ASSAY and Drug Development Technologies

Dr. Ivan Pushkarsky from the Department of Bioengineering at UCLA and Forcyte Biotechnologies, Inc. was awarded The President's Innovation Award at the Annual Society of Biomolecular Imaging and Informatics meeting held in San Diego, September 2017. All cell types produce mechanical forces to serve important physiological roles. Since aberrant force-generating phenotypes directly lead to disease, cellular force-generation mechanisms are high-value targets for new therapies. Despite knowledge of their significance in disease, drug developers have had limited access to force-generating cellular phenotypes, especially in the context of high-throughput screening. To serve this valuable need, we have developed a platform microtechnology called “FLECS” that can acquire robust contractility data from 1000s of uniformly patterned single cells simultaneously and seamlessly integrates with the 96- and 384-well plate formats to facilitate large-scale drug screens. This perspective discusses the challenges facing existing laboratory methods for measuring cellular force in the context of drug discovery. It then provides an overview of the FLECS platform, describes how it was designed to overcome many of these challenges, and discusses some exciting work already accomplished with FLECS. It concludes by highlighting the platform nature of the technology and the potential value that it promises for a myriad of drug development efforts.

Jessica Lacoste from the Donnelly Centre at the University of Toronto was awarded best poster at the annual Society of Biomolecular Imaging and Informatics meeting held in San Diego, September 2017. Her work focuses on characterizing the protein localization of variants involved in rare disease. The current works and future directions of research in rare disease are summarized in the following overview.

Protease-activated receptor 2 (PAR₂) is a proinflammatory G-protein coupled receptor (GPCR) that is activated by inflammatory proteases, and its activation initiates signaling pathways that modulate the nonselective cation channel transient receptor potential vanilloid-4 (TRPV4). PAR₂-dependent opening of TRPV4 has been attributed to kinase activation, but the identity of the responsible enzymes is unknown. Deciphering the signaling pathways involved in the PAR₂-dependent opening of TRPV4 may yield new targets for pain treatment. This study has identified specific kinases that are involved in opening TRPV4, using a selective screen of short interfering ribonucleic acid (siRNA) SMARTpools, which individually targeted all human kinases, in human embryonic kidney 293 (HEK293) cells that stably express inducible TRPV4. This screen is unique because it uses a real-time assay measuring intracellular calcium with Fura-2AM dye. From the primary screen, subsequent confirmation screen, and on-target messenger ribonucleic acid expression analysis, we identified two kinases as crucial to the PAR₂-dependent opening of TRPV4 in HEK293 cells, mitogen-activated protein kinase 13 and with no lysine kinase 4. In conclusion, this study describes a powerful new application of siRNA knockdown to identity signaling molecules that are responsible for the PAR₂-dependent opening of TRPV4, which will help elucidate this signaling process.

High cancer drug development attrition rates have provoked considerable debate about whether the two-dimensional tumor growth inhibition high-throughput screening assays used in pre-clinical lead discovery adequately reflect solid tumor complexity. We used automated high-content screening image acquisition and analysis methods to compare fluorescent drug uptake, accumulation, and distribution in Cal33 and FaDu head and neck cancer (HNC) monolayer and multicellular tumor spheroid (MCTS) models. Ellipticine, idarubicin, daunorubicin, and doxorubicin were studied because of their fluorescent properties and broad anti-tumor activities. HNC MCTSs were generated in 384-well ultra-low attachment plates where compound exposure, image acquisition, and analysis could be performed in situ. Fluorescent drug accumulation in Cal33 monolayer and MCTS cultures was linear with respect to concentration, and appeared to achieve steady-state levels within 10–15 min of drug exposure, which were maintained through 30–45 min. Drug accumulation in monolayers was independent of cell number and/or density, and every cell achieved uniform drug concentrations. In MCTSs, however, drug accumulation increased as the number of cells and sizes of the MCTSs became bigger. Drugs exhibited restricted penetration and distribution gradients, accumulating preferentially in cells in the outer layers of MCTSs relative to those in the inner cores. Cal33 monolayers were 6-, 20-, 10-, and 16-fold more sensitive than MCTSs to growth inhibition by ellipticine, idarubicin, daunorubicin, and doxorubicin, respectively. In Cal33 MCTSs exposed to ellipticine or doxorubicin for 24 h, MCTSs were smaller and although they still exhibited drug penetration and distribution gradients, the fluorescent intensity difference between outer and inner cells was reduced. After a 24 h exposure, both drugs had penetrated throughout FaDu MCTSs, consistent with drug-induced death of peripheral cell layers enhancing drug penetration. The increased resistance of MCTS cultures and their ability to recapitulate drug penetration and distribution gradients argues strongly for the deployment of these more physiological models in cancer lead discovery. MCTSs have the potential to enhance the correlation between in vitro potencies and in vivo efficacy, and ultimately may lead to improved cancer drug approval rates.

There is a large amount of information in brightfield images that was previously inaccessible by using traditional microscopy techniques. This information can now be exploited by using machine-learning approaches for both image segmentation and the classification of objects. We have combined these approaches with a label-free assay for growth and differentiation of leukemic colonies, to generate a novel platform for phenotypic drug discovery. Initially, a supervised machine-learning algorithm was used to identify in-focus colonies growing in a three-dimensional (3D) methylcellulose gel. Once identified, unsupervised clustering and principle component analysis of texture-based phenotypic profiles were applied to group similar phenotypes. In a proof-of-concept study, we successfully identified a novel phenotype induced by a compound that is currently in clinical trials for the treatment of leukemia. We believe that our platform will be of great benefit for the utilization of patient-derived 3D cell culture systems for both drug discovery and diagnostic applications.