Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation

Predicting the cellular response of compounds is a challenge central to the discovery of new drugs. Compound biological signatures have risen as a way of representing the perturbation produced by a compound in the cell. However, their ability to encode specific phenotypic information and generating tangible predictions remains unknown, mainly because of the inherent noise in such data sets. In this work, we statistically aggregate signals from several compound biological signatures to find compounds that produce a desired phenotype in the cell. We exploit this method in two applications relevant for phenotypic screening in drug discovery programs: target-independent hit expansion and target identification. As a result, we present here (i) novel nanomolar inhibitors of cellular division that reproduce the phenotype and the mode of action of reference natural products and (ii) blockers of the NKCC1 cotransporter for autism spectrum disorders. Our results were confirmed in both cellular and biochemical assays of the respective projects. In addition, these examples provided novel insights on the information content and biological significance of compound biological signatures from HTS, and their applicability to drug discovery in general. For target identification, we show that novel targets can be predicted successfully for drugs by reporting new activities for nimedipine, fluspirilene, and pimozide and providing a rationale for repurposing and side effects. Our results highlight the opportunities of reusing public bioactivity data for prospective drug discovery, including scenarios where the effective target or mode of action of a particular molecule is not known, such as in phenotypic screening campaigns.