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Accounting for Artifacts in High-Throughput Toxicity Assays.

Jui-Hua Hsieh1

  • 1National Institute of Environmental Health Sciences, Durham, NC, USA. jui-hua.hsieh@nih.gov.

Methods in Molecular Biology (Clifton, N.J.)
|March 16, 2022
PubMed
Summary

A new data analysis pipeline effectively identifies compound activity in high throughput screening (HTS) assays. It handles artifacts and provides robust activity metrics, improving drug discovery efforts.

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

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Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Assay Development

Background:

  • High throughput screening (HTS) is crucial for compound activity identification.
  • Assay artifacts like autofluorescence and noise can complicate HTS data interpretation.
  • Traditional potency metrics (EC50) may not fully capture compound activity profiles.

Purpose of the Study:

  • To develop and outline a data analysis pipeline for handling artifacts in HTS assays.
  • To enable robust compound activity characterization using binary or continuous metrics.
  • To demonstrate the pipeline's application using Tox21 estrogen receptor (ER) assays.

Main Methods:

  • Developed a data analysis pipeline to address systematic and nonsystematic assay artifacts.
  • Utilized Tox21 estrogen receptor (ER) β-lactamase assays as a case study.
  • Incorporated counterscreen assays for artifact identification and mitigation.

Main Results:

  • The pipeline successfully handles assay artifacts, improving data reliability.
  • Compound activity can be characterized using multiple parameters beyond EC50, including point-of-departure (POD) and weighted area-under-the-curve (wAUC).
  • The pipeline effectively identifies agonists and antagonists and mitigates artifactual results.

Conclusions:

  • The developed data analysis pipeline enhances the accuracy of compound activity identification in HTS.
  • The pipeline offers a flexible framework applicable to various assays with concentration-response data.
  • This approach improves the efficiency and reliability of drug discovery screening processes.