Every day, clinicians snap pictures of patients’ brains using a variety of diagnostic cameras such as electroencephalograms (EEGs) and magnetic resonance imaging (MRIs). They use these snapshots to monitor disorders such as Parkinson’s disease or cancer.
In 2018, Brian Pepin founded a new health tech company he named Rune Labs because he thought that this imaging data was underutilized. He saw each piece of clinical data as an ancient alphabetic rune that no one tried to translate, so he decided to find ways to understand the brain’s alphabet by aggregating and analyzing data collected in the clinic with the hope of improving patient care.
Ro’ee Gilron is the lead neuroscientist at Rune Labs.
Credit: Rune Labs
Since then, Ro’ee Gilron, lead neuroscientist at Rune Labs, has spearheaded the company’s efforts to collect and analyze clinical data from patients with neurological disorders such as Alzheimer’s and Parkinson’s diseases. He hopes that these efforts will help clinicians better identify the right type and dose of a particular therapeutic and aid researchers in developing better treatments. Rune Labs is currently tackling questions about Parkinson’s disease treatment by scouring data collected from Apple smart watches and the biotech company Medtronic’s deep brain simulation devices (1).
Why did you join Rune Labs as lead neuroscientist?
As a postdoctoral researcher at the University of California, San Francisco, I worked on deep brain stimulation devices. These devices are commonly used to treat Parkinson’s disease, especially in the later stages of the disorder, and they are remarkably effective at relieving symptoms. But until recently, none of the brain stimulation devices responded to patients or their changing needs. For example, pacemakers sense what the heart is doing and then respond to that. I worked in an academic setting with five or ten patients and tested an experimental device that could not only stimulate the brain, but also sense brain activity. There is a vast gap between doing proof of concept experiments in an academic lab and developing a therapy that can be adopted in the real world; scaling studies up is a lot of work. That’s what Rune Labs is doing, and that excited me.
What new strategies are you using to move therapies into the real world more quickly?
We have two parallel strategies at work. The first is a clinical decision support tool. We take data that’s collected on a platform called StrivePD, which is an app that was initially developed by patients with Parkinson’s disease to help manage their own care and understand their symptoms. The iPhone app works with a smartwatch that measures symptoms. We package the data collected from this app so that the clinician can make better decisions about how to change the medication or the stimulation a patient receives. That’s happening right now. We are constructing this clinical support ecosystem in partnership with Medtronic and their deep brain stimulation devices.
We analyze the collected data to identify trends that offer insights into the patient population. For example, some patients respond better to one therapy than to another. We want to analyze the data and find what characteristics these patients share.
The heterogeneity in neurological disorders such as Parkinson’s disease is a major reason that a lot of drug trials run by small companies get shut down. What’s so exciting about our platform is that we have much higher resolution tools to look at each individual patient in a longitudinal way. Instead of a regular clinical trial where scientists may measure one outcome in the patients taking the drug, we measure multiple outcomes over long periods of time.
How does the data inform clinical decisions?
We’re working with universities, hospitals, and private clinics all over the country to participate in a trial where patients with Parkinson’s disease use StrivePD in combination with a prescribed Apple watch. A team of researchers from Apple recently published a paper in Science Translational Medicine specifically looking at the device’s ability to detect tremors, a cardinal symptom of Parkinson’s disease, and dyskinesia, a common side effect of drugs used to treat Parkinson’s disease (1). The watch passively monitors a patient’s involuntary movements. When a patient’s medication isn’t effective, they have tremors. If a patient takes too high of a dose, they can experience dyskinesia or involuntary, fluid movements in the face, legs, or arms, and muscle spasms. Using this information, clinicians can better optimize a patient’s dose or the amount of stimulation their device provides. This can be used in clinical trials to help determine proper dosage and better measure responses to therapy in a highly heterogeneous environment.
What other diseases can you tackle with these strategies?
There have been a few trials treating severe depression with deep brain stimulation devices. This is not a first line therapy; every other type of therapy has failed in these people. The initial trials in the 2000s failed to meet their endpoints, but there is renewed interest there. We help support some of these trials by digitally tracking patients and marrying the information from the wearable and deep brain stimulation devices.
Overall, this is a tractable approach for improving patient care. Clinicians tell me that this data influences their clinical decision making. That’s a good sign that we’re on the right track. On the pharmaceutical side, our databases are growing and we can start to use them to identify patients with commonalities. What could this data mean for potential, new disease modifying therapies? It’s exciting.
This interview has been edited and condensed for clarity.
Reference
- Powers, R. et al. Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson’s disease. Sci Transl Med 13 (2021).
