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Related Concept Videos

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...
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...
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...
Types of Toxins01:36

Types of Toxins

Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
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Environmental pollutants like...
Drug Toxicity: Dose-Dependent Reactions01:24

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...

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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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Is computational toxicology withering on the vine?

R D Combes1

  • 1Robert_combes3@yahoo.co.uk

Archives of Toxicology
|March 6, 2010
PubMed
Summary

Developing accurate computational toxicity prediction models requires careful validation and data selection. Applying structure-activity relationship landscape analysis ensures better chemical space coverage for improved in silico toxicity predictions.

Area of Science:

  • Computational toxicology
  • Cheminformatics
  • Predictive modeling

Background:

  • Developing predictive computational models for chemical toxicity presents significant challenges.
  • Key issues include model validation (internal and external), selection of appropriate physicochemical data, and characterization of structure-activity relationship (SAR) landscapes.

Purpose of the Study:

  • To discuss the difficulties in developing predictive computational models of toxicity.
  • To highlight the importance of characterizing SAR landscapes using recent methods.
  • To provide recommendations for improving model predictivity.

Main Methods:

  • Discussion of internal and external validation strategies for computational toxicity models.
  • Examination of methods for selecting relevant physicochemical data.

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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)
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High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents (HPHC)

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  • Application of recently published methods to characterize SAR landscapes from training sets.
  • Focus on ensuring adequate and continuous sampling of chemical space.

    • Main Results:

    • Characterizing SAR landscapes is crucial for understanding model performance.
    • Adequate sampling of chemical space is essential, particularly when external validation is limited.
    • Discriminate selection of molecular descriptors is vital for model accuracy.

    Conclusions:

    • Developers of in silico toxicity prediction systems should utilize SAR landscape characterization methods.
    • Ensuring comprehensive chemical space coverage improves model reliability, especially with limited external validation data.
    • Combining robust data selection, descriptor choice, and SAR analysis enhances the predictivity of computational toxicity models.