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Biolinq Introduces New Silicon Tech Opens Next Chapter of Glucose and Analyte Detection · Vanguards of Healthcare by Bloomberg Intelligence

0:21
Speaker 1: Welcome to another exciting episode of the Vanguards of Healthcare series.
0:24
Speaker 1: My name is Matt Henrickson, the medical technology analyst at
0:27
Speaker 1: Bloomberg Intelligence, which is the in house equity research platform
0:31
Speaker 1: of Bloomberg LP. We're pleased to have Biolink with us today,
0:35
Speaker 1: including CEO Rich Yang and co founder, president and CTO
0:40
Speaker 1: Jared Kandy. Biolink is a privately held medical device company
0:44
Speaker 1: that is developing biowearables, including Shine, the first glucose sensor
0:49
Speaker 1: with no hypodermic needle for insertion. Rich and Jared, thank
0:53
Speaker 1: you both for joining us today. Glad to be here.
0:55
Speaker 2: Thank you, Thanks Matt, And why don't.
0:58
Speaker 1: We just start with both of your career paths. Jared,
1:02
Speaker 1: I might start with you first because as a co founder,
1:06
Speaker 1: you know, you took the steps to see an unmet
1:10
Speaker 1: need in really what a market that seems to be
1:13
Speaker 1: innovating very very quickly. So just curious your thoughts of
1:17
Speaker 1: how you came about founding this company.
1:20
Speaker 3: Yeah, for sure. So the technology came out of uc
1:23
Speaker 3: San Diego, which is where I did a PhD in
1:25
Speaker 3: biomedical engineering. I met my co founder Josh there, and
1:30
Speaker 3: we did see that diabetes was one of these markets,
1:33
Speaker 3: one of these industries that had just been transformed by technology.
1:37
Speaker 3: But if you kind of looked at the way sensing
1:40
Speaker 3: was being done up until this point in time, the
1:43
Speaker 3: actual mechanism of sensing hadn't changed much. The electronics were changing,
1:47
Speaker 3: the wearables were changing, but the actual way of getting
1:50
Speaker 3: that information hadn't changed. And we saw that glucose was
1:54
Speaker 3: a very valuable marker, but we also saw that there
1:58
Speaker 3: was probably dozens of other marks that could be very
2:00
Speaker 3: interesting to measure. So that's really what got us going
2:04
Speaker 3: on this pathway was, you know, glucose is incredibly valuable
2:08
Speaker 3: for people with diabetes, but how can we expand that
2:11
Speaker 3: to other markers? And then also how can we make
2:13
Speaker 3: it easier for people with diabetes and without diabetes to
2:17
Speaker 3: get access to their metabolic information. And so that's really
2:20
Speaker 3: what got us started on this journey.
2:22
Speaker 1: Yeah, and Rich you joined a little bit later, but
2:26
Speaker 1: your career was very well established in the diabetes space beforehand,
2:30
Speaker 1: So why don't you let the listeners know your career
2:33
Speaker 1: path I got you to biolink sure.
2:36
Speaker 2: I was one of the early product managers for the
2:40
Speaker 2: very first c GIM that was ever approved by the FDA.
2:43
Speaker 2: That was in nineteen ninety nine. So as an early
2:46
Speaker 2: product manager in a new category, in a new domain,
2:49
Speaker 2: it was it was a tough run, right, There was
2:51
Speaker 2: no reimbursement, the product was retrospective, it was not wireless
2:56
Speaker 2: at the time, so there was a lot of education
2:59
Speaker 2: travel to fit ffty plus countries, educating doctors into chronologists
3:03
Speaker 2: on the technology and why it's meaningful. And did about
3:07
Speaker 2: eight years at Metronic Diabetes and went on to spend
3:11
Speaker 2: nearly a decade at Dexcom in a strategy role. So
3:15
Speaker 2: that is where we saw the birth of reimbursement for
3:18
Speaker 2: this category. A lot of great work done by the
3:21
Speaker 2: JDRF back then to help establish reimbursement for people on insulin.
3:27
Speaker 2: And went on to see connectivity interoperability, so it started
3:33
Speaker 2: really becoming a gold standard for people on insulin. So
3:37
Speaker 2: nearly twenty years in the space, but twenty eight years
3:40
Speaker 2: as an operator. In about five years ago, Jared asked
3:43
Speaker 2: me to come join a CEO, and when I looked
3:45
Speaker 2: at the technology, I couldn't say no.
3:47
Speaker 1: Yeah, and I mean we will jump into the technology
3:50
Speaker 1: in a few minutes. But this is an interesting episode
3:53
Speaker 1: for me personally because we're talking about. Usually in these episodes,
3:58
Speaker 1: I talk about an antiquated technology that's being disrupted by
4:02
Speaker 1: new innovation. This episode has the potential to talk about
4:06
Speaker 1: a gold standard that has disrupted a previous standard of
4:10
Speaker 1: care with new technology. And so I think we just
4:14
Speaker 1: need to take a step back and walk through house
4:18
Speaker 1: CGMS disrupted the glucose monitor market in the first place
4:23
Speaker 1: over those twenty years and what you saw expand and
4:26
Speaker 1: how that technology expanded over those like two decades.
4:29
Speaker 2: Sure, I think glucose has been the first marker that
4:35
Speaker 2: really transform transformed the wearable market. Meaning for people with
4:41
Speaker 2: diabetes on insulin, the single greatest thing that needs to
4:45
Speaker 2: be mitigated is when you have too much insulin or
4:47
Speaker 2: too much intense activity in the severe hypoglycemia. And so
4:52
Speaker 2: there was no other way to mitigate severe hypoglycemia. And
4:56
Speaker 2: so the birth of real time alerts and alarms and
4:59
Speaker 2: feedback so that you can mitigate some of your hypoglycemia
5:02
Speaker 2: and reduce hospital admissions and mortality rates. And so it's
5:09
Speaker 2: been the single best implementation of a wearable technology to
5:14
Speaker 2: save lives. And so we've seen the penetration into the
5:20
Speaker 2: insulin using patient population climb, and it's still not where
5:24
Speaker 2: it needs to be, but with reimbursement, broad access in
5:28
Speaker 2: the manufacturers at scale right now it is one of
5:31
Speaker 2: the single it's probably the best performing medical device in
5:34
Speaker 2: history in terms of number of users, access, reimbursements, and
5:38
Speaker 2: so there's a much broader now need for metabolic health
5:43
Speaker 2: literacy outside of people with diabetes using insulin. But it
5:47
Speaker 2: really evolved around having that continuous feedback in real time.
5:52
Speaker 2: Glucose is the next vital sign. It's just it's been
5:55
Speaker 2: really hard to get that information continuously, and we're the
6:01
Speaker 2: first needle free approach at doing that.
6:03
Speaker 1: Well, why in twenty years ago, why was it so
6:06
Speaker 1: hard to be able to get that continuous readings of
6:10
Speaker 1: the glucose levels, Because you know, I'm thinking, like the
6:13
Speaker 1: antiquated way is, you know, you prick your finger right
6:16
Speaker 1: four times a day and so basically get four readings
6:19
Speaker 1: a day. Now you're doing if I did my math right,
6:22
Speaker 1: you know, five minutes between readings. That's two hundred eighty
6:25
Speaker 1: eight readings without pricking your finger. So how what was
6:29
Speaker 1: the technology to get to those two hundred eighty eight
6:33
Speaker 1: readings accurately.
6:35
Speaker 2: In vivo sensing is incredibly hard. For a market this
6:40
Speaker 2: big you typically would have would have dozens, if not
6:43
Speaker 2: hundreds of players in market, And that just goes to
6:47
Speaker 2: show how hard it is to take something that was
6:50
Speaker 2: used for point of care testing and the technology that
6:54
Speaker 2: built billions of units per year in test trips was
6:57
Speaker 2: very difficult to translate into EVO performance with the appropriate
7:02
Speaker 2: degree of accuracy to mitigate hypoglycemia to see all the
7:06
Speaker 2: trends in real time. That continues to be the single
7:09
Speaker 2: greatest barrier to entry for any new technology coming in
7:14
Speaker 2: is figuring out the stabilizing chemistry, the manufacturing scale processes
7:20
Speaker 2: for NBVO sensing. So how do you put a live
7:23
Speaker 2: enzyme on a filament or on the tip of our microsensors.
7:27
Speaker 2: Have it survived sterilization, have it survived dry storage, and
7:32
Speaker 2: that when you put it on the body that it
7:34
Speaker 2: actually works right after warm up and be accurate enough
7:38
Speaker 2: for therapeutic decision making. So that continues to be a
7:44
Speaker 2: technical feat that very few have been able to accomplish
7:47
Speaker 2: and to be able to get through the FDA.
7:48
Speaker 1: Yeah, and that's probably why now you just see the
7:51
Speaker 1: dex common the abbots of the world being able to
7:53
Speaker 1: dominate the market because they've been able to establish that
7:56
Speaker 1: type of stabilizing chemistry. As you mentioned, you know with
7:59
Speaker 1: those two in the market, they're mainly in the type
8:02
Speaker 1: one space and they're starting to get into the type
8:04
Speaker 1: two diabetic patient population. How is that patient opportunity changed
8:11
Speaker 1: throughout the years?
8:12
Speaker 2: This is the largest population of people that need a solution.
8:18
Speaker 2: Finger sticks alone are not enough, right, So one or
8:21
Speaker 2: two finger sticks a day won't give you any trends,
8:24
Speaker 2: right and for the type two, the broader Type two population,
8:28
Speaker 2: what is even more meaningful is context. So providing a
8:33
Speaker 2: glucose number alone may not be enough because for people
8:38
Speaker 2: that don't take insulin, the numbers there's nothing they can
8:42
Speaker 2: do about them. So you're more than just a number.
8:46
Speaker 2: So we took an approach to make sure that we
8:50
Speaker 2: have glucose information combined with activity and sleep, so we
8:54
Speaker 2: were able to see and capture behavior right and glucose
8:59
Speaker 2: information so we can derive appropriate insights to drive sustainable
9:04
Speaker 2: opportunities to create new habits in hopefully sustainable behavior change.
9:09
Speaker 2: So context is the key here in a world where
9:14
Speaker 2: we have so many wearables. Now we have smart rings,
9:16
Speaker 2: smart watches, glucose sensors, and it's challenging to integrate all
9:23
Speaker 2: of this information on the back end by the user
9:26
Speaker 2: through different sources to make sense of all that information.
9:30
Speaker 2: So we decided to make it available to everybody, all
9:34
Speaker 2: in one wearable device, so you don't have to synchronize
9:36
Speaker 2: day and time stamps, try to coordinate all the information
9:39
Speaker 2: and have it there and have it be insight driven.
9:43
Speaker 2: And we put inspiration into everything that we do, so
9:47
Speaker 2: to inform is one thing. To inform and inspire has
9:52
Speaker 2: been our charter. So if we capture this information, how
9:56
Speaker 2: do we provide a better user engagement to drive healthier decisions?
10:01
Speaker 1: And I think we'll definitely jump into how to integrate
10:05
Speaker 1: all those into one device, because when you mentioned the ring,
10:07
Speaker 1: the watch, all those things, one of the reason why
10:10
Speaker 1: I haven't personally gotten into those is because I'm like, well,
10:13
Speaker 1: do I need ten different apps for ten different readings?
10:16
Speaker 1: So definitely I'm gonna be personally curious about that. Going
10:20
Speaker 1: back to the contexts for type two, is it just
10:24
Speaker 1: as simple as you know, I had an extra bagel
10:28
Speaker 1: for breakfast and I see that rise in the glucose measurements. Therefore,
10:33
Speaker 1: I know I need to fix my diet better to
10:36
Speaker 1: be able to maintain the time and range or is
10:39
Speaker 1: there more complicated understanding of the you know, glucose health
10:45
Speaker 1: for these patients.
10:47
Speaker 2: This is where behavioral science comes in. So how do
10:51
Speaker 2: we support sustainable behavior change and support cognitive restructuring. Well,
10:56
Speaker 2: three things are required for that to happen. Real time
10:59
Speaker 2: feedback massively when teachable moments arise. So how do you
11:03
Speaker 2: give that feedback in real time when a teachable moment happens.
11:07
Speaker 2: You can't tell when you're gonna have a teachable moment.
11:09
Speaker 2: So this is why we decided to create a wearable
11:14
Speaker 2: experience where we have the sensor on.
11:16
Speaker 1: The forearm of all places.
11:18
Speaker 2: Right, So, glucose sensors are known to be placed on
11:20
Speaker 2: the back of the upper arm on the abdomen. In
11:23
Speaker 2: the early years, we moved it to the forearm because
11:27
Speaker 2: it is highly visible. It is the least touched area
11:31
Speaker 2: on your body. And we put a color code to
11:34
Speaker 2: display on the wearables so that we can let you
11:37
Speaker 2: know when your glucose excursions happen in real time through
11:41
Speaker 2: color schemas. Right, everybody understands color schemas. You don't have
11:44
Speaker 2: to translate it into seventy different languages. And so when
11:47
Speaker 2: you're in your target range, we have this blue led
11:49
Speaker 2: light that glows on and off, and when your glucose
11:53
Speaker 2: levels are high, it turns yellow on and off. And
11:56
Speaker 2: so if I drink a glass of orange juice and
11:58
Speaker 2: I can see in my porpheral vision and that my
12:00
Speaker 2: wearable turn from blue to yellow. That supports cognitive restructuring
12:05
Speaker 2: because now I can relate orange juice caused a hide, right,
12:08
Speaker 2: And once I have that correlation, this then triggers that
12:12
Speaker 2: teachable moment. Well, what do I do about it? I
12:15
Speaker 2: could finish that glass of orange juice and I can
12:17
Speaker 2: stay yellow, and I can see that yellow on my forearm.
12:20
Speaker 1: But it allows me.
12:22
Speaker 2: To finish that glass of orange juice, and I could
12:24
Speaker 2: take a ten minute walk and fifteen minute walk and
12:27
Speaker 2: I can see that yellow come back down to blue.
12:29
Speaker 2: So now I've learned, well, orange juice made me spike,
12:33
Speaker 2: but I can do something about it and bring me
12:35
Speaker 2: back down. Or I could choose to stop drinking that
12:38
Speaker 2: orange juice so I'm not yellow for too long. And
12:43
Speaker 2: it also supports well, maybe I don't choose orange juice
12:46
Speaker 2: next time, because I know how long it takes for
12:47
Speaker 2: me to come back down.
12:49
Speaker 1: Yeah, or I was even thinking like, oh, how much
12:50
Speaker 1: orange juice could I drink before it gets yellow? Which
12:54
Speaker 1: is another kind of yeah, same thing cognitive like understanding
12:57
Speaker 1: of how your body's working and everything.
12:59
Speaker 2: Yeah. Context, So this was really surprising for us. In
13:04
Speaker 2: our development studies, we have a million hours of human
13:09
Speaker 2: clinical performance data. We saw that people with type two
13:12
Speaker 2: diabetes with an a one CE of eight percent spending
13:16
Speaker 2: fifteen hours a day in yellow, fifteen hours a day
13:20
Speaker 2: in yellow, and they have no idea that they're high
13:24
Speaker 2: for fifteen hours. Why because they're not pricking their finger
13:27
Speaker 2: during the times in which they you would be high,
13:30
Speaker 2: so they would not know. And they're not wearing conventional CGM,
13:33
Speaker 2: so how would they know. So I can't imagine seeing
13:37
Speaker 2: that yellow light for fifteen hours straight and not doing
13:40
Speaker 2: something about it. Yeah, So that was a really big
13:42
Speaker 2: aha in surprise moment for us. Another way of thinking
13:45
Speaker 2: about it is the sensor is always on. So if
13:49
Speaker 2: you have an app and you have information that's sending
13:52
Speaker 2: alerts and alarms tore on an app, well, most people
13:54
Speaker 2: start to snooze those things because oh, I drink orangeuice.
13:57
Speaker 2: I know I'm high short, I snooze it, and they
13:59
Speaker 2: never see how long they stay high until you look
14:03
Speaker 2: at it retrospectively.
14:04
Speaker 1: All right. Actually, in my case right now, I just
14:06
Speaker 1: left my phone on my desk, so we go. If
14:08
Speaker 1: I'm here at the recording studio and I have a
14:10
Speaker 1: spike because I drank some juice, then I have out
14:12
Speaker 1: on it. So yeah, and.
14:13
Speaker 3: One thing I'll I did you know? Our metabolism is
14:16
Speaker 3: incredibly complicated, right, So getting this contextual information is incredibly insightful.
14:23
Speaker 3: And I think one of the big takeaways that many
14:25
Speaker 3: people have realized is that it doesn't take this intense
14:28
Speaker 3: amount of exercise if you're glucose. Is I had to
14:30
Speaker 3: get it to come back down like which was saying
14:32
Speaker 3: ten ten minutes, fifteen minutes, and your glucose can come
14:35
Speaker 3: back down into range. And it's it's kind of that
14:38
Speaker 3: combination of moving your body and what you eat that's
14:41
Speaker 3: really important. And I mean, for example, right, you know
14:44
Speaker 3: we're we're here in New York. Every time I come
14:46
Speaker 3: to New York, I got to get a New York bagel.
14:48
Speaker 3: I don't usually eat bagels, right, but I get read
14:51
Speaker 3: this morning.
14:51
Speaker 2: Yeah.
14:51
Speaker 3: Yeah, if I'm wearing a sensor. I don't want to
14:53
Speaker 3: I don't want to eat a bagel, but I went
14:55
Speaker 3: for a run beforehand, right, And if I go for
14:56
Speaker 3: a run, if I run for five miles afterwards, I
15:00
Speaker 3: eat a bagel and my glucose won't rise nearly as high.
15:03
Speaker 3: So it's all of that contextual information that's really insightful and.
15:06
Speaker 1: Then sounding like too, like oh, if I eat the
15:08
Speaker 1: bagel before I run, or if I run before the bagel,
15:10
Speaker 1: you can see the trends and how that differs and everything.
15:12
Speaker 2: So even the timing in order of what you eat
15:15
Speaker 2: over a meal, for example, eating the high fiber foods first,
15:18
Speaker 2: like vegetables first before your protein. All of these things
15:22
Speaker 2: make a difference. And so so if we do nothing further,
15:29
Speaker 2: the metabolic health literacy opportunity here is profound. Everybody should
15:35
Speaker 2: know their relationship with the foods they like activity or
15:39
Speaker 2: lack of in sleep. And so because all of this
15:44
Speaker 2: leads to a pillar in metabolic health that could extend
15:49
Speaker 2: human health span, it is one of the most important
15:51
Speaker 2: pillars to understand. We just haven't had the tools to
15:54
Speaker 2: allow it to be easily used needle free for mainstream adoption.
16:00
Speaker 1: Yeah, and yeah, that's perfect segue. Let's jump into the
16:02
Speaker 1: tool now the shine. How did that development process start?
16:08
Speaker 1: And you know how, it's not simply I can work
16:12
Speaker 1: on this overnight as like my PhD project and get
16:14
Speaker 1: it done. What was the process to kind of develop
16:19
Speaker 1: and understand that this could work as a viable sensor.
16:22
Speaker 3: Yeah, it was. It was far from overnight, that's for sure. So,
16:26
Speaker 3: I mean the original concept came from looking at the
16:30
Speaker 3: drug delivery space. Actually, so if you look at drug delivery,
16:33
Speaker 3: what the literature shows is that if you deliver something
16:36
Speaker 3: like insulin, for example, into the skin, the body absorbs
16:40
Speaker 3: it very quickly, versus if you deliver it into the
16:43
Speaker 3: subcutaneous tissue it takes quite a bit longer. So this
16:45
Speaker 3: initial theory was, well, hey, if the body absorbs things
16:50
Speaker 3: like drugs more quickly in the skin, we should be
16:52
Speaker 3: able to measure things in the skin as well. And
16:56
Speaker 3: the reason for that is you're right next to this
16:58
Speaker 3: capillary bed that basically perfuses all of our skin, right,
17:01
Speaker 3: So all of our thermoregulation happens through this capillary bed
17:06
Speaker 3: in our skin. So for example, when you're out for
17:08
Speaker 3: a run and your body, you know, just trying to
17:11
Speaker 3: cool off those blood vessels dilate and therefore your body
17:15
Speaker 3: can get rid of that heat more easily. And so
17:18
Speaker 3: we've got this really nice capillary bed that's you know,
17:21
Speaker 3: about a millimeter below the surface of the skin, and
17:23
Speaker 3: so our thought was, if we can measure close to
17:26
Speaker 3: that capillary bed, it should be a really good location
17:29
Speaker 3: for sensing. And so that was the initial concept, and
17:33
Speaker 3: the first years were really around proving that dermal sensing
17:36
Speaker 3: is viable, that you could actually measure in the skin
17:39
Speaker 3: and then it correlates well with the blood. Sometimes the
17:44
Speaker 3: conventional wisdom would be, oh, well, hey, if you're not
17:46
Speaker 3: as deep in the body, maybe this doesn't correlate as
17:49
Speaker 3: well with the blood. But because we're right next to
17:53
Speaker 3: this capillary bed, it's the opposite.
17:55
Speaker 1: Well, it's actually very interesting because that was like the
17:57
Speaker 1: reason between fingersticks and subcutaneous cgms was the fact that
18:03
Speaker 1: having the access directly to the blood allowed you to
18:05
Speaker 1: measure the blood glucose levels in the blood directly versus
18:10
Speaker 1: some sort of translation. Yeah, and so yeah, you would
18:13
Speaker 1: think a layman would conceptually think, well, that the subcutaneous
18:18
Speaker 1: is not as accurate as the blood. Then going for
18:20
Speaker 1: the route the dermal. Yeah, that's a very valid question
18:23
Speaker 1: to address.
18:24
Speaker 3: Yeah, and that was so that was the first question,
18:26
Speaker 3: and that's the first thing we had to show is
18:27
Speaker 3: that this works conceptually. So you know, the first years
18:31
Speaker 3: were really around building prototypes to be able to prove
18:35
Speaker 3: that it does correlate. And you know, even you know,
18:38
Speaker 3: Rich was very skeptical at first, Right, he's the first
18:41
Speaker 3: to admit that. At first, he's like, Hey, if you
18:43
Speaker 3: can't show me this works in people, this is this
18:45
Speaker 3: is not worth my time. But we were able to
18:47
Speaker 3: show that in our first and human studies were very successful.
18:51
Speaker 3: And then from there, really the next leap goes to, okay,
18:54
Speaker 3: well we can we've shown that we can track glucose
18:57
Speaker 3: in the skin. Now how do we get to something
18:59
Speaker 3: that is factory calibrated that doesn't need to be calibrated
19:02
Speaker 3: by the user. And that is really the biggest leap,
19:06
Speaker 3: and that's probably been the biggest leap for people with diabetes,
19:09
Speaker 3: is no longer needing to calibrate these systems. But in
19:12
Speaker 3: order to go from something that can be calibrated when
19:14
Speaker 3: you calibrate a sensor, you can remove that user to
19:17
Speaker 3: user difference. You can remove that sensor to sensor difference.
19:21
Speaker 3: Without that calibration, you're relying only on information you have
19:24
Speaker 3: during manufacturing. So that was really the next big step
19:28
Speaker 3: is how do we create something that is predictable right
19:32
Speaker 3: that I know how it's going to perform in you
19:34
Speaker 3: and I know how it's going to perform in me
19:36
Speaker 3: without any additional context or information. So that's that was
19:39
Speaker 3: really the next big focus was getting to that factory calibration.
19:43
Speaker 1: Okay, and then so then so it was during that
19:46
Speaker 1: kind of first in human study you also had to
19:49
Speaker 1: figure out whether or not the chip, the microchip that
19:53
Speaker 1: you're using, was able to work or not. Was this
19:57
Speaker 1: one of those things where you had to develop the
20:00
Speaker 1: chip component to understand it or is it something where
20:03
Speaker 1: you had to figure out what you were looking for
20:06
Speaker 1: in the dermal in the skin and then create the
20:09
Speaker 1: chip around that.
20:11
Speaker 3: Yeah, it was kind of We came at it from
20:13
Speaker 3: both anglis. So we knew that sensing in that space
20:17
Speaker 3: would be or should be a viable compartment for sensing
20:21
Speaker 3: from a chemistry perspective, we didn't want to totally reinvent
20:25
Speaker 3: the wheel.
20:25
Speaker 1: There.
20:26
Speaker 3: We have you know, this enzyme called glucose oxidase that
20:29
Speaker 3: really all the CGM systems use very robust you know,
20:32
Speaker 3: manufactured in high volume now because of all the systems
20:35
Speaker 3: that are on the market. So we said, well, can
20:38
Speaker 3: we take that same method of chemistry, you know, electro
20:41
Speaker 3: chemistry very robust, but we need to miniaturize it, right,
20:44
Speaker 3: because our microsensors are you know, they're roughly the diameter
20:49
Speaker 3: of a human hair and a fraction of a millimeter
20:51
Speaker 3: in length, So these are very very tiny. If you
20:54
Speaker 3: took the same chemistry from a traditional CGM and try
20:57
Speaker 3: to just PLoP it onto our system, it would engulf
21:00
Speaker 3: our entire chip, right, it's so small, So we had
21:04
Speaker 3: to miniaturize it. So we had to show that we
21:05
Speaker 3: can use the same techniques but miniaturize it to fit
21:08
Speaker 3: our tiny array. And in parallel, we had to develop
21:13
Speaker 3: this chip. And what we found is that because these
21:17
Speaker 3: devices are so small, it had to be made out
21:20
Speaker 3: of something that's very mechanically robust.
21:22
Speaker 1: Right.
21:23
Speaker 3: If you took most materials and you said, hey, this
21:25
Speaker 3: is half a millimeter in length and it's the diameter
21:27
Speaker 3: of a human hair, it's either going to bend or
21:29
Speaker 3: it's going to break. And that's really where silicon came in.
21:32
Speaker 3: Silicon is it's stronger than titanium, it's stronger than steel,
21:37
Speaker 3: and so it's very very robust, and you can make
21:39
Speaker 3: it very sharp, and you can use these techniques from
21:42
Speaker 3: the semiconductor industry to not only make these robust microchips,
21:48
Speaker 3: but you can also scale them. So we knew that
21:50
Speaker 3: not only do we have to be able to make
21:52
Speaker 3: a prototype and run a first in human study, we
21:54
Speaker 3: have to be able to make you know, millions, hundreds
21:57
Speaker 3: of millions one day, probably billions of these things. So
22:00
Speaker 3: that really was where all these worlds came together. Of
22:03
Speaker 3: kind of standard approach for electrochemistry, novel approach for making
22:09
Speaker 3: the sensor itself, and then you combine those things together
22:12
Speaker 3: with our novel methods of chemistry deposition, and you have
22:15
Speaker 3: the first introternal sensor.
22:18
Speaker 2: And to add to that, just really quickly, how do
22:20
Speaker 2: you validate that in humans? That is the big question.
22:23
Speaker 2: So how do you architect the geometry of these microsensors
22:27
Speaker 2: so that you know it's where it needs to be
22:31
Speaker 2: in the skin. So the thickness of the human skin
22:33
Speaker 2: is actually fairly uniform. It's in peer reviewed literature, and
22:36
Speaker 2: so we did digital biopsies of every person that wore
22:39
Speaker 2: the sensor so that we can see the depth of
22:42
Speaker 2: placement in the skin, so that we're actually in the
22:44
Speaker 2: POPULARI dermis, and then we can interrogate the signals for
22:46
Speaker 2: each one of these sensors in the skin. And that's
22:50
Speaker 2: how we knew we were at the right place at
22:51
Speaker 2: the right geometry. So it's incredibly difficult to do.
22:54
Speaker 1: Was that your aha moment? And they're like, Oh, I
22:57
Speaker 1: think it.
22:57
Speaker 2: Was the proof point that I needed to see, which
22:59
Speaker 2: is could these micro sensors generate a stable enough signal
23:03
Speaker 2: to quantify glucose information and other biomarkers in the answer
23:07
Speaker 2: was a profound yes.
23:09
Speaker 1: Yeah, let's talk about some of the other features, because
23:12
Speaker 1: we kind of quickly went over the color coded LED
23:15
Speaker 1: display on the device itself. I just want to make sure,
23:21
Speaker 1: first off, anything else with those LED color codes that
23:23
Speaker 1: you want to make sure we cover, and what other
23:25
Speaker 1: other kind of features on the form factor are there
23:28
Speaker 1: that maybe differentiated from kind of the currency gym products
23:32
Speaker 1: that are out there.
23:34
Speaker 2: Well, we lightly touched on yellow for high blue or
23:38
Speaker 2: target range seventy to one and eighty milligrams per ESTs. Later, well,
23:42
Speaker 2: when you drop a low seventy, a red light turns on.
23:45
Speaker 2: So people with type of diabetes not an insulin you know,
23:49
Speaker 2: don't see severe hypoglycemia very often, so you would rarely
23:53
Speaker 2: see that. But we also have an led light pattern
23:57
Speaker 2: to let you know that you're rising quickly even though
23:59
Speaker 2: you were still within target. So when you're in target
24:01
Speaker 2: range and you're blue, that's a good thing. And when
24:03
Speaker 2: you start climbing quickly, you'll see the blue light flash
24:06
Speaker 2: in two regions on the wearabole to let you know, hey,
24:08
Speaker 2: you're rising, you're approaching yellow right, So it's a little
24:11
Speaker 2: bit of an early warning. And so it is it
24:16
Speaker 2: is very easy for people to understand color schema. So
24:19
Speaker 2: we've done that. Now you get higher resolution information if
24:23
Speaker 2: you happen to have an app. Now our product is
24:25
Speaker 2: fully autonomous, so it works even if you don't have
24:28
Speaker 2: your phone. You don't need the phone to turn it
24:29
Speaker 2: on or to pair or sink. But if you have
24:32
Speaker 2: a phone, you have an app that will show you
24:34
Speaker 2: context around your glucose information. So we add we lay
24:37
Speaker 2: around sleep in activity, and so that's how we generate
24:40
Speaker 2: the insights to drive positive behavior change.
24:44
Speaker 1: Okay, so if I got this correct. It actually can
24:47
Speaker 1: detect when you're sleeping. Yes, okay, So that's kind of okay,
24:51
Speaker 1: and that kind of gets us into more of that
24:53
Speaker 1: integrating it all into one device that we'll talk about
24:56
Speaker 1: a little bit later. And so then you have the
24:59
Speaker 1: app as well. One of the things too, just you know,
25:02
Speaker 1: between the product development and you know the recent at
25:07
Speaker 1: de Novo approval is the just the pivotal trial. There
25:12
Speaker 1: was news about you know, enrollment ending. Was that data
25:16
Speaker 1: presented to the FDA before? Is that is that clinical
25:19
Speaker 1: data going to be provided and published at one of
25:22
Speaker 1: these diabetes conferences in the near term.
25:25
Speaker 2: Great question, and a really quick shout out to Amy Vandenberg,
25:28
Speaker 2: who's our head of Regula Storting Clinical. She really set
25:31
Speaker 2: a new bar and set a new record for fastest
25:34
Speaker 2: pivotal trial in CGM history. So fifty eight fifty eight
25:38
Speaker 2: business days, right, so that's quite incredible and from cution start,
25:42
Speaker 2: initial enrollment to finishing the data fifty eight business last
25:47
Speaker 2: really extraordinary. And so subjects had to come in to
25:50
Speaker 2: the clinic for twelve hours twice during their censor session
25:55
Speaker 2: to draw venus blood samples for for you know, twelve
25:58
Speaker 2: hours every fifteen to twenty minutes. And we had thought
26:01
Speaker 2: that there would be an incredibly high drop out rate
26:03
Speaker 2: because who would come in twice in one week to
26:06
Speaker 2: draw blood for twelve hours straight. Well, Amy did such
26:10
Speaker 2: a great job in her team in executing that we
26:12
Speaker 2: didn't have people that dropped out of the study. So
26:14
Speaker 2: we got it done in fifty eight business days and
26:17
Speaker 2: it's unprecedented. But yeah, we generated a tremendous amount of
26:22
Speaker 2: clinical information. We had fingersticks that the patients did from home.
26:27
Speaker 2: We had venus blood samples on two in clinic days.
26:30
Speaker 2: They had commercially available CGM being worn at the same time.
26:35
Speaker 2: So we generated a very profound amount of information for
26:40
Speaker 2: the FDA for review. And Amy's philosophy is to collaborate
26:44
Speaker 2: with the FDA over communicate, and so when we did
26:47
Speaker 2: finish and when we did generate our data, by the way,
26:51
Speaker 2: all of the data was blinded to us, but to
26:55
Speaker 2: the data that was generated that we can get in
26:58
Speaker 2: front of the FDA. She actstually held pre sub meetings
27:01
Speaker 2: with the FDA let them know how we did, so
27:04
Speaker 2: there were no surprises when we submit. There was no
27:06
Speaker 2: precedence to this, right, there's no semiconductor biosensor that's ever
27:10
Speaker 2: been approved a needle free intradermal sensor. So how do
27:13
Speaker 2: you how do you go about that? So in you know,
27:16
Speaker 2: this is where Amy really excelled in making sure that
27:18
Speaker 2: the FDA was well informed of every.
27:20
Speaker 1: Step of the way. Yeah, and that's I mean the
27:22
Speaker 1: fact that you use you know, venus draws, you use fingersticks,
27:25
Speaker 1: and you use cgms as all kind of as comparables highlights.
27:29
Speaker 1: You know, because it's such a new technology, you want
27:32
Speaker 1: to make sure that you scientific grigor Yes, exactly, and
27:36
Speaker 1: so then you know, but going back, is that something
27:39
Speaker 1: that we could the public can see that information that
27:42
Speaker 1: data published maybe a DA next year. I know DTM
27:46
Speaker 1: is coming up in November. Is that something that we
27:48
Speaker 1: can all look forward to.
27:50
Speaker 2: Yes, we intend to publish and Jared will be speaking
27:53
Speaker 2: at the scientific sessions. We are applying to present formal
28:00
Speaker 2: so hopefully we get accepted, and so Jared will be
28:03
Speaker 2: speaking on the clinical trial results.
28:05
Speaker 1: And the.
28:07
Speaker 2: First opportunity where we have Jared speaking on some of
28:10
Speaker 2: the pivotal trial data will be at the DTM conference.
28:13
Speaker 1: Okay, yeah, that's and they they made it in person
28:17
Speaker 1: again for the first time in a few years, right, yes, yeah,
28:21
Speaker 1: but it also is that is that one out in
28:22
Speaker 1: California or yeah, because California is much as much nicer
28:27
Speaker 1: than Bethursday. I remember my but I don't know if
28:31
Speaker 1: I can make it out for that one, but I'll
28:33
Speaker 1: be looking forward to that record speed and getting the
28:36
Speaker 1: clinical trial through. How did the Denovo approval process then? Yeah,
28:43
Speaker 1: I mean it sounds like that. You know, Amy did
28:45
Speaker 1: a great job of making it as streamline as possible.
28:48
Speaker 1: But you know, anytime you have a DiNovo approval a
28:51
Speaker 1: new product, what were some of the questions that the
28:54
Speaker 1: FDA was asking you about this? Such a new technology
28:57
Speaker 1: out there in the markets.
29:00
Speaker 2: The most profound. I mean, CGM has been around for
29:04
Speaker 2: a few decades, so there's a lot of rigor around
29:07
Speaker 2: the clinical evidence in performance data you have to show.
29:11
Speaker 2: But a new layer of manufacturing controls this is a
29:15
Speaker 2: big one. The manufacturing controls are really profound. So it's
29:22
Speaker 2: a very high bar to meet. Not only did Amy
29:27
Speaker 2: execute on the fastest pivotal in our domain, and it
29:31
Speaker 2: was one time we had one shot at doing it,
29:34
Speaker 2: we didn't have enough working capital to repeat the pivotal trials.
29:37
Speaker 2: If we didn't meet the endpoints. But this was also
29:40
Speaker 2: one of the fastest approvals by the FDA for a
29:44
Speaker 2: new CGM category. So PMAs, as you know, take a
29:47
Speaker 2: very very long time when there's a new category if
29:50
Speaker 2: there's no predicate. So two hundred and seventy days. So
29:55
Speaker 2: for something this dramatically different from conventional sensors to be
30:00
Speaker 2: in under a year was also an extraordinary feet And
30:04
Speaker 2: we're very grateful to the FDA for their collaboration because
30:08
Speaker 2: without their long hours and guidance and support, this would
30:11
Speaker 2: not have happened.
30:12
Speaker 1: Yeah, and I'm just thinking like a regular five to
30:14
Speaker 1: ten K approval can sometimes last over six months, yes,
30:16
Speaker 1: and so yeah, yeah, I hear you. And so maybe
30:19
Speaker 1: I'll start with the the technical side here. I noticed
30:23
Speaker 1: in the approval itself it was called and let me
30:26
Speaker 1: just make sure I got my notes down glucose range
30:28
Speaker 1: monitoring system. That's different from the integrated continuous glucose monitoring
30:34
Speaker 1: system that kind of the dex comms and the abbots have.
30:37
Speaker 1: What is the Is that something I'm diving in too
30:41
Speaker 1: deep into the difference in that the classification name or
30:44
Speaker 1: is that just the start of the technology and who
30:49
Speaker 1: what the or what the technolog or yeah, to whom
30:51
Speaker 1: the technology is approved for sure.
30:53
Speaker 2: So this is the first fully autonomous bi wearable right
31:00
Speaker 2: for continuous glucose monitoring, and what that means is because
31:04
Speaker 2: we show glucose excursions through an LED light that tells
31:08
Speaker 2: you what range you're in. This is really a time
31:11
Speaker 2: and range sensor. So for metabolic health, time and range
31:15
Speaker 2: is becoming one of the most important metrics above and
31:20
Speaker 2: beyond a one C And the only way you get
31:22
Speaker 2: time and range information is if you have a sensor
31:24
Speaker 2: on that's measuring continuously, so you can see how much
31:27
Speaker 2: time you are in target range and how much time
31:29
Speaker 2: you're above and below. So we just happen to have
31:32
Speaker 2: it in a fully autonomous nature. So if you're going
31:35
Speaker 2: to have that information in real time feedback using color
31:39
Speaker 2: schemas that is glucose range monitoring, it works even if
31:42
Speaker 2: you don't have a phone. So one of the things
31:44
Speaker 2: that we really had to work around is for people
31:47
Speaker 2: that don't use insulin, what do they do with a number,
31:51
Speaker 2: because let's just say for example, you're high, well, there's
31:55
Speaker 2: no acute medication decisions this patient population would be because
32:00
Speaker 2: they're not taking insulin.
32:02
Speaker 1: Right, So they don't they don't need to calculate that
32:04
Speaker 1: into their.
32:05
Speaker 2: Correct correct So how do you inspire positive behavior change,
32:10
Speaker 2: drive cognitive restructuring without a number? And so we really
32:14
Speaker 2: wanted to simplify the use of time and range information
32:20
Speaker 2: and make it simple for everybody to understand the immediate
32:24
Speaker 2: impact of the foods that you may be eating right
32:27
Speaker 2: in your activity levels, in your change in sleep patterns.
32:31
Speaker 2: So we did that and because it's semi quantitative information,
32:35
Speaker 2: it is considered glucose range monitoring. So it's very different
32:39
Speaker 2: from the ic gym category where you could actually have
32:42
Speaker 2: interoperability with insulin delivery devices. Our sensors are not intended
32:47
Speaker 2: for insulin dosing and so it's not intended to be
32:50
Speaker 2: integrated with insulin pumps or insulin pens, but it's really
32:55
Speaker 2: a standalone device.
32:57
Speaker 1: Interesting and so that's also about now being in the
32:59
Speaker 1: type two non insulin space, it is a much larger
33:02
Speaker 1: opportunity than that insulent intensive space. Yes, and so you know,
33:06
Speaker 1: with that market opportunity there, it makes me think about
33:11
Speaker 1: well dex comms and abbots, you know, type two non
33:15
Speaker 1: insulin intensive products Stello and lingo, and it gets me
33:21
Speaker 1: to just basically how you order those products because you
33:24
Speaker 1: don't need a prescription for those two. You just go
33:27
Speaker 1: to their website and order it online. And maybe maybe
33:30
Speaker 1: at some point they do the pharmacy channel, but it's
33:33
Speaker 1: you know, right now, directly in their website and it's
33:35
Speaker 1: all out of pocket. Is the Shine monitor going to
33:39
Speaker 1: be a similar commercial design for you, guys?
33:44
Speaker 2: Great question. We are bilin Shine is a prescription product today, okay,
33:50
Speaker 2: and we intended for it to be a prescription product
33:54
Speaker 2: because there we believe will be an opportunity for some
33:58
Speaker 2: form of reimbursement. Now, while people that are not using
34:01
Speaker 2: insulin may not need sensors full time all the time.
34:06
Speaker 2: So the big question is could we provide enough benefit
34:10
Speaker 2: on a cost neutral basis to what fingersticks cost to
34:14
Speaker 2: payers and health systems and large self insured such that
34:16
Speaker 2: there could be some form of partial reimbursement for periodic use.
34:21
Speaker 2: And so we will be aggressively looking at this question
34:24
Speaker 2: to see if we can generate outcome studies and cost
34:29
Speaker 2: benefit studies so that some form of partial reimbursement for
34:33
Speaker 2: periodic use could be made available. This is one of
34:36
Speaker 2: the most important tools for people with diabetes that are
34:40
Speaker 2: not on insulin, if you don't fingerstick often enough, and
34:43
Speaker 2: if you're not using conventional CGM and it's not reimbursed
34:46
Speaker 2: for conventional CGM, well, what can we do to help
34:49
Speaker 2: with metabolic health literacy? And one sensor year, one sensor quarter,
34:54
Speaker 2: one sensor per month. What is that schema? So these
34:57
Speaker 2: are the things that we're attempting to answer in the
35:01
Speaker 2: in the near future.
35:02
Speaker 1: Interesting, Yeah, because you know, we talked earlier about you know,
35:05
Speaker 1: the context of the type two is very important. Seeing
35:08
Speaker 1: those trends, making those changes and everything is important. Yes,
35:11
Speaker 1: I mean it could be curious to see because you know,
35:14
Speaker 1: like I said, the costs, you know, and I could
35:16
Speaker 1: easily see something along the lines of, you know, once
35:19
Speaker 1: a year before your annual visit to you know, be
35:23
Speaker 1: your primar care position, or if you go if you
35:25
Speaker 1: end up going to see an endocrinologist maybe, I mean,
35:29
Speaker 1: I don't know the frequency of going to an endo chronologist,
35:31
Speaker 1: but if it's got to be more than once a year, right,
35:33
Speaker 1: so maybe you do would be ahead of that appointment.
35:36
Speaker 1: So then the endo chronologist has an understanding of how
35:38
Speaker 1: your trends work and everything.
35:40
Speaker 2: Whatever it takes to mitigate disease progression, right, and there
35:45
Speaker 2: is a window of time to reverse type two diabetes
35:48
Speaker 2: and and this could be a very important tool in
35:51
Speaker 2: the fight against diabetes. Now, so we have a design
35:56
Speaker 2: philosophy to develop products that are scific to the indication.
36:01
Speaker 2: So for our future product pipeline coming for people that
36:08
Speaker 2: use insulin, we would have the numbers, the trends, the
36:12
Speaker 2: graphs similar to all the other CGM. So our products
36:16
Speaker 2: will be designed specific to the indication. So, the metabolic
36:21
Speaker 2: health literacy is still far too low and if we
36:25
Speaker 2: could raise that bar, then how much can we save
36:30
Speaker 2: health systems? Right, So if we can find the twenty
36:32
Speaker 2: percent of people that are consuming eighty percent of the costs,
36:36
Speaker 2: this is one of the non invasive tools needle free
36:41
Speaker 2: that could be deployed, that could be deployed mainstream. And
36:48
Speaker 2: so you'll find that the first year of our commercial
36:51
Speaker 2: efforts will be used to really tailor that real world
36:56
Speaker 2: evidence in the feedback that we're getting from users and
36:58
Speaker 2: to really drive the right protocols on the frequency of
37:01
Speaker 2: where that would derive the greatest benefit.
37:03
Speaker 1: Yeah, and so how how is that commercial strategy going
37:08
Speaker 1: to be initiated? Are you going to just look at
37:10
Speaker 1: you know, certain select primary care physicians, or do you
37:14
Speaker 1: have to start with the endochronologists. Is there a certain
37:18
Speaker 1: region you're starting off with first because you don't want
37:20
Speaker 1: to expand too far, too quickly, And then just you know,
37:23
Speaker 1: I'll just throw this in while they're too What are
37:26
Speaker 1: the investments that are needed to kind of get that
37:29
Speaker 1: manufacturing capacity, because you know, we were talking about millions
37:31
Speaker 1: and even billions of sensors at some point, and we
37:35
Speaker 1: were talking about just you know, the stabilizing the chemistry,
37:38
Speaker 1: both in the sterilization and in you know, storage and
37:42
Speaker 1: everything like that. So how do you connect all those that,
37:46
Speaker 1: let's say the trifecta of commercialization into kind of your
37:50
Speaker 1: overall strategy.
37:51
Speaker 2: Sure, you can imagine we don't have time for organic scale.
37:57
Speaker 2: So the use of capital and the use of funds
38:01
Speaker 2: that we raised one hundred million dollars Series financing and
38:05
Speaker 2: the large bulk of the capital that we raised will
38:08
Speaker 2: go towards manufacturing scale and the foreseeable future. We have
38:13
Speaker 2: to hyper scale from day one. There is no organic
38:15
Speaker 2: scale pathway here. And also then to make sure that
38:19
Speaker 2: we get to the insulin dosing claim as fast as
38:23
Speaker 2: possible in our next generation product, so that we have
38:26
Speaker 2: a product roadmap and pipeline for people with diabetes that
38:29
Speaker 2: don't use insulin and for people with diabetes that are
38:32
Speaker 2: on insulin. And so we will be partnering with commercial
38:38
Speaker 2: partners to help establish key channels for us and do
38:42
Speaker 2: the heavy lifting around the early evidence with the health
38:45
Speaker 2: systems large self insured and so there's a huge commitment
38:49
Speaker 2: from our pending potential partners to actually help drive those initiatives,
38:53
Speaker 2: fund those initiatives because that could lead to more progressive
38:58
Speaker 2: opportunities for some of reimbursement and expanding the category. Very
39:02
Speaker 2: interesting and actually you just mentioned the insulin dosing as
39:04
Speaker 2: kind of the next label expansion. What are the steps
39:08
Speaker 2: and maybe for you to go through about getting from
39:13
Speaker 2: that range label to then the more precise DOSNE information,
39:19
Speaker 2: because that then that could open up a big opportunity
39:23
Speaker 2: for those who are wearing insulin pumps and being able
39:25
Speaker 2: to connect with just you know, that simpler sensor.
39:29
Speaker 3: Yeah, for sure. So there's a few steps required to
39:33
Speaker 3: get there, and there's a couple of areas of focus.
39:35
Speaker 3: One is extending the duration of where so you know,
39:38
Speaker 3: the standard devices that are out there today are around
39:41
Speaker 3: fourteen days. So that's one area of focus. Is one
39:44
Speaker 3: on our wearable side, you know, improving the battery life,
39:47
Speaker 3: you know, updating our connectivity so that it's it's fully
39:51
Speaker 3: connected to the phone.
39:52
Speaker 1: Oh, it's interesting, I didn't even think about that. What
39:54
Speaker 1: is the where time for this initial product that's in
39:57
Speaker 1: the market.
39:57
Speaker 3: Yeah, the Gen one is five days, but yeah, we've
40:01
Speaker 3: now you know, been running studies out to seven and
40:04
Speaker 3: ten days showing that the approach is viable for extended duration.
40:08
Speaker 3: So now it's around building the system around it. So
40:11
Speaker 3: updating the wearable hardware, which today is NFC, so you
40:15
Speaker 3: scan to get any additional information.
40:17
Speaker 1: Over the app.
40:18
Speaker 3: For the insulin using population, you have to have that
40:21
Speaker 3: bluetooth connectivity so that you know, for example, in the
40:24
Speaker 3: middle of the middle of the night, if you need
40:26
Speaker 3: to be awoken, if you're hypoglycemic or something, you have
40:29
Speaker 3: to have that bluetooth connectivity. So that's that's something we're
40:32
Speaker 3: focusing on. And then the third is you touched on manufacturing. Really,
40:38
Speaker 3: before you can launch a product into the insulin using population,
40:40
Speaker 3: you have to be at scale, right, we can't say hey,
40:43
Speaker 3: you've just you know, you've got Type one diabetes. You
40:46
Speaker 3: sign up to use a biolink sensor, and a few
40:49
Speaker 3: months in, oh, hey we're out of stock, Sara, we
40:51
Speaker 3: can't send you another sensor. Right. That's just a terrible experience.
40:54
Speaker 3: So that's really the next phase is scaling up manufacturing
40:57
Speaker 3: so we can meet that demand. And then also with
41:00
Speaker 3: that scale comes the level of precision improvements that you
41:03
Speaker 3: get with really fine tuning those processes. So it's it's
41:06
Speaker 3: the wearable, it's the scale. It's kind of combining all
41:10
Speaker 3: that together.
41:10
Speaker 1: Yeah, and then you know, maybe we'll just you know,
41:12
Speaker 1: round out. I mean, you've got a lot of developments
41:15
Speaker 1: going on near term. But you know, we started the
41:17
Speaker 1: conversation talking about how to integrate all those you know,
41:21
Speaker 1: sensors and devices into one, you know, just one integrated device.
41:28
Speaker 1: Sounds like you guys have a thought process there, and
41:31
Speaker 1: so normally in these kind of situations, I ask kind
41:33
Speaker 1: of what inning we're in with you know, biowearable technology.
41:38
Speaker 1: It sounds based off of what you were saying earlier,
41:40
Speaker 1: we're in the very very early innings.
41:42
Speaker 2: Yes, we're in the first inning. So we didn't we
41:46
Speaker 2: didn't have any legacy architecture for hardware. We don't have
41:50
Speaker 2: a large install base of people that need supply reorders
41:54
Speaker 2: today immediately, so and we're at economies of scale with
41:57
Speaker 2: chips in sensors that we can build into the wearable
42:01
Speaker 2: that provides context. So it made it easy for us,
42:04
Speaker 2: and our single greatest advantage right now is that we're
42:06
Speaker 2: small and nimble, so we could take advantage of all
42:09
Speaker 2: of the technologies that are available in the consumer electronics
42:13
Speaker 2: space and put them into our wearable so that we
42:15
Speaker 2: can provide a well rounded, holistic view, and that allows
42:19
Speaker 2: us to really deliver a personalized experience. What we would
42:23
Speaker 2: like to get toe is a hyper personalized experience. And
42:25
Speaker 2: what I mean by that is we're in the first
42:27
Speaker 2: inning of bio wearables because there's a lot more we
42:32
Speaker 2: can do above and beyond glucose. Right So, Jared has
42:36
Speaker 2: shared in some of our public presentations at technology conferences
42:42
Speaker 2: that we've done lactate continuously, We've combined glucose and lactate
42:46
Speaker 2: on a single chip continuously, we've had our first in
42:49
Speaker 2: humans for cortisol and protein. So we're in the very
42:54
Speaker 2: very first inning because once we can combine multiple analytes
42:59
Speaker 2: and it doesn't cost to us anything more to add
43:01
Speaker 2: new additional chemistries on our platform because we're the first
43:05
Speaker 2: precision membrane deposition chemistry for an electrochemical sensor, and the
43:10
Speaker 2: amount of chemistry that's on the tip of these microsensors
43:12
Speaker 2: is really negligible, so it really is a factor of
43:16
Speaker 2: manufacturing maturity and that's necessarily incremental cost to do these things.
43:22
Speaker 2: So we can almost get to a metabolic panel if
43:26
Speaker 2: you can have latate glucose, cortisol, ketones all in one patch.
43:32
Speaker 1: And so.
43:34
Speaker 2: We're nowhere near the second inning or third inning. There's
43:36
Speaker 2: a long way to go.
43:37
Speaker 1: And this is just.
43:38
Speaker 2: Also one vertical. So while metabolic health is important, you
43:42
Speaker 2: can see that as we establish additional analytes and get
43:46
Speaker 2: them on our microsensors, we can expand to cardio metabolic health.
43:50
Speaker 2: And then there are so many other verticals in women's health,
43:55
Speaker 2: right why don't we have hormone sensors today that support
43:58
Speaker 2: women's health? And then there are opportunities in oncology.
44:02
Speaker 1: So really.
44:05
Speaker 2: We're in the very first pitch and the very first inning.
44:08
Speaker 1: And let me just say it's actually interesting because it's
44:10
Speaker 1: like you're combining all these into the one microchip. Is
44:14
Speaker 1: it that the well, let me first start off, because
44:16
Speaker 1: the microchip has multiple sensors, multiple microsensors. Yes, would each
44:23
Speaker 1: of those micro sensors be able to sense all of
44:26
Speaker 1: those readings that you were just highlighting.
44:28
Speaker 3: Yeah, so each chip has multiple inputs. So even when
44:32
Speaker 3: we're measuring glucose, that's one of the differentiators is that
44:35
Speaker 3: you don't have just one channel of glucose. You have
44:37
Speaker 3: multiple channels of glucose coming in. So it's almost like
44:40
Speaker 3: having multiple sensors that gives you redundancy when you're measuring
44:43
Speaker 3: one marker. Now, the vision from day one was how
44:46
Speaker 3: do we do multiple together? And that is really how
44:50
Speaker 3: you get the full metabolic picture. And then as Rich mentioned,
44:52
Speaker 3: beyond that, so yeah, on one wearable, now the user
44:56
Speaker 3: doesn't know, you know exactly where this information is coming from.
44:59
Speaker 3: But on one wearable you have these multiple micro sensors
45:02
Speaker 3: in each one of those micro sensors can be a
45:05
Speaker 3: different analyte that we're measuring.
45:06
Speaker 1: Okay, So instead of having like twenty sensors, as you're
45:09
Speaker 1: saying with the redundant glucose measures, you could have ten
45:12
Speaker 1: for glucose and you can have ten for other metabolic readings. Okay,
45:16
Speaker 1: that's kind of that's that's actually Yeah, does sound like
45:18
Speaker 1: girly innings of being able to get all that done. Well,
45:21
Speaker 1: Rich and Jared, thank you both so much for joining us.
45:24
Speaker 1: Uh sounds like it's gonna be an exciting uh nine
45:26
Speaker 1: innings of baseball here.
45:28
Speaker 2: Yeah, thank you. We're humbled to be here, so thank
45:30
Speaker 2: you for the great questions.
45:31
Speaker 3: Yeah, thanks so much, Matt.
45:32
Speaker 1: Yeah, and thank you to our producer Mariam for all
45:34
Speaker 1: the behind the scenes work she's doing, and to our
45:36
Speaker 1: listeners for tuning in today. We hope you join us
45:39
Speaker 1: for future episodes. If you like to stay up to date,
45:41
Speaker 1: you can click the subscribe button on Spotify or your
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Speaker 1: favorite streaming platform. Take care.
46:04
Speaker 3: Pass us bases