Development Of Hyper-Polarized 129Xe Gas Magnetic Resonance Imaging-Based Molecular Probe for Early Detection of Alzheimer’s Disease

About the Research Project
Program
Award Type
Postdoctoral Fellowship
Award Amount
$100,000
Active Dates
July 01, 2015 - June 30, 2017
Grant ID
A2015344F
Mentor(s)
Mitchell Albert, PhD, Lakehead University
Goals
Alzheimer’s disease (AD) is believed to be caused by a protein structure called amyloid oligomers, which are amyloid beta (Aβ) peptides that have been converted from individual molecules into a chain that has different molecular structure and properties. These amyloid oligomers occur on the surface of brain cells in advance of Alzheimer’s symptoms. I propose to create a molecule which will bind to these oligomers and can be imaged using magnetic resonance imaging (MRI). This method would provide up to 10 years advance notice of AD. Early detection would permit preventative therapies to be started earlier, when they are more likely to be effective.
Summary
Our goal is to develop MRI techniques that can be used to detect AD up to 10 years before the start of Alzheimer`s symptoms. Currently, brain imaging of Alzheimer`s patients only confirms a diagnosis, whereas our approach seeks to predict a future diagnosis allowing for earlier treatment. Our approach is truly interdisciplinary as it combines biophysical research, synthetic chemistry and nuclear physics.
To accomplish this goal we plan to find molecules called affinity tags that attach to amyloid oligomers, which are proteins believed to cause AD. After being administered to patients, the affinity tags will attach to amyloid oligomers in their brain. Once we have identified affinity tags for amyloid oligomers, we will attach them to a molecule which can be detected by MRI. We will then test this molecule using MRI to ensure that it both attaches to amyloid oligomers and provides a detectable magnetic resonance signal. Next we can test these molecules in people with a family history of Alzheimer’s disease and track them to see the predictive effectiveness of our MRI techniques.
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