Dassault Systèmes SE (DSY) Earnings Call Transcript & Summary

Hi. This is Stacy Pollard. I'm the European software analyst for JPMorgan. Today is the 10th of January 2022, and this is part of the JPMorgan Healthcare Conference. During this session, we are speaking to Sastry Chilukuri, the Co-CEO of Medidata, a Dassault Systèmes company. For background, Sastry is also Founder and President of Acorn AI, Medidata's data science business. We've also got Arnaub Chatterjee, the SVP of Acorn AI; and Callie Gauzer, Director and Investor Relations. Some quick logistics, this session is 40 minutes long with 20 minutes of presentation and 20 minutes of Q&A. [Operator Instructions] Please keep those questions focused on the topic of Medidata and Life Sciences and not related to Q4 financials. Dassault is currently in pre-earnings quiet period. Sastry, thanks for joining us today. The floor is all yours.

Thank you, Stacy. Welcome. Welcome, everyone, to Dassault Systèmes update on Life Sciences & Health Care. We are really excited to share the progress that we've made over the last few years as a combined company as well as talk a little bit about our future ahead. Next slide. I'm joined today by my colleague, Arnaub Chatterjee, who is the SVP for Acorn AI, our data science business. Next slide. We are at a very exciting time for life science, as is evident through all of the presentations here at the JPMorgan conference. The industry is unlocking new frontiers in speed and complexity of therapeutic innovation. Technology, patient engagement, AI and data platforms are absolutely critical to accelerate these therapies to patients. And capital markets and investors are backing these trends with significant investments. We are the industry's leader with over 7,000 ongoing clinical trials with all of the world's leading sponsors as well as partners. Our multiyear investments in areas such as patient centricity, data and AI are paying off really well. This also creates a foundation to accelerate our growth. Our next-generation life science cloud platform brings together real-time AI and rich customer engagement to the point of use and closes the loop across research and discovery, development, manufacturing and commercial. Nobody else in the industry offers this kind of a platform. Our focus for 2022 and beyond remains on helping the industry, including biopharma, medtech and diagnostics, bring these new therapies to patients faster, attract the best talent in health tech worldwide and continue to drive innovation in this important space. Let me now spend the next 15 minutes talking through each of these areas. Next slide. Over the last 2 years, we have seen incredible progress across the industry on biologic platforms. mRNA, RNAi, cell and gene therapy, CAR-T therapies have all come to age. And over $1 trillion of investment in various kinds has flowed into the biologic platforms. Consistently from all of our customers, we are hearing a need for similar platforms at scale for technology, data and AI that allow them to replicate the speed, agility and innovation that we all witnessed in the manufacturing and delivery of the COVID vaccines across the entirety of the portfolio. How can we make our entire portfolio move at the speed of the COVID programs is a question that we consistently hear, and the role of technology enabling this is clear as well. Consequently, the capital markets have invested north of $200-plus billion through various sources to advance these technology platforms. Jointly, combining the biology platforms and the technology platforms allows us to accelerate and derisk the speed of therapeutics getting to patients. Next page. We are the largest player in the industry, and our mission is to power smarter treatments and healthier people. These are a few of the metrics that give you a flavor for our size and scale in the market. We are #1 by market share with over 8,000 customers and 20 of the top 20 biopharma CRO and medtech clients. Our current TAM is about $8 billion, and we estimate the potential addressable market as high as $20 billion. In 2020, 17 of the top 20 selling drugs were developed on the Medidata platform, and 75% of oncology approvals between 2015 and 2021 were supported on our platform as well. This gives you a feel for the scale at which we operate. Next slide. The financial metrics that we had outlined at the time of the acquisition of Medidata by Dassault Systèmes is still relevant right now. We continue to grow in the 13% to 15% year-on-year growth, and more details around the financials have been shared during the quarterly earnings call, and we are on track with all of the other financial metrics or exceeding them. The growth levers that we had originally outlined around continuing to grow market share with Rave and Attach continue to expand our patient centricity and decentralized trial platform as well as continuing to scale up our data analytics and AI have paid off extremely well over the last 2 years. We continue to optimize the resource allocation across our companies as well as continue to make progress on the end-to-end platform that connects manufacturing, clinical development, research and discovery as well as commercialization. Next slide. And this is really the fruit of our work over the last couple of years. Because of the progress we've made around patient centricity as well as around AI, we're now able to bring that together with our core transaction systems that we provide. So we support a vibrant ecosystem of 8,000 customers and partners, nearly 8 million patients, 95,000 physicians, 30,000 sites and 145-plus countries. Our real-time AI that has won numerous awards is based on a foundation of 26,000 clinical trials, 7,500 of which are going right now, and 1 billion images were collected in 2021 alone. We've taken these interactions with the ecosystem and the insights from the real-time AI and embedded it into the core processes that power the enterprise. Our systems span everything from clinical execution to clinical operations and decentralized trials, evidence generation, patient engagement, research and discovery, manufacturing as well as commercialization. Next slide. And the power of our platform came through with a couple of clients over the last year. The first is Moderna. We played a very core role in partnering with them on their COV19 vaccine program. The Phase III program involved 30,000 patients with 98 sites, 28 million data points, and all of this was executed in a matter of 5 months. The scale of speed and agility is unprecedented in our history. And we had several of the core leaders of Moderna, including Mel Ivarsson as well as Marcello, the Chief Digital Officer, join us at our next conference to talk about our experience working together. We deployed a wide range of innovation in partnership with Moderna. We took a hybrid and decentralized approach to study execution given all that was happening with the COVID pandemic. We used real-time AI to identify sites to predict where the pandemic was headed as well as to be able to identify rapidly data quality issues and patient-level issues. There were 30,000-plus patients who are providing real-time sensor and outcome data that we had to collect in a compliant and robust manner. We put in place all kinds of workflow innovation that allowed them to be able to make changes to the study as they were going real time as well as be able to get to this unprecedented speed that they were able to achieve. And finally, we piloted breakthrough use of real-world evidence that allowed them to connect their clinical trial data with the real-world data using Medidata Link that allowed you to be able to support better insights in patients, be it around breakthrough infections or be it around some of their past history to explain some of the safety-related events. All of this together was instrumental in the speed that Moderna was able to achieve. And a large question that both Moderna as well as other players in the industry are asking is how do we replicate all of these capabilities across the entirety of our programs as well as the entirety of our portfolio. Next slide. The other example that we have is how we close the loop all the way from research and discovery through clinical development, through manufacturing with one of these leading CAR-T manufacturers to be able to enable the bedside to bench to bedside kind of therapies. We support everything all the way from collection of samples to testing and processing of it to be able to develop the medicines and to be able to deliver the treatment back into the patient. It takes a wide range of capabilities across Medidata as well as Dassault Systèmes to be able to enable these kind of innovative platforms that deliver these treatments at scale. Next slide. Those were 2 of our core examples that show you our platform at scale, working both with Moderna as well as this large manufacturer around personalized therapies. Let me now shift gears and talk a little bit about the innovation that we're putting in place across each of our core capabilities. The first is in study conduct. We continue to drive innovation and scale in Rave EDC. We supported over 4,500 study starts in 2021 alone, and that's a record for us and we believe a record for the industry. To be able to support this kind of scale requires world-class privacy, security, compliance and quality for which we are known. The studies themselves are growing in complexity, and we're going beyond eCRF to support all kinds of data, from ePRO and eCOA to streaming data coming out of devices and instruments to video visits that need to be collected in a compliant manner as well as tokenization that allows us to connect with real-world evidence. And the innovation that we put in place with some of these covered programs is now available at scale for all our customers, be it around the intelligent workflows and interim study lock as well as data cut and mid-study changes and eSource or autonomous medical coding or integration with randomization and trial supply management and supply forecasting. Next slide. If we look at clinical operations and decentralized trials, COVID-19 has permanently changed the interactions between sponsors, sites and patients. What used to be managed in a traditional way no longer happens. Because of COVID, sponsors needed the flexibility around how they interacted with sites, either physically in person or remotely. And sites and investigators needed the flexibility to be able to interact with patients either remotely or through in-person interactions. Next slide. But being able to manage this kind of a complex clinical trial execution requires a platform to orchestrate a large number of interdependencies. It's not just eConsent or video visits or eCOA. It's actually a dozen-plus capabilities that are needed to be able to run decentralized trials in an integrated manner because remember, decentralized trials are not an on-off flip of a switch. They're much more of a dimmer. Depending on the therapeutic area you're operating in, the complexity of the studies as well as how you want to design your study, you probably need more or less of a decentralized element. And ultimately, you need everything to flow seamlessly back into the sponsor so that you have a compliant record around the entirety of the study execution. Next slide. And then during the course of the COVID pandemic, we speeded up our pipes to be able to get a real-time view around what was happening in the industry. We ingested all kinds of data, including tremor data and sensor data to understand traffic patterns in various cities to predict opening and reopening. And this allowed us to create real-time views of what's happening at a patient level, a site level, a sponsor level and build these central cockpits that create all kinds of agility that are needed to be able to manage studies in a complex environment. And these live analytics have forever changed the responsiveness and the speed and agility with which sponsors can execute a particular study. Next slide. And all of these capabilities, both the decentralization I talked about as well as the live analytics are now embedded as part of the core transaction systems. Everything from a CTMS to an eTMF to the financial management systems as well as the risk management systems, all have the analytics and the interactions built live at the point of use so that it's very seamless to the end user around where these insights are coming from, and it makes it easier for them to act on them. That is our vision and our plan around how we're executing on clinical operations and decentralization. Next slide. Our patient engagement and experience has grown tremendously over the last couple of years. COVID has changed the nature and the extent to which patients readily want to be part of a clinical trial process. So in addition to the eConsent, we've launched a whole set of new capabilities, including myMedidata, which is a patient-focused app, eCOA as well as Sensor Cloud, which allows us to rapidly collect data from patient sensors and instruments. And all of this is embedded within Rave so that it's seamless to the sponsor around how the data is collected as well as how the data is being managed, and it all happens in a compliant and secure manner for auditability and traceability with the regulators. Next slide. To double-click on a few of these capabilities, myMedidata closes the entire loop that allows sponsors to be able to find patients, learn more about them to get them into recruitment, get them to electronically consent, capture patient-reported outcome data, be able to monitor their visits remotely, collect digital endpoints out of Sensor Cloud as well as ensure that all of this data is returned back in a secure and compliant way, back to the sponsor as well as back into the patient. And that is really the vision for myMedidata that closes the loop around the patient and is an integral part of enabling decentralized trials. Next slide. With our Medidata registries, it creates a platform for us to be able to follow a patient throughout their entire journey, not just during the duration of a clinical trial but also after the trial for post-trial engagement as well as in the future, recruit them back into future clinical trials because this creates a location to be able to stay engaged with patients. Next slide. That was our patient centricity-related offerings. Evidence generation, this is an area that has shown tremendous progress as well as promise over the last few years for us. The first is synthetic control arms. We now have multiple instances where we have used historic clinical trial data to replace or augment the entire traditional recruited control arm. This allows companies to be able to both use it for regulatory approval as well as for internal decision-making to be able to make better decisions around which drugs to move forward and which programs to accelerate. While control arms are great to be able to simplify and reduce cost and reduce risk around the execution of studies, what we found is making some database and AI-based decisions very early in the program allows you to be able to improve the overall probability of success of a program. And that's really where our trial design offering comes in. As an example, we have the industry's largest CAR-T data set that cuts across multiple sponsors. And there are 120-plus sponsors currently active in CAR-T, and they would all love to get access to these data sets and the insights coming out of it to be able to understand what kind of patients to target, what kind of safety events to model as well as what kind of endpoints that they should be selecting to be able to improve the odds of success of their program. And finally, with Medidata Link, we have the industry's only software that allows you to be able to connect the patient's clinical trial data with their real-world data. And by embedding it in Rave, we're making the injection and use of real-world data seamless for all of the sponsors. To give you a better flavor about the work we're doing in evidence generation, I want to talk a little bit about synthetic control arms. Next slide. Recurrent glioblastoma is a horrible disease that has terrible outcomes for patients. And because of this, we've been working with 2 clients in this space to build synthetic control arms to use for regulatory submission. We got approval from both the FDA CDER as well as CBER to use our control arms to either replace or augment historically created control arms that require recruitment of patients. Because of this, we've been able to dramatically reduce the amount of development time by up to 6 months as well as significantly reduce risk and cost of these programs. To give you a better flavor of what is entailed in building these synthetic control arms as well as why we're so excited and why it's important, I want to play a video that we created with Dr. Fahar Merchant, who is the President and CEO of Medicenna that's highlighted over here. Next slide. Let's play the video.

Glioblastoma is by far one of the most aggressive forms of brain cancer. In most cases, you're expecting survival outcomes of these patients to be a few weeks to a few months at the most. If you look at very recent past few Phase III clinical trials with different therapies that are being used for recurrent GBM, You see that they show good data in a Phase I clinical trial, the mETC show you some good data in Phase II clinical trials. But eventually, when you get to the registration trial, the pivotal Phase III clinical trial, that's where these drugs end up failing when you conduct a Phase III registration trial. The big challenge is that patients know that there is an equal chance that half of them will end up in a control arm where they'll get the standard of care and the other half will get the experimental therapy. These patients know, the families know that there is a finite time period. And if they are then assigned to a standard of care arm, which has not improved outcomes for patients, they quickly move away. And that makes it very difficult for a company like us to do a successful or to conduct a successful Phase III clinical trial without having so many patients dropping out from the study. And this is where the external control arm or the synthetic control arm plays a big role. This is where Medidata Acorn AI came into play for medicine. They had this incredible ability to generate a synthetic control arm. Instead of relying on literature data, we would be able to collect data from patients that have been matched nearly equivalent to the patients that were treated in our clinical trial. We are substantially derisking what we might do in a Phase III clinical trial by making sure that the patients that we treated in our treatment arm were identical to the patients that would have received standard of care. Very quickly, we got the full team from Medidata contributing and making suggestions that literally rolled up their sleeves and got into all our data and guided us carefully in terms of not only how we were to analyze our data but also suggesting ways to improve the clinical trial design. And they were essentially part of our statistical group not only from the very beginning but all the way to our meeting with the FDA, preparing our package to submission to the FDA. What we are able to get from the FDA was, in fact, the very first design of a Phase III registration trial, where the majority of patients would come from a synthetic control arm or an external control arm. So that was a big accomplishment for us. And when you talk about dealing with the impossible, and this is basically what we are able to accomplish, suddenly the odds of patients being allocated to a control arm is dramatically reduced and therefore allows companies like us to conduct a clinical trial efficiently, quickly, faster and, of course, at a lower cost, eventually getting the drug to the patient much more sooner, particularly in these kind of diseases, which are life-threating. The reason they come into a clinical trial is the hope that they will get something better out there. We end up creating a situation which is satisfying for the patient. But more importantly, we set the stage the next therapeutic approach, next combination approach or for another kind of disease instead of GBM, perhaps we can open the door to other companies pursuing these challenging diseases and take our route. And hopefully, they'll learn from that. That's what makes you wake up first thing in the morning and go and do this.

Great. We're really excited about our work with Medicenna, and I get goose bumps every time I see this video. If we keep going, we still have a few more slides here. Next slide. And then to close out our rest of our portfolio, on the research and development side, in our BIOVIA portfolio, our analytics allow better precise targeting for target selection to be able to build AI-driven therapeutic design as well as AI-driven biologic cell and gene therapies that reduce overall cycle time by 50% or 20%. And next slide. As part of our manufacturing process portfolio, we support everything from CMC development and tech transfer and monitoring. Our ONE Lab solution has shown the boost to lab productivity and efficiency by 25%, improved tech transfer times by 30% as well as reduce tech transfer times by 30% by using some of our solutions. Next slide. So what this shows is really the breadth of our offerings all the way from research and discovery through clinical development, evidence generation, patient engagement as well as manufacturing. Nobody else has the scale that we have and nobody else has the breadth that we have. So as we start to think about our path forward, the last few years have been really hectic for all of us. A tremendous amount of technology has been thrown at us either on our personal lives or in terms of our professional lives. And it's not that easy for all our customers to be able to get the full value of the technology that we are releasing. So we need to work closer with our customers to be able to bring the therapies -- to help them bring the therapies fast to the patients. Second, there has been a tremendous war for talent across the industry as well as across all sectors. And we need to continue to attract the best talent around our shared mission and culture worldwide. What we have found in our HR and people mission stories is really to be able to talk about our mission and the work that we do. When we show our candidates, videos like the Medicenna video that talk about the purpose of what we do, people truly care. It's really exciting, and we're able to make a difference in the world. And it's really that mission that attracts top talent to come work with Medidata and Dassault Systèmes Life Science. And then finally, we need to harness all of the data and all of the innovation that we have to be able to continue to drive the next set of innovations at the intersection of biology, technology, data and AI. Next slide. So that really is a quick summary of what we are and what we do and where we're headed. With that, why don't I invite Stacy back and answer the questions.

Great. Thank you very much, Sastry. Really interesting presentation, and thanks for the video. You kind of answered one of my questions on the video, which was great. [Operator Instructions] But let me start with a few from my side, a big picture question. As a technology company coming to a health care conference, can you describe where you fit in and how you think tech is impacting the broader health care industry?

Yes, absolutely, Stacy. A couple of things. The first is health care in many economies represents as much as 20% of GDP, and it continues to grow, and some projections showed growing as high as 40% and becoming unsustainable. Technology is probably one of the big solutions that can allow -- bend the cost curve as well as truly deliver better outcomes for patients as we continue to make progress across all aspects of innovation. And then finally, in the slide that I had shown at the front of my presentation, as the biologic platforms continue to evolve, they're really looking to partner with technology platforms that allow them to scale and essentially replicate what Moderna and BioNTech and Janssen were able to do with their COVID programs. Every single head of R&D is asking a question, how do we use technology to be able to replicate the speed and agility that we brought to our COVID programs, and that's really what we see the role of us as Dassault Systèmes Life Sciences as well as technology as a whole health care sector.

Yes. No, tell us more about Medidata acquiring Acorn AI and how does that work together and then how it merges with Dassault Systèmes as well.

Absolutely. We started Acorn AI about 3 years ago around the time of JPMorgan, and very quickly in our early days, we became part of Medidata. And the goal of it was about 10 years ago, the founders of Medidata had the foresight to start to get secondary use rights on the data that was going through our platform. It's not our data to sell, and we don't sell data, but we build analytics solutions. And the mission of Acorn was truly to use all of this data that's going through our pipes to figure out how we can accelerate therapies to patients, be it through the use of synthetic control arms that take out risk in programs and cost in programs or some of the intelligent trial offerings that I was showing earlier that create this real-time view around the overall execution of clinical trials and taking out bottlenecks and allowing companies to be more agile. More broadly, the Dassault Systèmes mission is really around simulation and modeling, and this core of data science and analytics that we talk about in AI fits very well with the Dassault Systèmes theme of bringing modeling and simulation and virtual twin experiences to life sciences. So there is an incredible synergy between the data that's flowing through the Medidata platform, the data and analytics solutions that Acorn has been able to build as well as the broader modeling and simulation capabilities that Dassault Systèmes has deployed across other sectors.

So this is perfectly a segue to a question from the audience. Do you plan to use your AI outside of the clinical trial space? Please.

We absolutely do. So if you look at 2 examples that I'll give you, the first one is in CAR-T. So in CAR-T therapies, all of us are aware of the CRS adverse event that happens with a select number of patients. And it's awful. And this has actually resulted in trials being shut down, programs being put on hold as well as several patients unfortunately dying because of this adverse event. Now what we've been able to build by having a cross-industry data set is to assemble the largest data set in the industry of CAR-T patients. We have 2,400 patients right now, and we expect that to get to 4,000 patients. And we've been able to curate this data at an incredibly granular level and build predictive models to be able to better understand which patients are at what kind of a risk for either safety or for efficacy. And on the safety side, we build a CRS algorithm that predicts north of 85% of patient who's at risk of developing CRS. Now not only is this a tool that can be used in clinical trials to be able to derisk programs. It also becomes a tool that practitioners can use before administering a CAR-T therapy for a patient. So it takes you one step closer to get the right patient to the right drug at the right time by better understanding their safety and adverse event profiles. The other area that we're able to bring these kind of analytics is around launching and commercialization of drugs. As we come towards the end of a clinical trial, rather than try to figure out how to detail the drug in the market, we look much more at the body of evidence, be it around the safety profile or the efficacy profile, to understand where exactly does this drug fit in the overall market and the standard of care and what should the medical messages be and what should the body of evidence be to be able to better help physicians deliver the right drug to the right patient at the right time.

Yes. No, I guess we've answered part of my next question, which is how valuable is the data component. But really, how exclusive is that to Medidata? And what does the competition look like out there?

Nobody else has clinical trial data. What everybody else can talk about is real-world evidence, but we all know real-world evidence has gaps as well as it has lack in continuity as well as it's quite noisy. The second thing that when we've done head-to-head comparison of our data against the competition out there that we keep finding is our data has depth. Real-world evidence, broadly speaking, probably has 20% of the data fields that are collected as part of a clinical trial. So we bring a lot more richer information around individual patients as part of the clinical trial. And then the third is a lot of the data that we've seen is not real time, whereas we have a real-time view on 7,500 studies that are happening, and we can tell you at any given moment what each of the sites are doing or what's happening in a particular therapeutic area. But to your point about exclusivity, this isn't our data. This is data that are crown jewels of our sponsors who are trusting us with it. And they're also trusting us to be able to create cross-industry insights like the CAR-T example that I was using that allows all of them to benefit. Unless all of the CAR-T manufacturers consented to give us access to this data to be able to create that kind of a cross-industry solution, this would not be possible. The nearest provider has about 50 patients of CAR-T. It's impossible to stitch together a view that truly cuts across to be able to say here is what is happening at a class or a drug level. Same thing with glioblastoma. Unless all of these manufacturers decided to pull together their data, it wouldn't be possible to build a synthetic control arm that truly serves the needs of everybody operating in that particular class of drugs.

Interesting. Let me take another step back and say, can you talk through your sort of big picture TAM as well as where you think your estimated market share is and a little bit of deep dive on the competition, who you really do see on the ground?

Yes, absolutely. So if you take a big picture step back, our models show that technology as a whole can create north of $100 billion in value for the industry. This is through accelerating innovation, allowing you to create new kinds of drugs as well as significantly derisking programs and getting them to market faster. We believe that our TAM of that $100 billion of value that's created is about $8 billion today given the space that we play in. And we believe by expanding our offerings in some of the areas like manufacturing and commercial as well as discovery and research, we can probably get to about $20 billion of TAM. That is a really large place to play. Now in some areas, we have tremendous market share. As I was showing earlier, 17 of the top 20 drugs in the market right now were developed on our platform, or 75% of oncology approvals happen on our platform. That talks to the depth of the market share we have. But overall, in the TAM that we laid out, we believe we have about 10% to 12% of market share.

Okay. And you're talking about -- yes.

And in terms of -- go ahead, Stacy.

No, no, no. I was going to say also, can you talk about growth expectations? I mean you've been talking about mid-teens growth. What's really driving that, I mean, other than maybe both industry and you specifically?

Yes. The overall industry continues to be incredibly valuable, and we continue to see a lot of investment coming to the space and growth in this space. But for us particularly, the couple of areas of investment that we made around Patient Cloud as well as Acorn have paid off well. And we believe we're still in very early days of both of these businesses, and they themselves can become as large or larger than Medidata stand-alone. So that really is a really exciting spot for us to be in. And as we continue to innovate, we believe we are going to plant the seeds for the next Acorn and the next Patient Cloud that are going to start to become as big in 3 years from now when we're talking.

Let me interject another question from the audience here. Has there been an integration of Medidata AI and data and Dassault Systèmes simulation model to offer virtual clinical trials?

We are working on it. We are working with a few clients and partners to see what this modeling and simulation look like, but it's still very early days, and we're still trying to figure out what the definition of that looks like. But we do believe that in the coming days -- years, we are going to see a significantly more mature approach around modeling and simulating both what a clinical trial would end up becoming, be it around the endpoints you pick or the protocol complexity you define as well as getting better predictions in there around the actual operations of the study in terms of how it's executing. It certainly is a huge focus for bringing both of our company's capabilities together. It was not about cost synergies. It was always about creating the next set of innovation.

Yes, always. That brings me to another -- maybe just quickly to scoop back around to the competition because I'm sorry, I interrupted a little bit before. But I hear, of course, discussions in the market, people talking about Veeva, although I guess they're a bit more on the commercialization and compliance side, but sort of how you compete with them, how you compete with maybe Oracle and who else you see out there.

Yes. Oracle and Veeva are probably the 2 large players in the market. And we respect our competitors a lot. They make us better, and they continue to push the innovation boundaries in the marketplace. But our scale in the place that we play is truly unmatched. We started 4,500 studies in 2021 alone, which is probably much larger than all of the others put together. And we continue to operate at that scale. We also see a lot of much smaller competitors that are incredibly innovative, but they don't -- may not have the same scale that we have. If you look at the decentralized trial space, there are a lot of early newcomers coming into this space. And what we realized is you need to be much more of an orchestrator of a diverse set of capabilities for these studies to run successfully versus offer 1 or 2 point solutions. Similarly, we see a lot of innovation happening in the AI space, and we continue to monitor that closely to see where we could have partnership opportunities with the players. So that -- it is a very frothy market with a lot of competitors that are both large and small, and we continue to watch it very carefully.

And just a quick -- to what degree have you been able to leverage Dassault's 3DEXPERIENCE platform? I'm not sure if you're there yet or if that's something that's exciting to you.

It absolutely is exciting. And for the last few years, we've been -- since the acquisition, we've been working very closely to try to figure out what micro services we should be integrating both ways, which micro services from the 3DEXPERIENCE platform do we want to be able to put on the Medidata side so that those capabilities are available today as well as the other way around on what micro services on the Medidata side should we be offering on the 3DEXPERIENCE platform. Because what we don't want to do is put ourselves on a trajectory to say we're going to do this big build that in 5 years brings all of this together. We much rather focus on what customer value can we bring today by pulling services from one side or the other. And the closed-loop example that I had shown you around the bedside to bench to bedside really shows how a lot of these services are coming together.

Sastry, I've brought us right up to the end of the time period almost. Let me end on the final question. Hopefully, it's a good big picture way to close. Can you elaborate on your life sciences vision over the mid and longer term? In a few seconds, of course.

As health care continues to grow, we believe that this is going to be a huge part of the economy and what we do. Part of that seriousness of Dassault Systèmes in life science is what we've communicated in previous investor and earnings calls where we're organizing ourselves around 3 broad sectors of infrastructure and cities as well as manufacturing and life science. And Tarek and Glen, when he was with us, played the role of Chairman and Vice Chairman of Life Sciences to be able to truly chart us on that course. And as we think about our 3 horizons of growth in the short, medium and long term, in the short term, it's truly about closing this loop across the life science value chain, all the way from research and discovery through development, through manufacturing and commercialization because we are the only player that has all of these capabilities. And when you look at the 3-circle slide that I showed around the AI, the ecosystem and these transaction systems, nobody else can actually bring all of these together. The next piece is as the question actually talked about, which is are we going to go beyond just life science to start to tackle some of the problems in the broader health care space. And that certainly is an aspiration for us. And in the medium term and the longer term, that's really where we want to look at to be able to say, how do we take these capabilities and these innovations and bring the broader health care sector closer to the life science space. And also in that growth trajectory, our AI capabilities become more broad-based. And modeling and SIM capabilities as well as our patient connectivity and patient-centricity capabilities become a lot more central to that.

That's great. We've come to our end of time. Sastry, thank you very much for a wonderful presentation and for your very interesting insights. Thanks, everyone, for joining.

Thank you.