Abstract

Application of Multiparametric High Content and Pathway Profiling Technologies to Advance Phenotypic Screening Across Disease Relevant Models

Neil Carragher, Principal Investigator, University of Edinburgh

Cell based phenotypic screening is re-emerging as a valuable strategy for unbiased discovery of new drug targets and first-in-class medicines. While it is often anticipated that a well-designed phenotypic assay will faithfully represent human disease, it is unlikely that a high throughput primary phenotypic screen will truly represent the full complexity of disease pathophysiology. It is also unlikely that initial hit compounds from a large diverse chemical library screen will have sufficient potency or selectivity to support target deconvolution. Thus, if the primary phenotypic screen does not adequately represent disease or inform drug mechanism-of-action significant challenges and bottlenecks will appear in lead-identification, target deconvolution and target validation. We describe the development of novel assay methodology and informatics approaches to support quantitative analysis of multiparameteric high content phenotypic compound profiles generated across a genetically distinct but disease relevant panel of 8 breast cancer cell lines. The development of these methods supports our objectives for further understanding drug mechanism-of-action at genetic, epigenetic, and post-translational pathway levels to progress preclinical development and inform clinical positioning. We further present our iterative phenotypic screening strategy, which focuses on sub-library screening of small boutique chemical sets across informative/context based phenotypic assay panels. Using an agile strategy that combines ligand-based inhibitor design and phenotypic screening in an iterative manner, we demonstrate the rapid discovery of an orally-available ATP-competitive kinase inhibitor that displays high selectivity and potent antiproliferative and anti-invasive activity against breast cancer cells.