Alzheimer's disease (AD) affects more than 5 million people in the US. Although the FDA recently granted approval for a disease-modifying therapy for AD, questions remain regarding the efficacy of removing amyloid plaques for delaying cognitive decline. The conflicting results of clinical trials of anti-amyloid agents as well as the 99% failure rate of trials of other classes of AD treatments are rooted in an incomplete understanding of the complex mechanisms resulting in AD, and how the response to treatment may vary in the individual. Personalized optimization of treatment of AD will likely play a central role in future management and counseling of patients. Such treatment will be facilitated by the growing availability of electronic AD brain data. Although to date there are no clinical markers that can easily distinguish AD patients, nor predict AD risk, mathematical modeling and computational techniques could be helpful for constructing a personalized brain environment virtually to predict AD risk and therapeutic response. This project aims to provide an AD personalized prediction via validating a mathematical model on a multi-institutional dataset of AD biomarkers. Personalized therapeutic simulation studies for AD patients will also be performed via the validated mathematical model. Through their involvement with this project, graduate and undergraduate students will receive interdisciplinary training in both mathematical biology and AD research.
The long-term goal of this project is to develop an operational understanding of AD using an integrated mathematical modeling approach, based on clinical, omics, imaging, and other biomarker data to accelerate drug discovery. The project will use publicly available data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to develop a personalized multifactorial mathematical model of AD progression that identifies patient-specific triggers, predicts disease trajectory, and simulates therapeutic response. The investigators plan to accomplish the objective through three specific aims: 1) Validate a newly developed mathematical model of AD using patient data from the ADNI database; 2) Expand the AD model by integrating spatial information available from multidimensional imaging biomarkers; 3) Develop personalized therapeutic plans for AD patients via mathematical modeling. At the completion of the project, the investigators expect to have a personalizable, data-driven mathematical model that will yield future personal biomarker trajectory predictions as well as model-optimized single or combination therapeutic strategies. The resulting model should enhance the ability to predict AD trajectory at an individual level and thereby accelerate personalized treatment.
This award is being co-funded by the Division of Mathematical Sciences (DMS) Mathematical Biology and Computational Mathematics programs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date
|8/1/21 → 7/31/24
- National Science Foundation: $220,000.00