Zoom Meeting (Information posted at the end of the announcement)
Protease activity sensors for noninvasive diagnosis and monitoring of pulmonary diseases
Effective management of disease requires access to high quality and accurate information about disease state. As science and technology have evolved, the history and physical exam, once the foundations of the diagnostic workflow, have been supplemented with modalities that allow physicians to peer inside the body, oftentimes literally, and acquire information that would be otherwise inaccessible from the outside world. To gain maximal information about a disease, a promising approach would be to administer probes that sense disease activity and emit a signal that can be read out externally. To this end, our group has developed a class of diagnostic nanoparticles, termed “activity-based nanosensors” that measure dysregulated protease activity at the site of disease and release a reporter that can be detected in the urine. Because proteases are directly implicated in multiple disease processes, including cancer, activity-based nanosensors have the potential to enable quantitative, noninvasive, and real-time monitoring of disease activity.
Respiratory diseases are the leading causes of death and disability in the world, owing in large part to the constant exposure of the lungs to the external environment. Though this accessibility makes the lungs vulnerable to carcinogens and pathogens, it also provides a unique diagnostic opportunity. In this thesis, we aimed to optimize activity-based nanosensors for lung disease sensing in two diagnostic settings: early detection and treatment response monitoring. Finally, we sought to establish a generalizable pipeline to rationally design such tools for human disease.
To assess the ability of activity-based nanosensors to detect lung cancer, which causes more deaths globally than any other cancer, we delivered a multiplexed panel of sensors via intrapulmonary administration in two genetically engineered mouse models of lung adenocarcinoma. We found that our sensor panel diagnosed localized lung cancer in both models, detecting tumors as small as 2.8 mm3without false positives from benign lung inflammation. We then evaluated this approach in mouse models of malignant and benign pulmonary disease undergoing treatment with small molecule inhibitors. We observed dramatic treatment-induced shifts in pulmonary protease activity in both models, enabling rapid, noninvasive, and quantitative evaluation of drug response. Finally, we established a suite of ex vivo assays that enabled the bottom-up design of a protease-activated diagnostic probe, opening the door for translation to human disease. Collectively, this thesis provides a framework for the clinical development of activity-based nanosensors for pulmonary disease diagnosis and monitoring.
Thesis Supervisor:
Sangeeta N. Bhatia, MD, PhD
Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science, MIT
Thesis Committee Chair:
Daniel G. Anderson, PhD
Professor, Chemical Engineering and Institute for Medical Engineering and Science, MIT
Thesis Readers:
Lecia Sequist, MD
Director of Center for Innovation in Early Cancer Detection, MGH; Landry Family Associate Professor of Medicine, HMS
Richard O. Hynes, PhD
Daniel K. Ludwig Professor for Cancer Research, MIT
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Zoom information
Topic: Jesse Kirkpatrick: MEMP PhD Thesis Defense, May 8, 11AM
Time: May 8, 2020 11:00 AM Eastern Time (US and Canada)
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