Super-resolution microscopy network analysis: Drug target validation for cystic fibrosis

超分辨率显微镜网络分析：囊性纤维化的药物靶点验证

• 批准号: 538851-2019
• 负责人: Nabi, Ivan
• 金额: $ 21.31万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697784
• 关键词: Super; resolution; microscopy; network; analysis

The goal of genome-based precision medicine is to selectively target drugs to responsive patients. This is particularly important in cystic fibrosis (CF), where ~75% of eligible patients, carrying the F508del-CFTR mutation, do not show demonstrable improvement in lung function with the clinically-available drug Orkambi. Orkambi treatment costs >$250,000/yr and is not formally covered by publically funded health-care in Canada at this time. New drugs in phase 2 clinical trials may be more effective than others for individual patients. To identify which patients are most likely to benefit from CF modulator drugs, it is important to study CFTR at the protein level as patient-patient variation is not only dependent on genomic differences (e.g. specific CFTR mutations). We will apply machine learning based network analysis of single molecule localization super-resolution microscopy (SMLM) to develop an imaging-based assay to identify patients likely to benefit from a particular therapeutic. Optimization of therapies for individual patients will increase drug efficacy and substantially reduce cost of treatment for Canadian CF patients. SMLM generates resolution ten-fold beyond the diffraction limit. However, quantitative approaches to analyze the large data sets of 3D point localizations generated are limited. We developed a novel and powerful approach to model SMLM particle localization data sets, transforming 3D localizations into a network by virtual connections between the points. This approach allows us to determine molecular architecture, i.e. how proteins within a protein complex interact. We now propose to refine and apply the technology to CFTR and thereby validate our analysis pipeline for personalized medicine applications in CF and other diseases with a known drug target. We will partner with Quorum Technologies, a Canadian microscopy company, to commercialize the software for use by super-resolution microscopy users and pharmaceutical companies worldwide.

基于基因组的精准医学的目标是选择性地将药物靶向到有反应的患者身上。这在囊性纤维化（CF）中尤其重要，约75%的携带F508del-CFTR突变的符合条件的患者在使用临床可用药物Orkambi后，肺功能没有明显改善。Orkambi治疗费用为每年250 000加元，目前尚未正式纳入加拿大公费保健。处于第二阶段临床试验的新药对个别患者可能比其他药物更有效。为了确定哪些患者最有可能从CF调节药物中获益，重要的是在蛋白质水平上研究CFTR，因为患者之间的差异不仅取决于基因组