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

使用具有计算特异性的相位成像进行无标记细胞周期分类

基本信息

批准号：
10547446
负责人：
CATALIN CHIRITESCU
金额：
$ 22.5万
依托单位：
PHI OPTICS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10547446
关键词：
3-Dimensional Agreement Antineoplastic Agents Binding Biological Assay Biological Products Biophotonics Breast Cancer Cell Businesses Capital Cell Culture Techniques Cell Cycle Cell Death Cell Proliferation Cell physiology Cells Classification Clinical Trials Colon Complex Computer software DNA DNA Repair Detection Development Disease Drug Design Drug Targeting Dyes Excision Expenditure Feasibility Studies Fluorescence Funding Goals Growth Hour Hyperplasia Illinois Image Imaging Device Individual Interphase Cell Interview Investigation Investments Label Learning Legal patent Length Letters Location MCF7 cell Malignant Neoplasms Measures Methods Microscopy Modality Morphology Nucleic Acids Optical Instrument Optics Pathway interactions Performance Pharmaceutical Preparations Pharmacologic Substance Phase Photobleaching Phototoxicity Population Preparation Privatization Procedures Process Regulation Research Resolution SW620 Sales Sampling Scientist Series Signal Transduction Small Business Innovation Research Grant Specialist Specificity Specimen Staining and Labeling Stains Stratification Stress Structure Technology Time Tissues Toxic effect United States National Institutes of Health Universities Work anti-cancer base bioimaging cancer therapy cell fixation commercialization deep neural network design verification digital drug candidate drug development drug discovery embryo cell embryo tissue experience experimental study fluorescence imaging fluorophore high throughput screening imaging modality innovation instrument lead optimization live cell imaging monolayer novel novel therapeutics optical imaging pre-clinical product development programs prototype research and development response to injury sample fixation single cell analysis success technology development tomography uptake user-friendly

项目摘要

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. .

项目总结/摘要尽管在癌症药物发现方面的投资，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，由于缺乏疗效和不需要的毒性细胞通常处于休眠状态，并在收到信号时进入细胞周期的活动阶段。该途径减少或破坏，并且癌症治疗倾向于靶向分裂细胞，非分裂细胞因此，细胞周期分类是临床前癌症药物治疗期间的关键性能指标。候选人分层目前基于荧光的ID方法需要长的样品制备时间，并且遭受光毒性和光漂白。Phi Optics与生物制药高管讨论客户发现和研发专家透露，需要更快，更准确的细胞周期阶段分类，临床肿瘤治疗药物开发（命中优先化、命中到先导优化和缩短先导优化阶段）。这个小企业创新研究第一阶段项目建议研究开发一个证明- 用于癌症药物开发的概念细胞周期检测和分类仪器。仪器将使用伊利诺伊大学厄巴纳分校尚潘分校开发的创新数字染色方法：计算特异性成像（PICS）。PICS结合了非破坏性定量相位成像 (SLIM（一）以权力为中心。因此，可以进行准确性和特异性的细胞周期测定但没有细胞标记带来的不便。一旦可行性得到证明，工作将在第二阶段继续进行，以开发实验室工作台光学系统。用于对多种候选药物进行高通量细胞周期阶段鉴定和评分的仪器。该仪器将被商业化到研究和生物制药市场，以更快地提供（100倍）更高的通量）和在临床前阶段对癌症候选药物进行更准确的分层。.