Afternoon, or evening as the case may be, and welcome to Simulations Plus' Investor Day. I'd like to thank each of you for joining us, our shareholders, analysts, partners, and members of the broader life science community. Today is an opportunity to step back from the day-to-day and evaluate where we are, where the industry is headed, and where we are positioning Simulations Plus for the future. Let me briefly outline the agenda for our session. We'll begin with an overview of our vision, mission, and values, followed by company highlights and a look at our history of growth as we approach our 30th anniversary. We'll then move through the major components of our business, the industry environment and the challenges and opportunities shaping drug development, our software product strategy, and fiscal year 2026 roadmap, our scientific services organization, and we'll conclude with a Q&A session.
This is an important moment for our industry. Biopharma is undergoing significant transformation: the adoption of AI, the shift towards cloud-native scientific computation, the move away from animal testing, and the growing reliance on Model-Informed Drug Development. These changes are accelerating, and they're redefining the means to develop safe, effective therapies efficiently. It is also a defining moment for Simulations Plus. We are shaping our technology, our services, and our operating model to meet the inflection point, strengthening our leadership while building the integrated ecosystem our clients need for future innovation. Today, we'll take a deeper dive into that ecosystem, our capabilities, our roadmap, and the exciting developments ahead in fiscal year 2026 and beyond. Our vision at Simulations Plus is to improve quality of life through innovative solutions.
Our mission is to create value for our clients by accelerating the discovery, development, and commercialization of pharmaceuticals and other products through innovative science-based software and consulting solutions. Our values, which include innovation, respect, integrity, commitment, and wellness, shape how we operate, how we collaborate, and how we serve our clients and communities. These principles matter because they reinforce what's ultimately at stake. Every model we build, every workflow we automate, and every service we deliver contributes to the decisions scientists make about therapies that reach patients. They give our clients confidence, they anchor our employees in meaningful work, and they help investors understand the long-term value creation potential that drives our growth strategy. As we define our brand more clearly, we've adopted a statement that captures both our scientific rigor and our role as a trusted partner.
Simulations Plus enables innovators across the drug life cycle to explore confidently, distilling scientific complexity into data-driven success. This is who we are. It's also who we are becoming: a company that illuminates complexity with clarity and empowers discovery with confidence. Let me speak briefly about what sets Simulations Plus apart today. Our leadership is rooted in the depth of our scientific engines: GastroPlus, MonolixSuite, ADMET Predictor, DILIsym, and our QSP modeling tool, Thales, which have earned trust across industry and regulatory agencies for nearly three decades. Clients rely on us not only for our software but also for the expertise of our services organization and the strength of our longstanding regulatory relationships. We are a company defined by both scientific excellence and technological innovation. This combination is rare.
It is our differentiator, and it is becoming even more powerful as we integrate AI, cloud compute, and workflow orchestration into a unified ecosystem. As we look ahead, it's meaningful to reflect on how far we've come. In 2026, Simulations Plus will celebrate its 30th anniversary, a milestone built through both organic innovation and strategic acquisitions. This combination of innovation and acquisition has enabled us to expand across the entire drug development life cycle, from discovery to development, through clinical operations and into commercialization, and as we look forward, we are continuing to evolve, shifting from point solutions to an integrated cloud-enabled ecosystem that supports our clients' expanding needs. We operate in a large and steadily growing market with accelerating adoption of biosimulation, AI-augmented modeling, and digital clinical operations. Regulatory agencies continue to champion Model-Informed Drug Development and new alternative methodologies.
Clinical trials are becoming more complex, placing greater pressure on precision, predictability, and operational efficiency. In a competitive therapeutic landscape, commercialization strategies increasingly depend on data-driven insights. Our total addressable market, or TAM, is large at approximately $12.5 billion, $4 billion of which is traditional biosimulation and the other $8.5 billion related to clinical trial training and medical communications, which we are now addressing through our acquisition of Pro-ficiency a year and a half ago. All of these forces play to our strengths and expand our opportunity. Before we dive into the sessions, I want to introduce the members of our executive leadership team who will be presenting today: John DiBella, Chief Revenue Officer, a 22-year veteran of Simulations Plus who has contributed to everything from early GastroPlus development to leading our global sales and marketing organization, Dr.
Jonathan Chauvin, Co-Chief Technology and Product Officer with 19 years of product development experience. Jonathan joined SLP through the Lixoft acquisition six years ago. Eric Guffey, Co-Chief Technology and Product Officer with 17 years of software and product leadership. He joined through the Pro-ficiency acquisition in 2024, and Jill Fiedler-Kelly, President of Services Solutions, a founder of Cognigen more than 30 years ago and a cornerstone of our scientific services practice since the 2014 acquisition. I'm proud of our leadership team, each of whom has guided the final steps of our reorganization into a client-focused, functionally integrated operating model, strengthening our ability to deliver solutions to our clients consistently, predictably, and with greater cross-functional alignment. With that, I'll now turn the floor over to John, who will discuss the industry environment and customer priorities.
Thank you, Shawn. And hello, hello, everyone. I'll start with the environment in which our clients are currently operating. The pace and scale of change across drug development is reshaping what the industry needs, and it directly influences our strategy. In the early years of Simulations Plus, modeling was used in a limited way, what we think of as the model-supported era. Teams used simulations reactively to justify or interpret experimental or study data. The model supported what was seen but did not drive the future design, helpful but not central to the decision-making process. Then, starting in the late 2010s, we saw the shift to the model-based era, where modeling was used proactively to start influencing drug development plans, support regulatory submissions, and help teams make more consistent decisions. Experiments and studies were then designed around the model's predictions, but still on a project-by-project basis.
Fast forward to today, where the environment demands something much more integrated. We've entered the model-informed era. Modeling and simulation are no longer supplemental. They are foundational. They shape program strategy, de-risk key portfolio decisions, and increasingly serve as the quantitative backbone required by both regulators and internal governance, and the industry is feeling pressure from all sides, accelerating this transition. The first source of pressure is economic. It now costs more than ever to bring a new therapy to market, and development timelines continue to lengthen from early discovery through late-stage execution and launch. Every client, whether a large pharma or emerging biotech, is being forced to rethink how they increase efficiency and reduce uncertainty. They simply cannot afford empirical-only development or siloed functions.
They need trusted predictive engines and fast iteration cycles, fully integrated workflows and novel learning approaches to streamline clinical operations, and decision-ready insights that extend through regulatory approval and commercialization. The second source of pressure is scientific complexity. Today's pipelines include RNA therapeutics, cell and gene therapies, degraders, antibody-drug conjugates, radiotherapies, targeted biologics, modalities that require mechanistic understanding and multiscale modeling. Empirical trial-and-error approaches break down when biology becomes nonlinear or patient variability matters. Clients need technology that helps them anticipate, simulate, and explain outcomes long before entering the clinic. The third pressure point is technological expectation. Our clients now expect modern digital infrastructure: cloud access, collaborative environments, integrated data layers, automation, and AI assistance. They expect modeling ecosystems, not isolated tools. They want interoperability across biosimulation disciplines and learning systems. They want modeling embedded directly into their SOPs, not bolted on after the fact.
These pressures are reinforced very clearly by regulatory momentum. Regulatory agencies around the world have published guidance frameworks and position papers calling for broader use of Model-Informed Drug Development, non-animal methodologies, and quantitative justification. Regulators want more modeling earlier in programs with more transparency and reproducibility. And as AI becomes part of the development process, regulators are signaling that AI must be paired with mechanistic models, explainability, and scientific grounding, not simply automation or speed. In short, the external environment is no longer nudging the industry towards modeling. It is pulling it. And that leads to one of the most important trends that we're seeing: companies are actively redesigning and reimagining their processes around modeling and simulation. Large pharma clients are rewriting Standard Operating Procedures to require modeling from discovery through pivotal studies. Emerging biotechs are building cloud-native model-informed workflows from day one.
Teams want standardization, scalability, and reproducibility across all programs, and they want to augment their internal capabilities with partners like Simulations Plus, who can provide science, technology, and implementation support, and this is where our commercial orientation has evolved. We're not simply selling licenses, we're partnering with organizations to help them transform how they work. Through a unified go-to-market strategy, we're helping them modernize workflows, integrate new modeling modalities, adopt AI thoughtfully and responsibly, and help clients experience Simulations Plus as a single strategic partner spanning discovery, development, clinical operations, and commercialization. The integration of our software and services creates new pathways for expansion within existing accounts and accelerates adoption across new segments. Sales will harness this alignment to expand account depth by positioning Simulations Plus as an end-to-end partner and ensuring every major client is engaged with at least two or more solutions across the product lifecycle.
To accelerate cross-selling and solution selling by linking Model-Informed Drug Development with simulation-based training and commercial offerings into comprehensive value propositions that address clients' most pressing portfolio needs, and to deliver customer experience excellence by operationalizing our data-to-customer strategy to ensure personalized engagement, responsiveness, and measurable improvements. Each sale within the ecosystem drives incremental value. Software adoption enables service engagement. Service delivery accelerates data generation, and data insights reinforce product stickiness and new opportunity creation. This flywheel effect is expected to result in sustained client account growth, higher margins, and a compounding base of recurring revenue, so my takeaway is this: our role is shifting from tool provider to ecosystem partner. We're helping clients build integrated centers of excellence, connect siloed functions, and adopt and scale AI-powered MIDD across global organizations, and that shift is perfectly aligned with where the industry is going.
This sets the stage for Jonathan and Eric to walk you through how our platform strategy addresses these needs directly.
Thank you, John. For nearly 30 years, Simulations Plus has shaped the scientific backbone of Model-Informed Drug Development. Our modeling and simulations products, GastroPlus, MonolixSuite, ADMET Predictor, DILIsym, and our QSP platforms are used across the industry and inside regulatory agencies around the world. They guide choices about dosing, safety, exposure, and patient risk. With Pro-ficiency, the same scientific foundation now extends into clinical operations where protocol fidelity, timing, and human factors influence trial outcomes, and we're extending the same foundation into commercialization where exposure, dose justification, and safety guide labeling and market access. We are no longer a discovery and biosimulation company. We now support discovery, development, clinical operation, and commercialization, all grounded in the same scientific core. They are deterministic. They are reproducible. They are grounded in validated scientific principles. This matters because the industry is quickly changing. Teams are distributed across partners and geographies.
Cloud compute is expanding what scientists can simulate. AI is generating vast numbers of hypotheses more than any scientist can process. This leads to the question, "What can I trust?" In our industry, companies will answer that question by addressing the following: What is grounded in biology? What is grounded in data? What is traceable? What is reproducible? What supports the leap from simulations to clinical execution? Our position is clear. AI can accelerate the work that validates science and causes. Otherwise, AI is your guesswork. Cloud technologies can expand access, but scientific engines define its value. Training improves execution, but only when grounded in the same logic. Otherwise, cloud is simply distribution. This integration, scientific engines plus grounding intelligence plus operational fidelity is what sets us apart from our competitors. We are not just building better tools.
We are building an integrated and cohesive system that connects the entire drug life science from early discovery through commercialization. Historically, clients came to us for individual tools: a PBPK model, a population analysis that had made prediction, or a clinical readiness training reducing protocol deviation. Each provided value. But today, clients need more than just isolated results. They need coherence. They are asking for integrated workflows, automation that reduces manual effort, reproducibility across teams, AI that operates inside validated scientific boundaries, and a consistent way to connect modeling with operational decisions. This is not an incremental evolution. This is a structural shift and once-in-a-generation opportunity. AI, cloud, and validated science are converging, and the companies that act now will shape the next decade of Model-Informed Drug Development. That's why we've evolved. Everything from our architecture and roadmap to our R&D investments and our service strategy.
It is what is driving us to move from individual products to a unified AI-orchestrated ecosystem. In this ecosystem, our engines remain authoritative. Our architectures make them interoperable. Our AI is grounded in science. Our cloud layer is optional, additive, and secure, and our operational training aligns with the same underlying logic. Competitively, this is where we stand apart. Some competitors lead with AI but lack scientific validation underneath. Others lead with cloud platform but rely on fragmented modeling capabilities, and others have strong scientific engines but no unifying architecture, no integrated workflow layer, and no connections to clinical execution. Simulations Plus is uniquely positioned to deliver unifying scientific modeling, AI-assisted reasoning, cloud-scale compute, and clinical operational training inside a single validated ecosystem. Eric will now take you through how the architecture works and what it unlocks.
Thanks, Jonathan. Everything we're building rests on a simple principle. Our scientific engines remain authoritative. The architecture around them makes them far more powerful. To make that possible, we've organized our architecture into four connected layers. Together, they turn individual tools into a coherent system. At the base are our validated modeling engines you may know. They remain the mathematical and biological foundation of everything we do. And critically, our scientific R&D teams provide the fuel and refinement that keep every scientific engine advancing. New methods, new models, new validation, new regulatory alignment. They are deterministic. They are regulatory aligned. And they are continuously progressing. Above the engines is our powerful composition layer we call Vienna. Vienna provides a place where all our engines connect. It standardizes how data is prepared, transformed, and parameterized. It enables consistent workflows across products. And it makes our engines interoperable without rewriting them.
Above the composition layer sits our grounded intelligence layer. This introduces AI copilots that operate inside validated scientific constraints. They help with data assessment, parameter recommendations, scenario generation, model interpretation, and draft reporting. Importantly, they don't replace scientific judgment. Rather, they amplify it by capturing repeatable patterns and making them readily accessible. Every recommendation these copilots make is tied to the underlying scientific structure. By keeping computation in the loop, the AI-driven work is explainable, traceable, and aligned with regulatory expectations for reproducibility. Above grounded intelligence, our orchestration layer automates everything into end-to-end workflows. It can automate multi-step scientific and operational processes, capture provenance and version history, enforce auditability, and ensures results can be reproduced, reviewed, and scaled across teams. It turns steps into repeatable enterprise-ready workflows.
Earlier this year, we launched S+ Cloud, a scaled, secure, compliant platform built on technology gained through our Pro-ficiency acquisition and matured for scientific workflows. It provides unified identity and access, secure, compliant infrastructure, workspace-based compute, integrated telemetry, and on-demand access to engines. It is important to note that S+ Cloud is not a replacement strategy. It is an augmentation strategy. It does not replace local environments. It mirrors them. S+ Cloud adds scale, collaboration, and governance. But the architecture itself remains the same, both in desktop and in cloud. This is how we modernize without disrupting the work our clients rely on today. Across all layers, the architecture is open and extensible. It integrates with external data sources, models, and systems because our clients operate in diverse scientific environments. It meets clients where they work rather than forcing a single deployment model.
When you connect engines, composition, intelligence, orchestration, and cloud into a single structure, clients gain capabilities that simply weren't possible before. This unlocks, first, faster cycles. Processes that once took weeks can be reduced to hours. Reusable pipelines, hybrid compute, and AI assistance remove bottlenecks. This allows teams to spend more time interpreting results and less time assembling them. Second, it provides traceability and reproducibility. Every input parameter and model version is captured. This creates a transparent record that supports internal review, external collaboration, and regulatory interactions. Third, with cloud and orchestration, we can truly provide team-based modeling. Teams can share workflows, reuse validated patterns, and collaborate across geographies with consistent processes. This reduces variability and strengthens institutional knowledge. Fourth, we also gain compounded connected value. Each product becomes more valuable when connected through a shared architecture. A modeling workflow can feed an operational one. AI copilots can support both.
Cloud execution can accelerate either. Fifth, it accommodates trustworthy AI. With this architecture, our AI operates inside validated scientific boundaries and is transparent and traceable. This accelerates decisions without sacrificing control. Our AI stays inside the boundaries that matter. Sixth, it expands our lifecycle reach. The same architecture supports early discovery, translational modeling, clinical readiness, and operational execution using one consistent framework. Clients are already experiencing parts of this ecosystem today. Its full value unfolds over three horizons. Over the next 12 months, we're completing the structural backbone. In practice, this means that more engines are exposed through unified interfaces. Declarative pipelines become standard. AI copilots embedded in real workflows. S+ Cloud identity and workspace infrastructure maturing. Early cross-product workflows emerging. This is where clients begin to feel work becoming faster, clearer, and more consistent.
In the unification period of the following 12-36 months, modeling, intelligence, and operational workflows are expected to begin speaking the same architectural language. This will include a maturation of cross-engine templates, convergence of scientific and operational workflows. Our AI expands across modeling and readiness use cases. And teams adopt multi-product workflows instead of tool-by-tool usage. In short, our workflows begin to feel seamless across all phases. On a long-term horizon, it will become a cohesive ecosystem where discovery, development, and clinical operations flow through a continuous feedback loop. AI supports teams across disciplines. Insights move naturally from early development into later decision-making. And cloud-scale compute supports increasingly complex modeling demands. It becomes an environment where the value of the ecosystem reinforces itself over time. Our competitive advantage becomes self-reinforcing, not just better software, but the operating system for Model-Informed Drug Development, clinical readiness, and lifecycle intelligence.
I'll now turn the floor back over to Jonathan to explain what opportunities this unlocks for our business.
Thanks, Eric. What you've seen so far is how we are building an ecosystem that accelerates science and operations. Now, let's talk about what that means for the business because the same architecture that accelerates scientific and operational works also strengthens our business. Let me highlight four areas. The first one, new revenue opportunities. Our unified ecosystem supports workflow-based products, cross-product bundles, some premium AI copilots, cloud-based collaboration features, tokenized usage models, clinical operations and commercialization modules, portfolio-level workflow packages. These are a natural extension of the platform itself, not entirely new product lines, but new ways of accessing value. The second one is higher customer lifetime value. When products connect, we expect higher adoption within each account, broader enterprise expansion, deeper integration into customer workflows, longer, more resilient customer relationships. Clients stay longer, use more, and expand faster because the systems make their work better.
The third one is pricing and packaging evolution. Our annual license with model remains the foundation of our business. What changes is where value is created as the ecosystem matures and how that value becomes accessible. As we unify engines, compositions, intelligence, and orchestration into a single architecture, we expect to unlock new, more stable layers above the core scientific engines without modifying the underlying licensing constructs. Three principles guide our pricing evolution. New value sits above the engines, not inside them. The deterministic engines remain licensed as they are today. New monetization comes from the layers that make these engines more accessible, more connected, and more productive. Composition becomes a licensable access surface. As we standardize how clients interact with our scientific engines, this composition layer becomes a product of its own. Clients can license the AI connectivity of our engine and cross-engine integrations that accelerate their work.
Running intelligence introduces optional premium layers. AI copilots, parameter intelligence, scenario reasoning, model interpretation, draft reporting all operate on top of validated scientific structures. These copilots will follow a licensing-based model aligned with the user's role in scientific domains with usage transparency and full adaptability. Cloud capabilities expand the commercial model. S+ Cloud allows us to introduce complementary models that sit alongside annual licenses. Tokenized usage models for selected cloud-based services, usage-based compute for large-scale multi-engine simulations workload, and much more. These options allow clients to scale access according to program needs while keeping their core product licensing stable and predictable. The fourth one is strengthening our execution engine. Three internal pillars work together. Product is where engines, AI, cloud, and workflow come together. R&D advances the science, contributing new methods, models, and validation, and defining the science standard our ecosystem must meet.
Services accelerate delivery and feed insight back into the ecosystem. This creates a flywheel of scientific rigor, product innovation, and real-world validation. Success in this space comes down to five fundamentals, and we lean in all of them. First of all, a trusted scientific core. Our engines are widely used and scientifically validated, including use within more than a dozen global regulatory agencies. No one else integrated scientific engines, operational training, and lifecycle workflows the way we do. The second one is an architecture that amplifies the science. We don't dilute the science. We build around it. We have interoperable workflows, a grounded AI, optional cloud scale, integrated operational systems. These structures amplify our scientific strengths. Third, solving the workflow gap. Clients need a coherent way to connect modeling, data, and execution. Our architecture gives them a consistent, reusable, auditable way to do exactly that.
The fourth one is a grounded intelligence. Our AI works inside of validated scientific frames that keeps it explainable, reproducible, and trustworthy for scientific and operational use. Finally, the fifth one is the scalable ecosystem design. Because each product sits within the same architectural language, every new capability strengthens the wall. It's a long-term advantage built on computing value, not only a single tool. That concludes our section on our product strategy and roadmap. I'd like to hand the floor over to Jill, who will discuss our scientific services capabilities. Jill?
Thank you, Jonathan. Good afternoon, good evening, good morning. I'm Jill Fiedler-Kelly, President of Services Solutions at Simulations Plus. Now that you've heard our product vision, I want to share how our services complement that as part of our overall company strategy. Importantly, our services teams are power users of the Simulations Plus tools and platform, applying our software daily in real-world development programs, stress-testing workflows at scale, and translating hands-on client experience into actionable feedback that continuously strengthens our product ecosystem. Our scientific consulting teams across PBPK, QSP, PKPD, and clinical pharmacology help clients navigate some of the most complex decisions in drug development with clarity and confidence. While our clients vary from emerging biotechs to the world's largest pharmaceutical companies, their needs are remarkably consistent. They want to reduce uncertainty, avoid unnecessary cost and delay, and make decisions they can stand behind.
Our role is to provide the insight and the scientific judgment that allows them to move forward with confidence. At the same time, every engagement serves as a validation loop for our technology. Client use cases, regulatory interactions, and edge conditions identified in consulting projects directly inform software enhancements, workflow optimization, and roadmap prioritization across the Simulations Plus platform. Across every discipline, our approach is consistent. We interpret complex biology, accelerate critical workflows, and help teams advance their programs with a higher degree of confidence and a lower degree of risk. This work is deeply rooted in scientific rigor, transparency, and integrity, values that are core to who we are as a company. Our PBPK solutions team develops and applies mechanistic models that integrate in silico, in vitro, and in vivo data to predict human pharmacokinetics and drug product performance.
These approaches are broadly recognized by global health authorities and have become an essential part of modern development programs. Among other high-impact applications of PBPK modeling are first-in-human dose selection and prediction of drug-drug interactions. Using validated workflows, our PBPK team helps clients reduce development costs, compress timelines, and avoid unnecessary human and animal studies, and after collaborating with regulators, industry partners, and academic researchers, and supporting hundreds of approved products, our experience gives sponsors the assurance that their modeling work can withstand regulatory scrutiny. Our QSP solutions team builds quantitative systems pharmacology and toxicology models that represent disease biology, drug mechanisms, and patient variability across biological scales. These models allow companies to anticipate outcomes long before clinical data are available.
Some of the high-impact applications of QSP and QST modeling include predicting efficacy and safety potential a priori based on mechanisms and representations of biological pathways, cellular interactions, and feedback processes, and increasing the likelihood of clinical trial success through optimized study design and dose selection. A distinguishing strength of our QSP/QST practice is the emphasis on both efficacy and safety. By examining both sides of the benefit-risk equation mechanistically, we help sponsors refine development strategies that are more likely to succeed clinically and translate effectively to patients. Our clinical pharmacology and pharmacometric solutions team supports clients across the full quantitative decision space, from preclinical PK to late-stage exposure response analyses. Leveraging MonolixSuite and advanced pharmacometric methods, we provide population PKPD and exposure response analyses, non-compartmental and concentration-QT assessments, pediatric extrapolation and model-based meta-analyses, data curation, regulatory documentation, and embedded team support.
These analyses give sponsors the evidence and clarity required to make faster, better-informed development decisions. And as development programs move through the regulatory submission process, findings from these analyses form the backbone of understanding regarding determinants of efficacy and safety to bolster solid dose justification arguments. Every consulting engagement operates within a robust quality management framework, validated software, QC code, and verified scientific conclusions. This ensures our clients can rely on our work in high-stakes regulatory settings. We also have former U.S. FDA experts on our team who help sponsors anticipate regulatory expectations and understand where modeling will have the greatest impact. Their guidance helps reduce the likelihood of additional information requests, delays that can be costly for any development program. Equally important, our scientists translate complex modeling outputs into clear, actionable recommendations.
In a world where teams are often dispersed across functions and geographies, this clarity accelerates alignment and decision-making. Achieving regulatory approval is not the finish line. Success increasingly depends on how clearly science is communicated to clinicians, payers, and other stakeholders. Our commercialization solutions team uses Panorama and AI-enabled workflows to transform scientific and model-based insights into education, strategic content, and competitive intelligence assets that support uptake and understanding across the product lifecycle. These services connect early development, medical affairs, and commercial teams with the same clarity and consistency that guide R&D decisions. As the use of biosimulation and associated quantitative analysis methods has become an integral element in the fabric of modern drug development programs, understanding of the strengths and advantages of alternative methods has evolved as well. With this understanding has come an awareness of the complementarity of the various model-based methods.
A unique strength of the Simulations Plus consulting practice is the breadth of our expertise with different modeling approaches. It is this foundation that allows us to offer multidisciplinary support using multiple approaches together, not in isolation. This key differentiator leads to more robust insights for decision-making. I'll now provide a few case studies to illustrate how this multidisciplinary approach translates directly into faster timelines, lower cost, and reduced development risk for our clients. Case study one, QSP and CPP synergy for first-in-human and phase II optimization. In this case study, a small biopharmaceutical company was preparing for their first-in-human study of a novel drug where late surprises could have resulted in costly delays or program failure.
We developed a bespoke QSP model that predicted the efficacy of their novel compound, but also identified immunogenicity, the drug's ability to trigger an immune response, as a major potential driver of variability in drug behavior before the first patient was ever dosed. By revealing this risk months earlier than traditional approaches would have, the client avoided costly protocol amendments and accelerated their first-in-human study design by an estimated three to six months, saving several million dollars in potential rework and delays. Equally as important, our predictions of patient response to therapy revealed the potential for a far greater efficacy advantage for this compound over current treatments, allowing for more confidence around prioritization of assets and acceleration of program-level decisions.
As data were being collected during the first-in-human trial, we integrated the observed data to refine the QSP model and ran advanced pharmacometric analyses to quantify how intrinsic patient factors and antidrug antibodies contributed to the variable drug behavior. With these insights, we simulated phase II dosing paradigms and optimized the phase II plan without additional exploratory studies, effectively reducing the time to a confident phase II design by roughly one-third and improving the client's probability of technical success. The combined expertise of our QSP and CPP teams provided the sponsor with a unified mechanistic understanding of risk, reduced the need for additional clinical experiments, delivered a more predictable and cost-efficient path forward, and ultimately improved patient safety in phase II. In our second case study, it's population PK and PBPK and QSP strategy driving faster formulation and regulatory success.
In this instance, for another emerging biopharmaceutical company in the metabolic disease space, our clinical pharmacology and pharmacometrics team provided strategic support across their entire program, resulting in an efficient route to proof of concept. After defining the clinical pharmacology development plan, we performed population PK analyses and identified an opportunity to optimize the formulation strategy through PBPK modeling with GastroPlus. This insight allowed the sponsor to select the final formulation without running additional costly clinical formulation bridging studies with potential associated savings of more than $1 million-$2 million and shortening the development timeline by four to six months. Our population PK findings also fed directly into regulatory documentation. By proactively addressing drug-drug interaction concerns, we helped streamline the client's regulatory interactions and reduce the likelihood of requests for additional analyses or studies, a common cause of multi-month delays.
Additionally, our analyses de-risked the safety profile and guided dose selection optimization for later-stage clinical trials, helping to reduce the number of study arms, thereby lowering costs and providing earlier access to relevant informative data. Our medical and scientific experts also engaged targeted key opinion leaders to pressure test key assumptions, helping the sponsor make confident decisions earlier in development. The next stage, integration of all clinical and mechanistic data into QSP models, will further shorten the pathway to proof of concept by allowing simulation of efficacy boundaries and responder phenotypes before running expensive clinical studies. This end-to-end approach provides the sponsor with a higher confidence, lower cost, and faster path through early development. One of the most meaningful shifts in our industry is the FDA's roadmap to reduce reliance on animal testing through new approach methodologies, or NAMs.
Recent guidance from FDA provides further direction for streamlining non-clinical safety assessments for monospecific antibodies based on an integrated knowledge-based risk assessment. This direction aligns closely with the strengths of Simulations Plus. Our services teams are actively implementing NAM-aligned strategies using Simulations Plus software today, providing mechanistic, scalable, and regulatory-acceptable alternatives to traditional in vivo studies, and allowing us to refine workflows, validate regulatory acceptance, and rapidly incorporate emerging data types and methodologies into our platforms, well ahead of broader market adoption.
Our NAM-aligned platform capabilities include GastroPlus, which supports virtual species translation, dose optimization, and first-in-human predictions, applications that can reduce and, in some cases, replace non-clinical animal studies in early development. MonolixSuite, which enables model-based translation from sparse preclinical data into human predictions and supports efficient in silico study design. And our biologic sim platforms, well-positioned to incorporate emerging experimental approaches such as organ-on-a-chip data, helping evaluate and improve liver safety for large molecules, including monoclonal antibodies. Our NAM-aligned scientific services consist of our PBPK and QSP/QST teams who develop mechanistic validated models that integrate standard in vitro and in silico data to predict human PK, PD, and safety with confidence, and our regulatory experts who help sponsors interpret evolving FDA expectations and determine how NAMs can be incorporated thoughtfully into development and submission strategies.
As NAM adoption continues to grow, the combination of our platform technology, our depth of scientific expertise, and our regulatory insight positions Simulations Plus as a trusted partner in this next chapter of model-informed development, one where high-quality predictions and mechanistic understanding play an increasingly central role. By continuously translating real-world client experience into ecosystem enhancements, our services organization validates the relevance, durability, and competitive advantage of the Simulations Plus environment, creating a virtuous cycle of innovation, adoption, and growth. Simulations Plus services are not peripheral. They are a strategic asset that ensures clients navigate development with confidence, develop greater skill with and affinity for our platforms, and bring better medicines to patients faster and more efficiently. I'll now turn the presentation back to Shawn.
As we come to the end of today's program, I wanted to recap the themes that framed our discussion this morning and connect them to the long-term story we've unfolded together. At the start, we reviewed the foundations of Simulations Plus, our mission, our values, and nearly 30 years of scientific innovation that have helped define the field of Model-Informed Drug Development. We highlighted our leadership position, the strength of our scientific engines, our trusted relationships with global regulators, and the evolution that has brought us from a single PBPK tool in 1998 to a company that now touches discovery, development, clinical operations, and commercialization. Those highlights weren't meant as a retrospective. They were meant as a lens, a way to see clearly who we are so we could then look confidently at where we're going, and over the course of today, you've seen that journey arc in full.
You heard from John about an industry at an inflection point, one where the cost, time, and complexity of drug development demand new approaches, where regulators are pushing hard toward MIDD and NAM, and where AI-generated insights require a robust scientific backbone. The stakes are rising, and the market is moving toward integrated, transparent, cloud-ready workflows at a pace that is accelerating. Jonathan and Eric then revealed how we're meeting this moment, not just with a collection of tools, but with a modern orchestrated platform that can turn opportunity into impact. You saw how our scientific engines form a deep and durable moat, how the S+ Cloud provides the secure and compliant backbone, how Orchestrator brings coherence to workflows that once lived in silos, and how AI copilots, grounded in validated science, are beginning to reimagine what scientific productivity looks like.
Together, these pieces form the ecosystem shift we described this morning. From standalone tools to unified modeling environment, from local computation to cloud execution at scale, from manual modeling steps to AI-assisted workflows, from isolated analysis to traceable, reproducible pipelines, from point licensing to enterprise pathways built for the future. And Jill showed how our services organization complements and amplifies that platform. They're not an add-on. They are interpreters, validators, and accelerators of client success. Their multidisciplinary expertise turns our technology into outcomes and helps de-risk adoption for organizations that depend on accuracy, transparency, and regulatory trust. That brings me to my final point I'd like to leave with you. Simulations Plus didn't just participate in the first 30 years of Model-Informed Drug Development. We helped build the scientific foundations of it. Now the industry is entering its platform phase.
Because our ecosystem is rooted in validated science, grounded in regulatory credibility, and architected for AI and cloud-scale computation, we're prepared to define what comes next. This is our moment of convergence. Science plus cloud plus AI plus services becomes one coherent platform, a platform designed not just to accelerate workflows, but to create confidence, confidence for scientists, confidence for regulatory partners, and confidence for the investors who are backing a durable, future-ready business. Thank you for spending this time with us today. Thank you for your partnership and your support. We look forward to continuing this journey with you as we enter the next era of Model-Informed Drug Development, integrated AI-enabled and cloud-ready. Let's move now to Q&A.
Hi, everyone. Thank you for joining today. Our first question comes from David Larsen from BTIG. How large is Pro-ficiency software and services business, and how is growth in this area?
Hey, Dave. Thanks for the question. Our Pro-ficiency acquisition brought us two revenue streams. First, the Pro-ficiency training module platform that is sold into clinical operations. And secondly, the med communications service-based business, which supports the commercialization process. Both of those businesses, as indicated upon acquisition, like our biosimulation solutions, saw a step down in revenue in the back half of this past year, fiscal year 2025 for us. Both are moving forward since that inflection point quite well, both on the software side, the training module platform side in terms of building up its momentum back and contribution of revenue, as well as the service business, which overperformed in the first quarter, contributing to our service accomplishments. TAM for both of those businesses, as we've referred to, are quite large.
The acquisition of Pro-ficiency doubled our TAM opportunity going forward and are both positioned well to contribute growth into the future.
The next question comes from Matt Hewitt of Craig-Hallum. How has the introduction of the FDA's NAMs guidance changed the amount of R&D dollars being allocated towards simulation and modeling? And are your customers shifting resources?
Matt, I can't look under the covers in terms of allocation of budgets. We certainly come into our fiscal year 2026 and the new calendar year of 2026 with some budget momentum in the industry. We saw that contribute to our first quarter results, certainly positive indications out of the JPM conference that budget uptake in modeling and simulation budgets, AI budgets universally are being funded. How much is being driven specifically by the NAM opportunity, I can't speak to, but it certainly is something that those clients that are in that space, therapeutic space that's being focused on, are looking at in terms of what can get us to that weight of evidence threshold to reduce animal testing. But the announcement is, globally, yet another indicator. John DiBella spoke to the pulling, not pushing, of the regulatory world in terms of the expanding use of modeling and simulation.
The NAMs announcement is a good example of that, and that which has spurred growth in modeling and simulation over our history and no doubt will continue to push it into the future.
The next question is from Brendan Smith of TD Securities. Regarding FDA's NAMs roadmap, what has been your client experience thus far in terms of demand and new customer inbounds? To what extent do you expect this to drive upside to current revenue estimates? And how long do you expect it will take to see this upside? In other words, where are we in the hockey stick curve of NAMs-related adoption/upswing?
Thanks, Brendan. I smile only in terms of hockey stick. We're in a world of adoption of new techniques and procedures that typically don't hockey stick, but slowly build over time. Certainly, it's a topic at the top of the priority list in terms of clients. As the guidelines came out a few weeks ago or early December, and that iterative process of commentary and finalization of those has gotten a lot of discussion, continuing discussion going forward, continuing scientific debate as to what really is possible here. But what's possible will be achieved in baby steps over time. Revenue contribution will follow, as most of these endeavors, initiatives by the FDA have proven in the past, as more modeling and simulation is applied there. Ultimately, modeling and simulation departments within our clients get larger. More software is required to populate their desktop and make them effective.
Our consulting services business, in terms of clients that draw us in to work on these sorts of projects, that will uptick over time. So again, a lot of focus, a lot of anticipation. It's a very positive step forward and endorsement by the regulatory bodies. That financial impact will roll in over time.
Next question is from Scott Schoenhaus of KeyBanc. How should we think about your near and midterm tech objectives in terms of growth and margin impacts for the business over the next 12 to 36 months?
Yeah, thanks, Scott. Yeah, we're excited. Our business is enhanced tremendously, our business opportunities as we move forward by our roadmap, as we've described here, our tried-and-true licensing business of our engines, our products to date, opportunity and strength of growth going forward exists as a baseline. The incremental revenue opportunities, as described by Jonathan, are abundant and really taking us into a world in which, while we've earned our keep, if you will, from the modeling and simulation department, our clients have grown independent AI budgets, ecosystem budgets internal to their organization that we believe we will be able to access in this rollout of product strategy, enhancing our dollar opportunity within our clients. Impact on the financial side, first place to go is in terms of R&D budget.
You've seen and we spoke, as we announced our first quarter uptick in R&D spending that is already baked into the guidance that we provided at the beginning of the fiscal year, a result of our reorganization last year and focus of resources within the greater organization. That R&D spend level, we believe, will be consistent in its allocation and supportive of our growth in terms of the technology roadmap on a go-forward basis. So no additional incremental step up in R&D spending is contemplated. Over time, much of the revenue upside opportunities that we will be pursuing will be on the software side. As you know, our big impact in terms of our gross margin overall is our mix of software and services.
The roadmap rolls out incremental software opportunity, revenue-generating opportunities going forward that should, in time, enhance and contribute to improvements and even dive into the future.
Next, we have a follow-up question from Brendan at TD Securities. Regarding your commentary on pricing evolution, can you give us a sense of how much each additional add-on would cost per customer, for example, cloud, composition as a service, premium AI, etc.? and how should we think about the relative ramp in revenues as these roll out over the next 18-24 months?
Yeah, Brendan, we don't have the price list drafted yet. So individual pricing of these modules, which are still being developed. We've introduced some of this technology with our GastroPlus release last year. It'll continue to roll out as we reach our typical milestones in terms of upgrades to Monolix, ADMET Predictor, etc., down the road. So it'll continue to roll out. Some of the AI functionality and some of those layers is still being defined, and their definition will obviously translate to value, which will translate to pricing. So no specifics at this point in time in terms of price list of those incremental opportunities.
But the definitive value of them to our clients in terms of accelerating workflows, in terms of supporting efficiencies from that discovery through commercialization phase. The value of these technologies will produce very strong ROI presentation to the clients and would lead to incremental revenue to the company going forward.
Next question is from Constantine Davides of Citizens JMP. You mentioned wanting to be engaged with two or more solutions across the product lifecycle. Can you provide a little bit more color around that objective, where you've been most successful in the past and where is the most opportunity in terms of product that has historically been underpenetrated?
Yeah, that spreadsheet that every company has of all of its clients posted against all of one's products and services. Our opportunity set in terms of cross-selling has improved over the years, but still creates a vast opportunity. Clients that utilize all of our solutions across all of discovery through commercialization. The white space is quite healthy and clear. Our success in the past, across the board a bit, but ADMET Predictor and GastroPlus are very closely linked and integrated, and success there in the past, somewhat more than elsewhere. Very indicative of when the products are highly integrated, that ability to cross-sell is enhanced. And so as the integration and interoperability across all of our platforms steps up over time, that will support that cross-selling activity.
That sequencing on the biosimulation side of ADMET Predictor, GastroPlus, Monolix, and our QSP solutions, as you saw in Jill's presentation, the application of those in silico or modeling modalities to address the same use case or a use case utilizing different modeling techniques is growing, and therefore, our ability to, with an integrated, more integrated platform, cross-sell across those platforms, I think is going to step up as we move forward into the future.
Next, we have a follow-up from Matt Hewitt at Craig-Hallum. There have been a number of press releases lately regarding pharma and software companies partnering up. Is that something Simulations Plus will explore? If not, why not?
Yeah, absolutely. The client base is, depending on size and shape and where they're at financially, investing in building internal ecosystems to support their AI strategies. And through that effort, are looking for partners to supply the componentry of those ecosystems. This roadmap that we presented today has not been done in isolation and has been done in close endeavor with several of our large pharma clients. In time, a broader technology relationship with our clients might translate from partnership sort of scenarios that are not licensing of individual product by individual product, but by partnership and relationship economics that may be reflected in the way we build our relationships with customers in the future, yes.
Next question is from Max Smock and Christine Rains of William Blair. Given your recent revenue breakdown reporting changes, can you please map out how each of your product offerings, Gastro, ADMET, QSP, QST, etc., map to discovery, development, clinical operations, and commercialization in terms of percentage of revenue across both software and services?
Yeah, I'll provide the mapping. Certainly, our earnings release and slide deck show the breakout there. Discovery represents our ADMET Predictor solution, which is sold into discovery. Development, clinical development is where you'll find GastroPlus and Monolix and QSP, the solutions that are used in the clinic. Clinical operations is our Pro-ficiency training module platform. Commercialization does not have a software revenue contribution. It is net communication services only. If I go back and run through the continuum again, discovery, there's occasional projects there, but very limited service revenue in that segment. In clinical development, that's where the breadth of Jill's team and our consulting services is allocated. And in clinical ops, no services. Again, commercialization, fully services.
A follow-up question from Scott Schoenhaus. Your slides indicate overall TAM is expected to grow mid-teens over the next few years out to 2030. And this is in line with your prior comments about how the industry on average grows annually. But medical communications is expected to grow only around 6% in your slides. Is there a way for you to take market share and grow at a more accelerated pace in the next few years?
Yeah, medical communications is a business that strategically fits into our SLP business as an indicator, an involvement on our part in a space that's going to enhance in terms of its being data-driven in a world of personalized medicine. Medical communications is a very large TAM of companies that support the biopharma industry in terms of introducing their drugs commercially after approval. We're going to look for those opportunities that leverage data most significantly. And that's where our growth will be most enhanced on the commercialization side.
Another follow-up from David Larsen at BTIG. Is fiscal 2026 guidance being reaffirmed, and if not, why not?
Guidance is reaffirmed that we provided before. No change in our guidance here. Our focus in this call is to present our product roadmap. Certainly, the product roadmap supports our expectations for the coming year and beyond. No change in guidance.
Next, we have a question from Jeff Garro at Stephens. How will your sales and marketing teams prove out the ROI of the composition or connectivity layer as a product that merits incremental payments to SLP?
Good question, Jeff. I'd say our validation of expectations in terms of impact sources in two ways. One, I mentioned we've not developed this in isolation. Those clients that have been involved have contributed just that validation in terms of the value of what we're working on. And then secondly, I point to one of the key benefits of having such a strong service organization. The enhancements and benefits that we are bringing to our clients through our software enhance and are benefited by our service organization and our performance of this type of work on behalf of our clients. And therefore, validation of the ROI, we benefit from it as well. So internal validation is very key here.
A follow-up question from William Blair. What do you see as your long-term sustainable growth rate for software services and the overall company? When can we expect to get back to this level of growth?
Yeah, good question. We have historically pointed to a biosimulation primarily growth rate of 14%-15%, which validated in terms of third-party research analysis. We've operated in an environment in which growth has during cost-constrained era of the last couple of years been plus or minus to the 5% level, if you will. Certainly, a more stable market can get us to 10% in the coming time. But I think with all the emphasis in terms of data-driven model development, the support provided by the regulatory environment, the new opportunities to enhance the drug development process that are coming from advanced technical technology, AI, access to data, speed, computational speed, all of these will contribute to continued evolution of the drug development process in the direction of modeling and simulation. Can we see 15% or better growth into the future? Boy, there's some that are really pointing to that.
I'd like to get back to the 10% level and then move from there, but certainly, the opportunity set bodes well for accelerated growth into the future in the long run.
At this point, there aren't any further questions, so I'll hand it back to you, Shawn, for closing comments.
Thanks, Lisa. Appreciate everyone's attention and participation in the investor presentation that we've made today. Certainly available for follow-up, further questions, and discussion. I hope it's been beneficial to you to give you an idea of where we are headed with our business and the strategic opportunity it presents for us in our client world. Thanks again for attending and look forward to talking to you again soon. Take care.