title: Using the BioAssay Ontology for analyzing high-throughput screening data.
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.
authors.affiliation: Discovery Sciences, AstraZeneca R&D Mölndal, Mölndal, Sweden linda.zander-balderud@astrazeneca.com.
authors.forename: Linda
authors.initials: L
authors.lastname: Zander Balderud
authors.affiliation: Discovery Sciences, AstraZeneca R&D Alderley Park, Alderley Park, UK.
authors.forename: David
authors.initials: D
authors.lastname: Murray
authors.affiliation: Discovery Sciences, AstraZeneca R&D Mölndal, Mölndal, Sweden.
authors.forename: Niklas
authors.initials: N
authors.lastname: Larsson
authors.affiliation: Center for Computational Science, University of Miami, Miami, FL, USA.
authors.forename: Uma
authors.initials: U
authors.lastname: Vempati
authors.affiliation: Center for Computational Science, University of Miami, Miami, FL, USA.
authors.forename: Stephan C
authors.initials: SC
authors.lastname: Schürer
authors.affiliation: R&D Information, AstraZeneca R&D Mölndal, Mölndal, Sweden.
authors.forename: Marcus
authors.initials: M
authors.lastname: Bjäreland
authors.affiliation: Discovery Sciences, AstraZeneca R&D Mölndal, Mölndal, Sweden.
authors.forename: Ola
authors.initials: O
authors.lastname: Engkvist
mesh_terms: D001681:Biological Assay; D019295:Computational Biology / Q000379:methods; D057225:Data Mining; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods; D057166:High-Throughput Screening Assays; D006801:Humans; D015203:Reproducibility of Results
keywords: BioAssay Ontology; PubChem; assay design; detection technology; high-throughput screening