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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...

Drug Toxicity: Dose-Dependent Reactions

Drug Toxicity: Dose-Dependent Reactions

Drug toxicities can be stratified into pharmacological, pathological, or genotoxic based on their mechanisms. The incidence and severity of these toxicities generally increase with the drug's concentration in the body and exposure time.Pharmacological toxicity is evident when the therapeutic effects of drugs overshoot into adverse reactions in a predictable, dose-dependent manner. Central nervous system (CNS) depression from barbiturates is a classic example, with effects escalating from...

Toxicokinetics: Overview

Toxicokinetics: Overview

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...

Mutagenicity and Carcinogenicity

Mutagenicity and Carcinogenicity

Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...

Toxic Reactions: Overview

Toxic Reactions: Overview

When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...

Pharmacokinetic Models: Comparison and Selection Criterion

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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.

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Related Experiment Video

Updated: Jun 10, 2026

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Computational techniques for the prediction of toxicity.

D J Livingstone¹

¹SmithKline Beecham Research, The Frythe, Welwyn, Herts. AL6 9AR, UK.

Toxicology in Vitro : an International Journal Published in Association with BIBRA

|August 10, 2010

Summary

Computational toxicology prediction methods, including the APEX system for identifying toxicophores, show promise. While APEX achieved 75% accuracy, other systems reach 85-90% for predicting mutagenicity.

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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Area of Science:

Computational toxicology
Cheminformatics
Structure-activity relationships

Background:

Computational methods for predicting biological activity have existed since the 1960s.
Early applications in toxicology prediction faced criticism.
This review examines three distinct toxicity prediction approaches.

Purpose of the Study:

To review existing computational methods for toxicity prediction.
To evaluate a new system, APEX, for identifying toxicophores.
To assess the performance of APEX in predicting mutagenicity.

Main Methods:

Review of three computational toxicity prediction strategies.
Application of the APEX system to a literature dataset of mutagenicity.
Training and testing the APEX system on a dataset of 105 compounds.

Main Results:

The APEX system achieved a 75% success rate in predicting mutagenicity.
Comparable prediction systems, trained on larger datasets, reported 85-90% accuracy.
The study highlights the potential and limitations of current computational toxicology tools.

Conclusions:

Computational toxicology offers valuable tools for predicting toxicological endpoints.
Newer systems like APEX show moderate success but require further development.
Ongoing advancements in computational methods are crucial for improving toxicity prediction accuracy.