Simulations Plus, Inc. (SLP) Earnings Call Transcript & Summary

Well, thank you, everybody, for being here today. I'm delighted to introduce Shawn O'Connor, who's the CEO of Simulations Plus. Thank you for joining us, Shawn. I'll turn it over to Shawn to give a brief overview of Simulations Plus and the platform, and then we can go through questions as well as any questions that we have from the audience. So over to you, Shawn.

Very good. Thank you, Mark. Thanks for having us here for the conference. Simulations Plus will be celebrating our 30th anniversary existence as a company this coming year. So not a new player into the marketplace and biosimulation is not a new functionality and supportive drug development either. Beginning in the '90s, the use of a combination of technology and science, mathematics, statistics, along with chemistry, physics and biology led us to the development of in silico models, models of biology, models of disease state, model of drug characteristics, all in support of providing efficiency, accelerated time lines to the drug development process. Our platform of solutions span the gamut of application in discovery, early-stage discovery, the lead optimization process running through into the clinic, preclinical translational impact with biosimulation into the lab, out of the lab, in and through animal testing, first-in-human and into Phase II and Phase III clinical studies as well as post-approval applications of modeling and simulation that supports bioequivalence waivers, the elimination of additional clinical trial support for formulation changes in the manufacturing of drugs. Biosimulation has been in place for many years now, gaining acceptance, both from a scientific perspective, from a drug sponsor perspective, and importantly, along the way regulatory support in support of using in silico approaches in that regard, probably not the first mention of FDA support in animal testing areas is a recent example of the expanding use cases for biosimulation that has supported the growth and adoption and yet a continuing long road map of further application of biosimulation into the future. Our business is primarily a software licensing business. We provide tools, modeling capability and models, predeveloped models for our clients and their internal resources to deploy and use. We also have a scientific consulting service component to our business to support our clients who need additional capacity and/or have not reached that stage of building an internal department and outsource for these biosimulation support along the way. Basic overview of the company, open to questions.

That's great. Well, thank you for that background, Shawn. And I -- look, I mean obviously, you do a lot of different things as you talked about kind of over the value chain for drug development. Help me think about where in the drug development process you're delivering your services. Is that in the sort of discovery phase? Is it the preclinical phase? Is it in the clinical phase? Maybe those applications kind of vary. But I think as people think about AI and drug discovery increasingly today, they sort of think about the early stage design of a drug. I'd be curious as you sort of think about the way you guys deliver value, sort of how that looks.

Yes. We touch the full continuum of drug development, obviously, some focus areas. When you look at the success ratios of out of discovery into the clinic and through the various stages, there's ripe opportunity to improve the batting averages along the full continuum. Our earliest stage applications is through a product called ADMET Predictor. ADMET, absorption, distribution, metabolism, toxicity. It's a tool that deployed AI, machine learning back in the '90s to provide predictive characteristics of a molecular structure based simply on its drawing, if you will. And that utility is applicable in the lead optimization process, along with other inputs, other tools contributing to the identification of drug candidates to take into the clinic. That said, that represents maybe 15% of our support to our clients. The majority of our biosimulation tools are deployed once the drug candidate moves into the clinic. Several areas of high emphasis. Our PBPK modeling tool, GastroPlus, that approach in terms of modeling and simulation is significantly utilized in translational medicine. So taking that candidate into the lab through animal testing and into first-in-human testing. PBPK approaches allow for a tremendous amount of predictability and design input into animal testing and early efficacy toxicity assessments for first-in-human trials. That approach, that PBPK approach, though, is as well utilized further into the clinic. And as I referred to before, it also is used in post-approval changes, bioequivalence waivers as well. PK/PD is the third area, significant area of modeling and simulation, biosimulation support. And that is a technique and a platform that is really focused on profiling drugs, drug characteristics. And its application is in dosing regimens and patient population stratification in a world in which maybe the big blockbuster applies to every patient, drug opportunities are drawn further and further between personalized medicine drives us into stratifying patients and identifying patients that the efficacy-toxicity profile trade-offs are applicable to subsets of populations and PK/PD modeling is a key tool in that endeavor. All of that leads to the development of protocols for clinical trials and our input into those clinical trials is significant on the front end as well as simulating those clinical trials to assess if the likely outcome is adequate or tweaks and changes to that protocol will improve the likely success of those clinical trials downstream.

Great. Well, thank you. I think it's interesting you talk about having used AI/ML in your products for decades now. I think there's a perception that certainly #1 topic at this conference appears to be kind of AI. And I think increasingly, people are focused on the impact of AI on drug discovery over the last few years and the potential for novel accelerated drug development. I'm curious from your seat, obviously, you've been doing this for a long time, how have you seen the drug development process changed recently with the advent of sort of some of these newer tools. Are we at the cusp of a fundamentally new way of developing drugs? Or is this kind of more of an augmentation of existing research?

Yes, Mark, it took us 10 minutes before we got to AI. I'm surprised that it took us that long to get there. AI is revolutionizing everything we do in life and drug development is an area of high potential in terms of the application of AI as a tremendous tool in the kit here to accelerate the time frames, analysis of data and certainly something that we have in our toolkit. We see tremendous advancement focus in terms of the use of AI in early-stage drug discovery, the search for new targets, biomarkers and candidates based upon the gathering and accessing of wider populations of data to pinpoint and input into candidate opportunities to move into the clinic. AI is a great tool. It's -- it flounders without the scientific input and drug development components there. So the industry is making strides in that regard. Probably, the first challenge encountered is data in -- the quality of data in is equivalent to the analysis that comes out, and it's heightened the industry's focus in terms of how do I collect and manage in warehouse our data and position it for use in these areas. Ultimately, we've seen some increases in terms of candidate identification. And as those candidates move perhaps more quickly, but more abundance into the clinic, biosimulation value then kicks in. Identification of targets that may be higher and predictable, success are great. It funnels and leads to more opportunities for more drug programs, but still have that [ gauntlet ] of drug development requirements to play out on a go-forward basis. So it's an exciting introduction into our field, both in the context of the candidate delivery that it brings as well as how we can utilize that AI technology as well in our biosimulation approach.

And I'm curious, Shawn. I mean, obviously, AlphaFold predicting kind of 200 million protein structures potentially. Obviously, that creates a lot of opportunity for you in terms of incremental demand that might come into the funnel. But as you sort of think about your business today, it sounds like you've been using AI/ML for a long time. What do you see as kind of the core opportunities for AI within your business? What investments are you making to take advantage of some of these advancements?

Yes. Where does AI provide value? It provides value in search and find data a bit more quickly. It can assist in terms of interrogating those data sets more quickly and with higher throughput. Importantly, we think of AI and other industries and robots, the agentic AI capabilities that come into play in our world, I think, are going to be the most relevant, improving data management and data interrogation. But in the end, the productivity of our scientists, the building of models, the assessment of models that based upon the data I have, what will be most impactful to a decision at hand is not an overnight process. There's an assessment of that data, the concept of what model works, the logistics of building that model, running that model, perfecting it, eliminating hallucinations of a biosimulation approach and then updating it as more data arrives, bearing fruit to that model over the 10-, 12-year cycle time of a drug. That's a lot of work effort by that modeling scientists. An environment in which the -- one of the gating items for biosimulation adoption has always been the scarce resource of the scientist that is well positioned to do this type of work. Introducing agentic AI to automate many of the steps of this process or at least accelerate their speed to completion and putting that scientist into a world of evaluate and where he can deliver his scientific knowledge, not on the building of the model, but in terms of the evaluation of the model and improvement of that model is an area ripe for improvement in terms of expanding the productivity of modeling scientist community, which allows for a more rapid deployment and adoption across all of the biosimulation use cases that exist out there. And if AI is contributing more candidates into the mix, that wave of incremental work effort can be responded to with more efficient model building and biosimulation.

Yes, lots of opportunities as you point out. And I think one of the perceptions of AI is that it's going to make things easier, software easier to develop through the deployment of solutions, all those type of things. I think as you think about the entry of large, sophisticated technology players, people developing their own models. I'd be curious, you obviously got a very long history of doing this validated models, large data assets. Like what do you think of as the competitive advantage to kind of building training AI models in the biosimulation space that Simulations Plus has?

Yes. We've been in this business for 30 years now. And the advent of AI tools and whatnot certainly provide capabilities over there. But I can't overemphasize the contribution, the knowledge on the science side. We saw a tremendous inflow of capital into AI, life science startups focused in the discovery space that encountered very quickly. This is not translating facial recognition capabilities into drug development, maybe in terms of MRI reading, imaging type of things, but the knowledge of -- deep knowledge from a basic science perspective, physics, chemistry and biology from a medicinal perspective in terms of disease states and disease knowledge. The combination of the great tool with that knowledge is 2 very important ingredients into the mix, and that's where we've been living for 30 years. A number of other sort of moats in the business here. Data, data is very important. And it's not just simply accessing data. There's a wealth of public data that's available, could be accessed by most anyone. There's proprietary data. We, through the years, have had many collaborations and partnerships with pharma biotech companies in which we've gotten access to proprietary data that have informed our models, our algorithms that drive those models. But access to the data is not the only factor. It's your knowledge in terms of curating that data, if you will, into meaningful data sets. Simply put, you've got data from 5 different clinical trials with regard to a specific class of drug or target, whatever it might be. But the differential protocols on those trials requires how do they combine? How do you compare that data in a useful way that may be appropriate from a data management point of view, but not from a therapeutic knowledge point of view? Tremendous curation capability. There's a couple of other barriers there that are very significant. The investment in our biosimulation tools that has been made over the years by our clients doesn't get ripped out and replaced very quickly. That's a significant investment, both in terms of standard operating procedures, IT, infrastructure and leading ultimately to regulatory moats. The FDA is open to the use of biosimulation, open to the use of AI. But at the same time, AI is sometimes a black box. Those black box answers are there with regard to well-established GastroPlus, Monolix, ADMET Predictor platforms offered by Simulations Plus utilized by the FDA internally, very acceptable when a client drug sponsor comes to the table at the FDA and presents analysis that is output from our platforms going to the FDA with a coded up solution that leads to some analysis puts the FDA in a, "Hold on, let's look at the source code. Let's give us some more time frame to make this analysis." There's a good regulatory moat there as well. All in all, it requires us to continue to be adept and fast moving, continue to update and improve both our algorithms, the content of our models as well as the functionality of our products that basically translate an ability to use an AI tool to embedding that AI tool into the workflow of a scientist where I think our expertise comes into play significantly.

And I guess on the topic of regulators, I mean, I think biosimulation, to your point, has been around for decades. But I think a lot of people really stood up and took notice in April this year when the FDA came out and sort of announced a road map to reduce animal testing in preclinical safety studies and that the adoption of computational modeling as a tool would be part of achieving that. I'd be curious to hear from you, how you think about the opportunity that opens up for Simulations Plus. And where are we in the adoption curve today versus where you see as an opportunity to go in the next 5, 10, 15 years if the FDA continues to lean into this as a potentially interesting innovation that can start to replace some of the legacy clinical trials.

Announced in April, doubled our revenues in the May ending quarter. No, not quite that fast. I'll start at the high level. Biosimulation has grown over the years with a series of expanding use cases. There have been animal testing efforts or announcements of uses of biosimulation in the past, not specific to animal testing, but in other areas that generally take a window of time. A period of debate and analysis, scientific debate leading to guidelines and the definition of that barrier. What boxes do you need to check in order to diminish or eliminate an animal test. And that process is commencing post the announcement in April. And it will play out its course. And like these similar announcements in the past are wind in the sails in terms of adoption of biosimulation and growth of Simulations Plus that accrue over time and build support for our growing business. The animal testing one will be a little bit more, I think, controversial. There'll be debated on both sides. The sensitivity of the confidence in toxicity safety that comes through animal testing, how do you rely on in silico input to avoid that. I mean, the over and under that I get is that this is a 3- to 5-year sort of time window before we start seeing INDs approved without animal testing. No doubt, I would anticipate that as the guidelines get defined those first pilot cases will not be -- yes, we'll waive animal testing. There'll be -- yes, let's do that. By the way, let's do a small sample size animal test alongside it to confirm our belief here. These things will develop over time. On topic of [ AF ] discussions with clients today in terms of what do we think it means, and that will build over time as the guidelines become better defined to start ultimately driving, okay, we need more scientists to do this biosimulation software revenue to Simulations Plus and/or consulting input from Simulations Plus as well. In today's world, we do a lot of work in that translational medicine phase. Our product, GastroPlus, has 12 different species, models and that it's used in order to predict how it's going to perform in an animal test in the development of the protocol for that animal testing to take place and in the evaluation of how can we reduce population sizes in animal testing. The bar has been efficient animal testing. That bar now becomes how do we eliminate animal testing. And that will focus on, okay, let's take that predictive capability and skip the animal testing input that comes in that sequence to reaching a bar, a confidence level that allows you to go direct to first-in-human functionality is that for there. The tightness of those predictive analysis that will be scrutinized. Is there more and different data from organ on the chip solutions that can provide more input to refine those algorithms and enhance the predictive capability here, all to be sorted out and look forward to it because as we've seen with other initiatives of this nature, they then drive, most importantly, a significant advancement in terms of cost and time line to get through these steps, but those ROIs deliver revenue back to Simulations Plus. We look forward to participating in this evolution as it takes place. Be patient. It's not overnight, though.

Yes. Well, look, I think if there's one thing that's true of the industry, it's the shared objective of bringing more effective, safer drugs to market. Look -- so to your point, I think, I expect we'll continue to see innovation and driving towards that goal. I guess nothing is linear in the way it sort of plays out. Patient obviously is an important virtue. I think it's no surprise to anyone that the biopharma biotech demand environment has been challenging over the last few years. How do you see the current demand environment? Any insights you can share into how your customers are seeing the world?

Yes. Yes. It's -- I mean, there's -- the headlines are well known. Starting really a couple of years back in terms of patent expiration and IRI pricing interrogation and leading to certainly this year the introduction of tariff and most-favored nation pricing and FDA reductions, on and on and on, shock to the system in terms of our client base. And it's an industry that typically when surprised hunkers down and slows down. It tends to digest those headlines and stabilize and move forward. But it has been a cost-constrained environment, a low-funding environment. I think those headlines will come and go. If you look behind and don't get distracted by those headlines, underlying is a challenging step down in ROI and drug development. The return on the massive investment that is made has continued to decline, screaming out for improvements, efficiency, time line, accuracy, success rates along the way in terms of 5,000 molecules into the system to get on approval. Those batting averages need to improve. And my confidence in biosimulation is high given the fact that underlying all the comings and goings of headlines, that improvement in ROI, biosimulation is a key contributor to an improving ROI of a drug development process. And so while we wish the environment was stronger. We wish clients would. Unfortunately, I can spend X to save Y and the return is great in terms of more biosimulation, but I'm constrained in terms of making X investments that certainly a good -- the biotech without funding can make those investments and large pharma is slow to make those investments today. You fight through that. We've executed pretty well. During this time frame, we expect to continue that execution and revenue growth into the future, and times will get better.

And I think, obviously, the long-term environment as you sort of point to remains very much intact. And then perhaps you could spend a little bit of time on some of the strategic business realignment that you announced and sort of how that kind of puts you up for growth in the future.

Yes. Just completed in the past quarter or so, reorganization. Simulation Plus over the years has grown both organically and through the acquisition of other key components to the biosimulation portfolio of capabilities that we have today. And that growth experience led to a company that was kind of portfolio managed in terms of separate entities. And over the last few years, we've kicked off and completed this year a consolidation into a more functional organization, breaking down the individual groups, combining ourselves into one software development business, one service business down the line. While from a bean counter perspective, that creates all kinds of efficiencies internally, the real driver has been that while these biosimulation solutions, much like our clients' organization have grown up in silos, PK/PD modeling, that segment of the scientific community developed that approach. QSP modeling, a separate sort of group, addressing types of problems in the continuum of drug development, we see more and more today that these methodologies have increased their impact when used together and in a combined way to address problems from a multi-disciplined approach leading to better decisions. And -- so I think we're a little ahead of the curve with our clients. But our approach here now is here's a suite of platform software solutions that your -- the impact in terms of your development program will be enhanced by their combined use, our scientific consulting group, as opposed to I've got a problem, can you help me out? What can we do from our PK/PD group to solve it? It comes into a multi-disciplined group. And right from the start, gets the input across the domains in terms of how can we most efficiently shed some light on the challenge that you that you face. So our go-to-market strategy is evolving to maybe a little bit ahead of our clients, but where our clients are headed in terms of their more holistic look at biosimulation and its impact. In addition to the efficiencies from an internal perspective, the integration of those products, the development of overriding cloud technology, AI components that can be developed once and applicable across our platform as opposed to our own development of the product by product, also provides some acceleration in terms of our road map in terms of improving our platforms and their capabilities for our clients.

Thank you. We talked a lot about AI. We've talked about FDA innovation. Obviously, you've talked about some of the strategic realignment you're doing to take advantage of kind of growth. I mean as you sort of think to the future, what do you see as the biggest opportunities for Simulations Plus? And how do you -- what does your road map look like in terms of new market solutions, innovation to take advantage of that?

Yes, really driven by what we see in the science and the regulatory directions. We come back to animal testing as an opportunity that, again, we've always got some lead time. This is an opportunity for us to get ahead of the curve and be positioned for the reality of those applications 3 years down the line. Biosimulation has -- I've been in the space for 30 years. We've leaped over the sort of adoption, the acceptance of biosimulation as a useful tool in this process. And yet every year, we find new ways to apply it. So staying current in terms of whether it is a small molecule versus biologics. Our early days where we cut our teeth in the cardiac arena, which was top of stack in terms of R&D development. Today, oncology, other therapeutic areas are top of list in terms of improving -- creation of more data that improves the biosimulation modeling effort and predictive accuracy in these areas. So we look to be guided by advancements in science, where is drug development turning in terms of therapeutic areas or type of therapeutics and regulatory guidance to continue what is a long runway of biosimulation value that we can bring to the drug development process.

All right. Well, thank you for your time, Shawn. We really appreciate it and lots of opportunity in front of the platform and look forward to following along.

Thank you much, Mark.

Thank you.