Label-free cell cycle classification using Phase Imaging with Computational Specificity

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Project Summary/Abstract Despite investments in cancer drug discovery including high-throughput screening and structure-based drug design, very few anticancer compounds pass late stages clinical trials due to lack of efficacy and unwanted toxicities. Cells are normally dormant and enter the active segments of the cell cycle when signaled. This pathway is diminished or disrupted in cancer afflictions and cancer treatments tend to target dividing cells while sparing non-dividing cells. Cell cycle classification is thus a key performance indicator during pre-clinical cancer drug candidates stratification. Current fluorescence based ID methods require long sample preparation times, and suffer from phototoxicity and photobleaching. Phi Optics Customer Discovery discussions with biopharma execs and R&D specialists revealed that faster and more accurate cell cycle phase classification is needed to for pre- clinical oncotherapy drug development (hit prioritization, hit-to-lead optimization, and shortening of the lead optimization phase). This Small Business Innovation Research Phase I project proposes to study the feasibility of developing a proof- of-concept cell cycle detection and classification instrument for cancer drug development.. The instrument will use an innovative digital staining method developed at University of Illinois at Urbana Champaign: Phase Imaging with Computational Specificity (PICS). PICS combines non-destructive Quantitative Phase Imaging (SLIM and GLIM) with the power of AI. Cell cycle assays can thus be performed with the accuracy and specificity of regular fluorescence but without the inconveniences associated with cell tagging. Once the feasibility is proven the work will continue during Phase II for the development of a lab bench optical instrument for performing high-throughput cell cycle phase identification and scoring for multiple drug candidates. The instrument will be commercialized into the research and bio-pharma market for delivery of faster (100-fold higher throughput) and more accurate stratification of cancer drug candidates in pre-clinical stages. .