Good morning, everybody. Luke Sergott, I cover life sciences tools diagnostics at Barclays. With me, I have William Feehery. Feary?
Feehery. Right.
That's right, Feehery. Andrew Schemick of, . I guess we could start off here. The business isn't as well known as some of the other ones. You guys haven't been public for very long, but you've been in the business for 15+ years that you've built. Talk about the high level dynamics that you've seen over the past year and how you ended the year, and then really the underlying growth drivers within the industry that support your mid-teen+ growth.
Yeah, thanks. Yeah, the company's been around in one form or another for quite a while. It's a benefit since we've had, you know, the benefit of years of building our models and having them accepted by the FDA and by the industry. We've been public for just over two years now. You know, before that, we had a whole series of private owners so over the years. The, you know, the underlying driver of our industry really is, or of our company really is the number of drugs that actually get approved. We make about 70% of our revenues from in the late stage in the clinical phase.
We're kind of, dependent on, you know, the FDA continuing to approve, say, 40 to 50 new drugs a year. The bulk of the benefit of what Certara does has to do with the reduction of the use of human clinical trials, which is the largest portion of the spend. That tends to grow quite rapidly as the drug gets towards the end. Having said that, we have a pretty widespread customer base. We have, you know, round about 2,000 customers. The top 50 are, say, 50% of our, of our business. The other, you know, the other 50% is in the other, in the other part.
What we think about this is, you know, everywhere a drug is being developed, you know, Certara is A, we wanna be there, B, we can be quite relevant. We try to catch companies when they're small or when drugs are early stage so we can stay with it over time. That part of the business hasn't really changed. I'd say the one thing that's probably changed as we went public is we've gotten a little bit more experience around how to deal with public markets and how to talk about our forecast and that type of thing. Andy, do you wanna contribute there?
No, I think that covers it.
No. All right.
Yeah. When you think about, I mean, the two businesses that you, that you report on, the software and the services, but really what you're offering is just the overall software, and then you're using the service, you're essentially renting out somebody that can run it for you, correct?
That's true. We have about 30%-35% of our business is straight software sales, could be either term or subscription license. We have the other part of our business is services business. The bulk of that is our biosimulation services, which we're doing what you talk about, which is largely we're hired by companies that don't wanna buy our software to basically use our software to deliver the project.
Those companies could be small companies that don't have the, you know, the internal groups or maybe they only have a couple of drugs, so it's not worth it to buy the software. They could be large companies that want, you know, customizations.
They wanna talk about the expert who wrote the model, those types of things. A little bit more traditional services there. We'll do everything you need from a regulatory basis, regulatory strategy. We'll do regulatory writing, publishing. That part of the business, we have it for strategic reasons. It's important for us to know what we're talking about from a regulatory standpoint for our biosimulation clients. We have it because it's a highly profitable part of our business where we get, you know, we get a lot of our customers because we started working with them earlier, quite early, and we know a lot about that drug.
Okay. That's been the, over the past couple quarters, the regulatory side's been softer.
Right.
Talk about what's going on there. Then we can talk into the overall demand environment.
The regulatory part of our business is about 20% of our business. Over the last year, it did get a bit softer. It slightly, actually slightly shrunk last year, that part of the business. What I'll say about it is it still is quite profitable, so it's not dilutive to the margins of the business. We like it from that standpoint. And it gives us a lot of credibility from what we're doing at biosimulation. I mean, that said, you know, the bulk of our capital allocation goes into investing in our biosimulation business, it's probably not surprising that the growth rates are different on those types of things.
If you look at the dynamics specifically in regulatory, I think there were a lot of things that happened, during COVID and after COVID as, basically trials, you know, either got delayed in starting or delayed in finishing. That kind of affected the length of time between when we book projects and actually do them in regulatory. You know, the other dynamic in that particular part of the business is that there are fewer larger projects there. You know, it tends to get a little more lumpy than our biosimulation business where we have literally thousands of projects. You know, it tends to grow much smoother.
Got it. On the biosimulation side, I mean, you have the Phoenix software. There's all these different markets that you bring to the table. Talk about the overall portfolio construction here and where you guys play across that whole pre-clinical to phase 1 workflow.
Okay, that's a good point. We have several different projects, products. We work on the software side. The one that's probably the most known for is called Simcyp. That is our for biosimulation software.
Mm-hmm.
It tends to start, in being used during pre-clinical phases.
Mm-hmm.
We charge less for it in that phase, you know, that tends to be fairly small number of trials in pre-clinical. You know, as a drug becomes successful and moves into human trials and into late-stage clinical trials, we use the Simcyp CROs and, you know, the amount of revenues that we can acquire also expands there. If you look at our product called Phoenix, which is a little bit different, it's used for analyzing pharmacokinetic data. The bulk of that is used, you know, either in pre-clinical or in, you know, after the clinical. You're analyzing data that comes out of the out of trials.
Right.
It gets used a couple different places. Then the third big product we have is Pinnacle 21. A little bit different product. The idea behind Pinnacle 21 is it's a data validation product. When you go to submit your clinical trial data to the FDA, it needs to be the CDISC format.
The FDA uses Pinnacle 21 to determine whether or not you've met the standard and whether they'll accept the data. It's, you know, everybody uses it at that portion where you submit to the FDA, and then it gets used earlier in the cycle as companies try to, you know, ensure that their data, you know, basically meets the standard they're gonna eventually need to go to the FDA.
Yeah. Okay. The, you know, the Pinnacle was just a recent acquisition for you guys. Like you said, it's always good to buy the software that the FDA uses because that's pretty much spec then.
Well, it is, but it's also strategic for us.
Yeah.
Pinnacle 21, you know, fundamentally, it's a, kind of a pinch point for data. It's something, you know, clinical trial data is gonna flow through Pinnacle 21 as companies validate that it meets the standard.
Mm-hmm.
That happens really in a couple different places. One is when you go to the FDA, but increasingly it's being used as pharma companies interact with their data providers, their labs, or their clinical trial providers. They can also use Pinnacle 21 to say, "Hey, you know, I have a contract with you. In order to determine whether you've met the terms of the contract, I want you to show that it meets Pinnacle 21 standards.
Mm-hmm.
It gets used that way. You know, if you kinda think about it, you know, if you have a piece of software that everybody flows their clinical trial data through, then we can build other interesting things on. Things like data repositories, data analysis. You know, it kinda opens up a whole market of, you know, the armies of biostatisticians that pharma have to...
Yeah.
kinda manage all the data coming through there.
Okay. That's helpful. You talk about, I mean, it's a software business, but it's mostly consumption-driven. One of the drivers that you guys talk about for maintaining your growth rate is increasing the amount of subscription volume. Talk about the dynamics here and why would the pharma customer move to the subscription model if they only have three projects or four projects in the pipeline? I mean, talk about the dynamics there.
Primarily, just to clarify that point, the subscription model is more a reflection of the GAAP revenue recognition model.
Mm.
That's an impact when you're comparing us to historical periods. Historically, even go back two years, our software products were sold 50% in term licenses, 50% on subscription.
Mm-hmm.
As we've introduced new products or done acquisitions, we've seen an increase where we ended last year with, about a mix of 60% subscription, and we see that going to 65%. The point being that whether it's a term license or a subscription license, we have high stickiness with our customer, and our customer renewal rate over $100K. There's not been one that's left the company. It's more a reflection of when making comparison year-over-year to be a greater percentage of our revenues that's deferred because of the licensing model.
Got you. With that sticky customer base, where would they go? I mean, there's not a lot of other options out there.
Um-
What would be the rationale for not using it?
In terms of our model or historic numbers, the extent that we see churn in customers it would be with you know, a startup company that's not successful.
Yeah.
If it was one of our larger or more significant customers, we're really only impacted by, drug failures.
Mm-hmm.
-or adverse regulatory events we call them. That's baked into our growth algorithm. That's been societal. What happens there is, there's a period of time where the company, you know, deciding what their strategy is for that program or for other programs and where to allocate those resources. We stay embedded with that customer. There's a revenue recognition gap that's baked into our...
Yeah. I got it.
To answer your question, look, all good ideas have competition. There's always competition. There's always options, right? The way we think about things is twofold. One is. You know, we have the most experience with drugs actually getting approved with the FDA. There's a good reason to use our solutions versus others. The second is, it is fairly sticky in terms of once you have a model that's working for your drug, you know, it's hard to switch that. I mean, you have to have a big push to wanna switch that out.
Mm-hmm.
As long as we're delivering, you know, investment in continuing to expand the uses and the capabilities of our software, we think that we can, you know, keep that under control.
Yeah. Okay. Kind of talking about, sticking with the guide here and then your growth algo. You guys for, low double digits to mid-teens, which is slightly below your historic target. Talk about what you guys are seeing here, and any type of commentary you give on recovery and how the quarter's progressing.
Sure. All of our KPIs that we track, new logos, book-to-bill, et cetera, we're seeing status quo, so healthy, healthy business fundamentals. If you look at our book-to-bill at the end of the year around 1.22, that's historically translated to mid-teen revenues growth.
Mm-hmm.
In terms of the guidance, we tend to overweight the most recent historical performance as opposed to future expectations in putting out guidance. That's been our methodology for some time.
Okay.
You know, 10%-15% is consistent with our growth rate in 2022, next to 2021, which did have some benefit from M&A. I will say that, specifically what's factored into that is the reg group delivering low single digits growth, which historically that group's grown in line with the overall company metrics.
Mm-hmm.
Because of some of the volatility coming out of COVID and the regulatory point of view in some of our China-related business there, just expecting that to recover this year, but at a slower pace than the recovery than maybe the potential of the business is long term. There's also an impact on the revenue recognition, what we discussed early from the software subscription revenue versus license revenue.
Okay. That's helpful. From the margin perspective, since I talked about regulatory being really accretive margins and that's now what you guys are targeting for the year and how that's going to progress?
Yeah. We're targeting mid-thirties. We still see the company in a point in time where there's significant growth investment opportunities in terms of continuing to build out the company that where we are in terms of kinda industry and regulatory adoption is still relatively early to the potential of the overall market size.
Mm-hmm.
We're comfortable that at the mid-30s EBITDA margin, that gives us enough, you know, organic capital to reinvest in sales and marketing and growth initiatives. Some being, we just acquired a company, Vyasa, earlier in the year.
Mm-hmm.
Came with a team of developers, but not a full commercial footprint or marketing strategy or product strategy. There's some integrations going on there. We'll be making continued investments on the sales and marketing and the R&D side on an organic basis, and that's gonna hold us at that mid-30s margin as opposed to, you know, expanding.
Okay. Let's talk about Vyasa. I've never heard of the company. I didn't come across it. Give us the one-on-one on what it brings to the business. What is the opportunity here, and just really dig in on that.
Well, ever since we went public, everybody's asked us about AI, our answer has always been that Certara's built up their models from mechanistic side. We've been modeling known science. That's been the foundation of the company. We've always said that at some point, marrying the two of these will make sense, right?
Modeling the known part using AI to think about all the unknown parts which are significant. You know, we've been looking for different solutions and we started working with Vyasa over one year ago. What we saw with these guys is they have built a very solid deep learning tool. It's using a lot of the technology that you know...
It was kind of interesting that we bought that and all this stuff about ChatGPT broke out, but it was really coincidental. We've been working with it for a while, really. They've also built it so that you can plug into lots of different data sources very easily. That's really what you want as you bring in deep learning, which is you wanna be able to bring in lots of different data sources, ask questions across as much as possible, that's what the pharma wants. The immediate interest for us was that this can plug into a lot of our existing software. It becomes an additional feature for a lot of the software that we sell.
Obviously, we believe we'll have, you know, if we add it in, we'll have an upcharge for that. You know, we're delivering something that our clients have been asking about and that the technology, you know, direction has led to for a while. You know, the reason you haven't heard about them is it was a small company. They developed a really interesting tool.
The thing about AI is, you know, it's not very useful by itself. You need to marry it with an expert in certain things. You know, I think we bring a big platform to them that they didn't otherwise have. Obviously, they can plug into a lot of our software. I think this is gonna be a good partnership for us as we go forward. We don't have a material prediction in our, I'll give Andy's comment here, but, we don't have anything material in for this year, partly because we've got some engineering work to do to plug into all of our software.
Mm-hmm.
Maybe we'll exceed that. We'll see how this, how it goes. We're all quite excited about it. You know, like I said, you know, it's quite a hot topic lately, partly because there's a lot of really interesting new things you can do with this that were unable to be achieved before.
There's not ChatGPT for regulatory submission.
No, it's not ChatGPT for regulatory submission. No. That's what. Yeah. You know, one of the things, we had a recent press release in one of our software products we actually haven't talked a lot about called D360. It's a software product that's used in discovery to sort of act like a search engine across all of the chemicals that a company or a pharma company might have made over the years, right?
By plugging that in, now we can, you can do cool things like say, "Hey, you know, I'm interested in these three targets. Use the AI to tell me what targets that are very similar to that maybe I also ought to go look at." Right? That's very interesting to pharma. You know, it's you know, you know, we're getting a lot of interest in terms of like, you know, things like applications like that that we couldn't otherwise provide, so.
Yeah. Anything to accelerate the-
Right.
the development curve.
Right.
Okay. That's helpful. When you think about, you know, like about having that as an upcharge and you're plugging that in, but when you're selling the total portfolio to pharma, you have, say, it's a big pharma, and they have all these failed programs in the past, but they maintain all that data. Do you have to convert that data so that the software can read it?
No. It actually is capable of... I'm not gonna give all the technology here, it's basically capable of plugging into structured or unstructured databases and basically creating its own hierarchy of what's in there so that it can be searched without doing that kind of creation. It's not like, you know, the technology people talk about in the past used to talk about converting everything and creating a data lake.
Yeah.
That's not what this is. Now we leave all the databases where they are, you plug into it, and it kind of pulls from all of them. It does it itself.
Exactly. Cool. Oh, man. Hopefully, they don't do it to this call sign. All right. Somebody just hit me up. I didn't ask you guys early on at the top of SVB exposure. Have you guys done any analysis on that for your clients?
We have done analysis on SVB. We don't have a direct banking relationship with SVB. In terms of SVB and the other impacted banks that we've seen out there, we've looked at the incoming wire transfers over the course of 2022 from all of these, you know, group of five or six banks. It is less than 1% of revenues. Similar immaterial in terms of our current AR balance. We don't expect any impact from that.
All right. You guys are like you're net cash, net cash positive from those guys?
Yes.
Okay. That's good. I guess, as you're having those conversations, I guess the indirect knock on as you're talking to a lot of these customers, talk about their R&D budget. Has their philosophy started to change and starting to tighten up?
I would say our bookings remain quite strong. The reality on Certara is that the percentage of the revenues that we get from the emerging biotech sector, which is the one that everybody questions around their funding sources, is, you know, a couple percent of the revenue. Not a really significant part. It's not necessarily 'cause we don't have them as customers as it's just because we don't charge. You know, we don't have significant revenue products for that. It's more like we just.
Yeah.
do a project here or there to keep our-
Yeah.
name in their heads, right? In terms of the larger pharma, a lot of our clients have, you know, significant dollars particularly to invest, particularly coming out of the pandemic. At least, you know, I think, you know, we're as aware as everybody about all the macroeconomic things and risks that are going on, but we haven't really seen it hit the company.
It's also probably such a small part of their spending and often so core to the getting the drug through the pipeline.
Yeah. I also argue that you know, if push really came to shove that I don't think you
Right.
Other things we're not, we're just not, to your point, just not the big-
Yeah.
the big dollar figure to go after. It's not where you're gonna cut the funding.
Okay. That's helpful. Lastly here, as we think about, you know, continue to do M&A, talk about holes or areas of portfolio that you'd like to build out on, especially as you look out in the next year or two.
Well, you know, we've talked in general in a couple areas. One is that we get most of our revenues from clinical, but a lot of the decisions get made in pharma. You know, we thought about, you know, generally just expanding our footprint in preclinical into discovery. M&A is not our only opportunity.
We've launched a few more products in preclinical, for example, and we're investing in D360 in discovery. You know, the way I think about M&A is more we're in an interesting space. We wanna have flexibility to do something when it's available, but if it's not, we've got plenty of interesting internal opportunities to fund them, and those are, you know, quite attractive. We've also... You know, now we're getting into the AI space.
It opens up interesting opportunities around, you know, data that we might wanna collect that's relevant to biosimulation. You know, over time, we have done lots of little tuck-ins in services. Most of these I kinda think about as almost like acqui-hires.
You know, we get 50 people or 100 people at a time. It's easier than hiring them at a time. We do those whenever we find them at a reasonable price and, you can expect we'll, you know, do them 'cause they're good for shareholders, and they're a good way to grow the company with some good talent out there, so.
Makes sense. Well, that's our time. Thank you.
All right. Thanks.