A MICROFLUIDIC AND MACHINE LEARNING-ENABLED SMARTLAB FOR AUTONOMOUS REMOTE EXECUTION AND ITERATION OF MULTISCALE LIVE CELL ASSAYS FOR DRUG DISCOVERY

支持微流控和机器学习的智能实验室，用于自主远程执行和迭代多尺度活细胞测定以进行药物发现

• 批准号: 10505107
• 负责人: MARY LUDLAM
• 金额: $ 33.15万
• 依托单位: CAIRN BIOSCIENCES, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2022-06-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10505107
• 关键词: Address; Artificial Intelligence; Biological Assay; Cells; Cellular Assay; Data Analyses; Data Storage and Retrieval; Development; Drug usage; Feedback; Goals; Health; Human; Infrastructure; Interruption; Machine Learning; Microfluidics; Monitor; Persons; Reagent; Risk; Scientist; Secure; Time; assay development; base; cloud based; cost; drug discovery; experimental study; human error; improved; innovation; instrumentation; operation; pre-clinical; prototype; remote control; wasting

Preclinical drug discovery assay development depends on the presence of scientists to develop, initiate and monitor assays. Interruptions and errors in the execution of these activities incur extra time and expense to restart experiments and the waste of previously expended time and reagents. The innovative SmartLab framework envisioned by this project addresses the significant, costly risks of interruptions and errors in preclinical cell-based assays by enabling their remote, autonomous initiation, execution and iteration. Advantages of the SmartLab framework include (1) reduction in risk of human error; (2) unimpeded continuation of experiments when in-person lab operations are interrupted and (3) maximized experimental efficiency through adaptive experimental feedback. Collectively these advantages will benefit human health by dramatically improving the robustness and efficiency of preclinical assay frameworks used for drug discovery.

临床前药物发现试验的开发依赖于科学家的存在来开发、启动和监测试验。这些活动执行中的中断和错误会导致重新启动实验的额外时间和费用，以及先前花费的时间和试剂的浪费。该项目设想的创新SmartLab框架通过实现远程、自主启动、执行和迭代，解决了临床前细胞分析中中断和错误的重大、昂贵的风险。 SmartLab框架的优点包括：（1）降低人为错误的风险;（2）当实验室操作中断时，实验可以不受阻碍地继续进行;（3）通过自适应实验反馈，最大限度地提高实验效率。总的来说，这些优势将通过显著提高用于药物发现的临床前测定框架的稳健性和效率而有益于人类健康。