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John: Welcome to another edition of the Impact Podcast. This is a really special edition, and we’re so excited to have with us today Dr. David Klimstra. He’s the chief medical officer and the co-founder of Paige. Welcome to the Impact Podcast, David Klimstra.

Dr. David Klimstra: Well, thank you, John. I’m so happy to be here. Really nice to be able to chat with you.

John: Well, happy to have a New Yorker on with me. I know you’re in Upstate New York today, but I know you do a lot of your work in the city and I’m so thankful for you joining us. I’m in Fresno, California today, but it feels like we’re together and we’re just having this nice conversation.

David: Yeah. Very good.

John: David, I’ve learned a little bit by meeting some of your colleagues and studying a little bit about what you’ve created, a Paige Prostate. I want to get into that today. It’s so important for our audience to hear. But before we do that, I’d love you to share a little bit about the Dr. David Klimstra journey. Where did you grow up? How did you want to become a doctor? And how did you even get on this path to start with?

David: Yeah. Well, at this point in my career, it’s a long story, but I can make it concise. I’m actually a Midwesterner. I grew up outside of Chicago, but I’ve been in New York for 30 years. So, I can talk like a New Yorker if I have to, but I went into medicine because I was always interested in science and developed a passion to provide care for people who needed it and specifically had an interest in cancer. So going through medical school and exposed to a variety of different specialties, that was at Yale, I became interested in pathology because I really enjoyed getting into the details of the disease and understanding why a disease happens. I’m also sort of a visually oriented individual. I think that certain people are drawn to careers that use their inherent interests and I always like the visual aspects of things. So, looking microscopically at tissues, which is what pathologists do, was very appealing to me.

John: Were any of your mom, dad, or grandparents doctors or in the medical profession before?

David: No. My father was a researcher and ended up as an administrator in a pharmaceutical company. So he was in a healthcare-related business but not an MD, but I had a family friend early on who was a pathologist that inspired me a little bit to go into medicine, and then I kind of said, I’m not going to follow Bernie[?] and be a pathologist just because Bernie was a pathologist. But as I went through the different disciplines, as I said, you kind of gravitate to things that touch your native abilities and interests, so.

John: I appreciate your humbleness, but you just sort of let it out in a very quiet way that Yale was your medical school. As I’ve learned over the years through my good friends that happened to be doctors as well in different specialties and they’re Yale graduates, Yale probably is the best medical school in this whole country, if not, not the world. So, congrats for that. That’s not just a throwaway comment that didn’t get by me, Doc.

David: Okay.

John: That’s a heck of an accomplishment and good for you. So you become a doctor, you go to Yale, you become a doctor. You start practicing in New York City, or where did you start practicing your [inaudible]?

David: Well, so after you finish medical school, you go through a residency and, at the time, probably the leading diagnostic pathologist in the world, a fellow called Juan Rosai, was the head of anatomic pathology at Yale. I did a rotation with him as a medical student. I got to know him and very quickly became aware that this is the person I wanted to train with. So I stayed at Yale for my residency, which was 3 years of anatomic pathology training to diagnose disease by looking at it microscopically and applying a variety of studies to the tissue to fine-tune things. As I was finishing my third year of residency, I was supposed to stay and do a fellowship with Dr. Rosai, and then he left Yale to go to Memorial Sloan Kettering. So I said, well, what am I going to do? This is the guy I was supposed to train with. He says, don’t worry, you can come with me. So I signed up in 1991 for a one-year fellowship at Memorial Sloan Kettering, and I stayed there for 30 years.

John: He was your mentor and is he still alive? Does he happen to be still alive?

David: No, regrettably, he passed away about a year ago and he left Memorial in 1999. So he was the chair of the department, After he left, I stayed there and I went up through the ranks, I became the chief of surgical pathology and, ultimately, I became the chair of the department in 2011. So for 10 years, I was the chair of the department at Memorial Sloan Kettering.

John: What an exciting thing. Now, you go to Yale, but you got to be the understudy to, maybe during the time of his life, one of the greatest pathologists on the whole planet. So, what a great blessing and honor.

David: Yes. I was enormously privileged to be able to do that. Yeah.

John: So now, you’re a pathologist, you’re obviously at the top, top, top of your profession. Thank God for these people like you out there doing the important work you did as just strict[?] pathologist. Where did you get the entrepreneurial streak to make you want to become an innovator and entrepreneur and go out and create Paige? For our listeners and viewers out there that want to learn more about Paige, which we’re going to learn a lot more about the rest of this interview, you could go to www.paige, P-A-I-G-E, .ai. You go to www.P-A-I-G-E.ai. So, where did you get this streak from, Doc?

David: So, yeah, it’s interesting how this evolved. When I became the chair of the department, my overriding mission was to try to introduce any useful new technology to help us better diagnose pathology tissues.

John: Okay.

David: This ran the spectrum from genetic testing, advanced protein screening and digital pathology. So digital pathology, basically, means instead of looking at glass slides using a microscope, you scan the glass slides. It makes a digital counterpart and you look at them on the computer.

John: Okay.

David: It sounds straightforward but it’s been very slow to penetrate into practice. The reason is because it’s an expensive transformation. The scanners that you need cost a quarter million dollars a piece for a large department. We had thirteen of them at MSK to scan less than the entire volume of slides that we produce. So it’s something where we need to develop tools to make this worthwhile. The so-called return on investment is challenging. So, what we decided to do was to hire engineers, mathematicians, and computer scientists to take these digital images. Because once they’re digital, you can now manipulate them. You can now study them in a way that you can’t do with a glass slide, using what’s known as computer vision technology to analyze those images. The computers can learn to analyze them in a similar way that a human will analyze them.

The difference is that you can feed a computer 10,000 of these things and let it work on it for a week and it learns from that huge volume. You can imagine how long it would take a human to accumulate that same experience. So we started doing this as a research tool and gradually began to realize that you could build these artificial intelligence models in a research environment, but they were not robust enough to actually use them. So how are we going to take this? The whole idea is to introduce these innovations into clinical practice. How are we going to take an innovation that we created in an academic department with limited resources and make it robust enough, get FDA approval, get it generalizable so it works in everybody else’s department? The only way to do that is to have a company that can raise capital and create the infrastructure to do this.

So, with my colleagues who cofounded Paige, we decided that we would do this and form a company, spin-off from Memorial Sloan Kettering that would use the data that Memorial had and the computer technology that my collaborators had developed to produce clinical-grade products that could benefit patients.

John: Let’s go break this down. First of all, there was a problem. You saw there was a void in the marketplace from just doing the analog typical way you did pathology historically and that you were trained to do, and just your own eyes, laying your own eyes on it. Now, you had a way to put it against other sample sets in a technological way and build a database that would allow clearer results, a clearer path forward for the machine[?] tested.

David: Yes.

John: So, when you say scale, obviously, you were able to build a great database at Memorial Sloan Kettering because of where you are and the volume of people you see. What did you mean on a meaningful scale level to make it a real business enterprise? How big did that database have to grow and how big did you have to get it for what you want it to be meaningful that would have benefit outside of just MSK, and also be able to then clear that other major hurdle of getting FDA approval?

David: Right. So, it’s a great question. I think that the fundamental issue with machine learning is data. The challenge to this is that you can teach a computer to discriminate two different things.

John: Okay.

David: A nice example that I’ve heard is you can show a picture of canine animals to a computer. Some of them are wolves and some of them are dogs. So you tell the computer, these are all wolves and these are all dogs, and ask the computer to figure out how to tell them apart. The problem is the computer did it, but when you dig down into it, the reason that it figured it out is because all the wolves had pictures with snow in them, and the dogs did not. So the computer wasn’t actually looking at the animal. It was looking at something else.

In order for these things to work, you need to train it with every conceivable scenario. So you would need 10,000 pictures of wolves, some of which would have snow and some of which would not.

John: Right.

David: We adapted technology to enable the computers to learn by themselves essentially. So, if you wanted to, for instance, train a computer better to recognize a wolf and a dog, what you would do is go into that picture of the wolf and circle just the wolf and say, don’t look at the background, just look at this, and same with the dog. You could do that for a couple of hundred pictures, but once it starts getting beyond that, I mean, you can imagine how much agony it would be for someone to go in and a little. So, what we’ve done is we’ve trained the computer to learn by itself. All it knows is somewhere in this picture, there’s a wolf, and somewhere in this picture, there’s a dog. But if you show it enough pictures over time and you let it iterate on this repeatedly, it learns, and that’s what we were able to do at Memorial Sloan Kettering because we have 25 million slides.

So for any disease, and we’re going to talk about prostate cancer, we have tens of thousands of slides of prostate cancer, which is a data resource large enough to train these very data-intensive models.

John: So, I have to tell you just a quick, funny anecdote. I’m not an AI person. I’m not an engineer by trade. I’m an entrepreneur. I happen to be asked to speak at a conference in the fall of 2019 in Armenia.

David: Okay.

John: So at this big wonderful dinner, I was invited to sit at a table and the last speaker was this guy, he got up there. He’s 6’3″ and I’ve never seen him in my life. He’s Armenian and he’s telling the whole audience that the future of medicine is AI and AI is going to be powering so many great solutions in the future.

David: Yeah.

John: Again, I didn’t know him. So after he got off the stage, it was a little mock stage, really, it was just this ballroom setting, but he had all of our attention. I was riveted, and I don’t know anything about this stuff. I was introduced to him after and that happened to be Noubar Afeyan. So, fast forward six months later and he’s on Bloomberg, and MSNBC, and CNN with Moderna, I said, oh, I guess I really knew he was out to something back then. But, obviously, we didn’t even know about COVID back then. So then when I read about what you did and how you’re also taking AI, and now applying more, how do we want to call it, Doc, is it optical AI in your situation? Is it more optical-enacted AI?

David: Yeah. Computer vision is a good term for it.

John: Got it.

David: It’s training from images.

John: Right.

David: Yes.

John: So now, as you continue to scan more and more images, your database only becomes better, richer and smarter at what it does, right?

David: Correct.

John: So let’s talk about the journey. So when did you get together with your cofounder partners at MSK and say, okay, we’re going to incorporate or create an LLC or whatever you did, what year was that?

David: We started working on this in 2016, I believe. I mean, these ideas percolate a little bit. You’re having poppy[?] together and you say, geez, this is going to be tough to do in this academic environment. We need to raise some money and power this up and make a company. Those discussions went along. They weren’t going anywhere until we met Norman Selby. He’s old Mackenzie[?] guy. He’s a business guy. He’d been involved in founding many companies. He loved the idea and the three of us work very closely together with some other colleagues to figure out how to actualize this.

John: Okay. So let’s take a pause. Was Norman a client, a patient? Is he just a friend just through social networks?

David: No, he’s actually a board member at MSK. So, he was familiar with what we were doing at MSK in general.

John: Got it.

David: Once we all realize that there was something here, we started talking about, how can we move this forward? So that was maybe 2016.

John: So how much money did you have to raise to start to see if your vision can get traction?

David: Well, how much do we have to raise is one question, how much we did raise is 25 million.

John: Perfect. Okay, that’s what I really meant but you’re right. Did you really want to raise more, was that the point? Did you really want to…

David: Well, the company’s raised now 220 million after three funding rounds. So, yes, we did want to raise more and we did raise more.

John: But you got it. But like you and I are of the age that we remember the old stone song, sometimes if you don’t get what you want, you get what you need. You got what you needed, obviously, right?

David: Yeah. We did. Yeah, certainly enough to get things rolling. Yeah.

John: Got it. So then how did it play out? Where in the journey did you know, when you went to bed at night, that your original coffee chat vision with your colleagues was really going to work? You really were going to eventually get this to where it really was going to change the world for the better.

David: Well, so that’s a great question because a lot of what we did was a projection. Even at the point where we were doing fundraising, I mean, we had created these computer vision models. But as I said before, they were created in a smaller scale way in an academic environment. They had not been raised up to the level that they could be generally used. So, people had to go bet on faith that the technology would work, that the data were adequate to train these, that they could get FDA approval. When did I realize that this was a reality?

John: Yeah.

David: I mean, you could say it was two months ago when the FDA finally approved it, but it was earlier than that. I mean, I was a skeptic. I am a skeptic. I mean, pathologists are skeptics by nature, and I was like, is this really going to work? But we kept trying it, and as you started to see the output and the results, it was very encouraging and then we started running clinical studies to see how pathologists used it and they said, wow, this is great. We love it. It’s helping us. So it wasn’t like a lightning strike, but it was a gradual realization sometime in the last two years that this is actually going to be possible and it’s going to work.

John: So you took your results and you packaged them in the way that they need to be packaged and you deliver them to the FDA. How long did that process take and what was their general feedback? I know you just were approved just a mere few weeks ago. Explain that lead up of packaging it, sending it over, and then that waiting period and I assume there’s a Q&A period as well?

David: Right. So our journey with the FDA was not the typical journey. We applied for and received what’s known as breakthrough status, which gives us special access. Typically, when you apply to the FDA, you present them what you want to do and they say, no, this is not right, you need to fix this, and you go away and you come back. It’s not a dialogue. When you have breakthrough status, it gives you special access and sort of a sprint period when you have a lot of one-on-one discussions with them and they advise you on what you need to do. This is very important because our device was the first ever to be considered by the FDA for artificial intelligence in diagnostic anatomic pathology. There was never any other approved device prior to ours.

So we had to work with them to devise an appropriate study that was statistically rigorous, that considered all of the different scenarios. Diagnostic pathology is a very complicated endeavor. It’s not like a blood test, where you put in a sample and out pops a result. It’s an interpretation of structural alterations in a piece of tissue. So, ensuring that a machine can help a pathologist do that better requires extraordinarily rigorous validation. Actually, you asked me how long it took. It took three years. We started in 2017. The first time we went to the FDA, we are just saying this to you, we didn’t really know what we were doing, but they didn’t either. So it worked out well.

Typically, that first visit to the FDA, they have a couple of people meet with you in a conference room and say, okay, well, here’s what you need to do. We showed up there, we’re fifteen people, which was a testimony to the level of interest the FDA had in looking at this kind of a product. They’ve never seen one before and they were really curious. They sent people from radiology, pathology, other disciplines to come in and look at this product. So, we were honored to be able to work with the FDA on this to help establish the parameters that were established for our approval. Now, set the roadmap for anyone else who wants to bring a similar product. So they’re going to have to meet the same standards that we did. It was quite a journey.

John: So, now, you get approved. This product you’ve created at Paige is called Paige Prostate.

David: Yes.

John: For our listeners and viewers who want to find Dr. David Klimstra, his colleagues and the great work they’re doing at Paige, please go to www.paige, P-A-I-G-E, .ai. Paige Prostate, explain the differential level. Now that it’s approved by the FDA, what is this going to mean in the months and years ahead for prostate pathologies getting diagnosed and creating a path forward for the patients that are lucky enough to have this applied to their pathology?

David: Yeah. So, the important thing to understand, as I said a minute ago, is that pathology is an interpretive discipline. So, some things are subject to professional judgment. It’s also a matter of finding a needle in a haystack. So I don’t know how to explain this without some graphics, but you can imagine you’re looking at a microscopic slide or that you put it on your computer or not. It’s like looking literally for a needle in a haystack. There’s an acre of real estate there and somewhere within that real estate, there may be cancer, and it can be extraordinarily tiny. So because of these factors, the practice of pathology is imperfect. It’s like any other judgment. Sometimes the judgment is spot on. Much of the time is spot on and sometimes it’s not, and sometimes you find everything that’s there and sometimes you don’t.

So the reason this is so impactful is because there are things the computers are better at than humans. Looking for the needle in the haystack is one of them. So, if you have an acre of real estate, a computer can look at that in 7 seconds and tell you if there’s something there, once it’s trained to recognize what it’s looking for. That’s what Paige Prostate does. It looks at the entire slide and it points out for the pathologist any areas that it thinks are likely to be cancer. Then the pathologist can focus their attention on those areas. If they’ve looked at the slide already, they turn it on and they realize, oops, here’s an area I didn’t see or here’s an area that I thought was maybe suspicious, but now that Paige Prostate has pointed out, I realized that this is cancer.

So, the benefit to the patient is it improves accuracy and improves detection. It reduced the number of diagnostic errors in our study for the FDA by 70%. So, it’s not an autonomous diagnosis. It still requires the pathologist to look at that image and say, yes, that’s cancer. Or maybe I need to do some additional studies.

John: Right.

David: But I realize that there’s something here that I should look at more closely.

John: Wow.

David: But it works with the pathologist, it’s an additional tool to help them be more accurate.

John: So, it’s 70% more accurate. Help me because I’m not a doctor nor do I follow FDA besides what we’ve all been following with COVID. Is 70% literally off the charts when it comes to improving and creating a compelling reason for them to approve you? Does ten usually get drugs passed if it was a 10% and you blew the doors off? Give me some sort of context here.

David: Yeah. I wish I could tell you that there’s a magic number that they would approve.

John: Okay.

David: There is not. Obviously, it has to be statistically significant improvement which [inaudible].

John: Got it.

David: It also has to prevent going the other way, right? If it finds more cancers, but it also calls things cancer that aren’t, that’s not good, right? So, it’s got to be balanced and it was, it did not result in false-positive diagnosis, but it did eliminate by and large the missed cancers. That’s huge for patients. There’s so much that flows from pathology diagnosis. People don’t realize how critical pathologists are to their treatment. Their doctor is making a decision based on that pathology report and the doctor has no basis to question it, right? They can’t look at the slides themselves. They take the pathology report and that is dogma from the standpoint of dictating treatment.

John: So you get approved, Doc, and I’m sure that was a happy moment in your career and your life. Obviously, it should be, and what a great thing you’ve done with your cofounding partners. The champagne, the bottle cracks open, but now the Paige is turned.

David: Okay.

John: What’s the future? I assume one of the greatest opportunities and challenges that you’re facing now is, how do you now democratize this great technology that you’ve created?

David: Yes. It’s a huge challenge, bigger than you would think, because in order to use this, you have to be in the digital environment. They can’t use this with a routine microscope. You have to have digital pathology. So, in order to use it, we need for the pathology community to embrace the digital format. Sadly enough, as I said, this is an expensive transformation, it’s going to take a lot of these kinds of tools for pathologists to say, you know what, I want to be in a digital environment because I can use Paige Prostate. I can use Paige Breast. I can use this detection system the Paige built that will tell me whether this colon cancer is a bad one or not. We’re working on a whole fleet of these things.

So, what’s next is that we have to get to this tipping point, where these tools are so powerful, that the pathologists want them and the doctors want them and the patients want them. The patients say, I want to go to a place where I can have computers help with the diagnosis because I’m confident this will be more accurate.

John: Right.

David: That will push the field to embrace the digital environment that it hasn’t done so far.

John: So, I assume given your status just as a leading pathologist at MSK and around the world, there’s some sort of pathology annual conference that you get to go to. This is the pitch you’re going to be doing there. What is the numbers? So if I’m a pathologist at Cedars-Sinai in LA, which, of course, is another great leading institution, et cetera, what do I have to do? Is it just me convincing my partners, let’s buy some of these machines, or how complicated or simple is this whole, basically, generational shift now?

David: Yeah, it is complicated. I’m not going to trivialize this. We did this at Memorial Sloan Kettering, started it in 2008 and it’s still going. It’s a culture shift.

John: Got it.

David: It’s possible to simply dive into the deep end of the pool and say, I’m going to buy a bunch of slide scanners, I’m going to plug them in and we’re going to be digital on January 1st.

John: Right.

David: Most places don’t do that. They buy one. They use it for research and teaching and they gradually get used to how to use it. Then they buy another one and then they say, maybe we should have some of these slides digital. It takes a long time. So, the real impediment is the return on investment. It’s not just the pathologist, you have to convince the administrators and that’s your partner, they’re the ones with the purse strings, right? So, we have to be able to show that we’re going to be more efficient. We’re going to be safer. We’re going to have better patient care. We’re going to get reimbursement for doing this. All of these economic arguments that will allow administrator to embrace the cost of the transformation. That is a community effort. That’s something that there’s societies, there’s a digital pathology society that works on this, the College of American Pathologists and all these advocacy groups that are helping to build the use case. So we can say, okay, let’s do this.

John: Got it, understood. Obviously, the total addressable market is massive here, not only in the United States but around the world. If I’m a pathologist and I read your success rate here, how you got FDA approval, and I’m listening to this podcast and I’m listening to you say 70% more accurate, just from a business standpoint, from an administrator, a business standpoint, from a doctor, like you said, the whole ecosystem of people that are involved, it sounds like from the ground up, clients like me, patients like me should be asking their doctors, when are we going to get this? Then once they get it, once the pathologist at Cedars gets it, do they or immediately they won before they build up their own database? Are they able for just, say, Paige Prostate, are the urologists and the doctors at Cedars able to tap into your database in terms of getting accuracy from day one when it comes to the Prostate?

David: Yes.

John: Wow.

David: Because the basis for this FDA approval is that this product has been trained on 30,000 cases from 800 different institutions, so it generalizes.

John: Got it.

David: This is not something that you plug in and it continues to learn from your cases. It’s already trained. So you plug it in, and on day one, you can turn it on and it works in the cloud. So, by the time your digital slides are ready for you to look at, the algorithm has already run and it will immediately show you the results. Yeah.

John: So Paige Prostate is off to the races. Like you said, you raise a lot more money now for the socialization process for [inaudible] out there.

David: Yeah.

John: But, you’ve developed a technology just like what we’ve read with Moderna and all these other RNA companies, Pfizer, that has other applications. A couple of minutes ago, you mentioned Paige Breast. How many other pathology subsets that have such high stakes and are such large diseases, such as breast cancer, prostate cancer, and others, will you be able to take your great Paige technology and apply it towards?

David: Another great question. I mean, people think of cancer as one disease and it’s actually hundreds of different diseases. Even within an individual organ, breast cancer is not breast cancer. It’s ten different subtypes of breast cancer. Every different organ and every different subtype is special and requires training and requires significant numbers to train the algorithms. So, the answer is that, in theory, you could build an algorithm for every different kind of cancer and every different diagnostic scenario about the cancer. It’s not just diagnosing the cancer, it’s describing what grade is it, how severe is it, how far has it spread. Does it have any other features that we need to know about that might predict response to therapy or prognosis outcome?

So, in theory, you could develop hundreds of these. In practice, you have to pick things that are going to be beneficial to a lot of people. Prostate cancer was the first one to start with. There were some economic reasons behind that. There were some simple workflow reasons behind that. Breast, obviously, a very important cancer that we need more information about who needs treatment and who doesn’t need treatment, what kind of treatment they need. Lung cancer, colon cancer. I mean, the common cancers are, obviously, the ones where it would help the most people.

So, we already have on the roadmap about four or five different other cancer types. Then within those cancer types, we have specific questions that we’re trying to address that are relevant just to that cancer type. Then on top of that, there are some issues that transcend every different kind of cancer. Probably a lot of people have heard about immunotherapy. It’s the hottest new thing. Yeah, okay. So, even though therapy is amazing, it can completely eradicate a cancer, but it only works in certain cancers. How do we figure out which ones those are? That’s a very difficult question and we’re struggling to do that. The answer is that you devise different so-called biomarkers that you can put on the cancer and say, okay, this one looks like it’s going to respond. This one looks like it’s not. By the way, you want to know which ones aren’t because these drugs are extremely expensive.

John: Right.

David: Right? So, what if you could do that with a digital algorithm like we’re developing, just from the routine mythology and say, this is going to respond in that? What if it was applicable, no matter what type of cancer you have? I mean, that’s the kind of thing that we are very excited to do working on because it has such broad applications, both commercially and for patient care.

John: So exciting. So now, Paige Prostate approved, when do you start moving down the road on the other verticals to start working towards building up their data sets that [inaudible] towards approval as well?

David: Luckily, for us, the data that we have is licensed from MSK. So we already have four and a half million slides digitized, which is, as far as I’m aware, the largest collection of pathology data in a digital format. So we were already moving well down the road on many other things. Breast will be the next clinical tumor type that we’re developing and the algorithm is more complicated because there are more questions to answer about breast biopsies than prostate biopsies. So it’s more facets to the algorithm. But many of these are already in progress, there are teams working on them. The data are already ingested at Paige and being analyzed right now. So, we’re hopeful that we’ll be able to release a number of new organ-specific algorithms within the next year.

John: Doc, how many pathologists are there in the United States? When you’re looking at your universe of how many people that you’re hoping to convince, this is the right way, this is the better way to go and this will help us be better at our profession. No such thing as perfect but this will help them and the doctors that they serve and the patients that you serve be better and come up with better results. How big is that market both here in the United States and around the world? Is it just…

David: Well, and that gets at a very important point. So there’s thirty some odd pathologists in the United States.

John: Okay.

David: But we have a much greater number of pathologists per capita than many other countries. Surprising, some of these other countries, like Germany, has a much lower proportion. If you start looking at underserved countries, it’s almost unbelievable how few pathologists they have. Four or five in a whole country. So, we use the word democratize and we use that word a lot because this technology isn’t intended to be a standalone diagnosis.

John: Right.

David: But, where you have no access to pathology care, it has the potential to be amazingly impactful, just to help triage patients and get them into the right general bucket. So, the market size, global market size is enormous. You look at the number of cases of cancer every year in the US alone. If we can provide additional incremental, useful information on even a subset of those, that’s billions of dollars in market.

John: As a businessman, now, taking off your doctor hat, what’s the next step? Do you have to raise a lot more capital or do you access the public markets because now the public markets seem so much more post-COVID, which we’re moving towards a post-COVID rolling? It seems like the investment world, the equity markets are very excited about now more health care, good health care, preventative health care. When you’re talking to advisers and other smart people in New York and other people that you have access to, which is big, I’m sure, what’s the next step as a business enterprise?

David: Right. Well, I’m not businessman, by the way, I’m a doctor, right? I’ve gotten into businesses a little bit, but I’ve heard it said that any startup with revenue is clearly not going in the right direction. But, that being said, I think at this point, getting revenue is actually something we should be doing. There’s many ways that we can do that by using the product we’ve built and by working with other organizations who are interested in the space.

John: Right.

David: Partnering with pharmaceutical companies and other testing laboratories to implement our technology in assessing their patients for drug trials or predicting outcomes, and things like this that are being done. So, that’s certainly one objective. The other objective is to push this digital adoption that I told you about it. You can’t use our products unless the laboratories are digital, and we can’t get more data until they’re digital. So, it’s a kind of a catch-22 that we have to help them get into the digital environment and then the data available to develop and validate these will grow exponentially. So that’s clearly part of our goal and we partner with other companies in this regard, who build the scanners that scan the pathology slides, because they have the same ambition that we want to make sure that this technology is available for every patient. So that’s the immediate ambition.

John: So after prostate, will come breast, and after breast, will come colon, you think, in terms of your subsets?

David: Yes.

John: Wow, that’s so exciting.

David: I didn’t mention but there’s bladder in the mix there. It’s a less common cancer, but we had another reason to work on that so there’s bladder cancer algorithm. We’re interested in almost every different major tumor type. So there are other things in the pipeline, but the ones you mentioned are the closest.

John: So exciting. Doc, this has been great. I want to let you have the last word before we have to say goodbye for today, but we’re going to continue to have you back on just to share this incredible journey and these important breakthroughs in medicine that more people should learn about and ask their doctors about and actually request, so they have the best available health care to themselves and diagnostics to themselves like we all want. So, I want to give you the last word on the months and years ahead for you.

David: Yeah, thank you so much. I mean, it’s been a great conversation and I think you’ve drawn out a lot of the important facets of what we’re doing and why we’re doing it. I mean, pathology has sort of struggled for a long time because nobody knows what we do. It’s behind the curtain almost entirely. I think my closing comment would be that we’re in possession of enormous quantities of data in medicine. A lot of it comes from pathology. Genomic testing, genetic sequencing of tumors. A lot of these studies that we do are generating these huge data sets, almost so much that people don’t know what to do with it. So our ambition is to find a way to bring all of these big data sets together in an integrated fashion that best informs treatment, incorporates everything. Even radiology, laboratory testing, the digital images I’m talking about, genetic sequencing, and computers can integrate that together and produce a much more informative assessment than any one of those technologies by itself.

So, if you ask me, where is this entire field headed, that’s what I’m going to say. It’s big data analytics that will pull all this together and make it much more manageable. So that’s my ambition. I want to see pathology leading in analyzing the data related to patients’ cancers and other conditions.

John: Dr. David Klimstra, you’re exactly the reason why we do this show. You’re making a huge impact. You’re making the world a better place. We’re all so grateful for you and all the work you’re doing at Paige. Again, for our listeners and viewers that want to learn more about what Dr. David Klimstra and his colleagues are doing at Paige, please go to www.paige, P-A-I-G-E, .ai, paige.ai. Learn more about all the great technologies, Paige Prostate and the other great subsets that the Doc is working on. Doc, we’re going to have you back so you can continue to share your success and journey on making the world a better place and making us all have a chance to be healthier and live better and longer lives. Thanks again for all you’ve done.

David: Thank you, John. It’s been a pleasure.

John: This episode of the Impact Podcast is brought to you by Closed Loop Partners. Closed Loop Partners is a leading circular economy investor in the United States with an extensive network of Fortune 500 corporate investors, family offices, institutional investors, industry experts, and impact partners. Closed Loop’s platform spans the arc of capital from venture capital to private equity, bridging gaps, and fostering synergies to scale the circular economy. To find Closed Loop Partners, please go to www.closedlooppartners.com.