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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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Studies that assess how a drug is absorbed, distributed, metabolized, and excreted (ADME) at toxic doses are termed toxicokinetics. Understanding toxicokinetics helps predict adverse drug reactions (ADRs) and manage toxicity in humans.Toxicokinetics differs from pharmacokinetics mainly in the dose levels studied, with toxicokinetics focusing on higher toxic doses. The kinetics at these levels can be non-linear due to altered physiological processes. Toxicodynamics examines the relationship...

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Updated: Jun 12, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Public databases supporting computational toxicology.

Richard Judson¹

¹National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA. judson.richard@epa.gov

Journal of Toxicology and Environmental Health. Part B, Critical Reviews

|June 25, 2010

Summary

This summary is machine-generated.

Computational toxicology uses chemical structure and in vitro data to predict toxicity. This review covers challenges, technologies, and major databases for developing these predictive models.

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Area of Science:

Computational toxicology
cheminformatics
toxicology

Background:

Developing predictive models for chemical toxicity is crucial.
These models require large datasets of in vitro and in vivo data.
Publicly available databases are essential for compiling this data.

Purpose of the Study:

Review challenges in creating toxicology databases.
Describe key technologies like relational databases, ontologies, and knowledgebases.
Summarize major databases used in computational toxicology.

Main Methods:

Literature review of computational toxicology databases.
Analysis of challenges in data compilation.
Description of technological components (databases, ontologies, knowledgebases).

Main Results:

Identified key challenges in database development.
Described essential technologies for data management and integration.
Summarized prominent databases utilized in the field.

Conclusions:

Database development faces significant challenges.
Technologies like ontologies and knowledgebases are vital.
Existing databases are instrumental for advancing computational toxicology research.