Leo Trautwein Presents Know Labs at LSI USA '24

Leo Trautwein  0:03  
So, Know Labs. We are based in the Northwest in Seattle, we're developing a non invasive medical diagnostics platform, we use radio frequency for a platform. So we can basically measure multiple analytes in the body. Our first application is focused on blood glucose monitoring. So our first application is a blood glucose monitoring device. We started this five years ago, when we started this journey, we decided that we needed some principles to guide us throughout this journey. Firstly had to be accurate. So as accurate as any other CGM in the market, CGM is here thinking of Dexcom freestyle, from Abbott, Medtronic, and so on. It has to be affordable, it also has to be paying for it. Of course, if it's non invasive it is, by its nature going to be paying for it, it has to be convenient. So the user has to be capable of moving around the body. So it's not limited to one location, and also giving the patient more freedom. So if they want to use it now, and then remove it for a few hours, January might say they want to go to a party, or let's say they want to go swimming, they have that option, and also to be scalable platform. So that's why we went to radiofrequency. So we can we can detect and monitor more than just glucose. Our focus has been glucose, but I'm going to show someday again other analytes who have patents that that cover more than 100 analyte. So we have done tests on ketones, who have done tests in oxygen, metabolic drugs, so think of ibuprofen, Tylenol, alcohol, so the list goes on. So it's a scalable platform. So with that meet, they know you, they know you. It's our fourth generation of the technology that we have built. I have it here it's a functioning device. We have 15 units in house that we use for our trials, we're in the process of building another 50. And by the end of the year, potentially another 1000 of these so we can do large scale trials of them. So they know you use no needles, you can see the sensor in the back. It's 100% non invasive. Like I said, it uses radiofrequency spectroscopy. I'll explain that in the next slide. Continuous monitoring, but it can also be used as a spot monitoring, it's up to the user real time data. So there is no lag in the in this in the values that we are predicting. Because when you're using proxies, such as interstitial fluid, there is some lag. We don't have a lag, no consumables, so no disposables. That helps it to be very affordable. It's a wearable, we can wear it to two forms, either with a strap opha arm strap or with an adhesive patch. So it basically snaps on this little case. And it's powered by our proprietary AI. So how does it work, it has three components. The RF generator controls the signal, there is an antenna array, we went through more than 250 different arrangements to get to this one that's very stable, repeatable, and that RF generator emits a signal through the antenna array creates this energy field, the energy field goes into the body around 13 Young limiters. So we are getting data points from venous blood, capillary blood, and interstitial fluid. That's why we believe we're delivering higher accuracy. And it's captured from the other internet, the concept is a mid energy, as the concentration of that energy changes, it changes how much energy is absorbed by the body. So by looking at how much energy is absorbed, we can predict the amount of glucose in the body and other analytes. Quickly, just to show some data, this is actually in vitro data. So this is how the device works. We do a frequency sweep starting 400 megahertz to 4400 megahertz, that's the value that we collect on the receiving antenna. And those are the curves that are built by each sweep. These are curves for fructose glucose and ketones. It's very I just wanted to show here because it shows very clear the device identifying different different analytes. And then the next slide is really focused on glucose, left one in vitro, right one in vivo. So that's Flow Medical, from clinical trials. So what you can see by the different colors is the different concentrations of glucose, both in vitro and medical. It really just to show here, the power of the sensor and its capability of identifying different concentrations. Science for us is important. So we started first two years, three years focused on miniaturization of the sensor. Then we got into validation 2021. We did our first study with Mayo Clinic. In vitro, we prove the 100% accuracy. Basically we can put it in that study we could put any sort of solution on top of it in that case we use alcohol, bleach and salt So water is each one of those components. And we can say precisely the concentration in that solution. There are some like party tricks that I do, sometimes we can put some metal cubes on top of here. And I can say if it's copper, titanium, magnesium. So once you understand the RF signature of the of the metal or the solution, or the body, you can start identifying it. So we started after doing safety tests, we started our clinical tests in 2022. There was a very exploratory study, not just glucose, we did glucose, oxygen, ketones and some of those other analytes. For glucose, we achieved a 5.8% MRD, super low. But it was also a very small study, when we started like following IRB protocols. Our Mar D went to 20%. And we have been working on on decreasing it. Since 2022, we decrease from 19%. So let's call it 20% to 11.3%. That's the study that we published last year. major difference here is as we get more participants, as we get more data, we'll have been able to refine our algorithm. Most recent study, I was in Newtonian last two weeks ago presenting this at the attd, which one of the largest diabetes focused conferences, we delivered an ARD of 11%. In our most recent study, that was for 31 participants, type two participants. So with that, we were able to get there on the hyper glucose range in addition to the normal glucose range. And we also use venous blood as a comparison, the area of the data that I showed you in the previous slide was using a CGM at the X corner freestyle as a comparison data. Interesting this data, in addition to the 11.1% is when you look at the variance on the ranges, so sometimes it's easier to deliver accuracy on the normal glucose range, so 70 to 150 milligrams per deciliter. But once you started going to very high, or very low, you'll lose that accuracy. So what we were able to do here is that even in hyper and hypo, we maintain the same accuracy, which shows a big value for the for the user. Just a little bit of our history. So we started in the board, miniaturized assessor, I have what we call the gen one here, this was a benchtop version, so the user would just have to raise their arm on top, if to use in a continuous way. This is what we created that will allows us to start doing testing outside of the lab. So things were just not on the benchtop and wired everywhere. Until we got to our Gentoo. Both devices, the gen one and the gen two that we call know you. They are mobile, controlled by a mobile app. So they have Wi Fi Bluetooth capabilities. How we use the gen one I kind of showed, so you just rest your arm, or you put your palm on top of it. The genuine is not our go to market factor, but it will help us to help us with the clinical trials. But there are some applications for it as well. This is our Gentoo not going to spend much time here the major difference from the gen one was it's 85% More in volume, and 75% lighter than the Gen gen one, how we use it. were envisioning use on top of the arm, forearm and on the abs. So what's happening now, they want to go to each one of these but primarily data collection. As we collect more data, we can improve accuracy. Our goal is to deliver an MA rd under 10%. And that's what we're working for. We're not seeing that as a big challenge. It's just a matter of collecting more data, instead of my 31 PARTICIPANT studies we need to do 100 200 300 to get there. So why don't labs I would say we're an emerging leader in the space of why the glucose monitoring global innovator, we have more than 250 patents that that protect this technology. So it is something that we pay a lot of attention to, we're focused on the medical device path, we're not focused on the wellness path and we'll go for investors ask us that question. Are you gonna go to over the counter wellness, that's not our goal. And then lessonly is a platform technology. Glucose is just the beginning. There is so much more that we can do with it. That's it. Feel free to stop by if you want to. I was gonna say test the device but ran out of battery after testing so many times, but feel free to schedule some time and we'll we'll share more of our stories. So thank you