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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...
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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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Drug toxicity quantifies the harm a compound causes to an organism, varying by dose and potentially impacting whole systems or specific organs like the liver. Toxic reactions may arise from venomous insect or spider bites, with effects ranging from mild symptoms to severe outcomes such as brain damage or death. Common forms of acute poisoning include ethanol intoxication and overdose of pain or fever medications, with substances like GHB and heroin being particularly lethal at doses close to...
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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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Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood at designated intervals to ensure the drug concentration stays within a therapeutic range. This monitoring is crucial for optimizing individual dosage regimens, enhancing therapeutic efficacy, and minimizing drug-related toxicity. TDM is vital for drugs with narrow therapeutic windows, significant variability in pharmacokinetics, and a clear correlation between plasma levels and...

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Human Pluripotent Stem Cell Based Developmental Toxicity Assays for Chemical Safety Screening and Systems Biology Data Generation
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Data governance in predictive toxicology: A review.

Xin Fu1, Anna Wojak, Daniel Neagu

  • 1School of Computing, Informatics and Media, Richmond Road, Bradford, BD7 1DP, UK. x.fu1@bradford.ac.uk.

Journal of Cheminformatics
|July 15, 2011
PubMed
Summary

Data governance is crucial for high-quality predictive toxicology data. This review highlights gaps in current data sources and proposes solutions for better data management and accessibility in toxicology research.

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Area of Science:

  • Toxicology
  • Data Science
  • Bioinformatics

Background:

  • Advances in data storage necessitate improved data governance in predictive toxicology.
  • Dispersed toxicity data requires better management for effective utilization.
  • Current data quality focuses on storage aspects like accuracy and completeness.

Purpose of the Study:

  • To review predictive toxicology data sources and their data governance features.
  • To identify current challenges and future needs in toxicology data management.
  • To address gaps in toxicology data quality assessment.

Main Methods:

  • Review of seven widely used predictive toxicology data sources and applications.
  • Focus on data governance aspects: accuracy, completeness, integrity, metadata, availability, and authorization.
  • Analysis of current problems and desirable needs in data source development.

Main Results:

  • Identified lack of systematic data quality measures in current predictive toxicology data sources.
  • Highlighted the need for better metadata management and flexible user authorization.
  • Revealed gaps in existing data governance frameworks for predictive toxicology.

Conclusions:

  • Data governance is a key challenge and opportunity for predictive toxicology.
  • Improved data governance can lead to high-quality, accessible toxicity data repositories.
  • Further research is needed to develop robust data governance frameworks.