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Related Concept Videos

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

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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...
100

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Updated: Mar 16, 2026

High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
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Accounting Artifacts in High-Throughput Toxicity Assays.

Jui-Hua Hsieh1

  • 1Kelly Government Solutions Supporting NTP, 530 Davis Dr., Morrisville, NC, 27560, USA. jui-hua.hsieh@nih.gov.

Methods in Molecular Biology (Clifton, N.J.)
|August 13, 2016
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Summary

A new data analysis pipeline effectively identifies compound activity in high-throughput screening (HTS) by managing assay artifacts. This method enhances compound characterization using binary or continuous metrics for better drug discovery insights.

Keywords:
Assay artifactsConcentration-response dataData analysis pipelineHTSPoint-of-departureTox21qHTS

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Area of Science:

  • Pharmacology
  • Biochemistry
  • Assay Development

Background:

  • High-throughput screening (HTS) is crucial for identifying active compounds.
  • Assay artifacts like compound auto-fluorescence and noise can hinder accurate activity interpretation.
  • Traditional potency metrics (EC50) may not fully capture compound activity profiles.

Purpose of the Study:

  • To develop a robust data analysis pipeline for HTS.
  • To address and mitigate systematic and nonsystematic assay artifacts.
  • To enable comprehensive compound activity characterization beyond traditional metrics.

Main Methods:

  • Implementation of a data analysis pipeline for HTS data.
  • Utilizing Tox21 glucocorticoid receptor (GR) β-lactamase assays as a model system.
  • Incorporating counter-screen assays to identify and filter out artifacts.

Main Results:

  • The pipeline successfully handles assay artifacts, improving data reliability.
  • Compound activity was characterized using both binary and continuous metrics.
  • Demonstrated applicability to agonist/antagonist identification and artifact detection.

Conclusions:

  • The developed pipeline provides a reliable method for compound activity identification in HTS.
  • It enhances compound profiling by accounting for artifacts and offering diverse activity metrics.
  • The approach is adaptable to various lower-throughput assays with concentration-response data.