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

项目摘要/摘要 尽管在癌症药物发现方面进行了投资，包括高通量筛选和基于结构的药物 设计，很少有抗癌化合物通过后期临床试验，因为缺乏疗效和不受欢迎 毒物。细胞通常处于休眠状态，并在发出信号时进入细胞周期的活跃阶段。这条路 在癌症的困扰中减少或中断，癌症治疗倾向于以分裂细胞为目标，而不是 非分裂细胞。因此，细胞周期分类是临床前抗癌药物的关键性能指标 候选人分层。目前基于荧光的ID方法需要较长的样品制备时间，并且 遭受光毒性和光漂白。Phi Optics客户发现与生物制药高管的讨论 而研发专家透露，需要更快、更准确的细胞周期时相分类来为预 临床肿瘤治疗药物开发(HIT优先级、HIT-to-Lead优化和缩短领先时间 优化阶段)。 这个小型企业创新研究第一阶段项目建议研究开发证据的可行性- 用于抗癌药物开发的OF概念细胞周期检测和分类仪。这台仪器将 使用伊利诺伊大学厄巴纳香槟分校开发的创新数字染色方法：阶段 计算特异性成像(PICS)。PICS结合无损定量相位成像 (Slim And Glim)拥有人工智能的力量。因此，细胞周期分析可以准确和特异地进行。 具有规则的荧光，但没有与细胞标记相关的不便。 一旦可行性得到证明，第二阶段的工作将继续进行，以开发实验室光学工作台 用于对多个候选药物进行高通量细胞周期时相识别和评分的仪器。 该仪器将商业化进入研究和生物制药市场，交付速度更快(100倍 更高的吞吐量)和更准确的临床前阶段的候选抗癌药物分层。。

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. .