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Related Concept Videos

Toxicokinetics: Overview01:21

Toxicokinetics: Overview

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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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Toxicity Testing in Animals01:23

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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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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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Types of Toxins01:36

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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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Toxic Reactions: Overview01:26

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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.
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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Application of dynamic topic models to toxicogenomics data.

Mikyung Lee1, Zhichao Liu2, Ruili Huang1

  • 1NIH/National Center for Advancing Translational Sciences, Rockville, MD, USA.

BMC Bioinformatics
|October 22, 2016
PubMed
Summary
This summary is machine-generated.

Dynamic Topic Models (DTM) effectively cluster time-series gene expression data, revealing hidden biological patterns. This computational approach enhances understanding of gene regulation dynamics in response to various perturbations.

Keywords:
ClusteringDynamic topic model (DTM)Latent Dirichlet modelTG-GATEsTimes-series gene expressionTopic modelingToxicogenomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Biological systems exhibit dynamic behavior over time, especially after environmental or chemical insults.
  • Understanding temporal molecular events is crucial for deciphering biological mechanisms, particularly in response to drug treatments.
  • Interpreting complex time-series gene expression data necessitates advanced computational algorithms.

Purpose of the Study:

  • To introduce and evaluate the application of dynamic topic models (DTM) for the analysis of time-series gene expression data.
  • To identify and interpret hidden patterns and biological meanings within temporal gene expression profiles.

Main Methods:

  • Application of dynamic topic models (DTM) to a large time-series toxicogenomics dataset.
  • Analysis of gene expression data from rat livers treated with 131 compounds over four time points (4, 8, 15, and 29 days).
  • Clustering of compounds based on shared modes of action (e.g., PPARɑ agonists, COX inhibitors) derived from topic distributions.

Main Results:

  • DTM successfully identified hidden patterns in time-series gene expression profiles.
  • Drugs with similar modes of action were effectively clustered based on their temporal expression patterns.
  • DTM-generated sample clusters demonstrated greater coherence in functional categories compared to traditional clustering methods.

Conclusions:

  • Dynamic Topic Models (DTM) provide a powerful computational method for analyzing and clustering time-series gene expression data.
  • DTM probabilistically represents dynamic features, offering insights into gene regulation over time.
  • This approach enhances the understanding of dynamic biological system behavior and gene regulation patterns.