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What data you need, where you get it – and most importantly – what you do with it, all determine whether a medical device will be successful. Of course, every technology has its own set of caveats, but it always helps to hear from people who have been there.
In this episode, VP of Business Development Andy Rogers, and Senior Electrical Engineer/Partner Jake Cowperthwaite, both of Key Tech, talk with Steve Schaefer, CEO at CoolTech, about the quest for data with CoolStat: a new way to manage patient temperature in fever that can develop following a stroke, traumatic brain injury, seizure, or metabolic encephalopathy.
Speed-to-data determines go-to-market success for medical devices. You need to inform critical decisions with user data, technical demonstration data, and clinical data. We interview med tech leaders about the critical data-driven decisions they make during their product development projects.
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All right, everybody.
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Welcome to the first official episode of
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MedTech Speed to Data.
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A Key Tech podcast.
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I'm your host, Andy Rogers from Key Tech.
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Excited to be here.
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So what are we talking about?
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It's medtech speed to data.
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What's the critical data that startup companies
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and global companies developing new products?
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What data is most critical to their ventures?
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Early in development. Late in development.
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And what key decisions are folks
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trying to make with this data?
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We're going to break it down with our guests.
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Our first guest today, Steve Schaefer.
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Steve, welcome to the show.
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Thank you. Thank you. Pleasure to be here.
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All right.
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Jake Cowperthwaite, also co-host here today.
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Partner, Senior EE at Key Tech. Jake.
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Thanks for joining the show.
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Hey, Andy. Hey, Steve. Excited to be on today.
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So before we get into it here
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Steve, I'm going to make you blush a little bit.
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Very, very excited
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to have you on the show.
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It's going to be a great interview.
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So, Steve,
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Steve's an executive with concentration
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on taking innovation
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from concept to commercialization
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at life science, medical device
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and technology companies,
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broad experience in all kinds of areas
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sales, marketing,
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operations, business
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development, accounting
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and financial management.
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I had to reread this a few times here, Steve,
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but you've completed over $1,000,000,000.
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That's with a B
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in transactions,
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including over a quarter billion dollars
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raised for start up and growth companies.
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That's legit.
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Very excited that you're on the show here.
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Currently, you're CEO of CoolTech.
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We'll talk about that today in detail.
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But prior roles included co-founding
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medtech company, CSA Medical
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with the true freeze product
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based in Lexington, Massachusetts, now,
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and also leading a modern health multi-million dollar
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health care advisory company.
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So, Steve, again, thanks for joining me.
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Yeah, my pleasure.
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So let's get right into it.
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Very excited. So.
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So I want to ask you, this is your second
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medical device venture.
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I know you've been exposed to others,
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But why are you in
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the medical devices business
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if you don't mind my asking?
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I don't mind it on the fact it's
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it's personal.
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The reason why
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I keep coming back to
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medical device, which is very difficult industry,
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is that I've seen firsthand the impact,
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that innovation can bring to improve
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people's lives, even save people's lives.
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Early on in my career, I was a CPA
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a partner in a CPA firm,
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one of my partners.
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Years later, developed esophageal cancer.
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And unfortunately, he died
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despite the fact
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that there were treatments
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available for him.
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So a couple of things went wrong.
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One is he didn't
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go to the right type of doctor
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to properly diagnose him
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and give him the correct treatment plan.
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And two,
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there were innovations
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that were just down the street
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that could have, and most likely
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would have completely cured him.
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But he died and he left
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two kids behind.
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It was really tragic.
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But that story gets repeated all the time.
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And I was actually the
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co-founder of a company
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that developed the treatment for that,
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which is now part of STERIS Corp.
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and it's called True Freeze.
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And it would
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it could have ablated the disease tissue
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allowing healthy tissue to regrow
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and most likely he'd be with us today.
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So it means a lot to me.
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Both my parents passed away
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too early from cancer.
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And so you know, anything we can do
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and there is a lot
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we can do with the incredible inventions
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that are out there
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to bringing from,
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concept to actually out there
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helping people. That's why I do it.
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And it's very impactful.
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Steve, I know that background.
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So, you know,
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thanks for that that description.
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And yet every day,
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you know, we're working on products like this
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and it's both
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you have to catch these diseases early
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and you have to know how to treat them
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and you have to monitor them
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like this continuous
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cycle, prevent, detect, treat, monitor.
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So there's a lot of innovation out there
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in all those areas.
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So can you tell us a bit about Cool Tech?
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Tell us about the company.
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How long you've been there?
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Just that kind of high-level overview.
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Sure, so I've been involved
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since the inception, but full time
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since the middle of 2020.
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So about a year and a half in my current role
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and you know, Cool Tech
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has a platform technology
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for evaporative cooling.
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And, you know, you'd think
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we're going to have a HVAC conversation,
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but actually there are
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profound medical applications for it.
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One is through a product called
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Cool Stat,
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and that's to cool people
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who need to get cooled after
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a traumatic event.
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And the other is a pain management platform,
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a key product being developed called MiHelper.
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And that's for migraine and other
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pain and neurological disorders.
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Got it.
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So so there's two products,
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and we're going to focus on each.
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Let's start with the Cool Stat
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platform, the In-clinic product.
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What is the status of that product?
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And can you talk about
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some of the high-level features there?
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Sure, so CoolStat was developed
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to provide a better way
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a more cost effective way, a way without side effects
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to cool people after they've had a heart attack,
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a stroke, a seizure,
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traumatic brain injury.
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Any reason why
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they might develop a fever that can't be controlled,
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which will probably kill them?
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Or
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they need to be cooled in order to help
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preserve their brain
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and their heart and other vital organs.
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So CoolStat,
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it's different in that it uses simply dry room air.
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No chemicals, no drugs, just dry room air.
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To trigger an evaporative process
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so it blows air through the nose,
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across the nasal turbinates
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The body has to give energy
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for that liquid to change phases from liquid to gas.
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The evaporation and that energy extraction
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and pulls out heat and creates cool.
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Just like if you get out of the shower
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on a hot summer day
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and the breeze blows across, you feel cold
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even though it's 90 degrees outside.
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It's the same principle,
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but we use it to systemically cool the body.
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And that really cool thing about it is that
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people don't shiver normally.
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Any type of cooling, you go outside.
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Forget your coat. It's 20 degrees out.
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You're going to shiver all right?
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You're going to fight it.
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You want to bring your body back to a normal temperature,
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and that shivering
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it makes it intolerable.
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And so other cooling methods that use surface
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cooling people shiver terribly
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and they have to paralyze them,
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which is not something that you want to have to do.
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CoolStat that doesn't trigger that response.
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So we're really excited because it's very cost effective,
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extremely easy to use.
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It's the only portable option in the market.
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It is not cleared yet by the FDA.
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So it's still in studies only.
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So I think you answered my next question.
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It's still in studies here.
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We'll talk about the clinical study
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and what key data is important.
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But I think it's worth asking, like
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and I know you join the company a year and a half ago,
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but could you describe to our audience, like
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if you're aware of this, like what what early data
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was collected on this general therapy to say, hey,
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you know, this is worth an actual product?
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I mean, we know that that there are
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targeted temperature management
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products out there that actively cool
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externally and things like that.
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But how did you kind of know that this was
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this was a viable product.
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So the initial data was,
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you know, animal model testing.
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We used a porcine model.
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So we essentially cooled pigs
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using the same method
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and we measured the cooling efficiency.
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And we also measured the
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the feedback, the biofeedback
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from the animals to determine that it was safe.
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And that was the most important thing.
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The other thing is that we matched it up with the
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what the market opportunity in the market needs were.
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So you could prove that you could do
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it in an animal model.
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It's probably highly transferable
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to other mammals, in humans,
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but does anyone care?
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You know, should you commercialize something?
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what worth does that have to the health care industry?
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And so when those two match up
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and this is something that's needed,
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there's a potential for a compelling advantage.
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And the early preclinical work is showing great results.
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That's when it makes sense to move to the next stage.
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And everything has to go through stages
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and prove its worth through each stage.
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So the next step was, you know, early feasibility and
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safety testing
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with the prototype before the device is actually available.
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And then you develop
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the device
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or something close to what might be the commercial version.
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And then you move it into more advanced
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clinical testing
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designed to get clearance and to produce the evidence
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that will be required for adoption of it,
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you know, once it gets cleared.
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Yeah.
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So I want to dig into the animal study
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just a little bit more here real quick.
00:10:42:00 - 00:10:45:07
So you're claiming that
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the therapy effectively cools, right?
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So in a pig study, right?
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Like what?
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Obviously, you're collecting temperatures
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at various parts of the body, whatever, or of the pig.
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And what did the data
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what were you looking for specifically to say yes?
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This this will work or might work in humans.
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Specifically we were looking for a rate of cooling
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and we were looking at different parts of the body,
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how it cooled.
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Did it just cool locally?
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Was it systemic?
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We were looking at the flow rate.
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How much air do you have to blow through?
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How many liters per minute are required
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and what is the efficiency of that cooling
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if it takes two days to call somebody one degree,
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that's not going to work.
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So we were looking at the efficiency, the cooling,
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the flow rate
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because the amount of flow has to be tolerable
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and has to be practical to to deliver.
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So those are the main inputs.
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So you're showing feasibility on a bench.
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Now, clearly
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you need to do a trial, right,
00:11:57:08 - 00:11:59:15
because there wasn't a predicate device.
00:11:59:15 - 00:12:03:06
Can you describe to the audience what your regulatory
00:12:03:06 - 00:12:06:15
pathway is for the CoolStat platform?
00:12:06:15 - 00:12:09:17
Yeah, I mean, pretty much, even if there is a predicate,
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you have to do trials to show equivalency at a minimum.
00:12:15:03 - 00:12:16:21
So what
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we needed to do, because there was no predicate,
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was probably to do a little bit larger
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of a study than you otherwise have to do
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because the methodology of the cooling system was
00:12:29:08 - 00:12:30:23
completely different.
00:12:30:23 - 00:12:33:03
So we set up a multicenter study
00:12:34:14 - 00:12:36:13
that had an escalation to it.
00:12:36:13 - 00:12:39:05
So the first thing we wanted to do is cool people
00:12:39:05 - 00:12:40:10
get them to a certain
00:12:40:10 - 00:12:41:13
target temperature,
00:12:41:13 - 00:12:45:05
which was normal temperature or normothermia
00:12:45:05 - 00:12:46:15
within 4 hours,
00:12:46:15 - 00:12:49:23
and then maintain that temperature for 8 hours
00:12:49:23 - 00:12:52:16
and make sure that it worked and it was safe.
00:12:53:06 - 00:12:56:11
So we did that for the first ten subjects.
00:12:56:11 - 00:12:59:12
And since that worked well, we moved into the next phase,
00:12:59:12 - 00:13:01:18
which was doing the same thing.
00:13:01:18 - 00:13:03:23
But then cooling them for 24 hours.
00:13:04:22 - 00:13:07:13
And the device has a closed loop system.
00:13:07:22 - 00:13:11:10
So you basically set a desired temperature end point,
00:13:11:10 - 00:13:14:18
and it will induce that temperature and maintain
00:13:14:18 - 00:13:16:02
that temperature.
00:13:16:02 - 00:13:18:06
The system controls will do that.
00:13:18:06 - 00:13:19:11
So that worked well.
00:13:20:18 - 00:13:22:12
However, when we
00:13:22:12 - 00:13:26:00
went and looked at the device data, we realized
00:13:26:00 - 00:13:29:21
we were getting good results, but it was suboptimal
00:13:29:21 - 00:13:32:21
that there was a potential to change
00:13:32:21 - 00:13:36:03
the patient interface, to make it more efficient,
00:13:36:03 - 00:13:38:05
to make it more robust,
00:13:38:05 - 00:13:40:12
to make it even easier to implement.
00:13:40:23 - 00:13:43:23
And so we made a major shift.
00:13:43:23 - 00:13:45:05
We paused the study.
00:13:45:05 - 00:13:46:21
We went back to the FDA.
00:13:46:21 - 00:13:49:13
We said, here's the data.
00:13:50:08 - 00:13:52:11
It worked. It was safe.
00:13:52:11 - 00:13:54:16
But it can be better.
00:13:54:16 - 00:13:55:23
So we want to change it.
00:13:55:23 - 00:13:58:01
And when we changed it,
00:13:58:01 - 00:14:00:18
we went back and we did a dose escalation.
00:14:00:18 - 00:14:04:03
So we started at an even lower flow rate.
00:14:04:15 - 00:14:06:22
And then when we proved safety
00:14:07:08 - 00:14:08:18
for the first three subjects,
00:14:08:18 - 00:14:10:15
we moved it up to a higher flow rate.
00:14:10:15 - 00:14:13:02
And now we're in our final flow rate.
00:14:13:02 - 00:14:15:18
And it has worked incredibly well.
00:14:17:04 - 00:14:20:00
And we have three more subjects to complete the study.
00:14:20:10 - 00:14:20:20
That's great.
00:14:20:20 - 00:14:21:18
I mean, it seems like,
00:14:21:18 - 00:14:25:07
trial design is a science in and of itself.
00:14:25:07 - 00:14:26:11
And you're learning
00:14:26:11 - 00:14:27:14
and I think
00:14:27:14 - 00:14:30:02
most of the guests on this podcast
00:14:30:02 - 00:14:33:05
are going to tell stories of pivoting or improving
00:14:33:05 - 00:14:36:14
or changing, you know, their study along the way.
00:14:36:14 - 00:14:39:12
I do want to get into,
00:14:39:12 - 00:14:41:20
obviously, your product development
00:14:41:20 - 00:14:44:06
process and specifically,
00:14:44:06 - 00:14:45:21
you know, so we're going to state the obvious.
00:14:45:21 - 00:14:47:16
The critical data for your venture
00:14:47:16 - 00:14:49:19
is proving efficacy in the clinic.
00:14:49:19 - 00:14:53:17
And so you needed a clinical trial device.
00:14:54:10 - 00:14:57:02
So is this clinical trial device
00:14:57:15 - 00:15:01:03
your commercial device or or not?
00:15:01:17 - 00:15:04:14
Or can you describe the differences between
00:15:04:14 - 00:15:06:10
what you're using in clinic?
00:15:06:10 - 00:15:08:06
And maybe that was by design
00:15:08:06 - 00:15:09:17
and what you know,
00:15:09:17 - 00:15:11:18
you ultimately will be we'll be launching with.
00:15:13:01 - 00:15:14:16
Great question.
00:15:14:23 - 00:15:18:19
You always want to start with the end in mind.
00:15:18:19 - 00:15:23:04
So understanding, what the future commercial product
00:15:23:04 - 00:15:26:23
requirements are as best you can early on is important.
00:15:27:15 - 00:15:30:20
Now, there may be some features and functionality
00:15:30:20 - 00:15:33:16
that you feel are non-essential to prove
00:15:34:06 - 00:15:36:10
the basic tenets of your device.
00:15:38:06 - 00:15:40:12
And they may cost a lot of money to implement
00:15:40:12 - 00:15:43:07
So you may decide in your trial device
00:15:43:18 - 00:15:47:22
that it may not be as sleek, it may not be as slick,
00:15:47:22 - 00:15:50:05
it may not have all the bells and whistles
00:15:50:05 - 00:15:53:03
that you eventually want to have to commercialize,
00:15:53:03 - 00:15:57:07
but it gets the job done and it's faster to get it done.
00:15:57:07 - 00:15:59:23
It's cheaper to get it done.
00:15:59:23 - 00:16:02:19
So that's normally the case.
00:16:03:10 - 00:16:06:15
And even when you get a commercial, there's normal
00:16:07:00 - 00:16:10:19
evolution and iterations of it as you go forward.
00:16:10:19 - 00:16:14:11
But I think we did a pretty thorough job
00:16:14:11 - 00:16:16:19
of getting it to the point where it could be the
00:16:16:19 - 00:16:17:16
commercial device,
00:16:18:15 - 00:16:20:15
especially with
00:16:20:15 - 00:16:23:16
the pivot we made within that study,
00:16:23:16 - 00:16:27:13
we went back to our product development partner, Key Tech.
00:16:27:13 - 00:16:30:23
They help us engineer the new interface
00:16:31:10 - 00:16:34:14
to reconfigure and validate
00:16:34:14 - 00:16:37:11
the device and the controls and software
00:16:38:11 - 00:16:41:07
And so we've essentially kind of moved to what
00:16:41:07 - 00:16:45:05
a commercial device should be within the study period.
00:16:46:01 - 00:16:49:04
And, you know, it was it was a cost effective
00:16:49:04 - 00:16:52:10
and allows us to go, you know, immediately
00:16:52:10 - 00:16:54:18
post clearance into commercialization.
00:16:55:09 - 00:16:56:05
Got it.
00:16:56:05 - 00:16:57:05
Now, that's that's great.
00:16:57:05 - 00:16:58:00
I mean, ideally, yeah.
00:16:58:00 - 00:16:59:10
You're doing your trial
00:16:59:10 - 00:17:00:11
with your commercial device,
00:17:00:11 - 00:17:03:07
so I'm glad you're glad you're there.
00:17:03:07 - 00:17:06:00
So now that we we talked a little about the device
00:17:06:00 - 00:17:09:00
let’s talk a little bit more about the clinical data.
00:17:09:00 - 00:17:12:18
So you mentioned 4 hours, 8 hours, ten subjects.
00:17:12:18 - 00:17:13:19
24 hours,
00:17:14:22 - 00:17:15:20
you know,
00:17:15:20 - 00:17:19:09
how are you collecting the temperature data on the human
00:17:19:09 - 00:17:22:13
and, you know, and how did you determine,
00:17:22:13 - 00:17:24:00
you know, the size of your trial
00:17:24:00 - 00:17:26:03
and that that data was sufficient enough
00:17:26:03 - 00:17:28:01
to, you know, for the study?
00:17:28:08 - 00:17:29:08
So
00:17:30:07 - 00:17:33:02
it's a little bit different with such an objective
00:17:33:02 - 00:17:35:22
measurement as temperature.
00:17:36:10 - 00:17:38:06
There's validated temperature
00:17:38:06 - 00:17:42:03
monitoring probes and software to store it.
00:17:42:03 - 00:17:44:13
That's commonly used in the hospital.
00:17:45:00 - 00:17:48:10
And we just piggyback into that.
00:17:48:10 - 00:17:50:19
So it's not like an outcomes study
00:17:50:19 - 00:17:53:15
where you're saying, okay, because we cooled them,
00:17:53:15 - 00:17:56:13
how many people improve their mortality
00:17:58:01 - 00:17:59:09
it's not like that at all.
00:17:59:09 - 00:18:04:16
So the number of subjects you need is rather small,
00:18:04:16 - 00:18:08:14
and it all depends on how consistent your results are.
00:18:08:14 - 00:18:10:01
If they're all over the place,
00:18:11:17 - 00:18:13:23
then that's an issue.
00:18:13:23 - 00:18:16:15
You're going to need a larger sample size
00:18:16:15 - 00:18:19:01
and you're probably going to need a control
00:18:20:18 - 00:18:23:09
with something objective like temperature management.
00:18:23:09 - 00:18:27:07
It's really you either lower their temperature,
00:18:27:07 - 00:18:28:20
or you didn't, right?
00:18:28:20 - 00:18:30:18
Having a randomized controlled trial
00:18:30:18 - 00:18:32:18
really doesn't make sense because
00:18:32:18 - 00:18:35:10
it's such an objective measurement.
00:18:35:10 - 00:18:38:23
So we, you always work with the biostatistician
00:18:40:02 - 00:18:42:10
and you know, especially in a randomized
00:18:42:10 - 00:18:45:15
controlled trial, it's a completely different story.
00:18:45:15 - 00:18:46:11
I mean, you're going to
00:18:46:11 - 00:18:50:17
look at what effect size you think you will have,
00:18:50:17 - 00:18:54:05
what difference there'll be between the control group,
00:18:54:23 - 00:18:57:05
what's the placebo effect within that control
00:18:57:05 - 00:19:01:05
group, what's the expected efficacy in the treatment arms?
00:19:01:05 - 00:19:04:19
And they do their power analysis and they figure out
00:19:04:19 - 00:19:06:17
for a certain confidence interval,
00:19:06:17 - 00:19:10:04
you need this big of a study for ours
00:19:10:23 - 00:19:12:09
for temperature management.
00:19:12:09 - 00:19:15:20
It hasn't hasn't been that complex
00:19:16:16 - 00:19:18:22
So we basically picked a practical number
00:19:19:12 - 00:19:22:15
that the we thought the FDA would agree is sufficient.
00:19:22:15 - 00:19:23:08
Got it. Yeah.
00:19:23:08 - 00:19:26:21
So it wasn't too involve there in the analysis.
00:19:26:21 - 00:19:31:06
So the devices are in the field,
00:19:32:01 - 00:19:33:00
how many are in the field
00:19:33:00 - 00:19:36:00
or how many are in the in the trial I should say.
00:19:36:00 - 00:19:37:17
And, you know, what infrastructure
00:19:37:17 - 00:19:39:16
did you have to put in place,
00:19:39:16 - 00:19:42:12
you know, to support those devices?
00:19:42:12 - 00:19:45:08
We have three centers each center has two
00:19:45:08 - 00:19:48:18
devices, a primary and a backup.
00:19:48:18 - 00:19:51:07
And we had to do a whole lot of work
00:19:51:07 - 00:19:53:14
leaning heavily on our development partner
00:19:54:01 - 00:19:57:06
to create those devices, to monitor
00:19:57:06 - 00:20:00:08
those devices, to make changes to devices
00:20:00:08 - 00:20:03:12
When we made the amendment.
00:20:04:14 - 00:20:07:12
So it's a lot of work.
00:20:08:00 - 00:20:11:16
We also look at every
00:20:12:23 - 00:20:16:10
subject that's enrolled and treated.
00:20:16:10 - 00:20:19:22
We look at the device data right afterwards.
00:20:19:22 - 00:20:24:14
So every second the device is storing the performance
00:20:24:14 - 00:20:25:23
pretty much everything
00:20:25:23 - 00:20:29:14
from what the temperature is, the flow rates, the pressure,
00:20:29:14 - 00:20:33:03
the patient temperature, all that's recorded.
00:20:33:03 - 00:20:37:05
And so we analyze that to see if there are any issues.
00:20:38:11 - 00:20:42:05
Like, for example, we had a subject where the staff
00:20:42:05 - 00:20:45:13
accidentally turned the machine off for a period of time.
00:20:46:12 - 00:20:47:21
That's not good.
00:20:48:01 - 00:20:48:15
Yeah.
00:20:51:20 - 00:20:52:17
Got it.
00:20:53:08 - 00:20:54:20
So I guess, Steve,
00:20:54:20 - 00:20:55:20
you mentioned that you're
00:20:55:20 - 00:20:59:03
fortunate enough to have a relatively small sample size
00:20:59:21 - 00:21:01:16
but still, I'm sure it's a lot of effort.
00:21:02:21 - 00:21:03:14
I guess
00:21:03:14 - 00:21:05:08
my question would be,
00:21:05:08 - 00:21:06:20
even with a smaller sample size,
00:21:06:20 - 00:21:08:08
how do you get access to patients?
00:21:08:08 - 00:21:10:07
Like it's not like you could just walk into a hospital
00:21:10:07 - 00:21:12:00
with a, you know, new device
00:21:12:00 - 00:21:13:18
and ask the clinicians to use it.
00:21:13:18 - 00:21:15:02
So could you take us through
00:21:15:02 - 00:21:17:06
how you find patients and get access to that?
00:21:17:06 - 00:21:18:07
Sure.
00:21:18:07 - 00:21:22:10
So for this study, patient selection is
00:21:23:11 - 00:21:25:16
it's highly concentrated.
00:21:25:16 - 00:21:27:15
They're all
00:21:27:15 - 00:21:28:22
unconscious.
00:21:28:22 - 00:21:31:05
They're all on breathing machines.
00:21:31:05 - 00:21:33:06
They're in the neuro ICU.
00:21:34:15 - 00:21:37:02
They're very easy to find.
00:21:37:02 - 00:21:41:07
They have to meet rigid inclusion and exclusion criteria.
00:21:41:20 - 00:21:45:03
So you have to screen hundreds of subjects
00:21:45:03 - 00:21:49:12
to get down to the very few that might get consented
00:21:49:12 - 00:21:52:06
and then you have to have the hospital's
00:21:52:17 - 00:21:56:07
clinical research staff get that consent.
00:21:56:07 - 00:21:59:07
And since the patients are unconscious,
00:21:59:07 - 00:22:03:05
it has to come from a legal authorized representative.
00:22:03:18 - 00:22:05:18
So you have to get what's called an LAR
00:22:07:03 - 00:22:07:17
to get them
00:22:07:17 - 00:22:11:14
into the study and not everybody consents to it.
00:22:11:14 - 00:22:15:09
You know, that's not always the first thing on their mind.
00:22:15:09 - 00:22:19:05
Their loved ones on a ventilator or other life
00:22:20:10 - 00:22:22:10
support device.
00:22:22:10 - 00:22:25:14
So so the staff comes through,
00:22:25:14 - 00:22:28:18
they explain the study, the risks and benefits
00:22:28:18 - 00:22:31:06
and potential benefits, I should say.
00:22:31:06 - 00:22:35:08
They consent them and enroll them in the study.
00:22:35:08 - 00:22:38:16
And for this type of study takes a long time
00:22:39:20 - 00:22:42:20
if a center enrolls a subject a month.
00:22:42:20 - 00:22:45:04
That's fantastic.
00:22:45:04 - 00:22:47:17
So, Steve, I got to ask that the last couple of years
00:22:47:17 - 00:22:50:18
have been tough, the COVID pandemic.
00:22:50:18 - 00:22:53:08
How did that impact your enrollment?
00:22:53:08 - 00:22:55:21
Yeah, it's been dreadful in many ways.
00:22:57:02 - 00:22:59:11
The human toll on people
00:23:00:05 - 00:23:02:21
in all the death,
00:23:02:21 - 00:23:04:22
the health care workers
00:23:04:22 - 00:23:08:17
exhausted, the staff shortages
00:23:10:00 - 00:23:12:06
the potential for contagion
00:23:12:06 - 00:23:15:15
having an aerosol generating procedure
00:23:16:15 - 00:23:17:23
like using CoolStat
00:23:17:23 - 00:23:20:19
it's blowing air through a patients nose
00:23:21:18 - 00:23:22:19
adds another element.
00:23:22:19 - 00:23:27:02
So obviously no one with COVID should be in the study.
00:23:28:00 - 00:23:30:17
And so it's made it incredibly difficult
00:23:30:17 - 00:23:33:02
even access to the centers, normally
00:23:33:02 - 00:23:36:16
for clinical study your team would be in
00:23:36:16 - 00:23:40:04
the air shoulder to shoulder
00:23:40:12 - 00:23:43:17
you know, looking for opportunities to enroll patients
00:23:44:01 - 00:23:48:17
and their physical presence within the hospital.
00:23:49:01 - 00:23:51:00
Not during COVID
00:23:51:00 - 00:23:53:07
couldn't come anywhere near the building.
00:23:53:07 - 00:23:55:16
So it has made it more challenging.
00:23:56:00 - 00:23:56:13
Sure. Yeah.
00:23:56:13 - 00:23:58:18
Sounds like additional logistical challenges
00:23:58:18 - 00:24:00:11
additional logistical challenges
00:24:00:20 - 00:24:02:12
I guess next thing I'm interested in
00:24:02:12 - 00:24:05:21
is just the process of handling the data.
00:24:06:00 - 00:24:09:02
So, you know, how is it collected?
00:24:09:02 - 00:24:12:18
How is it stored and then how is it reviewed?
00:24:12:18 - 00:24:15:05
You know, where does it go once you collect it?
00:24:15:05 - 00:24:16:03
Great question.
00:24:16:03 - 00:24:20:13
So it starts out being recorded on predetermined
00:24:20:13 - 00:24:21:21
what are called CRFs.
00:24:21:21 - 00:24:25:05
So there are forms that tie to the protocol,
00:24:26:14 - 00:24:28:07
what data
00:24:28:07 - 00:24:29:01
to be gathered and
00:24:29:01 - 00:24:33:02
how it's to be gathered is all, you know, pre-specified.
00:24:33:02 - 00:24:36:13
So it actually starts out on paper and then it is entered
00:24:36:13 - 00:24:41:05
into an electronic data capture software system
00:24:41:05 - 00:24:44:14
and a database that's been built and validated.
00:24:44:14 - 00:24:48:12
We use a a partner called Med Net on that
00:24:48:12 - 00:24:53:17
and then that data is aggregated, it's validated.
00:24:53:17 - 00:24:56:07
And we also in this study
00:24:56:07 - 00:24:59:16
have the luxury of the device data as well.
00:24:59:16 - 00:25:01:22
So we can match up with the device
00:25:01:22 - 00:25:04:15
said happened to what the human said happened
00:25:05:11 - 00:25:09:21
when they recorded it off their devices.
00:25:09:21 - 00:25:11:07
So it's a good way
00:25:11:07 - 00:25:14:16
to make sure that there's consistency in the data.
00:25:15:02 - 00:25:17:01
Sure. Makes sense.
00:25:17:01 - 00:25:19:18
So how soon can you see the data are you looking at,
00:25:19:18 - 00:25:22:01
you know, daily or weekly?
00:25:22:01 - 00:25:23:14
You know, when are you able to get your eyes on it?
00:25:24:19 - 00:25:28:04
So we can see it usually within 24 hours.
00:25:28:22 - 00:25:32:06
And we look as soon as it comes in.
00:25:32:06 - 00:25:34:07
There's no reason not to.
00:25:34:07 - 00:25:37:08
In this case, you know, it's not a blinded study.
00:25:37:08 - 00:25:41:11
There's no you know, there's no randomization.
00:25:42:03 - 00:25:44:16
So you really want to see what happened
00:25:44:16 - 00:25:46:01
as soon as possible.
00:25:46:01 - 00:25:47:05
And if there are any issues
00:25:47:05 - 00:25:48:10
you want to address them,
00:25:48:10 - 00:25:51:02
you know, before the next patient gets enrolled.
00:25:51:02 - 00:25:54:10
So it might have been the training of the staff
00:25:54:10 - 00:25:57:12
and how to administer the therapy,
00:25:57:12 - 00:25:58:13
maybe there was something
00:25:58:13 - 00:26:00:08
that didn't work as well as you wanted.
00:26:00:08 - 00:26:01:23
You can see that in the data.
00:26:01:23 - 00:26:03:23
You can take corrective action.
00:26:03:23 - 00:26:06:16
So we look right away plus for curious
00:26:06:16 - 00:26:10:10
because we want to see how well it worked, we’re human.
00:26:11:05 - 00:26:12:07
And what do you do if
00:26:12:07 - 00:26:16:12
the data doesn't look like you expect it to
00:26:17:14 - 00:26:18:23
in a trial?
00:26:18:23 - 00:26:21:06
Yeah. So safety is paramount.
00:26:21:06 - 00:26:24:07
So you're always going to look for
00:26:24:20 - 00:26:26:11
any potential safety issues
00:26:26:11 - 00:26:29:03
and address them appropriately.
00:26:29:03 - 00:26:31:20
And if there’s safety issues,
00:26:31:20 - 00:26:34:07
you're going to follow the procedures for that.
00:26:34:07 - 00:26:36:01
Fortunately, we haven't had
00:26:36:01 - 00:26:39:08
those issues so it's all about performance.
00:26:39:08 - 00:26:41:07
And did it work on every patient?
00:26:41:07 - 00:26:42:11
No.
00:26:43:00 - 00:26:44:11
Did we see some reasons why?
00:26:44:11 - 00:26:47:11
Yes. Have we corrected most of them?
00:26:47:11 - 00:26:49:15
Yeah, I'd say we corrected all of them.
00:26:50:15 - 00:26:53:01
And on some subjects that have worked better than others,
00:26:53:01 - 00:26:54:21
someone who is 500 pounds
00:26:54:21 - 00:26:58:17
It's a lot more to cool than someone who's 120 pounds.
00:26:58:17 - 00:27:03:09
So then you start to understand some of the dynamics of,
00:27:03:09 - 00:27:05:10
you know, subject variability
00:27:05:10 - 00:27:10:07
and you can start to formulate how you might
00:27:11:12 - 00:27:13:09
be able to market this in the future,
00:27:13:09 - 00:27:14:06
what instructions
00:27:14:06 - 00:27:17:14
you'll get of what the labeling will look like.
00:27:17:14 - 00:27:19:18
There's a lot of rich
00:27:19:18 - 00:27:23:00
and interesting things that can be learned.
00:27:23:03 - 00:27:26:07
When you're looking at the data beyond safety.
00:27:27:04 - 00:27:30:13
Is there kind of a Go No-Go threshold where,
00:27:30:13 - 00:27:31:03
you know, if
00:27:31:03 - 00:27:32:20
after some period of time
00:27:32:20 - 00:27:34:21
the results aren't looking so good,
00:27:34:21 - 00:27:37:22
you might stop the study and redirect things?
00:27:38:10 - 00:27:40:15
Yeah, absolutely.
00:27:41:05 - 00:27:43:01
You could say,
00:27:43:17 - 00:27:46:18
if it were the case that it's ineffective
00:27:46:18 - 00:27:48:06
and it's not worth
00:27:48:06 - 00:27:51:10
going forward and you can just stop a study.
00:27:51:10 - 00:27:53:21
It doesn't have to be for safety reasons.
00:27:53:21 - 00:27:57:13
It just might not merit going any further.
00:27:58:13 - 00:28:00:22
You may find that it's suboptimal,
00:28:00:22 - 00:28:03:09
but there's a way to correct that, which we did.
00:28:05:00 - 00:28:07:14
And so, you know,
00:28:07:14 - 00:28:09:00
it all depends on
00:28:09:00 - 00:28:11:16
what endpoint you think is commercially viable
00:28:11:16 - 00:28:13:00
you think is commercially viable
00:28:13:00 - 00:28:16:23
and whether it's worth marching forward or not.
00:28:17:21 - 00:28:19:06
And I and I quite frankly,
00:28:19:06 - 00:28:22:07
I thought we had really good results,
00:28:22:07 - 00:28:25:20
but just not like incredibly compelling over
00:28:25:20 - 00:28:27:12
what was out in the market.
00:28:27:12 - 00:28:30:01
It might have been better,
00:28:30:01 - 00:28:32:06
but when we made a change now I think we have
00:28:32:06 - 00:28:35:03
something that's, you know, a night and day improvement
00:28:35:15 - 00:28:39:02
beyond improved versatility.
00:28:39:02 - 00:28:41:11
I think that now we're going to be able
00:28:41:11 - 00:28:43:05
to prove some pretty big advantages.
00:28:43:05 - 00:28:46:08
And this is that with the added irrigation feature.
00:28:46:11 - 00:28:48:10
Well, we basically we switched
00:28:48:10 - 00:28:50:11
from this is very technical.
00:28:50:11 - 00:28:51:23
So listen carefully
00:28:51:23 - 00:28:55:20
for blowing air through two nostrils at the same time.
00:28:55:20 - 00:28:58:07
Through just blowing air through one nostril.
00:28:58:07 - 00:29:00:23
So now the air can go in one nostril and out the other,
00:29:00:23 - 00:29:01:17
kind of like a
00:29:02:23 - 00:29:05:00
neti pot flow pattern.
00:29:05:13 - 00:29:08:16
Before it had to go
00:29:08:16 - 00:29:11:03
down the back of the throat and out the mouth.
00:29:11:03 - 00:29:13:19
So what happens if the patient closes their mouth?
00:29:13:19 - 00:29:15:08
Well, the pressure will rise
00:29:15:08 - 00:29:17:23
the flow will be reduced by the device
00:29:17:23 - 00:29:21:00
and they won't get the dose that they need.
00:29:21:16 - 00:29:24:02
Now it works 100% every time
00:29:24:16 - 00:29:27:10
in terms of delivering the desired flow.
00:29:27:10 - 00:29:30:15
Well, one thing, and this might be a tough question,
00:29:30:15 - 00:29:33:02
I know clinical trials are heavily regulated,
00:29:33:02 - 00:29:37:01
as they should be, but is there any data
00:29:37:01 - 00:29:38:18
that you wish you could collect
00:29:38:18 - 00:29:39:23
but you're just not able to
00:29:39:23 - 00:29:42:02
because of regulatory constraints?
00:29:42:02 - 00:29:42:21
Yeah, absolutely.
00:29:42:21 - 00:29:46:22
There's there's a lot. The most significant is
00:29:48:00 - 00:29:50:20
we had a desire to study
00:29:51:13 - 00:29:54:18
beginning cooling out of the hospital
00:29:54:18 - 00:29:56:22
at point of first response.
00:29:56:22 - 00:29:59:23
You know, now it takes 4, 6, 8 hours
00:29:59:23 - 00:30:02:09
before people start to get cooled.
00:30:02:09 - 00:30:04:15
And it's potentially too late.
00:30:05:04 - 00:30:08:14
I mean, every minute is precious, much less hours.
00:30:08:14 - 00:30:11:17
And so our vision and it still is
00:30:12:03 - 00:30:16:02
was to be able to because we're the only portable option
00:30:16:02 - 00:30:19:10
is to be able to put it on the ambulance,
00:30:20:17 - 00:30:22:22
equip the paramedics with it.
00:30:22:22 - 00:30:25:11
But we weren't able to do that because,
00:30:26:20 - 00:30:30:13
we weren't able to get a community consent
00:30:30:13 - 00:30:34:13
because the patients are dead or unconscious at a minimum,
00:30:35:12 - 00:30:36:22
they can't get consent.
00:30:36:22 - 00:30:39:20
And so in order to start a therapy right away,
00:30:39:20 - 00:30:42:23
you have to have what's called a community consent.
00:30:42:23 - 00:30:46:07
And they didn't think we were ready to do that
00:30:46:07 - 00:30:48:11
until we proved the safety
00:30:48:11 - 00:30:50:15
and efficacy within the hospital.
00:30:50:15 - 00:30:52:02
So I would love to do this study,
00:30:52:02 - 00:30:53:23
but we can't, not now.
00:30:53:23 - 00:30:55:05
One day.
00:30:56:06 - 00:30:57:20
That's interesting.
00:30:57:20 - 00:30:58:18
Okay.
00:30:58:18 - 00:31:01:18
So, I mean, I feel like we've touched on
00:31:01:18 - 00:31:04:09
what data is important how you're collecting it.
00:31:05:13 - 00:31:09:06
I guess when will your trial be completed?
00:31:09:21 - 00:31:13:12
you don't have many patients, but are you close?
00:31:13:12 - 00:31:15:05
We only have three more to go.
00:31:15:05 - 00:31:17:08
So, yes, we are close.
00:31:18:15 - 00:31:20:13
I'm going to have to go get my crystal ball
00:31:20:13 - 00:31:23:12
to tell you how long it will take to get those three.
00:31:24:12 - 00:31:26:18
I hope we'll be done in April,
00:31:28:02 - 00:31:29:14
possibly sooner.
00:31:30:09 - 00:31:34:09
So, Steve, let's go into the lightning round here.
00:31:34:09 - 00:31:37:07
Let's have a little fun. All right.
00:31:37:07 - 00:31:41:03
We'll focus on the CoolStat platform for now.
00:31:41:21 - 00:31:44:03
So you've had success
00:31:44:03 - 00:31:45:00
developing products,
00:31:45:00 - 00:31:47:19
getting them through trials and on the market.
00:31:47:19 - 00:31:50:07
So what advice do you have for
00:31:50:07 - 00:31:53:21
entrepreneurs that are that are fundraising right now,
00:31:54:09 - 00:31:55:22
trying to get
00:31:55:22 - 00:31:59:16
traction showing showing these early concepts,
00:31:59:16 - 00:32:01:08
you know, without clinical data
00:32:01:08 - 00:32:04:20
What advice do you have for for these startup entrepreneurs
00:32:04:20 - 00:32:05:19
to get where
00:32:05:19 - 00:32:07:14
where you are now in the driver's seat
00:32:07:14 - 00:32:09:02
towards the end of a trial,
00:32:09:02 - 00:32:11:05
looking at the commercialization?
00:32:11:05 - 00:32:14:00
It's tough out there in medtech
00:32:14:00 - 00:32:19:12
to get funding, to get early funding is even harder.
00:32:19:12 - 00:32:23:06
And the traditional venture capital firm
00:32:23:06 - 00:32:25:15
who will come in at a very early stage,
00:32:25:15 - 00:32:28:12
like a concept stage preclinical.
00:32:28:23 - 00:32:32:08
They really don't do that anymore.
00:32:32:08 - 00:32:35:03
So you're you're forced to source
00:32:35:03 - 00:32:40:01
your funds from private investors, angels
00:32:40:01 - 00:32:43:19
small funds, maybe state run funds
00:32:43:19 - 00:32:46:07
and high net worth individuals.
00:32:47:12 - 00:32:49:02
The other sources is grants,
00:32:49:02 - 00:32:52:17
very important, so that kind of angel money
00:32:52:17 - 00:32:56:20
and grant money is your target when you're at that stage.
00:32:56:20 - 00:32:58:01
And there you want to have
00:32:58:01 - 00:33:00:10
something that people can identify with.
00:33:00:10 - 00:33:03:00
Maybe they have a personal connection
00:33:03:00 - 00:33:06:02
to the desire to bring a solution for the disease
00:33:06:02 - 00:33:09:07
or the need for a diagnostic.
00:33:09:07 - 00:33:10:14
You've got to do your homework.
00:33:10:14 - 00:33:13:23
You've got to be able to lay out, you know, the plan,
00:33:13:23 - 00:33:15:14
what it's going to take to get there,
00:33:15:14 - 00:33:17:23
what it's going to look like when you get there.
00:33:17:23 - 00:33:20:19
The business model will look like
00:33:20:19 - 00:33:21:20
you've got to
00:33:21:20 - 00:33:24:01
go through all the standard business plan stuff
00:33:24:01 - 00:33:25:03
all the standard business plan stuff
00:33:25:03 - 00:33:27:15
and the more primary and secondary research
00:33:27:15 - 00:33:31:21
you have to back up your story, the better.
00:33:31:21 - 00:33:35:16
Yeah, I was listening to a medtech podcast,
00:33:35:16 - 00:33:38:00
not this one, but a different one.
00:33:38:00 - 00:33:41:11
And one of the it was a kind of a news
00:33:41:11 - 00:33:44:05
report and reimbursement,
00:33:44:05 - 00:33:45:13
which is not surprising here.
00:33:45:13 - 00:33:48:19
But the reimbursement projections
00:33:48:19 - 00:33:50:21
is one of the most important
00:33:50:21 - 00:33:53:05
sort of indicators for success.
00:33:53:05 - 00:33:56:20
So can you talk a little bit about reimbursement on
00:33:56:20 - 00:33:59:13
for targeted temperature management for CoolTech?
00:33:59:13 - 00:34:00:11
Sure.
00:34:00:17 - 00:34:01:04
Yeah.
00:34:01:04 - 00:34:04:11
How it's going to get paid for is critical.
00:34:05:18 - 00:34:07:15
There is the possibility that
00:34:07:15 - 00:34:10:12
you can create new reimbursement
00:34:12:14 - 00:34:15:15
and even if you get a code,
00:34:15:15 - 00:34:17:06
doesn't mean you're going to get coverage
00:34:17:06 - 00:34:19:06
by commercial insurers.
00:34:19:06 - 00:34:21:23
So you're going to end up in the end
00:34:21:23 - 00:34:25:06
you have to have the data to support that
00:34:25:06 - 00:34:27:09
it's cost effective.
00:34:27:09 - 00:34:30:09
So there's not just advocacy, it's cost effective.
00:34:30:09 - 00:34:32:06
It makes sense.
00:34:33:16 - 00:34:36:14
So for temperature management,
00:34:36:14 - 00:34:39:23
these patients are in intensive care.
00:34:39:23 - 00:34:43:09
They're admitted, they fall under DRGs.
00:34:44:13 - 00:34:47:18
It's a package deal for care for them.
00:34:48:12 - 00:34:51:00
And so the reimbursement
00:34:51:00 - 00:34:53:21
the hospital gets to care for that patient
00:34:54:20 - 00:34:56:10
is the reimbursement.
00:34:56:10 - 00:34:57:20
And so, how does
00:34:57:20 - 00:35:01:04
CoolStat doesn't get specifically covered or won't,
00:35:02:01 - 00:35:05:18
but it'll be part of the costs they already incur
00:35:05:18 - 00:35:08:05
and it will allow them to reduce those costs.
00:35:08:05 - 00:35:10:12
What is the rough cost equation?
00:35:10:12 - 00:35:12:02
On on this product?
00:35:12:02 - 00:35:15:18
Is it that you're reducing the number of days
00:35:15:18 - 00:35:18:16
in ICU potentially and of course, saving the life, but
00:35:20:15 - 00:35:24:15
is it as simple as reducing the time in the ICU
00:35:25:19 - 00:35:29:11
that that really is the value you're communicating?
00:35:29:11 - 00:35:31:19
Well, that's a huge value.
00:35:32:10 - 00:35:33:18
When someone spikes
00:35:33:18 - 00:35:36:13
a fever that can't be controlled by medication
00:35:37:17 - 00:35:40:01
and it's raging out of control,
00:35:40:01 - 00:35:43:09
they have to reduce it.
00:35:43:09 - 00:35:46:02
So it's something that they're compelled to do.
00:35:46:02 - 00:35:49:05
And now how they do it today
00:35:49:05 - 00:35:52:22
is use other devices which are much more costly.
00:35:52:22 - 00:35:56:00
So there's the cost of the device itself,
00:35:56:00 - 00:35:58:01
you know, A versus B.
00:35:58:01 - 00:36:00:05
And if ours a third of theirs,
00:36:00:05 - 00:36:03:04
then that's the savings.
00:36:03:04 - 00:36:09:00
There's also the lack of shivering as a side effect.
00:36:09:00 - 00:36:11:19
Which they need all these costly medications for.
00:36:11:19 - 00:36:15:12
So the reduction of
00:36:15:12 - 00:36:17:14
of those pharmaceuticals
00:36:17:14 - 00:36:21:13
and lastly would be if you if you do anything
00:36:21:13 - 00:36:25:22
to lessen their stay, then that's very helpful as well.
00:36:25:22 - 00:36:27:05
So if they don't have to be as
00:36:27:05 - 00:36:29:15
deeply sedated and paralyzed
00:36:31:03 - 00:36:33:02
to avoid the shivering,
00:36:33:02 - 00:36:37:03
then it stands to reason that they will be up
00:36:37:03 - 00:36:39:12
out of the ICU faster which is big bucks.
00:36:40:14 - 00:36:41:00
Yeah.
00:36:41:00 - 00:36:44:05
We don't have the data to support that yet, but
00:36:44:05 - 00:36:47:00
I think it's pretty logical conclusion.
00:36:47:19 - 00:36:50:10
That's for another trial, I suppose
00:36:50:20 - 00:36:54:07
So, you know, we are you know,
00:36:55:07 - 00:36:57:23
there are a lot of companies that
00:36:57:23 - 00:37:00:23
they're leaders, they're confident in their R&D team
00:37:00:23 - 00:37:02:14
either they have an internal team
00:37:02:14 - 00:37:04:10
or they're working with outside
00:37:04:10 - 00:37:07:12
product development firms and manufacturers.
00:37:07:12 - 00:37:10:20
And so now, you know, like you, it sounds like
00:37:10:20 - 00:37:14:06
they're shifting their attention to the commercial stage.
00:37:14:16 - 00:37:16:20
So we're still in the lightning round.
00:37:16:20 - 00:37:18:12
It's a very, very slow lightning round here.
00:37:18:12 - 00:37:19:17
But let's let's keep going.
00:37:19:17 - 00:37:23:09
What are you doing
00:37:23:09 - 00:37:27:02
to prepare to shift
00:37:27:02 - 00:37:30:23
from a preclinical pre-commercial
00:37:30:23 - 00:37:34:21
to now a commercial, potentially commercial organization?
00:37:35:15 - 00:37:36:06
What are you doing?
00:37:36:06 - 00:37:37:01
And maybe what advice
00:37:37:01 - 00:37:40:15
do you have given that this isn't your first rodeo
00:37:40:15 - 00:37:42:11
to prepare for a commercial company?
00:37:42:11 - 00:37:44:22
Yeah, frankly, we're not doing anything right now.
00:37:46:11 - 00:37:49:00
We foresee the sale to be very targeted.
00:37:49:00 - 00:37:52:07
There's only like 140 hospitals
00:37:52:07 - 00:37:55:09
that even take care of patients like this.
00:37:55:09 - 00:37:58:15
So we don't think we're going to need a big infrastructure.
00:37:59:04 - 00:38:00:15
For people preparing,
00:38:00:15 - 00:38:05:00
it's all about how this sale is going to be made
00:38:05:00 - 00:38:06:18
and what process you'll have to go through.
00:38:06:18 - 00:38:09:03
So if you're selling $1,000,000 laser
00:38:09:23 - 00:38:11:08
it's going to go through a whole different,
00:38:11:08 - 00:38:14:11
you know, capital acquisition process that people
00:38:14:11 - 00:38:17:18
you would need to sell that are very different.
00:38:17:18 - 00:38:19:13
They have a different background
00:38:19:13 - 00:38:21:23
it might be a sale to a physician,
00:38:21:23 - 00:38:25:03
might be a sale to a nurse,
00:38:25:03 - 00:38:27:13
who is right on the front line,
00:38:27:13 - 00:38:31:19
could be a sale to an acute care center.
00:38:31:19 - 00:38:35:06
It all depends on how that eventual sale process
00:38:35:06 - 00:38:36:04
will be made.
00:38:36:04 - 00:38:38:13
That determines the type of infrastructure team
00:38:38:13 - 00:38:40:06
that you need to put together.
00:38:40:06 - 00:38:40:22
Yeah.
00:38:40:22 - 00:38:44:11
So another question for you, like what industry groups or,
00:38:45:07 - 00:38:49:05
resources have you tapped into to really help
00:38:49:05 - 00:38:51:01
you get to where you are today
00:38:51:01 - 00:38:53:06
with your startup medical device company?
00:38:53:06 - 00:38:56:22
I would say that the NIH has been a great partner
00:38:56:22 - 00:39:00:05
both for temperature management and for pain management.
00:39:00:05 - 00:39:03:22
We've done large SBIR grants,
00:39:04:14 - 00:39:08:09
cooperative research with academic institutions
00:39:08:09 - 00:39:10:23
we work locally with Johns Hopkins,
00:39:10:23 - 00:39:13:14
University of Maryland and MedStar,
00:39:14:11 - 00:39:16:20
and they've been great partners.
00:39:16:20 - 00:39:19:06
So those are the main organizations,
00:39:19:06 - 00:39:20:08
as you go forward,
00:39:21:10 - 00:39:22:02
there are a
00:39:22:02 - 00:39:25:21
lot of like nursing associations that might be relevant
00:39:25:21 - 00:39:29:20
that need to know about your product can help you,
00:39:30:07 - 00:39:31:23
you know, if you're helping them,
00:39:31:23 - 00:39:33:01
they want to spread the word
00:39:33:01 - 00:39:36:02
about things that will help them do their job better.
00:39:36:23 - 00:39:38:00
All right.
00:39:38:00 - 00:39:41:08
I think that concludes our our lightning round, Steve.
00:39:41:08 - 00:39:44:18
So thanks for answering all our questions.
00:39:44:18 - 00:39:47:20
And I think this concludes our first episode
00:39:47:20 - 00:39:51:06
of MedTech Speed, the Data Key Tech Podcast.
00:39:51:06 - 00:39:54:04
So, everybody, thanks for joining Steve.
00:39:54:04 - 00:39:56:11
Thanks for coming on the episode Jake.
00:39:56:11 - 00:39:58:13
Great insightful questions
00:39:58:13 - 00:40:00:07
related to the trial and the devices.
00:40:00:07 - 00:40:03:22
So that's it. Thanks, everybody.