Despite investments in cancer drug discovery including high-throughput screening and structure-based drugdesign, very few anticancer compounds pass late stages clinical trials due to lack of efficacy and unwantedtoxicities. Cells are normally dormant and enter the active segments of the cell cycle when signaled. This pathwayis diminished or disrupted in cancer afflictions and cancer treatments tend to target dividing cells while sparingnon-dividing cells. Cell cycle classification is thus a key performance indicator during pre-clinical cancer drugcandidates stratification. Current fluorescence based ID methods require long sample preparation times, andsuffer from phototoxicity and photobleaching. Phi Optics Customer Discovery discussions with biopharma execsand 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 leadoptimization 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 willuse an innovative digital staining method developed at University of Illinois at Urbana Champaign: PhaseImaging 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 specificityof 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 opticalinstrument 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-foldhigher throughput) and more accurate stratification of cancer drug candidates in pre-clinical stages. .
Public Health Relevance Statement: Project Narrative
Phi Optics proposes to develop a proof-of-concept cell cycle classification instrument for cancer drug
development by combining label-free quantitative assays with the power of AI. The instrument will provide more
accurate cell cycle phase classification that is needed to stratify pre-clinical oncotherapy drug development (hit
prioritization, hit-to-lead optimization, and shortening of the lead optimization phase)
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