Good afternoon, everybody. I'm Luke Sergott. I cover life science tools and diagnostics here at Barclays. With me, I have William Feehery, CEO of Certara and newly minted CFO John Gallagher. Welcome aboard.
Thank you, Luke.
Thanks for making it down, guys.
Appreciate it.
I kinda wanted to start off because you, I mean, you've been public for a while, but I think that there's still a decent amount of education on your business. I think that we all ultimately oversimplify what you guys are doing into, "Oh, you're running a computer program to reduce the amount of, you know, data or, or not data, or number of samples that are needed, or, or mouse models." So, like, as you think about, you know, you know, Phoenix, and you have Simcyp, and you have all the other applications that you guys provide across the drug discovery to development portfolio, maybe, like, level set where, like, how you guys are used throughout the drug paradigm to take, like, drug discovery molecule all the way through the clinic.
I know that's, again, you have tons of different small businesses there, just, like, as you're thinking about, you know, the key major steps.
Yeah, no, I appreciate the question and it's a good opportunity to just remind everybody what we do. So the core of Certara is based on the idea of using models during drug development. So pharma grew up by doing clinical trials, and clinical trials are always gonna be necessary for proving drugs. But the fact is, there's a lot known about the science of a drug that you can take advantage before you go into human clinical trials, which are expensive and risky, and all of those, and sometimes patients are rare, for example. So, you know, that's the concept. By you know, so if you wanna do model, so what we call that is model-informed drug development, the concept of what we believe needs to happen.
You know, the kind of the three legs to that stool is one is, and I'll talk a little bit briefly about them, but one is biosimulation.
Mm-hmm.
One is having experts do this kind of work because it doesn't exist everywhere in pharma, and it's pretty complicated. And the other one is around the data flows that go through pharma, right? So we're, you know, by doing simulation, we're taking into our software lots of data that comes from labs and clinical trials, and then it's kind of a big mess of all those sources coming into pharma. We need to standardize that, normalize it, analyze it, produce something that makes some sense to a regulator coming out of it, right? So, you know, biosimulation is what we're probably the most known for. There's not a lot of companies in that area.
But the idea there is, you know, up till now, most of our models have been what we call mechanistics, where we're taking into account the known science of how a drug you know, gets into a body, moves around, concentrates in the body, is eliminated, and what it and, as we go forward, even more and more about what it does when it's there. So a lot you'll hear a lot of companies talk about picking a drug candidate. Certara is more around, "Okay, you have a drug candidate. There's a billion ways that it could fail in a clinical trial.
Mm-hmm.
So let's, let's start to think about all of what's really gonna happen when it gets out there in a point in, in a real human and in a population of humans, which, in fact, vary in many, many different ways in a clinical trial and when we get in a big population. 'Cause that's really where the cost is, right? There's lots of drug candidates that come up. They fail for all kinds of reasons, toxic reasons, or, you know, everything else. And if you can start to predict that before you waste $100 million on a clinical trial, you can add a lot of value to pharma. So broadly speaking, that's what our software is designed to do. Now, we were formed over many years, a lot of acquisitions. From the outside, it could look complicated.
We have a number of different software and service products that basically hit this same topic at various points during the drug development cycle. When we started, we were primarily in the sort of clinical phase. Over time, we've been, you know, pushing our ideas into the preclinical phase and increasingly into discovery because we kinda believe that there's an opportunity to unify all of this, right? But, you know, I guess the way to think about this, there's not a lot of companies that have that are out there like us. It's a little nerdy if you really look at some of the science of what we do, but it's successful, right? We have literally hundreds of drugs that have gone through the FDA, where you can look in, you know, their filings.
You can see that they use Certara biosimulation to make an argument to the FDA that their drug should be approved or that it should, you know, that you can use simulation instead of a clinical trial. And that's a lot of value that we deliver. There's still a ton, you know, we're not the biggest company in the world. We're growing, actually. But there's still a tremendous opportunity to continue to grow this as we go forward. And that's what we're doing both organically and inorganically.
Okay. I wanna I wanna come back to the data piece there, but you know, as you as you think about the different demand trends within the market, so we have, you know, on the we're starting to get the biotech funding environment back, which is going to help later-stage trials on that part of the market segment. Pharma's been tightening their budget on some of the later-stage work. That was probably last year. That's starting to be released. We're starting to see some positive trends from other peers that you're hearing about the positive trends in pharma. On the drug discovery side, it seems like there's still some prioritization or still some budget tightening that's, that's going on.
Like, give us a sense of, from where you guys sit, how the orders or the conversations you're having across those two different markets, if you will, you know, and how that you see that progressing throughout the year.
Yeah, I think that the two trends that were negative last year that affected us is one was the continued downswing in funding for biotech. So we saw a lot of our smaller customers. There was a lot of turnover last year. Some of the other ones, you know, they were, you can say, they're price-sensitive, but they didn't really know where their next round of funding was gonna go, or where it was gonna come from. So that was a factor last year. It started maybe a little bit before. The other one was, you know, I think particularly, you know, at the second and third quarter, we saw the larger pharma companies reevaluate a lot of their portfolio.
I would say that was primarily because of the Inflation Reduction Act and a rethought of a rethinking about what they wanted to do. You know, that was a the I would say a transitory effect. I mean, they, they, they took a pause. They rethought it. They killed some things. They started some things. For us, it meant that, you know, the pause in work that was kind of unusual last year. I would say, you know, what we're seeing recently is, you know, pickup in the biotech funding environment. And I think, the inflation it's hard to argue that the Inflation Reduction Act is positive for pharma, but I think it's been absorbed, and, and they've made their choices, and they're moving on. So as a result, we're kinda where the other companies are. Like, we're kinda cautiously optimistic about where we're going in 2024.
I don't think we're calling for it to be a boom year in the area, but, you know, it's, you know, it's certainly looking positive, compared with where we were last year.
Yeah. So it's kinda like you're bouncing along at the bottom, and you're just kinda waiting for stuff to turn on.
No, I mean, there's definitely signs of life. Our bookings in the fourth quarter were pretty good. You know, we're seeing, you know, a fair amount of activity from really across the spectrum.
Mm-hmm.
In terms of, you know, interest in our products and sales and that kinda thing. So I think, you know, we think that as we go through the year, hopefully, we'll see a pickup in, as we even as we go in the second half of the year. I mean, biotech funding just kinda restarted, so it will take a while for that to flow through to, you know, to us, for example.
Yeah. And on I particularly the services piece of the business rebounded, especially on the bookings. Kinda walk through, you know, think about why, you know, why would services kinda rebound before the software bookings or am I completely off on that?
Well, yeah. Actually, software really did, didn't perform badly at all last year.
Yeah.
We grew 15%. So that was fine. It was the services that were more volatile because of the trends I was talking about. You know, our services are really a lot of them we call them technology-enabled services. And the original idea we had was we have a lot of software. Some of our clients can use it to different degrees. Some of them are very sophisticated. Some of them are small. And so a lot of our services are right around using our technology to help clients do what I talked about earlier.
Mm-hmm.
You know, I think at the end of the year, I think, you know, you had multiple things going on. I think in some of the big pharma companies, they had budget to spend. So there's a little bit of a budget flush in the fourth quarter. That's okay. That's real work.
Mm-hmm.
And that benefits us going into this year. There's a little bit more confidence about what they wanna work on and what their portfolio needs to be going forward. So no.
Yeah. And I guess then from a services, it's like you said, it's a tech-enabled, so they don't have it, it's probably more on that. That's like almost like a full service where the biotech or whatever pharma doesn't have that person in place that can run the software, and they basically.
Yeah, it's very. So if yeah. And there's a lot of different customers.
Mm-hmm.
We have about 2,200 customers or something like that. So we really have a very broad base, which is good, you know. And that's what there's a, you know, portfolio effect there. But, you know, if you wanna take the prototypical biotech company, you know, they could be what you said. They don't really have the internal experts. They don't wanna hire them.
Mm-hmm.
They want, so they're—you're—please use your software and give us the answer, right?
Yeah.
But it's not quite that clean. Even the bigger pharma companies often hire us for services because this stuff can get pretty technical. And so if you're working in an area, and you want, you know, one of our experts, or you want the person who actually wrote that code to come in and help you, they'll also bring it in too, right? So there's opportunities to really unite these, and that's been kind of one of the themes behind how we built the company.
Right. And then so on, you did the sales reorg. Talk about how that kinda fits in with that paradigm.
Right.
You know, what you guys actually changed or, or why, why you changed it.
Right. So when we went public in 2020, we had a very small sales force. We don't disclose the exact numbers, but let's just say it was smaller. Since then, we've been investing and you can kinda see it in our numbers. We've been investing to grow that up. Over the last couple of years, we've really spent a lot of time focused on the software business. We've built up, I think, a good, decent sales force in terms of we have a good process. We know the pipeline. So we're doing the things you'd expect. On the services side right now, though, we still have a business where there's a lot of seller-doers.
Mm-hmm.
It's not unusual in consulting to see that. And in fact, it's inevitable. I mean, you know, clients always wanna see the, the SME come in and write the proposal and talk about what you're gonna do and that type of thing. But there's an opportunity there to, you know, put in a lot more process there to take some of the load off our really senior consultants who are doing a lot of selling. And, you know, that'll pay off for a bunch; it'll pay off a couple different ways. So one is, as I said, strategies to you know, there's many, many opportunities in this business to sell software and services together. So by having the one sales force that carry both, we expect to get opportunities there.
The second, specifically on the services side, there's an opportunity to basically just make this more predictable and also to take some of the load off our consultants who are, you know, pretty high-priced, you know, experts so they can do more work and spend less time kind of, you know, doing all the process involved in sales and screening and all that kinda stuff.
So it's more about just kinda building out the technical infrastructure that can support those guys.
Yeah, exactly.
So they can get out and do the BD.
Right. I don't think there's a lot of risk in this. Sometimes I'm asked, "Hey, you know, is there any way this" I mean, you know, we've got a pretty good relationship with a lot of customers.
Mm-hmm.
I just think that there's an opportunity right now to kind of, you know, basically make the company a lot more efficient. I mean, we're starting to do nearly 800. We have over 800 consultants right now, so.
Yeah.
We're starting to get to a critical mass where you can sell in a different way in a profitable manner, so.
Okay, and then, so with it within that reorg you started two or three quarters ago, and then when you were talking about it on the call that it's, you know, really gonna start to prove too cute. Kinda, you know, what's the visibility there, and what gives you the confidence? Like, what's the early feedback from establishing that type of structure for your sales force?
Yeah, well, one of the key changes that we still had actually, we implemented this last year, but we really didn't get the kind of compensation structure until January 1st. So one of the catalysts 'cause we can't change it midstream during a year. So one of the key catalysts for us now, in pointing to going forward for growth is the fact that we can align sales compensation structure, and really, you know, start to get even more benefit from the structural changes we made.
All right. And then so as that kicks in, is that, you know, kinda weighing on that as we think about the, the 1Q or the 2Q margin, and it improves from there? Is that kinda be some seasonality there as the compensation kinda kicks in?
Yeah, I mean, so we called out investments that.
Yeah.
That we're making this year, and that's the reason for the 31%-33% EBITDA margin guide. Those investments will be; you'll start to see them show up in Q1.
Mm-hmm.
We will be gating them during the quarters of the year and be monitoring how they're.
Yeah. So it's not gonna be just, like, one massive drop in 31 days.
Exactly. No, it wouldn't even be possible to frame it that way. But you will see it start to come in Q1, and then but it'll also come in the subsequent quarter.
All right.
And by the way, too, that's all on the OpEx line.
Right.
So it's in R&D investment for developing our software platforms. And it's also in sales and marketing given the reorg commentary that we just made, so.
Okay. I wanna go back to the data piece that you're bringing in. And just talk about how the data piece, I mean, there's so much data being produced. How has that evolved, and how sophisticated is that getting? And then, I mean, there's so many different disparate data sources coming in, and then how that fits in with, you know, Pinnacle 21 and establishing the standard. And, you know, what other pieces could you add to that to help improve the efficiency for FDA to have just one type of data aggregation?
Yeah, that's a great question. So Pinnacle 21, when we bought them, was used for data validation for data that goes to the FDA. So the idea behind that that the founder had there was brilliant was, you know, the FDA wants all of their clinical data to be submitted in CDISC format. Somebody has to determine whether, in fact, it meets the standard. Pinnacle 21 was used for that. And that's kinda what Pinnacle 21 was used for. But the vision going forward has been, you know, the problem that the FDA has, pharma has in a big way, right, because they, they take data from CROs and from labs. They have contracts with these companies. They want the data to come in and come from some kinda standard format, ideally.
Or if it's not, they have to send it off to a, you know, a data team to basically standardize it and sort it, you know, sort it out. And so, you know, they can use the, the new versions of Pinnacle 21 that were coming out to basically police their own contracts with, with their data suppliers. And that brings benefits to everybody because now if data's gonna come in standardized, it can be analyzed more efficiently, cheaper.
Mm-hmm.
We can use it more in biosimulation. Down to the end, you know, when you're gonna submit it to the FDA, you don't have to go, you know, through this whole process to kinda restage the whole thing the way people are doing right now. So I think a lot of our clients see that as a pretty big advantage. We just bought a company, Formedix, which basically tacks onto that and lets us basically interact with all the EDCs.
Mm-hmm.
You know, basically, that's even extending the reach of Pinnacle 21.
You're moving it upstream more, and then you're kinda layering on. That's really helpful.
Right. So it's, you know, and as you know, like, you know, as a very small portion of drugs that are worked on get submitted to the FDA. So the market is much bigger as we move back.
Yeah.
You know, backwards, in this particular case, right?
Once you standardize that data, it becomes the analysis that much easier across the pharma companies.
Yeah. The pharma companies spend. They have huge teams, enormous amounts of money. A lot of it gets outsourced to, you know, other countries. But there's tons of people who basically just try to validate and standardize data in pharma. You have to do it, right? Otherwise, you can't do analysis. You can't sort it. And so attacking that cost, and it is not just a cost in terms of people. It's a cost in terms of time. It's a cost in terms of potentially making mistakes.
Mm-hmm.
You know, maybe even missing the big picture of what you want what you wanna do and what you wanna say to the FDA, right? So if you can standardize that in the overall process, there's a huge financial advantage to opportunity in pharma. But also, it brings in the opportunities of biosimulation a lot more, right, because we play you know, we need all this data too to really expand biosimulation to the levels that, you know, kinda got clued into this market.
Yeah. And, I mean, you just talked about from Formedix and then the Medidata repositories. When pharma, when you're doing that data analysis, is there a part of your business where you're actually able to keep that data and kinda create a data lake moat for yourself? Or is this just?
Yeah. So what I'd say is, the advantage of SaaS software and software platforms is that you tend to get the client's data in one place where you can at least see it. We don't own that data, that data. We don't make any claims for that data. They would never accept that if it's just not our business model. However, if you know where it is and you know what performance it is, you can go in and sell them additional things. So there's a potentially a, a big benefit to the company as opposed to where they are now, which is there's stuff in SharePoint all over the place. And, you can't, you know, if someone leaves, you lose a lot of it, right, so.
Okay. And that's ultimately, like, from Medidata on the back end of Pinnacle, and that's that fits in.
Right.
Let's talk about a little bit about the Applied BioMath.
Right.
It's not the nerdiest company name I've ever heard.
I like it.
I kinda like it. So you talk about this bearing being strengthening the QSP part of the business.
Right.
You know, 101 what that is, and then really how that fits into the overall portfolio and the workflow that we're kinda digging through now.
Yeah. So QSP is, you know, within the scientific area, it's the hot area within biosimulation, right? So, you know, there's various parts of biosimulation. In this particular case, we're trying to model exactly how the drug interacts with its target, as opposed to, say, genetics moves through your body and other important things. There are, you know, Applied BioMath and we are probably the two biggest independent players in an admittedly small market, competing with each other a little bit less than you'd think. They were, you know, a Boston-based group. Everybody came out of Harvard and MIT, and so they have a good reputation within a very important customer segment that we'd like to attack.
But I think the real vision was to basically just get a critical mass of people in this area 'cause we see it as an important area of development for the future. Number one, there's a ton of demand for this type of work within our clients.
Mm-hmm.
But there's an opportunity to unite it with what we're doing in our Simcyp business to really expand that as well in terms of more software play as well, so.
Is it 'cause the Simcyp originally, it's mostly like you're talking about, like, ADME, PK, PD, so, like, toxicity prediction?
Right.
Now this is almost an efficacy prediction of whatever the drug target is based on that area.
Yeah. But you kinda need both, right?
Yeah.
So being able to tap into Simcyp's, you know, proven validated models that have been used by many people across the industry is a big advantage because no one has to rewrite that. Nobody has to go back and validate that their models are accurate and prove to the FDA that they did it right.
Mm-hmm.
You can just tap into that. So it's a way we kinda think of this as, you know, Simcyp's gonna get the line around Simcyp's gonna be much bigger than just PBPK. It's gonna start to.
Yeah.
Include all the other concepts.
Yeah. All right. So, I mean, the key question is, was there an AI component to Formedix or Applied BioMath, or was that kind of a misread on our part?
We bought a SaaS, a different AI company last year.
okay.
Vyasa . There's opportunities all over the surface. It's a higher-up portfolio for AI. And I realize that every company up here is probably talking about AI.
It's like,
But we bought a company pretty early on in this. We've already implemented features. We already have revenues coming in from our AI features. And, and I think that we've taken a very practical approach to implementing specialized AI within our market.
Mm-hmm.
Now, AI this year has been great. I mean, from a marketing tool, everybody wants to talk to you about AI. And that's great, but it won't last forever, right? We're gonna switch to, okay, you know, there's a zillion ideas. You know, what ones are really gonna work, and who's, you know, what, what's the winnowing out that's really gonna happen in this market? We have products today. We have a pipeline of AI products. Most of our almost all of our existing software products can have either already we have implemented some AI features, or they will have it going forward. But, you know, just if you just think of it from a broader perspective, it enables us to take into account unstructured data that either we wouldn't have taken into account before or would be very expensive. Think of, like, people re you know.
Handwritten stuff.
Well, yeah. Reading papers and looking for the science in it, trying to find data that's in obscure places. And so the ability to expand biosimulation with that and take into account data sources and provide inform you know, provide results that we couldn't have otherwise is a pretty exciting thing for us.
Just make the model smarter.
Yeah.
All right.
Absolutely.
What are the key applications that you're seeing for right now? You know, you said I know you talked about it being used at some point of your, your portfolio, but, like, what are the near-term trends that are being used for majority? And then where do you think I mean, obviously, we're gonna have, like, AI drug discovery in the next three years off you guys. Like, so, like, where are the low-hanging fruits for the technology in your opinion?
Yeah. The three well, the two or three areas we've talked about, right? So we've implemented it in our discovery product. It's kinda like I call it, like, a Netflix feature. Like, if you like you know, if a drug discovery scientist likes these five molecules, it will suggest, you know, other ones that he thinks are similar. So that's pretty cool. It's drawn a lot of attention.
If they buy it through you, there's an extra drop there on the margin for it.
Oh, I hope you're kidding me, you know. So, you know, nothing's, you know, providing value here, right?
Yeah.
So, we are using it internally in our regulatory group to actually write regulatory documents.
Mm-hmm.
At some point, that'll be an interesting, you know, product we can launch internally. We do have 200 people internally writing regulators. They know what we're doing in that area. There's an opportunity for specialized products, so that's another one we've talked about. And then, a third one is, you know, we're enabling a lot of our customers to take into account their internal databases and create a custom GPT.
Mm-hmm.
Kind of thing where they're looking at their own internal data and also maybe in production with, you know, external sources like PubMed or something and creating a custom GPT, which is totally within their boundaries, keeps their data private. So those are not the only things we're doing, but those are, you know, probably three early ways where I can say confidently, like, we have something pretty good, and we're using it. It's working. There's a pipeline of product improvements that will come out, and, you know, we'll see how this plays out as we go forward.
Great. Last one here as you think about, you know, Certara continues to roll up and how that fits into the overall LRP. But, you know, as you're looking at the business of the portfolio now and having a better understanding of the workflow, I think I pointed out a few areas where you might be looking. But just give a sense of where you're looking to bolt on those additional technologies and continue to expand the portfolio. Like, what's a hole that you guys have that you'd like to fill?
Well, we're fortunate to be in a really interesting area with a lot of opportunity to expand. You know, we have done some inorganic expansion. You know, we've tended to focus on things that are software-based 'cause we'd like to, you know, you know, increase our percentage of revenue coming from software. You know, I would say there's opportunities in all the areas I talked about. In biosimulation, there's probably not a lot of things out there, but there's some bolt-on technologies we can, you know, we're always interested in. Some of it is accelerating our own development plans. Sometimes it's by decision. There's opportunities for us to get into the discovery area potentially in a bigger way.
You know, one of the things about the industry, you know, as we look out there, sometimes we just get surprised with, you know, there's lots of good ideas that maybe we don't even have, right, that you can incorporate depending on the motivations of the founders, right? Awesome.
Great. Thank you. That's all the time we have.
All right. Great. Thanks.
It's a pleasure.
Appreciate it.