The Role of Historical Bioactivity Data in the Deconvolution of Phenotypic Screens

Abstract

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 highaffinity 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.

Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection

“…The Fisher’s exact test was calculated to indicate the probability of obtaining a prediction distribution at least as extreme as the one observed. This statistical measure was selected since it has been shown to be used for calculating enrichment for the deconvolution of phenotypic screens …”

Phenotypic Screen for Cardiac Regeneration Identifies Molecules with Differential Activity in Human Epicardium-Derived Cells versus Cardiac Fibroblasts

“…To follow up the singleton hits or very small compound clusters, chemically similar compounds from the AstraZeneca compound collection were identified. These were selected based on multiple fingerprint methods using extended connectivity fingerprints, an in-house implementation of the Daylight fingerprints, and a text-based molecular similarity search. , In parallel, we investigated the annotated biological activity of the hit molecules to generate early hypotheses for biological targets which may be driving the proliferative phenotype . Subsequently, diverse compounds were selected which modulate the enriched biological targets.…”

“…21,22 In parallel, we investigated the annotated biological activity of the hit molecules to generate early hypotheses for biological targets which may be driving the proliferative phenotype. 23 Subsequently, diverse compounds were selected which modulate the enriched biological targets. These chemical and biological target approaches provided an additional 90 compounds which were screened in the EPDC proliferation assay, giving a total of 200 compounds being progressed in the screening cascade (Figure 3a).…”

Knowledge from Small-Molecule Screening and Profiling Data

“…Haney 10 illustrates the importance of visualization and understanding the underlying distribution in high-content data sets. Smith and Horvath 11 offer a novel approach to the challenging area of phenotypic screening analysis, while Bornot et al 12 show the value of using historical data to aid data analysis.…”