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Related Concept Videos

Toxicokinetics: Overview01:21

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...
Bioactivation and Tissue Toxicity01:25

Bioactivation and Tissue Toxicity

Bioactivation is a metabolic process that transforms less reactive substances into highly reactive metabolites, initiating tissue toxicity. This transformation can lead to various toxic effects, including carcinogenesis and teratogenesis. Reactive metabolites are classified into two main types: electrophiles and free radicals.Electrophiles are electron-deficient species and are produced primarily by the enzyme cytochrome P-450 during the metabolism of compounds containing carbon, nitrogen, or...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
Toxicity Testing in Animals01:23

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...
Toxic Reactions: Overview01:26

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,...
Mutagenicity and Carcinogenicity01:25

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...

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Related Experiment Video

Updated: May 18, 2026

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
05:34

Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods

Published on: June 6, 2025

Accessing, using, and creating chemical property databases for computational toxicology modeling.

Antony J Williams1, Sean Ekins, Ola Spjuth

  • 1Royal Society of Chemistry, Wake Forest, NC, USA. williamsa@rsc.org

Methods in Molecular Biology (Clifton, N.J.)
|September 26, 2012
PubMed
Summary

Computational toxicology leverages chemical data and open-source software to build predictive models. This approach aids in understanding chemical harm mechanisms and prioritizing testing, reducing costs and improving safety assessments.

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Area of Science:

  • Toxicology
  • Computational Chemistry
  • Data Science

Background:

  • Toxicity data generation is costly and essential for computational toxicology.
  • Repositories of chemical property data support the development of predictive models.
  • Structure-toxicity relationships are key to understanding chemical hazards.

Purpose of the Study:

  • To provide an overview of available data and software for computational toxicology.
  • To explain how these resources can be used to generate predictive toxicology models.
  • To highlight the value of computational approaches in prioritizing chemical testing.

Main Methods:

  • Utilizing mathematical, statistical, and computational modeling approaches.
  • Leveraging public domain toxicity and chemical property data.
  • Employing open-source software for model generation.

Main Results:

  • Established methods for building structure-toxicity relationships.
  • Demonstrated the utility of computational models in predicting adverse effects.
  • Showcased the increasing availability of public data and open-source tools.

Conclusions:

  • Computational toxicology offers a cost-effective alternative to traditional testing.
  • Predictive models enhance understanding of chemical mechanisms and human health impacts.
  • Accessible data and software empower the community to develop and apply predictive toxicology tools.