Literature DB >> 25512330

Using the BioAssay Ontology for analyzing high-throughput screening data.

Linda Zander Balderud1, David Murray2, Niklas Larsson3, Uma Vempati4, Stephan C Schürer4, Marcus Bjäreland5, Ola Engkvist3.   

Abstract

High-throughput screening (HTS) is the main starting point for hit identification in drug discovery programs. This has led to a rapid increase of available screening data both within pharmaceutical companies and the public domain. We have used the BioAssay Ontology (BAO) 2.0 for assay annotation within AstraZeneca to enable comparison with external HTS methods. The annotated assays have been analyzed to identify technology gaps, evaluate new methods, verify active hits, and compare compound activity between in-house and PubChem assays. As an example, the binding of a fluorescent ligand to formyl peptide receptor 1 (FPR1, involved in inflammation, for example) in an in-house HTS was measured by fluorescence intensity. In total, 155 active compounds were also tested in an external ligand binding flow cytometry assay, a method not used for in-house HTS detection. Twelve percent of the 155 compounds were found active in both assays. By the annotation of assay protocols using BAO terms, internal and external assays can easily be identified and method comparison facilitated. They can be used to evaluate the effectiveness of different assay methods, design appropriate confirmatory and counterassays, and analyze the activity of compounds for identification of technology artifacts.
© 2014 Society for Laboratory Automation and Screening.

Entities:  

Keywords:  BioAssay Ontology; PubChem; assay design; detection technology; high-throughput screening

Mesh:

Year:  2014        PMID: 25512330     DOI: 10.1177/1087057114563493

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  4 in total

Review 1.  Reporting biological assay screening results for maximum impact.

Authors:  Evan Bolton
Journal:  Drug Discov Today Technol       Date:  2015-05-02

2.  RegenBase: a knowledge base of spinal cord injury biology for translational research.

Authors:  Alison Callahan; Saminda W Abeyruwan; Hassan Al-Ali; Kunie Sakurai; Adam R Ferguson; Phillip G Popovich; Nigam H Shah; Ubbo Visser; John L Bixby; Vance P Lemmon
Journal:  Database (Oxford)       Date:  2016-04-07       Impact factor: 3.451

3.  BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry.

Authors:  Igor V Tetko; Ola Engkvist; Uwe Koch; Jean-Louis Reymond; Hongming Chen
Journal:  Mol Inform       Date:  2016-07-28       Impact factor: 3.353

4.  Using C. elegans Forward and Reverse Genetics to Identify New Compounds with Anthelmintic Activity.

Authors:  Mark D Mathew; Neal D Mathew; Angela Miller; Mike Simpson; Vinci Au; Stephanie Garland; Marie Gestin; Mark L Edgley; Stephane Flibotte; Aruna Balgi; Jennifer Chiang; Guri Giaever; Pamela Dean; Audrey Tung; Michel Roberge; Calvin Roskelley; Tom Forge; Corey Nislow; Donald Moerman
Journal:  PLoS Negl Trop Dis       Date:  2016-10-18
  4 in total

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