New approach may help design shorter, more powerful trials to evaluate drugs for Parkinson’s disease

Testing whether a new drug has an impact on the progression of Parkinson’s disease takes years, in part because the disease often progresses very slowly. Many patients with Parkinson’s disease experience no worsening of their symptoms during a typical clinical trial, even without treatment.

Now Scripps Research scientists have developed a tool that analyzes genetic and clinical data from patients with Parkinson’s disease to predict who is most likely to progress rapidly. The approach, described in npj Parkinson’s diseasewill allow clinical researchers to select patients most at risk and design shorter, more powerful trials to evaluate drugs for Parkinson’s disease, they say.

If clinicians are able to only enroll patients in trials who are expected to progress, they can see much faster results and move this area forward faster.”

Ali Torkamani, PhD, lead author, professor and director of genomics and genome informatics at the Scripps Research Translational Institute

Parkinson’s disease is a progressive disorder of the nervous system and affects approximately one million people in the United States. The first symptoms are often barely perceptible tremors, and over the years the disease progresses, eventually affecting movement, posture, facial expressions, speech and eating, as well as pain and dementia. However, the order and speed at which these symptoms worsen varies greatly from person to person. Over the course of a single year, for example, many patients do not worsen, making it difficult and time-consuming to study the effectiveness of drugs in slowing that progression.

Torkamani, along with colleagues at Scripps Research and collaborators at Takeda, who are developing experimental treatments for Parkinson’s disease, set out to better predict this short-term progression in patients being considered for inclusion in clinical studies aimed at slowing this progression. sickness. They analyzed the progression over 12, 24 and 36 months of patients enrolled in two existing cohorts: the Parkinson’s Progress Markers Initiative and the Parkinson’s Disease Biomarkers Program. In total, the team used data including genetics, clinical exam information, brain scans and treatments, on 879 patients.

Overall, 529 patients were found to be “progressors” during the first 12 months of the study, with a significant worsening of their symptoms, while 350 were grouped as “non-progressors”. Torkamani’s group used a machine learning approach to develop a model that could predict, with 77% accuracy, which group patients belonged to.

“This model worked by combining different aspects of comprehensive disease profiling,” says Torkamani. “Genetic risk factors were the strongest predictor, but other factors were also important to include.”

Some of the strongest signals, he says, included whether a patient had a mutation in LRRK2-; this known risk factor for Parkinson’s disease makes patients more likely to develop early-onset disease, but then their symptoms progress more slowly.

For now, the model has no clinical value for individual patients, because there is no drug that slows the progression of Parkinson’s disease. However, researchers hope that being able to choose “progressors” for clinical trials will make it easier and faster to identify these types of drugs as the field advances.

“Right now, these clinical trials are large and tend to take two to three years,” says Torkamani. “We hope to allow smaller trials that are on the order of a one-year timeframe.”

The Scripps Research scientists also plan to expand their model to attempt to predict other aspects of Parkinson’s disease. For example, can genetic markers predict which Parkinson’s patients will develop psychosis or depression? The same approach they took in the current study; integrate clinical and genetic information; could also be useful for analyzing the progression of other neurodevelopmental disorders.