SPEAKER_00 0:03

The decades of women's health conditions have been chronically underdiagnosed, misunderstood, and underfunded. My guess today is important to think that Google Miles is the father and seal of milestone dynamics, a biotechnology company developing a non-based, AI-powered biotechnostic platform focused on detecting women's health conditions further and more accurately with more than 15 years of experience in biotechnology diagnostics and artificial intelligence. Today we're going to explore the future of diagnostics, the role of AI in medicine and why the next major healthcare breakthrough may not be treatment, but rather early detection. Welcome to the joke, yeah.

SPEAKER_01 1:03

Thank you so much for having me.

SPEAKER_00 1:08

It's my pleasure. Why don't you just kind of take us to the to the origin, to the journey? Like, why do you decide to focus on women's health and in particular endometriosis?

SPEAKER_01 1:21

Yeah, good question. So I decided to go after this uh this mission because uh I I grew up in New York City and I lost both my grandparents as teenage as a teenager. Um they both passed away from leukemia, and unfortunately they caught it too late. And so my year old daughter Sydney was uh unable, she didn't get the chance to meet them. And it's because early detection wasn't available to them. And so as a result, I decided at that point I wanted to spend my career working on early detection, and I realized that there was a significant gap that biology usually involves very large data sets, and biologists generally don't have the background to handle this data, and data experts lack the biology background oftentimes. So if you want to turn these large data sets into something that can be used in the clinic, um, it's quite tricky, and you need to sort of have this mix of those backgrounds. And so uh I decided at that point that I wanted to become one of the first bioinformatics PhDs in the United States. And uh since then, uh I've spent uh over the you know, over the last decade plus, I've been working in genomics and diagnostics and AI to turn that biological data into something that can be applied in the clinic. And uh during all this time, uh a loved one was suffering from chronic pain, and she went from doctor to doctor, but no one really could figure out what was wrong. And after 12 years, a surgeon diagnosed her with endometriosis. And I just remember thinking to myself, why didn't they see this on the test they ran? Uh what took so long? Like, I I couldn't figure out what you know how how we ended up in this place, and I I realized that early detection is completely missing from women's health, uh with the exception of breast cancer. Women's health just does not have early detection. And uh, you know, carrying that forward, my daughter is genetically high-risk friendometrios as well, and she needs better solutions so that she doesn't spend her entire 20s begging doctors to take her seriously. And so uh I left a job that I had for the last 10 years at Agilent, and I founded this company, Milestone Gynomics, and um, as you said, we're we're we're building the first AI-driven early detectable platform for women's health, and the first target is a non-invasive blood test for endometriosis so that women can die get diagnosed at the first onset of their symptoms.

SPEAKER_00 4:02

Yeah. Um, Gregory, without getting too technical, are you able to kind of review how the science works? Where do you acquire the data to be able to drive the early detection?

SPEAKER_01 4:15

Yeah, good question. So over the last couple of years, we've built some partnerships with some uh great folks on our team. Uh two of them are Drs. Shanti Molling and Nick Nick Fogelson, who are uh gynecologic surgeons that are in Portland, Oregon, two of the top surgeons in the world in this particular field. And they have generously offered to provide us with patient blood samples at no cost. We just paid for shipping in order to um in order to test our our our methods. And you know, the the key to what we're doing is you know, based on my story, it would sound like that I would have gone into oncology research with with my grandparents and all that before what happened um with the endometriosis story. And I did spend most of my early career in oncology, and I realized that they learned a lot of lessons uh the hard way. Um it took 80 years for folks to realize how complex the disease was, and for them to realize that they needed to understand the biology better and use uh more complex tools for diagnosing it. Things like genomics and artificial intelligence, and once that happened, everything else unlocked. So in the late 90s, early 2000s, oncology realized that understanding that biology, the complexity, that cancer is not one disease, it's it's a collection of diseases that all behave differently, but share a certain set of hallmarks. Uh, once they did that and they identified how it worked, everything else changed. They've started to become more targeted therapies, preventive care, insurance coverage is pretty universal for anything that's not experimental at this point in the oncology space. So learn seeing all that, but seeing how long it took made me realize that like we need to apply the learnings and sort of fast track this in women's health and try to figure out if there's ways to understand the complexity. And so, what we do is we look at three different types of signals uh from the blood, not just one. And a lot of companies they start by looking at one type of signal, whether it's a certain kind of protein or a certain kind of marker. We look at three different signal types. Uh, some of them are coming directly from the disease, and some of them are coming from the the body's natural response to the presence of endometriosis, so that we get the biological picture. And that's what cancer figured out about 10 years ago and in their uh blood-based diagnostic journey. And so we're trying to repeat that here.

SPEAKER_00 6:50

Understood. Um so for our listeners and viewers, do you mind uh kind of answering uh for the question one kind of metriology? Like I mean, you and I toy uh do you mind uh the kind of second open work and around the technology? What's it a personal connection for you that that uh can take you that uh that we want to focus on it? Or what's it kind of like the the fact that your your your relative spent 12 years going undiagnosed and trying things that didn't work, kind of like kind of walk into it?

SPEAKER_01 7:40

Yeah, so yeah, that's a good point. We should start at the beginning. So um so endometriosis is a chronic disease that causes a wide variety of symptoms. It's caused by cells that are similar to that of the uterine lining, the endometrium, that end up in other parts of the body. So you can have these cells that are like those in the endometrium end up in your intestine, in your colon, in your lungs. It can even end up in your brain and spine. Uh those are a little bit more rare, but these cells end up in places that they shouldn't be, and as a result, they end up causing chronic pain and um gynecologic symptoms, but also uh it's the leading cause of infertility. It causes 50% of infertility cases, and surprisingly, it impacts uh more than one in ten women worldwide. So you it's the number is probably closer to one in seven or one in eight, and yet uh most folks don't even realize they have it, or some folks don't realize don't really know what it is. It's uh one of these areas that's just there's been a blind spot to it for a very long time. Um, many women have just been told that uh it's it's normal to have period pain like this, it's normal to have painful intercourse or uh to to just deal with it. And and the worst, what I've seen with many of the patients is that they end up told being told by their doctor that they're imagining their pain and that it's all in their head. And so um it's really unbelievable that we're in 2026 and that's still, you know, that's where we are right now. Um the symptoms are very nonspecific, so you can't just bundle them all together, and the care is uh separate too. So you can end up going to a GI doctor for what you think is uh Crohn's disease or something inflammatory in your in your bowel or your intestine. You can go to an OBGYN because of your painful periods, you can end up going to a surgeon because you think you have appendicitis or or gallstones, and when they proceed with that procedure, when they go forward with that procedure, they can't figure out why you're still in pain afterwards. And so at that point, a lot of times that's when they may tell you that you're imagining it. So the most important part of this is that 90 more than 95% of the time, you cannot see the disease on imaging. So you can't really screen for it, you can't really test for it in any way. Uh generally, it's a diagnosis of exclusion unless you are one of the hundred or so world experts who know how to identify the disease just from an interview in 10 minutes. And so, for example, you know, the two surgeons that we're working with that are getting us the samples, they tell us, yeah, we can identify endometriosis 99% of the time just by talking to the patient for 10 minutes. But that's not really the norm here. And uh, so for that reason, the diagnosis becomes delayed. Uh, most primary care doctors and OBs do not want to suggest surgery as a first-line option, which is the only way to really diagnose it definitively. So patients will end up going on these diagnostic odysseys where they get misdiagnosed with all sorts of conditions before they finally, if they're lucky, see the right person who can suggest something, you know, something that's gynecologic or endometriosis. And it's uh it's an inflammatory disease. There's been a lot of debate about what the root cause is, it's still not really known, uh, which is why our company uses the cancer analogy so frequently. Um, our our whole motto is uh women's health conditions like endometriosis behave like cancer, so we should diagnose them and treat them like cancer. Cells that end up in other parts of the body, that that's that's how cancer behaves in in certain ways. And the the damage that it causes, the the outcomes, all that is very similar to the way that cancer operates. And only in the last several months a paper came out that showed that all eight hallmarks of cancer are shared by endometriosis. So we're talking about growing your own, they okay, they grow their own blood vessels, they avoid the immune system, they have the cells grow uncontrollably. All eight of the key hallmarks of cancer are also shared by endometriosis. And so uh the fact that we are not treating this with the seriousness of a disease like cancer, uh, to me is from you asked, you know, how did I get here? Is it the personal story? It is. Uh, that's a big part of it. Um, when I was working in the oncology space, I was trying to develop a blood test for cancer while seeing this person go through that struggle and realizing that there are answers here if we can just diagnose people, especially diagnosing them early. There are treatments that are available. Uh, they're not perfect, but the earlier you catch the disease, the better those treatments work, just like in cancer. And um, so there there were a few different reasons for why I felt like this was the direction to go in. The personal story, the biology made a lot of sense. Um, the access to samples, you know, for free samples, because folks just need they just needed this so badly that they're offering to give us free samples in order to push this forward. Um movement within the community was very, very inspiring. So we were very pleased with that too. And so it allows us to develop this thing um faster and uh and cost consciously at the beginning so we can show that it works. And so that that's sort of what we're finalizing now. And uh July 1st is when we expect to have our pilot study results, and once that comes out, then we're gonna raise an institutional round and run a larger study and launch.

SPEAKER_00 13:43

Amazing. Um I mean the obvious question for me is um well twofold. I I guess I'm starting off with twofold questions, but one is the current condition uh if this could be a terminal condition. And two, if this is a current condition that in your case, um somebody went on the hypnosis for 12 years what kind of like um what is what is a delay diagnosis actually cost anxiety, not just financially, but uh emotionally and economically. I mean, I would I would expect that it's gonna be pretty debilitating when the when when a woman is in pain.

SPEAKER_01 14:25

Yeah, so um I'll start with the first question. So when the disease is allowed to grow and spread, the um the likelihood that it invades the neighboring organs is is higher. So I I know many folks who have had these what they call lesions, endometriosis lesions. They're basically the the types of scarring and growth where the endometriosis cells should not be. Um these cells can end up in your colon, for example. And I've know folks who have had part of their, you know, part of their uh part of their digestive system removed as a result of this. So it's very it's slower moving, it's not, it's it doesn't have the immediate urgency the way the cancer does, but the longer you wait, the more damage it does, the further it can spread. And um, and like cancer, you know, the harder it is to keep to have the treatment work. So if you can, if you were able to treat endometriosis in the first couple of years of experiencing the symptoms, it's more likely that hormone therapies will work. It's more likely that the surgery you have will take because you have to have surgery to remove those lesions. If you wait longer, it's possible that you don't get it all and then it can grow back. And so, in about 30% or so of the patients that have surgery, it comes back. And I can tell you one story. So on our team, we have uh Abby Finkenauer, who is a congresswoman from Iowa, who's a women's health advocate, passed some amazing legislation, came out with her story of endometriosis. Her parents believed her and they pushed things forward very early in her life. And as a result, three months ago, she had her first child. And so that's you know, that's that's the kind of difference the early detection can make in this space. Um, whereas I've spoken with folks who didn't get diagnosed for over 30 years, and they said they forgot what it was like to be out of pain, they they they missed their window to have a family. It it was devastating. And so um I would say that there was almost universal and a universal agreement when I asked the community what would a non-invasive test have done for you if it was available, and you know, 99% of the folks said, Yeah, this would have changed things significantly. And um, and as you said, there's like there's financial implications too. So um, you know, and it goes toward there's several different angles. I know that there's like a you know a business aspect of this podcast. So if you're talking about um just simply lost productivity and medical costs, you're talking about $200,000 per NOPatient over the course of the 10-year diagnostic delay for every yeah, for every missed patient. For the OBGYN, you know, reproductive experts, uh, you're talking about a lot of IVF treatments that were doomed to fail before they even started because they didn't diagnose it. So you're talking about uh probably about $2.4 million in failed protocols per practice if you're dealing with about a hundred a hundred couples or so every year. Uh so that that's a huge cost, not to mention the additional clinical services. Um, on the primary care side, you're talking about all these extra visits that are unnecessary, that adds up to you know a couple thousand dollars a year. There's also the insurance companies um, in terms of how much excess cost there is, if they could spend even two or three thousand dollars for a test like this, like we would like to price it lower than that, but even if they ended up paying two or three thousand dollars for this kind of test, you're talking about uh cost savings of um about fifty-five thousand dollars per patient. And that's included, and yeah, and that's include so that's uh yeah, that's obviously significant that's a significant incentive for them to want to test rather than going down this rabbit hole of diagnoses and doctors' visits and ER visits. I mean, I can't tell you uh the folks that I've watched how many times they end up in the ER with pain. Um, and so that just add up. And um, so there's there's a lot of financial uh benefit for for a solution like this as well.

SPEAKER_00 18:48

It it just I mean, you you you attacked a whole bunch of patient costs. And do you say one in eight to one in ten women suffer from this?

SPEAKER_01 18:58

Yes, it's uh the statistics are one in ten, but the expectation is that uh there's probably a lot of undiagnosed women out there, so it's probably closer to one in seven or one in eight.

SPEAKER_00 19:13

Back of the napkin calculation, if there's four hundred uh million people in in the United States, 200, let's say that 50% of them are women, so it's 200 million. If it's one in ten, we're talking 20 million women maybe suffering from um endometriosis in the United States.

SPEAKER_01 19:29

About that, so I I would caveat this is that endometriosis tends to stick with reproductive age women. So girl, young girls mostly, mostly I'll say, don't get it until they're in their teenage years. And there are a lot of folks who, when they have endometriosis and hit menopause, that the symptoms do resolve for them. But generally we stick to like 15 to 45 or so, like our the range for our study, I think, is about 18 to 42 for the women that we recruit into our study. Um, so it's probably a little bit, yeah, it's not quite 200 that much, but it's it's a lot.

SPEAKER_00 20:08

But but but even if half of the women were in uh in in the in productive uh uh age, that's that's still a very significant number. Uh I mean it's it's two white guys and a black guy, but would this have been the case if this was a man's disease?

SPEAKER_01 20:26

No, I and that's uh I mean that's where cancer again, if cancer comes in. Like obviously cancer is devastating, and it it it's it's it's horrible. Um but I I can tell you that if you take an example cancer, say colorectal cancer, which impacts, I believe, 32, there's 32 new cases out of every 100,000 people. So we're talking about quite a low incidence rate, right? Um, but cola guard is out there and there's screening for it, and there's there's a movement behind different cancers. You know, breast cancer was the first big one back in the 90s. Uh, everyone just got tired of their wives and their sisters and their moms dying of this thing. And with colorectal cancer, the incidence rate is is so low. And so imagine for a one in ten incidence rate, or if you want, yeah, if you want to make it a little bit lower because it's only reproductive age. So the fact that a lot of a lot of women don't even know what it is. And I I know women who said, Yeah, my mom just said this is normal. Like even their mother is saying it because they didn't understand it, because we're told just deal with it. And so, yeah, we're we're two white guys discussing this on two white men discussing this on a podcast. But um, it will yeah, there's no way, there's no way that this would have uh gone on for this long. And uh if if this was uh disease affecting the whole population or or or men, um, and it just continues to push forward the You know, some of those inequalities that have been per per persistent. I mean, there's stories about women getting home getting getting sent home from the ER with heart attacks because their symptoms weren't the same as what had been explained.

SPEAKER_00 22:13

Wow.

SPEAKER_01 22:13

There's different um drug studies. I think it was it wasn't even legal to include women in clinical trials until like the 90s, I think. So they did like experiments on ovarian drugs using men as the safety study cohort. I mean, there's just some unbelievable, disappointing, you know, history here. Um, but you know, we're in 2026, so you figure it shouldn't still be like this, and it is. So it's um it's time that we kind of change change the the dynamic here and change the.

SPEAKER_00 22:46

I mean, like I I'm kind of curious. Um another difficult question, but do you think that with that women talk about maybe a scientific problem, a finding problem, problem? I think I know the answer to the second question, but like if I think correctly, uh the kind defined dynamic is to build a platform, but platform is not necessarily just limited to um in the material, it's it's it's to be able to identify unlimited data.

SPEAKER_01 23:20

Yeah, I I would it's it's a loaded question because there's a lot of different factors that go into this. Um I would say that uh you know all of the above sort of fits the right answer to that. Um I would say so genomics really wasn't popular until the mid to late 90s, early 2000s, and genome sequencing didn't really become popular until about 10 years after that. You're talking about 2006, 2007, 2008. Um, and the cost was still kind of high at that point. And those are the technologies that really allowed cancer to move forward in that you know it with the progress that I described earlier. And so uh it's not only the last 15 years that we've had the tools where we could really accelerate the type of work that we need. With cancer, the invention of the imaging technologies changed a lot. You didn't have to wait until you could see or feel the tumor before you went and saw a doctor. And unfortunately, with these women's health conditions, you can't really see a lot of these conditions on with with imaging. So when you can't see it and the symptoms are very nonspecific, that's when some of the um the surrender, if you will, that they just I don't know what I'm supposed to do about this. The genomics gives us that the the clue that it allows us to see on the molecular level what's happening in with the biology, but there's not a lot of folks who really know how to do that and also can apply it to like a clinical, it's only again last 15 or 20 years. You know, my my education formally finished in 2012, and I learned how to use genomics for you know for clinical interpretation and clinical application, but it's a very specialized field, and I think to be able to get the kinds of teams you need in place to answer these kinds of difficult questions, deal with the complexity of the disease and the biology and the data sets themselves, it these are hard problems too. So you can say, well, I don't know why nobody came up with a drug for endometriosis sooner. Well, we don't know why why it happens. So why don't we try it happens? Because we haven't diagnosed it yet. We haven't diagnosed it properly, and so it's this uh self uh circular, you know, circular process where um or chicken and egg type of scenario where it's really hard to break in unless you can find a way to visualize or understand the disease. And but uh, you know, there's all this significant social component to it as well. That it's um you know, there weren't a lot of women in politics, there weren't a lot of women that were able to push this. Um, I highly recommend everybody read the AOADX report that came out after JP Morgan in January. Um investors were very skeptical about the power of women's health startups to make return on investment. And um there are three amazing founders of this comp this diagnostics company, women's health company, that decided to reclassify certain companies as women's health companies to demonstrate that if you just call breast cancer women's health, if you just call reproductive health women's health, all of a sudden it everything, you know, all of a sudden you're talking about billions of dollars in return. And so when investors give you the excuse, well, women's health doesn't make money, that's why I'm not investing, they can say, actually it does. Actually, it does. You just don't realize that we're calling it oncology, we're calling it you know infertility, reproductive health, we're calling it uh autoimmune disease. This is women's health, and it's something that needs to be um addressed in that way, and so there's a there's a fight there, and it's it's important that we try to make the most of this moment because it's getting the attention. You see, I think Melinda Gates just allocated a whole bunch of money for women's health and menopause. And um, I think uh Bezos' ex-wife did the same. And um, I during I think Biden's administration, they allocated a bunch of money for women's health too. We really need to kind of make the most of this moment so we can take advantage of it and and and get this thing uh moving ahead.

SPEAKER_00 27:43

And so I mean obviously the natural question for me to follow it is you have PhD in bioinformatics, how is AI helping you to accelerate the diagnosis?

SPEAKER_01 27:57

Yeah, good question. So um traditional bioinformatics looks at different kinds of signals uh coming from the body, and it tries to interpret them. But there are large numbers of signals. We're talking some cases that can be with RNA, like what I'm looking at is one of them is RNA, there can be 20,000 signals or more that you're looking at, and you need to try and figure out which signals are important, which signals are just background or noise, or or just inner, you know, they're they're confounding in some way. So uh that's one way. There are other uh there are other kinds of signals that can have millions, millions of different kinds of signals. And so someone can't just look for that data in spreadsheets and pick out from a million rows or or a million columns with hundreds of data, you know, hundreds of data points, what might be important. You have um so you have all these different markers, they all have different scales. So it's imagine if you're like looking, I'll use a baseball analogy. So if you're trying to compare like a pitcher's number of innings with the batting average, like they're two different scales. Batting average is a percent, the innings are you know a count, you know, a certain kind of count. So there's different formats for the data, and we need to be able to find a way to combine those together in order to make sense. So the AI, the machine learning, the traditional machine learning methods that are being used for this, they select markers of interest, they figure out the ways in which those markers contribute. So if you imagine you and your friend are moving a couch and you're really strong, and and your friend is not so strong, so you have to figure out how much is my strength contributing to moving the couch versus my friend, so weighting those things. And so the machine learning algorithms learn the weights for each of those molecules. Maybe there are certain cancer-related molecules that are really, really contributing to endometriosis, and then there are certain molecules, they only contribute to like 10% of the patients in a weak way, but they're still worth looking at. And so the AI helps us to combine those into a signature that then can be applied for new samples that we see, and to make a prediction that way. It's it's similar to the way Google image recognition. If you give it like 100 pictures of a cow and a hundred pictures of a sheep, it figures out okay, that the sheep have like fluffier wool, and the cow may have horns, and you know, you know, the the you know, the and the shape of the face is different, and it identifies like what features are most important for distinguishing between those images, and then you hope when you give it a new picture of a sheep that it can correctly predict that. And so uh it's about learning the molecules which are quantum quantified that are most important and contributing, and then testing it with a set that hasn't been seen before and see like how do we do? And uh goal for us is with the pilot study, we'd like to get at least 80% accuracy. It's very modest, but because we're only dealing with a 50 patient pilot, it's it's quite a low number. Um, early early signs seem to be showing that we're gonna maybe do a little quite a bit better than that, which was just promising. Um eventually would like to get the performance over 90 percent. And uh I think that's that's kind of the most important thing. And um, if we can get there in symptomatic patients, then you're you're talking about uh a change in the average diagnostic Odysse from like 10 years on average to like under one year.

SPEAKER_00 31:37

Wow. Um, so in order to accomplish this, what's the biggest bottom like just the ability to be able to acquire blood samples um and and inject it through through your platform and kind of determine like the biomarket and be continuously reinforced and be become more precise? Like, how can you accelerate the journey?

SPEAKER_01 31:58

Yeah, so there's there's a couple of things I've learned along the way. Um, at one point, uh I thought the blood samples were gonna be the bottleneck, and yeah, it they come at a certain speed, you know. Then you're not gonna get two, you know, 200 of them tomorrow. But the once you get part once you partner with the right surgeons, you get into a good flow and you can you can have things moving along pretty reasonably well. The chant the challenge that a lot of early stage founders run into in the biotech side. So with tech, you can sort of build something in your garage. You can coat it in your garage, you can like eat ramen noodles, and you not pay yourself for six months, and you can have some sort of most you know minimum viable product that you can then try to get some customers to adopt. With biotech, you need to show that it works, and the experiments can't be done in your garage. You need a lab, you need samples, and you need consumables that you can, you know, whether it's antibodies or uh other kinds of markers or or um test tubes or or or pipette tips, like all sorts of disposable things that cost a lot of money. And so finding a way to be lean about that and be responsible while still making sure that the science is good is probably the most difficult thing because you you're trying to explore an idea, not a prototype, an idea, and you're trying to get like a friends and family around to get to find that based off of you know that I can do this, and the idea sounds pretty cool, but we don't we can't just get in the garage and build something easily. And so usually the way biotech inventions come about is there's either someone does their PhD work and they spin it out of there, which they've been working for between five and eight years on some project, making like $25,000 a year under their professor, and they can do it that way, or they spin it out of a large company, or a professor can spin it out of academia as well. So, for someone like me, I took an unconventional approach. This was not my PhD project, and I didn't make it spin it out of a large company. So the largest bottleneck was probably a combination of financial and trying to figure out the best ways to run this as lean as possible while still making sure it was scientifically rigorous. And um we were meant we managed to do it. We originally thought we were gonna need about $750,000 to run the study. Um, we've done it. Uh we we should be able to finish with $150,000. We're only raising $250,000. So yeah, we we really were able to cut it down based off of um a significant contribution from the scientists we hired, um Papia Orti, who's just been amazing as well. Uh, we looked over the protocol and realized there were a couple of inefficiencies that we were able to remove, and that really saved us a lot of time and money.

SPEAKER_00 34:47

So um it's I think the story is extremely compelling, especially the size of the market, the each of the create the the economic impact also emotional impact. But yeah, yeah, it's you know, 50% of our investment, 50% of our honor is like if there is if there's an investor, that's an right investment. I mean, obviously, like how to biotech investors, but if you have somebody in mind.

SPEAKER_01 35:16

Yeah, so uh good question. So uh I mean we're we're closing our our friends and family round, you know, that that's that that's a little bit smaller level. We we've got enough to uh file our IP and and and finish our studies, and so the last funds would be used for a little bit of automation, a little bit of uh regulatory stuff. But the next round is what we're really looking forward to. So basically, since January 1st, we we've raised this small round, we've hired the scientists, we rented out the lab space in Palo Alto, it's a shared lab space, nonprofit Stardex Med. I'll give them a shout out. Um it's um dirt cheap lab space. We're spending like $1,300 a month or something for lab space where all the equipment is there. We we've been really, really lucky to be located in the Bay Area so we could do it this way. Um we uh and we've been making great progress. We uh we we sent out uh an update to to our to our uh investor and non-investor community in the last week. Uh we've sequenced uh nearly 20 samples uh so far of the 50 targeted that we've had, and uh the the results are looking quite good. So we envision when we finish the 50. Our target right now is July 1st to release the the pilot study information uh and and accuracy results based off of that cohort. At that point, we will raise a um a larger, much larger institutional round. So at that point, we're looking at women's health investors, whether it's VCs or angel groups, we're looking at diagnostic investors, folks who are looking in like the early detection space. Um so investors that are interested in that. We've we've had a lot of uh a lot of excitement from early conversations that we've had with with investors like that who say, Oh, we can't come back to us after July 1st, and we we want to see like what the what the results are gonna look like. So uh a lot of pressure on us to make sure it look that it's uh that it performs the way we we want it to. And you know, transparency is extremely important for us. So we want to make sure that um, especially with the the history and the diagnostic space and some of the um the stuff that happened with like Theranos and Elizabeth Holmes, we we need to make sure the reputation is is rehabilitated and and that folks are fully privy to to to to how the tests are doing. But yeah, so basically, like in about a month we're gonna start raising that larger round. And so the institutional investors that uh maybe have already done this, uh this space, or an investor that's looking to unlock that that women's health um inflection point. Uh, we've had some folks who don't necessarily do directly diagnostics, but they realized, oh, that this is this is a place like this is what happened with cancer. We can we can replicate this here. So this is worth this is worth throwing throwing uh a little bit of investment into so we can uh propel it forward. And uh now that we've we've all nearly de-risked the science, you know, but we're getting very close to de-risking that science fully, um, the next next part will be running a larger study and running a larger validation and um building uh our lab developed test. So another another benefit of this space is that you uh diagnostics don't really require FDA approval at this point. It's it's a um lab developed test um pipeline that allows you to go to market uh to get early adopters the opportunity to have the test, and uh you still publish papers with your performance, you still uh make sure that everyone's fully aware of how many samples you tested it on and what the performance was, but it allows you to not only get to market sooner, but it allows you to adapt the test. So if you run, we're gonna run the next study, is gonna be about 250 to 300 patients. After we finish that, we're gonna run, we're gonna release the test as a V1, but we're also gonna run a thousand patients or more validation study. And if we can tweak that test, make it go from 92% to 96%, we can implement those changes and incorporate them into our a V2 version, you know, a V2 version of the test and keep iterating. We can also build other applications into the test like adenomiosis, um, polycystic variant syndrome, which is now uh now called the MOS. Uh we can go into the infertility space, um, as well as post-surgical monitoring, so recurrence monitoring, which is what happens in cancer as well. So we envision this as a complete solution, not just for endometriosis, but for women's health early detection. And so by taking this uh non-FDA approach, we can keep adding new applications to the test as we go, and that allows us to reach more more women and help more uh help a larger market or help a larger population.

SPEAKER_00 40:15

How uh in that minority, it it would be a two-tunk if we can go from episode non-invasive uh diagnostic to something that like in this case something could be diagnosed earlier uh and dealt with earlier. How important you talked about like that you're blessed with the five that you in Powell out, but like how important is that you're based out of the Bay Area and that it's just such a confluence of of innovation and capital in the Bay?

SPEAKER_01 40:47

Yeah, I I don't think I could have done this anywhere else. Um I think uh I moved here because of so I you know in 2015, well, 2012 I visited for the first time here and I just fell in love with the area. I met some folks and always loved everyone's trying to build the next awesome thing here. And this is like the bio hub, it's the tech hub. But um a lot of people really trying to do ambitious things and try to change the world in good ways, not not only not only bad ways. And uh and I met a lot of these folks, and I realized that like this is the place I needed to be if I wanted to try and um implement an ambitious idea like this, and uh you know, uh a thing like Stardex, where you can rent lab space for that kind of price with all of the the equipment and the camaraderie there. Like I've become friends with so many people in the lab, and then they have this incubator just you know, next you know, right in the same space where every they have three or four cohorts a year where they try to introduce it's mostly for Stanford folks, but they allow visiting founders to to join if you have recommendations. It's just this ecosystem of uh entrepreneurship. And uh I'll tell you, like most of my friends and family around came from the relationships that I made there. It was just like some of the friends that I've made there, some of the contacts and colleagues who I've seen how they work, they've seen how I work, and we just talk every day about how we're doing the struggle, the pain, the the good things. It's all uh it's all you know combined in this uh sort of like an emotional support group for folks who try to do these these entrepreneurial types of endeavors. And uh and it's wonderful. It's uh it's something that I I wouldn't really trade for anything. And and of course, as you said, the funds are all here. So um, you know, I'm throwing I'm throwing a couple of events in San Francisco, some dinners on the topic of how the next big liquid biopsy is actually going to be in women's health, not in oncology. And um which is basically liquid biopsy is the term for uh blood-based or liquid-based can't uh disease detection, so non-invasive detection. So I'm hosting a dinner for investors you know in San Francisco to do that. It's it's hard to do that anywhere else. People can just sort of leave work and come to come to our dinner and we can talk about uh you know where where where those opportunities are and and what we need to have a lot of the financial things that we've just discussed, like where's the where's the return on investment, where's the uh you know the opportunity and value.

SPEAKER_00 43:28

So with with that context in mind, what uh is the uh metric or the signal that would give an investor confidence? Is it is it the the accuracy rate uh if if an investor look at mouth and dynamic what is the the the most important metric they want to look at?

SPEAKER_01 43:54

Good question. So uh I would say that uh there's four main buckets that we really That we look at our competitors and we say, How can we do it better than they do it? And these are the ones that I would encourage the investor to look at. So the first is blood-based approach versus some other kind of fluid or other. So there's companies that are doing modified types of biopsies, there's folks that are collecting menstrual blood as a marker, and you know, those are still somewhat invasive. You know, folks with endometriosis, it hurts to collect menstrual blood. Like a tampon hurts, uh menstrual cup hurts, there's a lot of user error that can happen there. Also, if someone comes to their doctor and they have abdominal pain, that's considered a medical emergency. And so is your doctor really gonna say, all right, we should go to the hospital, we'll test for gallstones and appendicitis, immune, you know, some sort of immune issue or infection. And in two weeks, when when you can test for endometriosis, then you can that's not realistic. Like you're not gonna wait two weeks, three weeks for for that to happen. So one of the aspects is like I think the peripheral blood, which is blood from your arm, that approach is going to be very important to for a diagnosis. The second one is um to be mindful of is does this make a binary decision, or is there is it a something called the traffic light system, which is a red, yellow, green? Red is you know, you have the disease, green is you don't have the disease, and yellow is we don't know. It's okay to have this, but your cohort for that shouldn't be very large. And there are a lot of companies who came out and they said we have 90% accuracy, but they don't tell you how large the yellow light cohort is, and if they don't tell you, it's likely 30%, 40%, 50% of the patients. No insurance company is ever gonna pay for that. And most folks who are desperate for a solution are not gonna pay out of pocket for that either. So that's another one. I would say that when you're looking at the biggest metric is is gonna be sensitivity and sp of the two are gonna be sensitivity, specificity. So the sensitivity is gonna be how many cases do we catch versus how many do we miss? So if there are 10 patients with endometriosis, can we capture nine of them? That would be pretty great. If we capture five of them, that's not so great. But then you have specificity, which is of the patients who are healthy, how many do we correctly identify as healthy? And so if again, if there's 10 healthy patients, can we identify nine of them as healthy? That's pretty as long as your incidence rate is okay, and I can explain that later. But if your sensitivity goes up, usually your specificity goes down, and vice versa. So it's it's a challenge that happens in machine learning and mostly in most statistics. If you try to make your accuracy on one side better, the accuracy on the other side gets worse. So oftentimes the question is which one do you pick? Would you rather miss a few cases of endometriosis, or would you rather scare people into thinking they have endo when they don't really have it? And so in cancer, this is a significant problem. They've decided to err on the side of avoiding false positives, so falsely diagnosing patients versus correctly diagnosing patients. In this space, I would think that it's probably better. That's probably a similar approach that we'd want to take. We don't want to send folks for unnecessary surgery. But oftentimes, uh the patients who have well, the patients who already have endo but don't know it will end up just going through the same process they're going through right now. And so it's probably better to err on the side of the specificity, making sure that the people that we say have endo definitely have it versus over overcorrecting. So that that's one of the keys. And then I would say the the the other key bucket is you want to make sure that the the AI algorithms are correctly testing on patients who have never been seen by the learning algorithm before. This is something that we've seen from certain companies where they it's like again going back to the cow and the sheep analogy with Google image search. It's as if you gave as your next, so you've trained it on a hundred of each of each picture, and then you give it one of the pictures it already saw. And so basically it can memorize, it can memorize the answer rather than actually knowing what a picture of a cow looks like. There are a couple of companies that have done this, and um, and so though that's something that investors also want to ask when doing their due diligence here is um what is your AI algorithm doing and what is your held out set, your independent data set, look like? And is it truly independent from the learning algorithm that you that you built? And so I would say that is uh really critical as well for them to ask about if if we're having the same conversation 10 years from now, Gregory, um what breakthrough do you hold that we're celebrating? My ulterior motive in this, so we said like diagnosis accelerates everything. So my ulterior motive is that by looking at all these they call multi-omics, but it's basically like all the different signals that we're looking at. By looking at these signals, I think we can find signals that either we haven't seen before that may be indicative of drug targets, or we may see signals that we have seen before have been identified as drug targets, but not necessarily for the disease that we think. So the biggest one that we see now is like Ozempic. So at first it was a diabetes drug, and then they realized, oh, this thing helps women and men, and everyone loses a lot of weight. And then they realized, oh, we're starting to see like in patients with Alzheimer's that when they're taking Ozempic, it's you know, or or any of the GLPs, I shouldn't just stick with Ozempic, that they are uh slowing down the previous that it's actually helping a little bit with that off label uh as sort of a uh you know sort of a collateral type of effect.

unknown 50:43

Yeah.

SPEAKER_01 50:44

And you'll if you if you look up endometriosis and Ozempic, you'll see that there's a little bit of promise there as well. And my hypothesis, uh it's starting to be, I think, validated a little bit, is that most of these, most of the most horrific diseases are inflammatory processes that are either uh out of control in one direction or out of control in another. And so whether cancer or cardiovascular diseases and strokes and diabetes and dementia and Alzheimer's, but also endometriosis, I think they all fit into this inflammatory type of process. Ozempic reduces inflammate inflammation, so of course it's gonna reduce inflammation for folks who have inflammation in their brain, it's gonna reduce it for folks who have inflammation in uh the urine lining or in other places. There's this uh overarching inflammatory uh theme. So it could be Ozempic or GLP1s, but I do think that we will find markers that will be more specific than that. Maybe it's just some kind of drug that targets osteoporosis. Uh who knows? I know that in the last six months, UCSF came out with um fascinating paper that linked 600 different diseases as comorbidities with endometriosis. Wow. So that means that patients with endometriosis are more likely to experience one of those diseases. And many of those diseases have nothing to do with gynecology. There were eye conditions, there were uh migraine headaches is the most common one, but there's eye conditions, there were certain kinds of cancers. So this this comorbidity thing means that this is a systemic disease, which means that there are likely drug targets that we can use that we just haven't figured out can be applied in that place. So is it possible that one of these drug targets, these drug targets and one of these drugs that's being used for some random disease that we never thought about could be applied for endometriosis? I think that's a way to faster track, that's a faster track to you already had the safety studies, you just sort of jump into uh a phase one trial, and in a few years you may have something that's actionable that goes beyond the standard treatment, which is hormone therapy, uh really severe hormone therapy, or the surgical option, which is either uh excision or hysterectomy, which is about 90% effective.

SPEAKER_00 53:15

Wow, wow, amazing. I I know that we're coming at uh coming to the end. Um my favorite question to end the to end the show is uh you can pick um what's the best advice you've ever received, or what's the kind of thing that somebody has ever done for you.

SPEAKER_01 53:39

I would say for the the first question, I would it would be uh uh one of my close friends uh he said that in in the entrepreneurial world you need to look for the side door that like the front door and the back door are not gonna open for you the way that the side door was. And when I was doing uh cancer cancer diagnostics, we were having this trouble because you need cancer samples and they're very expensive, and we didn't have the money to pay for them. And one day this this friend came to me and he said, What about like what if you tried to demonstrate this in dog samples first? Yeah, he's like, you know, you can get those samples for free. It's just this this this side door, the side door approach is has uh you know, it was such a genius. He's not a biologist, he's not a computer, he's a chemist. And he just came up with this idea and it was just so brilliantly simple. And so now like no matter what I do, I always look for like is there a way for us to kind of take that side door in? And um it's it's been eye-opening. It helped me find um it helped me find a lot of the the paths in in this current journey, whether it'd be getting the samples for free now or uh having a scientist on board and and having them uh save us all this money, like it, you know, getting the the passion involved, but also like find finding people who who can help in the right ways is has been like a really useful side door for me and uh for the team. And so that's been really great. And uh yeah, I think in terms of the kindness, I think there's just been so many, kind so much kindness throughout all of this. Um I've spoken with so many folks in the endometriosis community, uh advocates, policymakers, patients, and I want to hear everyone's story because it's all they're all slightly different, and they're all wanting to help. Uh, even ones the folks who really can't help. I've I found that like you know, the ways that I need help, that that community can help in certain ways, and then there's other ways where I that just doesn't work, but they all want to help, and they all really want to see this happen, and they really want to see this succeed. And I am trying to live, have this company live sort of practice that that way. I want everyone to win from this. If if I end up with a few less, you know, a few less percentage points of equity and it and allows everyone who participated to win from this, then like I'd like to repay that kindness in any way I can. And it's uh but it's been so much, I can't even I can't even name it. It's been so many of them. So it's been wonderful to see. And um I really want to pay it back. Yeah.

SPEAKER_00 56:30

That's been done. It it's it's unique to the biotechnology community in terms of like people being so um magnanimous and supportive of one another.

SPEAKER_01 56:42

Uh I think every community has some of that, and then there's always going to be uh you know the diversity of you know bad actors or folks who are a little bit less in that boat. I think I I just I try to operate with the way the world I want the world to be, and uh try to put that good out into the world. And I'm not naive, I understand you know that once you get into VC space, you have to be like act in a certain way and and be a little bit tougher. But I want to make sure that I try to like hold those principles and the values that got me here, and uh and hopefully it works. It it's uh I think bio has been good about a lot of that. I even when I was in my my full-time job, I remember if you just explain folks why things went wrong, most of them will accept the fact there'll be a little bit of a delay if you just you're just up front and you talk shop with them a little bit rather than giving them some you know nonsense about you know you know or jargony type of response. So um I've learned a lot from all these different worlds and even the corporate world, like it taught me a lot about um different kinds of people and and and and how how how to figure out like what works best for them, and and so I just learned so much, and I I I'd like to kind of put that back out there. And I I think there's people like that in all of these different spaces. I think you just have to uh find them and yeah, yeah. I think there's a lot of good good ones out there, and we gotta we should highlight those more.

SPEAKER_00 58:18

Uh that's uh that's a good note to end on. Um really enjoyed the conversation. Um thank you so much. Uh hopefully we can uh write the story around uh around it today and and uh um with the story driving read, hopefully we can find the writing better that that can back the journey back to MIP and uh drive drive secure uh my donor.

SPEAKER_01 58:46

Yeah, that would be wonderful. It was uh it was uh amazing being on here. Thank you so much for inviting me. Uh if anyone has any questions, uh you can reach me at Greg at milestone gx.com. And uh you can look me up on LinkedIn. I'm happy to you know set up a chat with anyone who wants some more information. And but yeah, thank you so much for having me on. I really enjoyed this, and uh I look forward to coming back to you when we have some results and hopefully we'll have something else to talk about.

SPEAKER_00 59:16

Absolutely. Thanks, Greg.

SPEAKER_01 59:18

Thank you.