Ram Yalamanchili: Hey, George. How are you?

George Magrath: I'm doing great. Good to see you, RA.

Ram Yalamanchili: Yeah, you too. Um, thanks for, uh, uh, uh, making time and, uh, maybe let's start with a quick introduction about yourself and then we can start.

George Magrath: Sure. So I am a physician, an ophthalmologist, and I have worked in industry for the past 15 or so years, um, in different roles.

I've worked as an equity analyst, in New York. I've also worked in drug development and as a service provider. And at my last job we were, a company called Lexitas. It was a small company that ran ophthalmic clinical trials and we one time we're running 40, 50 studies, and I left there a couple, well, a year and a half ago, and joined what is now Opus Genetics, which is a company that's dedicated to developing gene therapies for blinding conditions of childhood.

Right. So childhood blindness. So we have two, um. Uh, two assets in the clinic. One is for a condition called lc five. The other one is just now about to start with clinical studies, which is best one. And so we're super excited, um, to have a portfolio of gene therapies. It come from the University of Pennsylvania.

Ram Yalamanchili: Um, thank you. And I, what I find fascinating about your journey is the multiple roles and hats you want, uh, throughout your career. Uh, I feel, uh, uh. Very related to that kind of a goal because I've also come from a very similar sort of multi role perspective into the clinical trial industry. Um, you know, as you know, I've, been a, uh, tech entrepreneur for yeah, first half of my career.

Um. You know, coming from a computer science background and trying to break into healthcare, about nine years ago, uh, is challenging. It's not the easiest market or, or the, uh, uh, space to get into. I think, uh, it, like yourself, it requires a certain amount of, um, expertise and training, which, uh, if you're coming from a pure compute, um, background, that's not quite clear.

Fortunately, uh, what I've found is a opportunity to work with a few physician scientists like yourself as co-founders. I built a company called Lent Bio, and uh, what we've done at LNT is, develop a few molecular diagnostic assays, uh, in oncology. So the process of, building a company like that, going from a small biotech company with an idea.

Fundraising, uh, working through the product development cycle, your protocol development, your study development, recruiting, uh, working with many parts of the ecosystem like your CRO sites, uh, various other vendors. So we did a, I would say, a full spectrum of all that work, and I've certainly gotten to learn quite a bit about this process.

Uh. And, uh, ultimately we had a, an outcome where we were, uh, able to exit the business to Roche, uh, uh, end of 2019. And I, I began my journey in terms of thinking about what do I do next? And, uh, one of the things which I was pretty fascinated about and quite sure about also at that time was. The potential for AI to be, uh, you know, a much bigger, part of our thinking than, than it has been, I would say in 2021 or 2020.

So, uh, and I also was looking around opportunities and I said, well, what is the area where there's the maximum impact? Because there's impact from automation and bringing, uh, into this.

It's entirely a human, uh, and, uh, human in intelligence driven market. And, uh, I think there's plenty of opportunity for us to really expand the bandwidth, which this, uh, market brings. Uh, I think we should be able to more with less, but at the same time significantly improve the quality, reliability, and consistency.

So, so, um, so yeah, so that's kind of where we are and, um, uh, what we're doing right now. Uh.

George Magrath: Yeah, it's amazing. It's so complimentary to what we're doing. You know, I, um, my first experience with AI in clinical trials was really around imaging, right? We were using computers to identify patterns in images that might predict which patients would be the best.

Responders to certain new medications and, and that's it. That's super cool stuff, right? It's a very practical outcome. But what you guys are doing is incredible from an efficiency standpoint and from a quality standpoint because so much of clinical trials is, um. Is is sort of, um, re a repetitive type work, right?

Or work that, um, that can absolutely be, um, be, be handled by the computer. And, and so I know that. Our, um, current trial working with you guys, that's exactly what we're doing and has led to faster startup, you know, lower, um, queries. Um, about every metric has been improved by using your system. And so I'm absolutely very grateful for your journey and getting into what you did.

'cause it's made a direct and immediate impact on our lc five trial.

Ram Yalamanchili: No, it's, it's really great to work with, uh, with you and your partners, or the sites you bring in. And, uh, yeah, we're, we're very excited. I feel what we're doing right now is really just the first innings, or the second innings.

There's so much to do. Mm-hmm. So much more to build on. And, uh, as we see right, intelligence is starting to really, um, develop into a place where I think we can, we can certainly see where the trend is going, uh, over the, so ask you something, George. Uh. Tell me more about your particular trial. Maybe the, the opportunity that, the design, um, how, how you're approaching it.

George Magrath: Yeah, so this is, this is super interesting. this is really the core of the value proposition of AI or, or computer learning and. In our program, which is that we have a lot of programs. We have seven of them in our pipeline. but the trials are all very similar. It's just looking at different genes, so, you know, uh, in, in the world, in the world of inherited childhood.

Blindness. You know, there's about 280 different genes that can cause this. And what we're doing at Opus is we're targeting 'em one by one in a highly efficient conveyor belt, sort of string of pearls type approach. And so for us, it's all about, it's almost like manufacturing, right? It's all about consistency and efficiency with.

These things. And so lc five is just the first. And you can see our pipeline has seven. And so what I'm really hopeful for is that we can have significant cost savings, significant time savings by implementing with your system on the first trial, and then just having the computer help us replicate it each.

Time. We do, we do the same, the same thing with a different gene. And, and so that's the real core value proposition of why we're particularly interested in this. And, to get to your question about exactly for the lc five trial, really the, um, the interesting thing about this one with ai, um, was that there are a lot of different assessments in this one.

there's a lot

of potential variables, potential different data points that we need to capture. And the AI system was able to come in and absolutely, I. Give us a great first step on that.

So in the LCA five trial, and specifically we're using AI and we're using particularly your platform for, for a number of reasons, and the first reason really is around the amount of data sets that we have, like the different.

Data that we need to capture from all the endpoints and the ability of the computer to really be able to target in on that in a way that I haven't really seen clinical trial databases ever do in the past, at least with the ease and the efficiency that you guys are. We're able to do startup and able to do to, to execute.

And so, so that was really huge. The, the integration with the site is really great too. I've, I've used that as, you know, on a different project in the past before Opus, um, where we, uh, utilize that on a study that was more high volume, high throughput, and the ability to integrate into the system.

Identify patients give workflows to different roles within the practice made all the difference in the world. And as you know, from that practice and in that particular phase three study, I think they enrolled a hundred patients in that trial. So it was a very high throughput, um, uh, effort with, with, with your technology

It would've been extremely difficult. We would've needed multiple additional personnel to execute in a traditional fashion in that trial.

Ram Yalamanchili: Yeah. And that's, uh, really, uh, you know, the crux of it, right?

I think when I look at the burden and the way we currently design trials, uh. It's sort of lost in translation where I think we all design for what's best from our lens. But there's so many stakeholders in this whole, uh, ecosystem that it's very hard to sort of optimize or, you know, and it's, I think it's very easy probably to suboptimize for yourself.

And I really think that's what's happening, uh, across the board. And, uh, you know, sites have massive burden on the, uh, uh, on the execution side of these trials. Similarly, you know, CROs or sponsors also have equally, uh, high amount of burden, right? Because your sites are your bedrock. And if sites are burdened, that burden does translate back into, um, uh, what I call upstream.

So it could be monitoring, which is burden because of the site's lack of, um, you know, streamlined workflows. So I think one of the things I touch upon is. Because you've seen us and you've, you, we've worked together both on a site side of things as well as the sponsor side of things where with Opus on the site side.

I think I'd like to understand, you know, as a sponsor, what are some of the challenges you've seen in the past and also as a CRO, because you, you ran Lexi, I'm sure you have a lot of perspective on. Running many, many trials and working with sites. So what are some challenges you've seen

George Magrath: what you said first was incredibly insightful. Right? So we, when we developed for a trial, we developed one system, but yet the stakeholder, there's so many different stakeholders that have such different needs and the ability to tailor to each of those makes.

Everybody's workflow more efficient. And I think that's exactly the answer to your question, is that what I've seen from being on the sponsor side from the CRO side, um, and from the site side and from running trials on your platform,

the it, the ability to customize for different workflows is huge because as a sponsor, what do I care about? I care about enrollment rates, I care about queries, I quick, I care about, um, getting the data and ensuring the quality of the data. Um, things, things like that, right? Um, audit preparedness, all the quality metrics.

From a site side, when I'm trying to enroll patients, you know, it's, as a PI I care about, okay, am I making sure that I'm doing all the right things in the right order, exactly like I should. From a coordinator standpoint, she's trying to get in touch with the patients, figure out which patients on the schedule or the right patients to enroll in the trial.

When do we do follow up? All the logistics of that. Have they, you know, do we have the most recent consent forms? Do we have, are we doing everything? Like we're supposed to. It's incredibly executional. Um, and then from a CRO side, you're trying to, you're basically in the middle trying to manage both of those.

so it really is an interesting concept to have a one system that you program that then will pull out and create amongst itself different workflows for different, users, different, different roles. So that's a very, very cool thing.

Ram Yalamanchili: Yeah. And, you know, stepping back, right? I think, uh, when I was in my sponsor role, uh, uh, at my previous firm l mm-hmm.

Uh, what was fascinating is you ultimately were, uh, you know, beholden to the performance of your site or sites. I would say

George Magrath: a hundred percent. And yeah.

Ram Yalamanchili: Everything we were doing, you know, like for example, um. Unlike my experience prior to lexion, which was mostly tech entrepreneurship. you have certain metrics which are usually defined by product launch, some amount of early revenue, some arr, some few logos of customers, and that's really how you go about and, you know, raise your capital and sort of continue towards the journey of, uh, building the company.

And my experience building Lent, uh, has shown me that, you really are beholden to your investors' interest in the type of data you're able to present and also the wins of the market, you know? Mm-hmm. Are you in a space where there's funding available or not? we went through these cycles where there were periods when you could raise and there were others where whatever you're working on is not where the market is, uh, in terms of being able to fund you.

Right.. And the other thing on the site side is, you know, obviously after starting Tilda, uh, you know, I, I took this approach of not trying to build a platform or any type of technology without fully understanding the whole picture. Both sponsors, sites as well as CRO.

Um, I have worked in a sponsor in a CRO relationship prior to this. But I would say the first half of our journey, or the first third of our journey with Tilda, uh, about a year and a half has been, really working at the site level. Uh, we, we purchased a site. We, uh, went in and we really got to understand the challenges of running a site, the, what it's like to be a coordinator, where it's like to be a owner of a site.

And really like getting at the nitty gritty details of how do you manage 20 studies in parallel, uh, at a site all the issues you face. Right? And in this process, I also got a chance to really get into the details of coordinating and working with the site staff, working with patients, um, you know, talking to, many sponsors, CROs, who would come to our site and talk about putting a study,

And what's fascinating is so much of this is essentially up to the coordinator. coordinator is the most underappreciated role in the entire, clinical trial picture. I understand. Um, you know. Somehow that was just not part of the calculus.

when I was at Lexan, this was not a discussion we've had at, our board meetings or our, uh, regular sinks within, uh, within our team where we said, you know, we have 23 sites or something, and how do we make sure all of our coordinators are happy? They're actually mm-hmm. Uh, motivated, they feel empowered, they're gonna do the work so that we can get the data.

Mm-hmm.

Ram Yalamanchili: Having taken this other side of it, being a coordinator for, about a year and a half at, at Tilda, I really started to appreciate a lot about the challenges. Right. Um, you know, I would say we had multiple roles in a single job. I would say, uh, you know, you, you had from managing feasibility, budget negotiations, contracts, um, then ultimately doing your trainings startup, uh, at the site.

recruiting patients, calling all the patients, working through the backlog, bringing them in for screening, performing the screening, collecting the data, putting into the EDC, doing all the quality and query management. Uh, of course there's regulatory, like you mentioned, the IRB, uh, communication and interaction

and then finance, you know, ultimately.

Going back and looking at our own process and trying to do this with no AI or automation or what we call AI teammates. it's a place where, you know, to me that was just unfathomable, at least where we are today, because you can bring so much efficiency when you sort of adopt, AI into some of these day-to-day workflows.

And you really collaborate with an ai and some of these, uh, workflows are starting to become very, very intelligent, just from where, where the feed was going. Yeah. So, um, yeah, no, I, I, I completely, uh, get where

George Magrath: Yeah. You know, the inter, you know, the interesting thing is that you're exactly right. The site coordinators and the staff at the site are the ones who make the studies go, and they're the ones who produce the data that a lot of times your study.

Falls upon. Right. I've seen that over and over in studies. you know, whether it's imaging data or ancillary things like that, that the technicians are doing. and that the doctor's reviewing and, and, um, and a hundred percent like. At sites I've been around and at the site I used to work at it, you'd have a number of protocols on your desk, and the coordinators were the ones who decided which one was at the top, and it was decided based on, you know, their ability to execute it.

And how comfortable they felt with it. And so it really is very critical to execution. Like it goes down, it goes down to the site coordinator, it probably is the most critical position in a study. Um, right, right there with the pi, you know? Yeah. Couldn't agree more.

Ram Yalamanchili: And, and speaking of your own, um, site experience as a PI and, uh mm-hmm.

Working on that phase three study, which, uh, which we worked on mm-hmm. Uh, with your team, I think you had one coordinator managing what about a hundred patients at that point You were the top enroller in that study, right? Uh, to me that was, that's really the power, right? How do we build that kind of bandwidth into this model?

Where you can enable sites to perform so much work at a very high quality and ultimately be able to really push the frontier of innovation. Uh, the, the current model has really like burned down in many ways. I don't see how we can sustain this going forward unless you do something different.

George Magrath: I, I haven't even thought about that, but you're exactly right. We enrolled a hundred patients across four surgeons with one coordinator who did, who did all the work through your system. And uh, and it was the top enroller for that site, for that study. And it's by far. Like, like normally you would staff something like that with probably three or four people.

Um, so it really did increase the efficiency of our site coordinator by, um, by, by three or four x and, and that individual I. You know, executed well. Really enjoyed the trial actually, even despite that volume wasn't burned out, wasn't flustered. she enjoyed the trial. I mean, she'd have done another hundred if the trial had allowed it.

so it really is powerful. It's very cool. Yeah.

Ram Yalamanchili: No, I, and I, I remember, the experience of working through that process and yeah, having these reactions, like, wow, like, you know, I, I collected all my source data and from there on anything related to working with external systems, working with mm-hmm.

IRB or monitoring, querying. A lot of that was happening pretty much through the AI teammate, which we were able to provide. And I think that's the power, right? And I don't see a world where we all wouldn't have our own, uh, AI enabled teammates. I mean, for what you do, what I do, and what, what certainly a site and a CRO and a sponsor would do.

And I think that's really the vision where we're going. Uh, I. Opportunity to build right now is not just the ai, but the interface through which you can work with an ai. So, uh, what is the collaborative layer which you can build so that we can work with an ai? Right. And I think traditionally, if you look at just, you know, hiring teams and working with, um, uh, within our own teams.

As, uh, as humans, we have the capability to interact. We have emotions, we have personalities. Uh, we can work on different modalities. You know, we can share different applications on which we can bring context, uh, very quickly. These sort of things are not really there yet. Uh, when, let's say you use chat, GPD or the chat interfaces, a very limited sort of an interface for perform.

But I think our goal and what we've built is really just that building a platform through which you can work with multiple AI-based teammates. You can collaborate with them, you can train them, you can teach them, you can monitor them. And I think I see a future where this sort of a thing becomes norm, uh, normal across the board.

Right. I think it should, it should be the way we expand the productivity we all have. I think ultimately I want to touch back on something you said about the pipeline and the way you are thinking about Opus. Um, and, you know, the opportunities of running multiple different trials, um, uh, you know, in a, in a quick succession,

And I think when we first talked about it, I was explaining to you the main motivation, at least for me, why we're doing this at til, is I really do think the current bandwidth, of how many trials we can run, how many patients we can recruit, uh, how big the, the, um. The space for biotech innovation can be is really curtailed because we have not had any meaningful expansion in the amount of infrastructure in clinical trials over the last 10 years.

In fact, I would say, I would argue that the amount of infrastructure shrunk because we've lost quite a few physicians who would normally have done. Uh, research and there's data out there that shows that it's been, it's been going down, or at least we have lost some during the covid period and we haven't recovered back.

That is a, a problem for the industry, right? I think when we think about how we push the frontier. I mean, especially now, it's exciting because we have, uh, really strong models on, on the biology side, protein folding side, you know, like let's say alpha folding, um, models like that. So what, to me that showcases is you are expanding the number of targets you can potentially come out with at the very early stage of the development cycle.

And, you know, that's great and wonderful, but once you get into the clinical model, into your human studies. Where is the bandwidth? we currently do not have that bandwidth and we need to do something to expand this maybe a.

I think you're, you're going at something which is very similar in rare disease and, uh, yeah, we'd love to touch, touch more on this, right? It's,

George Magrath: this is the core of our business proposition, right? So we're developing treatments for, for rare diseases, right? For, for diseases that affect a thousand kids. In the United States, like small stuff, but so meaningful because these thousand kids are going blind and we know the technology works, right?

I mean, Luxer was the first one approved by Spark and it, it worked And so, you know, running the clinical trials is. Is, is so important to be efficient, right? if you're in a big indication where if you're after diabetes, obesity, something like that, then the numbers make sense, you know, in, in, in big pharma to go after those indications and run the trials with a lot of money and a lot of spend and a lot of time.

But when we're going after these targeted diseases,. So in order to make it viable and to make the company real, we have to be efficient. And that's exactly why I'm so thrilled that you guys are doing what you're doing because otherwise these kids wouldn't get treated right.

I mean, it just wouldn't be viable. the bigger picture for the country or for the world, is that the ability to use AI or the ability to use computer learning? in clinical trials to improve efficiency, allows us to treat diseases we otherwise would not be able to treat.

Ram Yalamanchili: Judge, what's stopping you from doing this without, you know, let's say AI or any of these latest technologies, right? Like for example, when you are in your CRO role. I'm sure you've dealt with customers like yourself. yes. A pipeline of small in, you know, rare disease indications. Yes.

Or, you know, even studies which are not technically rare. Uh, yes. But why are we not able to basically enable opportunity?

George Magrath: I've watched, I've watched a number of companies from the periphery when I was a service provider. You know, just not be able to get drugs developed because of the cost and timelines involved.

it's just like any other business. Right? And, and that's the thing is to, to impact these patients, you have to create a viable business or else it won't get across the finish line. So it's, it's an interesting concept that not any other industry really has to worry about, which is that you're trying to do good for society by treating these patients.

You have to build a real business that's viable to be able to do that. and I've seen it so many times where it's great science, it really looks good, but the pathway just isn't viable from a commercial standpoint. And the project dies, you know, because it takes too long, it's too expensive.

that's too hard to find the patients, you know, that, that kind of stuff. and that's fundamentally what I'm trying to solve from my standpoint is how do I run these trials as lean efficiently with as high quality as possible? Right.

You know, using every tool we can. And a big part of that is AI and through you guys, honestly. if I'm developing these seven projects and I'm developing them at price points or at timelines that, um, that are used by bigger indications, then they may or may not make sense.

So it's all about, it's all about efficiency, you know, getting these things to patients, proving they're safe for efficacious, and then trying to get 'em approved.

Ram Yalamanchili: And I think I, I found something interesting what you said, right? it's not just about, you know, how, how much your overall budgeting is. It's also the timeline.

And I think one other thing which I want to point out is it's also about how you can paralyze these multiple approaches at the same time. Mm-hmm.

Mm-hmm.

Ram Yalamanchili: I think you could argue that yes, you might have the resources, but how do you paralyze seven trials at the same time and sort of go at it at the.

You know, running like two or three trials. Right. And do you find that as a, as another, um, sort of lever you need to think about?

George Magrath: Oh, a hundred percent. Like most companies my size, you know, can concentrate on like one asset and, and we are just concentrating on one right now. So, but, but as this thing unfolds, and if it does, what we hope it does, the goal is to unlock the ability of a

small team, to be able to execute in parallel a number of trials, right? And that's what will drive value for our shareholders. That's what'll get treatments to patients faster. That's what will move science forward for this. And so that's absolutely the goal.

, for a small company, the answer can't be. Well, if I need to run seven trials, I'm gonna hire seven times the number of people. that just isn't a realistic business proposition. It needs to be how do we leverage what we have from technology to increase the efficiency of the team that we have?

So that's, that's what we're trying to unlock.

Ram Yalamanchili: And it's also not guaranteed that if you do expand your resource by seven times, that you actually have seven times the bandwidth. Right. I mean that you probably have more, uh, scale at that point.

George Magrath: And Yeah. And as you know, you, you expand like that, you start to have to add in layers for management oversight, . You know, that come with having that many more people. Absolutely. Yeah. Yeah.

Ram Yalamanchili: And yeah, just touching on this, on the aspect of quality, um, you know, I have seen how, uh, traditional monitoring works, the traditional service industry works, uh, around this.

And as you know, it's, entirely driven by a person going to the site or maybe going through the systems.

Looking at

Ram Yalamanchili: all the documentation and all the data which is coming in and really doing like forensic level monitoring on top of this. And I'm just curious from your perspective, how do you look at the role of AI going forward?

Right, because, um, uh, you know, I'm, I'm sure certain parts of it cannot be done through ai, and some can be only done through an actual monitor who's, on site, . But, um, how are you thinking about that going forward? Uh,

George Magrath: well, so, I think one of the big advantages of using computers in this is it is really pattern recognition early in real time.

Because what we're trying to do, it, it's a quality first of all, is the number one thing for us, right? If we're not running high quality trials and the rest of it doesn't matter. So quality is always number one for us and quality to us, Will likely always involve humans to some degree,

What saves me time? What saves. You know, on quality, what's what, what's best for patients, really, honestly, at the end of the day is if you can recognize patterns in, in poor quality very quickly and be able to mitigate them very quickly. So a computer does that in real time with the data, you know.

the traditional way to do it with monitors is very hard to do it in real time. It's, it's, it's, and one of the first applications that I used AI for that was, for a different company that was more image based, was looking at the quality of images in real time. And, the patient is sitting with the coordinator in front of an imaging machine.

The image gets taken. And before that patient even gets up out of the seat, the quality is reported back to the coordinator. So if the retake needs to happen, it happens at the point of care. Right. So it's not, it's not a human looking at it that afternoon and then they have to call the patient to come back and retake the images or anything like that.

It's, it's happening in real time and doing that throughout the dataset is critical because the worst possible thing you can have happen. Maybe not the worst possible thing, but one of the, one of the bad things that can happen is if you get systemic patterns of poor quality in a dataset that isn't corrected quickly.

And so that's, that, that, that's a huge, huge thing.

Ram Yalamanchili: Yeah. And uh, what you're pointing out is actually very interesting because we see this often with sites we work with as well, because. You know, the monitor is doing quality management on behalf of the sponsor, but I think

mm-hmm.

Ram Yalamanchili: Would also need to do the same on their behalf for themselves, because mm-hmm you're ultimately responsible from the quality of work you're performing from an FDA perspective.

And, uh, we have an inspection readiness, uh, product where, you know, we have a AI teammate, which is a quality teammate and is essentially checking all the work being performed at the site. And essentially in real time reporting where you are with your inspection readiness and if there's any like, you know, uh, areas which need to be buffered up or there, there are areas where we need to, uh, go back and, and, uh, like you said, maybe an informed consent was not the right version and you, you had to recon concern that patient or it could be a missed mm-hmm.

Uh, report somewhere, which, uh, is not part of your binder, not part of your dataset. Mm-hmm. And so what I've found fascinating is, uh. Even in the most incredibly, like efficient sites and uh, uh, places where they have, um, you know, a, a really tight quality program, um, you, you know, you still cannot get to the accuracies, which we're getting through the current, uh, AI based implementations.

And I think it's just not on the quality side. I've found that even on finance side, for example, one of the, uh, articles I read recently said something like 95% of the sites do not bill for all the work they've performed. And, and it's, it's, it's reasonable, right?

Because Yeah. In, in an industry like healthcare, I think we've evolved. Very strong systems over the last few decades to be able to solve for that exact same problem, which is

billing.

Ram Yalamanchili: And you have this EMR, you know, infrastructure, which, I think some would argue physicians like yourself might argue the EMRs are actually more billing systems rather than healthcare.

Uh, you know, patient record systems. And we don't have anything like that in clinical research. These stakes are high and, uh, it's very easy to miss, uh, you know, a certain set of invoices or a certain set of, um, uh, you know, visits you performed, unscheduled visits, certain drugs you might have used, which need to be passed through, uh, to sponsor.

So it's just, uh, you know, everywhere we look in clinical research, I find the burden to be on somebody to make sure like everything's going well and it's. I don't think it's a sustainable model. and hence we see this whole concept of burden, right? Whether it be sponsors, CRO side or side side. Um, and then just sort of slows, slows the whole process down for all of us who are, uh, ultimately trying to get more shots on the globe.

George Magrath: your point on the finances is so poignant. You know, it's so true. The, um, the, the clinical side of things like the clinical care, um. The EMRs do such a great job of capturing, you know, I mean that's one of the biggest advantages honestly, is that they capture the billing and they capture how you're supposed to appropriately bill for the work that has been done.

And on the clinical trial side, that's completely ignored by the software to the. Extent of my knowledge, and the, that burden is put on, on people and I think that that is something that's probably a very low hanging fruit for sites to be much more efficient. Yeah.

Ram Yalamanchili: And talking about the imaging based AI views in the past from a quality perspective, right?

Yeah. I find it fascinating because today the problem I, at least from, uh, you know, looking at ,it both from a biotech perspective, uh, as a biotech founder and, and now from a site perspective, the place where you can give me a technology like that as a coordinator and for me to actually use it just does not exist right now.

Mm-hmm. None of the systems I have used, or I can, you know, CRO would possibly gimme or a sponsor would gimme. It has that capability where, you know, you can bring these external integrations very quickly and enable you to do things which would otherwise be, uh, done manually. And I think, I feel like everybody benefits from this, right?

What's missing here? Have, have you looked at a ways to bring, you know, transformative technologies like that into the clinic or into the

George Magrath: Yeah. You know, it's a, it's a, it's, it's a wide open field, right? I mean, we, we did it with a very homegrown solution. Um, and, and to be quite honest with you, I spent most of my time on an imaging problem that's slightly different.

Um, you're, you're talking about a very practical problem that. Needs to be solved. The problem that I've spent most of my time on though is, is, is in, in an era of personalized medicine, that's, the exact tag that this goes under. How do you make sure that the treatment you're giving has the highest likelihood of if.

Affecting the patient positively. And so can we use the computer to assist the physicians, assist the investigators, assist the sponsors in selecting the patients whose biology is most amenable to whatever treatment you're evaluating. we actually just got some of our work on that in Diabetic Eye Disease accepted by the American Journal of Ophthalmology.

So we're publishing on that very shortly. but every patient is different and everybody's biology is a little bit different and. We have treatments that work for some people, but don't work for others, and you really wanna just not treat patients who are not gonna respond and make sure that patients who will respond have access to whatever the treatment is.

That's the goal. And I think that using imaging and using computer vision and computer learning for that is one of the. Honestly, it's one of my passions, right? It's something I think will be so good for so many people if we can actually figure that out.

Ram Yalamanchili: Yeah, and I think to your point, right, there are amazing technologies out there.

There's, there's people working on these problems, which are, uh, clearly published and they have great, um, I would say, uh, benchmarking looks good. Pulling that into a trial pretty easily and building a platform, which will allow sponsors to do that or even yes, to put it onto a platform so that you can be part of a trial.

I think that marketplace style platform, where you can very easily like design your trial. Pull in the right components, the right teams collaborate with them. Yeah. All that seems to be missing in this industry. It's, it's like the app store,

George Magrath: you know? Exactly. Yeah. Like you go, like, you go in the app store and you're like, we, we wanna use this, this, and this.

Yeah. That doesn't exist. And it's hard. I like, when I was on the CRO side, it was almost like you, you hit on, you hit on something that's so real. There's so many different technologies out there, but actually implementing them in a clinical study gets extremely complex. Very fast for a myriad of reasons.

And so having a platform where you can actually have, like, you know, and you'll know this better than me, but like an API or some sort of plugin, you know, my very crude way of calling it an app store is sort of how I envision it. But, um, that would be. So meaningful for clinical trials. You know, we do it in rare, in rare disease.

We see it because we're using really specialized equipment that's targeted towards that particular pathology. And it may not be widely used because it's only applicable to a certain number of patients. And so having in easy interoperability is, it is a, is an unsolved problem right now.

Ram Yalamanchili: Yeah. No, absolutely.

Yeah, and I think, uh, couple of things which I, which come to my mind when you're saying that is I think ultimately you can do all of this if you had enough resources. Mm-hmm. I mean, that's, that's well understood. But being able to go from a protocol to a first patient, first visit in a time of, in a matter of, hopefully a week or two.

Right. Something like that. Yeah. In a very short timeframe where you. Use majority of your resourcing to really like check and validate and make sure whatever your, uh, platform or AI has, has sort of set together. Yeah. And then being able to run multiple studies on parallel, uh, with architecture. Right. And then you have a, a series of, uh, you know, workforce, which I call AI teammates.

Constantly checking, making sure everything's going well, making sure your quality is on right place, making your startup is on right place. Um, and then, you know, collaborating. I think all of these seem to be the type of things which we should shoot for, and that's, that's what we're going for. And, you know, ultimately the way I look at it as companies like yourself will be able to deliver and effectively compete, you know, in a world where you're saying like, I've got seven assets and I wanna take you to the trials, uh, and I want, I wanna deliver value to my shareholders.

And, um, yeah, I mean, you can go two at a time or you can go seven at a time, but this, uh, hopefully

mm-hmm.

Ram Yalamanchili: Amount of resources are, uh, or even, even the resources, I actually feel, uh, at least on the capital side. I, I feel like people or, or companies which differentiate with their strategy and can effectively show that they're not distracted when they run these many programs, probably can raise a lot more capital anyway, right?

Mm-hmm. There's probably enough capital. It's just that on a risk adjusted basis, it doesn't work out if, uh, uh, you know, if you're distracted . But if you can unlock that productivity and you can show that you as a platform or a team can manage multiple assets.

And hence you need, you need to raise more. I feel like that shift might happen very quickly, I would think just on the,

George Magrath: I I think it could, right? I mean, you prove that and then the, the capital allocation will be there. Right? Um, and, and it, um, and it just goes to show you the power of this is so far, has not been unlocked.

Right. Because, people haven't. You know, been able to really execute against it. So this, the, the, the new technologies and stuff that's being built, you know, I mean, how amazing would it be if every biotech out there could, could do more assets or do more science than they're currently doing?

I mean, you would see such an amazing, impact on our society if that happened on large scale.

Ram Yalamanchili: I and I, I believe that those days are not too far away. George? Yeah. you know, some of the offshoots of, alpha fold, Google's, uh, um, uh, products, right? I mean, that's what they're saying.

They're saying we will have a, a, uh, a drug for every target almost, right? and that's great. I think in a science perspective, maybe we can simulate certain parts of biology and, and figure out the right protein or the right, uh, antibody design. Um, but. Bringing it into the clinic, bringing it into, into the research pipeline, and then ultimately seeing that kind of maybe a thousand, 10,000 fold expansion bandwidth.

Um, I'm, I'm, I'm looking forward to that data 'cause I think yeah, we would need, certainly, we would need, you know, many, four more people working in our industry when that happens. And, and we certainly will need equally number of. AI teammates working alongside us. Right. So I, I really do think it's an age of abundance, which is mm-hmm.

Which can get unlocked

George Magrath: uh, right now the bot, the bottleneck is in clinical development, right? You've got alpha fold and many others that are identifying novel targets and identifying how to drug.

Targets we thought were undruggable. but right now we haven't seen the same sort of efficiency gains on clinical development to match that. And that's, uh, I think that's exactly what you guys are trying to unlock. I mean, it's super cool and it, it could be, it could be very impactful.

Ram Yalamanchili: Yeah, no, I would say I've certainly seen different approaches to solving this particular problem, right?

Clinical development certainly is a bandwidth. Mm-hmm. And I think you could argue maybe we don't need the number of patients we try to recruit. Maybe there are better ways to design a study or, you know, use synthetic data, synthetic patients, uh, you know, control arms. I'm sure you've seen all these, uh, uh, sort of, uh, ways to reduce the amount of work we would need to do on the clinical development side.

But where we are focused and where I'm passionate about is even if you were to do that, I still think there's a hundred times thousand times four increment in the amount of work we would need to do in, in clinic. Yeah. As in like with patients and with, with clinics and with, uh, physicians. And that simply is just not possible right now.

Everything right now seems at the brim of whatever capacity we have, and hence, um, we need some new opportunities to really improve the bandwidth. If you kind of look at our, our way of, um, bringing AI into this field, we think about it from a perspective of how can I improve, um, the bandwidth of a certain persona.

So if it's a site, it's a coordinator, it's a quality person, it's a regulatory person, it's a finance person. If it's a sponsor or a CRO, we have, uh, different personas. They, they generally have data managers, project manager, site activation, process of. Identifying personas which are operationally very burdensome, and bringing them in and training certain aspects of their workflow through an ai.

And, uh, and essentially saying, this is how you expand the bandwidth. Now you can do 10 times more work. Right? Um, and I'm very positive. I think like in general, this trend will continue to develop because, the world's, uh, mission today seems to be the pursuit of AI singularity.

I think, all the resourcing, all the smartest people seem to be working on some sort of AI to deliver, uh, a very superior intelligence. So. Uh, you know, it's, it's gonna happen and, uh, I think we all need to be prepared to take advantage of this, new model. Right. And, uh, um, go from there.

George Magrath: Hundred percent. Yeah. And we've loved, we've loved working with you guys. You know, I think we've got, we've got an amazing. It is just an amazing fit with our pipeline and, and our, and our model, right, where we're developing an ultra rare disease to unlock efficiency with you guys without sacrificing inequality.

And so it's, um, it, it's, it's, it's fun. Be at the tip of the spear.

Ram Yalamanchili: I'd love to see 70 assets in your pipeline, George.

George Magrath: Yeah. Me, me too. One day.

Ram Yalamanchili: Yeah. I think you told me that there were, uh, what was it, like 200 plus targets, which are, the opportunity is large, right. From a rare disease. There's, there's

George Magrath: enough.

There's

Ram Yalamanchili: enough.

George Magrath: There's enough of these genes that need to be developed to keep us busy for quite a while.

Ram Yalamanchili: Yeah.

Um, opportunities which get unlocked and the benefits and the impact

George Magrath: is you, you know, if there's ever any question about how important it is to be doing this kind of stuff, what, I would encourage you to watch the KOL event, the physician event that our company did in December where they described in detail the life-changing effects in the first three patients we treated in the lc five program.

It's traumatic and it's, it's motivating. So it's, um, you know, we got like, we got 280 to go.

Ram Yalamanchili: I, I have a, a, a fun anecdote there. When I first heard about your, uh, company, uh, I, I heard through our CRO partner who, who's, who's, uh, working with you. And, uh, um, I Googled it and then I found this, uh, New York Times article, and I think the headline, or at least the abstracts or something like, uh.

You know, somebody who had no vision all their life was able to see Yeah. For the very first time. Yeah.

And

Ram Yalamanchili: I, I, I was just thinking this is like sci-fi stuff, right? This is unbelievable. Yeah. And I read through the article and I, I was just completely blown away. I was just that, how, how is this even possible?

How can somebody who's, who's been blind all their life and yeah. Then start thinking about what are the implications then, right? I mean, what happens when you, for the very first time in your life actually see, and, you know, yeah. How process all that, it must be

George Magrath: for the fir the first patient in our trial.

Um, had, was treated a year and a half ago now, and he was 39 years old when he was treated. He had been blind since he was one, and he got formed vision for the first time in his life. You know, he learned what a ceiling fan looked like, what a skateboard looked like. I mean, it was. It was, it was cool. Yeah.

Ram Yalamanchili: Yeah. I mean, just hearing it as like such an emotional, uh, uh, thing. Right. So that's great. I, I, I love the work you're doing and we're so happy to be part of, uh, journey you guys are on. Uh, yeah. No, this is frankly why we're doing it. I, you know, this is motivating, you know, gotta keep, keep pushing and, uh, work on this.