High-throughput Organoid Drug Efficacy Screening Platform

Combining microfluidics, synthetic biology, tissue engineering, and machine learning to tackle efficient drug screening

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Overview: Clinical treatment of glioblastoma, a disease that kills over 10,000 Americans annually, is limited by the lack of a scalable, physiologically-relevant model for testing therapeutics. As part of the Duke Undergraduate Biotechnology Research Group, I developed NODES, a high-throughput organoid-based drug screening platform to characterize treatment efficacy in common glioma variants. We designed a non-invasive reporter device that quantifies the drug response of mutation-specific glioma cells in a mini-brain co-culture model, grown in a droplet-based system. Aside from wet lab research, I modeled our reporter system, which detects oncometabolite levels throughout brain tumor development, to improve device characteristics and help developed a machine-learning based image analysis pipeline for organoid screening.

GCS relation: Drug discovery is a complex, time-consuming, and expensive process. This is especially true for orphan diseases. My research is about how, as engineers, we can make this process more efficient by developing complementary platforms for research. By recapitulating the brain microenvironment, NODES has the potential to accurately characterize drug responses, offering new hope to patients in their fight against this lethal disease.

Supervisor: Dr. Cameron Kim and Dr. Zhaohui Wang (Director at Woo Center for Big Data and Precision Health)

Duration: May 2020 - November 2021

Total Hours of Completion: 400 hrs

Recognitions: This project is sponsored by the Lord Foundation, Woo Center for Big Data and Precision Medicine, Duke Biomedical Engineering, and Bass Connections.

Collaborators: The Duke Undergraduate Biotechnology Research Group and Xiling Shen Lab