Researchers have created a mathematical model that can accurately predict Alzheimer’s disease progression in individual patients, paving the way for personalised treatment and faster development of therapies.
According to the model, the disease onset and rate of progression varies on a case by case basis, meaning that some patients will be more likely to benefit from certain therapies than others.
The simulator could also be used by pharmaceutical companies to rapidly test multiple drug therapies and patient scenarios, increasing the speed and cost of drug development.
The discovery by researchers at Penn State University and Duke University Medical Center was featured in SIAM News, a publication of the Society for Industrial and Applied Mathematics (SIAM).
“When you have enough data to draw from, it’s possible to simulate diseases on a computer with a very high degree of accuracy,” said Wenrui Hao, Associate Professor of Mathematics at Penn State University. “We’re at a point now where we can use this math to reliably suggest personalised, optimised regimens for Alzheimer’s disease, and that’s incredibly exciting.”
Mapping individual Alzheimer’s disease progression
The team’s model relies on clinical biomarkers for Alzheimer’s disease, including fluid markers for the amyloid protein that is responsible for plaque build-up in the brain, cognitive decline scores from pencil-and-paper testing, and MRI brain images.
By using comprehensive, publicly-available data from the Alzheimer’s Disease Neuroimaging Initiative in the model, the researchers map individual disease progression simply by adjusting different biomarker parameters to match the real-world data – ultimately achieving a high degree of accuracy.
“This model makes predictions from a place of understanding, telling us not only whether a patient is likely to develop the disease within five years, but here’s why and here’s what’s actually going on in their brain to explain that,” said Jeffrey Petrella, Professor of Radiology and Director of the Alzheimer Disease Imaging Research Lab at Duke University Medical Center. “The most important benefit is that we now have an accurate model that we can use to perform virtual experiments with different types of interventions, quickly and digitally.”
In silico drug trials
Earlier this year, the researchers and their collaborator, Suzanne Lenhart, Chancellor’s Professor of Mathematics and Cox Professor at the University of Tennessee Knoxville, completed two in silico drug trials that used publicly available data to test a recently FDA-approved therapy, Aduhelm, an anti-amyloid therapy that removes plaque in the brain, as well as another similar drug in the pipeline, shown to slow the rate of cognitive decline.
By simulating the drug’s performance in individual patients, they rapidly modelled the outcome of short-term (78 weeks) and long-term (10 years) treatments and discovered that when treatment begins by age 60, it’s possible to reduce cognitive decline by five percent. These findings closely matched the results of the corresponding real-life clinical trials.
The researchers expect their model will be ready for use in a clinical setting within five years. To further refine it in the meantime, they are currently looking to collaborate with pharmaceutical companies to access more detailed patient data that pertains to individual biomarker trajectories.
In the future, the model could also be applied to study other inflammatory diseases such as lupus, multiple sclerosis, and chronic pancreatitis.