Updated: Jun 30, 2026

Micro-dissection of Enamel Organ from Mandibular Incisor of Rats Exposed to Environmental Toxicants

Micro-dissection of Enamel Organ from Mandibular Incisor of Rats Exposed to Environmental Toxicants

Published on: March 29, 2018

Meal Duration as a Measure of Orofacial Nociceptive Responses in Rodents

Meal Duration as a Measure of Orofacial Nociceptive Responses in Rodents

Published on: January 10, 2014

Area of Science:

Toxicology
Computational Chemistry
Drug Discovery

Background:

Chronic toxicity assessment is vital for toxicological risk evaluation.
Understanding the link between chemical structure and chronic toxicity is limited.
Experimental chronic toxicity testing is complex and challenging for quantitative structure-activity relationship (QSAR) studies.

Purpose of the Study:

To develop a predictive in silico model for chronic toxicity.
To correlate chemical structures with chronic toxicity using computational methods.
To address the challenge of limited QSAR studies in chronic toxicity.

Main Methods:

Utilized a dataset of over 400 compounds.
Employed two-dimensional (2D) chemical descriptors.
Applied multivariate analysis for model development.
Validated the model using leave-one-out cross-validation.

Main Results:

Achieved a root mean squared error (RMSE) of 0.73 (logarithmic scale) in cross-validation.
The model's predictive error is comparable to experimental variability (0.64).
Identified bioavailability as a key driver of chronic toxicity, with additional contributions from specific chemical moieties.

Conclusions:

The developed in silico model provides a reliable method for predicting chronic toxicity.
The model can effectively estimate safety concerns in the absence of experimental toxicological data.
Findings highlight the importance of bioavailability and specific chemical features in chronic toxicity prediction.