Neurons
Source: Rost-9D / Getty Images
Brian Pepin
Brian Pepin

In mid-September, three-year old Rune Labs, a brain data company empowering the development and delivery of precision neuroscience therapeutics, announced it had secured $22.8 million in a Series A financing that will allow it to broadly expand its team of software engineers. Co-founded by CEO Brian Pepin and CTO Miro Kotzev, the company aims to bring the power of data generated by the brain to help inform the development of new therapeutics for diseases such as Parkinson’s disease and other neurological disorders, while also providing a platform for clinicians to tailor care by having a longitudinal view of their patients’ diseases and symptoms. A day after disclosing the investment in the company which also includes past executives from Roche, Pathfind, Evidation, and Robin Health, CEO Brian Pepin took time for a conversation about the company’s focus and technology with Clinical OMICs Editor in Chief Chris Anderson.

 

Chris Anderson, Editor-in-Chief, Clinical OMICs: Prior to co-founding Rune Labs, what were some of the things that were resonating with you in the work Google Health/Verily?

Brian Pepin, CEO, Rune Labs:  Going back a little bit before that, I’ve been working in this intersection of engineering and neuroscience for a little longer than a decade, starting, really, with my graduate work at Berkeley in brain machine interface. I was working on designing systems which could interface with the brain and help us investigate things like the Parkinsonian neural circuit, to help us build neural prosthetics for people.

I came initially to Google because I was following some folks that I had worked with there, which became the smart contact lens program—very early on at the time—which rolled into the diabetes platform and was rolled into Google Life Sciences. But at Verily, I had this interesting view, because we were doing work in diabetes and immune-oncology—data pipeline work; data-driven therapy work. And there was this idea for how in some of these other areas like oncology, data driven medicine was emerging, both clinically and as a sort of vehicle for developing new therapies based on high-quality human data and targeting for efficient clinical trials.

At the same time, my interests were more on the neuroscience side. For a time I was running the Verily half of a joint venture that we were doing called Galvani, which was a neuroscience joint venture with GSK developing therapies for autoimmune diseases like rheumatoid arthritis that were based on stimulation of the nervous system. I was being exposed to stuff that was happening in Parkinson’s, deep brain stimulation with folks like Medtronic; or in Parkinson’s, and Alzheimer’s drug development with folks like Biogen and I could see there was data-driven (work) going on, like in oncology. But the picture is very, very different on the neuroscience side—the care pathways are not data-driven.

And so you get Parkinson’s, and there’s not  clear set of say your fluid biomarkers, your imaging to then tell you what kind of Parkinson’s you have and what kind of therapy is right. And also there is a low rate of success on clinical trials—not a lot of new therapies coming to market. The animal models that are used in these neuroscience therapies don’t really recapitulate disease.

So that was the background that I was sitting in, and then I started to see evidence through an emerging explosion in the clinic, of lower cost imaging, more accessibility. But there was also some inflection point (technology), like the new Deep Brain Stimulation device from Medtronic that does direct brain sensing. Now you have 100,000 plus Parkinson’s patients over the next several years, they’re going to have direct brain sensing. Here’s a potential source, a window into what’s actually happening in the brains of people over time. And that might be to start building this neuroscience data platform company that can, on the one hand, support precision neurology, but also can partner with folks on the pharma side to bring therapies to market.

 

Can you tell me a little bit about how you’re planning to deploy the $22.8 million Rune just raised in the Series A financing?

Pepin:  It probably won’t surprise you that our team is mostly software engineers, given what we’re doing. We would like to significantly grow that part of the team, so a lot of the funds are  going toward that.

 

Tell me about the different data sources your platform uses and how it obtains these data.

Pepin:  We work directly with patients and clinicians in the context of clinical trials, but also now in the context of routine care—we’re going right to the source, in terms of what we’re bringing in from the patient. We have partnerships with Medtronic and Abbott that allow us to fairly automatically bring in data those devices. But we also are engaging patients through our mobile app, and also through an Apple Watch integration and bringing out a lot of real world context for that data. So, it’s quantitative sensor data on the watch—things like step count, heart rate—and then case reports, different medication symptoms, to build that time series context of the right data. We bring it all back to the clinician on a dashboard and they’re interacting with the data, leveraging that to engage with the patient, and look for patterns. We are also working on some forward-looking things to bring in genetic variant information and fluid biomarkers.

This provides a really cool view, over time, for the clinician to see if things vary over days and weeks via what’s happening with the patient. And we also get the clinical snapshot to help contextualize that. So, we have this rich brain data, but it’s got all this context built around it that makes it usable for clinicians and also makes it also usable for like researchers who are looking to do Parkinson’s patient phenotyping or look at developing better endpoints for trials.

 

Based on the data you are collecting and providing to clinicians will you need to be entering a regulatory approval pathway for your platform?

Pepin:  There are a few different approaches there that we’re considering. The core of what we’re doing is essentially reading data, and then providing it back to the dashboard. We’re not telling the clinician what to do. We’re not making any medical claims. We’re not really, you know, changing the risk profile of any therapies. So, although we take HIPAA compliance, GDPR compliance, and data privacy very, very seriously, we’re not FDA direct regulatory facing right now. As we go forward, though, there are some applications where we might have some of the edge elements of our platform dip into that. For example, we have been leveraging a toolkit from Apple to do tremor and distribution monitoring, that may eventually evolve into an FDA-approved tremor and dyskinesia monitor. But for right now, it’s just the raw data presented in a form that clinicians can read it, and then they can make their own decisions.

 

What was the gap you saw that needed filling when you and your co-founder Miro Kotzev launched Rune Labs?

Pepin:  Pepin: If you were to go in and watch your average Parkinson’s visit today, the first 20 to 50 percent of that visit is sort of an anthropological expedition by the clinician to figure out what’s really going on with the patient. With Parkinson’s—and all of these neurological disorders—they are very time varying. At any given time, in the clinic, they’re only going to be presenting a narrow range of their symptoms. And so this whole conversation (with the patient) has to happen. What we’re providing is enough data over enough time, in the right context, and with enough ground truth for what’s going on in the brain, that these conversations can be short.

[The physician can say]: “I can see from this information that you’re having problems specifically in the morning with dyskinesia. Tell me about the drug you take in the morning, Oh, actually, you’re taking four pills, when you should be taking two.” They can have these conversations very, very quickly and spend more of the visit on actual quality clinical care.

The second thing is, if you’re in the clinic, you can only see what you can see, right? You can see symptoms, you can see movement things, but you can’t know what electro-physiologically is going on in their brain. We give (clinicians) a peek into that window, which gives additional information to how—at a patient specific level—they’re responding to drugs; how they’re to responding to other therapy or are other therapies working or not working during sleep; how their disease is changing over time. As we enroll more patients into the platform, it starts to become this idea of neural fingerprints, where folks have similar outcomes with these therapies, and you can start to do more of that pattern matching. That’s the idea. That’s a little higher bar that we’re headed towards, but right now, at the very least, we’re providing much higher quality information for conducting routine clinical care.

 

Since you are collecting data from patients on a regular basis, I assume this can also allow doctors to see how effective the treatment they prescribe is?

Pepin:  Yes. Traditional measures of efficacy and Parkinson’s are still very symptom-based. I think the really cool thing that we can see is what effect is the therapy you are prescribing having on the underlying network dynamics of the brain? Are you actually changing something fundamental in the brain? And then what about the brain? Are you bringing it closer to what it would look like in a normal person or homeostasis? Some of these questions you haven’t been able to ask—the data hasn’t been there. But if you are treating the disease or trying to modify the disease over time, that’s essential info.

 

You’ve mentioned the company’s current focus on Parkinson’s. Are there other diseases and conditions that you are working on?

Pepin:  We’re building up a small cohort in multiple sclerosis right now and bringing in some really interesting data that is essentially electrophysiology sent from the spinal cord. I’m pretty excited about that. We also have a few smaller pilots for very severe psychiatric disorders, under the umbrella of treatment resistant depression.

 

Can you explain how Rune Labs is able to leverage deep brain stimulation devices used to treat Parkinson’s and other conditions, as well as other nervous system stimulation devices?

Pepin:  Deep brain stimulation devices, the spinal cord stimulation devices, the transcranial magnetic stimulation systems, neuromodulation devices are special, in some ways, because (they generate) all of this high-quality data. In the case of something like deep brain stimulation,  you can get low noise, high signal physiology, essentially for free as it’s being generated.

It’s being generated not just in a clinical snapshot—you’re seeing it over time. You’re seeing circadian cycles. You’re seeing what it looks like on a good day and what it looks like on a bad day for a patient. And you’re not asking the patient to go out and you know, wear an EEG head set 24/7, they are just going on living their normal lives. Also, we’re not talking about tens of patients and hundreds of patients, there are thousands. So very meaningful data sets. It’s what everyone has been wanting for the last 10 years and we have a way of delivering it quickly.

This site uses Akismet to reduce spam. Learn how your comment data is processed.