Saama Technologies – Suresh Katta, Founder and CEO and Greg Simpson, VP and Head of Marketing

Announcer (00:06):
Welcome to Cowen Insights, a space that brings leading thinkers together to share insights and ideas, shaping the world around us. Join us as we converse with the top minds who are influencing our global sectors.
Charles Rhyee (00:25):
Welcome to the Cowen FutureHealth Podcast, a part of Cowen's fifth annual FutureHealth Conference held virtually this year on June 24th and 25th, 2020. Over the past five years, the Cowen FutureHealth Conference has brought together thought leaders, innovators, and investors to discuss how the convergence of healthcare, technology, and consumerism is changing the way we look at health, healthcare and the healthcare system.
(00:45):
My name is Charles Rhyee and I'm Cowen's healthcare services analyst. In this episode, we talk about AI-powered data and analytics in clinical development. In the digital age, data and analytics are being applied to just about everything and nowhere more than in drug development. The biopharma space is always looking for ways to accelerate and optimize the clinical development process. And to help us explore this topic, I'm joined by Suresh Katta, founder and CEO, along with Greg Simpson, vice president and head of marketing of Saama Technologies, a clinical data analytics company that leverages AI to integrate, curate, and animate structured and unstructured, as well as real-world data, to deliver actionable insights for biopharma. Welcome, Suresh and Greg.
Suresh Katta (01:24):
Thank you.
Greg Simpson (01:24):
Thank you.
Charles Rhyee (01:26):
So maybe start with Suresh, perhaps you could share a little bit of your background and how you came around to founding the company?
Suresh Katta (01:33):
Thank you, Charles, for this opportunity, to Cowen Health, FutureHealth. We are living in really odd times in our lives. I appreciate you guys are conducting this kind of sessions and having us participate in it. I happen to work in Silicon Valley for a very innovative, creative company. That's where I really learned about how to take very large volumes of data and simulate it, and visualize it. That's what we did.
(02:05):
That company went on to grow from being a startup to a $5 billion annual revenue company. I just happened to be part of that. That's where I got this. From that background, I felt I should apply this kind of stuff, what I learned there for different industry verticals. That's how I got started out with Saama.
Charles Rhyee (02:27):
Great, that's helpful. So then let's dive into Saama a little bit. Tell us a little bit about what the company does and a little bit more about how the platform works.
Suresh Katta (02:38):
Thank you. We had a data analytics company, as you know, we wanted to, over the years, from data analytics, that's how we started with multiple different industry verticals. Just to give you a quick rewind for how we were, around 2010 or 2011, major things happened in the industry. We felt one was a cloud was finally becoming real. Second big data was just getting defined. Third, was a more algorithmic, open source-based algorithms were becoming part of life. That's when we felt it was important to build out a big data analytics platform. We started working on that early part of last decade. Then we evolved into turning that into today's clinical development platform.
Charles Rhyee (03:34):
Great, and then as you look at that in particular, you guys talk about Saama's life science analytics cloud, talk to us a little bit more about that, and how it works and the purpose of it, and what's so unique about it?
Suresh Katta (03:50):
Yeah. What we found, is one of our earliest customers were Genentech, where we spent quite a bit of time there, learning about that inefficiencies involved in today's development piece. What we felt, was whether you are a study manager or a clinical research associate, or all the way up to chief medical officer, we felt they were all struggling with getting a single source of truth about the studies they are conducting. It's a very complex, whether it's phase one, all the way to phase three, it gets more and more complex as it evolves.
(04:32):
One thing we felt, was that they were dealing with all sorts of data sets coming from all over the world, all over the sites, all sorts of sites. And sometimes several thousands to tens of thousands of patient information, with the complexities that got involved, that we felt that our platform was quite well fit for that one, we started evolving that platform into trying to figuring out how do I collect the clinical trial operations' data to clinical trial patient data, to clinical trial safety data, to clinical trial supply chain data, to clinical trial. All the different types of data sets that flows through.
(05:14):
And we felt, if we are able to connect all these pieces, automated we integrate those pieces, without any human being involved, then turn that into analytics-ready, thanks to some of the work that was done in the industry by TransCelerate, MCC, [inaudible 00:05:32], some of the standard bodies had come up with hundreds of metrics, how to measure clinical trials' effectiveness. What we were able to do, is take our platform and codify all those metrics into our platform, very quickly. Irrespective of what persona you are, whether you're a chief medical officer, or all the way down to clinical research associate, everyone needs to measure certain things as they conduct the clinical trials. Our platform became home for all of these different personas, for them to manage their part of clinical trials through these metrics.
Charles Rhyee (06:08):
So, Suresh, that's really interesting. If I stopped to think about, I guess it makes sense, the fact that the information is coming from so many different places. So it's not so standard, but what are some reasons behind why is the data so disparate during trials from biopharma? Is it not possible for them to think through this a bit before, during the designing of the trials?
Suresh Katta (06:34):
Yeah, Charles, it's a great question. That's a same thought I had as we got involved. This is what happened too, maybe let me start with one major strategic mistake. It was supposed to be an advantage for pharma industry. I, in my opinion, that turned into a mistake. They had a lot of repeatable processes they need to go through, as they conduct clinical trials. And they felt it has to be variable cost, "Can I outsource this kind of repeatable processes to someone?" And that's how CRO industry won.
(07:13):
Today CRO industry is very big, and big pharma learned to outsource clinical trials to CRO industry, and CRO industry, second big mistake was it was not about outsourcing. It was about the modern debate. They built a model where it's billable hours, how they charge the CRO. Depending on project manager, how to study manager, how to clinical manager, how much to their particular area manager hours. That's how they charge. Now, it becomes actually a confrontational, this, pharma wants to get the drug to the market as fast as they can, whereas CRO industry want to stay on the clinical plan as long as they can, because that's a very, generates more billable hours.
Charles Rhyee (08:00):
So that's easy, so in other words where the way it organically built up, those crazy these inefficiencies, that they're now stuck, and it's hard to go back. So it makes sense that they need a different type of platform to handle that. How were biopharma companies handling these tasks then before someone like you?
Suresh Katta (08:23):
This is what I'll say, one more point, if I would bring up, our has done incredible stuff over the last century, I would say, in the modern medicine. We have been the pioneers in this, and we have been able to attract a lot of young kids going to becoming a scientist, academic researchers. And pharma industry has been blessed with a ability to attract the best of the best minds to work on a new molecules, new therapies, all these pieces. However, that industry never realized there isn't room for, there is a need for technology, get engineering minds to come in. So it became part of the process. They attracted a great scientists to come into pharma industry, or become great scientists once they joined the pharma industry. But however, that side of it, they never attracted great engineering minds to come.
(09:24):
If I can go a little bit, my background in Silicon valley, if you look at chip, when I did my thesis work on my master's program, my thesis work was putting more transistors into a chip. And at that time I got my thesis work done for putting a 100,000 transistors in a chip. Today, same chip has 5 billion transistors in a chip.
(09:50):
So what they did, was the science people or the people in the chip industry, I attracted the best of the best engineering minds to join them. With the same size of chip, same cost, we applied something called [inaudible 00:10:07], that is every 18 months, it will double the speed, and half the cost. And this has been true for the last four or five decades. Whereas pharma industry did not apply that. Pharma industry went through raising the cost from couple of hundred million dollars to now couple of billion dollars or more. That's how, because they never attracted the technology people to come in.
Greg Simpson (10:30):
So one of the ways they do it, was with spreadsheets. I've met with clients, when we showed them a demo of our platform. And they've said, "You know what? Everything I see here is the same stuff that people come to me everyday and ask for." And oftentimes, when it's not something that has already been asked, they either work with a SaaS programmer within their company, or they issue a change order to the CRO, both cause additional time to get the answer. If it's with the CRO, it's additional cost. Then if there's a different question that comes up, they have to go through the same process over and over again. So that's the life that they live today. It's just inefficient, and they're not able to get answers from their data right away.
Charles Rhyee (11:12):
That's great. Thanks for that, Greg. Maybe then let's take this to Saama a little bit more directly and talk about the business model. If we have CROs, and the old way of doing things, maybe billing on an hourly basis, or on a project basis. Talk to us about the business model for Saama, and the value prop that you're bringing to your clients?
Suresh Katta (11:36):
Yeah, great question. This what we are trying to do at this point, digitization of the world has changed at all of us around the world today. Same way in the pharma industry for drug development, what we were able to see, is there is an automated way to connect with the sites automated way to connect to the different kinds of CRO systems, as well as their internal systems. We were able to connect that, so that we were able to bring in all the data dynamically on an ongoing basis, by minute by minute. Or whatever, nanosecond by nanosecond, rather than wait for weeks, months, that kind of stuff.
(12:14):
By doing that, what we have been able to do, is pharma companies can subscribe to our platform, rather other than do anything else like they used to do. That is procure hardware, procure software, procure implementation teams. All that, we were able to build those. So our business model is really great, for them to just sign up with us. Then we are able to show them what they need to see, how their studies are going. All that we need to do, is connect to their systems. That's what we've been able to bring to the table.
Charles Rhyee (12:53):
And when I and how quick can you get somebody up and running? How long does it take? And maybe compare that to how long it typically takes for, let's say a typical CRO to get a project up and running? And maybe that's not the right comparison, what they would normally do, versus how quickly you can get them up and running. Maybe that's the better way to ask it.
Suresh Katta (13:11):
Yeah, Charles, if I can give this answer in three buckets. One is there is small biotechs, who have a very limited amount of money they raised through IPO, or their private equity or some kind of venture fund. Second, mid-size companies of pharma, then large sized one. In the small one, they are not have technology teams or anyone to do. So they depend on someone telling them what has happened or what's happening with the study. That's where they see our platform as a great way to jump in, and speed up their clinical trials. Whereas a mid-sized one, they have small IT departments. And they're continuously looking, they are very aspirational, midsize pharma. They're very aspirational, they aspire to become one of the large pharma/ and several of them, I don't need to name them, but you are aware of those people who are already in the market.
(14:13):
And then the larger one, today, they have a big IT department, or they have system integrators, biggest system integrators. So if you look at these three, big pharma, it takes months, and sometimes, I would say several quarters for them to do what our platform does in days, today. Okay, for small biotech, they're able to get, as things happen, they're able to see things for themselves, without calling anybody, without spending any money on CROs, that's what we have been able to do. This is again, I just don't want to confuse you. CROs do a lot of things, great things, I don't want to take away that.
(15:00):
That what we are able to digitize, is some of the data-related stuff, over-all process related stuff. What we have been able to digitize. We do not go into the site to do any work. We let CROS do that. CROs do bunch of different things. Labs, we don't go to labs to do that. We connect to the lab's software, so that we can get your lab results dynamically as it happens. So [crosstalk 00:15:29] we are eliminating some of the manual processes, very laborious, erroneous processes, which take a lot of time, as we have been able to eliminate for the pharma industry.
Charles Rhyee (15:44):
Thanks, and then that helps clarify things for me as well. Maybe then talk about the competitive landscape? It sounds like it's a really large market opportunity here. It sounds like for the most part, biopharma is still struggling with this issue? When you're in the market today, what types of companies are attempting to do something similar to what you are?
Suresh Katta (16:07):
I think my colleague, Greg, earlier pointed out to some extent, like every industry, spreadsheets is our biggest competition. There are a lot of people in the company. I'll tell you, one of my clients, they said, every time they have a meeting with their internal monthly meeting on the studies, to go through each study, where it is, what it is, it is week to 10 days, every manager have to work on spreadsheets to build that out. They do 30 days of work, and then they do 10 days of actually collection of data.
Greg Simpson (16:48):
And I'll say [crosstalk 00:16:53]-
Suresh Katta (16:52):
[crosstalk 00:16:53] still, they cannot answer questions, because a guy in New Zealand, gives his in his own format, that spreadsheet, whereas the guy in Germany is giving it in his own format. Then at the higher level, head of operations, or head of clinical studies, they'll go crazy trying to connect all these points to figure out, "What do we do? Or what do we don't do?" That's our biggest one competition, to be honest with you. Greg, you wanted to say something?
Greg Simpson (17:24):
Yeah, I just wanted to add, what I hear is a lot of times when they get together to talk about the data, they spend more time trying to rationalize the data. Then at the end of the meeting, once they finally figured out, "Okay, we're all talking about the same thing," then they can make strategic decisions on what to do with the data they have. So that's one of the challenges with the way things are today, is that the technology totally makes a moot point where you have one single source of truth, and everyone's looking at the same data and getting the ability to interrogate it, and ask their questions.
(17:53):
And they come to a meeting with that foundational knowledge of what's going on in the clinical trial. Then they can spend their time talking about what decisions they need to make. Versus trying to rationalize why does one spreadsheet say one thing and something else that's something else, and one system has it this way, another system has it that way. So it really puts people at a much further, closer to able to make strategic decisions, rather than trying to rationalize and understand what it is they're looking at to make those decisions.
Charles Rhyee (18:22):
[inaudible 00:18:23]
Suresh Katta (18:24):
Charles, if I could add one more thing there? Depending on how rich you are as a company, you will get a more fancy consultant system integrators to come in, and have it three year-project to build out a data lake for this. And things are moving so fast, that data lake becomes obsolete. Many large pharmas already publicly made a statement, "I spent $300 million trying to create a data lake. Now it has become a data swamp. I just don't know what's there, and what's not there." And when midsize or small they're struggling with. So our competition is to some extent, this fancy money that goes into system integrators.
Charles Rhyee (19:08):
That's interesting, and that reminds me, I hosted a panel on a similar topic years ago, and a large bio biotech company was on, a chief technology officer, talking about exactly that, "Well, we're building this big data lake, and this is how we're going to be thinking about digital." It'd be interesting if I circle back and see and see how that project's going at this point. But so maybe talking about customers and partners then, your customers will include some of the largest, clearly, most innovative biopharma companies around. You talk about like some of the Pfizers, Gileads, and Regenerons of the world. Maybe talk about some of the work you've been able to do with them, and how quickly you were able to work with them. Are these, do they start at the enterprise level? Or are you typically starting with certain projects, or certain divisions when you're working with companies, particularly at the very large end of the scale?
Suresh Katta (20:09):
Yeah, good question, Charles. I'll maybe give you an answer and then give you one example here. The smaller the organization, they try to bring us in as an enterprise solution. Okay, bigger the organization, then you try to bring in us one specific problem you're trying to solve. That's how I look at our customer base, [inaudible 00:20:34]. You are somewhere in middle, you are doing multiple things, not necessarily one, all the things, but not necessarily just one thing. Okay, in between. That's how I ... Then given that, I'll just give you one example of, you asked the example. When you are conducting clinical trials, especially big pharma, they're conducting several hundreds of them, on an ongoing basis, actively. So many therapeutic areas, at least 10, 20, 25 therapeutic areas they're working on. So you generate queries as you're conducting clinical trials. And these queries runs into, on a daily basis, thousands and thousands, and tens of thousands of them.
(21:21):
And they have very expensive clinical data managers, not only they pay for those guys at CROs, they also have a duplicate of those people, same people, inside pharma, because pharma has outsourced same clinical study to three CROs, not one. Because one CRO happened to be specialized when they're in Germany. Another CRO happened to be in New Zealand, another CEO who happened to be in US. So they had to contract multiple CROs to do this work. So inside of pharma, they need to have a clinical data manager sitting on top of all these different clinical data managers, to process these queries.
(22:04):
What the issue is? What we were able to show, is with our deep-learning, machine-learning algorithms, we were able to process these claims, process these query segment, queries, in an automated way. They're called something called, "Golden query." Golden query is, without any human touch, query was either generated or query got closed. Both sides, able to do that. So when we are able to do thousands of them at day, or multiple thousands of them at day, you can think about all the savings they get in the big pharma. Instead of trying to hide a $300,000 clinical data managers, which that's what it costs to hire those people. Now, same clinical data managers are able to do more studies inside the pharma. Rather than they're limited to one or two studies. Now they're able to do something like five to 10 studies because of our platform.
Charles Rhyee (23:05):
That's pretty interesting. And is that, when you guys go to the clients, is that a key thing that they're looking for? Or is that a by-product of what they're looking for? They realize after the fact, there's also a savings element to that?
Suresh Katta (23:19):
Yeah. With all due respect to my colleagues, or my clients, in pharma industry, that great scientific minds, they are trained very well on the regulatory environments, highly regulated industry, their mind thinks that way. Their mind doesn't think about technology that can be applied.
Charles Rhyee (23:38):
[crosstalk 00:23:38] certainly-
Suresh Katta (23:39):
So that's a uniqueness, what we bring to the table. Where we are able to add to their complexities of highly regulated environment, and highly scientific environment, how do we bring technology to speed up things? Most of the time, we are teaching them. There are some bright people there, don't get me wrong, that have been able to attract some right people, technology-people there. Even one of the big pharma, who had made CEO announcements about how they're going to save 500 to $700 million by drug development. He openly said after about a year of effort, "I'm not able to attract great engineering mind [inaudible 00:24:23]. They always go to Silicon Valley companies. I'm just not able to neither attract them or retain them." Okay, so that's the uniqueness of what we bring to the table. As we are able to attract the great scientific, not scientific, engineering minds, who can compliment the scientific minds in the industry.
Charles Rhyee (24:44):
When I'm hearing you talk about the platform here, what strikes me though, is, I mean, you talk about your biopharma clients, but does it make as much sense also being the platform for CROs themselves? Because particularly the largest CROs, they operate in multiple countries, multi-regions, and at the same time, and they have to deal with probably systems at the local level, similar to how bio-pharma looks at it. Is that an area that you're targeting for growth as well? Or is that a little bit different at this point?
Suresh Katta (25:19):
Yes, I think, eventually we think CROs also will like that, this kind of platforms, they're forced to adapt. But how the industry working today, Charles, is CROs would like to do what sponsors tell them to do. So until the sponsors tell them to use our platform, there will be a slow growth inside CROs, because CROs have a vested interest to protect their billable hours.
Charles Rhyee (25:47):
I see what you're saying. So, okay, I understand that, but, okay. Yeah, let's move on a little bit here. If we look earlier this year, obviously you alluded to at the very beginning, that we're going through some very unprecedented times here. Early May, you announced the launch of a new COVID-19-command center and using the, your LSAC platform, and you're partnered with, forgive me if I pronounce it wrong, [Index AI 00:26:20]?
Suresh Katta (26:20):
Yeah.
Charles Rhyee (26:22):
Their [inaudible 00:26:23] Platform? Maybe talk to us a little bit about that and how you're helping people during this process and trying to cope with the pandemic?
Suresh Katta (26:33):
Yeah, good question again. Look, as we were thinking through building this platform, one of the things, one of my colleagues, brain behind this, said this, "We felt pharma industry is very broad, the development industry is very broad and very deep. And we cannot be one single provider for doing all the clinical analytics for this." What we did, was we expanded our platform, like a iOS platform on Apple, or an Android platform. And what it was about this, [inaudible 00:27:08] capabilities, they can build their applications on top of our platform.
(27:12):
So now we have bunch of partners who have come in, building different apps on our platform, and that's the way we are allocating to, coming back to your COVID-19, we felt Index.AI Had a great translational sciences, this is a multi-level mix capabilities. And by choosing the patients, right patients for COVID-19, whether it's a vaccine, or it's a therapy, you're making, we need to select the right patient. So we were able to get, very quickly, from Wuhan China, to Korea, to other places, and load up all the data we could get on the COVID-19 patients. We were able to show, through biomarker stuff, which are the patients, who would be perfect for this vaccine clinical trials. What says, "Who are going to be better for therapy?"
Charles Rhyee (28:06):
That's interesting, so when we look at some of the clients that you have, like such as Regeneron and Gilead, who are clearly at the front of some of the development that's going on right now to combat COVID-19, have they been using this offering here to try to find patients to enroll in their trials?
Suresh Katta (28:25):
I'll just say this, several of them are using our platform today. We are very proud of that, since there is no public announcement from some of these companies, I'm trying to hold my excitement about it. But what's a good, is a several of them, as well as small biotechs, which only we heard those names now in last three months, they are also in the high-profile right now. We are having different level of discussions for different applications in our platform. They're exploring how to speed up their clinical trial.
Greg Simpson (29:01):
One of the differences, is this is purpose-built for COVID-19. We have a lot of other technology companies out there, that are saying you can use their existing technology for your COVID-19 trials. This is purpose-built. That's why we partnered with Index AI. So it's in our minds, everything that someone running a COVID 19 trial would need, all in one place. Not just our traditional LSAC platform, that they can use on a COVID trial, but something purpose-built.
Suresh Katta (29:28):
And one of the things we did, was there are almost close to 70,000 scholarly articles written about COVID-19 by academic researchers and physicians and the scientists. And we felt there was a need for our platform to access all this ever-growing set up. When we started doing that, our thinking was, "Anybody conducting COVID-19 clinical trial, whether it's for a vaccine side or therapy side, they need to check, look at what other scholarly people are saying about this. Whether they are in China or whether in India, Singapore. Doesn't matter which place the scientists are that have published these things.
(30:13):
So what we were able to do, is not only connect this entire database on an ongoing basis, every day we are refreshing. One of our clients came to us and said, "Hey, we are running into problem with selecting principal investigators, who would be the perfect one to conduct our clinical trials, investigators?" Within Friday night, we got that call, we were able to turn on. By Sunday night, we went through several iterations, we were able to get them 7,000 plus principal investigators around the world, they could use as their first take on selecting the investigators for COVID-19 clinical trials.
Charles Rhyee (30:53):
That's pretty amazing. Yeah, that sounds exciting. Then obviously we'll look forward to see what happens, on the development-side, hopefully sooner, rather than later, obviously. Even before COVID though, you have working with, making announcements. I think there's one back in February, with Pfizer? You signed an agreement and it sounds like they signed on to deploy your platform. Talk a little bit about what drove that decision? How did that dialogue go? And what were the key problems that Pfizer was looking to address, and try to solve for that led them to you?
Suresh Katta (31:37):
Yeah, as you know, Pfizer is one of the largest big pharma, and that they had done, unsuccessfully, several attempts at using data analytics platform for a period of time, and some things that have succeeded for them. But they were looking for some very identified problem statements they felt that could be solved by a data analytics platform like ours. That's how they came to us. We received a lot of data points from the raw data, we were able to receive, that's part of the data center. In short, running an RFP process, we would rather run a process. It's very similar to hackathon. That is, you have a well-defined set of major problem statements.
(32:26):
Then you take the data sets, and overnight you quote it, or you come up with some solution out of the hackathon. But it's similar kind of concept, but not naturally overnight, it was several weeks, rather than several months of several [inaudible 00:32:42] RFP process. They were able to squeeze this into several weeks, and able to get the problem statement they defined for us. They give us the data set, and our platform was able to deliver what they were looking for. That's how they selected us. This was mainly, they were focused on the clinical data management-side, they felt they are overwhelmed with the data sets they get on a daily basis. They wanted a system to process that.
Charles Rhyee (33:10):
How do you see the industry evolving from here, and the future for you guys? And I guess maybe more specifically then, any kind of key milestones or things to look out for, that you're excited about coming down in the next year or so? Something that we should all pay attention to?
Suresh Katta (33:29):
Yeah, one of the things I'll say, there are some organizations talking to us right now, end-to-end drug development to be done, just using our platform. That's the future I'm looking for. Where someone is bold enough to say that, "I need to have a command center in my hand," like Houston command center to manage the entire Apollo-program, or Falcon-program at Space X. Similar way, they need to have a command center to manage the entire drug development from the end to end. That's, I'm going around, not doing one piece or two pieces here or there, looking at how do I line up a couple of them. We know that this industry will follow one, two successes we have, then the rest of the industry will follow. These are the big milestones we are looking for over the next 12 to 36 months, how we are going to evolve as a platform of choice for that development.
Greg Simpson (34:28):
Charles, I'll just add that I think a lot of the use of technology that's been expedited because of the virus, will continue in our opinion. So there's a lot of value of using something like remote monitoring. So the clinical research associates don't have to go to all the sites. They have all the information right in front of them. Why would you want to go back once people can start traveling and have people be unproductive when they can have information from all the sites in front of them in one place? So we really see that the virus has accelerated the digital transformation, and we think it's going to stick, and we're going to see it evolve even more and more quickly.
Charles Rhyee (35:08):
Yeah, that makes a lot of sense. And obviously this pandemic changed a lot of things, and certainly helps maybe accelerate some of these trends that you guys are envisioning?
Suresh Katta (35:17):
Look, what our thought process are, earlier I mentioned, we wanted to find a way to apply [inaudible 00:35:24] so that we can take that cut, which has been going up into multi-billion dollars over the last two, three decades, and turn that down into coming down to $300 million per drug cost. That's what our vision is. I think it's quite doable, we feel, today, rather than spending, because batting average has been low for the industry. They hit too many walls, they take too many wrong roads. Then by the time they come to realization, they have spent already tens of millions, or hundreds of millions of dollars, happening. With our platforms, we feel we can minimize that, and increase the batting average so high. And get that drug development timeline squeezed into much more like three to five years, rather than today, it's taking 10 to 12 years.
Charles Rhyee (36:14):
Yeah, and who knows? Maybe that will help also lower overall drug costs as well and pricing. Yeah. So that'd be a, I can't say we've seen it yet, but that would be something certainly to hope for in the future for all of us. With that, I think, we've run out of time here. I really want to thank you both, Suresh and Greg, for joining us today. I really appreciate this discussion. And I think our listeners will find this very exciting to listen to you, and we all look forward to hearing more news coming out of Saama.
Greg Simpson (36:43):
Thank you, Charles.
Suresh Katta (36:45):
Thank you, Charles.
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