The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens.
A substantial challenge in phenotypic drug discovery is the identification of the molecular targets that govern a phenotypic response of interest. Several experimental strategies are available for this, the so-called target deconvolution process. Most of these approaches exploit the affinity between a small-molecule compound and its putative targets or use large-scale genetic manipulations and profiling. Each of these methods has strengths but also limitations such as bias toward high-affinity interactions or risks from genetic compensation. The use of computational methods for target and mechanism of action identification is a complementary approach that can influence each step of a phenotypic screening campaign. Here, we describe how cheminformatics and bioinformatics are embedded in the process from initial selection of a focused compound library from a large set of historical small-molecule screens through the analysis of screening results. We present a deconvolution method based on enrichment analysis and using known bioactivity data of screened compounds to infer putative targets, pathways, and biological processes that are consistent with the observed phenotypic response. As an example, the approach is applied to a cellular screen aiming at identifying inhibitors of tumor necrosis factor-α production in lipopolysaccharide-stimulated THP-1 cells. In summary, we find that the approach can contribute to solving the often very complex target deconvolution task.
AstraZeneca R&D, Discovery Sciences, Computational Biology, Macclesfield, Cheshire, UK aurelie.bornot@astrazeneca.com.
Aurelie
A
Bornot
AstraZeneca R&D, Discovery Sciences, HTS, Macclesfield, Cheshire, UK.
Carolyn
C
Blackett
AstraZeneca R&D, Discovery Sciences, Chemistry Innovation Centre, Mölndal, Sweden.
Ola
O
Engkvist
AstraZeneca R&D, New Opportunities, Macclesfield, Cheshire, UK.
Clare
C
Murray
AstraZeneca R&D, Discovery Sciences, Computational Biology, Macclesfield, Cheshire, UK.
Claus
C
Bendtsen
D000818:Animals; D000911:Antibodies, Monoclonal / Q000737:chemistry; D002460:Cell Line; D019295:Computational Biology / Q000379:methods; D055808:Drug Discovery / Q000379:methods*; D004797:Enzyme-Linked Immunosorbent Assay; D057166:High-Throughput Screening Assays; D006801:Humans; D008070:Lipopolysaccharides / Q000737:chemistry; D051379:Mice; D010641:Phenotype; D011336:Probability; D011994:Recombinant Proteins / Q000737:chemistry; D014409:Tumor Necrosis Factor-alpha / Q000737:chemistry
D000911:Antibodies, Monoclonal; D008070:Lipopolysaccharides; D011994:Recombinant Proteins; D014409:Tumor Necrosis Factor-alpha
compound library; computational inference; data analysis; data integration; phenotypic drug discovery; target deconvolution