Dassault Systèmes SE (DSY) Earnings Call Transcript & Summary

All right. Good afternoon, everyone. Welcome to the first day of the Healthcare Conference. I'm Elizabeth [indiscernible]. I'm an associate in healthcare coverage group. And I'm very excited to introduce Sastry Chilukuri, the CEO of Medidata. Take it away.

Thank you, Elizabeth. Thank you, everyone, for joining us today. Can you hear me okay? Perfect. We're a mission-driven company at Medidata, for those of you who don't know, powering smarter treatments and healthier people is really what the organization has focused on. And I want to talk about a few key messages today in terms of where we're going. With over 6,000 treatments currently under development that all of us are listening to during the course of this conference, we have an opportunity collectively as an industry to address the significant unmet medical need across the world and curing diseases as well as slowing the progression of many of these disorders. We believe technology, patient engagement, AI and data platforms are going to be critical to accelerate the development of these therapies and get them to patients faster as well as unlock significant enterprise value. Medidata continues to be the trusted platform for life sciences. Our next-generation cloud platform brings together business processes, real-time AI, rich customer experiences all together at an unprecedented scale, and I'll talk more about that. And as we are in year 4 of the integration with Dassault Systèmes and Medidata, we are committed to moving the life science industry beyond just digitization to actually virtualization, making the virtual twin of a human a reality. And we've made tremendous progress on that front over the last 12 to 24 months. So as I double click, this is really what our scale looks like. In terms of reach, we have 9,500 active customers worldwide, 1,500 new customers since 2020, 7 million patients on our platform and 95,000 physicians. And more importantly, as we look at the relevance, over 70% of novel drugs that were approved in 2022, were developed on our platform. Over 50% of clinical trials run on our platform as well. And when we look at depth, we have about 28,000 studies and over 1 billion images that are collected in a single day -- sorry, in a single year. I wish it was a single day. If we look at the financial metrics themselves, this is what we had communicated to the Street at the time of the acquisition of Medidata: 13% to 15% year-on-year growth, 200 basis point improvement and continuous growth in operating margin. And if you follow those trends, at the time of acquisition, we were about $600 million in revenue. And if we come in at the high end of that range, we're closer to $1.2 billion right now. And we've doubled the number of employees as well from 2,000 to 4,000. If you look at the growth levers, they continue to be pretty consistent, continue to win with Rave and Rave Attach, connect to the broader patient and the health care ecosystem, use data analytics and AI as a differentiator that allow us to generate both unique insights as well as actions, optimize the resource allocation and synergies across the broader Dassault Systèmes, and then finally, build this end-to-end platform for life science that unlocks new opportunities. To dive deeper into what's going on, when we go talk to our customers, they're all excited about the left-hand side of building these world-leading biology platforms. It could be the mRNA platform, the RNAi, the CRISPR, the cell therapies, the gene therapies. And what they're really interested in is a world-leading technology data and AI platform that collectively allows them to accelerate and derisk the overall therapeutics to the patient. When we look at the broader clinical ecosystem, it's changing very rapidly. If you look at the sites, they continue to be overwhelmed by the amount of demand that they have in terms of the overall clinical trials, and they're increasingly turning down studies because they just don't have the capacity and they have shortage in staff to be able to support all of these. If you look at the regulators, the nature of evidence continues to evolve where they're much more accepting of historic clinical trial data as well as real-world data as a portion of the evidence package. And there is a question to the sponsors about how do you start to bring all of this together? Patients are more engaged than ever in their overall care and you're continuing to see a lot more data being contributed by patients as they continue to be much more involved in the clinical trial process. And sponsors are trying to navigate through this new reality of the ecosystem as it continues to evolve, and that's really where we want to take our platform. So if we look at our platform and the evolution of the platform, we want to be the platform that brings together a 360 view of the patient that collects all of these different forms of data, not just the eCRF, but the labs, the imaging, the EMR, EHR, real world as well as sensors and eCOA. We don't want AI to be a data science project off on the side, we want it to be integrated where the work actually happens and a core part of the platform itself. And we want to be able to make this experience across sites, regulators and sponsors seamless. And all of this is on our clinical cloud, which has the security and scalability to be able to support over 20,000 clinical trials. And this is some of the value that our customers are realizing by using our platform. Using the next generation of data management has allowed customers to take out about 5.5 months in their overall development time. Using evidence generation and trial design, they've been able to save 6 months by reducing the number of protocol amendments. The AI-powered study execution and decentralization is saving about 2.5 months by picking the right sites as well as intervening earlier with underperformance sites. Our patient engagement and experience solutions have 95% compliance in terms of what patients are contributing around their data for eCOA as well as with sensors. And more broadly, when we look across the Dassault Systèmes portfolio, our research and development, our portfolio has been able to reduce cycle time by 50%, and our manufacturing improves the lab efficiency by 25%. And finally, with our synthetic control arms and virtualization, we have been able to save 12 to 18 months in the overall development time. Each of these is worth millions of dollars in the clinical trial space. And when you start to multiply this out collectively across all of these interventions and applications of technology, as well as across the entire portfolio, it translates into billions of dollars of value being created for our customers. And several customers have been able to put the entire platform in action. Last year at JPMorgan, we talked about our work wit on Moderna their COVID-19 vaccine. And this year, we want to talk about the work that we're doing with a large enterprise client on their CAR-T therapy. What we were able to do using our synthetic control arm was to be able to save up to 2 years in their overall development time by replacing an entire control arm using historic clinical trial data, which resulted in billions of dollars of value. And beyond that, we've been able to use our data and analytics to identify the right patient population to target to design a shorter trial. We've been able to use real-time AI to be able to identify the right sites as well as workflow innovations to be able to reduce the time from first patient visit all the way through last patient last visit and database lock. And all of these collectively represent a tremendous amount of value as well as faster treatments to patients in really life-threatening situations like ALL. So to double-click a little bit into what our road maps look like, in next-generation data management, as we move beyond eCRF data to be able to cover the entirety of data getting acquired, we want to be able to drive real-time pipelines that drive standardization and enrichment so that you can get to action a lot faster. And last year, in 2022, we introduced the 10 breakthrough innovations that allow you to do that. A few examples are Medidata Link, which is our solution that allows you to connect clinical trial data with real-world data, which won the Reagan-Udall Foundation Award for innovation and regulatory science last year. Similarly, what we're doing with designer is you build it once and you configure Rave once and it propagates across the entirety of the platform so that it saves significant amount of time required to do study builds. And then with one experience, we are creating a single unified experience across patients, sites and sponsors so that it is truly seamless. And Medidata Detect allows you to be able to identify issues in data real time as opposed to waiting until database lock to be able to identify outliers and issues in the data. So these are some of the investments that we continue to make in core Rave as well as what we're doing to be able to drive insights and speed to market. If you look at study execution and decentralization, the nature of a site is fundamentally changing. It could be the patient's home. It could be the pharmacy near the patient because sometimes you need the drug to be refrigerated end-to-end. It could be a direct shipment to the patient's house itself as well as what a visit is, which used to be an episode when they came in into a physician's office, could be a video visit on their iPhone as well as what used to be a collection of data from a patient in the event of their visit into a physician office could be what comes out of their sensor. And in this kind of a decentralized world, which 90% of studies now are going to be, sponsors need to find a seamless way to be able to manage the data as well as to be able to manage the customer experience. Who does a customer call when they have an issue with one of the sensors that they have, do they call the sponsor, do they call the site or do they call a partner? So really, our decentralization solution integrates with a broader portfolio to create a single seamless experience for the patient that is running this trial in a decentralized environment. And beyond that, we've combined that with live AI so that it gives you real-time insights around how a site is performing, about how do you meet your diversity targets as well as how do you build better and more reliable forecast that the industry is asking for? Our decentralized trial solution continues to be leading in the industry where we're building feature functionality across the entire continuum, all the way from live video visits and sensor data to eCOA, eConsent and patient recruitment. And finally, as Dassault Systèmes, we're continuing to move beyond just digitization of life science customers to actually virtualizing. We virtualize everything as Dassault Systèmes, from airplanes to your kitchen to cars and trains. And as we start to think about virtualizing clinical trials, the first step of that process was our building of synthetic control arms, where we're replacing an entire control arm where you no longer need to recruit patients into it to be able to actually use historic clinical trial data and simulate what a control arm looks like. And we have 3 FDA and EMA acceptances of the control arms that we've submitted. And to date, we've created more than 500 patients into these control arms. And we continue to drive this at scale. The second big innovation that we've done is around synthetic patients. Synthetic patients is when we combine data from multiple patients and bring it all into one single patient that has no connection back into the real world from a privacy perspective, from a compliance perspective or from an IP perspective. And what this allows you to do is run all kinds of trial design as well as simulations because this data is really without boundaries in terms of what you can do as exploratory analysis. And to date, we've delivered over 150,000 synthetic patients to our customers as subscriptions of these data sets. So these are some of the examples of the virtualization and action that we are driving in the life sciences care. And we'll continue to be able to drive a lot more of that as we bring together Medidata and Dassault Systèmes in the coming years. And then finally, as a company, as we look at our priorities for 2023 and beyond, there continue to be the 3 big things. The first is what I said at the beginning of the presentation, we're a mission-driven organization, and we're focused on helping our customers and patients get access to therapies faster. The second is we want to continue to track the best talent around our shared mission as well as this intersection of biology and technology. And then finally, we want to continue to drive innovation; some of the examples that I shared earlier at this intersection of biology, technology, data and AI. So that's really where we're headed in the coming years. So with that, let me end the formal presentation and should we open it up for questions?

Thank you very much, Sastry. We're going to do -- I'm going to ask a few questions as you guys get your questions together, and then I'll open it up to the audience in about 5 minutes. So I think let's start off because you talk a lot about innovation, I mean what you have done in the last year. And I was just wondering if you could expand a little bit on that. What has been Medidata's biggest innovation on the product side since Dassault acquired Medidata?

So a few things that we've been talking about in our portfolio is the innovation around the 3 core pillars of our portfolio. The first is the core, which is Medidata Rave plus a lot of the transaction systems that run the clinical operations. The second is around our AI portfolio. And then the third is around patient centricity. And if you look at what we've been able to achieve over the last years, each of these areas have a lot of really exciting developments. The first is the threat and breakthroughs around Rave that I talked about, and we had a slide on. Rave was a 20-year-old product. It's the work horse of the industry, more than half the clinical trials in the industry run on it. And we're continuing to drive more innovation and more feature functionality into Rave. And it's everything from supporting the various data types beyond the eCRF to actually getting more real-time visibility into the data as well as creating a more unified expect that spans it. The second is if we look at the AI business, we won multiple awards in the last year. We got the award from Reagan-Udall Foundation around innovation and regulatory science for Medidata Link. We have 3 synthetic control arm approvals from FDA and EMA. And at ASCO, we presented our findings around the CRS algorithm that now has 90% likelihood of predicting a patient's risk of developing cytokine release syndrome on a CAR-T therapy. It's unprecedented and something that we are really excited about. And that's the innovation that we continue to drive on the AI front. And then in Patient Cloud, we continue to drive this unified experience of the patient in the decentralized study environment. So I can't pick one, but this is a portfolio of innovation that we've driven in the last year across -- and the last few years across our entire platform.

That's terrific. And thank you for expanding on that. I guess as a follow-up to that, given the tougher macro environment, are there any particular strengthens and weaknesses to call it in that context with the portfolio of Patient Cloud, Rave data management and in Medidata AI?

The first is clinical trials are reasonably resilient to recession. You can't just stop a trial midway. You have to get it through, it's unethical for patients as well as in terms of the company. And then as a company, you want to continue to invest in R&D because that's really what you want to -- that's what's going to define our future. So we continue to see a pretty robust demand across clinical trials. And then secondly, our revenues are co-related to clinical trial starts, but they're not directly related to it for multiple reasons. The first is majority of the trials run on our platform, and we continue to get share from competitors versus actually trying to create new demand. And then secondly, a lot of our revenues are tied in enterprise contracts. So these are multiyear contracts that are independent of the amount of volume of trials. So even if the volume goes up or down a little bit, it doesn't really change our revenues that much.

Okay. Awesome. And let me just -- let me continue actually on this track. So during the Q3 2022 earnings call, COO, Pascal Daloz said that Medidata AI and Medidata Patient Cloud make up more than 1/3 of Medidata's overall revenue. Do you expect their revenue contribution to further increase going forward? And what is your outlook here?

Yes. So if you take a step back, since the time of acquisition, we were, as I mentioned, a $600 million company, and we came in at the high end of our growth targets, so we're now close to $1.2 billion. And in Q3, I think Pascal announced that we grew 16% or 17% year-on-year. So we've hit new records in terms of our growth numbers internally, and we feel very bullish about our growth going forward. Our employee satisfaction is at an all-time high as well as our innovation and the awards for the innovation continue to grow. So we're absolutely going to continue to execute across our entire portfolio. and Medidata AI and Medidata Patient Cloud are 2 of the strategic bets that we made. When I started at the company, Medidata AI was very tiny, if at all, and that's what I found it. And then Medidata Patient Cloud was still in its early days, and we made deliberate investments in both of those areas as we grew our employee base from 2,000 to 4,000 employees. And those investments are going to pay out because that's really the bet for the future that we are making.

Great. And speaking about this portfolio that's been growing, can you talk about areas of functionality you think would make sense to acquire further in or to build out?

So our acquisitions that we continue to look at are around these 3 core areas of the Medidata Core Platform; Patient Cloud and Patient Centricity as well as AI, and there are several areas that we continue to look at. And when I talked about the feature functionality and road map that we're building, that's really where we're looking for complementary acquisitions or partnerships. We still haven't found anything we like yet at prices that we like. So we'll continue to keep looking, but that's certainly an area that we continue to explore.

Okay. Great. And let's sort of switch gears here to the competitive landscape and -- sorry, all right. So would you share or say a little bit more about your biggest competitors in the EDC segment? And can you also comment on your market share in the same space; I know you mentioned that you're gaining market share. So I'd love to hear more about that.

Yes. So our market share in the EDC segment, we estimate is north of 50%. And if you look at some of the stats that we showed, the relevance part is more important because 14 -- 13 of the top 15 drugs of 2022 were developed on our platform. Over 70% of FDA approvals in 2022 were on trials on our platform. We continue to be extremely relevant to the activity in the industry, and that's the number that we care about a lot. When we look at our competition, we believe Rave is the industry standard for running highly complex studies, and we continue to see that in every metric from our market share to our bid wins, et cetera. And the innovations that we're talking about is really setting a new bar in terms of where the future of data management and data collection and clinical trials looks like. So we feel very confident about our competitive position compared to the strength of Rave and continuing to strengthen Rave. And by bringing the core of Rave together with the AI and decentralized trials as part of our road map and the platform story that I was talking about, it really creates a vision for the industry about how to run these clinical trials of the future.

That's great. And now we're going to open it up, and thank you very much for answering all of my questions. We're going to open it up to the audience. Does anybody have any anything?

I was wondering if you could just compare the difficulty of standardizing the real world data versus the clinical trial data that you've done with Rave and that created such a wonderful moat for you guys? I don't know if you can do that in man hours or whatever metric you prefer?

That's a great question. So I haven't done standardization of real-world data. So I can't give you a specific quantification of the complexity of it. But what I'd explain to you is the academic complexity of the problem. And what I mean by that is if you have completely unstructured data, we have a lot of tools to be able to navigate through it and search it; Chat, GPL and Google and all the others. If you have structured data, it's really easy because it neatly follows columns and rows. What we have with clinical trial data and EMR data is the snowflake problem, where it's structured data for an NF1 and everyone configures it slightly differently. So there isn't any toolkit that can allow you to be able to do it easily. So you consequently have to do an enormous amount of manual work to be able to do it. You can build a lot of tools that simplify how people do it, but ultimately, you need this human in the middle. And the number that the industry uses on a clinical trial to standardize one trial is like 400 man hours. So you can start to put that together in terms of what the overall complexity of this data standardization problem is.

Impressive, $600 million to $1 billion -- $1.3 billion, excellent. A couple of questions. And you have entire portfolio in Dassault in terms of entire research and development portfolio, maybe one lab or the ELNs and all the way through Medidata. Do you have any plans to look at it -- look at the entire portfolio together? That's my first question. The second question on the synthetic control arm, very interesting area. In terms of your data source, do you look at the entire public data source? Or your -- is that derived from your Medidata clinical trial base?

Yes. It's derived from Medidata clinical trial, the second one, and that's what makes the data regulatory grade. And in the first one question that you had, yes, absolutely, right? I think as we go through our integration, and we are now in year 4 of the integration, we got through the easier things of G&A as well as creating a single sales force that allows us to get to customers in a unified way, but now we're starting to look at the real portfolio synergies, which is how do you start to bring manufacturing and labs and research with clinical trials. And there's a significant amount of value, and we're trying to figure out what the connecting points are. Is there a way to be able to do [ recipe ] transfer from clinical development into manufacturing to be able to speed that up? Are there like lab productivity that you want to be able to drive in clinical trials? And are their modeling and simulation capabilities from things like synthetic control arms that you can pull much forward into the discovery and research phase to be able to improve overall testing and likelihood of success. Because ultimately, the biggest problem we're trying to solve for all of our customers collectively is how do you improve the overall probability of technical and regulatory success for Phase III trials? It has been stuck at about 10% for over a decade. And how can technology and AI and all of these capabilities allow you to move beyond that 10%?

Just a quick question. See, what is the percentage accuracy of your AI, if we have to do remote source data verification versus the data which has been captured through EDC?

I don't have the exact number for you, and I can get it for you, but it's certainly a big area of development for us, and I'm happy to follow up in terms of what the specificity of that use case is.

Because that's actually a very critical piece because now we have the regulators, they actually want to verify the source data with the data which has been captured through EDCs.

Yes. So one of the things that we're doing is I talked briefly about -- no, I did not talk. One of the innovations we talked about was Rave Companion, which is this EMR-EDC connectivity, which allows you to be able to drive the source data verification with 100% accuracy to be able to pull the data, the right data from the EMR into the EDC. So that should simplify your source data verification problem.