Computer Models Used to Predict Progression of Parkinson's Disease in Patients

Jack Murtha
SEPTEMBER 26, 2017
GNS Healthcare, Michael J. Fox, Parkinson's Disease, Neurology, Artificial Intelligence
If Parkinson’s Disease advanced at similar rates in each patient, researchers have long said, it might be a simpler challenge to tackle.

Yet that dilemma might be one that artificial intelligence can overcome. GNS Healthcare, a precision-medicine company, announced today that it used patient data to pinpoint genetic and molecular markers of faster motor progression in people with Parkinson’s Disease. Researchers created computer models that were proven to “essentially” predict the rate at which the disease matures in each person, according to the organization.

So what does that mean? For now, according to GNS, smoother and speedier clinical trials.

“With accurate predictors of rates of progression, we will be able to remove uncertainties from drug development and patient response, reduce the number of clinical trial enrollees required by as much as 20 percent, and speed up the development of effective new drugs,” GNS Healthcare CEO and Chairman Colin Hill said.

GNS used REFS, its causal machine learning and simulation platform, to crunch genetic and clinical data from 312 Parkinson’s Disease patients and 117 healthy people. That formed the computer models, which simulated how the LINGO2 gene, a second genetic variant, and demographic factors could affect the rate of progression.

A study confirmed the findings. The company broke the news in the journal The Lancet Neurology.

The move once again illustrates the power of AI in medicine. While the field has been slower than some to adopt such technologies, experts claim that is about to change across the board.

But exactly what results these high-tech steps may produce is unclear, for now. Even so, GNS officials believe this predictive power will go on to aid researchers and ultimately Parkinson’s Disease patients.

“There is still so much to understand about the progression of chronic, debilitating illnesses like Parkinson’s Disease,” Jeanne C. Latourelle, a study co-author and the director of precision medicine at GNS, said. “The validation of our models in this study underscores the power of our REFS technology and its ability to accelerate the development of effective therapies for patients in need.”

Patient data came from an initiative sponsored by the Michael J. Fox Foundation for Parkinson’s Research, according to GNS.

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