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

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.