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Nanotechnologies for the Research, Detection, and Treatment of Cancer and Allied Diseases

Daniel Heller, Memorial Sloan Kettering Cancer Center

Thursday, Oct 10, 2019
4:30 pm to 5:30 pm | 60 Oxford Street, 330

My laboratory develops nanotechnologies to address problems in the diagnosis, treatment, and research of cancer and allied diseases. Two such examples include:

Implantable nanosensors for cancer detection: The early detection of cancer could lead to improved therapeutic responses and vastly improved patient outcomes. We aim to identify cancer biomarkers within the body at early disease stages, permitting detection before symptoms arise. We are developing implantable nanosensors, using the unique optical properties of carbon nanotubes, to facilitate non-invasive detection via optical detection through living tissues. The sensors could enable early detection of cancer in people at high risk for the disease, in successfully treated patients to monitor recurrence, or in patients who are undergoing treatment to inform clinical decisions.

Machine learning-enabled precision nanomedicines: Therapy based on personalized medicine—the genomic context of a patient’s disease—has become a leading strategy to treat cancer. Small molecule drugs such as kinase inhibitors, which target key effectors of cancer signaling pathways, constitute a major component of this strategy. However, such drugs can affect the same signaling pathways in healthy tissues, which often leads to dose-limiting toxicities. To address this issue, we realized that the development of targeted nanoparticle drug carriers with diverse personalized drug cargoes often requires complex synthetic schemes that are generally difficult to predict, execute, and control. We developed a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Speaker Bio

Head, Cancer Nanomedicine Laboratory
Bristol-Myers Squibb/James D. Robinson III Junior Faculty Chair
Associate Member, Memorial Sloan Kettering Cancer Center
Associate Professor, Weill Cornell Medicine, Cornell University


Irene de Lazaro del Rey


Nick Grall