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

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.