Computational Analysis Supports "Single Disease" MS Theory

NOVEMBER 03, 2017
Jared Kaltwasser
Pablo Villoslada, MD, PhDA new computational analysis of multiple sclerosis (MS) bolsters the case that MS is a single disease, despite the wide range of symptoms and progression rates evident in different subtypes of the neurological disease.

The study used computational simulations to evaluate whether it’s possible for a diverse spectrum of MS phenotypes to be reproduced from the same underlying mechanism. The answer was conclusive, a fact that could have major implications for the way researchers aim to treat the disease. 

Pablo Villoslada, MD, PhD, director of the August Pi Sunyer Biomedical Research Institute at the University of Barcelona, said his study highlights the neurological damage caused by MS, something that seems to continue in the background regardless of how quickly a particular patient’s disease progresses.

“Our simulations support the concept that MS induces a chronic inflammatory process that damages the brain from the beginning, in an accumulative way, as well as with superimposed relapses,” he told MD Magazine. “Preventing relapses is very important because they disturb patients’ quality of life and because the produce disability. For this reason, early treatment of relapsing MS is so important, but not sufficient.”

Even if relapses are prevented, the brain continues to sustain damage from the disease, potentially resulting in long-term increases in disability. Villoslada said physicians therefore ought to be vigilant even when an MS patient shows no sign of relapse.

Those conclusions are based on a mathematical model Villoslada and his team developed to mirror MS. The model was developed using data from 66 MS patients who had been tracked for 2 decades.

To verify the data from the initial data set, the team took data from another set of 120 patients who were tracked with a 3-year follow-up. 

“We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses,” the authors wrote.
 
In addition to shaping scientists’ and physicians’ understanding of MS, the research could also have implications for how the disease is treated.

Villoslada said the findings could theoretically help elucidate a personalized medicine approach to treating MS. After all, if the mathematical model can predict the different courses the disease could take, they could also in theory predict the way an individual patient’s disease might progress. Those predictions could become roadmaps for individualized care.

“Based on the risk analysis obtained from such an approach, patients and physicians will be able to make informed decisions,” Villoslada said. 

In order to make that a reality, Villoslada said the next step would be to collect quantitative data from patients in databases. The research team could then use its mathematical model to predict disease prognoses for those patients and then test their accuracy.

For now, Villoslada said the research ought to serve as a reminder to physicians to keep a close eye on MS patients, even when it doesn’t seem as though the disease is progressing.

““[T]his is a call for not relaxing the surveillance about the disease in the absence of relapse because the disease will remain burning inside the brain,” he said.

The study, “Dynamics and heterogeneity of brain damage in multiple sclerosis," was published online in PLOS Computational Biology last month.

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