Ro’ee Gilron, PhD, Lead Neuroscientist at Rune Labs – Interview Series

Ro’ee Gilron, PhD, is the Lead Neuroscientist at Rune Labs, a software and data analytics company for precision neurology, supporting care delivery and therapy development. StrivePD is the company’s care delivery ecosystem for Parkinson’s disease, enabling patients and clinicians to better manage Parkinson’s by providing access to curated dashboards summarizing a range of patient data sources, and by connecting patients to clinical trials. For therapeutics development, biopharma and medical device companies leverage Rune’s technology, network of engaged clinicians and patients, and large longitudinal real-world datasets to expedite development programs. The company has received financial backing from leading investors such as Eclipse Ventures, DigiTx, TruVenturo and Moment Ventures.

What initially attracted you to the field of neuroscience?

I fell in love with the field of translational neuroscience after my research experience working with epilepsy patients in the Epilepsy Monitoring Unit (EMU). A lot of work has been done with these patients over the years, leading to amazing discoveries in speech, vision, and motor control, the area in which I focused at the time. After doing basic research for my graduate work I wanted the ability to work with patients specifically on the diseases they’re suffering from. This motivated me to apply what I have learned in graduate school about motor control and engineering into working with Parkinson’s patients with deep brain stimulation devices.

Could you discuss some of your early work with deep brain stimulation (DBS) devices?

Over the last decade of my career, I’ve been fortunate to be in the right place at the right time. I joined the lab of Philip Starr, a neurosurgeon at UCSF Health, and at the time, he was working with experimental DBS devices. The lab was working on getting an investigational device exemption (IDE) to collect the necessary data required to support a premarket approval application with a select group of patients and clinicians trying to develop next-generation therapies with DBS.

An exciting component of this work has been the new capabilities of the devices that were being developed at the time. There’s a brain stimulation device that we worked to develop from end-to-end which involved designing the interface, working with the device, and programming it.

Rune Labs describes itself as a software and data analytics company for precision neurology. Could you define what precision neurology is?

We’re taking the playbook from cancer research over the last 10 years. We used to think a trial failed because only 5% of patients responded. We now realize that if you take all that data and aggregate it across all different types of cancer, sequence the genomes of tumors, and take the perceived ‘failures,’ you have a much more personalized therapy to provide these patients. Now, you’re not treating a breast cancer patient but treating a very specific type of tumor that’s been sequenced from a cancer patient. The treatments are incredibly personalized. This revolution in cancer had a major impact on patient survival rates, and now we’re trying to learn from that experience.

In neurology, we’re still stuck, to a certain extent, with ways of evaluating some disorders that have been around for the last century. We’re trying to usher in a future where all these incredibly sophisticated devices are strapped to a wristwatch and paired with smartphones, collecting detailed information about patients to help them and their clinicians make better decisions about their therapy. We want to use this data as a foundation to develop new neurological therapies and bring them to market.

There have been very few breakthroughs in Parkinson’s disease over the past decade, why is this such a difficult disease to tackle?

It’s multifactorial in Parkinson’s disease. We don’t have the perfect target and most of the therapies that we have today aren’t changing the course of the disease, only treating the symptoms, including DBS. It’s challenging to develop new drugs. Parkinson’s, and many other disorders, unfold over a period before symptoms even manifest. You can live a very long time with the disease being stable, making it difficult to assess the efficacy of new drugs in a traditional way. Methods for measuring clinical benefit, like questionnaires, aren’t always able to accurately capture impact, especially with the diversity of disease symptoms in a 500-patient trial. There’s a very limited number of molecules to be able to test.

However, there’s a theory that if you had a much deeper way to phenotype patients and track them to gather greater detail over time, you may be able to observe an effect that you wouldn’t have been able to previously. This could require a shorter length of time like weeks or months, accelerated thanks to data collected from wearables like the Apple Watch.

What type of data is Rune Labs collecting from wearables like the Apple Watch to sophisticated deep brain implants that can accelerate the development of therapies for Parkinson’s?

With the Apple Watch, we have 510(k) clearance from the U.S. Food and Drug Administration to measure a patient’s tremors and dyskinesia minute-by-minute. We worked together with Apple on this technology, which allows us to be more richly focused on patients each day, week, and month. This isn’t possible when you look at only a patient’s clinical scoring. With the Apple Watch, we can collect a vast array of data that allows us to do deep phenotype annotations. In addition to these validated responses, it also collects vast information about the patient. This can include patterns of their mobility from step count to step length, to other validated metrics  like double support time or walking in symmetry, which relate to the patient’s probability of falling –  a big concern for Parkinson’s patients and a large contributor to disability. We’re also tracking sleep activity and exercise, which studies have shown are beneficial. Exercise is one of the only things that is beneficial for Parkinson’s symptoms over long periods of time.

Additionally, we’re using this data to help phenotype patients with DBS devices in a subset of more advanced Parkinson’s patients. For this, we’re using Medtronic-manufactured devices that can sense brain activity. We’re tracking a lot of information about patients’ electrophysiology that’s coming from deep within these nuclei that produce pathological networks in patients. This approach allows us to characterize patients in a way not possible before.

How does this data assist Rune Labs with offering predictive personalized therapies?

We’re coming at this from a patient-first approach. Right now, communicating all of the options available to a person with Parkinson’s can be difficult, as clinicians have to sort through a lot of these therapies for their patients. One of the things we think better data might be able to help with is improved predictions, like recommending a patient receive a DBS device because they’ve been experiencing a lot of motor fluctuations with oral medications. We can help empower the patient to have that conversation with their clinician. Another example is if a clinician can see from the data that their patient has been experiencing a lot of dyskinesia, they can recommend changing their drug formulation. There are many new drugs and devices on the market and we want to empower patients to explore all options.

In addition, we work with device manufacturers like Medtronic that potentially in the future can offer real-time suggestions to patients, like particular medications or whether the inhaled or injected modality is best suited for them.

Another thing that we’re working on in the DBS device space is being able to take a patient’s outcome data, like their symptoms, and combine it with the electrophysiology data that is being collected from their brain. Putting those two data types together to come up with a recommendation about how to effectively stimulate a patient’s brain can, in the future, be integrated into clinical trials. There are already some examples of this being done that have helped identify biomarkers for Parkinson’s disease progression.

With all of the data has been collected, has Rune Labs been able to identify biomarkers for Parkinson’s disease progression?

I think we have some early leads that are very promising in terms of biomarkers. Published data show that there are certain characteristics that contribute to an increased risk and more rapid progression of the disease, like sleep abnormalities or cognitive issues. The platform that we have can measure these symptoms. What’s exciting about this is the potential it has to positively impact patients. Patients are wearing these devices and capturing these patterns over long periods of time, which is necessary to develop biomarkers given the timespan of this disease is measured in decades.

Rune Labs has also been working on a spinal cord stimulator to help Multiple Sclerosis patients, could you discuss some of the science behind this?

Multiple Sclerosis is a neurodegenerative disease and, like Parkinson’s, there isn’t a cure today, but there are drugs and disease-modifying therapies that help patients alleviate their symptoms. These drugs essentially lessen the overreactive immune system that causes the body to attack itself in MS. At Rune, we’re investigating a novel MS treatment that would use a spinal cord stimulation device to help manage the neuropathic pain associated with MS. What’s unique about this approach is that, like Parkinson’s disease, you can use this device to access the nervous system.

A goal in neurology is to design an adaptive brain implant that can respond in real-time to brain waves to treat dozens of diseases. What are some of the core challenges behind building this? 

There are many core challenges behind building an adaptive DBS (aDBS) device. The main challenge is recording and stimulating the same target. There are several factors that can impact signal fidelity, and even prevent the use of a device in some patients. A recent study found that using an implantable pulse generator (IPG) in the right chest at a distance from the electric dipole of the heart can mitigate electrocardiogram (ECG) contamination and thus lower the probability of ECG artifacts in available sensing contacts. Along with ECG artifacts, DBS electrode cable movement can be observed as causing large transients in brain signals. Both aforementioned artifacts contaminate broad bands of the frequency spectrum and therefore, potentially prevent threshold-based control policies from effectively reacting to the target biomarker and leading to an uncontrolled increase or decrease of stimulation.

The development of clinically sustainable aDBS systems will bring new challenges that are technical in nature, including artifact-free interaction with brain activity. Many of these challenges could be addressed by pairing additional wireless external devices with the implants to support physiological and behavioral tracking while increasing the precision of the patient-tailored control strategies.

Thank you for the great interview, readers who wish to learn more should visit Rune Labs.