Latest / Diagnosing Dx / Could High Frequency Monitoring Transform Autoimmune Care? Kavya Swaminathan

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Could High Frequency Monitoring Transform Autoimmune Care? Kavya Swaminathan · Diagnosing Dx

• David Filby: Welcome to the show this week, Kavya. How are you? Kavya - Kascaid: I'm doing very well, David. How are you? David Filby: Very good, thank you. Where in the world are you? Kavya - Kascaid: I am in Castro Valley. That's on the East Bay in the San Francisco Bay area. I'm calling in from home because commuter traffic time. David Filby: And you must be surrounded by a really robust early stage diagnostics biotech industry and loads of people. You're in the world hub of the industry. Kavya - Kascaid: Absolutely. And within a microcosm there too, we are in MBC BioLabs in San Carlos. That's where the lab and office are. And it is a thriving ecosystem of just these early stage startups. As a matter of fact, that's why we decided to go there. One of our friends, Zach, who's a founder himself, was one of the first people we interacted with, took advice from, and when it was time to find a space, we were just like, okay, that's where we gotta be, right? David Filby: That's great. That must be comforting to be going through inevitable early stage challenges as a founder and be surrounded by other founders in the same space. Kavya - Kascaid: 100%. The ecosystem is fantastic. it's a unintended plug for MBC, but they deserve it. It's a fantastic ecosystem. I highly recommend it. David Filby: Well, we've got quite a lot to get into. I've been excited about recording this. Obviously you and I have had this plan for a long time. So I'm actually interested myself to find out what's happened since we first connected, probably over a year ago. But before we do that, why don't you introduce yourself, your career and background, and sort of bring us up to speed on what you've been doing up until Kascaid. Kavya - Kascaid: Absolutely. So I am a immunologist, proteomics scientist by training. So I did my PhD at the University of Sydney. I studied the influenza virus, the virus that causes the flu, studying antivirals to it, and understanding how it evolves to evade the antivirals we do develop. And then wrapping up my PhD for my postdoc, I wanted to study the host immune response because it became clear that we're playing a lot of catch-up you're trying to chase after the virus. So understanding the host immune response was really important. So I moved to Stanford to start my postdoc, and I was studying the host immune response to infectious disease when I first started. And that led to what I think was almost seven or 8 years at Stanford, where just started studying. immune response in people generally speaking. Initially because there was just a lot of overlap in the systems that respond when you have an infection versus you have an autoimmune disease or really when the immune system really decides to go wrong, it's so many different networks that come together. Just had a lot of collaborations. I had a fantastic mentor, Josh Elias, that also allowed me to explore these various realms. So by the time I was wrapping up, I was just generally studying immune dysregulation. And that was very close to my heart and I really enjoyed that. A lot of people stay in academia for 8 years and they're like, oh my God, it was miserable. But for me I had the time of my life. And when I moved to industry that was just post pandemic, most of my motivation then was actually in the early cancer diagnostic space where a lot of what we were doing and a lot of what we continue to do now is target tumor-derived DNA because they're excellent, very robust markers, but if you're measuring a tumor-derived DNA, there's already a tumor. So essentially the hypothesis is that your immune system starts to chatter. Before a tumor takes hold. And I generally have this hypothesis around most diseases, which is, there's an immune component that starts signaling really, really early. As a matter of fact, I start most of my talks by by saying, outside of being hit by a car, right, everything about your health state is probably being determined by your immune system. And and so capturing that was really important, and my journey in the cancer diagnostic space was essentially bearing out that hypothesis that we do have these action molecules, proteins, that will be able to signal things way earlier than an actual tumor takes hold. and that was about half a decade in the early cancer diagnostic space. I spent a lot of time in the trenches. So the first company I was at, I was one of the first single digit employees that was hired there, one of the first R&D hires. So built out lab, built out teams, bought everything out from little pipette tips to big instruments. And really that was the startup ecosystem adopting me and me feeling very at home there because you can feel the impact you're having every day. You're working with your hands, you're doing a lot of things. And so I guess it was natural that when we felt the need to go beyond what we were working on and identify this massive gap, based on which Kascaid was essentially founded. We fit right into the startup ecosystem. David Filby: Well, I would love to know as someone who enjoys research, right? And who's loved that, how did you end up being an entrepreneur and founder? Kavya - Kascaid: not in very deliberate ways. You know what I mean? It's not like one day I woke up and I was like, you know what I want to be? I want to be a founder. I would say that Kascaid exists because we saw that there is a problem to be solved. and I think it's one of the reasons we seamlessly fit in, right? it's not all the time, but a lot of times, especially when you're heavily in the research space, there's a lot of I have a solution now let me find a problem in the world to fit it into that happens. and for us it was quite the opposite. It was essentially like, oh my god, there's this gap and we need to find a way to solve this. Because we feel like the industry itself will not be able to move forward if we don't fix this gap, right? Actually one of my co-founders, Austin, has a way of saying this, why did you start Kascaid? It's like, because we had to. David Filby: That's always the right answer, isn't it? Find th there's th Find the problem organically and then and then work backwards from there, try and solve it. I love it. So give us an introduction then into Kascaid. Who are you? What are you doing? Kavya - Kascaid: So Kascaid is actually me, my co-founders, Austin Hall and Scott Ferguson. What we essentially do is tap into the interstitial fluid to capture immune biomarkers. And the reason we do this is because the gap we've identified is twofold. Immune markers. I've told you how I feel about them and why I think they're really, really important. They are also critical for the chronic immune disease space, which by the way impacts tens of millions of people. Something like 1 in 6 people have diagnosed chronic immune disease. A lot of people walk around yet to be diagnosed with chronic immune disease. It's not dramatic like cancer, for example, right? Like where the mortality rate is really high. And so there's a lot of focus on research there. We actually found that in the chronic immune disease space there's a lot of neglect around precision medicine, but there's a lot of opportunity because a lot of people are suffering. And what's preventing us from being able to deliver that kind of care in the chronic immune disease space is the fact that we don't have good biomarkers of diagnosing the disease, of understanding how the disease moves, or even understanding how treatment is working. And we certainly don't have the frequency with which we need these measurements, right? So disease is constantly evolving. These people, even when they're on medication that works, is they're on borrowed time before their medicine stops working. And we get these snapshot measurements from people maybe once a year. And doctors have absolutely no way of knowing. It's that game of playing catch up and the problem is a lot of this is permanent damage, right? rheumatoid arthritis is one of the primary indications we work with. Once your joint is damaged, there's no going back. We're just trying to fix more damage, which is why it's really astonishing to hear doctors and patients talk about it, because we still do throw gum at the wall to see what sticks. an actual conversation we've heard of patients repeat with their doctors earlier that day, was like, oh this medication you're on has stopped working. Would you wanna try a different category of medication? Or should we try to put you on a biosimilar for the same? The patient doesn't know. The doctor clearly also has no idea. And so it feels wrong that we're doing this in 2026. And by the way, if we just flew by this and said, look, patients are suffering a little bit, we're getting by, that would be a different story. An astounding stat is that we spent something like $70-$75 billion on just mismanaged medication in chronic immune disease. And a lot of this, surprisingly, is also just doctors over-medicating their patients, right? Because hey, I'm not gonna see you for the next 6 months. I don't know how your disease is going to evolve. I'd rather have you over-medicated than under-medicated. So let me do that. And one patient we talk to ends up at the ER every three months. Because of side effects and complications from her medication, right? So unanimously we all agree that if we're able to get one, high quality measurements of immune markers, and if we're able to get high frequency measurements of these immune markers and actually understand patient trajectories, we can intervene so much earlier and have so many better outcomes, just primarily from a health point of view, but also clearly financially for the system. David Filby: Okay, and is that why ISF is the key to your product? because of the quality of data you're able to retrieve? take me through that. Kavya - Kascaid: Both those things actually. So when I started talking about Kascaid, the patients and the disease story just takes front and center stage always because that's top of mind to us, right? The scientific nerdy part of why ISF is the answer. Actually, if you'd asked me over a year ago, just before Kascaid actually came into official existence. ISF was the choice because why we came together to start Kascaid was because we realized the limitations of blood-based diagnostics, especially for this really important category of immune markers. We haven't as a society, found a more efficient way to do blood testing. Essentially a lot of diagnostic testing involves venous blood collection, and it requires processing a blood sample. You usually want serum or plasma to do your blood tests. And a lot of the bane of my existence as a early cancer diagnostics researcher came from the fact that there was so much pre-analytical variability that would come in the time blood was collected. And in the interim before it got to me when it was stored and transported. So in the simplest of terms, the biomarker signals we were trying to measure would degrade in storage or transportation. Sometimes it would degrade and other times, the cells in the blood sample would be, releasing molecules I was trying to measure. So you would not know. Again I will quote Austin, we were essentially measuring how sick a blood tube was, not really how sick a person was, right? and so it became apparent that there were inherent challenges to - by the way if you're doing early tumor DNA and things like that, you don't have all of these issues. But if you're trying to measure these inherent intrinsic immune signals, it's hugely problematic, right? So we started essentially looking into what are alternative sources that can give us high fidelity signals in this immune marker space. And stumbling upon ISF was mostly because it was like, it's accessible, but it's also cell-free. Right. so, at least the primary problem of it cells might be confounding things, it was not there. And ideally, because the ISF is the same fluid that a continuous glucose monitor taps into. So the thinking was if you're able to put a biosensor, you can literally get a real-time readout of the signal that you're trying to measure without any of this need for storage, transportation, and things like that. The second most important understanding that we came to when we started recognizing this gap in the chronic immune disease space. we were measuring immune markers, it made sense that chronic immune disease is like the first indication given the scope of it it it for all intents and purposes, it feels like a health and economic crisis, right? what's going on in the chronic David Filby: Yeah. Kavya - Kascaid: immune disease space. It also became apparent that the timing of these measurements was way more important than we initially gave it credit for. Patients go through a lot of flare in autoimmune disease, so immune system just even when your medication is working, it overreacts sometimes, even despite your medication. So if me as a patient show up to my doctor today and said, Hey, I think I'm having a flare, the timeline between me self-reporting a flare and my doctor actually getting to test. Whether I have a flare or not could be 2 weeks, right? He or she writes me a note, and then I have to set up an appointment with Quest, then maybe that's a week out, and then I show up there, and then it takes another 3-4 days for the blood test to get there, and then another 3 days for my doctor to be able to see it. But then I probably didn't have a flare at all. Right? so this huge gap in being able to intervene in a timely way, right? And again, even something as simple as my follow-up appointment, I got on a new medication six months from today is my follow-up appointment. If my medication has not worked for 6 months, I have accumulated damage in my joints for the whole 6 months, and we're only catching up to that 6 months later, right? We feel like the trajectory of how things are evolving can only be captured if we caught these things way more frequently. And speaking with doctors, so we build a lot within the ecosystem, which we're gonna talk about too for sure. Doctors think that something like a weekly cadence would be extremely helpful for them to be able to see how disease moves. David Filby: So take me through the product now. So where does the product fit in end to end? Kavya - Kascaid: Essentially the entry product for us is to understand treatment response, right? And the goal within this is twofold. So initially the only plan was to be like, okay, we need to catch when treatment stops responding really early. But again, this is another key learning from doctors. By the way, patients stop and taper medication on their own will and accord a lot. Right. This is something that we learn because they may have a cold and they may not want to be on the immunosuppressant they're on. They may not be feeling good and they're not sure why that is. So they taper their medication, so doctors want to recognize this. They also know that their patients are over medicated. One of the primary things doctors would like to do is to taper the dose of medication, if possible. Right. So it's both understanding when a drug is working really well. And now if doctors are able to see within a week if you're tapering your medication is bringing your disease back, they would feel a lot more comfortable saying, no, no, just just go back on your OG dose instead of being like now 6 months have gone by and you've been under medicated, right? So that's an unlock that we learn from doctors. That's one of their primary things that they would want to do as a part of treatment monitoring. But also understanding much earlier than this 3-6 month of waiting to be able to intervene and say, hey, it's been 4-6 weeks and there's absolutely no improvement at all. In fact, it seems like you're getting worse. Let's get you in for an evaluation and try to put you on the next line of treatment. Right? So we do this thing called step therapy in autoimmune medication, which is like we need to start you off on a baseline of drugs and watch it not work before we put you on the next thing. And it's fine, it's that way. I would like to reach a place where we have enough data and we're able to build such robust models in the future where we're able to look at a patient profile and say, you need to go on this category of biologics for treatment to be most effective for you. That would be the ideal future where it's not this trial and error. But even within the time we're there, the initial indication is to intervene a lot more earlier, a lot more frequently, and give people a much better treatment outcome experience. David Filby: And how does that actually fit into people's lives? So how are they able to give you the access to this data so frequently? How does it actually change people's lives like that? Kavya - Kascaid: So for us, the earliest version of the deployment, which we expect to have as early as the end of this year, would be to have patients send their ISF sample. So maybe this is also a good time to tell you one of the key learnings we had. So when we were like, okay, we're gonna tap into ISF. And and because ISF was starting to be a part of the lexicon in the diagnostic space, at least publications, academic publications. We felt like there were going to be technologies that we could easily license that would allow us to, collect ISF from patients in a slightly more decentralized way because you think about it, you're like, there's continuous glucose monitors, they're super decentralized, they're at home, you can just snap it on. Surely people have figured out how to sample ISF. did not exist, right? there's bench top systems that allow you to collect ISF, but if you wanted a little thingamajig that a patient could put on in their home and access their ISF either to be read out right away or even just collected. We do in lab assays right now that didn't exist. So we actually spent the last almost year building up to a prototype that allows us to to be able to do that. Right. And that's been one of the key things that we at Kascaid we've had to develop a way to access ISF outside the context of driving around to people, which by the way we do. a lot of our patient data that we we collect now, we track a few patients longitudinally for several weeks on end. We actually drive around to their homes and sit down with them and collect their samples. But to move away from that to be able to scale and deploy. We will have patients essentially collect their ISF and send it into us. The cool thing is it's way more robust because it's cell-free. We are able to store and transport ISF for way longer than a typical blood sample would be able to. David Filby: Now let me ask you this, because there's a couple of really interesting points there. Do at some point do you see the opportunity long term because obviously you're still early on and we'll come on to where you are in your journey at the moment. But do you see the long term opportunity as potentially developing an at home device? Kavya - Kascaid: Yes, certainly one of the key offerings. So maybe I can tell you what the absolute ultimate long-term version is, and then we can work back from there, right? We think that the fundamental data layer to change how reactive diagnostics is in this space needs to be built. That's our mission, right? to understand disease trajectories in a way with these high fidelity, high frequency data sets across patient populations. there's so many... the disease keeps evolving and we use some clinical manifestations as shorthand and so when disease evolves they become highly specialized categories within those. But your immune system rarely goes wrong in one specific way, right? so it's usually a constellation of immune dysregulation that evolves, changes sometimes responds to treatment, other times it does not. It's something that we need to generate the data layer to be able to better understand. And instead of this stopgap plug situation we're doing now where we think for the next two years you may be under control. Thing needs to change. We need to be way more proactive, we need to diagnose these way earlier because there was a time where a lot of autoimmune disease was like a 40s and 50s disease. It's trending way earlier now. Right. And David Filby: Mm. Kavya - Kascaid: all these women in their 20's starting to get diagnosed means we should as a society draw more attention to it. it's sad that we don't when it's when it's older people, but well, you can't now just spawn it off on Medicare anymore, can you? So I think it's important to be more proactive in how we do these things and essentially we wanna build a data layer to facilitate that. AI is getting really good, but I stand by my assessment that the model is only as good as the data you give it, and if it's garbage in, it's going to be garbage out. So essentially that's what we see ourselves as doing, building that fundamental data layer. But in moving towards that, there's a lot that we will accomplish, which is essentially, and the means to generate this data is an at-home device. Initially, for us right now, being able to gather a lot of broad data is very helpful because when we first started running these assays, the goal was, let's demonstrate that there are biomarkers that are concordant between ISF and blood, and that would be a product. It's becoming very apparent that would be just shortchanging ISF. I don't know, it's like taking feathers from a goose that lays golden eggs, you know what I mean? you really need to dig in. David Filby: I get it. Kavya - Kascaid: Yes, right. so I think understanding more of what ISF has to offer for us in terms of robust biomarkers for these various indications is one of the focus. So that's actually the shift from just going in with an existing panel of a handful of things to actually understand ISF better initially, but it is essentially moving towards finding those robust set of biomarkers. And having at-home measurements that can be done. I still think we will continue to characterize people more broadly, even over time, maybe with lesser frequency than on a weekly cadence. But that fabric or data layer we're building essentially will be poised to transform early diagnostics, biomarkers, but also understanding therapies, right? right now we are starting to appreciate that people stop responding to treatments, and biologics are the new order of the day which allow you to do this more precise way of intervening with the immune system but when a biologic stops working we actually don't have good data to guide if your TNF alpha inhibitor has stopped working what should be the next line of treatment should I supplement that Treatment? Should I replace that treatment? We have no data, no real-world evidence to guide any of this. We will essentially create that data layer by monitoring patients, thousands of patients, over months and years. David Filby: And then using that data layer you intend to move earlier in the process as well, in terms of diagnostics. Kavya - Kascaid: Absolutely, right? Because capturing how a person evolves over time, how their immune system evolves over time, is critical to be able to capture that. And in a way learning what those early signals are, right? Much like many other diseases, rheumatoid arthritis, for example, if you have those autoantibodies, you already have the disease, right? But there's no way for us to capture you earlier than your symptoms for most people. Right? Unless I know that you have a genetic predisposition and you are one of those motivated people that are like, no, I need to stay on top of this. An average person shows up to their doctor because their joints are hurting. And then it's just playing catch-up from there on. Right. So understanding these things much earlier. Also, by the way, even within that example, about 30% of people present with seropositivity, so at autoantibodies. The remaining percent of the population are seronegative, which means they don't necessarily have the auto antibodies, but they still have the disease, right? So there's a lot to be understood here. And fundamentally I'll make the same argument again and again. We lack the data to be able to do this right. David Filby: So let's zoom out slightly in terms of let's talk about the company objectively. So where are you in your life cycle as a company and what are the sort of milestones that you've already achieved up until this point? Kavya - Kascaid: So we are over a year in. So as a working company that that last year, about May, June is when we closed our pre seed round. That was the first and most important milestone because it allowed us to exist and do the things that we wanted to do. And when we first started, ISF as a matrix for doing what we want to do was a hypothesis, right? We didn't have a lab, we didn't have any testing. We had some early signals from other people's findings and papers that you can measure a few things in interstitial fluid, but whether or not you could make robust measurements of a lot of these relevant biomarkers, whether they would in any way, shape, or form reflect real biology, all of these things were TBD. So in the past year, that's the most important thing that we have uncovered, right? I like to say, when we started Kascaid I still had a mortgage. So and well I still have a mortgage but the the biology standing up to it was fundamental to us existing as a company and bringing bread on the table. So de-risking that has been huge for us to show that these molecules can be measured and they do truly in fact reflect disease has been important. The other milestone that has been tremendous is essentially partnerships that have allowed us to build within this ecosystem, right? that I think is by far the biggest milestone here because the biological de-risking could not have really happened in the way it did without those partnerships. So we're partnered with the Global Healthy Living Foundation. They're a Not-for-profit that works with a lot of chronic immune disease patients, a lot of rheumatoid arthritis patients. They have a massive network of hundreds of thousands of patients and doctors within their ecosystem. It was really important for us as a company to not build a thing and hope that we can fit it into something within the world, right? given that we've identified a problem, it made sense to actually work with the doctors and patients. So any aspect of our business model, whether that's doctors, patients responding to the data we're generating, falls apart if patients don't use it and doctors don't find it actionable. So every step along the way, we've been walking with them through understanding what a patient's day-to-day is like, what they struggle with, and so building around that and getting doctors' input on what data would actually be actionable. It was actually one of our doctor advisors that initially told us that treatment treatment response is where the biggest gap is at because we came in from the diagnostic space, clearly diagnosing chronic immune disease early is a massive unmet need, but the more urgent, more actionable, more tangible way of deploying for us would be treatment monitoring because it's very clearly defined, right? from a monitoring someone point of view, we could baseline somebody before they go on treatment pretty easily because then they become their own baselines. And then as through time they traverse their treatment path, we will be able to capture how they evolve over time. And that the doctor said was way more tangible for them and way more urgent while we build towards a larger let's diagnose this early. David Filby: That's great. So the pre seed round you raised last year, where does that take you through to? Kavya - Kascaid: For us, the pre seed essentially was well, we started with the idea that we'll raise a pre-seed to do just the scientific de-risking, right? Over subscription helped us go way beyond that. One of the learnings we had along the way was we needed to figure out a way to access it in our social fluid in a more decentralized way. So a lot of our funds have gone towards building towards that and starting to generate the data within the patients, right? Understanding it's values. So a lot of our time and effort and and capital goes towards those two key things. Understanding the data and the frequency of data that we're able to generate with patients and how doctors can react to that data, but also building the prototype towards the access and the readout component of that is a lot of where we spend our time now. David Filby: That's awesome. And so once you've got early deployment, where do you see commercially the strategy going and where do you see the biggest upsides for Kascaid and the product? Kavya - Kascaid: There's two main commercial angles to our product, and one leads to the other, right? the first one primarily is the ungodly amounts of money the medical expenses add up to in taking care of these patients, both in terms of over medication, medication that doesn't work, and side effects and such that take these patients to doctors if not emergency rooms on some regular cadence. We think we can stop cap a lot of those things by offering patient monitoring to intervene earlier when treatment isn't working, to intervene earlier when treatment is working and taper people off, but also catch flares as they happen. So you don't actually have to let this get worse in a way that you end up paying more to get that treated, right? So one of the things that we like, It was a nuanced conversation with a doctor under like when we showed data for a for a flare from a patient and how it's an N-of-1, so I will caveat that before I explain what we saw in the data. We actually capture signals that preceded a patient officially complaining that they're going through a flare, right? if that's able to bear out in a population scale and you as a doctor and patient can do a lot of things here. you could decide to just take it easy for a weekend, right? if you had plans to be at a festival or something like that, probably not do that, right? Or should I put you on prednisone? Should we just have you not take your medication? these are all decisions you can make if you have days in advance of knowing what's actually imminent in terms of an immune flare that's happening to you, as opposed to taking medication, having side effects to that, or or not having an arena in the right way, show up at the doctor's office again. So for us, the early commercial deployment essentially is on the treatment monitoring managing side of things. And then I will go back to the main financial upside of our model as being essentially the data layer that we're building, right? so that would lead to early diagnostics products, but also certainly therapeutic targets and we are eager to engage with partners that would benefit from that kind of a data layer. David Filby: Sounds great to be honest. I don't want to talk too early on it, but the signals we tend to see of companies that are even more advanced in terms of maybe Series A and the ones that we tend to see that go on to be successful are saying all the same things that you are, to be honest, th they find a problem and that they work backwards, they don't just they're not in love with their science and trying to force it into clinical workflows. That just never works. They value partnerships and other opportunities in terms of what they can do with their data rather than trying to produce a test, implement a test at the just into people's lives, and that is that's a long hard road. That probably brings me on to a really relevant next question in terms of like regulatory pathways and reimbursement, how does that tie into the product development? Kavya - Kascaid: as a medical device, you need to be cleared by the FDA, right? So there's two steps to it. One is just the accessing ISF, because we do have little microneodles that enter your skin. It needs to be in the lease cleared by the FDA. and then as we generate the data to support the treatment monitoring claims, we will get it cleared for that specific use case, the which is the prerequisite for getting the code and getting it reimbursed. What we like about that path for us is actually twofold, right? One and this is my personal favorite because having seen a lot of companies try to scale up from smaller scale studies to their pivotal clinical trials and those usually struggling to hold up the data from one to another one of the things that we've been very cognizant about is generating the early data within the same intended use population that we're actually going to run our pivotal clinical study. And people underestimate what that means, right? Because when you try to go from animal studies to to suddenly into a population, that's where things fall apart. We've been very cognizant about not using pig skin and stuff like that. We build around people, generate these data around people. So when we do our power calculations and actually take this to the road on the pivotal clinical study, it's as close to what we initially generated as possible. And oftentimes, this is the difference between running single-digit million clinical trials and high double-digit million clinical trials, right? So doing that in a capital but more importantly, time efficient way is very important. And getting cleared for the FDA use cases essentially. Also from a treatment response point of view. In dromatoid arthritis, there's actually existing reimbursement codes around these. I think doctors feel like they would be more inclined to use it if they thought that the data was more frequent and more usable and more actionable. And so building in that space gives us more confidence, right? you just need to show that there's a clinical need and clinical action on the other side of it. So building within that ecosystem we feel confident about being able to get there. David Filby: That's great. And so now you're a founder, you've raised capital, which is, tough in itself. But you're obviously really passionate about the challenges and the gaps that exist in the diagnostics industry. What's been interesting to you as a first time founder and also where do you see the diagnostics industry objectively and where are we going? Kavya - Kascaid: I've been in enough startup environments to know that you wear a lot of hats. But Founder, and first of all, I want to use this opportunity to say that I have nothing but the tremendous amount of respect for solo founders, but I will also say I have no idea how they do it. the reason I keep my head above water is because of Austin and Scott every day. And even within that I will say that just the scale of work, right? everything from bookkeeping to talking to customers, to keeping your eye on the product, and even with us dividing and conquering, it's a lot. So the scale and scope of things that demand your attention is something that I have been very humbled by. So I think that I would say it's probably not the sexiest thing as a first-time founder, but it is certainly the thing that I would say to anybody that wants to become a first-time founder, that is something you need to be aware of. And I would also say that you start to appreciate the things that you are going to learn for when you want to do this again, right? you already and I think I'm very okay with knowing that I'm going to get some things wrong. And I think that that's really important because it's very easy to just start going back into this oh my god, I can't like hindsight is always 2020. And I think again, I will lean on my co-founders and our extensive set of advisors. So we actually have a lot of very experienced people in our orbit. And we lean on them very heavily. One of them literally told us that the best advice he could give us was to not take advice from anybody, which I think is generally good advice. But having all of those voices of people that have done it and hear where they tend to converge and where they diverge has been tremendously useful because I've certainly stepped back in these moments and been like, I would not have done it that way. And sometimes they work out, sometimes they don't, right? Like, but having more experienced voices has been tremendously valuable. David Filby: Yeah, that's great. And so with Kavya - Kascaid: Yeah. David Filby: the industry at large, where do you think we are and where do you think we're going? Kavya - Kascaid: Yes, I really do like this question. I'm very happy for where we're at because at least within the startup space there's a lot of hope, right? I've been very excited by some of these exits that have come about for diagnostics companies where I think people had given up on even holding their breath. So I'm happy to see, the billion to one, the personalist free node, all of these companies having exits have has brought a breath of fresh air. People have stopped shying away from claiming to be a diagnostics company. I got this piece of advice, again, one probably the category of advice that you maybe should not take to. They told me not to talk about us as a diagnostic platform at all, right? it's like, nobody's gonna give you money. And you know what? Whether or not we branded ourselves that way, people that had been burnt by diagnostics still wanted to stay away, right? I think that will change from just a funding and companies coming up with ideas in this space. I'm very hopeful for that. I still think that there's so much potential here. I see a lot of movement and buzz in the consumer space and in the early diagnostics realm, right? And then I'm excited to see how that moves along. On the one hand, it's very exciting to see that consumers want to take health into their own hands, which is fantastic because the system for some reason is not very incentivized to move towards that, and that the healthcare system and I'm eager to see how as a innovation and startup community we respond to that, right? I think that building within the clinical and biological context is really, really important. And I'm hoping that the scientific rigor and filling the gaps that exist right now is where we see a lot of innovation and movement with this new breath of fresh air. So I am very excited. I think AI is going to be very useful in building a lot of things. It's not quite taken hold the way it's taken over therapeutics, which I'm happy about because I think we're all recognizing that filling the gap is something essential we need to do in order to get there. And I'm very eager to see all the ways in which we get there across the board. David Filby: Thank you for that, Kavya. That's great because I think you've made some some fantastic points. And I love that you're optimistic about the industry. You I think it's interesting you're not the first person by a long way to tell us that they were told to not brand themselves as a diagnostics company and and that's fine. There's many a reason in the past that unfortunately has caused a bit of a black mark and reputation in investors' eyes, but I think every passing quarter we move away from that and things look better. But the way you're going about this early stage company rather than product, I think is fantastic and I think there's so many early indications that you're on the right track with regards to being open minded for about not even just open minded about partnerships but excited about partnerships and what they can bring to the company, even if it means you have to give something away, or whether it's revenue, profit, or equity or whatever in the business, you're excited about getting the product out there and really filling that gap. And think that's a massive point. But it also feels like commercially, as a company, you've got so many options, feels like there's so many options where you go from treatment response to what you do next, whether it's an extension of the product earlier, later, whether it's what you do with data, you've created a device, for yourself that I assume has some commercial value if you wanted it to, right? I'm sure there's other companies that would love to be using that device to measure ISF for other things. I don't know. I love I love this. I love talking to early stage companies that I'm excited about, but I love it when they come, prepared with like. the right mindset and having done things and I feel that that's what we get with you. So this is a great story to get out into the industry. So thank you very much for coming on and sort of sharing the story. We're really keen to follow it along. I'm sure we'll get you back on the show in the future to talk about the progress you guys have made. Kavya - Kascaid: Thank you, David. This has been a fantastic conversation and thank you so much for all the very thoughtful questions and providing a platform to I think, I will say this, we are getting a breath of fresh air in diagnostics now, but you've been doing this for when diagnostics has been in the doghouse for a little bit, right? Like in and fighting the good fight, getting this message out there because AI is important, it's taken over a lot of our field and a lot of the limelight and a lot of this is well deserved but also I will argue if people are not healthy they're not gonna use your I don't know cool image generation product you know what I mean like. David Filby: I know exactly what you mean. But thank you, Kavya. It's awesome. We'll follow along and we look forward to speaking to you again soon. Kavya - Kascaid: Likewise, thanks David. Have a good one.