Certara, Inc. (CERT) Earnings Call Transcript & Summary

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.

Very well, thanks for making it down guys.

Appreciate it.

I kind of wanted to start off because -- 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 ultimately oversimplify what you guys are doing into, "Oh, you're running a computer program to reduce the amount of data -- or not data or number of samples that are needed [indiscernible]. So like as you think about your Phoenix and you have Simcyp, 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 paradigm? You take like the discovery molecule all the way through the clinic. And I know that, again, you have tons of different small businesses there, just like as you're thinking about the major steps.

Yes. No, I appreciate the question. 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 will always be very 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 -- all of them and sometimes patients are rare, for example. So that's the concept. So if you want to do model -- so what we call that is model-informed drug development. It's a concept of what we believe needs to happen. And the kind of the 3 legs to that stool is, one is -- I'll talk a little bit briefly about it but one is biosimulation. 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 -- by doing simulation, we're taking into our software, lots of data that comes from labs and some of the trials and 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 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, up till now, most of our models have been what we call mechanistics. So we're taking into account the known science of how a drug gets into a body and moves around, concentrates in the body is eliminated and what -- 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. So let's start to think about all of what's really going to happen when it gets out there in a real human and in a population of humans which, in fact, vary in many, many, different ways, in a clinical trial or in a big population, because that's really where the cost is. So there's lots of drug candidates that come up. They fail for all kinds of reasons, toxic reasons or everything else. 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 can 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 pushing our ideas into the preclinical phase and increasingly into discovery because we kind of believe that there's an opportunity to unify all of this, right? But I guess the way to think about this, there's not a lot of companies that have -- that are out there like us. It is -- 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 their filings, you can see that they use Certara biosimulation to make an argument to the FDA if their drug should be approved or that it should -- 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 -- we're not the biggest company in the world. We're growing nicely 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 want to come back to the data [indiscernible] there. But as you think about the different demand trends within the market. So we have 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 has 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 years 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 going on. Like, give us a sense of from where you guys sit, how the orders or the conversations you're having across that -- those 2 different markets, if you will and how that, that -- you see that progressing throughout the year?

Yes. I think that there are 2 trends that were negative last year that affected us. One was the continued downswing in funding for biotech. So we saw a lot of our -- all our customers. There was a lot of -- some over last year, some of the other ones, they were -- you can say they're price-sensitive but they didn't really know where their next -- the next round of funding was going to -- when or where it was going to come from. So that was -- that's been a factor last year, started [indiscernible]. The other one was, I think particularly in the second and third quarter, we saw the larger pharma companies reevaluate a lot of their portfolio. And I would say that was primarily because of the Inflation Reduction Act and the rethought of -- rethinking about what they wanted to do. That was, I would say, a transitory effect. I mean, they took a pause, they rethought, they killed some things, they started some things. For us, it meant that pause in work that was kind of unusual last year. I would say what we're seeing recently is a pickup in the biotech funding environment. And I think it's hard to argue that Inflation Reduction Act is positive for pharma but I think it's been absorbed and they made their choices and they're moving on. So as a result, well, kind of where the other companies are, like we're kind of cautiously optimistic about where we're going to 2024. I don't think we're calling for it to be a boom here in the area but it's certainly looking positive compared with where we were last year.

So it's kind of like you've been bouncing along the bottom? You're just kind of waiting for stuff to turn around?

No. I mean there's definitely [indiscernible]. Our bookings in the fourth quarter were pretty good. We're seeing a fair amount of activity from really across the spectrum in terms of interest in our products and sales and that kind of thing. So I think -- we think that as we go through the year, hopefully, we'll see us pick up even as we go into second half of the year, I mean, biotech funding just kind of restarted. So it will take a while for that to flow through to us, for example.

Yes. And particularly the services piece of the business rebounded, especially on the bookings, kind of walk through -- if you think about what -- why would services kind of rebound for the software bookings or am I completely off on that?

Well, yes, actually, software really didn't perform badly all last year. We grew 15%. So that was fine. It was the services -- were more volatile because of the trends I was talking about. 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 -- clients can use it in 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. And I think at the end of the year, I think you had multiple things going on. I think some of the big pharma companies they had budget spends, a little bit of a budget flush in the fourth quarter. That's okay. That's okay, that's the work on that, that benefits us going into this year. There's a little bit more confidence about what they want to work on and what their portfolio needs to be going forward. So, no.

Yes. And I guess from a services, let's say, like you said, it's a tech-enabled, so they don't have probably more on, I think, almost like a full service where the biotech or whatever pharma doesn't have that person in place that can run the software and maybe [indiscernible].

Yes, it's fair. So if you had -- there's a lot of different customers. We have about 2,200 customers or something like that. So we really have a very broad base -- which is good. There's a portfolio effect there. But if you want to take a prototypical biotech company, like people who say they don't really have the -- they don't really their own experts. They don't want to hire them. They want -- so they're -- you place your software and give them the answer, right? 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 want one of our experts or you want the person who actually wrote that code to come in and help you, they'll also bring him in to you, right? So there's opportunities to really unite these and that's been kind of the -- one of the themes behind how we built the company.

Right. And then so you've been -- you did the sales reorg. Talk about how that kind of fits in with that paradigm. What do you guys actually change? Or 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 small. And since then, we've been investing and you can kind of 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 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 expect. On the services side, right now, though, we still have a business where there's a lot of seller/doers, it's not unusual in consulting to see that. And the fact it's inevitable. I mean, clients always want to see the SME come in and write a proposal and talk about what you're going to do and that type of thing. But there's an opportunity there to 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 that will pay off -- or a bunch -- it will pay off in a couple of different ways. So one, like I said, digesting -- there's many, many opportunities in this business to sell software and services together. So by having one sales force to carry both, we expect to get opportunities there. And 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, sort of pretty high-priced experts, so they can do more work and spend less time kind of doing all the process involved in screening [indiscernible].

Is this more about just kind of building out the technical infrastructure that can support those guys, so they can get out and do the BD.

Right. I don't think there's a lot of risk in this in terms of, "hey, is there any way this is," -- I mean we've got a pretty good relationship with a lot of customers. I just think that there's an opportunity right now to kind of basically make the company a lot more efficient. There's nearly 8 -- over 800 consultants right now. So starting to get to a critical mass where you can sell them in different way, in a profitable manner.

Okay. And then so within that reorg, it's probably a couple of quarters or 3 quarters ago and then we're talking about it on the call that it's really going to start to improve in 2Q. And what's the visibility there? And what gives you the confidence, like what's the early feedback from [indiscernible] and that type of structure for your sales force?

Yes. Well, one of the key changes that we still had to make, so we implemented this last year but we really didn't get the compensation structure until January 1. So one of the catalysts -- we can't change it midstream during a year. So one of the key catalysts for us now and pointing to going forward for growth is the fact that we can align sales compensation structure and really start to get even more benefit from the structural changes we made.

All right. And then so as that kicks in, is that kind of weighing on that as we think about the 1Q or the 2Q margin that improves from there, does that kind of be some seasonality there as the compensation kind of kicks in?

Yes. I mean -- so we called out investments that we're making this year and that's the reason for the [ 31 to 33 ] EBITDA margin guide. Those investments are all, will be -- you'll start to see them show up in Q1 but we will be gating them during the quarters of the year and be monitoring how they're...

Yes. So it's not going to be just like one massive?

Exactly. No, it wouldn't even be possible to frame it that way. But you will see it start to come in, in Q1. And then, of course, it will also come in, in the subsequent quarter. And related to that, following on the OpEx line, 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 gave.

Okay. I want to go back to where the data piece that you're bringing in. And just talk about how the data piece you find, 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 Pinnacle 21 establishing the standard? And what other pieces could you add to that to help improve the efficiency for FDA to have just one type of data aggregation?

Yes. That's a good 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 -- the founder had there was brilliant, the FDA wants all of their clinical data to be submitted. You can see this on that. Somebody has to determine whether, in fact, it meets the standard. Pinnacle 21 was used for that. And that's kind of what Pinnacle 21 was useful. But the vision going forward has been -- the problem that the FDA has, pharma has in a big way, right? Because they take data from CROs and from labs. They have contracts with these companies. They want the data to come in and come in some kind of standard format ideally or if it's not, they have to send it off to a data team to basically standardize and then sort it out. And so they can use the new versions of Pinnacle 21 that were coming out to basically police their own contracts with their data suppliers. And that brings benefits to everybody because now data is going to come in standardized. It can be analyzed more efficiently, cheaper. We can use it more in biosimulation down to the end, when you're going to submit it to the FDA you want to go through the whole process, kind of restate 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 Formedix, which basically tacks onto that and lets us basically interact with all the EDCs. So basically, that's even extending the reach of Pinnacle 21.

So you're moving upstream more than you're kind of layering on that Formedix?

So -- and as you know, like a very small portion of drugs that worked on get submitted to the FDA. So the market is much bigger as we move back, backwards [indiscernible].

Yes. And then once you standardize that data it becomes, the analysis becomes that much easier across the pharma companies [indiscernible].

Yes, pharma companies spend -- they have huge teams, enormous amounts of money, a lot of it gets outsourced to 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 an analysis, you cancel it. And so attacking that cost. And it's 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, maybe even missing the big picture of what you want to do and what you want to say to the FDA, right? So if we can -- if you can standardize that in the overall process, there's a huge financial opportunity in pharma but also it brings in the opportunity to biosimulation a lot more, right? Because we play -- we need all this data too, to really expand biosimulation to kind of get included into this market.

Yes. I mean you just talked about Formedix and the [indiscernible] data repositories In 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 kind of create a data lake remote for yourself? Or is this just...

Yes. So what I'd say is the advantage of SaaS software and software platforms is that you tend to get the clients data in one place where you can only see it. We don't own that data. That data, we don't make any claim for that data. They -- we would never accept that and this is not our business model. However, if you know where it is and you know what point is this, you can go in and sell them additional things. So there's a lot of -- these are potentially a big benefit to the company. As opposed to where they are now, which is, there is stocks and share point all over the place and you can't -- if someone leaves, you lose a lot of it, right? So...

And that's ultimately like Formedix on the back end of the clinical and that's exactly fits in. Let's talk about a little bit about Applied BioMath. It's about the nerdiest company name I've ever heard. I can't lie it. So you're talking about this being strengthening the QSP part of the business. 101, what that is and then really how that fits into the overall portfolio and the workflow that we're kind of digging through now.

Yes. So QSP is, within the scientific area, it's the hot area within biosimulation, right? So -- there's various parts of biosimulation. In this particular case, you're trying to model exactly how the drug interacts with its target, as opposed to, let's say kinetics outlives your body and other important things. They're -- they -- Applied BioMath and we were probably the 2 biggest independent players in an admittedly small market, competing with each other a little bit, less than you think. They were -- it's 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 act. But I think the real vision was to basically just get a critical mass of people in this area because we see it as an important area of development for the future. And so #1, there's a ton of demand for this type of work within our clients 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 a more software play as well. So...

And is it -- is this Simcyp originally, it's mostly like you're putting [indiscernible] so like toxicity prediction and now this is almost a efficacy prediction of whatever the drug target is based on the [indiscernible].

But you kind of need both, right? So being able to tap into Simcyp's 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 the FDA that they do it right, [indiscernible] just happen to that. So it's the way -- we kind of think of this as -- Simcyp is going to give -- the line around Simcyp is going to be much bigger than just PBPK. It's going to start to improve all the [indiscernible]. Yes.

Expand into [indiscernible]. Yes. And so I mean the key question -- was there a AI component to Formedix or Applied BioMath? Or was that kind of a misread on our part?

We bought a different AI company last year, Vyasa. There's opportunities all over the surface -- the product portfolio for AI. And I realize that every company up here is probably talking about AI. But we brought a company pretty early on in this. We've already implemented features. We already have revenues coming in from our AI features. And I think that we've taken a very practical approach to implement a specialized AI within our market. 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 going to switch to, okay, there's a zillion ideas, which ones are really going to work? What's the winnowing out that's going to 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 just from a broader perspective, it enables us to take into account unstructured data that we wouldn't have taken into account before or would be very expensive. I think like people write -- handwritten stuff, 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 results that we couldn't have otherwise, it's a pretty exciting thing for us.

This makes models smarter.

Yes, absolutely.

All right. What are the key applications that you're seeing for it right now? You're saying or you talked about being used in some point of 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 planning to have like AI drug discovery in the next 3 years like you guys, like so -- like where are the low hanging fruit for the technology in your opinion?

Yes. And the 2 -- well, the 2 or 3 areas we've talked about, right, so we've implemented it in our discovery product, just kind of like, I call it, like the Netflix feature. Like if you like -- if it's a drug that probably scientists like these 5 molecules, it will suggest other ones, that it thinks that are similar. So that's pretty cool. Has drawn a lot of attention.

Let's say buy through years and this will actually drop the overall margin for you.

So nothing is providing value here, right? So we are using it internally in our regulatory group to actually write regulatory documents. At some point, that will be an interesting product we can launch that -- we do have 200 people internally writing regulatory, so we 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, we're enabling a lot of our customers to take into account their internal databases that create a custom GPT kind of thing, where they're looking at their own internal data and also maybe in conjunction with 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 probably 3 early ways where I can say confidently, like we had something pretty good and we're using it. It's working. There's a pipeline of product improvements that will come on and we'll see how this plays out as we go forward.

Great. Last one here, as you rolled out -- as it continues to roll out and how that fits into the overall LRP. But as you're looking at the business of the portfolio now and having a better understanding of the workflow, I think, coming out of 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, there's -- we're fortunate to be in a really interesting area. There's a lot of opportunity to expand. We have done some inorganic expansion. We've tended to focus on things that are software-based because we'd like to increase our percentage of revenue coming from software. And I would say there's opportunities in all of 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 -- we're always interested in. Some of it is accelerating our own development plan, sometime it's our decision alone. There's opportunities for us to get into the discovery area potentially in a bigger way. And one of the things about the -- as we look out there, sometimes we just get surprised where it's been going. There's lots of good ideas, I mean, we don't even have but they can incorporate depending on the motivations of the founders, right?

Awesome. Great. Thank you. It's all the time we have.

All right. Great. Thanks, Luke. Appreciate it.