Note to self for next year, not 4 back-to-backs. Good morning, everybody. Hope you're well. I'm Dave Windley with Jefferies Healthcare Equity Research. We're very pleased to have Certara here. I'm assuming if you're in the room, you are familiar with Certara, but if you're not, they're a leader in biosimulation in the drug development process. Joining us today is the company, or are the company's CEO, William Feehery, and CFO, John Gallagher. John's been in the seat for 15- A little more than a year. A year, yeah, but I was gonna say 15 months. Yeah. Yeah, pretty close. You're about right, Dave. Thank you. So, thanks very much for being here. Really interested to talk about Certara today, and I wanted to start just get you started, Bill, with kind of landscape question, which is, you know, biopharma customers have been through—we've been through the pandemic, then we've been through kind of an underfunded period. We're going through a, you know, some concern about the economy and then throw on IRA on top and a pricing regime that changes, you know, pharma's long-term outlook. And it seems like if we think not, you know, a quarter or a year or even five years, but maybe even a 10-year development cycle type timeframe that large pharma is probably in, that they need to pull some levers to be more efficient. Biosimulation and other, you know, software and AI probably are among the portfolio of things that they're thinking about levers to pull. So the question to you is, as you're interacting with customers at, you know, as high a level as you can, how's their appetite for biosimulation and the types of tools that you bring to the market evolving or changing? I'd love to hear. Thanks. Yeah. Thanks, Dave. I think that's a great question, and I've been talking about it increasingly this year. Because, you know, the way I see this is, you know, certainly the IRA has pulled a bunch of profits out of the industry. The pharmaceutical industry, you know, you can pull up whatever statistics you wanna look at. It's just not a very efficient development cycle over it. So if you start, you know, at preclinical, you still lose 90% of the drugs along the way, and you even have fairly large late-stage failures that occur at a rate that's quite expensive for the industry. So, you know, if you just play it logically, one of two things is gonna happen. Either there will be fewer drugs developed or the industry will find ways to be more efficient, and I think that we have a pretty good story about how that can happen, and we are, you know, we're starting to get a lot of lo... You know, we are starting to get interest about that. So, you know, what I talked a lot about people is that the science of drug development is around doing clinical trials, and the gold standard is a blinded clinical trial. But the business of developing a drug is about predicting the clinical trial outcome before you spend all the money doing the clinical trial, and that's what biosimulation's about, and that's how, you know, Certara is, I think, fortunately lined up in terms of the technology. We've been expanding our portfolio of software and experts to do that, and you know, I think the part of our business that's really focused on biosimulation has continued to grow through the kind of disruptions in the market, and we're seeing you know, pretty good interest as we go forward. That's excellent. And what I hoped you would say. I guess, you know, it risks getting into a lot of detail, but you have some biosimulation applications and use cases in what seem to me to be maybe early clinical that are well-established and well-proven and been used maybe for a long, long time. As I think you've described to me, since the IPO or even before, you're continuing to try to expand those use cases, and it seems to me, correct me if I'm wrong, that QSP is an extension of biosimulation that cracks open a pretty significant new area of use cases. And so if you could talk about, maybe where you see biosimulation's application going, like, where are the biggest areas of promise as you think about helping customers on this journey that you just described? Yeah. So thanks. You kind of alluded to it a little bit earlier. We have a lot of products, and if you kinda look at us for, you know, at a high level, it can seem really confusing. But when you get down to it, you know, biosimulation exists throughout the drug development chain, and we've got slightly different questions being asked at different stages, and you kind of think of our products as targeting those kind of decision points, where, you know, you're making decisions that are gonna change the course of that drug's development. So, you know, we've got a significant presence in preclinical because there you're deciding, you know, do I even wanna take this drug into human development? What's my dosing? You know, we're doing things like doing translational medicine, where we're trying to either avoid certain animal trials, avoid use of certain animal models, things like that. We're doing dosing, we're doing, you know, decisions around, you know, whether you want to take drugs forward based on what, what you'll know about the populations they'll go into. And so that's one area that we've got. Another area... And then QSP basically falls right there. And I'll come back to that in a second. Another area is, you know. Sort of later, later in clinical, where you're in late-stage trials, and you're saying, "Well, how do I get the size of these trials lower in a way that the FDA will accept?" So a classic example for that for us has been around drug-drug interaction trials and the ability of a lot of our clients to avoid expensive trials because you can, you know, you can satisfy the FDA's requirements and, you know, using simulation. So to come back to QSP, you know, what we're really doing there is we're expanding what we're doing in preclinical. You know, you're trying to develop a model of how that drug works in the body that's gonna—ideally, that model is gonna stay with the drug as it develops further. And so it's important from a—as a business standpoint, to get in early and to be predicting it. And some of the challenges with that business is, you know, this is called quantitative systems pharmacology. That's the acronym we use. Some people call it systems biology. There's a bunch of ones, but the idea we're trying to come up with a model of how that drug works in the body, where it's gonna go, what it's gonna do, and will it work? And you learn a lot of things, right? So sometimes you learn, hey, you know, the drug is active, but you can never get enough of it to the site. Other times you'll find out that it's likely to have a side effect that you, you know, you might not have, might not have found until you got into, in the trials. All of that is very, very valuable to a drug company as they're going into, into a decision. And the, you know, the trick for this is to having enough people and enough capability to, to produce a model that meets their timeline. So we, we can't say, "Hey, you know, give us 3 years, we'll develop a model." They're just gonna go develop the drug, and we're gonna do trials, and, and we'll be out. So a lot of what we've been doing together is putting together a critical mass of people that we can handle many different therapies and modalities. We're turning that into a software business, and then there's a lot around handling the data flow, so we can easily access the data we need to produce those models fast enough to be, you know, acceptable to our customer's timeline. So... That's that couldn't have been a better segue into my next question, which is around your acquisition history. So I went back and looked at the last two or three years, Pinnacle 21, Formedix, Vyasa, INDS, Applied BioMath. And I think each of those maybe fits, you know, is a plug-in to the answer that you just gave in one way or another. My question for you is kind of what is the theme that you would stitch through the acquisitions? Or, you know, if we were to say, you know, go back in time and say, Certara three or four years ago looked like this, and two or three years from now, we're trying to get it to look like, you know it was X, and we're now trying to get it to look like Y. What is that? So I, we believe that for biosimulation to really take off, you've really got three legs to the stool. So one of them is kind of the cool software that you guys probably know us for. We have Simcyp, we have Phoenix. You know, so if you looked at us, that's, that's kind of the, the cool, cutting edge stuff where we're doing a lot of the, the interesting science. But that, that in itself probably would leave us with a fairly limited business because you've got two other legs of the stool. So the other one is around what I've talked about before. It's around the data. So there's massive amounts of data in pharma. It costs a ton of money to find it, to standardize it, to process it, to analyze it, to get it in some form that you've tracked it back, and you can talk to regulators about it. A lot, a lot of pharma spending is really in that area, and like I said before, if we're gonna do modeling on a timeline that's meaningful, we need to be able to participate in that and to help out there. And then the third piece is the experts. So, you know, we're not providing generic models; we're providing specific models for you, for your case, for your drug. And so that requires a set of experts that we can bring to bear to help out. So our acquisitions have been targeted in those areas, right? So the one acquisition, I would say, was our biggest one, was Pinnacle 21. We did that, I'm thinking 2021. Q2 of 2021. Yeah. Exactly. Sorry, getting my years there. But, and so Pinnacle 21 was really designed to give us that leg in the data side. It also did more because, you know, we've been trying to become more of a software company over time, and that brought us in a kind of a basically a really good software company, where we could basically use their organization, their infrastructure, and their processes to kind of build that up in Certara. So that kind of gave us also a strategic leg up in being a software company. The other ones have been bolt-ons, tied into one of those three areas, and we bought, you know, we bought software that's been tied to preclinical modeling. We've recently our two recent acquisitions was, one was a significant QSP business, where we're bringing in a lot of the experts and some of the software for quantitative systems pharmacology. We brought in another company that's focused on metadata, which has to do with, you know, how you set up the your data collection across all of your EDCs and all of your labs to bring in data in a standardized format, so we can do this very quickly. So, you know, kind of think about it as, you know, we don't see ourselves as an acquisition machine. We have done a bunch of acquisitions, but most of them have just been we're in an area where there's a bunch of bolt-ons that start to build out this kind of core offering that we've got. Yeah. I think that's actually very helpful. The categories are helpful for me anyway. Maybe an unfair question because you haven't owned them all the same, you know, same period of time, but what would you point us to as being one of your... or say, the best performer in this acquisition portfolio? Well, you know, you, you kind of have to adjust for timing. They've all been successful from a financial standpoint. Now, being successful from a financial standpoint has to do with how you integrate them and also what you pay for them. So that, that's worked out. Generally, we've, you know, we're participating in private markets, where they're smaller companies, maybe not as well known sometimes. In most cases, we've worked with the companies for long periods of time, so we know the founders. We know what-- you know, you know, whe- whether they're gonna stay with us. We know how we're gonna integrate it, so we have a process in there, involved there. If you wanna say, what was our most successful one? It was certainly Pinnacle 21. It was our largest one, but you know, they have a really good franchise in what they're doing. We bought a software pipeline that's continuing to grow as they add more features, and we're you know, both finding new customers and moving people up in terms of the value chain and what they're paying us. And then, like I said, you know, we also got a core software engineering group that kinda helped us supercharge everything else we were doing in Certara as we you know, continued to move our percentage of revenues more and more towards software. Sure. And then on the first quarter, I think the first quarter was the first that you talked about your cloud portal. You know, you have all these products. You've been integrating them to the best of your ability, but probably have... You know, it takes a while for the client to recognize some of that, and probably still is an effort to get the client to realize that all of those things map back to Certara. And so how does the portal, you know, both enable easier use by your clients, and I also sense, maybe create more of a kind of a common brand impression to the client? Yeah, so linking all of our software together in a portal, you know, some of it's just very practical, right? So, you know, we've got single sign-on for all of our products, which really help, you know, our, you know, The IT departments of our customers love that. From a marketing standpoint, you log on, you see all the products you have, you see all the products you don't have, so that helps our, you know, our sales group. We can go in, and we can say, "Okay, well, you guys are using this. This is how much you're using them. You ought to think about these other..." So that helps that process, and it gets rid of a whole lot of infrastructure that all these products had to do that. But I think that strategically, it has a lot as well because what we're trying to do is we're trying to unite our products into one, sort of common framework that a pharma company can use as it's moving that drug through development. And in order to do that, you know, we wanna have the data basically flowing from one product to the next. So over time, that's what this portal's allowing us to do. So our products are... You know, this, this will be the, this will be the center part, where, you know, data is coming in, it's being standardized. If you have data, if you've got a product, if you've got use of our product in your, say, preclinical area, that data is there, and as you move into clinical area, those guys can tap into your data and use it in the products that are appropriate for them. So, you know, it gives us a bigger presence. It gives us a much bigger story, and I think from our pharma, for our pharma clients, it sort of sorts out the confusion you have. Because we do have this history of selling, you know, products to different parts of these very complicated organizations. So letting them see across the whole R&D portfolio is pretty valuable. Excellent. So to the point on the data fluidity through these products, can we interpret that that is live now? Like, are those capabilities live now, or are there still parts of the, kind of, the enablement that goes along with this portal that still need to be. Oh, no, this will migrate over time, but the portal's live. We're using it for all of the new releases of our software, so I mean, I could report late uptake, but the reality is, everybody's gonna uptake it, 'cause that's gonna be the only way to sign on to our products as we go forward. So that part's live. There will be... There's gonna be a bunch of work over time to, you know, to basically get all of the data to flow from one product to the next, and then, you know, to fill in the holes that, you know, we can see as we go forward. We still know a few here and there, and obviously we've been working on it over time, so. Maybe a different version of that question is, I know, like, Simcyp has been around, it's been versioned annually, but it's been around for a very long time. Is it cloud-ready? Or are the products all cloud-ready, or is there recoding that still has to be done to make them kind of cloud-type products? Yeah, great question. So Simcyp, you can buy it... You know, we can run it on the cloud, on Amazon, or you can buy it, or you can, a lot of our clients buy a really big box and run it internally 'cause it's cheaper, you know, than paying Amazon. So I would say we're kind of indifferent on that standpoint as to how you do it, and smaller clients might do one way, and larger clients, another one. Other products are more data-intensive. So if you look at Pinnacle 21, it only has a cloud version. It doesn't really make sense for that to even be, you know, based on the desktop, just the way, you know, the way that you're handling data as it moves from group to group around pharma as you're, as you're trying to get closer and closer to submission. So, you know, I think Simcyp, to answer your question, you know, where, where it runs is less, is less important than where the data it acts on resides, and, and we're basically building repositories where we can standardize the data and hold it. Not necessarily take it from one pharma client. I mean, that if it's their data, it's their data, but, you know, where they can, they can access it as their products move through the succession of our tools. Yeah. Before I leave this and go to maybe some numbers, 'cause I'm sure everybody's asking, like, "What, what the heck is Dave doing here?" I wanna talk, AI is a hot topic. The way I formed this question was, are Certara and biosimulation AI before AI was cool? I mean, is there an element of kind of AI that you were already using in your products, or asked differently, how does current AI influence where you go in your product roadmap? Yeah, I mean, I could take the bait and say, "Yeah, we were AI all along," but I guess get into the definition of what- Yeah, right. What is AI? Sure. Right. So, you know, what Certara was for many, many years was we were mechanistic modeling. Like, we looked at the known science, we'd build up models based on what was written in scientific papers. We carefully documented it, validated it. We got data to prove that the models were accurate, and we could go to regulators and show that, "Hey, this, in fact, matches reality, that, and you, and you should use it." Now, if we look at what I call AI right now, it's an additional set of capabilities on top of that. So we're interested in AI because it lets us tap into data that we either couldn't use or couldn't economically use. So there's lots and lots of data in pharma. There's lots of unstructured data that, you know, what we used to do with unstructured data was we would hire PhDs to go read papers and come up with models and document what they did and talk about it. Well, with AI, we can process a lot of that faster. We can start, you know, mapping the connections between, you know, the science. So we can start moving a lot quicker, quicker on that standpoint. And, and I think the other thing we can do with this is we can automate a lot of the workflow that costs a lot of money, both in the operations of what Certara does and what our clients do. 'Cause, you know, a lot of our clients, at the end of the day, drug development is just this massive operation of, you know, how do you collect a lot of data on your drug from labs and clinical trials, and, you know, anything you can think of? How do you standardize that? How do you make some sense out of it? How do you make a decision? And then ultimately, how do you report it out and make an argument to regulators and to payers? So, you know, the ability to automate that is a big opportunity for us in terms of just our core business and then, you know, helping our clients as well. Maybe I can throw a John question out here. So, on guidance, your revenue midpoint is, I believe, around 11% for this year. Maybe some carryover acquisition contribution to that that I'd ask you to quantify. But as you think about that 11% growth, your management has talked about trailing twelve-month bookings growth as being an indicator, which is obviously, you know, has been below that, you know, kind of around flat to maybe a little below flat. So you have kind of a bridge, a fairly significant bridge to get from where your bookings growth has been to where you think your revenue growth can be this year. Help us with that bridge. Yeah, sure, David. So thanks for the question. Our guidance on the year for revenue is 9%-13%, so the 11% midpoint, as you mentioned. On an organic basis, we said that that'd be mid-single digits, so that's the fourth quarter acquisitions that we did contributing during the course of this year. When you think about the bookings that you're seeing us achieve relative to the revenue achievement during the year, there's really 3 things that I'd point out as we move through the year that are a bit different than what we've seen in the past. One is. So the first of those is the backlog that we carried over from Q4 and into this year. So because of some of the slowness we had seen on the services side, particularly with tier two and tier three customers on creating bookings, starting projects, continuing projects, and finishing projects, we carried more backlog from 2023 into 2024 than what we'd seen historically, and that gives us more opportunity to convert for revenue. So that's point number one. Point number two is we're seeing stronger, we're seeing stronger in-quarter bookings to revenue, and you see that really when you look in sort of the... One of, one of the key proof points there is in the software results. So we had 19% software revenue growth on the quarter, and that's thanks to the many of the new products that we've rolled out during the course of the year, becoming bookings and then also converting to revenue. The last point that I'd make on, you know, why do we believe that, you know, that we can get there from a revenue guidance perspective is related to normal seasonality that we see. At the time that we did guidance, we said that the services business would be more of a first-half, second-half story, that the back half would be larger than the first half. That's not atypical for the business to achieve that, and we anticipate that to be the case this year also. Got it. Any chance you could either give us magnitude, dollar value on that backlog carryover element? Like, how big of a factor is... You know, I gather you're looking at a backlog relative to forward revenue, and that is a bigger number than it has been in prior, prior years. Can you give us, like, order of magnitude, how much bigger? So we haven't broken that out, but I guess what I'd point you to, though, is the Q4 bookings. So we saw a strong Q4 from a bookings perspective, and because of the dynamic that has existed with those bookings then being, one, it's been slower to get the bookings, but then, two, once we have the bookings, it's been a slower process to convert to revenue. And that dynamic with a large Q4 bookings quarter carries over into 2024. As a reminder, too, the other thing I'd add on to that, too, is that services, which is where we've seen some of that softness, a booking typically converts to revenue over a period of 1-2 quarters. Okay. Okay, that's helpful. All right. Great. So I think that brings us to time. Thank you to the Certara folks for being here and to everybody in the audience.