Oleg Grodnensky, Priveterra Capital - AMOI Studio Interview | LSI USA ‘23

Priveterra Capital is focused on disruptive opportunities in LifeSciences.
Oleg Grodnensky
Oleg Grodnensky
Managing Partner, Priveterra Capital


Ben Glenn  0:10  

My guest is Oleg Grodnensky at LSI 2023. Oleg, thanks for coming by the studio.


Oleg Gradnensky  0:18  

Thank you, Ben, great to be here.


Ben Glenn  0:19  

So you're going to be leading on panel. Day two. So this is day one at LSI. Tomorrow, what's your panel going to be about?


Oleg Gradnensky  0:27  

Well, we, we want to talk about data and how data particularly works in businesses, and how data is really the driving force of certain businesses and really bring up opportunities that are, you know, companies that we particularly from the fund perspective, thought that they're really going in the right places, as far as sort of their strategy and their business proposition within the healthcare continuum. And, you know, first off, I would say that this is sort of a new entrance into the LSI. Because primarily, we, you know, been here, this is my third time, most companies are kind of in the Series A series B around and sort of early in their commercial path, and wanted to bring more sort of more mature companies that have been around half, I would say, around 20 mil and trailing revenue, Mark, that, obviously, you've been done, gone through commercialization, product launch, and all the headaches that sort of come with it. Before you kind of get skin.


Ben Glenn  1:31  

I liked the idea that you're bringing companies that are more mature to sort of round out the early stage flavor that's at LSI. So what is it about the mature companies that they've been through that you think can help other entrepreneurs get ready for that stage?


Oleg Gradnensky  1:43  

Well, I think it's kind of I wouldn't say necessarily it's, you know, learn from other people's mistakes. But I think it's more about hearing, you know, their stories, how they were originally developing one technology, and then how data came into play that really made them rethink and sort of be the central piece and a cornerstone of their development strategies that help them essentially pivot their plans and commercial launch. And I think that when one of the things that I saw here at LSI, for last couple of years, when you talk to folks that are developing robotic solutions, when you start talking to them about well, what are you going to do about software? What's your strategy? And you know, most people just don't have an answer. Because for the most part, they're coming from a very different industry that coming from either it's an engineer, that's sort of a hardware person, or whether it's practitioner, and you know, they're not really sort of thinking that it's not just creating a device. It's actually everything else that this device does, and all the data points that this device produces and how that would actually influence how that device will further advance in its development stage and whether the commercial proposition that this device offers, is actually viable or not. And so most cases, I would say, it's always, as we've seen from, you know, most consolidators right now talking about, you know, strike from Tronic, etc, or Zimmer, for instance, I would say that, for them, it's really creating a data source that constantly sort of circularly fits back into its own system to provide to essentially provide more input, and then develop further and further a new proposition, right. And then whether it's additional software development piece that sort of gets back and then an add on to the existing product, or it's better improvement, right? So, so it's sort of, I feel that we're in an ever changing environment today, where you don't really have an answer. Because, you know, we've we look back at human genome, it's been, what, 20 years or even more, and we're still, we still still still still sort of haven't figured it out. Right. I remember when I was on Wall Street back in, you know, 1999, we thought we're gonna have, you know, there are companies going public at the time that had, you know, protein sort of chip that would enable you based on your DNA to tell you what kind of medicine you need to take based on your profile. You know, that was 24 years ago, right. So, so I think, you know, people tend to project a lot in most cases. And, you know, I think what, what, what most folks realize that it actually takes a lot longer, it takes a lot more grit, and it takes a lot more to develop to deliver a product that's going to stick that will have a customer base, and you constantly need to be developing and innovating. You can't sit still because you get a lot more data inputs. And those who actually are able to process and understand what that data does, will be probably in a much more advantageous position than the ones that are just sort of stay sitting still and monetizing their existing customer base So I think it's really critical to adjust the business strategy for any particular product and sort of, not to say that look, I got data, I'm going to do AI, I'm going to do you know, algorithms and whatever you want to call the sort of jazz worlds. But I think it's, it's really about actually delivering something that works. That feeds the system back into and provides critical output that actually has patient outcome. I think if you look sort of at the investor capital, that historically, you know, there's a lot less capital going into med devices than it's actually going into biotech, right? It's a very, very simple formula. If you look at Big Pharma, I would say 80% of products that are blockbuster drugs actually, were acquired at certain point, right? They're never actually generated internally. So and from the investor standpoint, you know, you always have this upside by investing into biotech company and consensually gets acquired before it goes commercial. Right. So and there's been a lot more acquisitions, obviously, it's got a feeling a whole system and medtech's been very different. And primarily, it's always sort of helping the big guy with, you know, with with tuck in acquisitions and sort of solving a little issue that may be incurred improves the margins, or does something better, and really didn't account for availability of technology that is essentially becoming very accessible, and essentially a commodity today, right. And it's becoming more and more ability to calculate and ability to sort of process data is increasing tenfold, or even 100 fold. And as a result, sort of combining device or procedure with data together, and then seeing, you know, preoperatively, or operatively, or postoperatively, and how that all kind of correlates in one ecosystem, and how all these data points correlate to each other, will really create an opportunity to actually derive better outcome. And also, you know, one of the things we realize talking to a lot of physicians and also to health systems is that for them, they're all focused on cutting costs and providing better outcome and essentially, refocusing their strategy after COVID to actually worry about the patient. And I think that's the driving force. And you know, second wall is so so the budget deficit currently half 31 trillion, big part of it is, you know, Medicare and Medicaid, so and that's not going anywhere. And it's not like our aging population is getting smaller. So I think, so to me, I feel this is the the opportunity to actually have direct impact from the procedure. versus, you know, a therapeutic, whether it's a it's a drug, or you don't know how it will react, right, that takes time here, it's very direct impact. It's very direct correlation between the procedure, and then how that actually correlates into patients, patient cells at the end, right? So you can track that entire process and understand what works, what doesn't, how you can improve, that essentially enables you to do better faster, and not make mistakes and to make system more efficient. And I think together, I just think it's it's a it's a very large data equation that is not solvable overnight. It's correct.


Ben Glenn  8:44  

So as an investor, how do you how do you find those companies that you think are best able to sort of be on that trajectory? What's the is there a secret sauce? Or is there a combination that you look for that that tends to work out better? What would you tell them?


Oleg Gradnensky  8:58  

Look, I think it's a combination of various factors, right? As an investor, you always have to think, view, how do you sort of how do you move from this point to the next? And you know, what's your exit? And as we have seen last year, there was no med tech IPOs periods in 2022. And, you know, I don't know how this year is gonna look, but it's very challenging, right? And then when you sort of start thinking, what's my path to exit? You have to consider all options, right? And of course from and you have to rely on fundamentals. I think that after pandemic, I would say in 2021. Everyone sort of lost their fundamental hat, because it was sort of primarily as you've seen in markets we had, you know, flying taxis we had we went to the moon and back And so and I think that the retail capital that's fueled this craziness, essentially, you know, resulted in the huge pushback today from public investors to really be sort of more conservative than they should be. And essentially, putting everybody back in kind of, you know, very, very bad places, I would say, right, so, but it's still cheaper to raise capital publicly than it is privately, because everything sort of triggers down, right. And so I would say that, you have to always kind of take into perspective markets, right. And that's why, for the most part, a lot of folks just sitting back on their capital and waiting today that and by the way, they've raised record amounts in 2021. So I think it's really has to be driven by profitability, by margins by TAM. And of course, you know, the management team, of course, that's, that's the key. So and it has to be sort of the kind of position the business towards more of a SaaS model than it is towards device, it's almost like, I would say, you you have a device is a is a is an invitation to a SaaS model, it doesn't work the other way, for the most part. So. So you have to be in a position to monetize, and continue to develop and in advance and improve the products we currently have. And then it sort of becomes this sort of circle, where, in my mind, I think, you know, medical technology will become essentially a SaaS model over time.


Ben Glenn  11:41  

It's very interesting, I love the combination products for Pharma. And I just feel like if pharma knew, like the things that have been used inside of, you know, that we take for granted and device that are trusted, tried, proven, and many of them off patent, and use that as a delivery vehicle. And so finding a way for now to say, well, now we're going to deliver data, we're going to deliver a better AI, or we're going to fuel AI, because we got devices that we know, can work that are therapeutic and are going to meet the clinical need, but it's also getting more information about the patient could be a whole new age of medical device.


Oleg Gradnensky  12:22  

No, you're absolutely right. And I think this is definitely, you know, I would say a couple of years ago, when you had a device that was collecting data, it was almost impossible to agree with the hospital to actually have any data back. And now I think everyone realizes that that's a common currency for our progress for a future because everyone is interested to share data in order to provide better outcome because everyone benefits provider payer, and the patient's at the end of the day, right. So it's almost an end, of course, you know, we have data privacy laws in this country, particularly when it comes to healthcare data that are the best, right, that's where you start. Everyone talks about data privacy in other areas, but I think that's a very tough one to go back from so but here, we already have an ecosystem that protects patients data, and then the working securely with information allows us to develop and create future directions that could help improve outcomes, right. And I think you kind of have to sort of create your own real estate first and establish your, I would say, call it plumbing, when it comes to software and infrastructure and being positioned to take advantage over whatever you find. It's almost you know, it's I guess, the gold rush you buy the territory and and you say, Okay, maybe I'll find gold here, right? But if you don't establish your territory, if you don't establish infrastructure, I think you're going to be at the loss so I think any company today needs to really focus on the data aspect of their business because there's always one there's always a concern or you know, everyone is always focusing on profitability, everyone is focusing Okay, what if I do this what happens right? If you're trying to sell someone a device, you have to think through what that means in terms of you know, in terms of bottom line right, and how that's actually going to impact whatever process you undergo right and and I think that's what sometimes folks are not really thinking it through effectively. And I see that quite a lot when you know, your review got your TAM wrong. You haven't thought through you know, there things that you just don't know that others are doing. And it could really change the game so so it's it's it's not easy to be an intrapreneur don't get me wrong. I think it's very tough. And, you know, but you have to sort of think through a lot more today than you had before. Because previously, the system was, didn't have this data aspect as you do now. And then that becomes this huge ocean, that essentially everyone is swimming in it. And you know, you're going to be eaten, or you can eat someone, right. So it sort of gives you an opportunity and risk at the same time. So we have to think about those things. Now, when you start something


Ben Glenn  15:28  

like that, you bring me think of the ocean. And I love the analogy of the blue ocean and the red ocean, you know, so that blue ocean is where it's just you clear sailing. And you can, you can go as far as you like, the red ocean is where there's all that competition, there's all that churn, there's all the other fish. So it sounds like your advice is to help these companies, you know, get your device, find your area, you know, stake out your claim, find that blue ocean, and then get the data you need to feed the rest of it as your device propels into the system.


Oleg Gradnensky  16:00  

Yeah, no, I think it's a, it's their way, it's kind of like the right way to think about it. But at the same time, there's no such thing as a permanent blue ocean ocean, because it's a matter of time, you only buy yourself certain amount of time, right until somebody else comes in. And I think today, every single big company, we're talking about Google, we're talking about Apple are trying to get into healthcare, because that's the most lucrative business to be in. And their technology, their financial abilities are, you know, you can build a company overnight with billions of dollars, right, if you want to. So, so I think, you know, you have to be cognizant of the fact that the competition is yours. And you're not thinking that you're protected forever, that's just the wrong way to, to approach the business. I think that, you know, there, there's been many attempts to kind of get into the field. And obviously, there's a lot of smart people, and a lot of capital behind those names. So and, you know, I think that you want to make sure that your whatever you're doing, you're protected, obviously, you know, from the IP perspective, and, you know, freedom to operate, and all those things, obviously, basic, but same time, you also need to think through how is your product will fit in the general ecosystem, if you're part of someone or something, or if you're collaborating with someone, how you can work together to create better outcomes, right? Because it's almost like once you have more data points, right, and you start adding, you know, your your DNA, or your you know, your blood work, your results, your your wearable data, I mean, it just becomes a nightmare of data, and then try to correlate how all these certain data points, you know, how you can create an algorithm out of it, it's gonna take some time. Right. And, you know, the fact is, we have open AI project and all these other things, and I think someone's developing something for healthcare, believe Google, it was so you know, it's it's the uptake is fast, right? But the beauty about healthcare is that data is protected. So you can't necessarily jump in like you could do in financial technology markets overnight, and you know, create a huge margin. Here, you have to be very cognizant of risks if you do something wrong, and if there's a data leakage or how that actually impacts the patient's life at the end of the day. So it's not the bank. It's not bank account. Right. It's so so I think they're the quality and the, the amount of work that goes into it is very different.


Ben Glenn  18:45  

Well, look, you've given us a tremendous insight into how data is going to change healthcare. Thank you for coming by the studio. Have a great LSI 


Oleg Gradnensky  18:53  

Thanks, Ben. I really appreciate the questions. It's been very fruitful discussion. I hope that you know, most companies here will take some notes from this from some of these topics, and hopefully it will be helpful


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