Good morning, good afternoon. Welcome to Moderna's headquarters. Thank you so much for taking the time today to be with us in person or to be with us online. Welcome to Moderna 2025 Analyst Day. As you know, our mission is really the North Star of this company. We want to work together with all our stakeholders to deliver the greatest possible impact to people through mRNA medicine. If you think about the near term, our strategy is really set up on two axes. One is to build a large seasonal vaccine franchise for high-risk population and to use the cash generated by that franchise to invest in oncology and in rare disease therapeutics. If you look at the seasonal vaccine franchise, we're already quite underway. We now have three approved products.
We have positive phase III in flu, positive phase III in flu plus COVID, and all of us are currently enrolling in phase III. If you look at oncology, 2026 is going to be quite an exciting year with a lot of data in oncology and a lot of new medicine getting into the clinic. PA is now in phase III, fully enrolled, and MMA is phase III already. If you think about the seasonal vaccine franchise, we think there's a lot of positives of that business. One, of course, is the tailwind of a growing older population. If you look at the numbers, there are 250 million people right now in the OECD countries that are qualified as older population, 65 and above, and of course, growing with an aging population.
If you look at Europe, and Stephen will talk a lot about Europe in a minute, there are actually 90 million people right now in Europe that are 65 and above. It is a larger population set of countries, and so it is an older continent. We think it is quite an interesting opportunity for us in terms of tailwind. If you look at the burden of disease, which is why we do what we do, we really think that those products, those medicines are really, really important to prevent disease, but even more importantly, to prevent hospitalization and to prevent deaths. If you look at the numbers, those are just U.S. numbers. They are very, very large numbers. It is up to 1 million people hospitalized every year. Think about the impact on those people, the impact on those families.
Many of us, you know, being young, do not always appreciate how traumatic a hospitalization can be in terms of how it impacts the quality of your life when you are older, when you have comorbidity risk. Sometimes a hospitalization, even if you survive, it might mean a drastic change of your quality of life because of a muscle mass that you lose during the hospitalizations. If you look at COVID, the numbers are also really high in terms of what we can do. We did not even spend time on RSV and so on. If you look at the burden of disease, it is really high. If you look at this franchise, there is some interesting seasonality, which I think is going to be really important for us in the flu plus COVID combo product.
We also think it's important with the mRNA technology speed, the ability to potentially go very late in strain selection. We see from time to time, as we spoke in the past, that you have a mismatch in flu, for example, between the strain pickup at WHO that are in the vaccines and what is circulating around the world, leading to more hospitalization, more disease, and more deaths. We think with the mRNA technology, we have shown now several years in a row that we can be informed by the FDA or the regulators, you know, in May or June, a new strain, and we can very quickly get ready in front of a season, which I think is a huge advantage of the mRNA technology. There are a lot of other benefits.
One of our platforms is, of course, manufacturing scale-up, and Jerh in a minute will give you a lot of details around that. It's because mRNA is a very flexible manufacturing process. We can literally a week make, you know, a COVID lot and then the week after make a flu product. As you see over the next few years as Stephen will describe the product launches, as we get more and more volume, we're going to get an incredible leverage of this manufacturing infrastructure that we have. The market access and reimbursements are pretty set up because healthcare providers know the burden of disease. If you talk to health ministers, they know that with an aging population and respiratory viruses, they have a massive financial challenge ahead of them. The lifecycle investment is very manageable because those products have a very long tail.
Of course, you need to update them. Sometimes you need to do seasonal studies, but this is very manageable. In the U.S., we have a very interesting setup where there's a strong incentive from the channel, especially retail pharmacies, to drive vaccination every season. Stephen will discuss in detail why we're excited about the next few years. What you're going to see, we set up a portfolio driving diversification of growth through new product launches. Like you're going to see mNEXSPIKE, we will fully air next year, launches fully in the U.S., launches in the rest of the world, potentially flu launches in 2027, and then norovirus, flu plus COVID, and then also geographic diversification. I know sometimes people are worried about the U.S., but if you think about it, we have now great partnership in the U.K., Canada, Australia. We're fully impact next year.
The European contract reopening for us, the ability to participate in Europe, which is a very large market in 2027. We're going to see product growth through launches and also geographic diversification that's going to drive growth in the next few years. Jeremy will walk you through some numbers. If you look at that respiratory franchise, you're going to see the next few years, you know, growth. The margins, gross margins are also going to improve because you're going to have additional volume in an existing infrastructure. As Jerh will share with you, the team has done great productivity improvement, and there's more to come. There's a lot of exciting projects that Jer and his team are leading, which will drive improvement in gross margin with those two factors.
As we've been talking for a year or two now, the cost of R&D for respiratory disease are going to go down. They're going to go down because those very large one-time phase III that you have to invest to build that franchise are basically concluding. They don't conclude the day of launch because you have a tail of investments as you have to finalize those studies, monitor the safety, which is very important. When those are over, you have basically a one-time investment, which is very large, which is a great bar to entry. You have many, many years, if not decades, where you can have those products driving revenues. When you look at this plus commercial in the U.S., it's mostly a B2B setup where we have direct access to the retailers because we don't go through PBMs in vaccines.
We do not need to add teams as we basically grow the portfolio. Actually, as we spoke in the past, a larger portfolio is a strength to our ability to get great contracts and talk about the products with the customers. If you think about it, growth on the top line, improvement of gross margin, lower R&D cost, and very stable through existing infrastructure, SGN. It is a very interesting improvement in operating margin that we are going to see over the next few years. If I now turn to the future, where we are investing the capital that we are generating through the vaccine franchise into oncology and into rare disease. Again, the team will spend a lot of time on the details. I want to be brief to start 30,000 feet. You are going to see potentially Intismeran with a launch in 2027.
As we talk about, the first IO with that is in 2026. If it's positive, we're working very closely and diligently already with a Merck colleague to be able to file very quickly, which could lead to a launch in 2027. If you think about PA in rare disease, PA is now fully enrolled for his registration study. We could see PA launch in 2028. For those of you that have not followed at ESMO or were not at the analyst day at ESMO, we shared some early but quite exciting data on 4359 in stage four melanoma NLEG patients. The team will walk you through some data. Because of that data, we accelerated the phase two this year to be able to see, is that signal real? Can we confirm it with the larger numbers?
Because that could be a very important product for patients and a product that is 100% owned by Moderna product. That's really for products that are in the late stage. If you look at earlier stage products, as I said, MMA is ready to go into phase III with a couple more exciting products in oncology, and the team will walk you through it, with an EBV treatment program that could be very important for patients with multiple sclerosis, an EBV vaccine that's also with interesting data that could be used potentially for mononucleosis infection, Lyme disease as our first bacterial vaccine, and CMV for transplant. Those are the two franchises we're really focused on, generating cash through a lot of financial discipline, sales growth, respiratory vaccine, investing the cash for innovation for the next wave of products.
We could see sustained sales growth for many, many years to come thanks to our platform. As you've seen what we've done in the last couple of years, it's a lot of financial discipline. I'm so thankful for the teams for what they have done under Jeremy's leadership and all my colleagues at the EC to drive cost across the entire enterprise. The momentum that we have is very exciting, as you saw on the Q3 earning call, which gives us confidence for the ongoing work and projects to come to be able to get the cost into the right place to drive profitability in 2028. Jeremy will walk you through it. There's a lot of productivity projects. There's a lot of digital investment, and there's a lot of AI tools also being worked through.
For those of you who are going to stay after lunch, we have set up a bunch of panels to walk you through just vignettes of examples where you have members of the team will come here to walk you through some of the tools we are using every day in the business to drive productivity through AI across the entire enterprise. With this, I want to now turn to Stephen to walk you through the more detail of a seasonal vaccine franchise. Jerh is going to walk you through manufacturing before Jeremy wraps up everything again at the enterprise level around financials for the next few years. The team will come. We start with seasonal vaccines with Jackie and her team. Then we will go into early vaccines before we take a break.
After, we're going to have a lot of exciting things to share in oncology and then rare disease before I have a very quick two slide close. We will be very happy with the team to take your questions. With this, I'm turning back to Stephen.
Thank you, Stéphane, and welcome everyone. Hello. It's good to see you again. I want to quickly summarize those drivers of growth that Stéphane just mentioned. There is a diverse portfolio of drivers of growth that we think will contribute to that steady financial performance we're looking for over the next few years as we invest in accelerating our company. Stéphane already presented this slide. I will just highlight that we do have a mix of geographic and product growth drivers.
Really, in the next two years, it is mostly geographic or market expansion into the U.K., Canada, Australia, into Europe, and into new regions, including Latin America. It is really as you look to the second and third years of the next three years that we will expect to see a substantial uplift from investments in our portfolio. That includes flu, our flu COVID combination product, and norovirus. I would like to click through those and just give you a sense of why we are bullish on these opportunities. Starting in 2026, we have multi-year strategic relationships with the United Kingdom, Canada, and Australia that we have been working on for the last few years. We announced most of them in 2023. This last year in 2025, we have licensed the three facilities that will actually supply those agreements. As a reminder, these are long-term multi-year contracts.
They involve us investing in domestic research and development, and they are part of a national security and biodefense as well as public health strategy at the core of each of these countries' approaches to protecting against viruses, but also protecting against future pandemic threats. That onshore manufacturing is now licensed and starting to deliver products. In the U.K., that covers 69 million lives. We had already referenced in prior financial statements that we have seen that first approximately $0.2 billion of revenue from that partnership in the first quarter. That was delayed from the fourth quarter of this year into the first quarter of next year. We will also be delivering vaccines for their fall campaign, first with COVID, then eventually with the rest of our respiratory portfolio. Canada, again, 41 million lives. We do have strong performance this year in Canada.
We are already delivering made-in-Canada COVID vaccines, hope to add additional products, RSV and mNEXSPIKE to those relationships. We expect to see a full annualized impact from that agreement starting in 2026. Third, Australia, 27 million lives, where again, we've now licensed that facility. We're hoping to make our first deliveries possibly even this calendar year. As we look to 2026, see the annual benefit from this strategic partnership. All of this in place and a major driver of growth for us as we look to 2026. The other driver for us that I'd highlight is mNEXSPIKE. mNEXSPIKE was our successful launch this year. I will remind you, we've only really launched this product commercially in the United States. The product was only approved mid-year, really in June.
We had a tremendous amount of work through the summer to get ready for the fall season, not just in manufacturing, but in market access and reimbursement and preparing the market for it. We are incredibly excited to see the market share that we have already grabbed in this first year of launch. 23% of the total retail shots in arms year to date has been from mNEXSPIKE. Actually, if you look in older adults, those 65 and above, that market share is almost a third. Really strong start. It has become our leading product in the retail channel as well as in that 65 plus and high-risk market that we really think is the future for us in COVID. Now, mNEXSPIKE, as we look to 2026, we hope to complete that launch.
We've only really had a half year this year, and we look to drive even better performance in the U.S. We expect to add many other markets. Europe, we're under review and hope to be approved. We've recently been approved in Canada, although we haven't launched the product yet this year commercially. Australia, Japan, and Taiwan all have submissions pending for approval. We are looking forward to continued strong momentum behind this, we believe, differentiated profile for mNEXSPIKE going into 2026. Okay, those are the two drivers for 2026. As we look to 2027, Stéphane already highlighted the role of continued market entry for us. First, in the European Union, we have been excluded from that market largely because of a pandemic-era contract that is now falling away in 2026.
If you look at the size of that market in the most recent year for which we have data, so 2024, there's about $1.8 billion of respiratory vaccine sales, $700 million from COVID, about $1 billion in flu, and an RSV market that's just starting to grow. This 2024 was that first year launch. We do expect that RSV market will get bigger as it expands. We have had a very small share there because of that exclusion from the market, only about less than $100 million. Clearly an opportunity for us as those barriers fall away. Why are we excited about 2027? Obviously, the end, the lapse of that COVID contract gives us an opportunity to compete for share in the COVID market. We will also have, we think, one of the most diverse portfolios. By 2027, that means an RSV vaccine that's approved.
We hope also Nexpike, a combination flu COVID and flu, in fact, five different products. We hope in that combination product, the most differentiated, an opportunity to compete in this very large, very established market where there's a high burden of disease, particularly in older and high-risk populations. Pretty excited about that as a market entry opportunity. We're not done there. While Europe may be the largest market we'll be entering, we are also looking to add additional strategic partnerships. I'll highlight one here that we announced, the government of Brazil announced September 5th, just two months ago, which is a productive development partnership, which has us transferring and manufacturing with a partner in Brazil, Instituto Butantan, for the delivery of COVID vaccines. We hope that starts delivering products as soon as 2027.
It's the kind of multi-year strategic relationship which allows us to get access to that market, but also grow our share and revenue opportunities. That was a recent announcement that we're excited about. There are several others that we're engaged on across Latin America and Asia-Pacific right now that would provide strategic access and growth of access for our products over multi-year agreements, much like we're doing in the U.K., Canada, and Australia for 2026. The other thing we'd highlight in 2027 is we'll continue to add product portfolios. Flu vaccine, we have filed for, we are in the process of filing for approval across the U.S., European Union, Canada, and Australia by January of next year. Just in the next two months, and some of those filings in flight as we speak.
Flu is a very large and established market, approximately $6 billion of sales, growing double digits. There is a large enhanced vaccine market, $2.5 billion in the most recent year. We think that will grow to almost $3 billion by 2032 that we believe 1010 is well positioned to compete for. A place that is for us a new opportunity. It is competitive. We recognize that. Through our strategic partnerships and the other infrastructure we're building, we believe we can go compete for it. Anything we get out of the flu market is growth for us. By 2027, we do hope that becomes a substantial contributor. Okay, those three drivers in 2027, mostly market entry and perhaps the beginning of an impact from flu.
As you look to 2028 is when we really expect to have the biggest impact from our pipeline investments. That's not to say that we don't hope these products aren't approved earlier. We do, and we will work to launch them as soon as we can get them approved. In most cases, it is how do we prepare to really drive growth, and we think that is more in the 2028 time horizon. First among those I would highlight is the flu COVID combination product. We've had very exciting data we've shared previously. I know the team will walk through some of that now. We believe flu COVID combination remains a substantial growth opportunity for us, both in the flu space and the COVID space. Some of the data, the reason why is highlighted here.
If you look in the most recent season, 2024, 2025 season in the U.S., where we have the best data on this, I'll draw your attention to the lower right-hand corner. 47% of those over the age of 65 who got a flu vaccine in that channel also got a COVID vaccine in that same day. It probably won't surprise you to know that when we do market research, people would prefer one shot over two, one arm over two. We see that as a real opportunity, both to increase the coverage. If that 47% could be higher, that means there's 53% of people that only got one shot, and some of them because they only want one at a time. There's an opportunity to expand coverage for COVID and grow that market.
There is also an opportunity to provide the convenience and cost savings associated with just that single injection administration. We really do see this as a growth opportunity for us across our seasonal vaccine franchise. Now, we have filed for submission in Europe, and we are on path with that review with the European Medicines Agency. The first potential approvals could actually be coming in 2026. EMA was filed last year, or sorry, in 2025. Health Canada has been submitted for approval, again, supported by the new flu efficacy data as a part of that just recently. Again, in 2026, those might be the first markets that we see approval. We are highlighting this as a 2028 growth driver because we will have work to do on market access and reimbursement and preparing to launch those products.
Actually, we think we're in good direction there in those markets. In the U.S., where we do expect to engage with, we're engaging with the FDA, we're waiting further guidance from them on what they would like to see prior to refiling the TAY3 program. Again, by 2028, we do hope to have gotten that approved and be driving growth across our regions and markets. The last I'd highlight is norovirus, which we'll talk a little bit more later today, I'm sure. Norovirus we see as a very big opportunity. There's a high unmet need. Currently, no products, over 155 million people in this country who either because of risk factors or age or occupational health risks or some lifestyle choices, they will want to be protected against norovirus. It is an opportunity for us to add another product into an established channel.
We do believe most of the vaccination will be through the retail channel, and it continues to be a seasonal disease, so it really fits into this portfolio that we've been building. As we look forward to those growth drivers, we do think we are incredibly well positioned, both from a market entry perspective and a product growth perspective. Focusing just first on the U.S., we have established the relationships with all of the key customers we need, our retail pharmacies, our government customers, as well as doctor's offices, hospitals, and integrated delivery networks. The strength of the Nexpike launch this year for us really highlights how we've really matured those capabilities and are in a good position. We don't believe we need substantially more capabilities as we add products in the United States market.
U.K., Canada, and Australia with those strategic partnerships are well positioned to deliver growth in 2026. As we add additional products, we will be adding value, but not a huge amount more cost. We do not need dramatically more capability. We will add value to those strategic relationships, but all of those products can be made in the same facility. As you have seen and you have heard us talk about, and I am sure you will talk about in a moment, our technology gives us incredible leverage and opportunity to do that. One place where we will do some investment in growth over the next couple of years is in the European Union to expand that capacity because, again, it is a very large, very established market, and we have some capabilities.
As I said, we've been generating some revenue, but it's a place that we do expect to see growth, and you'll see us invest in commercial in a disciplined way as we try to grow our presence in the European market, both with the current products and with new products as we move forward. We think we're well positioned on the current infrastructure to deliver this plan. There are places where we will have to expand as we go and make additional investments. Stéphane has referenced these, but just to quickly say, we are incredibly excited by the momentum behind Intismeran. I know the team will come and present that data. We do hope for first potentially pivotal data next year in 2026, and then we must get ready, if that is positive, to be launching the product in 2027.
Those will be some additional capabilities, but we have the benefit of Intismeran from a commercial perspective of having a partner in Merck. We work together with them to prepare that for 2027. As you look to 2028, we do expect to be launching our first rare disease program, Propionic Acidemia. That pivotal study or the pivotal phase two portion of that study is fully enrolled, and we're looking forward to that data in 2026, as you'll hear about. By 2028, we do hope that is a driver of growth for us as well. Our wholly owned 4359 program, where again, Stéphane referenced the phase two data, and I'm sure the team will give you a sense of that preview of why we're enthusiastic about it, but something we would need to be ready for in 2028.
While we're really focused on that respiratory and seasonal vaccine franchise, and we do really feel strongly that that sales growth drives us to break even, we also recognize there are opportunities, perhaps requirements that we invest a little bit above that to drive growth in some of these other areas, particularly oncology and rare diseases. Stéphane referenced the rest of the pipeline, but I would just say as you look to 2029 and beyond, there are even more growth drivers, even more important product contributions that we expect. We do expect we will get some leverage from a commercial and medical function perspective. In rare diseases, we'll add MMA. It is a very similar disease and commercial footprint to Propionic Acidemia. We believe as we launch that product, we'll be able to use the existing infrastructure.
We have multiple oncology products, oncology therapeutics in early stage disease, and we'll look to be very efficient as we build out our oncology presence. Rose and others will walk through those programs and why we're excited about them. As we get ready for 2029 and beyond, we do believe we'll have the infrastructure to launch them. The early stage vaccines, these are programs where we are paused in phase two often, waiting for that break-even moment so that we can accelerate some of these programs, EBV, Lyme, and CMV for transplant. We'll share some of that data today, some of why we're enthusiastic about those. All of those we believe will fit really comfortably into our seasonal vaccine franchise and commercial capabilities that we expect to build over the coming years.
With that, I'm going to turn it over to my colleague, Jerh, who will walk you through how we think some of that technical operations really enables that delivery and scale
Thank you, Stephen. Good morning, everybody. Welcome. It's really great to have you here and everybody online. I wish you a very good day. Our technology gives us incredible leverage. That's the word that Stephen said. I want to take you through why and what is it about our technology that's really important and what is it that allows us to drive great efficiency. I think efficiency, when I look at manufacturing, is tremendously important, not only when I think of efficiency that we can deliver speed, speed to market, but that we also have the capability of ensuring highest possible quality and ensuring the highest possible cost efficiency. That's what we look at when we look at our manufacturing network. Let me take you through a little bit of what the network now looks like.
It wasn't that long ago, actually, 2023, when we had over eight CMOs globally between Asia, between Europe and the United States manufacturing for us. Part of that manufacturing process was they would manufacture to a demand, and as the demand would change, there would be a take or pay. It started to get quite expensive. It was that that we started to pare down our CMOs, to reduce the amount of external CMOs based on quality, CMOs of really high reliable quality, great speed, and great cost efficiency, and at the same time being able to leverage our own manufacturing. Here in Massachusetts, we have Norwood, and in Norwood is where we make our drug substance, which is the active ingredient, the mRNA for the medicines that Stéphane indicated earlier and Stephen indicated. Maybe anecdotally, let me tell you about speed.
It was so exciting to hear Stephen talk about mNEXSPIKE 1283. It was by us having the ability to manufacture that in Norwood, allowed us to have great speed, the ability that already in June we're identifying what the variant is, and then actually have the drug substance made and be able to supply it in the United States and have the channels filled already in 2026 was a huge achievement, not only driven by the fact that we had the manufacturing here in Norwood, but to the amazing people and teams that we have driving that. That's one of the big benefits that we have now in relation to having drug substance in Norwood and then using Rovi as our contract manufacturer in Spain.
That's the network that's allowed us to drive the cost efficiency we have and the supply we have, not just the United States, but globally. We have now added, and I'll touch on them a little bit later, three manufacturing sites. Stephen talked about these three sites. Between these three sites, we have Lavelle in Canada, we have Harwell in the United Kingdom, and we have Clayton in Australia. As Stephen indicated, those three sites serve a population of 137 million people. These are a long-term partnership and agreements that we have with the United Kingdom that allows us a revenue stream that we can rely on and the supply reliability that these countries can rely on from us. We just announced, and you've read it, a new DP manufacturer in the United States. We are tremendously excited about that for many reasons.
If you just look at the logistics of making drug substance in the United States, then sending that drug substance to Europe with fantastic partners we have in Rovi, and then sending that back again to the United States, operating not only geographically across the Atlantic twice, but also within two different quality management systems. We also have, as I mentioned earlier, when you work with CMOs, we've exited quite a number of them because we wanted to optimize our take or pay. That's what drove us to announce, as we did yesterday, a new DP manufacturing facility here in Norwood. What this allows us to do is we now can manufacture drug substance in Norwood, and then in the same site, we can manufacture a drug product.
Not only are you taking out all of the logistic time, but you're operating under one quality management system, which means you don't have to wait for a release and approval before it leaves your quality management system and then goes into another. Because you're in one quality management system, you can move your product through the system much more efficiently. From a point of view of lean, you can remove a lot of waste, waste time. Not only then does this allow us actually even greater speed to market, even greater ability to optimize our channels, but also with Stephen's team from a commercial point of view to actually fine-tune our manufacturing based on the demands that are there. It's as close as you can get to real-time manufacturing based on demand. We are super, super excited by that.
The second reason we're super excited by it is the efficiency. If you look at drug substance manufacturing, we do a lot of that manufacturing already in the earlier months of the year, and then in the mid-months of the year, you're doing the drug product manufacturing. What we'll be able to do in Norwood is optimize our manpower to be able to work with our drug substance manufacturing operators, be able to train them to do drug product and start moving our people around. We have the one quality management system. We have the one quality assurance, the one quality control, the one engineering and facility services. The synergies we get from that is absolutely huge. Also, we are building out in a facility that already has the infrastructure.
We did not have to actually break ground to build a new infrastructure because we have optimized our footprint. We were able to build this in an existing footprint already. We had already pre-purchased some of the equipment. From a cash-out point of view, a lot of that cash is paid for this facility. It is very efficient going forward. Our total CapEx over the next number of years is not going to change. This also allows us to drive a lower cost per dose. It allows us, being in a position as volumes increase based on what Stephen presented, to continuously drive a gross margin expansion, which is critical for us.
I'm so excited by this because I think honestly what manufacturing delivers in Moderna is the ability to have top-class speed, the ability of highest possible quality, and at the same time, the greatest possible cost efficiency. That's the magic triangle for manufacturing anywhere. By us taking control of all of this, it allows us to optimize while at the same time being able to partner with a CMO so that we have the optimized efficiency for Europe, as Stephen already indicated, without having a large number of CMOs. We're at a very, very exciting part of the manufacturing journey of Moderna. It wasn't that long ago, not only did we have eight CMOs, but we were making a lot of our drug substance in Lonza in Europe. That wasn't that long ago.
It wasn't that long ago when we had only one manufacturing site, which was Norwood, trying to do all of this. Now we got Norwood, drug substance and drug product end-to-end here in the United States, made in the United States to supply the United States and supply other countries as well. We're tremendously excited by that. I see Tracy's here with us. We speak a lot about our physical assets, and we speak a lot about our digital assets, and Tracy manages our digital assets. I don't want to forget our people. We have top-class manufacturing associates that I would class as the best in the world, are definitely up there, who are behind all of this because it takes great intellect, great design, and great ambition that we have in our Moderna mindsets to be able to make this happen.
This is one of the things we're super excited by. What's amazing when you look at this slide is not that we have three sites in the United Kingdom, in Canada, and in Australia. I think what's amazing in this is two years ago, this was land. It is amazing to be able to build out these sites so fast and then to have them licensed already and to be in a position that they're already supplying, already supplying 137 million populations of these three countries.
This is a huge call out to the engineering team and the design philosophy we had, which was state-of-the-class excellence, the ability to use robotics, automation, to have a learning mindset, to look at all the experience we had from working with Lonza, look at all of the experience we had from working with CMOs, look at all the experience we had ourselves from working in Norwood and design that in so that these efficiencies are super efficient in relation to manpower and batches per person, but also super efficient in how we can use AI, robotics, and automation already from the start and not having to design it in later. What is also unique about these sites, and I would argue you probably will not find any place else in any other manufacturing, is that these three sites are exactly the same.
If you were helicoptered into one of the sites, blindfolded, you would not know which site you are in unless you could look out the window and see which flag is flying. They are designed exactly the same way. There was a huge benefit in this because we started off the installation and operation qualification in Canada, then on to the United Kingdom, and then to Australia. As we started to do installation qualification and operation qualification, and as part of that process, you find issues and you correct those issues, we were already then able to pre-correct them in the United Kingdom and pre-correct them in Australia so that those facilities were up and running even faster. We partner in Lavelle in Canada, and we partner in Harwell in the United Kingdom with CMOs so that we have a fully integrated vertical supply for those countries.
In Clayton, Australia, we make our own DP because we were not able to find that capability there. In relation to operational excellence, we are designing these facilities with optimal cost optimization so the type of COGS that we are going to deliver out of these facilities will be pretty much exactly the same as what we are delivering here out in Norwood. We have the same one quality management system, one manufacturing system, MS&T, quality assurance, engineering right across all of this so that we can drive efficiencies through this. As I speak about efficiencies, let me touch a little bit about margins. I think what you might find impressive on this is that despite a 40% reduction in total revenue across those two years, we were able to keep gross margin improvements pretty much flat. Why and how?
Firstly, that was driven by removing take or pays, starting to reduce down the amount of CMOs we have. We were able to take a lot of costs out of the system with that. The second thing we were able to do is drive a real ambition in relation to efficiency, robotics, automation. We were starting in Norwood with five operators per suite, and we set ourselves targets for three. We got to 2.2 and then hit 1.9. We drove an amazing efficiency. That type of efficiencies that we have in Norwood is what we have now sent out to the other sites. We are then able to apply what I would call lean, but modern lean, the curiosity of understanding the manufacturing process in great detail so that you can manage the quality envelope and that you can ensure that you're right first time.
Our drug substance manufacturing, for example, this year in Norwood for all of the products that Stephen indicated, the 1273, the 1283, the mNEXSPIKE, and the Spikevax, 100% success rate, no batch out of spec. Not only did we achieve that this year, but also we achieved it last year. In fact, last year, we had one batch out of spec when we pivoted when the FDA wanted to change the variant, and we stopped that manufacturing of that batch overnight, and the next morning, we were manufacturing as well. This is what gives me the confidence as when we see, and Jamey's going to touch on it later, as the volumes start to grow, we'll be able to drive an enhancement in gross margin because we've got the system in place. Also in relation to inventory and inventory management, we've made great progress.
What I would say is a lot done, and what Stephen said earlier in the presentation is a lot more to do. We have great ambitions ahead, and our game is not finished yet. There's a lot more ahead for us. That's a little bit about that. The other element of manufacturing is the personalized manufacturing, which is the Intismeran and Autogene. This is where we have the ability to manufacture personalized for per patient. The previous type of technologies that had this was CAR-T. What CAR-T technologies always had was a challenge in the COGS and the challenge in the manufacturing COGS. We built out Marlboro. Again, two years ago, Marlboro didn't exist. It was basically a brownfield site. We've built out Marlboro, not with just the ability to supply fast.
In relation to supply fast, we already have clinical supply of Malboro in September of this year, two years later. Also, the ability to ensure that we're managing the cogs right from the start. Because usually what happens is you focus on getting supply, you focus on regulatory, then you go, "We got that done. Let's work on the cogs now." We started working on the cogs before the facility was even built. I think it's that level of just ambition and determination to really keep efficiency at first and center is what allowed us to do that. In relation to Malboro, what we have is we have an end-to-end operation that's focused on cost, supply, and of course, quality. That basically is what we're going to look at as we build out Malboro.
The second thing when I say as we build out Malboro is we made sure we did not put all the capacity in there that we need for five or six years' time. We set it up so that we can do line by line. We have the ability to run out seven lines, and we start with one, and we will maximize that line. As that line reaches its full capacity, we will then have line two ready. Line two, we will have a higher standard, and I will touch on what I mean by that later. That higher standard, we will then go back and retrofit in line one, and then the same later for line three.
Not only have we the capability of adding to capacity only as we need it, so we're not carrying a depreciation shadow that's unnecessary, but also that we have the capability to consistently improve our manufacturing capability without having to do a major redesign. I think that's super, super efficient when you're charting out a journey to cogs reduction. Again, as I said earlier, we were planning and designing cost efficiency right from the start, not something that we wanted to do later. Not only are we really, really proud, and again, an amazing team there, but not only are we really, really proud that we've supplied clinical supply out of this facility already in September, but we're very confident that we got a really great path to a cogs reduction here.
This might give you a little, it's a sort of a very simple cartoon example, but it's illustrative just to show you what I mean. When we started off the manufacturing of INT in Norwood, we had an equipment footprint that was 120 sq ft. If you look at this cartoon, you see a picture of a person, and that was the size of the equipment roughly to manufacture for a patient. Several pieces of equipment involved, a lot of it once-only use and then destroyed. It had a cogs that was reasonably expensive. We've already, in the current configuration in Malboro, reduced the size of that down by three, driven efficiency in the design.
We're constantly looking at the design, constantly looking at what can be reused, how we put in cleaning validation, how we work with DFD and that, how we do briefing books with DFD and that. We've already made huge progress. This is what we have already in our Malboro facility. Already in our technical development facility in Norwood, we have already the future configuration, and we're already not only have we already designed it, we're already running pilot scale batches on it and testing them, again, a threefold reduction site. Not only does that mean then that when line one gets full, but line two, as we will build out line two, will be based on the future configuration, and then we'll retrofit that.
We have not only the ability to drive increased capability for the demand that's ahead for all of the different eight different INT products that are all the way through the different phases of clinical trial, but the ability to drive it on cogs. I think that's something that we've learned also from looking at other industries and what happened in the whole cart technology area. We're pretty excited by that. I'll probably finish up with probably three things. Jamey likes three things. I think the first thing is just a huge call out to our people. We have amazing people behind us. It's what gives me confidence that we'll be able to continue this journey. I think the second thing is we have dependability, not only in relation to quality, not only in relation to supply, but also in relation to cost management.
I think lastly is we never finish. We are ambitious. It's never good enough for us. It's a lot done and more to do. That is our attitude. We have plans ahead that can continue to drive not only the gross margin expansion improvements that I've indicated on the slide, but even better, particularly as we use AI and robotics to drive that in. You'll see some of our team later will take you through some of the AI and robotics that we already use. We already have agentic systems up and running in our manufacturing facilities, and it's quite exciting to see. Let me say my last word in relation to quite excited to see. I'd love to bring you all to Marlboro, and maybe that's for another day.
Let me give you an indication of what the site looks like in a one-minute video. That is real. That is happening right now. Thank you very much for being with us this morning. A real pleasure to hand over to Jamey.
Thanks, Jerh. All right. Let me also extend a welcome to everybody. It is great to see many of you, and thanks for joining live or on our webcast. As Jerh said, I do like to cover three things. The first is I will talk through where we are headed in 2025. I will provide a recap from our recent earnings call. I will talk through our financial framework for the coming years. Stéphane laid out the business strategy. Stephen laid out the commercial strategy. Jerh just walked through our manufacturing strategy.
I'll put a financial lens to all that as it pertains to the next three to four years. Then I'll wrap up with capital allocation. I really take that from an R&D perspective, which is where we are driving most of our capital from an investment perspective. I will then take it to a cash perspective and walk our cash balance over the coming years. Then I'll talk about our exciting announcement this morning, which was the loan that we just announced, which makes our balance sheet even stronger. I'll get back to that in a few moments. Starting with 2025, as a reminder, we narrowed our revenue guidance to $1.6 billion-$2 billion. That's comprised in the U.S. of $1 billion-$1.3 billion and outside the United States, $600 million-$700 million.
Inside the United States, we had provided for there's only one large variable left, and that is vaccination rates. We had provided for within this range in the United States that vaccination rates would be down 20%-40%. At the time of our call, season to date, we were down about 30%. We're now two weeks later, and it's down 28%. It's a little bit better than at the time of our call. We feel very confident within the range in the United States as we're in the middle of November now. Outside the United States, it really just comes down to two things: delivery timing, whether the deliveries will happen inside the fourth quarter or shift to next year, and then in a few countries, vaccination rates as well.
I think the punchline here is after another two weeks, we still feel very confident in our guide of $1.6 billion- $2 billion of revenue. From a cost perspective, we said we would take our GAAP cost down by another $700 million versus what we had said at the second quarter. Overall, it is over $1 billion out this year. We focus a lot on our cash costs, which you can barely see here, but on the bottom of that slide. We started the year believing that we would invest $5.5 billion, and now we believe our guidance at the midpoint is $4.6 billion. From a cash cost perspective, Stéphane mentioned it as well, we are taking out nearly $1 billion of cost versus what we thought outside of the year.
We said we'd make improvements to 2026 and 2027, which I'll get into in a few moments. All of this is really important as we target cash break even by 2028. That's a quick wrap on the year. From a financial framework perspective, I won't go through this chart again. Stephen and Stéphane have covered it. When I step back from it, I see 10 opportunities for growth over the next three years. We will guide future years at a different time, but right now, in 2026, we believe we will start growing, which is exciting. We believe that growth is up to 10% in the year of 2026. Come our fourth quarter call, we'll give greater specifics around that.
We want to see where the 2025 lands, and then we'll give a little bit more specifics around what the actual guidance is for next year, but you can expect that we will grow starting in 2026, and we believe every single year thereafter. I want to take a minute on cash costs. I said we were going to make improvements. Our previous estimate for 2025 was $5.1 billion. For 2026, it was $4.7 billion, and for 2027, $4.2 billion on a cash cost basis. You can see the gap cost numbers at the bottom. Basically, in a year, we're basically advancing one year. With the previous guidance that I just mentioned of $4.6 billion, we're already beating what we had thought we would do in 2026. We are going to do that again. In 2027, we thought we'd be $4.2 billion.
We're actually going to achieve that in 2026. Finally, we're going to actually take 2027 down to $3.5 billion-$3.9 billion. At the midpoint, that's $3.7 billion, which is another $500 million out. That is due to all the tremendous work across all the teams. Basically, if I step back, when we laid out this plan a couple of years ago, we thought we were at $9 billion in cash costs. We're going to hit $4.6 billion in two years, and we'll hit $3.7 billion. We knew that we could do this. We didn't know that we would accelerate the timing as much or that it would be as significant as what the teams have been able to achieve.
There are loads of examples across all the levers we talk about in terms of manufacturing efficiencies, procurement savings, what we can do to make our trials more efficient as well. The teams are just doing an exceptional job, and they're actually beating our own expectations. Therefore, we have confidence in being able to take 2027 down to something that starts with a three. That's it for the kind of financial framework over the next few years. I want to move to capital allocation. Our primary driver of investing capital is in research and development, which will evolve over the next few years. I think you can see two things from how it evolves. Number one is it will go down. That is largely we've been saying this for a long time.
As we complete the phase III trials in infectious disease, those will no longer be there come 2027 and 2028. I'll come back to that in a moment. I think the second observation is you can see the percentage of investment is going to increase in oncology. We believe as we scale down and have a portfolio of hopefully six products in infectious disease by 2028, that will just require a basic maintenance of lifecycle management for infectious disease to maintain that portfolio. You can see the large balance will be in oncology. The last couple of things I'd say on infectious disease is you can see it remains a little bit elevated in 2026 and 2027 for two reasons. One is our phase III norovirus trial. We're going to initiate another cohort this winter.
Therefore, we can see still that'll cover and carry into 2026. Our post-marketing commitments as it pertains to COVID, we do have those in the budget. Those will go into 2026, maybe stretch into a little bit of 2027. Largely, that's it. That's it from an infectious disease standpoint. All the phase III trials will be completed, and then we'll get to that lifecycle management. From an oncology perspective, we are super excited to invest behind this. We're obviously super excited to invest behind infectious disease, but that is completing. Everything that you're going to hear today from Intismeran or mRNA-4359 or our entire emerging oncology portfolio that Rose will walk you through, that will increase over the coming years.
We are still investing behind rare disease and autoimmune, but we want to see how those registrational studies read out before we continue to invest further. Either way, it's a relatively smaller investment versus from a patient population perspective as well as dollars perspective versus what we'll see from oncology or infectious disease. That's how our R&D is evolving, and that's where we're primarily putting our capital. I want to talk about the balance sheet. I had already mentioned at our Q3 call that we will end at $6.5 billion-$7 billion in cash, which is a net cash investment this year of $2.5 billion-$3 billion. We started the year at $9.5 billion in cash. We are going to have revenue of $1.6 billion-$4 billion or $2 billion.
We will have cash cost of $4.6 billion, which is a $2.5 billion-$3 billion net cash investment. As I walk that forward, we believe we'll have a net cash investment of roughly $2 billion in 2026. That is 10% growth on that revenue line plus an almost $4 billion cash cost. I said $4.2 billion. That should, in the ballpark, basically deploy $2 billion of capital, in which case we would have $4.5 billion-$5 billion by the end of 2026. As we turn to 2027, we're targeting $1 billion. We've had a mantra of investing $3 billion in cash in 2025, $2 billion in cash in 2026, and $1 billion in cash in 2027 before we break even. That is still our mantra. That is still what we're targeting.
We've provided here for a billion to a billion and a half because we'll have to see where the revenue line grows. There are all the opportunities that Stephen's already laid out for you, and we'll see what happens. Maybe it's a billion dollars, but if it's not exactly at that revenue line yet, maybe it's a billion and a half dollars of net cash investment come 2027. Therefore, we'll end at $3-$4 billion in cash, which is a very strong balance sheet before the year that we break even. We're excited by that. We'll see revenue growth as well as cost reduction. That'll basically leave us with a strong ending cash balance and a strong balance sheet of $3-$4 billion. You might ask, why did we then go out and announce an exciting loan this morning?
That is a strategic move to maximize flexibility here. Obviously, we're excited about that. We want to make sure that we are in control. I think if you look at the terms of this loan, we are quite excited about it. Number one, it's non-dilutive financing from an equity perspective. Number two, it's relatively low cost. Let me explain that for a second. You can see so far plus 550. Right now, that's about a 10% interest cost. We will just take the cash proceeds that we get, which the first draw is $600 million, and put it in the bank. The ultimate interest cost is the 550 spread. That is what we'll be sitting on for $600 million of this. The delayed draw component is just pure flexibility over the next two to three years.
That is at a very nominal cost. The average cost of that for three years is 1%. That gives us significant flexibility, which we're excited about. We're still able to use it for any general purposes. We can use it for business development. We can use it for share repurchases. This is a five-year loan. A lot can happen over five years. That is what gives us extreme flexibility and a lot of comfortability over the next few years. Just to update our 2025 ending cash balance, that means we will now end the year with $7.1 billion-$7.6 billion in cash because we'll draw $600 million.
Our liquidity will be over $8 billion at the end of this year, which we believe on a year that we are investing $2.5-$3 billion, and that net investment will reduce over the coming years. That gives us a lot of flexibility and a lot of comfort as we look at our financial future. If you fast forward to 2027, that means our liquidity will be roughly $5 billion. Going into a year that we break even, we believe we'll break even in 2028, on a cash basis, we will have $5 billion in liquidity. $3-$4 billion from a cash perspective, and then this extra $900 million from a delayed draw loan perspective, which we're really confident in. Overall, we really believe in the base plan, but this provides a lot more flexibility to us moving forward as well.
I want to summarize what I think are the key financial takeaways. I really believe this is a turning point in our financial story. That turning point starts with growth. We believe we will grow over the coming years. We've tried to lay that out for you. The first year will be up to 10%, and we believe we will continue to grow thereafter. I think that's a big change versus the last few years. The second thing is Jerh laid out a terrific story for gross margin expansion. I would say of this 10%, half of that is volume, and the other half is all the productivity drivers that we are driving.
We feel very confident that with revenue growth and with the outstanding efforts of the entire team and what they're driving from a manufacturing efficiencies perspective, that we will grow 10+% on the gross margin line. We will evolve our R&D investment from infectious disease, and we will take that down to a lifecycle management level by 2028. We will significantly invest into oncology and hopefully rare and autoimmune behind that as well. For the most part, it'll be an oncology story over the coming years. We are reducing our cash cost investment. By 2027, we will be $3.5 billion-$3.9 billion. That really gives us great comfort in seeing cash break even by 2028, great line of sight to that. We believe in this strong financial framework.
We believe that we will have $3 billion-$4 billion in cash absent a loan. This enhanced liquidity gives us a lot of flexibility for both uncertainties or opportunities that present in our future. With that, those are the financial takeaways that I'd like to wrap up with, and I will turn it over to Jackie.
All right. Good morning, good afternoon, and good evening. It's a pleasure, really, always to be with you again. This year, I again have the happy task of taking you through our scientific data with the team. I want to say, unlike Jamey, we're going to talk about many things, which actually is a super pleasure. You're going to see a wide variety of the team presenting data with me. I think that also is a representation of our success.
We've really built out a large team of investments who are representing, as Jerh said, an amazing team behind all of these data. With that, why don't we get started? We're first going to talk about our seasonal vaccine pipeline and some of the new data there. This has obviously been a big year for us in seasonal vaccines. In the U.S. alone, we've had three FDA approvals. That's starting with mNEXSPIKE, which, as Stephen explained to you, has been incredibly important to our U.S. business this year. That team has also pulled forward two additional supplemental BLAs, first for RSV, keeping up with our competition in having data in those high-risk individuals recommended for vaccination 18 to 59. In COVID, importantly, the only licensed product currently in the U.S. for pediatric patients.
You'll see representatives from the entire team who will talk to some of the data that we've generated this year. I'm going to finish the presentation on norovirus, which those of you who have followed this story, you'll say, "Wow, norovirus changed teams. What happened there?" We're now thinking, as norovirus gets closer to being a commercial product, norovirus more and more seems like it's going to share some characteristics with our seasonal vaccine portfolio. Like other seasonal vaccines, it has a period of intense transmission, and that's in the winter season. Like COVID, it also has a small bump in the summer, but we expect that most vaccinations are going to occur in advance of that winter season. Also, like COVID and like influenza, it really can vary in terms of epidemiology over time.
We are envisioning a world where maybe not every year, but some years, we may want to update that vaccine. We have gained some expertise, obviously, in COVID, learning to do that more with influenza this year, and ultimately, we will apply that to norovirus. With that, we are going to start our seasonal journey. I am going to hand the podium over to Darren Edwards, who is our program leader on that program.
Thanks, Jackie. I am really excited to be here to talk through our data on mRNA-1283, which is mNEXSPIKE as it is branded in the U.S. We have not only gained the approval, but also done the annual strain update and launched the product in the U.S. This is data that comes from our pivotal phase III study.
It is data that we were excited to receive, excited to report, and I'm excited to talk about it today. A little bit of a description of the trial before I get into the results. This was a phase III trial designed to assess the immunogenicity, the safety, and the relative vaccine efficacy of mRNA-1283 versus our commercial product mRNA-1273. Approximately 11,500 participants enrolled in this study above the age of 12 years and older. A single dose of either mRNA-1283.222 or 1273.222 was administered. That is the bivalent vaccine composition encoding the ancestral and the BA45 strain. This was used because that was the licensed product composition at the time the study was enrolled. First, a little bit about the participants in this trial. Characterizing the demographics and the baseline characteristics of these groups, they were well balanced between the two arms.
That's both in terms of age, with a median age of approximately 56 years for participants in the trial, breakdown between the different age cohorts, as well as by race and ethnicity, where the demographics were largely representative of the American population. One thing to highlight, though, is approximately 46% of the participants in this trial also had greater than one or greater comorbidities as defined by the CDC case definition. In addition to the demographics, we also controlled for prior SARS-CoV-2 infection with about 75% of participants that actually had evidence of prior SARS-CoV-2 infection, very characteristic of the time during which the trial was enrolled. In addition, most of the participants in this trial had three to four prior vaccine doses with a median time of about 10 months from the last vaccine dose.
Getting into the results from the trial, first, we look at solicited local adverse reactions. They were relatively well balanced between the two arms with a trend towards lower local reactions for the mRNA-1273 arm. The highest solicited local adverse reaction was for pain. For all, they typically had either grade one or grade two reactions that resolved after about one to two days. Now, looking at the systemic adverse reactions, we see they were well balanced between the two arms of the study, 1283 and Spikevax. Fatigue, headache, and myalgia were the most commonly reported systemic adverse reactions. Again, grade one or grade two reactions were most common. Again, they resolved in about one to two days. This study was designed to assess relative vaccine efficacy. To do that, we used the CDC COVID-19 definition for COVID-19 symptomatic disease.
That required a virological confirmation of SARS-CoV-2 infection via PCR, as well as the presence of one or more symptoms consistent with COVID-19 within 14 days of the positive PCR. Active surveillance was conducted in the study in order to capture cases. That included biweekly symptom surveillance conducted using our eDiary, as well as assessment of participants with symptoms for clinical evaluation and for collection of samples that we then used for PCR. Starting out with the top-line data from this study, the pre-specified success criteria was met for relative vaccine efficacy for 1283 versus Spikevax. This is indicated by the data that you see on the screen, where a 9.3% positive point estimate was seen for 1283 in relation to Spikevax in terms of prevention of symptomatic COVID disease.
The success criteria required the lower bound of the confidence interval to remain above minus 10%. That's what we see from these results. As we know, the people at greatest risk for COVID-19 disease are those older adults. Here we see a breakdown across three different age ranges. It was very nice to see that the highest point estimate that we saw from the study when we did an age breakdown was for those at highest risk for symptomatic and severe COVID-19 disease, that being the above 65-year-olds. There we saw a 13.5% positive point estimate in favor of mRNA-1283. As we look at the younger cohort, we see relative consistency between the two arms. The confidence intervals are very wide based on the limited case number and the limited number of participants in that age range.
I think we all know that it's not just age, but it's also the presence of risk factors or comorbidities that put people at higher risk for severe COVID-19. We wanted to do a post-hoc analysis of the results of the data from this study in order to give us a view on how 1283 is actually performing in prevention of COVID-19 disease in individuals that had these comorbidities. The top line in this figure demonstrates or captures all people in this trial, 12 and older, that had one or more comorbidities. Here we see a 17.5% point estimate for prevention of COVID-19 disease. For those that were above 50 years old, again, increasing the level of risk, looking at populations that had increasing risk. Above 50 years old and with one or more comorbidities, we see an even higher point estimate, that being 23%.
Above the age of 65 with one or more comorbidities, individuals that had the highest risk for severe outcomes, we see a point estimate of 28.6%. We are very happy to see these results. This study was not powered to assess prevention of hospitalizations. We were, in a post-hoc analysis, able to use the FDA-defined guidance, FDA severe disease criteria in order to do a post-hoc analysis to give us some view on how 1283 is actually performing in relation to Spikevax in prevention of severe disease. This is all built on the knowledge that Spikevax has been demonstrated consistently to be very effective in preventing severe COVID-19, not only in our pivotal efficacy trial, but also in real-world effectiveness studies that we conduct year over year for each vaccine composition that we gain approval for and launch.
We were able to identify 55 cases in this study that met the FDA criteria for severe COVID-19. In this assessment, we see a 38.1% positive point estimate in favor of mRNA-1283 versus Spikevax. Now, pivoting to the immunogenicity results from this trial. At the top, you can see the view of the neutralizing antibody responses against the two variants that were encoded in this vaccine composition, the original SARS-CoV-2 and BA45. For both of those, the non-inferiority criteria was met, that being a lower confidence interval above 0.667. Importantly, you see that the lower bound of the confidence interval is actually above 1. That indicates that mRNA-1283 is driving a higher immune response. Same thing, looking at serum response rate, the non-inferiority criteria was met, that being a lower bound of the confidence interval above minus 10%.
Again, the lower bound is above 0, again, indicating that 1283 is eliciting a stronger immune reaction than the very strong immune response that we have measured consistently from Spikevax. Again, we wanted to look at those individuals that were at higher risk. We did an age breakdown. In this assessment, you see that the highest GMR, the ratio between the titers elicited by 1283 versus 1273, was highest in the above 65-year-olds. Correlates very nicely to the relative vaccine efficacy results that we also measured in those age ranges. We also wanted to take a look at durability of this product in relation to the durability profile that we have consistently measured for Spikevax.
Here we're looking at one month, three months, and eight months, or sorry, six months, and looking at not only the GMR, but the overall titers that we actually measure for Spikevax and also for mRNA-1283. We see the GMRs are very consistent at these three different time points. Also, I think it's actually very important to highlight that the overall neutralizing antibody response that we measure for 1283 at six months over 1,000 is actually higher than the neutralizing antibody response that we measure for Spikevax at three months. It's been my pleasure to be up here talking through the pivotal phase III results that supported our licensure in the U.S. and are also supporting the reviews of our 1283 applications globally. Just a brief recap, mRNA-1283 was generally well tolerated, had an acceptable safety profile.
We met the pre-specified relative vaccine efficacy and non-inferiority endpoints with a point estimate of 9.3% for all participants included in the trial. We saw a trend for higher RVE point estimates with advancing age and with comorbidities. For all participants above the age of 65, we saw a 13.5% point estimate. For above the age of 65 with at least one comorbidity, we saw a point estimate of 28.6%. We met all pre-specified non-inferiority objectives for immunogenicity. That includes indication that mRNA-1283 elicits a higher immune response than Spikevax. That higher immune response was most evident in individuals that were older. We have plans, and those are actively underway, to augment this data with data from clinical studies of our currently approved LP81 vaccine composition. That data will be published relatively soon. We are also conducting real-world evidence studies again.
That data will become available as it accumulates. We're approved in the U.S. Steven shared that information. That is for the current LP81 composition. We're approved in Canada. We're targeting strain update for next year. We filed for and are targeting 2026 approvals and strain updates in other markets like Australia, Europe, Japan, and Taiwan. Thank you very much for your time. I think next, I'll pass it to Rafael to talk about flu.
Thanks, Aaron. Good morning. I'm really excited to give an update on our influenza program today. Quick reminder, as Stéphane said, influenza is a major source of morbidity and mortality worldwide. Up to 1 billion cases occur every year. In the U.S. alone, up to 130,000 deaths every single year.
What's also important to highlight is that age and chronic conditions are also playing a role in the increased risk for influenza. Influenza can actually cause downstream events like cardiac as well as pulmonary events like heart attacks, stroke, or COPD. As you all know, influenza vaccines are licensed. There's also an enhanced vaccine market with vaccines that have shown superior efficacy to the standard vaccines. A quick reminder to mRNA-1010. It encodes for the surface glycoproteins, the major immune response for the influenza vaccines, the hemagglutinin. There are some inherent advantages of our mRNA platform when it comes to influenza vaccines. It encodes the exact protein that is being recommended by WHO and other recommending bodies. It has no requirement to propagate the virus in cell lines or eggs, where you could sometimes get mutations that can actually change the antigenicity.
What's also important is downstream, we have reduced production times, which gives the potential of having later strain recommendations as well, which could improve the matching of the vaccine strains to what is circulating in nature. What I'm not going to show today, but we've previously shown that we actually get superior immune responses of our mRNA-1010 vaccine to both standard as well as enhanced vaccines. Today, I'm actually going to talk about the efficacy that we've seen in our P304 study that we conducted last year in the northern hemisphere. It was a large trial of 40,703 participants. We randomized them to either receive mRNA-1010 or a licensed comparator vaccine. Last year, as some of you might recall, was when first the change of removing B/Magadha happened from the vaccines. Not all regions made the change at the same time.
Depending on the region, we actually had a trivalent or quadrivalent comparator. mRNA-1010 was trivalent throughout globally. In terms of the study objectives, the primary objective was to show non-inferiority and superiority of mRNA-1010 versus the standard dose comparator in terms of efficacy against all influenza strains. We also looked at the safety and direct genicity of mRNA-1010. In secondary objectives, we also looked at specific match to the strains that were circulating and also the immunogenicity of a subset of participants to confirm the prior findings that we had in our previous studies. In terms of exploratory objectives, we also wanted to look at the impact of mRNA-1010 against medically attended ILIs, which are more rare events. They are, as I mentioned, really important as well in terms of the types of disease that you actually want to prevent.
If we look at demographics, you see again that it's a well-balanced group between mRNA-1010 and the licensed vaccine comparator. What I do want to highlight is the last line, which is the baseline high-risk conditions. More than half of the participants had some type of baseline high-risk condition, which included diabetes, asthma, obesity, as well as COPD and atrial fibrillation. When we go to first the reactogenicity, it's very much in line with what we previously had presented about mRNA-1010. We do see higher local reactogenicity, with the majority being injection site pain, grade one and grade two in nature, and transient. Similarly, for the systemic reactogenicity, we do see higher reactogenicity for mRNA-1010 compared to the licensed standard dose comparator. However, a low frequency of grade threee events and most reactions were grade one or grade two in nature.
The most reported systemic reactions were fatigue, headaches in both of the groups. This is, to me, the most exciting slide of all of it. Aaron has shown a very similar slide for COVID. Just to orient you, because we have a couple more slides, we have the geometric mean is the blue diamond on top, which is the 26.6%. We have the confidence intervals. You have those different lines that actually show the non-inferiority bar. They show the superiority bar and then the highest superiority bar. What you can really see is that the lower bound exceeds all of those bars, which really gives us a lot of confidence with those results standing for mRNA-1010 being, in fact, superior to the standard dose vaccine. This also holds true when we then break it down by strains.
The confidence intervals become a bit wider because, of course, you don't have as many cases for every single one of those strains. You actually see that we get that efficacy for both the influenza A and influenza B strains across, which is really reassuring to see for our vaccine. This I really like a lot because it really drives home that the waning that has been known for seasonal influenza vaccines for years is not a bigger concern for mRNA vaccines. In fact, actually, we see over the season that the separation of the curves becomes bigger, with mRNA-1010 actually showing constant protection throughout the season, which is really reassuring to see where you still see an increase of cases later in the season with the licensed standard dose vaccine comparator. You see the curve flattening out for mRNA-1010 and then staying separated throughout the season.
That's really exciting for us to see. A couple more breakdowns, which I think the drive home message is that we see consistency across all those different cross-sections that we can make. When we look at the different age groups in this study from 50 to 75 years and older, you see that we consistently got the relative vaccine efficacy relative to the standard dose comparator. If we go to the high-risk conditions, we again see the same picture. We see that frailty status we see from the fit to the most frail, the consistent higher relative vaccine efficacy of mRNA-1010. We see with the higher BMI that higher relative vaccine efficacy. That was really nice and reassuring to see. As I mentioned, we had an exploratory outcome to look at the medically attended ILIs.
If you go from the top down, you effectively see different levels of severity. It goes from urgent care visit all the way to hospitalization. For the hospitalization visits, we did not have as many cases. They are pretty rare events. You see that for urgent care visits, outpatient visits, and the overall encounters, we consistently see that superior relative vaccine efficacy. In terms of the summary, the reactogenicity, as I mentioned, was higher for mRNA-1010. However, most of the solicited reactions were grade 1 and grade 2 in nature and transient. We saw an overall acceptable safety profile for mRNA-1010. We saw higher efficacy across all age groups, influenza strains, including participants at high risk of severe influenza compared to the standard dose vaccine comparator. The efficacy was maintained throughout the entire season.
What I didn't show here, but I mentioned that subset of immunogenicity, we yet again saw the same superior immunogenicity as well. mRNA-1010 prevented more severe and medically attended influenza. As Steven alluded to earlier today, we are in the process of submitting filings. We are intending to submit to the U.S., EU, Canada, and Australia by January 2026. With that, I'm handing over to Christy to talk about the combination vaccines.
Hello, everyone. Christine Shaw, I'm the portfolio head for infectious disease and rare. Today, I'm going to first share an update on our combination vaccine against flu and COVID. This combination vaccine takes into the two components that you just heard Darren and Rafael talk about, our 1010 vaccine for flu and our 1283 vaccine for COVID-19. Despite available vaccines, commercial vaccines for both COVID and flu, there remains a high unmet need and burden of hospitalizations for these diseases. This visual on the left shows in the U.S. in adults above 75 years the amount of hospitalizations over the last two seasons for COVID in red and flu in blue. One of the reasons for this still significant burden is the low vaccine coverage rate, particularly for COVID-19.
You can see on the right that that coverage rate in the 75+ population is around 40% in the last two years. As we know, the flu vaccine coverage rate is a bit higher at 75%. As Steven shared earlier this morning, the individuals getting a flu vaccine, about 30%-50%, depending on the year in this age group, do get a COVID vaccine at the same time. We think that a combination vaccine against flu and COVID can do two things. One is it can help to increase the COVID vaccine coverage because individuals coming in to get their flu shot can now get a combination shot in one arm and one injection that covers both diseases and both pathogens. It will help for the convenience of those already getting both vaccines. They can get it in one shot instead of two.
Overall, this should reduce the burden of disease against these two different pathogens. Our 1083 vaccine, as I said, is a combination of the 1010 flu and the 1283 COVID vaccine. We have previously shared that the phase III pivotal study for this vaccine was successful and met the primary endpoints. I am going to review that study briefly and also share some of the durability antibody responses in the study. It is a randomized observer-blind active control study in which we assessed safety, reacto, and immunogenicity. The study is split into two age cohorts. Cohort A is above 65 years. Cohort B is 50 years-64 years. The reason we did this two cohort is that we are able to compare the 1083 vaccine to co-administered licensed comparator flu and COVID vaccines.
In the 65-plus, we can do that compared to Fluzone HD, which is an enhanced flu product. In the younger age cohort, the flu comparator is Flurix, which is a standard dose vaccine. This is the standard of care for flu vaccines in the United States. In both cohorts, the COVID vaccine comparator was Spikevax. There are about 2,000 participants in each of these cohorts. 1083 showed an acceptable reactogenicity profile in both of the cohorts. The majority of the reactions were grade one and two in severity. There was a somewhat higher reactogenicity profile for 1083 relative to the co-administered licensed comparator vaccines. The reactogenicity was a bit lower in the 65 and older cohort compared to the younger cohort. Here are the immunogenicity data. Again, we assessed this independently in the two age cohorts.
In each cohort, what we did is determine the ratio of the antibody response to 1083 compared to the antibody response to the licensed comparator vaccines. For flu, each of the four strains in the vaccine, we did this by hemagglutination inhibition assay or HAI assay. Those data are shown in blue. For SARS-CoV-2, we measured neutralizing antibody. Those data are shown in red. You can see for all of the strains in the vaccine, the antibody response met the predefined success non-inferiority criteria in that the lower bound of the confidence interval was above 0.667. We actually saw a higher response to the vaccine, excuse me, for the flu antigens H1, flu strains H1, H3, and B Victoria. These are the clinically relevant strains.
As Rafael shared, B Yamagata is no longer circulating and therefore not recommended to be part of seasonal vaccines going forward. In these cases, the response even above Fluzone HD was higher in that the lower bound was above 1. We saw similar results and just as importantly against the antibodies against COVID-19 or against SARS-CoV-2, sorry, in which the responses to 1083 are higher than the responses to Spikevax. This was true in both age cohorts. The new data sharing today is looking at the durability of that antibody response. We took samples six months after vaccination and measured the antibody response in the same assays. You can see in the top panel are the older adults. In the bottom panel are those below 65. The 1083 groups are in red.
You can see that throughout the time course in the study, the response to 1083 is higher or equal to that of the comparator vaccine out to the six-month time point. To conclude this part of, sorry, I'm losing my voice here, the 1083 showed an acceptable safety and reactogenicity profile compared to the comparator vaccines. It did meet all the co-primary immunogenicity endpoints and elicited a higher immune response against SARS-CoV-2 and the relevant flu strains in both age groups in the study. Because antibodies are an established surrogate of protection against both flu and COVID, and because both components of the vaccine have demonstrated efficacy in standalone pivotal efficacy studies, we think that the combination vaccine 1083 should also protect individuals from both COVID and flu.
As noted earlier in the presentations today, the filing for this 1083 vaccine is under licensure review by EMA. We also recently submitted application to Canada. We are waiting further guidance from FDA before refiling there. Overall, we're very optimistic that this vaccine can help reduce the burden of disease against both flu and COVID in the coming years. Switching gears to RSV, mRNA-1345, we are going to share a bit of an update on that vaccine as well. Let me just grab, thank you. All right, RSV still causes significant burden of disease in the U.S. despite there being vaccines. In this past year, you can see here there are millions of inpatient visits, outpatient visits due to RSV. There are hundreds of thousands of hospitalizations and tens of thousands of RSV-related deaths.
We know that vaccination is an effective strategy to reduce this disease burden. Our mRNA-1345 vaccine was licensed initially in adults 60 years and above. At this point, we have achieved licensure in 40 countries across the world, shown on this slide here. Recently, we have achieved expanded indication licensure in another age group in the U.S. and in Europe for individuals 18 to 59 years of age with underlying comorbidities. Today, what I'm going to share on this program are the revaccination results of both safety and immunogenicity from two studies, one in which we gave a one-year revaccination and the other which we gave a two-year revaccination. First, the study with the one-year revaccination. This was a phase III study, P302.
In this study, individuals who had received a RSV vaccine at day one received a revaccination about one year later with the same commercial dose level. We measured safety, reactogenicity, and non-inferiority of the immune response after the revaccination compared to the antibody levels after the first day one dose. Here is a visual of the participants in the study. This was a study in 50 years and above. About 543 participants were enrolled in the study, with about 60% of them above 60 years of age and a bit more than half of the individuals female. This revaccination was well tolerated, as you can see in the visual here, with local and systemic reactions, primarily grade 1 or 2, a median onset of one to two days, and a median duration of two days.
Now looking at the antibody results from this study, at the 12-month post-initial dose time point, you can see there still are measurable antibodies that are above baseline, about twofold above the baseline day one. When these individuals got a revaccination at that 12-month time point, the antibodies were restored up to the level seen after the primary injection, where we had demonstrated efficacy previously. This study also met the predefined non-inferiority success criteria, both against RSVA, which is shown here, as well as RSVB, not shown on this slide. Moving to the second study in which we now assessed revaccination after a two-year period. This study was actually part of our pivotal phase III efficacy study. We took a subset of the individuals and gave them a second injection two years after their first injection.
They were randomized to receive either the same commercial dose of mRSV or a placebo injection. It was a safety immunogenicity study. The endpoints were similar to that I just described for the 12-month booster study. This study was in 60 years and above. About 1,000 of them received the mRSV vaccine about two years after their first dose, as I mentioned. The median age here is 68 years and about 50% female. Here, about a third of the participants had an underlying comorbidity. A third of the participants were defined as obese. I'm sharing the reactogenicity data split across two slides, with this first slide being the local reactogenicity. Most of the events were grade 1 with median onset of two days and duration of two, and overall showing that this revaccination at 24 months was well tolerated.
Now the systemic adverse reactions showing a similar picture, where the reactions were mostly grade 1 and 2 with an onset of day two and a short duration, again showing well tolerated revaccination. The immunogenicity after this two-year booster is on this visual here, showing the RSV A neutralizing antibody responses. Similar to what I showed you with the 12-month booster here, once you go out to 24 months, you again still see antibodies restored or maintained above the baseline after the first injection. You can see it's about 4,000 versus 2,000, so about a twofold elevation still, which is actually quite similar to the level we saw in the 12-month post-vaccination in the previous study. Again, after the revaccination at this 24-month time point, the antibody titers were restored and reached a level similar to that after the primary injection.
In this study, we also achieved the predefined non-inferiority success criteria, both for RSVA, the visual here, and for RSVB, which is not shown. Summarizing the RSV story shared today, revaccination either at 12 months or 24 months is well tolerated. We were able to show that durability of the immune response does last out to at least two years. If you give a revaccination at 12months or 24 months, it does restore the immune response. It does meet non-inferiority success criteria. We think because of these results that revaccination at either of these time points would be expected to provide comparable vaccine efficacy to that after a primary dose. Therefore, revaccination has the potential to provide sustained protection against RSV, particularly in individuals with underlying comorbidities in which revaccination might be particularly beneficial.
We continue to monitor guidance from recommending bodies on RSV revaccination approach and timing. Turning back to Jackie for norovirus.
All right, thank you, Christie and team. I think you can see what a pleasure it is to work with this group of people every day. Now I'm going to talk about our newest seasonal vaccine, norovirus, and our progress there. Just as a reminder about why we think norovirus can be such a growth driver for us. Among enteric viruses, this is really the leading cause of diarrheal disease globally. It results in a substantial healthcare burden. I'll just say now that rotavirus is a vaccine-preventable disease in children, this is a virus that actually can impact both older adults and children. Unlike rotavirus, norovirus actually has its most severe disease in older adults and immunocompromised patients.
While highest incidence is in kids, greatest impact in terms of severity of disease is in older adults and immunocompromised patients. That really defines why we've designed our clinical development program the way we have, which I'll talk about in a couple of slides. The burden amongst older adults is also expected to rise along with societal aging and the increased need for institutionalized care. This is one of those viruses, just like respiratory viruses, that if you have a contained group of individuals, it's a fecal oral virus, meaning it's passed through inappropriate hand hygiene. You can imagine that where care is being given, it can just rip through one of those institutions. We think it's going to be important in those settings.
What you see in the United States, about 20 million infections leading to about 900 deaths, but 100,000 hospitalizations where people will receive supportive care, so rehydration primarily, and then also support for maybe organs that have been impacted by reduced perfusion. That's $2 billion of healthcare costs annually and lost productivity. Same numbers for the global environment, about 685 million infections each year with 200,000 deaths and about $60 billion lost in terms of healthcare costs and lost productivity. I mentioned that norovirus shares some attributes with other seasonal viruses. Really, it's the variability that's one of the key pieces. I alluded to this a bit earlier. Just to say the reason why you will see us pursuing a multi-valent vaccine is because there's actually quite a lot of distribution of genome groups. We've targeted the genotypes that are most prevalent year on year.
We have really looked across the last couple of decades to design the first version of the vaccine. There are actually both trivalent and pentavalent versions. We are going forward with the trivalent first. If that is successful, the pentavalent in pediatrics, where we actually see the greatest distribution of genome groups, will be how we move forward next. An important piece about the mRNA technology and why we think this is an ideal technology for norovirus, as I mentioned, with the genetic diversity of these viruses, is the ability to have a second option in terms of strategies for greater vaccine coverage, meaning rather than continually adding different strains the way we have done with pneumococcal vaccines, which leads to increased immune interference, it also can lead to challenges in terms of how many you can actually put in that syringe.
Every iteration of pneumococcal vaccine gets further away in development because it's harder to both develop and manufacture. With the mRNA technology, as we do surveillance, you can actually swap in and out strains as they become relevant or recede in relevance. This is actually a discussion we've already started with the FDA, thinking about a diarrheal vaccine maybe differently than people have thought about it before. I'm going to share with you our phase I clinical trial. We actually investigated both formulations, the trivalent, which is 1403, and pentavalent, 1405, for obvious reasons. We observed them in the same clinical trial so we could pick that formulation we were going to take forward into phase III in adults. It was a randomized observer-blind placebo-controlled trial.
It actually had 664 healthy volunteers, pretty big for a phase I study, but that's because of all of the treatment groups, so multiple dose levels in two formulations. We looked at both one and two doses. Why? In children who are primarily naive to the infection, we think we may need more than one dose to get to protection. In adults, because all of us have experienced norovirus at some point, one dose is really going to be sufficient. That's how we've gone forward into phase III. Those are the data I'll really be sharing. We've been following participants for 12 months after their last study injection. That's really to look at the persistence of those antibodies, to think about how long could this last. I'll be sharing some of that with you today. This was primarily or only a U.S. study.
Before we get into the immunogenicity results, I think sometimes it's important to really understand what we're measuring when we do these immunological assays and maybe to also help you understand why we think we have an understanding of how this vaccine might work and what makes us comfortable to make that next stage of investment. The assay that we've been primarily looking at is a functional assay, meaning we're not only measuring how much antibody we're making, we're also observing how that antibody interacts with the pathogen or with the human body to understand maybe how it might work. In this case, the histoblood group antigen blocking assay, and from now on, I'm going to say HBGA because it's a lot easier. HBGA is an antigen that's really important for the virus entry into those epithelial cells in the gut.
What we are looking to do is blockade that virus entry. This is really a binding antibody. For the sake of time, I am not going to go through the details of how the assay works. Just to emphasize, we are looking both at how much antibody we induce and also the quality of that antibody. We looked in two cohorts, both older adults and younger adults. On this slide, you see the older adults. As I mentioned, I think the target for this population is obvious. It is really because they experience the most severe disease. As we age, we are more susceptible to this kind of disease. As people age, they are more likely to be in settings that really favor outbreaks of this disease. What you see here are the three genotypes that are included in our vaccine.
You see the results from day one and day 29. Remember, I told you I'm going to share with you the single dose data because that's really how we envision this going forward, just like getting your single flu vaccine every year. In the case of norovirus, perhaps every other year, every third year remains to be seen, like with RSV. What we see is some seropositivity. It actually really varies across the different genotypes, as you might see at the day one level. Why? Interestingly, norovirus, some of these genotypes actually tend to evolve much more quickly than other genotypes where they tend to be more stable. Where you see lower pre-vaccination titers, that's in the genome group G24, where we see the most variability.
Understanding even whether we ultimately may be able to swap out G24 strains might be helpful in the future. What we see with all three of our genotypes, regardless of the pre-vaccination titers, is a really robust increase in antibodies and really going from 10 to the two to really 10 to the three, close to 10 to the four. We are really encouraged by these results, again, in this functional assay looking at how the antibodies are able to blockade that T cell surface antigen for viral entry. Now here you see the data in younger adults. It is really a similar story. Why younger adults? Younger adults, first of all, may be in occupations where they are really at high risk. Just like this virus can rip through assisted living facilities, it definitely rips through pediatric and adult hospitals every year.
If you are a healthcare provider who's being exposed to that, there's pretty high force of infection. There are other populations that may choose to be vaccinated as well, like, for example, parents of young children, if you've ever taken care of your child and then experienced the infection after your child. I used to be a mom whose kid liked to climb in my lap and throw up, never in the toilet. This is a virus that I would sign up to be vaccinated against. I'll put it that way. Safety data, again, as we show in all of our slides, dark blue are grade one, lighter blue are grade two, and the orange are grade three. We see our typical pattern in terms of the local reactions. In the different dose levels, we see in younger adults increasing rates with increasing doses.
What you'll see is, unlike some of our other vaccines, that band of orange is actually really small. We are very encouraged, actually, by the reactogenicity profile. This is something we see. There is generally an increase with increasing doses, regardless of the vaccine antigen that is part of the platform. The different antigens cause different levels of reactogenicity. Older adults are less reactogenic than younger adults. That is something we have also typically seen. As we talk about it now, we have moved to phase III. I mentioned to you the reason why we are really going forward with trivalent in adults, but with a longer view towards, if this works, moving to pentavalent and starting in the pediatric population. This is one of our large-scale trials, again, placebo-controlled and enrolled about 17,500 per arm. As Jamey mentioned earlier, we did do an initial northern and southern hemisphere season.
We're accruing some cases. The epidemiology was not in our favor, unfortunately, this year. We are going to need another season to accrue additional vaccine-matched cases, leading to an interim analysis that we're anticipating later in 2026. In summary, our single dose was really well tolerated and showed an acceptable safety profile. The functional antibodies really showed robust titer increases against the vaccine-matched strains. The single similar mRNA-induced titers were observed in both younger and the older adult populations, which is why our phase III is actually evaluating the vaccine in those 18 and over. We're advancing now into the third cohort and, in fact, have enrolled our first subjects in the U.S. The phase III readout is really going to be subject to those case accruals. As I mentioned, we are anticipating later in 2026, at least an interim analysis. Okay.
With that, I'm actually going to announce a pivot. We pivot a lot at Moderna. We're pivoting to have our break a bit early. Unfortunately for you, you're going to hear about latent vaccines after. I will introduce my colleagues who will lead into the oncology portfolio. I should say, unfortunately for me, because I have to talk to everyone after a break and the insulin effect sets in. We'll take how long, Lavina? Just 10 minutes. Ten-minute break. I'll see you back then on stage. Thank you so much for your attention. Hi, everyone. Thrilled that there are so many great conversations going on, which hopefully we can continue into the lunch and beyond. If I can ask, I know there are actually a few people still outside.
Maybe in the next 60 seconds or so, we'll get restarted just to get us back on track. Right. I hope you enjoyed your break. I'm going to now take you through a tour of our vaccines that are earlier in development before handing it over to the oncology team. What you see here are the latent and bacterial vaccines. We're going to talk for a minute about CMV vaccine, followed by our two EBV candidates, the prophylactic candidate and the therapeutic candidate. I will end with a quick talk on Lyme vaccines. I'm going to hand it over then to my colleagues who are really leading the charge in oncology. All right. CMV and transplant was obviously a huge disappointment to us that we were unable to demonstrate prevention of infection in seronegative subjects.
This was obviously our first step to really getting to the indication of prevention in congenital infection. I will say it was always a very high-risk program in the sense that we were studying seronegative patients and hoping to prevent evidence of any infection. As you may remember from our COVID and our RSV phase III trials, we were able to show some prevention of infection, but really at much lower rates than symptomatic infection or severe disease. That is pretty typical, not just in the vaccine world, but in general. You often see evidence of more severe disease being the first place that you have impact in infectious disease. Why do we believe in CMV transplant? One, it's a very different population.
While there can be CMV-negative transplant recipients, the reality is as you get older, you are more likely to have a disease that leads to a failure of some end organ or cancer. That means that transplants are actually weighted more at the older end of the age spectrum. These people tend to be seropositive. The pathophysiology of their disease, either because they're receiving a solid organ that has CMV-infected cells or because they themselves are already seropositive, once they receive their immunosuppression, that really reduces the immune control of latency of that virus and the virus expands. It can actually cause in immunocompromised people quite symptomatic infection. The risks that are associated with CMV disease in solid organ transplants and those with hemopoietic stem cell transplants or HCT, graft rejection.
CMV can actually cause a degree of inflammation and then immune destruction in organs. Since these organs are so very precious, we do not have enough for all the patients that need transplant. That is a huge issue in transplant medicine. Second is it can cause other kinds of end organ CMV disease. One of the most common in this population is CMV enteritis, causing a lot of diarrhea, potentially leading to dehydration. You can also see rarer forms like CMV retinitis, which is a threat to someone's vision. There are currently no approved vaccines. There are antivirals, and they are actually used pretty universally. Different centers use them differently. We will talk about that in a minute. The issue with them is, one, they have a high cost and they have their own side effects. They are typically not used forever in people with transplants.
What we often see is once you pull off the antiviral suppression, you do see a rebound even in CMV viremia, so the measurement of CMV circulating in the blood, particularly when the medication is first withdrawn. That's not a risk that actually decreases. There's that rebound no matter how long you keep that prophylaxis on board if you then pull it off. There are 47,000 organ transplants in the U.S. each year and 23,000 bone marrow transplants. That really leads to about 70,000 transplants in the U.S. each year. A smaller but still sizable market. Sterilizing immunity, as I mentioned, is really challenging in vaccine development. That's actually not what we're aiming for in the transplant population. As I mentioned, these are typically seropositive individuals. Once you're seropositive, you remain seropositive for life.
What we are trying to do is prevent that virus from reactivating in the immunosuppressed milieu. The way that actually transplant physicians measure whether or not once we withdraw or even when you're on CMV prophylaxis, whether that virus is reactivating is by looking for CMV DNAemia. We actually looked at CMV DNAemia in the phase III trial in healthy women. We actually did see that initially after infection, we're able to reduce that level of DNAemia. That actually gives us hope that this may be a place where we can see some effect. I'm going to show you what T cell responses look like in transplant patients in just a moment. We think it has the possibility to prevent the viral replication and reactivation. Our hope is that we can look in a smaller bone marrow transplant population.
If that looks positive, so limiting the investment upfront, but if that looks positive, talk about how we might expand use. In summary, the CMV is a disease that's associated with substantial morbidity and mortality. It's one of the key issues that transplant physicians across the spectrum have to manage. Latermavir is actually a suppressant that is approved. It's currently being used as standard CMV prophylaxis. It's really considered best in class. It's a way that you'll see in the next slide. We're considering that as we think about how this program could fit in with existing treatment. Despite its efficacy, it's also been associated with decreased CMV-specific T cell reconstitution in bone marrow transplant patients. As I mentioned, these drugs, like all drugs and vaccines, have their own side effects associated to them.
It also can lead to late-onset CMV infections. Again, once you decide to withdraw your prophylaxis, we often see a rebound reactivation. That is why we think the development of a safe and effective vaccine that you might be able to boost year on year might be a way to get on top of this remaining unmet medical need. We are studying CMV-positive adults who are over 18 years of age who received an allogeneic, meaning it is coming from themselves, stem cell transplant. We are looking at a slightly different dose level and slightly different schedule than we were in the primary disease. This is an accelerated vaccination schedule where we are giving three doses. That is happening after the initial reconstitution of the immune system. People are on their Latermavir prophylaxis up to day 100. We then start vaccination as they are reconstituting that immune system.
Six months into the trial, we're going to give a booster dose to see what it would look like if you needed to boost this over time. There's an mRNA-1647 group and a placebo group, and they're randomized one-to-one. In terms of the solicited adverse reactions, here you see grade one in gray, grade two in blue, and grade three in orange. As we might expect, actually, in a transplant immunosuppressed population, we see lower severity for most of the events. That's really because probably their bodies are not allowing them to have the same reaction to the vaccine that we see in healthy individuals. However, I was actually pleased to see that we do see some reactogenicity, meaning we're causing their immune system to do something in this case.
I want to show you actually what the T cell responses look like and why the T cells. T cells are incredibly important in suppressing CMV, and particularly CD8 T cells are important in controlling that infection and keeping it in latent mode. What I'm showing you here are the three antigens. GB is the antigen that was on its own. GH and GL and UL, they are part of the combination that makes up pentimer. We are looking at T cell responses to each of those individual antigens. We also have the CD4 at the top in the different shades of pink, CD8 in the different shades of blue on the bottom. Also what we see are the placebo versus the mRNA. This is really looking after multiple doses in the schedule.
As I explained to you, we're studying this in stem cell transplant patients. These are people who need to have their bone marrow and their cancer, importantly, completely ablated before we give them a transplant. That transplant is actually like a seed that grows into a reconstituted immune system. We're talking about people who are immunosuppressed, not only because they're on immunosuppressing drugs to prevent graft versus host disease, but because they're in the process of reconstituting that immune system. In the different shades, what you see are the level of polyfunctionality. Polyfunctionality really refers to the different kinds of cytokines and different kinds of activation and cell activity functions that you can see. Darker means better and more functionality. What you see at the top are the CD4 responses.
I mean, it's absolutely clear that there's many more in the 1647 group than we're seeing in that placebo group. We're seeing about a third, a third, a third in terms of polyfunctionality. Even at this early stage, we're seeing really reconstitution of a diverse CD4 response. CD8, similarly, we're seeing polyfunctionality developing. We always see lower CD8s. The fact that we detect them at such a different level than in the placebo group is really something that we feel strongly positive about, particularly in the GB space and in the GL, UL. What you're seeing in the placebo group there, as you see, it's kind of even throughout. People, even as they're reconstituting, have some kind of baseline level.
This probably is because we're needing some kind of activation, so they're getting a natural boost because they probably are having to control their CMV infection even while on Letermovir. In summary, the solicited local and systemic adverse reactions were mostly grade one and two. We didn't see any grade four adverse reactions reported. In a transplant population, I was actually encouraged to see that we can get them to stimulate some of those reactions. For 1647, the most common were injection site pain and headache, fever, and fatigue, similar to the rest of the platform. Our interim analysis in this population demonstrated that we can induce antigen-specific polyfunctional CD4 and CD8 T cell responses. As I mentioned, we think that's encouraging for controlling that DNAemia. Of note, our robust cell-mediated immune responses were observed as early as 77 days after transplant.
This is really speaking to the ability of this platform to induce the right kind of immune responses at a moment when someone really is at their worst in terms of immunogenicity. We're hopeful about that. Our next step is really to plan our phase II data readout. On the heels of our recent CMV findings, we'll be meeting with the MGH team to really talk about how we build in an interim analysis and look at what's happening in this study in the future. Okay. Now I'm going to move on to Epstein-Barr virus or EBV and our prophylactic vaccine 1189 and our therapeutic treatment 1195. These two vaccines are really meant to address very different populations. All of us, just like with CMV, tend to acquire an EBV infection over time.
As you get older, you're more likely to be exposed to this infection and become seropositive. Unlike CMV, there is a very obvious symptom associated with the severe form of this infection. It's infectious mononucleosis. What you see on the left here are data that we actually generated within our epidemiology team looking at the increase of seroprevalence with age depending on your location. What we know is that in high-income countries, you're a little bit slower to acquire the infection than in medium-income countries. What we were kind of surprised to find in the middle is that by the time you're getting to middle school, about 9-11, 12-14 years of age, you're looking now at more than half of individuals are actually seropositive.
This really indicated to us, we need to think about this as a vaccine that we would include with some of the other middle school vaccines like meningococcal vaccine and pertussis. Finally, I think really pertinent to the 1195 therapeutic program, EBV accounts for over 90% of cases of infectious mononucleosis. This is an importantly severe infection we can monitor and manage. The vast majority are going to be due to EBV. The annual incidence of infectious mononucleosis in the general population is at least 45 per 100,000. There is a way that we can measure this in phase III with the peak incidence of about 15 years-19 years of age. This is a program where we're really looking to establish ourselves and begin middle school, high school vaccination programs.
The reason why I say this is important for 1195 comes on the next slide, which is if you look at the conditions that EBV can be associated with long-term in terms of sequelae, multiple sclerosis tops the list. It is really when you get to having infectious mononucleosis, if you had the severe form of the infection, your risk is increased even above getting the infection itself. This is the interplay between the two programs. I will say our target indication initially is multiple sclerosis because of what you see in the top bar. We know that Epstein-Barr, which its latent infection tends to hide in B cells, is actually associated with a large number of cancers, particularly B cell lymphomas.
Being able, just like in the CMV program, to prevent that vaccine once infection is established to stay in its latent phase, prevent the lytic phase where it reactivates and begins to spread, we think is going to be incredibly important. Okay. Let's talk for a moment about that etiological link that I just referred to. Nearly a million people in the U.S. have multiple sclerosis. This is a progressive degenerative neurologic disease. People really are on all ranges of the spectrum. I think an important piece is when you're born, you have all the neurons you're ever going to have. As you grow, you continue to myelinate your nerves. They continue to spread. You are born with the cells you are going to have. Once those cells die, you don't get to replace them. Every neuron's precious.
Really being able to have an impact early in disease is incredibly important. That will become important as we talk about the phase two study we're currently enrolling. There was a landmark study that demonstrated that there was a 32-fold increase of developing MS once you seroconvert to EBV. This is one of the strongest links ever established between a virus and the potential sequelae happening years later. It was also previously established. We've talked about this in the past, that infectious mononucleosis is a risk factor. You can see on the bottom that the seroconverted have that 32% increased risk. What you see is that on the right, the risk of EBV plus a history of infectious mononucleosis is really increasing up to 2.3-fold over that history of EBV.
That's really an important population and why we think preventing infectious mononucleosis can be important in the prevention also of sequelae. All right. EBV, as we mentioned, is associated with several serious medical conditions that we think can be addressed through mRNA therapies. We talked about IM and multiple sclerosis. I mentioned to you that we believe B cell type lymphomas, of which post-transplant lymphoproliferative disease is one of them, is another place. I think it's important to also emphasize that in addition to prophylactic oncology, autoimmune diseases, there's a growing body of evidence. There was a recent paper in Science looking at the link of EBV and SLE or systemic lupus erythematosus. That's a potential place in the future. There actually is a chronic infection with EBV that's quite debilitating for patients.
These are all places that if we can see impact with our vaccine and therapy, we can look forward to further data generation. I mentioned to you we have these two programs, and I wanted to go through with you a little bit the difference between them. 1189 is primarily looking at lytic antigens. Lytic antigens are the ones that are responsible for rapid proliferation and cell-to-cell transfer. 1195, or the therapeutic version, also includes those lytic antigens plus two latent antigens. That's really important because, as I mentioned to you, our goal in these therapies is to not allow that latent virus to get to the lytic space. The virus actually, in its latency, expresses very different antigens than it expresses in the lytic phase. Only vaccinating against lytic antigens is unlikely to have the impact of keeping that virus suppressed.
That is really why we think we need to look in two distinct spaces. I am going to talk about the prophylactic vaccine first. This was another larger phase trial design. We started in adults 18 to 30 years of age. We then moved down to adolescents 12 to 17 years of age. That was really based on the epidemiology work we did where we realized we need a younger age group if we are really going to target the majority of infection. It actually incorporated multiple primary objectives. Looking at safety and reactogenicity, of course, but also looking at binding antibodies, so how much antibody we produce, and then B cell neutralization antibodies. Remember, I told you that B cells are the bad actors in this infection. Understanding if we can neutralize EBV-infected B cells is an important endpoint as well.
We're also going to be looking at epithelial cell neutralization and the impact on viral shedding as well as seroconversion. If we have cases of mononucleosis, we're looking towards those as well. There's also a phase two trial that's ongoing. In the phase I trial, there was a request from FDA to pause enrollment at one moment and look at safety data. We have single dose and two dose data in 12 to 17-year-olds. I think that's going to be helpful as we try to decide how many doses of this vaccine do you need. Again, as data continued to accumulate from that epidemiology study, we actually started again looking at an even younger age group, thinking we want to capture as many infections as possible. At this moment too, we were able to focus our dose ranging at lower doses.
That is really because we're not seeing so many differences in immunogenicity. This is a way, as I mentioned earlier, for us to manage reactogenicity. Okay. I'm not going to go through the data in the interest of time so we can move to oncology. You've seen it before, but the vaccine was generally well tolerated in adults and adolescents. Participants showed an increase in functional binding antibodies as well as they showed baseline EBV seropositive thresholds. Meaning we can induce antibody titers greater in those seronegative by vaccination than people experience through natural infection at baseline. We also reduced measurable viral DNA and the frequency of shedding in EBV seropositive subjects, meaning we can suppress reactivation and shedding in that case in the seropositives. The data from part A and B, meaning those 12 to 17-year-olds, are expected later in 2026.
We will have those phase two data in 2026 as well, hopefully helping us target how we take this program forward. Moving quickly to 1195, as I mentioned, our primary indication is going to be in multiple sclerosis. That's a disease really characterized by immune dysregulation of EBV-infected B cells, presumably by EBV. This may be one of the key underlying mechanisms, not only of disease initiation, but also of progression, poor EBV control over time. The vaccine mechanism of action is hypothesized to be restoring some robust immune control. Okay. Talking about the part one trial design, this again was in EBV seropositive subjects only. Again, we're targeting people that are already infected. Once you're infected, we can't make you uninfected. Hopefully what we can do is prevent some of the long-term sequelae of being infected.
We had two formulations and multiple dose levels. Once again, we're really looking at humoral immunogenicity, T cells also being important here, as I mentioned before, because latency really requires the help of T cells to keep those B cells in check. Looking at the reactogenicity, here again, you see the dark blue, medium blue, and orange schema. We actually saw relatively comparable reactogenicity across the different dose levels. We really are looking to select the optimization between as minimal a dose as possible while seeing the best possible immune responses. Here you see the binding antibodies. We have generated binding antibodies to the glycoproteins that are the lytic antigens. Remember, I told you that 1189 and 1195 overlap in this space. What we see is that in all of the dose levels, even out to day 317, they finish their dosing schedule.
It's a three-dose schedule at day 180. Seeing six-month persistence, they're staying above that dotted line, which is the natural infection level. I mentioned to you that neutralization is important here, particularly in the B cells. Again, staying above the limit of quantitation of the assay, that's in the gray line. That's true for the B cell neutralizing antibodies. It's also true for epithelial cells, which can also harbor a latent EBV infection. Finally, I wanted to share with you the shedding data. What you see here at the top, I think in particular, is the placebo group. These are all EBV-positive subjects. The placebo group is shedding EBV at a certain level over time. Both 1189 and 1195 are reducing that viral shedding over time.
Again, hopeful that we can have an impact in that lytic phase of the virus. Finally, I mentioned to you that particularly for 1195, we think that these T cell responses are going to be important. At the top, EBNA3A and LMP2B, these are those latent antigens I was referring to where we're going to need to exercise some immune control over those antigens in the latent phase of the virus if we want to prevent that proliferation. Seeing in the pale colors, the median responses being so much higher than the median responses, which are represented by the dark bar in those that receive the placebo group, is super encouraging to us. Just to say, we also see CD8 responses to the glycoprotein GH as well. These are the CD4 responses to the EBNA3 and the GH antigen.
Again, including 1189, actually on this, no, excuse me, the four dose levels on this slide, we see CD4 responses as well, both to the latent antigen, EBNA3A, and to the lytic antigen, GH. We are so excited that earlier this year, we started enrolling actually now in multiple sclerosis patients. Again, we're repeating the dose ranging study for two reasons. One, the safety here is going to be important. These are subjects whose primary disease is neurologic. As I mentioned, every neuron's precious. We have an expert DSMB really looking at the progression of those patients over time clinically. We're complementing that with looking at MRIs over time. One of the key clinical endpoints in EBV is a loss of myelination in the white matter. This is a way that we can follow very early patients over time.
This is known to be a clinical endpoint that actually precedes progression of symptoms. We will see between placebo and those receiving one of three doses if we can reduce the number of lesions over time. Okay. I think I am going to go to the summary for the sake of time. The interim analysis data demonstrate that 1195 is generally well tolerated. EBV seropositive participants show increase in B cell neutralizing antibodies. The vaccine is able to boost both CD4 and CD8 cells. The humoral and cell-mediated immunity persisted out to day 317, so about six months after that last injection. We reduced measurable viral shedding of saliva. It was actually both 1195 and 1189. Looking this year, we have a phase I part B data coming in younger subjects later this year. We also have the phase two multiple sclerosis study that is coming soon.
With that, we're going to move to Lyme disease. This is our bacterial vaccine program. You all have heard of Lyme disease, I know, especially up here in the Northeast. Lyme disease is the most common vector-borne disease, meaning an insect is carrying it in the Northern Hemisphere. The Lyme follows a bimodal age distribution. It typically affects children under 15. They're out in the grass playing in the summer and older adults. Interestingly, this is also a seasonal infection, but the season is reversed from those viruses we were talking about. It really tends to proliferate in the summer months when everybody is outside and enjoying being in the great outdoors. In the major geographies, there are about 475,000 cases in the U.S. each year and about 200,000 in the EU.
The symptomatic infection is actually one of the harder ones I found in pediatric practice to diagnose. It can masquerade as a lot of different things, but subjects typically develop a rash. That rash can take a lot of forms, but the target rash is the classic one. They have some nonspecific symptoms like fever, fatigue, headache, and joint pain. If it's untreated, it can lead to certain neurologic and particularly cardiac complications. It can lead to a cardiac arrhythmia. That is why we're very interested in preventing this infection. There's currently no human vaccine on the market. Why are we convinced that this could work through mRNA? The program has actually already been de-risked from an antigen perspective. There was a licensed vaccine, LymeRx, that targeted the same antigen. That vaccine was later withdrawn from the market.
It's actually the same antigen that we're targeting because it's a known mechanism of action. It actually works a little bit differently than the viral vaccines we've been talking about. We are looking to induce primarily antibodies. The protein that we target has the very creative name of outer surface protein A. The anti-OspA antibody that we produce, it's not actually protecting the individual person. It's protecting by transferring that antibody to the tick when the tick feeds. It gets an antibody dose as it gets also your blood. Those antibodies can kill the Borrelia organism, the responsible bacteria for Lyme disease in the midgut. That prevents transmission from human to host. It's kind of a cool mechanism. Like I say, it's been established in a previously licensed vaccine.
We have two candidates cleverly named after the dates or the years when there were various discoveries made about Lyme disease. The first recognizing Lyme disease in 1975 and then 1982 recognizing the pathogen that was causing it. 1982 is actually a monovalent looking at serotype one, which is basically the only serotype that is prevalent in the U.S. Europe is a bit more heterogeneous, excuse me, serotypes one through seven actually proliferate there. A multivalent vaccine, 1975, is targeted. We had a phase I study here looking at both the multivalent and the monovalent. In red and in blue, it was a three-dose study given at zero, month two, and month six. We are looking again at immune responses and the safety. Here you see the local and systemic solicited adverse events.
In this case, I told you we see some differences in antigens. We saw a few more grade threes, particularly at the higher doses. This is definitely a case where increased doses led to increased severity. There were no grade fours that were reported. Most of the reactions are still grade one to two in severity. In terms of antibody, though, the vaccine really induced robust anti-OpsA antibody responses. You see here on the bottom of all the time points at which we measured. People are negative at day one. At day 29, they start to have some immune response. It is really at that second dose that we start to see some increase day 57 to day 85.
There is pretty good maintenance of vaccine persistence, which we're kind of excited to see with a further boost, though, that happens at about six months. We are seeing the robust antibody titers not only to serotype one, which you see in the slide, also to serotypes two-seven. They did elicit dose-dependent responses. There is a bit of a decision to make here as we look at balancing reactogenicity with the immunogenicity. Both of the vaccines were generally well tolerated. They had an acceptable safety profile. They elicited robust dose-dependent anti-OpsA responses. We've actually decided to go to phase two in this program. We are going to be evaluating not only lower dose levels because we think that even the lower dose levels were really inducing robust responses, but we are also going to be looking, we are a platform company constantly improving.
We're going to look at applying some of those improvements that we've made in other programs to see if we can also improve that reactogenicity a bit. With that, I'm now going to hand over to my colleague, Kyle Holen, who will talk to you about the oncology portfolio.
Thank you, Dr. Miller. Thank you all for being here in Cambridge and joining us here at our headquarters building. For everyone online, thank you for taking time out of your day to talk to hear about our pipeline updates. We are going to have an overview of our oncology portfolio next. I thought what we do before we talked about our oncology portfolio is just have a reminder of why this work is so important, but not from my words, from someone who's lived this experience. Maybe what we'll do then is quickly move on to a video. Can you show the video, please?
Before my diagnosis, everything seemed to be going pretty well. I was active. I would go play baseball on the weekends with my friends. My wife is a big runner, so we would go on a lot of trail runs together. That is the lifestyle that we had as a family. When I was diagnosed at that time, it was a devastating diagnosis. Just about every negative thought you can imagine went through my mind, which is, "Are you kidding me? I mean, where did this come from? How did this happen?" Everything about your life becomes inconsequential. The only thing you can think about, the only thing you can focus on is the fact that this incredibly random, strange thing has happened to you. Because the treatment options that were available were not great. The recommendation that I got was surgical removal and chemotherapy.
Chemotherapy is a rough road for anyone who ever has to do that. I had the classic issues, which not being able to eat. Suddenly you turn around and you go, "I've lost 20 pounds." I'm a relatively thin person to begin with. Losing 20 pounds is a lot for someone like me. I realized how much chemotherapy and removing my entire left lung had affected me. I had to stop walking up a stairwell because I couldn't do it. I would get halfway and I would be done. It raised some really serious questions in my mind about what I was going to be physically able to do. It's really hard on the people around you. I became very tunnel visioned about myself and just trying to get better. Unfortunately, it took me away from my family in a way that I wouldn't wish on anyone.
I think future treatments ideally are going to be less invasive, less intrusive. Right now with our treatments, especially with chemotherapy, whether it's the fatigue factor or all of the side effects that go along with that, the ability to reduce that so that you can carry on with more of your regular life so that you can be there for your family and hopefully you can work at the same time. The ability to be able to continue to function in a meaningful way while in treatment would just be huge.
I'm a medical oncologist by training. I spent 10 years taking care of patients with cancer. I can tell you that his experience is not atypical. Nausea, vomiting, severe fatigue, hair loss, diarrhea, infections, hospitalizations, these are all side effects of some of the most brutal therapies that we give people with disease. In fact, one of my patients came in and he, unfortunately, was not able to walk on his follow-up visit. He was on sorafenib, which causes very severe hand-foot syndrome. I said to him, "Why don't you call me?
Why don't you tell me this was happening so I could have stopped your therapy so you could have walked into clinic?" And he said, "Doc, I wanted to survive." We have to come up with better therapies for people with cancer, therapies that honestly can provide the same efficacy, but not ask for the sacrifice on people's lives that they're currently going through with other therapy. I think we're onto something with our current platform. This is a snapshot of both 4359 and 4157 or Intismeran of the safety readout from both of these programs. I think a lot of us appropriately focus on the efficacy of these products. What I'd like to just do is take a minute and show you the safety of these products.
So far across our entire portfolio of cancer antigen therapies and Intismeran, we have not reached a maximum tolerated dose. The incidence of grade three side effects is vanishingly small. I can count on one hand how many times we've seen from monotherapy treatment grade three side effects from these therapies. I believe that this opens up a whole new window of opportunity for this type of treatment that hasn't been available for other treatments in the field, areas where we can treat patients earlier and precancerous lesions, areas where we can combine with other therapies that already have a fairly toxic profile where other treatments couldn't be added on. This leads us to a whole variety of different settings where we can administer these therapies. You can see here with Intismeran and other cancer antigen therapies, we've been able to explore adjuvant settings.
We've been able to explore neoadjuvant settings. For our cancer antigen therapy, we've even looked at early stage and precancerous settings for these types of therapies. With our T cell engager programs, we're looking at refractory metastatic settings. With our cell therapy enhancing and in vivo cell therapy, we're also looking at later stage disease. This allows us to create a portfolio that can have an effect across all different stages of cancer and multiple different types of cancer. This is a snapshot of our current oncology portfolio. What I'd like to emphasize is, yes, I'm excited about the number of programs that we have in clinic, but what's more important than the quantity is the quality. We firmly believe that every single one of these treatments can have a dramatic impact on patient lives. That's what we'll discuss with you today. We'll start out with Intismeran.
Dr. Brown will come and talk to you about the latest developments with Intismeran. I will then come back and talk to you about our next most advanced therapy, 4359, which we're all very excited about. Last but not least, we'll have our fearless leader for our research efforts, Dr. Loughlin, come and talk to us about all the really exciting and amazing products that are coming into clinic, either are in clinic today or will be coming into clinic very soon thereafter. With that, I'll hand things over to Dr. Brown, and she'll talk to you about INT.
Thank you, Kyle. Hi, everyone. It is nice to be speaking to you again about Intismeran. I'm not really sure how I'm supposed to follow Kyle's performance and then Keith's video. Hopefully what I show today is sort of our resounding belief of why we believe that Intismeran can make that type of impact for patients and a litany of patients at that. As Kyle alluded to, we are leveraging the power of the mRNA technology and the platform to develop a precision immunotherapy program overall. We're anchoring this on four different segments with distinct modalities and distinct potential to impact patients across a wide continuum of cancer care. The anchor and the lead of our programs is Intismeran autogene, which, as you saw on the slide, has a litany of clinical studies. That is what we'll double down on today.
If you're not resonating with Intismeran, this one is the one that we've called mRNA-4157, V940, INT. It's had a lot of different names and roles, but as its true form, it is an mRNA lipid encapsulated individualized neoantigen therapy. It is basically taking and starting with the patient and sequencing their tumor to identify the sort of most immunogenetic specific tumor targets to train the immune system. It is one medicine for one patient, and it is the lead in personalized cancer therapy. The way this works is, again, what we're doing is taking a concatenized mRNA, lipid encapsulating them, delivering that concatenator into a patient through an IM administration. Once in the body, the mRNA enters into an antigen presenting cell. We use host translational machinery to produce those neoantigens.
Those neoantigens, which are basically cancer-specific mutations, are presented on the cell surface to stimulate a repertoire of T cells, both CD4s and CD8s, that then become activated, but also trained to go out and seek other cancer cells or cancer cells and destroy them. We think that this mechanism is synergistic with checkpoint inhibitors who just tend to take the brakes off the immune system. We are essentially targeting cancer cells, activating the T cells to go kill them, and then hyperactivating them to keep them killing these cancer cells. That mechanism was officially tested in our phase two randomized study in high-risk adjuvant melanoma patients. This data was presented last year at ASCO for our median three-year follow-up. Essentially what we did is we took Intismeran in combination with pembrolizumab versus pembrolizumab standard of care in these high-risk melanoma patients who had their tumors completely resected.
We followed them to see if the combination could improve recurrence events. Basically, patients' tumors from coming back after they had their tumors removed. One thing I want to point out is that this was a November 3, 2023 data cut with that median of three years. We know in cancer that five years is usually the big landmark for clinical studies as a whole for durability. We are now in 2025. If we add, we are looking upon our five-year data cut emergently. The one piece in here is that this data cutoff was on November 3 with the approximately three years. Because five years is such a landmark, we are going to make sure we pass that landmark. The data cut here might be a little bit later than this November 3, but we are definitely tracking towards having that.
We're very excited to see the durability because we are very excited to see the three-year data. This is a refresh from that three-year cut. Basically, what you saw here is the primary endpoint, which was recurrence-free survival. The green line on top is essentially the combination arm. The yellow line is what happened with pembrolizumab standard of care. What you're looking at is recurrence events or death. From just a global three-year mark, we see a hazard ratio of 0.51, which means that we reduce the risk of recurrence or death globally for these patients by about 49%. If we sort of anchor on that two and a half year landmark, that means that three out of four high-risk adjuvant melanoma patients were disease-free at the time of this cut.
That was 20% higher than what they would have received if they received standard of care pembro. It's important to note that that delta of almost 20% is essentially what pembro showed against placebo, against nothing. No other study has been able to showcase this type of effect above pembro standard of care in this type of setting, which was very, very encouraging for us for this type of neoantigen therapy and the impact we could have on patients. Not only was this seen with recurrence-free survival, but this was also seen with the key secondary endpoint, which is distant metastasis-free survival. On this one, we see a 0.38 hazard ratio. In oncology studies, 0.38 is transformative. This is a huge treatment effect. You don't tend to see this, and it translates into almost two-thirds of patients not having distant disease.
Distant disease is important because what that means is we're preventing patients from having significant surgeries, additional systemic therapies, or death. Their risk is much higher when they have a distant event. What you see here, again, at about two and a half years is that almost 90% of patients on the study did not have this type of event. That's compared to 68% that would have had it with just standard of care. We're really maintaining that spread of 20% even in this profound endpoint. This is actually on point with the mechanism of action that I just showed you because we believe that Intismeran is supposed to train and activate the immune system to go out and clean up all these micro metastases and create long-term disease control.
Having this profound delta on DMFS is actually what we would have anticipated with the mechanism. It is not just about preventing these recurrence events. It is also about preventing death, right? As we heard from Keith's story, when a patient is diagnosed with cancer, it is about survival. Everyone really cares about survival. While it is very early, we do not tend to see overall survival in these adjuvant studies, especially at three years. We did see an initial trend of protection for overall survival. The hazard ratio here is 0.42, but if you look at the spread here from 0.11-1.5, it is big. That is just because there are so few events. What is encouraging about this OS trend is just how flat that green line is. It was flat through that median three-year follow-up.
It is one of the reasons we're so excited for what we're going to see with the five-year data that's coming. As Kyle alluded to when he started, we tend to focus on the efficacy picture. For an adjuvant setting that's curative intent, patients want good quality of life. They want to be able to continue running and traveling and having that same life that they had before they were diagnosed with cancer. Having a safe profile is amazingly important. Not only did we see in this three-year follow-up this efficacy benefit, but we also saw that Intismeran was generally tolerable, where the majority of patients had low-grade transient adverse events with fatigue being the most common. Not hair loss, not weight loss, not myalgias where they can't walk, but fatigue.
In addition, we did not see an increase in significant or serious adverse events when combined with chemo, nor did we see potentiation of the immune-mediated adverse events, which is what you see when you have IO/IO combinations. It really is well tolerated as a whole, and specifically for cancer patients, having a clinical benefit and a well-tolerated safety profile is truly differentiating, which gave us the confidence to move to this broad clinical trial program that we have now with Intismeran with our partner Merck. What we have here is not only the phase III adjuvant melanoma, which we'll talk about in a second, but we also have the two other registrational non-small cell lung studies that will hopefully benefit patients like Keith.
We have studies in renal cell carcinoma, muscle invasive bladder cancer, non-muscle invasive bladder cancer, metastatic melanoma, metastatic non-small cell lung cancer, and our phase I had expansion cohorts in PDEC and gastric. What you see from this picture is that we're hoping to impact and learn across a litany of tumor types and a litany of settings. We are really spanning with having solid foundations in the adjuvant environment, and we're beginning to start exploring the bookends to metastatic disease and perioperative. One of the reasons that we're actually comfortable looking at the metastatic space and the perioperative space is basically what you heard from Jerh, is we're optimizing our manufacturing. We're building our Marlborough facility. We're getting better at our turnaround times, and we have confidence that we will be able to deliver Intismeran to patients that need it most quickly.
In addition, because of the safety profile that I just showed you, it means that we're also able to combine with agents beyond pembrolizumab. In our metastatic non-small cell study, we're actually combining with pembro and chemo. Within our non-muscle invasive bladder cancer study, we're combining with BCG. In our phase I study, we're combining with different chemotherapies for PDEC and gastric. This program as a whole is really going to teach us a litany of things about Intismeran, tumor types, patient populations, combinations, and really sets the foundation for us to have a profound impact. What you can see based off all of this is that we have a series of learnings that are going to come 2026 and well beyond. Now, while everyone can pick their favorite study for a number of different reasons, our team is hyper-obsessing about our adjuvant melanoma phase III study.
This study was launched in 2023, and it looks largely like our phase two study. It is randomized in adjuvant high-risk resected melanoma patients, and it randomizes patients to Intismeran plus pembrolizumab versus pembrolizumab standard of care and has the recurrence-free survival endpoint, which is the appropriate clinical endpoint. The two major differences from our phase two study is first, we expanded the patient population down to stage twos, partly because pembrolizumab is indicated there, and we have reason to believe that we can impact that wide range of high-risk adjuvant melanoma patients. The other piece is this is a stage three study, our phase III study, so there are close to 1,100 patients that got enrolled. It was big, and it enrolled in record time.
I think one of the reasons that it enrolled in record time is that people are very excited for this phase two results that I showed you. At this point, it has been fully enrolled for quite some time, and we are sitting in a very awkward phase right now where all of us are amazingly excited to see the RFS and to see the endpoint here, but this is an endpoint-driven study, which means that we also do not want these events to be coming in too fast because we want patients to benefit. We are in this like, we really want to see it. We want to see the actual outputs to see how the phase III is going to play out and if it looks like the phase two, but we have to be patient because we also want that treatment benefit.
We want patients to benefit. That is the dance we're doing, and we are just patiently waiting right now for those events to occur. On the clinical summary, what I've basically told you to this point is that we have a manageable safety profile. We have a clinically significant improvement in recurrence-free survival and distant metastasis-free survival. We have encouraging trends in overall survival. We have launched a series of clinical studies, three of which started this year with NMIBC, metastatic melanoma, and metastatic non-small cell lung cancer. We have a line of sight for a lot of studies that are going to be coming 2026 and beyond, starting with the five-year median follow-up for adjuvant melanoma, but then each of those other studies are going to start reading out. Now, behind the clinical learnings, we also at Moderna want to lead the science in this space.
What I like about the Intismeran program is that it sits at the precipice of computational, technological, and biological innovation and understanding. At its heart is our deterministic BICS algorithm, which selects the neoantigens for patients using their specific DNA sequences and rank orders the ones that are going to be most immunogenic. The thing about BICS is that it was perfect for the science at the time, but as our clinical understanding evolves, as our understanding of cancer biology evolves, and just as the field overall evolves, we have the opportunity to think or rethink about BICS as a whole. That is going to take more science and more data. Built into our programs is translational endpoints or collections. If you reimagine the clinical study schema that I showed you for the clinical side, this is the phase two again.
Nothing's really changed for how it looks outside of showcasing where we're collecting patients' blood to do some translational work. We had patients that gave blood right at baseline before they had any treatment, patients that then had blood collected after they started pembrolizumab treatment, then they had more blood collected after they started Intismeran treatment, and finally at the very end of their treatment with pembro a year later, they had another sample collected. The data that we presented this year was two things. The first was to address the number one question we keep getting from a lot of folks, which is, do you really need an individualized therapy for neoantigens? Why don't you just use off-the-shelf? Isn't that a lot easier? That's the first question, and we'll show you that data. The second was, you've shown us immunogenicity.
You've shown us you can mount a T cell response, but do those T cells matter? How does that population look as a whole? That is the data that we presented this year. The first part is addressing the idea about, is a neoantigen therapy have to be individualized? The first piece is in this phase two study, the majority of patients did have an mRNA that spanned the full 34 neoantigens. The BICS algorithm was working to be able to select the right neoantigens for the majority of these patients because melanoma is a high TMB tumor. They should have a lot of mutations to choose from. That is not true for every patient. We are not forcing neoantigens if they do not have them. That is why you have this sort of range. Now, if we look at the bar graph, this is not patients, right?
We don't have 3,401 patients in this study, but we do have 3,401 neoantigens that were completely unique to a patient, which means that 99% of the neoantigens that we are picking, that BICS is picking, is clearly specific to each individual patient. What that means is that, no, we can't do an off-the-shelf approach for this. We have to tailor it to the specific person because the specific person's cancer is as unique as they are, and we have to generate a therapy that is going to treat them. The next part is we started looking at the idea of what do the T cells look like in these patients? Are they doing anything? Because core to the hypothesis is that we're selecting neoantigens. Those neoantigens are training the T cells. The T cells are going to go and clean up the tumor cells.
We're using PCR sequencing to characterize the T cell milieu of an individual patient and saying, does that change when a patient is treated with Intismeran? We're able to do this because actually your T cells are also unique, and they each have their own unique fingerprint. We can sequence them and see how they change over time. This first slide is basically showing what the T cell clonal phenotypes of the population looked like at baseline for both the combination arm and the pembro arm, and mapped that against if a patient had a recurrence event or not. This is basically a negative slide. It basically says it doesn't matter what your T cells look like to start with. That didn't impact outcome at all. What that means is that the treatment effect matters because baseline, your T cell and your immune system is intact.
This is where we're going to squint, but hopefully you'll take my word for the story. If you have interest, you can look at the SMR and the AACR picture. If we're going to look for the waterfall plot first, what we're doing here is now showing how the T cells change over time with Intismeran treatment or pembrolizumab treatment. The red is the top, and this is not the combination. What you see on the waterfall plot and what you're going to try to read is that 71% of patients that were treated with the combination had an expansion of their T cell population, which means that when they were treated with Intismeran, they actually had new T cells grow and adapt, and their population actually shifted, which was different than pembro, which most of the pembro patients did not have that effect
If you look at not only the sort of broad population of T cells, but how many were new, which is what we would want with the neoantigen approach, right? How many new T cells are we forming? Those that were treated with Intismeran actually had a higher proportion of new T cells, really, which is on point with the mechanism. That was not true for the pembro arm, which again, does not sort of fit pembro's mechanism. You could say, well, your patients were treated with pembro. Is not it doing something? The answer is, no, actually it is not here based off of the data. What you see is at screening, the population, the T cell populations are the same. When pembro starts, it does not really change. It is really once Intismeran gets on board that we start seeing these novel clones.
Not only are we seeing novel clones, but we're seeing clones that are only for those unique neoantigens that I told you about, not the shared ones. If you put basically all of this together, it's saying our algorithm is picking neoantigens. Those neoantigens are stimulating T cell responses that are new and that are changing the milieu of the T cells throughout the body to be able to have long-lasting impact. Now, the question is, what is that impact? Is it meaningful? Great. You showed me that you can expand populations that you want to. The answer is, yeah, it's actually important. What we see is these T cells are actually associated with recurrence-free survival.
What that means is it is important for Intismeran to have an impact, to have this clonal cell expansion because that means that these targeted, tumor-targeted, patient-specific T cells are now going out and cleaning up whatever cancer cells are left in the patient's body to create disease control and prevent recurrence. That this is true with a statistically meaningful impact. All of this basically is to say that mechanistically, we have confidence in what we're doing with BICS. Not only are we seeing clinical impact and a good safety profile, but we're seeing that Intismeran is doing what it's supposed to be. With that, for the translational summary, we're basically showcasing that the mechanism for what we hypothesized is actually playing out in clinic.
Not only is it playing out in clinic, it's actually making the clinical data also make sense and the fact that we are able to have this long-term disease control. The sort of next step here is to sort of tether this one step further and say, are all those T cells that I just showed you that are expanding, do they actually track back to the neoantigens that we've selected? And which ones? How can we optimize those neoantigens? How can we optimize BICS to create even better ones?
That is why when Stéphane and Stephen were talking about the excitement we have for the oncology portfolio in 2026 and beyond, it is really referencing the amount of scientific and clinical data that we will be coming with over the next couple of years, not only to help our learning, but to actually improve what we are doing as well. On that theme of excitement for 2026 and new data and the potential for the oncology portfolio, I am going to hand this back over to Kyle to talk about our 4359 program. Thank you.
Thank you. Our most advanced program is 4359. We also have other therapies in clinic that are part of our cancer antigen therapy portfolio, which include 4106 and then our Lynch syndrome program. 4359 has two targets, PD-L1 and IDO. These are targets that are well described and validated in oncology.
I don't have to talk to you all much about PD-L1. However, I do want to take one second to talk to you a little bit about IDO because we have some questions that we get about IDO and the importance in cancer. What I'll share with you is that we're not targeting IDO to change IDO functioning. Other programs that have targeted IDO have done so because they've wanted to introduce small molecules that change the downstream pathways of IDO. What we're doing instead is we're targeting IDO through a mechanism where we can use it as a flag where T cells can be directed towards that expressed protein and use it to both have an effect directly on the cancer cell as well as have an effect on the overall immune suppression that occurs in cancer.
We can change the immune environment by targeting IDO and PD-L1, but we can also have a direct effect on the cancer cell. We presented our safety data at ESMO in 2024. Most recently at ESMO in 2025, we released our first look at some efficacy data in an expansion cohort that we did in metastatic melanoma. These were patients that were checkpoint inhibitor refractory. As you all know, checkpoint inhibitors are standard of care for patients with metastatic melanoma. All of these patients had received multiple prior checkpoint inhibitors. All of these patients had metastatic melanoma, and they all received a combination of 4359 with pembrolizumab. These are some design features of the study design, as well as some demographics of the patients and some baseline characteristics.
We had two different dose levels that we assessed, a 400 microgram and a 1,000 microgram dose level. As I mentioned, both of these were administered in combination with pembrolizumab, and we had overall 29 patients that were enrolled in the study. However, only 25 patients were eligible for assessing response to treatment. I've talked to you a little bit already about the safety profile, but what I'm excited to share with you again is that grade three events were very uncommon. Even in combination with pembrolizumab, we did not see many grade three events. As Michelle described before, one of the concerns that many people have had when you combine treatment that has the potential to boost the immune system is that you might get an increase in immune-related adverse events.
Fortunately, for both the 4157 program and now consistently with the 4359 program, we have not seen an increase in immune-related adverse events. This is the first snapshot of the anti-tumor activity of 4359. When you look at the entire population, both the 400 and the 1,000 microgram patients, we saw approximately 24% of those patients with a complete or partial response to therapy. The disease control rate was even higher, and patients who had either stable disease or a partial or complete response increased to approximately 60%. You can see that near the bottom of the slide. We also have the number of prior therapies listed here. You can see that these patients all had many prior therapies and had progressed through these therapies. There was a highly refractory population.
What was more encouraging, however, was the data when we separated the patients by those who were PD-L1 positive, by TPS, or those who were PD-L1 negative. On this waterfall plot on the bottom of the left slide here, you can see patients who were PD-L1 positive represented in the red bars versus the ones that were PD-L1 negative in the blue bars. Every patient that had a partial or complete response was PD-L1 positive. When you look at the overall population who are PD-L1 positive, you can see now the response rate goes from 24% up to 67%. These responses were quite durable. You can see that from the spider plot.
On the bottom right here, the spider plot has these patients separated similarly to the waterfall plot where all the patients who were PD-L1 positive are represented in the red lines and the patients who were PD-L1 negative are represented in the blue lines. These lines represent changes in the size of their tumor over time. When the lines come down, that means their tumors have shrunk. The patients with tumor shrinkage had continued shrinkage of their tumor out for 200, 300, 400, 500, almost 600 days. Just as a reminder, this therapy is administered nine times every three weeks. The majority of these patients stopped therapy much earlier, but continued to have a robust and durable response to therapy. We've done some similar analyses similar to what Michelle had described around T cell clonality as well as T cell specific responses.
I'll walk you through the data on the T cell specific responses to PD-L1 and IDO. When you look at these slides, you can see the increase in the number of T cells with these either IDO-directed T cells or PD-L1-directed T cells. The patients in purple were the partial response patients. Those patients in purple all had increases in their T cell specific responses. What we can tell from this slide is there were other patients that did not respond who had T cell specific responses. Our takeaway from this is it's probably necessary to have a T cell specific response, but not sufficient. When you look at the novel expanded TCR clones, you can see for the patients that had a CR or a PR, there's quite dramatic increases in the novel TCR clones.
Whereas the patients that had stable disease or progressive disease didn't have the same dramatic increases in the novel T cell clones. This exciting preliminary data has led us to expand into multiple cohorts in this phase I trial. We now have an ARM2A. ARM2A is a trial where we have a combination of 4359 and pembrolizumab in frontline melanoma. ARM2C is an arm where we are combining 4359 with ipilimumab and nivolumab. ARM2D is an arm in patients who have second line and beyond melanoma, all with PD-L1 positive disease. This is a larger cohort to what we had originally described where all these patients will be selected for PD-L1 positive disease. We are also moving into other tumor types. We have ARM2B where we are assessing the response in patients who have non-small cell lung cancer and have TPS score greater than 50%.
This will also be 43-59 administered in combination with pembrolizumab. Okay. With that, I will move on to the next part of our presentation in oncology, Dr. Loughlin, who will talk to us about our early programs.
Thank you. Thank you so much. All right. We are going to start by building on what Michelle and Kyle already shared with you, moving into the cancer antigen therapy portion of the pipeline, starting with mRNA-4106. Now, similar to 43-59, but quite different from our individualized therapies, 4106 is designed to encode for antigens that are shared across many different patients. This includes many different types of cancer and all of the patients within those types of cancer. When we think about the design for this cancer antigen therapy, we specifically chose to include multiple antigens.
If you took a single patient, you would expect their tumor to express multiple antigens within this therapy. '410-6 is currently in a phase I study in advanced solid tumors and will be excited to share data once we have those available. I'm going to spend a little bit more time on the next program, which is Lynch syndrome, because this actually takes our cancer antigen therapies even earlier in disease to the point that we are trying to intercept disease before it truly becomes cancer. For those who are not familiar with Lynch syndrome, it's a heritable disease where these patients have mutations, so they have defects in the mechanisms your body naturally has to repair damage to your DNA. It can be mutations in a couple of different proteins, but basically, as your cells normally divide, occasionally they make errors in your DNA.
Your body has mechanisms that go in and correct that. If you have Lynch syndrome, that repair mechanism is deficient. You start accumulating mutations over your entire lifetime. As it turns out, these patients have an incredibly high risk of developing certain types of cancer over their lifetime. If you have one of these mutations, you can have about a 50% chance of developing colorectal cancer during your lifetime, and you can have very early onset as well. These patients go through considerable surveillance, colonoscopies every one to three years starting at really early ages to try to identify things like polyps in the colon and have those removed before they progress to cancer. What we have done in collaboration with the University of Oxford is identify these mutations, which happen to be shared between these patients.
With this therapy, we're actually training those patients' immune systems to identify the cells with those mutations before that truly turns into a malignancy. We're trying to intercept and train that immune system to remove those cells before these patients actually develop cancer. We're excited with Oxford. We're looking forward to starting this study next year. Now, I'm going to pivot pretty substantially in terms of the approach that we're taking to treating oncology. We've talked a lot about cancer antigen therapies and the idea that we were training your immune system to recognize cancer cells and kill them. With T cell engagers, it's a different approach. We are actually looking to guide your immune system to those cancer cells. We have two different types of T cell engagers in our portfolio.
The first focuses on proteins that are expressed on the surface of cancer cells. I'm actually just going to start the cartoon on the left-hand side here. Our lead program in the space is mRNA-2808. It encodes for a T cell engager that on the one side binds CD3, so it binds to your T cells. On the other side, we've actually multiplexed this product. There are three different targets that are present in multiple myeloma that we are able to go after with a single therapeutic. You can see them here, BCMA, FCRH5, and GPRC5D. We think this is really important in disease because if you have the ability to multiplex, you can really avoid antigen escape, which is a clear mechanism in cancer, and you can account for the fact that not all tumor cells will express every single antigen.
There is some heterogeneity in cancer, and we can go after multiple targets at once. This has actually played out in the field. There are recombinant protein T cell engagers already in use in multiple myeloma. If you take two of those and clinically combine them, it has already been demonstrated that you can improve response rates and that depth of response. In our lead therapeutic, we are able to multiplex three targets. Now, I'm using the word multiplex intentionally, not the word combination, because for us, this is one drug product from a regulatory perspective. We're advancing one product that can target three proteins. Additionally, as you move further into our T cell engager pipeline, we can encode other signals that can help the T cell engagers be even more efficacious.
We can include, for example, co-stimulatory signals that really help those T cells be more activated and add another layer of specificity to that activation. What we're showing here on the right-hand side is data from non-human primates where we were looking to see with 2808 depletion of a cell population that actually doesn't even express those targets very highly. These are healthy non-human primates, but really demonstrated to us that we could truly deplete those populations and that we're seeing a nice durable effect until those populations are able to come back. We're able to do this with both IV infusion as well as subcutaneous injection, which you can see here in the blue line. The second approach that I described for our T cell engagers—oh, I apologize. 2808 is actually already dosing patients in our phase I study.
For that, of course, our primary endpoint will be safety. Given the patient population, we should have a pretty good read on pharmacodynamics, so the impact on those pathogenic cell populations and the proteins that we secrete. We, again, will be excited to share those data as soon as they're available. The second approach that we're taking, rather than going after proteins that are on the surface of cancer cells, is actually focused on intracellular antigens, so proteins that are within the cancer cell. We're excited about being able to target those because it really opens up the target landscape for us. Proteins that are on the cell surface tend to be shared between cancer cells and healthy cells. There may be more on the cancer cells, and that's why we target them.
If we're able to go after intracellular proteins, it actually opens up a much larger pool of targets that we can pursue. These targets tend to be very specific to tumor cells. As Kyle and Michelle talked about, that safety and tolerability profile is really important for patients, and we think this will improve that as well. We are still able to multiplex and go after multiple targets with this approach or to encode some of those supporting proteins like co-stimulatory factors. We are pursuing this in partnership with Emmatics. Okay. We're going to transition to yet another part of our pipeline. We're going to talk about our approaches in cell therapy. I'm actually going to start by describing something that Moderna does not do, which is XVIVO cell therapy.
Our first approach in the cell therapy space is actually looking to enhance the performance of a partner's XVIVO cell therapy. For those who are familiar with XVIVO cell therapy, you might have heard about CAR-T. In certain blood cancers, CAR-T therapy can be absolutely transformative. In solid tumors, these XVIVO cell therapies have not delivered quite that same level of impact. We are looking to truly improve those outcomes with a focus in that space. How do we do that? You first start with your typical XVIVO cell therapy. You need to take the immune cells out of your patient. You need to engineer those immune cells. You need to try to remove the remaining immune cells in the patient to try to conceptually make more space for those engineered T cells to engraft as you infuse them back into the patient.
Now, after the engineered cells are back in the patient, we administer mRNA-4203. It's an intramuscular injection, and we have designed it specifically to encode for the antigen that is recognized by that engineered T cell therapy. mRNA-4203 is specifically designed for Anzu-cel, but it is not specifically designed for any individual patient. It will work for all of those patients. You're actually taking those engineered T cells after they're in the body and boosting them. You're showing them the antigen that they are engineered to recognize. They will get activated, they can proliferate.
We think this has the ability to enhance the performance of those engineered T cells because it has been shown clinically that when you have these T cells that are in a good immune state and that they are able to persist for longer in the body, those tend to be correlated with better clinical outcomes. Now, we are in an active phase I study in collaboration with Emmatics for this combination, and this is a true combination of mRNA-4203 and Anzu-cel. Our second approach in the cell therapy space is quite distinct from XVIVO CAR-T. It's actually in vivo CAR-T. As I said, we do not do XVIVO cell therapy. Our approach here is to actually engineer those T cells inside the body in the first place. For this approach, we actually use a lipid nanoparticle that is specifically targeted to T cells.
It is specifically going to T cells, and it encodes for that same CAR that you might have engineered outside of the body, but we can do all of this inside of the body. Importantly, this means you do not need to take the cells out of the patient. They do not need to have these tough conditioning regimens that take out the rest of their immune cells. You do not have this large individualized manufacturing component. All you do is infuse LNPs. We see this as having substantial advantages there. We can also rely on some of the differentiation that I described for T cell engagers. For example, we can multiplex. If you wanted to encode a CAR for multiple targets, you could do that with this platform.
If you wanted to encode other proteins that would help these T cells stay activated and go into that tumor microenvironment and be very efficacious, you can also do that. We see two application spaces for the in vivo CAR-T style approaches. The first is actually in autoimmune disease. There has been quite a bit of really promising clinical data of late that if you go into autoimmune diseases like lupus and you actually use CAR-T to eliminate and deplete those B cells, you can actually help those patients reset their immune system. They can go into remission for a remarkably long time. We think that is certainly an application for in vivo CAR-T, where the in vivo profile as well from a safety perspective will be really important to those autoimmune patients. We also think in vivo CAR-T will be highly relevant as you go into oncology.
As I mentioned, CAR-T in general has been incredibly transformative in the blood cancers, and we believe this will help us go into solid tumors as well. Our third approach in cell therapy actually focuses on a different cell type. Where CAR-T is very focused on T cells, CAR-M is actually much more focused on a different cell type, myeloid cells. The approach is still similar. We infuse patients, and using LNPs, we transfect different types of myeloid cells, and we're encoding a CAR. Now, those cells will move around the body in traffic, and when they get to the tumor, that CAR will recognize the antigen. Those myeloid cells can then actually ingest some of the tumor cells, so they're killing some of the tumor cells.
They will secrete other proteins that will help other cells in that tumor microenvironment to be able to kill cancer cells. Having actually engulfed and ingested a tumor cell already, the cells can present multiple antigens from that tumor cell. Michelle talked about how you get that broad T cell response even with INT. This is a similar concept where, even though we only encoded a CAR that recognizes one antigen, by the time you get to this part in your mechanism of action, you're able to really expand that response to lots of different antigens that are present within a cancer.
I know we moved quickly, but we did want to have a chance to share this part of the oncology portfolio with you and give you a sense of the diverse set of therapeutic approaches that we are able to take because we can leverage so many different aspects of our platform technology. With that, I believe I'm handing it to Dr. Rita Das. Oh, no, we're going to take a break. Lavina said we're going to take a break and bring lunch in. Yes. If I can invite everyone to please grab a plate of lunch and a beverage of your choice and bring it back in here, and we will resume the rest of the meeting. Thank you.
All right. All right. Hi, everyone. My name is—I'm going to bring us back from break.
My name is Rita Das, and I'm the clinical development head for respiratory and rare diseases. It really gives me great pleasure to focus on our rare disease portfolio today because, as a pediatrician, I've taken care of these children with inborn errors of metabolism and seen the impact on their lives and also the underlying progression, neurologic and otherwise. I'm really excited by Moderna's commitment in this space, and I'm very excited that we're getting closer and closer to bringing these therapies forward. First, I'll talk about propionic acidemia, which is where we're the most advanced. Propionic acidemia is a rare metabolic disorder, primarily diagnosed in infancy, that causes a huge amount of morbidity and mortality. It's very rare, ranging from 0.29-4.24 per 100,000 newborns, so it's actually formally in the ultra-rare category. It's caused by pathogenic variants in the PCC enzyme.
are two subunits, PCC A and PCC B, that stand in the way of the enzyme in the metabolism of proteins. When that metabolism is not going properly, you build up these toxic metabolites. Because this is so pervasive, PA is a multisystemic disease with not only these metabolic decompensation events, which can be life-threatening, but there are underlying neurologic, cardiac, endocrine, and immunologic manifestations. There are no approved therapies for propionic acidemia, and the management involves severe dietary protein restriction. As the disease progresses, the patients often progress to needing liver transplantation. Here is a bit more on the PA biology, which really shows why mRNA therapy is particularly well-suited to succeed in this space. As I mentioned before, there are two components of that enzyme, PCC A and PCC B, that come together and allow people to metabolize particularly proteins.
When that enzyme is not functioning, you build up these toxic metabolites that are damaging to the brain and other organs. For mRNA-3927, what we're able to do is encode the functioning of both PCC A and PCC B, package it in a lipid nanoparticle, and deliver it IV, but target it towards the liver, where then the patients are able to make these enzymes and correct their inborn error of metabolism. First, I'll talk about our phase I study, which is called Paramount. It's a global study that enrolled in multiple countries. The analysis from this study was presented in the ICIEM conference in Kyoto, Japan, earlier this year. The primary endpoints for this study were safety and tolerability, and a key exploratory endpoint was the reduction in metabolic decompensation events. I'll tell you a little bit more about that after.
The study design was a dose escalation design, so we started from a dose of 0.3 milligrams per kilogram delivered every three weeks, and we progressively increased that dose to 0.9 milligrams per kilogram delivered every two weeks. Now, the key inclusion criteria for the study were participants had to be greater than one year and had to have a confirmation of the PA genetic defect. The key exclusion criteria were if the patients were in grade three or four heart failure, which unfortunately happens in PA, or either had a planned or a history of liver transplantation. Now here are the demographics and baseline characteristics of the patients. Twenty participants were enrolled in this study, 18 completed treatment, and 17 participants entered the open-label extension study, and 10 are continuing to receive treatment even today.
The mean age was 11 years, and the range in age was from one to 26 years. Now, mRNA-3927 was well tolerated and had a manageable safety profile. In this study, we've administered almost 1,000 doses of mRNA-3927, and we've seen no drug dose-limiting toxicities. We've seen some serious treatment-emergent adverse events, as would be expected in this population of chronically ill children and young adults, but the adverse events that are related to treatment have been much fewer. Here's a little more detail on the adverse events that have been emergent, and these are fairly expected in this age group, and those that are related are mild to moderate. We also saw very few infusion reactions, and those were managed with conservative therapy. Just an overall summary for our PA program to date is that we've enrolled, sorry, 20 participants in Part one.
Thirteen participants have been dosed for over a year. There's been 43.6 years of cumulative patient experience of the study drug. The longest treatment was 3.1 years, and the median duration of treatment was 1.45 years. Here's a little bit more on these metabolic decompensation events. These metabolic decompensation events have occurred in both PA and MMA, which I'll talk about next. They're usually how these patients present first to medical care. They're either identified on their newborn screen, or they present with these metabolic decompensation events. They're a major contributor to both morbidity and mortality and also to these long-term irreversible sequelae, such as brain and cardiac damage. We had to discuss with the agency and agree upon a definition for these metabolic decompensation events, which we've done now.
The definition that we've come up with is that the signs and symptoms include vomiting, anorexia, lethargy, and seizures. There are also these observations of metabolic acidosis, which is a buildup of acid in the blood or high ammonia. There is often a need for acute medical care, emergency room visits, or hospitalizations. This is the scope of the definition that we've agreed upon with regulators. This is what's really, really exciting about the data to date. The red dots represent the spectrum of metabolic decompensation events in the study. On the Y-axis, you see the different doses, and on the X-axis, it's over time. Pretreatment is before that black vertical line, and posttreatment is after that black vertical line. It's individual within patient comparisons. You can see pretreatment, you see a lot of these red dots.
These patients are having multiple metabolic decompensation events every year. As you get past the vertical line, you see these events really decrease in frequency. As you go higher in dose, the 0.6 milligrams per kilogram and the 0.9 milligrams per kilogram, you see these events virtually go away. When you do the statistical analysis of this, you see that across all doses, the mRNA-3927 is associated with a 76% relative risk reduction in metabolic decompensation events. When you look at just the doses above 0.6 milligrams per kilogram, that comes up to an 83% relative risk reduction that's statistically significant. This is what makes us really excited about our pivotal study, which has completed enrollment. In summary, mRNA-3927 is well tolerated at all the doses administered with no dose-limiting toxicity.
All the IRRs were grade three or lower and resolved with conservative management. mRNA-3927 treatment continued to demonstrate sustained reductions in these metabolic decompensation events, with the highest benefits seen in those patients dosed at 0.6 milligrams per kilogram and higher. These findings support the clinical development of mRNA-3927 at that dose, 0.6 milligrams per kilogram, for the first treatment for patients with propionic acidemia. The registrational study is ongoing. This is part two of the study that I already presented. Target enrollment has been reached, and we're really looking forward to seeing the results next year. Also, since some of the neurologic decompensation happens in infants, we're also doing a dose-finding study in infants to bring the greatest benefit of mRNA-3927 across the spectrum of age in these patients. Now moving on to MMA, which is a related organic acidemia.
Like PA, MMA onset occurs very early in life, and it's associated with these MDEs and then this underlying chronic toxicity. The MMA defect occurs a little bit farther down the pathway than PA, but the enzyme that's responsible is the MUT enzyme. The downstream sequelae are very similar. It results in a buildup of these toxic metabolites from proteins and fatty acids because of an inability to metabolize them. Again, protein restriction is the mainstay of therapy because there's really nothing else curative available. Some patients have levocarnitine supplementation, and many of these patients progress to liver or kidney transplantation. The mRNA therapy, 3705, encodes the MUT enzyme. It replaces the MUT enzyme. It's packaged in the LNP, just like the PA. The LNP is specifically trafficked to the liver, where replacement of the enzyme restores the metabolism and the buildup of these toxic metabolites.
I'm going to present to you the phase I/II study. That's the dose-finding study for MMA. This was a first-in-human study, and it was enrolled globally as well. It was also a dose-escalation study. We started with a dose of 0.1 milligrams per kilogram administered every three weeks, sorry. Then we progressed to 1.2 milligrams per kilogram administered every two weeks. The key inclusion criteria were, again, MMA that was genetically confirmed to be due to a MUT deficiency. The age was greater than one. The key exclusion criteria were very similar. Children with the background chronic disease that was too advanced were excluded, and children who had a history of organ transplantation also were excluded. Here are the demographics of the cohort. Eighteen participants were enrolled across six countries worldwide.
The mean age was seven, and the age ranged from two to 39. Now, there's two different major phenotypes of the MUT enzyme. MUT0, where the enzyme is completely absent, and MUT minus, where the enzyme is not completely absent but has a decreased function. Here's the safety profile. The median duration of treatment was 99.6 weeks, so just above two years. All 18 participants finished their dosing in that base dose-finding study and continued into the extension study. Just above 860 doses were administered. The total patient years of exposure was 36.17. The safety profile was overall well tolerated. The infusion reactions also were easily managed, and there was no dose-limiting toxicity. Again, 18 participants have been dosed. The longest treatment duration is 2.3 years. The median duration is about two years. It was well tolerated, and all the participants are continuing in the extension study.
Now, here, for MMA, there is a plasma biomarker that we are hoping to use for our phase III study. We see that, and that's the plasma methylmalonic acid level. We see here that there is a greater than 50% decrease from baseline for participants who were treated with the mRNA-3705 at doses greater than 0.4 milligrams per kilogram every two weeks. The plasma biomarker is more evident on this slide. You can see here, the greatest evidence of the plasma biomarker decrease is seen in those MUT0 participants who are on the bottom. As you can see, almost all of the MUT0 participants, all the different doses are in the different colors on the right-hand side. The plasma biomarker, as you go up in dose, you see a very consistent decrease in the plasma MMA biomarker.
It is just the lowest doses that have the higher residual. This is a little bit less clear in the MUT minus phenotype. Now, again, the thing that we are most excited about in MMA as well is this reduction in these MDE events. This chart is set up the same way as the PA chart. The doses are increasing on the left-hand side. Time is on the X-axis. You can see pretreatment and posttreatment. Pretreatment is in the red circles, or the MDEs are in the red circles. As you can see, pretreatment, these children are having, these children and young adults are having quite a few metabolic decompensation events. As you go to posttreatment, you see a 91% relative reduction in the MDE events and a 75% relative reduction in MMA-related hospitalizations. We are very excited about our MMA preliminary dose-finding results.
In summary for MMA, mRNA-3705 was well tolerated in participants with the MUT-deficient MMA in this study. There were no dose-limiting toxicities and no treatment-emergent adverse events that led the patients to discontinue the study. We saw reductions in disease-related biomarkers. That was the plasma MMA levels that indicated improved metabolism. We also saw the reduction in the MDE events as well as the MMA-related hospitalizations at all doses greater than 0.4 milligrams per kilogram. MMA is ready for the pivotal phase III, and we aim to begin that in 2026. With that, I'm going to hand it back to Stéphane.
Thank you, Rita, and thank you to everybody who presented this morning. As I said this morning, I'm going to be quick, two slides. The first one is, as you heard me this morning, our near-term strategy is clear: drive sales growth through the respiratory seasonal vaccine franchise, drive profitability, drive cash to invest in oncology and rare. You've seen from the presentations that the oncology portfolio is really exciting and extremely differentiated from what you can see from other companies. We are really eager to see what this does for patients and how we can help patients.
As Jamey closed, if you think about what we're trying to do is drive a top line, drive a gross margin improvement, evolve our investment to diversify further away into oncology, reduction of project cash cost through every year in the process, and really drive a company back into profitability in 2028, and from there, deliver a lot of patients' impact. With this, I'm going to ask my colleagues to please join me, and we'll be delighted to take your questions.
We will take questions in the room as well as online. By yourself and tell us your question.
Great. Thank you very much for taking the day and for lunch. Yeah, I have to say, I never thought I'd have so many questions about manufacturing. In the interest of time, I'm going to kind of skip to some of the other stuff that I jotted down throughout the morning. If I may start, just with respect to the 1083 combo vaccine, what is the FDA waiting on? What do you need to hear from them? Is there a potential risk that you might have to do another study?
Yeah. You look, our efficacy data are relatively new. The FDA has not had the opportunity to review that data in the very in-depth way that they do as part of their review. I think what they really want is that we submit this flu file and really get to check under the hood. We certainly will have additional discussions.
Hi, guys. Yeah, thanks for hosting this today. This is Jarway here for Jeff Meacham at CITI. With the new debt facility, you guys mentioned that it's going to further bolster the balance sheet and open up optionalities. With that, what future opportunities could that provide, and how might that shape your view on which pipeline programs you might bring back online and/or partner out? Thanks.
Yeah, thanks for the question. I would say we don't know right now. We're prepared for both upside and downside. I think that's what this does for us. Right now, we plan to park the cash on our balance sheet, and therefore, bear a small spread from an interest rate perspective. We'll see how the next few years unfold. Whether that's pipeline increase on the opportunity side, whether that's pipeline increase, whether that's share buyback or BD, we'll see how that goes on the opportunity side. We'll also understand what's happening with our revenue line. We need to execute our base plan first. Our base plan is to break even by 2028, and that is what we are going to monitor to, to make sure that we do that.
This facility provides flexibility both for the opportunity side, but if there is something that happens, we're not pointing to anything. We believe in our base plan. Should something happen to the downside, it also provides flexibility on that side as well.
Yeah, I mean, if I could just add, I think as you asked the question about where we'd focus in the pipeline, we do have a large number of products to launch in that vaccine space, as we talked about. I think as Jackie and Rose and the team highlighted, there are some exciting programs either in the mid-stage development of vaccines, our EBV vaccine, Lyme, and others, and our early oncology pipeline that we're equally excited by. We're going to wait until we're ahead of plan on cost, which we've been lately, and showing we can grow that top line. Having the flexibility of the facility to be able to invest when we have very high confidence that we're ahead on that break-even target is going to give us an opportunity to accelerate that mid-stage pipeline.
Great. Tyler Van Buren, TD Cowen, thank you very much for the presentations. I wanted to start on the financial side. Can you elaborate on the assumptions behind the 10% revenue growth year over year in 2026? I think that's a little bit above consensus. The slide says it doesn't include flu, even though I think consensus does. Just curious to know if that growth is coming primarily from the partnerships piece with the U.K., Canada, and Australia. Do you have confidence in that because you're already deep in those discussions? Is that assuming stable COVID revenues or decline? The follow-up to that would just be with respect to your year-end cash balance exercise through 2028. That clearly has some sort of revenue assumptions through 2028 for 2027, 2028. How are you thinking about revenue in those years as well in that exercise?
Yeah, I'll start, and then feel free to add, Stephen. As it pertains to the up to 10% growth in 2026, there was a couple of questions in there. The short answer is yes, it primarily is driven by those three facilities, which is what Stephen tried to lay out today. We're not only in advanced discussions, we actually have it contracted and built. These facilities are built. The contracts are already in place. We're already executing on the contract for all three of them. There is some revenue as it pertains to Canada and our assumption this year and a little bit in Australia. If you remember, for the U.K., earlier in the year, we pushed out over $200 million in revenue. That will take place next year.
If you just think about 10% growth by itself, if we're $1.6-$2 billion or $1.8 billion at the midpoint, $200 million is 10% growth. That alone is sufficient. Now, we will have a fully annualized impact for all three contracts. Therefore, it will be a primary driver of growth next year. That's number one. Next spike share, as Stephen laid out earlier as well, is the other primary driver. You asked if COVID is flat, I think, as it pertains to the U.S. We can basically offset further decline, actually. With what we believe will happen outside the United States with the three contracts I just talked about and other countries, but these are three primary contracts, we actually believe we can offset and still grow up to 10% with a U.S. decline. We're hoping it doesn't.
We're hoping next spike will increase our share and value. Nonetheless, we still feel confident even with a little bit of decline. As it pertains to walking forward, the cash balance, yes, obviously, that has some revenue assumptions in there. I said that we would invest $2 billion in the year 2026. I said $4.2 billion of a cash cost target. We're $1.6-$2 billion on a revenue side. Take $1.8 billion if you're at the midpoint. If we grow up to 10%, that's $2 billion. That's technically a $2.2 billion net cash investment, but I'm kind of rounding here. We'll see where this year lands. If this year lands on the high side, then certainly $2.2 billion could be in play if we're at $2 billion plus 10%.
If we're on the low end, then we'll have to relook at it and understand what will happen. I still feel good about the overall $2 billion. As you look out a year, we said that we would invest net $1-$1.5 billion with a cash cost of $3.5-$3.9 billion. On the low end, a $1 billion cash burn would imply $2.5 billion of revenue. If we had $3.5 billion of cash cost and $2.5 billion of revenue, that's obviously a billion dollars of a cash burn. We're not trying to be specific about that. We're not guiding that number. We will continue to ebb and flow. Two years from now is a long ways away. In two years, we took out $4.5 billion of cost. What can happen in two years?
There's a lot that could happen in the next two years, which is why we're not guiding a revenue line. It gives you some semblance of understanding. We do expect revenue growth, not only in 2026, but also in 2027 and 2028. Stephen laid out the drivers earlier.
That's great. Remarkably detailed, as always. Jamey, appreciate it. I have to ask about INT before I pass the mic. Watching the Marlborough facility video and having Jerh walk us through the optimization, it's clear that you all are not only very excited, but have invested significantly in the future of Intismeran and INT in general. Can you just elaborate on, one, your confidence in the phase III readout next year and the powering of the primary endpoint? Two, if you need to show an OS trend for approval? Three, how confident you are that it will come next year based upon what you're seeing with the events and where you're at with the events?
Yeah, maybe I'll take a first stab at this and please ask my colleagues to chime in. This is probably one of the most de-risked phase III programs that happens in oncology because we have a randomized phase II trial that showed an incredible hazard ratio of 0.51 in RFS at our three-year data point. Given that de-risking, I'm very confident in the phase III program being successful. This is a blinded study. I can't tell you what the results are until we finally get to our first interim and then can unblind and check. In terms of the powering, we've made some fairly conservative assumptions on powering that study. Those conservative assumptions are much higher than a hazard ratio of 0.51. I think that gives us even more confidence that we'll hit the target that we're hoping to achieve.
Lastly, we currently are on track with our events to what we had discussed in previous guidance, which is third quarter of next year is when we hope this matures and we can do our first analysis. However, event rates are sometimes unpredictable, and we really need to wait until all those events can happen before we do our first analysis. Depending on the continued trend on those events, we'll have a better idea of when that first analysis will take place. Right now, we expect it to be in that third quarter next year timeframe.
I would just add a couple of things. It could come sooner than that. In fact, we really don't know. It's an event-driven trial. Throughout 2026, we'll be looking for it. We do expect it to happen in 2026. I would highlight that there are a couple of other either fully enrolled or largely enrolled studies. We highlighted some of them today, phase II studies, to be randomized studies that actually also could read out in 2026 and provide strong confirmation of what's possible with INT. The way we look at 2026 is there will be multiple readouts of mid to late-stage studies, obviously the first being the phase, not the first necessarily chronologically, but the one most anticipated being INT for melanoma. A lot of data coming in in very short order. We're looking forward to 2026.
All of them event-driven. We will wait and see.
Yeah, and maybe to add the last piece is also this is not only our investment. These investments with Merck as well. As you remember, Merck is investing half of all the investments in manufacturing. Marlborough, we are running it. Merck is financing half of it. Of course, for the studies and all the medical commercial readiness and so on. It is actually a huge commitment from a company who I think knows a few things about immunohology.
Hi, this is Adi Folkore from Evercore. Just on the topic of Intismeran and adjuvant melanoma, how do you view it in the context of the emerging data and potential use of Pembrol in an earlier neoadjuvant setting?
Yeah, it's a great question. We've looked very closely at how much neoadjuvant use is happening both for Pembrolizumab and for Nevo and IBG. Right now, we haven't seen a dramatic trend in those neoadjuvant rates. We don't believe that this is going to be a major factor. Those are also not approved uses of Pembrol or Nevo or IBG. They're not labeled. There's no plan from these companies that I'm aware of that they're planning on changing the label. If Intismeran is a positive study, it will be labeled as such. It'll be something that we can discuss with prescribers about the impact that this can have on their patients. I'm confident that we'll be able to make sure that this is changing the treatment landscape based on positive phase III data and based on a label that explains that.
Yeah, this is Kung Wong on behalf of Jina Wong from Barclay. We have three questions. First one, regarding the ongoing IP litigation with Arbitues, does it apply to the next generation spike? For Section 1498 defense, what's your strategy to defense for the government rather than for the American people? Second question, it is very exciting to see these new discovery assets. Given the cash guidance of the break-even in 2028, what's the priority list for all of those assets? Third question is for INT assets. Since we need to individualize the new antigen, what's the process time now and which steps could be improved? Thank you.
Let me start with the first question. As I said during the Q3 call, because we got the similar questions, we believe in our IP and we're going to be defending it actively. I won't comment further given there's a litigation coming soon.
Maybe I'll take some of the pipeline question. Look, I think we'll be data-directed. We are very excited to move forward with the mid-stage vaccines, with the early-stage oncology programs as we get clinical data over the next year or two. It'll ultimately depend upon where do we see the most compelling opportunities and the data from those ongoing clinical trials. Hard to speculate, but a large number of opportunities to invest as we start to continue to grow in 2027 and beyond. There was a turnaround time. The question was sort of how are we doing on delivery and turnaround time for INT and any opportunities to further improve that as we go forward from manufacturing?
From a manufacturing point of view, we're doing really well. I mean, compared to where we were 12 months ago versus now, we've improved our manufacturing turnaround time by 50%. We have plans to even continue to do that more. Looking at the whole needle to needle, we have plans to continue to drive that forward as well. Not only will that give us further capability to be there for the patients and their need, but it allows us also simultaneously to drive efficiency.
Just to give you a sense, across our clinical trials, there's 1,000-plus patients. You saw some of that data. We are already ahead of our target turnaround time for commercial launch. We are quite confident that we're already performing at a level that we need to. We'll always look to do better. It's actually even better than we saw in the phase II study. We are pretty confident we can achieve our target product profile.
This is across multiple countries around the world. Over 40 countries, we've been able to continue to achieve these really robust turnaround times.
Thanks. Miles Benton from William Blair. Jamey, I think we were here last year, and you put a $6 billion cash cost basis number up on 2028. Obviously, I have not seen the 2028 number yet, but I assume that is going to be closer to $3 than it is to $6. What has changed in 12 months that gives you the confidence there? How much of it is just efficiency versus a need to get that low because revenues are maybe not projected to grow as much as you thought 12 months ago?
It's a good memory. Yeah. We did say that we would break even in 2028 by $6 billion. At $6 billion. I think it's probably a bit of both, frankly. We continue to say that it is both prioritizing the pipeline as well as driving efficiency, to which our teams have done it faster and to a much greater level than what we could have ever anticipated. I think it's a little bit of both, frankly, that instead of being at $6 billion, I don't know if we'll be at $3 billion, but we'll certainly be at least $3.5-$3.9 billion. We have been able to take it down. It is a mix of both. I've been super encouraged by the entire team, how everybody has done it across every single organ function inside the company.
We continue to see greater opportunity, like I said earlier, more than we thought we could do and faster than what we thought we could do. That is a big driver of it. We definitely had to prioritize the pipeline as well. Everything that you have seen across all the products, you have heard our story. The candidates that we think will read out over the coming years that we believe will be launched in those respective years, 2026, 2027, 2028, have always been prioritized and will continue to be prioritized. We made a decision on a lot of the phase II trials, which is what Stephen was just referring to in the answer to the question, that we will not be able to take those to phase III until we actually see that we can break even and can afford those phase III trials.
That was some of the prioritization that we had to do as well.
Hi, this is Elizabeth Webster from Salvian Rector's team at Goldman Sachs. We wanted to ask about the opportunity for RSV to come into play in the growth story in a bigger way. Could that be an upside lever? Framing what you would like to see at the interim for the norovirus phase III next year?
Can I take the first step? We do hope RSV becomes a growth driver as well. It is built into some of those strategic partnerships. When we highlight U.K., Canada, and Australia, I'll remind you those are across our entire respiratory portfolio. Not just COVID and RSV and flu and the combination. All of those are built in. It is an important driver internationally to expand. As I highlighted, as we move into Europe, we do expect it to be a contributor as well. In the United States, it has been a very rapid launch in that first year. There has been a bit of a waiting game for the revaccination year for our opportunity to present itself. We did show up a year after that first wave of vaccination. We have had some wins. We won a VA contract.
We're slowly and steadily adding to our share. We're getting ready for that revaccination. That determination will be made by public health, not by us. As we expand internationally, we do expect RSV to be a contributor.
Yep. In terms of norovirus and what we're hoping to see at the interim analysis, I think it's pretty typical of what we want to see. We want enough cases to be adequately powered to give us a potential shot on goal in terms of vaccine efficacy. We also want to conduct a futility analysis. We're investing in a cohort here to capture a certain number of cases. We want to understand from our DSMB without unblinding data, but just are we in the right direction or not? We anticipate that later next year.
Hi, Lillian Songo from Lering Partners on behalf of Manny Furuhorn. I wanted to touch on the projection in terms of cost reduction, specifically as it relates to R&D regarding the vaccine franchise specifically. You'd mentioned that you expect a reduction given the completion of the phase III studies. I wanted to understand, can you give us a little color in terms of what type of post-marketing studies are baked into your assumptions as it relates to the two COVID programs as well as the flu program and the combo vaccine?
Yeah. Thanks for the question. We actually did bake into those assumptions the need to do some post-marketing work. Our assumption is that we will be starting actually imminently the post-marketing commitments that we have in COVID. One of them is actually already well underway. The second one is starting soon. We have planned that in flu we may need to do some post-marketing work, certainly on the safety side, potentially on the effectiveness side as well. I think the flu efficacy data is really what we were waiting for. It is controlled against flu vaccine that's licensed in the U.S. We are really confident in the strength of those data.
Maybe just a follow-up for the COVID component. Does it include post-marketing studies for the below 65 age?
It does, actually. We have taken FDA guidance, had some back and forth on what they need to see from that study design. Like I say, one of them, the immunogenicity one, is ongoing. We are looking to start the effectiveness one soon.
I think Jamey alluded to that. There is a little bit of an artificial elevation of that number compared to what we expect the maintenance to be in 2026 and a little bit into 2027 related to the new post-marketing commitments that the U.S. FDA asked for. Outside of the U.S. and the rest of the world, we have already really transitioned to that more standard post-marketing number.
Great. Thanks. I'll try to squeeze in two if I may. First, I really enjoyed the redosing data on Mresvi. I found that really compelling. Where are the regulators currently in terms of considering recommending redosing for adults or the currently approved population? I have another quick kind of high-level question if I may.
Yeah. I think maybe I'd say it's probably not a regulatory decision on when revaccination happens, right? Again, this is revaccination. The decision on when we see waning of efficacy from a respiratory vaccine and when that public health benefit is sufficient to justify paying for an additional dose in most countries will be made by NITAGs, not by regulators. Ultimately, in the U.S., that's the CDC ACIP. What you're starting to see in some of the real-world effectiveness data is, and this has come out for all three vaccines, there's pretty substantial waning by year two. By year three, we'll look to more data. We do expect it to be down. If you have high-risk factors, immune compromise of some form, it's actually happening even really after one year.
There is a rationale to be made to put forward for protecting those at higher risk even more frequently than, let's say, every three to five years. I would look for that to be the first place you'll see movement. Probably you'll see it through the recommending bodies first, not regulators.
Yeah, that makes a lot of sense. Kind of a high-level question. As we think about individualized neoantigen therapies versus shared antigen therapies, what do you really see as the future playing out? The immunogenicity data that you shared was pretty compelling. I mean, why would you ever not go with an individualized therapy if you're getting that kind of unique response to the INT? How do you guys kind of see this developing? Obviously, near-term, it'll be whatever it's approved for. Longer-term, when you have multiple approvals, where do you think INT might be appropriate versus shared antigen? Thank you.
That's a great question. A couple of comments I'd like to make about that question. One, we need to learn more about the populations that really would benefit from a personalized approach versus a shared approach. We're learning more about that. We'll learn more when we have our phase III data and when we have more data on 4359. I think there could be some populations that might benefit from one versus the other. Of course, the obvious challenge with INT, as amazing as Jerh is on getting that turnaround time to happen, there are patients with bulky metastatic disease who are diagnosed who need therapy immediately. They can't wait even six weeks to receive INT.
I think for those patients, it's really important that we have some off-the-shelf options that we can deliver to those patients and they can get the benefit of that therapy immediately. There's also a world where you can combine these therapies. You can say, hey, let's get the benefit of both to these patients. Let's give them access to an individualized treatment as well as start them off on an off-the-shelf product. That way, they could have a better chance of efficacy from these programs. I think there's a series of potential scenarios that could play out. We'll have to make decisions based on the ongoing data that come in. Anything you'd like to add, Rose, on that?
No.
OK. Or Jackie.
I think you covered it.
Great. Thank you.
Just one quick follow-up on RSV. Do we now have a correlate of protection? On the Lyme disease vaccine, I'm very excited about that. Would you expect this to be a vaccine we're going to be getting in every couple of years, every year? How should we think about that revaccination?
Yeah. Thanks so much for the question. Starting with RSV and the correlative protection, I think at one of these previous meetings, we actually talked about the work that we did to establish the correlative protection, which we had previously presented at ACIP. It has actually been pretty well received by the regulatory authorities. It is how, for example, we were able to expand the indication to 18-59-year-olds. We actually are utilizing that work a fair bit. As you saw, the immunogenicity data is really the booster data that we have generated in order to unlock that potential indication. Second question on Lyme disease and how we are thinking about dosing. I will say it is always dangerous to compare assay to assay. We know that assays can be different in different hands.
We're seeing incredibly robust antibody titers relative to some of the competitive data that are out there with all of those caveats in mind. Our thinking is actually we want to explore in phase II a bit how we can reduce that dosing, particularly in the primary series as much as possible because in that first off-season, three is a lot of doses to get into place. There's not a lot of wiggle room for people to miss doses. Subsequent to that, I think it's a similar story to RSV or noro as I've been describing. It's going to depend a lot on observing what happens in the real world. We would imagine that the booster actually could be administered as a single dose, just like with other boosters.
That could be done in advance of the season, obviously at a different preseason time than currently. It is the way that we're thinking about it as a seasonal vaccine that may actually be able to go a few years in between needing revaccination.
Great. Thank you. Woody Poglej from Bernstein on behalf of Courtney Brewing. Just wondering if you could talk about the program discontinuations announced this morning. Aside from CMV, what was the rationale? What was the framework you used to make the decision? Was the data not sufficient? Are there still options for partnering?
Maybe I'll start and ask you guys to fill in anything I miss. For the most part, it's product by product will have different answers. The broad strokes are, as we look to prioritize and drive towards breakeven, and we look at the next step investment, often in those programs, a large, several hundred million dollar phase III, we have a specific bar for the return on that investment. It has to really contribute meaningfully to our growth trajectory. We have to view it as more valuable for the long term than other programs we have in our late-stage pipeline across all of the pipeline, including oncology, rare diseases, and what we're doing in the vaccine space right now. The ones that we discontinued in different ways for different reasons didn't meet that bar.
They didn't feel like the commercial opportunity justified the level of investment necessary for us to do it, even if we viewed it as value creating. We have a very tight, we have a very high bar as we drive towards breakeven, as Jamey has reiterated again today.
Maybe I can just add, I've been actually asking for some of this clarity on some of these programs for a bit because, as Jamey mentioned, if we have to cut an R&D, once we go into human clinical trials, there's actually a burden of work regardless of whether you're actively vaccinating or not. Somewhere where we're winding down and it's not clear that that's a program we would prioritize, it really helps me manage the workload and the staff if we can just stop. I think that was another piece of it, to prioritize within latent what we would go and do next, which I think you heard from Stephen is very much EBV and Lyme.
OK. We'll take our last question from Miles.
Thanks for sneaking there. Just on the 1010 vaccine efficacy data, looked pretty compelling, quite provocative versus standard of care there. There are some safety considerations, obviously. Just wondering how you see that as a commercial opportunity if you did have a claim to superior efficacy to what we're taking today, especially in such a large market.
Yeah. That's why we highlight that as a really 2027 growth driver. Now, we hope we're filing now. We hope to have that product approved if everything goes well in a year. We will probably miss the 2026 season. We really want to step into 2027. We think it's an enhanced profile. Ultimately, regulators will get a chance to review that and offer their perspectives. We want to launch it globally. You will see us in the United States, which obviously is an enhanced market. Actually, across Europe, as I tried to highlight as well, we see this as a big opportunity. As I alluded to, it is built into our contracts, our strategic partnerships with the U.K., Canada, and Australia, and beyond. We tried to do two major growth drivers in each of the years.
The one place we allowed ourselves a third is in 2027 because it is between Europe, between expanding in Latin America through that partnership with Brazil and flu. We think there are multiple shots to get us there.
Great. Thank you, Stephen. This concludes the formal part of our annual analysis day. Thank you so much for coming. I suggest we take a 10-minute break to grab a coffee outside. For those of you who can stay, we have a team that has some interesting AI demos for you. Stay tuned. Speak soon. Thanks. Thank you, Stephen.
Thank you. Yes. I was just reading. Yes, I'm good now. OK.
Thank you. Thank you. Great job.
Yes, of course. Thank you.
I'm sorry we're so late.
Oh, thank you for being patient.
Learning and stuff. You guys want to.
Can I suggest we get started for the AI section of the team? Can we get started? Thank you. Good. Thank you so much for all of you staying here and those online to look at the interesting work that our team is doing. Just maybe a couple of words of intro. Moderna AI journey started actually a long time ago in our science team. They actually convinced me back in 2016, 2017 of the power of AI when it's used to tackle important product problems. They basically were working in the science to try to invent new enzymes using machine learning systems that they had built with our data scientists and our biologists to improve the capability of enzymes so that we could reduce manufacturing costs, to reduce purification costs.
The first time they showed me the enzyme that they had invented through a machine learning system, most people were extremely not convinced, as you can imagine. They did the work. They made the enzymes physically that the system told them was going to be better than the enzyme that exists in nature. The enzyme that was designed by the machine learning system performed as designed. That was a big aha moment for me to know that an enzyme that did not exist in nature, that came out literally of a computer with a lot of small biologists behind it, could do exactly what it was designed to do. At that time, believing that AI was going to be an important feature of our work, we started Moderna AI Academy. This is pre-pandemic.
We started training people across the business to understand what machine learning could do for them whether they are in HR or they are in finance, manufacturing, or of course, in the science team. You fast forward to, of course, the pandemic. We are kind of busy with the COVID vaccine. Of course, November 2022 with ChatGPT. Many of us played with ChatGPT over Christmas and had a little bit of a brain explosion and trying to understand where this was going. Because it was clear that the November 2022, December 2022 ChatGPT was the 1.0 version. You do not have to have a lot of imagination. We saw it ourselves with seeing the mRNA ride we saw over the last 10 years that if you have more compute coming, stronger systems, and more data, the technology was just going to get better.
We decided to really have Moderna starting to really incorporate into our culture and our work ChatGPT. We were, of course, very worried initially about, of course, not wanting to teach the rest of the world our stuff. We developed mChat, which was basically a version of ChatGPT just for Moderna, totally siloed off from the rest of the world before GPT Enterprise existed. Actually, GPT Enterprise came from a lot of discussion we had, like, I am sure, other companies with OpenAI to tell them we need a solution for Enterprise. What we can do on the internet does not work for us. Of course, GPT Enterprise happened. We stopped mChat and we really moved the company to GPT Enterprise, giving full access to people across the company.
I have to say, when I go to see my colleagues, whether it's in R&D or in manufacturing or in HR, or the GPT we use and I use, I'm really humbled by what's happening. It is really, really exciting. It is just the beginning. It's interesting to know, and I'm sure the team will share with you the number of GPTs we have across the company and how people are just changing their work.
Which is why, as you know, I asked a year ago to have Tracy leading HR and tech because I believe in the next few years, our job is to reinvent work across every line of work, to go back to what are we trying to do for the customers and figure out what do we do with humans, what do we do with digital systems, what do we do with AI, and what do we do with robotics. As you saw in the quick note with FacilityBiz, there's a lot of robotics work. There's more coming. As the price of robotics comes down and the cost of deploying robotics thanks in part to AI for validation, I think this is going to be a very exciting journey. With this, we're going to start with manufacturing.
You're going to see us going through a lot of functions, these little snippets. You'll see actual live demo by the teams who build those things. I think we're going to finish with science, which, of course, is where we think we have the most impact long term because everything else is mostly productivity, improving quality, which is really important. It's part of our course journey. We'll finish by the icing on the cake, which is how can we do more amazing science, adding machine learning capabilities to great scientists. With this, I'm turning to Jeff. Jeff, it's all yours.
Testing. Hear me? Good. Good afternoon, everyone. Thank you very much for the opportunity. Thank you, Stéphane, Jerh, Tracey, and others for the ability to not only speak about our work, but just to innovate every day using our large language models, our automation, our robotics. It's very fun to come to work. And I mean that truly. I'm Jeff Savard. I lead the INT manufacturing team based out of both Marlborough and Norwood, Massachusetts. Today, I have my colleague, Jason Manchester, who leads Norwood Drug Substance Manufacturing based out of the Norwood, Massachusetts site. Today, we're going to be talking about use of LLM and AI within a CMC manufacturing environment. Thanks. As a refresher, we intend to improve our profit margin by 10 percentage points over the next three years by 2028.
There are three levers that we're pulling to be able to do this: volume, procurement and productivity, and waste reduction. GP manufacturing touches on all three of these pillars. In fact, we are a key enabler of executing this strategy. We can increase our speed and productivity to decrease or maximize our utilization rate and decrease the amount of plant time we need to do things. We can create environments through automation and AI to have digital right first-time execution to increase our quality and minimize our waste. Lastly, we can use digital and physical tools to physically miniaturize the batches that we're producing to be able to do more with less to ultimately manufacture in the smallest footprint possible. We are a key enabler to this strategy, and we're really excited to be on this journey with the rest of the company. Next.
Within a plant structure, we have created dozens upon dozens, probably hundreds at this point, of custom GPTs that span end-to-end production from raw material receipt through finished goods release. These are used and deployed by hundreds of employees daily in a clean room operating environment and supporting environments in support of execution of not only the core manufacturing process, but also our supporting quality and business processes. These individuals are using these tools. They come from all spans and walks of life, from our operators to our entry-level associates to our technicians to our supporting technologists and our scientists as well. We have truly created an environment where we have a digital-first mindset, and we are automating and enabling automation wherever possible. Today, we are going to talk about two of the pillars that are featured here, that being production controls and quality management and inspection.
We're going to walk through an actual problem that an entry-level associate might face in the middle of the night in a clean room environment where they have to act quick to be able to salvage the quality of a batch. It is a real-world application, and you can see how use of the digital tools will enable faster resolution, lower downtime, or less downtime, and ultimately increase our consistency and increase our quality. What you'll see is the use of chain GPTs to be able to not only troubleshoot and diagnose an issue, but to assess the impact and then to run it through the full documentation cycle as well. It is a full end-to-end, and it's ready to be identified. We're quite excited about that. With that, I'll turn it over to Jason to provide the example.
Yep. And to just add on to what Jeff said with the second and third blue block here, the Pval GPT and the Pia GPT, these, to Jeff's point with the dozens, I think we have 450 or so in total GPTs. We have built a suite of tools that we can essentially plug and play. So regardless of what the defect is that is observed on the manufacturing floor, whatever the event is, we can swap out those, that second and third block down to cater to what we need to respond to the event. I don't know what just happened here, but trying to get into the GPT for the demonstration. There we go. Perfect. All right. All right. So what we have here is a live view of an issue that we had in our manufacturing process where we experienced the flow rate excursion.
This is something that it's not a common occurrence. It could happen at any point in time within our production schedule. It could happen overnight, on a weekend, on a holiday within a 24/7 manufacturing facility. What we have here is an operator interfaced with the GPT that is specific to the area in which this individual is operating. They've stated the problem that was observed, that we had a flow rate excursion, that our manufacturing process went into a hold state, and our pressure is high, and it's asking what we do next to restart the operation. We see quite a thorough set of responses in terms of coaching the operator to next steps, what physical steps to take place, what to look out for upon restart, and also required escalation points.
Who to engage, whether it's a manufacturing science engineer or a quality associate, is all covered within the immediate response. This is built with years and years' worth of real-time experience, real-life experience at the Moderna manufacturing facility where our MS&T engineers, manufacturing science engineers, as well as our process engineers, have meticulously documented years' worth of real-time experience with defects, problematic events in terms of equipment performance, and resolution that was required for those events. All of that has been built into the background of this GBT, as well as books of knowledge, we can call it, for the know-how that each of the individual engineers bring into Moderna. All of that has been codified into the brain, for lack of a better description, of the LLM here.
What we see here is a couple of interactions where the operator is continuing to state what we're seeing, that we're going to be opening up a deviation. What we've looked at and confirmed is correct. It's furthering the aim of the GPT to better inform the operator for what to do next. As you can see, there's quite the list of things that we can continue to look at. This iterates a couple of different times where the GPT correctly points us to look for auditory cues, so unusual noises, chattering on the pump that is controlling our flow rates. For this instance, that was truly the problem. The operator asks for a bit more of a description around the chatter, suggests that they remember hearing something. They want to look out for that in particular when we restart.
With this, the GPT informs the operator that we are in a safe, from a quality perspective, position to restart. It gives more of a know-how in terms of what could cause the chattering and what specifically to look out for. From a mechanical perspective, roller wear for this pump is an issue. That is exactly what this drills down to. Next steps here, we restarted. The sound was confirmed, and our GPT helped us to message the escalation to the maintenance department that would be coming to help us with that pump replacement, as well as a batch record comment, which really kicks us off in the quality lifecycle. It is taking all of the information that has been observed, been documented within the chat, and giving us a GDP-compliant right first-time comment for us to put in the manufacturing execution record.
From there, considering where we were in a process, this was part of a process qualification run. One of the second and third block that I referred to earlier, we utilize this to chain in the process validation expert GPT to assess the impact to the qualification campaign, which then gives us an output that we're able to archive for this and continue to use as we get further into the quality lifecycle. It gives us quite a lot of information here. What this is built with is all of Moderna's documentation, all of Moderna's SOPs, as well as years and years' worth of experience from the FDA, 483 guidance, things of that nature. Everything that we need to look out for in a GMP manufacturing environment.
We continue on to chain in our product impact assessment GPT, which again utilizes all of the know-how, the experience from our MS&T engineers, as well as all of our process documentation, process development documentation to assess the issue that we observe to see if there is actually product impact. With that, it gives us a product impact statement. We are then able to chain in a deviation assistant, which then almost instantaneously gives us a full deviation write-up, which can be seen down here. The final chain in this, it was an audit preparation GPT. It uses Canvas, so it slides the rewrite down. We interacted with a GMP audit support assistant that we call Verify, AI in capitals, for us to ensure that it is FDA ready. The deviation report meets all requirements. Impact is solid.
483 history has been reviewed against if this is a red flag or a tripping point. It gives us feedback, and then we're able to tie right back into the deviation guide to rewrite the assessment based on the report out from that audit support tool. End-to-end product identified on the floor. This event identified on the floor. This actually happened on the 12th of November, so it's relatively recent. It did happen overnight. End-to-end, this is able to get to a full deviation write-up, technically speaking, within seconds.
Overall, within, of course, five to ten minutes, an individual, an entry-level associate, was able to use essentially what would have been five to seven phone calls over the course of hours in the middle of the night to be able to appropriately diagnose, troubleshoot, and continue on with execution. Just to give you kind of a sense for how this will scale and how it scales every day. Thank you very much. That'd
be helpful. Yeah. Good afternoon. Hi. My name's Craig Kennedy. I head global supply chain for Moderna. As Stéphane said when he introduced, GPTs are used as cognitive amplifiers all through Moderna today. I can attest that all through the organization, GPTs are deeply used by employees every single day. We actually wanted to show you some use cases outside of GPTs today, and we have five use cases in the supply chain area that we're going to show you. Firstly, we're going to talk about the way we use robotics in a very rapid solution for our last-mile distribution. We've got some interesting things to show you there. We're also going to talk to you about how we use IoT devices and machine learning to track everything that we send in our critical markets to make sure that they get there on time and in the right quality.
We're then going to talk to you about a use case which takes those data that we've gathered from everything that we've done, uses really forward-looking machine learning to create agentic capability to manage those shipments without the need for human in the loop. We're then going to talk about the way we do global logistics and global control tower using machine learning to make sure that as we move goods around the world, that they get there in the condition that they're meant to at the right cost, at the right value for the customer. Finally, we're going to show you a use case that we use for machine learning for forecasting, for distribution planning to make sure that we actually have exactly the right product in exactly the right location at exactly the right cost point all the time.
Those are the five use cases that we're going to show you here today. Jamey is going to start off for us with robotics in the U.S. market.
Thank you. I'm Jerh Collins. I'm responsible for last-mile distribution in our US market. At the end of last season, we were presented with a challenge that we had to be fully FDA compliant, which means the PI, the product information leaflet, must be on or inside the carton, the saleable unit. Given our current manufacturing environment and the timeline required for that, we could not put it inside the carton. We realized we had to have a solution at the last mile closer to the customer. We did something a little unexpected, and we actually applied the PI on the carton. To do this, we enabled a robotics and automation journey alongside our partners, UPS Healthcare and Eclipse Automation, and created a solution that would ultimately enable cost savings, mitigate labor risk, and enable a scalable future for this type of activity.
This project normally, at many other companies, would have taken at least 18 months to implement and deploy. Here at Moderna, our team was able to do this in under 10 months. From concept and design straight through to build and out to production, we built an 8,000 sq ft system that sits at our UPS Healthcare facility in Kentucky that ultimately yielded us a 1.5 cartons per second throughput. It is a significant automation journey there. As I mentioned, we did something a little unexpected. Through our mindsets and being truly bold, it ultimately took a constraint that we had last year and turned it into a true competitive advantage. What that means is that we are actually able to claim being first to market this year. You can see that is my colleague and I actually hand-delivering the first doses to market for this season.
We have a little video to show you of our actual robotic solution. Yeah. And then if my colleagues could help kind of give you a little insight of what these PIs look like.
That's later. That's later.
That's the next one. These are samples.
Jamey is pointing.
Yeah. Excellent. You can see there that's our Scara robot decanting product out of the cases and putting them onto quite a conveyor journey there. This is actually our mNEXSPIKE product. This is a video of that. This is our new launch for this year. This is what we call our banding system. That's actually what was able to band the product that you guys are holding. All the while, something really critical about this system is that given our cold storage requirements for our product, we are in a frozen condition. We had to maintain time out of refrigeration and freezer. This system tracked all the way across the 8,000 sq ft every step of the journey for each of these cartons to ensure that we were fully compliant within our handling requirements. Ultimately, you'll see there really weren't too many people involved here.
At the end there, again, the case gets closed, scanned, and you'll see one person there at the end taking it off the line. It truly was quite an experience and something our team's truly proud of being able to meet product on time. In fact, an interesting stat is with this solution and other enablements we made this year, we were able to, after product release, deliver our first doses in under eight hours to customers. Okay. Yes. Now we're going to talk about these. As you can see, something that we're really passionate about is continuously improving our customer experience. Obviously, getting the doses to patients is of our highest priority. As of last year, we started implementing on a journey to enhance our customer experience by developing an industry-leading real-time ship and monitoring solution that utilizes these tag and track devices.
Feel free. What you'll see up here is actually what our customer receives. These are examples, again, not with real product in it, but these are examples of what our customer receives in the frozen storage condition. The tag and track device is in there. What that's able to do is provide us not only with location data so that we can ensure on-time delivery, but it actually provides us temperature data as well. That's critically important given our cold storage conditions, et cetera. What this year has enabled us to do is create an automated intervention notification solution, which essentially, although it is manual today, allows us to be proactive by projecting where the product will be at any given time and potentially reduce temperature excursions ahead of it reaching the customer. The cost-saving initiatives, but also a customer experience improvement.
We have now shipped over 75,000 units, shipments with this solution, and therefore have gathered quite a bit of data that will then give us the ability to build off of that as Craig will then lead us into.
One of the things we, as Jamey said, we track every shipment. We've shipped over 75,000 already. We track every piece of data from every shipment that we send out. What that has done has given us a huge amount of data to infer what shipments are going to be good and what shipments may not be good. Essentially, it's a fraud detection problem. You have a very, very small number of shipments that don't go well. When they don't go well, they are a huge impact on our customers, particularly in a seasonal business, a vaccination provider, your CVS, your Walgreens, or any clinic. When something turns up in one of those busy pharmacies that isn't ready to use, that's a problem for them. Yes, they get new product. Yes, we fix it. Our goal is to never pass on that problem to the customer.
The way we're doing that now is we're taking advantage of these data that we've collected. We've already built highly accurate inferential prediction models that allow us to know when a shipment is most likely not going to make it in the conditions that it needs to make. The reason that's important is because we can predict that now before the shipment arrives. What we're in the process of demonstrating is an agent that takes that inferential data, listens to these devices, which are here. They send every shipment every 10 minutes. That agent looks at what is happening compared to what we know is wrong. If it makes a determination that that shipment is likely not to end in the right conditions at the pharmacy, it is built to do a couple of things.
Number one, instruct the carrier to redirect the shipment so bad stuff doesn't turn up at the patient, at the customer. Number two, place a replenishment order such that the order goes out high priority immediately. Number three, tell the customer all automatically, all on its own, so it doesn't have to wait for human in the loop to actually make that occur. We're even looking at the opportunity to determine whether or not we can segment and take higher value customers and make sure they get preference if a lower value customer is en route that we could reuse as well.
It is because of the fact that we took access to all of these data that we've collected with the push that Stéphane and the EC and others have given us around machine learning and AI and actually being able to turn something into what will be agentic behavior as well. That is good for the customer. It is also good for our actual cost as well because we can tell even if it is going to excursion, whether it is going to excursion within the known parameters of what good product will be at the same time. We save that return as well. That is something we are working with right now. We are very focused on the U.S. market, but what Gerhard is going to tell you about is also how we do this worldwide and give you a demonstration of our worldwide logistics control tower too. Gerhard?
Yeah. So similar to what we heard from Craig, we tracked our shipments since, let's say, I would say the last five years. The challenge we had there is these were in different systems, fragmented data, non-validated data. With the control tower, we implement a tool where we through APIs can ingest data from carriers, from devices like this, from devices like the tag and track here, but also from packaging providers. For the first time we are moving into structured data, validated data, and an intelligence layer. This is how we move from within, let's say, three and a half months from selection in March, we wanted to be ready to supply into the U.S. market intercompany on our minimum viable product with this logger and our Kühne & Nagel shipments in July. This is something we have achieved.
If we quickly go into the tool that you see a little bit how that looks, we see here, can we go to a, if you go to the live shipments and then to my favorites, then you can see one of the first one. You can take the first one. This is now an intercompany shipment where you see the shipment is going here from Belgium, where we have our central hub, which is feeding into the different country locations. It's routed through the, this shipment was going to Newark, and from there it's trucked to Kentucky. If you quickly look at the devices, we see here, we have here perfect depth. We know exactly what the outside condition was, but inside it's perfect in minus 20. You see this is on track, is green. What this tool gives us is actionable insights already.
If something would happen and there's a delay or there's a temperature excursion, we are notified. This is the first step what we have here. We implemented this one, but then we got the feedback from the market that what the customers get in the U.S. for these nice boxes, what Craig and Jamey explained, this predictive and an alert system, they wanted to have it for the pallet shipments as well. We quickly pivoted, used the same device for the truck shipments, and that is how we can go into the next shipment that was the one you had opened before. We use it also for the shipments out of UPS to the wholesalers, and they get an email two hours, one hour, and 30 minutes before expected arrival time, plus a one-time link. They can see exactly the same here.
What we did as well, we have a geofencing solution. We have a light sensor in there. Whenever they unload it, it is automatically switching off the device so they do not get any false alarm. This is also what we then short notice implemented to really have control over the shipments, but also give the customer the insight, and they can plan better the arrival of the shipment. The last one, what we did, what we very saw a need is our critical release samples. We need to send to national agencies like CBER here in the U.S. We need to send release samples, and they are super critical for us. That is why we implemented, if you go to the last one, Joe, can you go so? We also put together with our partner QuickStart. It is a white glove courier. We just used their API.
We used their device. We are agnostic. We used their device. Yeah, no, this is, if you go back, it's the one with W. There we see that we have the same. It's the one with W on the right. If you go to the device as well, please, you see exactly this is routed now. You see here the routing is not going directly from Spain to the U.S. It's going through Frankfurt. That is on purpose to mitigate the risk because what you can see here, if you are in Frankfurt at the airport, but also in the U.S., we can store it in minus 20 degrees Celsius.
We mitigate the risk in case of an FDA or a custom hold that the product is getting out of range and we lose it, which would be incredibly unfortunate for the guys then delivering to the customer. What we build on top of that, we built the first robotic process automation. We have a central sample shipment or sample tracking tool where previously someone manually had to enter the actual status of the shipments. Now with a robotic process automation, this is done every two hours or every three hours daily. We remove manual work. What is next here? We are now four months. We have a lot of learning. The next step, what we want to do is automated temperature release. We want to take the validated data and enable automated temperature release feeding directly into our quality management system. That is one thing.
The second one, we want to do predictive thermal modeling. Using machine learning and AI to say we are not static on a box and know it is validated for 80 hours or 90 hours. No, we know that if it is shipped in two to eight or in minus 20, it is much longer. Gives us more flexibility and we can improve our cost basis because we do not need to urgently react on each and every delay because we have the confidence and the tool tells us how long it lasts. What is midterm? That is then the real fancy stuff, I would say. If you can go to the better version here, we want to move from a static dashboard into really a conversational partner.
You see here already the first preview, which is planned for mid of next year, where if you click on logistics, for example, you come into the office in the morning, you can talk to the tool. The tool took already the actions for you. No matter if you're there, it's not only then giving you the recommendation, it's taking the action for you. It's escalation, it's accelerating, or even like Craig mentioned here, it's returning. This is then also where we feed in our lane SOPs, our risk assessments, or even let the tool do for us the root cause analysis and the corrective action. This is planned for mid of next year.
This tool shows as well, we selected the tool in March, we started implementation in April, we went live in July, we added in end of July and beginning of August these additional solutions for the market in the U.S. for the Zebra samples. We added the robotic process automation in September. Now we hope that in December we can do the first automated temperature release.
Thanks, Gerhard. One final show. Joe is going to talk about the way we do distribution forecasting effectively by starting at forecasting likely shots in arms as a way for us to make sure we have the inventory in the right place, in the right condition at the right time so that we never miss a sale and that we have it at the lowest cost. Joe?
Thanks, Craig. What you see up here is a live dashboard of a machine learning algorithm that we have implemented here at Moderna. This is dummy data, so there is not real data associated with this, so there's no inferences to be drawn here. This is a probabilistic statistical forecast with a machine learning backbone. Where I say machine learning, because we're not just using the projections that were developed from the statistical forecast model, we're actually ingesting real-time data, both from shots in arms administered that we get through public databases, as well as some internal data that we collect, as well as some, and you can see here on the right, Google Trends, right? There are some other exogenous factors that we track that are correlated to what our shots in arms and administrations are going to be.
You can see here that when we're looking at forecasting, we're not just looking at just a single point forecast, right? This is a probabilistic forecast that helps us continuously learn about where and how many shots we're going to need to get up to our customers. If we want to skip down to there, this is just the US market on aggregate. If we go to the supply planning here, you can actually see we go down to the ZIP3 level. This isn't just about forecasting of what the total market is going to look like. This actually gets down into the local area of where we expect our consumption and where we expect our doses to be deployed. What that allows us to do is to forward deploy our inventory to satisfy customer need and in many cases before they need it.
We also do get some customer feedback back around what inventory they're currently holding. We can actually predict when they're going to need replenishments and make those recommendations out to our customers. Like we talked about before, we don't miss any vaccination opportunities for our customers, right? This helps to drive and ensure that we are optimizing where inventory is at the right time, at the right place, and the right value to the customer. Again, this is a model that continuously learns. As we get additional data on a regular frequency basis, we update these models. As we think about how does this impact for our future, right? When you think about the connected supply chain, it all starts with what the customer wants and needs.
Feeding that back into how much we make, when we make it, where we make it, and how we forward deploy that is a really big opportunity for us to optimize our supply chain.
Unless there are questions, that's it for us. Yep. Good. Thank you. All right.
We'll save questions till the end and we'll move to development now.
Sorry, can I ask your name? Thank you.
Good afternoon. My name is Suzanne Tracy. I have accountability for the transformation team within the pharmacovigilance area. We're actually going to walk you through how we've been using artificial intelligence within what is traditionally a highly conservative and regulated area. About a year and a half ago, because at Moderna, we do take advantage of artificial intelligence, we actually developed an AI center of excellence. Our vision being, how can we revolutionize patient safety by putting actionable processes in place that are aligned with our operational excellence? How do we do that in a way that integrates our capabilities for artificial intelligence while building scalable, repetitive, high-impact solutions?
How do we do all of this while utilizing artificial intelligence in alignment with our Moderna mindsets, but to do so in a way that meets the regulations from the health authorities and remains aligned with our North Star of patient safety? We actually developed a foundational layer where we have originated an SOP, a standard operating procedure for our software development life cycle. On top of that, we've evaluated all the guidances from the health authorities. The EMA, the FDA, along with other health authorities, have released guidance because they do see pharmacovigilance as a very data-intensive area, and everybody is fully invested in looking at artificial intelligence. There are very few companies from our benchmarking that have taken it to the level that we have at Moderna. We're going to show you some of those examples.
With the talent that we have in-house, Wen's actually going to take us through how we've used the compute platform and used that as a benchmarking. Let me go back this line to use that as the benchmark for our workbench platforms. Giving us an environment where we can develop our artificial intelligence solutions in a regulated and standard manner. At Moderna, we remain invested in expert in the loop. It's not just a human in the loop for us. In our area of expertise, we make sure that the human is actually a subject matter expert. All of this comes together so that we can enable all of our pharmacovigilance colleagues to focus on value-add activities. There's no sense in having our individuals at Moderna invest their time.
A lot of this does take an inordinate amount of time to review the data. How can we do that more efficiently? On the next slide, I'm actually going to introduce Wen. He's going to walk us through the workbench, how we are using our compute platform to really introduce our regulated use cases. He will hand it over to Andrew. Andrew will actually walk us through very specific use cases that we've introduced in pharmacovigilance. Looking at our PV regulatory intelligence, using artificial intelligence to redact personal identifiable information. In alignment with the regulations, we have accountability to look at all of the social media with reference to Moderna. How are we deploying artificial intelligence to use against that case? We are actually very focused on ensuring that our staff is focused on the higher value activities.
Along with these three use cases, we will be introducing savings in the millions of dollars. CSPV, so our clinical safety and pharmacovigilance team, is not lacking for ideas. We have 40 additional ideas in the pipeline. Because these are our highest impact ideas, this is where we've started this year. I'm going to turn it over to Wen. He's going to take us through the workbench.
All right. Thank you, Suzanne. My name is Wen Haoliu. I head up the data science and AI team at Moderna for the clinical development operations space. Let's rewind to early 2025. You guys have all heard of this term agentic AI kind of pop into the mainstream as a buzzword. We asked ourselves, is it real or is it hype? Right? I think about a year later, it's mostly still hype at most places. Right? The reason is because it's not actually a problem of technology. It's an issue about company mindset and culture. It's the willingness to change and reinvent ourselves and really adapt our processes to build them up with agentic automation in mind. That's what it's going to really take to take this to this next level. Right? I'm going to tell you about this platform that we built called Workbench.
This is one of our platforms that is enabling our AI-first strategy. Workbench has four main pillars. The first is data, followed by data access in a secure and governed way, the agentic frameworks themselves, and then the user interface. I'm going to go through each of these one by one. Data, this is the most critical and most important layer. No matter what anyone says, AI will not clean or read your messy data. It's just not, it just doesn't do that. This is where we found that most efforts stall because it's really a collaboration between people, process within the business, and digital to take an existing business process that someone wants to automate. We trace the data to where they live. It could be in Excel, it could be in SharePoint, it could be in digital systems like SAP, Syncade, Workday.
We bring them into our data environment and we map that data, we normalize it, we standardize it, and we push it back into this AI-ready data platform. This is just a traditional data warehouse, right? This step needs to be done. This is the really hard part. This part takes three to six months if the data is not in a clean state. You really can't do the rest of this without the foundation, right? I think this is where most efforts stall because people try to skip this step and just try to get the agent to read the raw data. Data access is extremely important in general, but especially so for a company like not just Moderna, but any pharmaceutical company where the access to data has regulatory implications, right?
In Workbench, within the platform, we built a custom role-based authentication system that is matched to our company's internal structure. What that means is if I assign a role of medical monitor to a user within Moderna, that grants that person access to certain patient data, right? It only grants them access to data within the clinical studies to which they are entitled to see. That level of access control is foundationally required in a company like Moderna in order to then attach our agentic framework and have agents explore data within our network, right? That is a foundational piece that we spent a lot of time thinking about and designing. Then you have the agentic frameworks themselves, which will access that data through the industry standard model context protocol. That's just how agents interact with data through a common interface layer.
The last piece is the user interface, which is really critical because these agents do not just run by themselves in a dark room in the back. Right? A user needs to be able to see what they are doing. Right? They need to see what agents are in my team, what actions they are doing, what workflows they are running, where they are stuck in a particular workflow, and be able to interact with that agent in order to provide feedback, provide the expert in the loop, and move the workflow along. That observability is extremely important. We are kind of limited in what we can do within ChatGPT. We have kind of pushed that to its limit where we have done today. We have done a phenomenal job doing that here.
The next level of this is providing a custom user interface where users can interact with agents in a more structured way. Okay? I'm going to take you in through a demo of the Workbench platform. This is kind of executing what I mentioned earlier, which is the hard part is cleaning the data. What I'm showing you now is, this is synthetic data, by the way, just to show you what this type of data this system has. We spent about three to four months working with the business within clinical operations and the finance team to take extremely messy Excel data from our CRO vendors to standardize them into a format which allows us to look at clinical trial spend at specific clinical sites across our portfolio. Right? It took us four months just to get this data in the system.
What I'm showing you now has nothing to do with AI. This is just a dashboard, but the foundation of it is the same foundation that will enable the AI use cases. Just to show you some of the information that this system has, what we're looking at here is data for a particular clinical study. You can see the total study budget, the evolution of our contracts as they are executed over time. You can see the numbers are going up a little bit. This combines our clinical operations data with enrollment and sites with our financial data, which is how much money we've spent on these locations. This type of dashboard will really give us insights.
For instance, if you are looking at the number of active patients and number of active sites over time, and you're seeing this number going down, but you're seeing your contract cost going up, that would be a red flag as an example. Right? What we really want to enable is instead of an analyst having to click through these interfaces, applying filters, going through every day and trying to find these insights, the agent has access to the same data. Right? It can just do this for you autonomously. That is the platform that we built. I'll show you an example of that. Within Workbench, this again is a demonstration prototype. It is not live yet in production, but it kind of gives you a view of the future.
The idea is that a business user can come into this system and create a financial assistant agent. This is done through configuration. They don't need to know how to code. The engineering team obviously will work with the business to build these agents. Once they're built, anyone can come in and configure this agent. This particular one in this demo example, we are looking at clinical sites that have very low enrollment. There could be a site that has little to no enrollment. Yet we have upcoming site visits, which cost us a lot of money. Right? This will give the clinical trial operations leads insights into which sites they might want to close early.
You can see in this agent, we're able to configure the number of days ahead of time that we want to look, the what percentile of the bottom performing sites we want to look at, what therapeutic area we want to target, and some prompt instructions which can be configured. Right? This looks a lot like ChatGPT, but this goes beyond the capabilities of ChatGPT. Right? This has much more customization and is much more fit for use for specific use cases. Once you configure this agent, you can execute this workflow. Normally, once you configure it, the agent can run on a scheduled job. It can just run in the background. Right? When you come in in the morning, you're going to get your insights. It's going to do all the analysis for you.
What I'm showing here is a manual execution of this workflow. This little side window that comes up is actually from a collaboration with OpenAI. They gave us early access to their front-end user interface component, which we used in this application, but it's fully connected to our own custom backend, which has all the data level security and everything that I mentioned in the first slide. You can see the agent is able to autonomously go into our MCP server, locate the right APIs, make the calls, break it down into steps through chain of thought, and do the analysis. This is the really important part because in ChatGPT, that's where it would end. You would download the results into an Excel sheet or you might read it and do something.
Because we're in a custom application, the output of that agent is actually put back into our database where the user can interact with these records. Right? Now the user can see, okay, I have a site here with zero enrollment, but 13 upcoming site visits. The savings here could be potentially this amount. You're able to open that record, interact with it, give the agent feedback. Right? I can say, this one looks good. I'm going to approve it. I can say, reject. That provides the feedback into the system to allow it to continue to improve over time. Right? Okay. Now if you just imagine this is a generic workflow that we built, imagine this applied to other areas, you know, legal, HR, finance, manufacturing.
You can kind of get a sense of the potential of this type of technology when you're able to put agentic AI against our internal company data. I hope you come out of this with the appreciation of how hard this really is because it takes a lot of effort to get the data right, but most importantly, to align the people and the process and get the business team involved with digital. I think at Moderna, we have the right people. We have the right mindset. We have the technology, for sure. That's the easy part, actually. If you saw the presentations earlier, like just to give you the sense of the mindset of the people here, right, it's the people that are driving these innovations. It's the people that are going to make this change possible.
That's what makes this a really special place. Okay. Next, I'm going to turn it over to Andrew Sems, who's going to talk about an actual use case we built and deployed to production using the platform that I showed you here, but only using a specific portion of this platform, which we were able to validate from a GXP perspective. This is a really big deal. Now we can run agentic workloads in a validated environment that will pass FDA inspection. Right? This is where we really get the benefits of this technology. I'll let Andrew talk about the three use cases there.
Thank you, Wen. I'm Andrew Sems. I work in the AI Center of Excellence with Suzanne. I'll cover three use cases for you quickly today. Starting out with MScout. This is a regulatory intelligence agent.
Biopharmaceutical companies are required to respond to various pieces of legislation and new regulations that break across the world. As you can imagine, monitoring 80-plus health authority websites, tracking all those changes, assessing them against our internal procedures, and then responding to them is a massive amount of work and is quite labor-intensive. Previously, this was a manual process. We had a regular cadence controlled by business process where stakeholders would do this. They would have to open up their browser, look at the website. You can imagine that takes quite a lot of time across all the different regulatory agencies and the things that we're tracking. That's where MScout comes in. MScout is our regulatory agent. It runs on a daily basis. Basically what it does, it pulls down all the information from regulator websites.
It compares them from the previous day to today. It looks for differences. It then translates from 25 different languages into English and then assesses that with a series of criteria looking for impact to our pharmacovigilance operations. Once that is running, you can see here kind of like under the hood, and this is part of the platform that Wen was talking about. It actually goes fully autonomous. Right? It has a trigger on a daily basis. It will kick off its daily monitoring, look at the 83 websites, and then send notifications to our experts in the loop who need to respond to that legislation. Here's just one example. Right? The International Council for Harmonization recently had a new GCP guideline that came out. It's E6R3.
In this case, you can see here for Argentina, the MScout went to the Argentina Health Authority website, translated it from Spanish to English, identified the fact that Argentina adopted this international guideline, and then sent this on to our LATAM colleague who's responsible to update our procedures according to ICH E6R3. Our expert in the loop will take a look at the summary here. We have similar buttons as Wen showed in the previous example to indicate whether or not this is relevant intel or not relevant intel. That feeds back into our model to track performance continuously over time and helps us understand when we make a change, like upgrading the AI model, if we're getting more accurate, less accurate as we go along. That is just one example. That one came out last year. This year, we're forging ahead.
This is one of the items that just recently came out. When we process patient records, there are global privacy regulations that we have to comply with. When a case has been closed, we then need to go into that source documentation, which could be on the order of 300 pages long, and redact all of the personally identifiable information, names, addresses, all of the above for PII. As you can imagine, highly manual, highly labor-intensive. We were also previously using a contractor to perform this work for us. By implementing this AI, we actually ceased that contractor contract, brought that capability in-house with our existing staff, and achieved 80% time savings because we achieved in the 90-percentile in quality. With that, I'll show you a demo of how this solution works. You can see here, this is within our environment.
We use an LLM that's internally hosted. No personally identifiable information leaves our data environment. The user will upload the file. They'll get an email when it's done redacting. They will then go and download that file. Once they download it, the AI is actually proposing those redactions. Right? You can see here, and this is synthetic data. All of the patient name, right, race, ethnicity, address, phone number, email. The user can then just look at the proposals. If it all looks good, they hit Apply All. It gets redacted. As you can imagine, this is a massive time savings across just one case that could have hundreds of pages that this is needed for. Of course, we're a global company. As you can see here, we built it to be multilingual across seven initial languages. This is a synthetic example for Spanish.
That's our second one, which recently went live. I wanted to preview a look ahead of what's coming in 2026. This is MDetect. We are also obligated to monitor social media. When someone mentions Moderna, mentions our products, we are obligated to look through that information and then see if there is any mention of side effects or product quality complaints associated with our products. We're also currently using a vendor to perform this. It's quite an expensive activity on the order of millions of dollars. We're looking to apply that same strategy, right, have an AI agent that helps us go through all of that data, take a first pass at it.
Our expert in the loop with our existing staff will then confirm the disposition of the AI classification and either identify that there's no reportable information or if there is, send it to a downstream system for processing. That will enable us in 2026 to also sunset that vendor relationship and bring that capability in-house as well. All right. I believe that's our presentation.
Okay. We're now moving into the G&A section. Maybe one of the examples will be more relatable to everybody in the room. Thank you.
All right. Hi, everybody. I'm Amanda Sorrento, and I lead our HR core here at Moderna.
My name is Nathan. I am a manager in technical operations for HR. I just manage projects across our HR stack.
Excellent.
I'm really excited to be here today to talk to you about what we've been doing with AI and HR. Our journey kind of really started by looking at how we can redesign work within HR. We started with some chatbots around benefits and around AskHR that had great success and productivity. We started to ask ourselves, how do we rethink the work within HR and in our core talent processes? About a year ago, we launched a GPT that is designed to support our year-end process. Really helping employees as a support tool and then really helping to aid and enable our managers to have really great year-end performance conversations with their employees. What we saw when we launched that was a ton of adoption. We actually saw a net effect.
We can't directly correlate it to this, but we did see a threefold increase in usage of ChatGPT across the enterprise for different purposes after we had launched that tool to the enterprise. We wanted to take it one step further from there and really think about now how do we aid in the development? How do we use ChatGPT and AI to really aid in the development of our workforce to build the capabilities not only that they need today, but for the work that they need to do in the future? Just for a little context setting, traditional development planning happens where an employee sits down, thinks about what their development is, then they meet with their manager, and they together come up with a plan.
Oftentimes, that plan is only as strong as the manager capability or the employee's desire to think about their development. We really wanted to make sure we were building something that fit within the Moderna context, really helped to elevate what we're trying to achieve as an organization. That's where we brought in AI and ChatGPT in particular to really design an AI-assisted process here. What we did was we took an API to Workday where all of our employee data sits, and it gives great context about what's going on. It has the role of the employee as context. It has that employee's objectives, which are ultimately laddering up to what our organizational objectives are.
We built an understanding of what the Moderna culture is, what our philosophy is around employee growth, and then how do we think not just about the skills and capabilities that an employee needs today, but what are those skills that an employee may need in the future, especially as technology evolves and all of our roles are rapidly evolving. That became the structure for the GPT that we then launched to the organization to help an employee write a really quality development plan anchored in context and then also really bolsters the manager because it has a consistent context behind it and thinking to really ultimately produce a high-quality plan. This was a recent launch for us. We just completed our cycle, actually, about a month ago.
We think our outcomes are going to be measured over time and where we're really going to see the amplifying effect to this. Over time, we'll really be looking at how does the capability of our workforce, especially in those skills and those areas that we think are critical for the success of the organization, how does that amplify over time. We think we'll see an increase in engagement. Right? Employees want to know they're being developed. They want to have that commitment. We ultimately think we'll see a reduction, and we'll be measuring for a reduction in voluntary attrition. More exciting out of this is not just what it does around this particular process, but what it can also serve as we think about the whole employee lifecycle and what more we might want to do with this.
Because the data then goes back into Workday, which is our source of employee data, we can then leverage that data to think about what are the other employee solutions that we're putting in place to really bolster our workforce and drive for the company. Things like internal mobility. We have incredible talent here. How do we understand where their skills are, what their motivations are, and then really create a system from that where we can move our talent into new experiences and new needs of the organization, including roles that do not even exist today because they're still emerging as technology evolves. We've got a data-rich environment to be able to really draw from in a unique way. We also can then really make sure our learning and development programs are truly anchored into the things that matter most.
We're focusing our energy with our employee base and what we're creating within HR to really drive those skills and continue to develop through the tools or through the programs that we may create as a workforce. While those are our longer-term measures, we have seen some really strong good success just on the process, which tells us there's a high level of engagement. We didn't design this with the intention of forcing everybody to go through. We designed it as something we wanted to draw the organization into. What we ultimately saw was 84% of our workforce went in and completed their growth plan without it being a mandate or a requirement, which tells us that it really was a beneficial tool that we created.
Pairing along with that, which really then points to the tool that we created, we saw 87% of our workforce utilize the GPT. We wanted to understand, did they not just go in first time and then walk away from it because it was not value-added? We measured the number of conversations that actually took place, which told us they actually went through the whole process and then ultimately put it back into Workday, put the information back into Workday. They really did find the value in the whole tool. That is where we see 70,000 conversations that took place utilizing those tools that drove to that 84% adoption.
Obviously, one of the net benefits that we get out of this is it takes a whole lot less time for an employee and a manager to really come up with a quality plan at the end of the day. I'm going to turn it over to Nathan now, who's going to show you the actual tool.
Thank you, Amanda. It's going to get personal because we're going to do my own growth plan together. Just, yeah, I'm hanging in there. This is a pretty standard interface. I'm sure you know what ChatGPT looks like these days. This is accessible throughout a variety of different ways. We had, obviously, some comms leading up to the launch of the growth plan. We've got links available throughout our ecosystem, My Moderna, which is our intranet, etc. You cannot miss this.
The usage rate just indicates that people have not missed this at all. We have made this to be super, super easy. It is essentially a semi-structured conversation that we are leading people through. We are going to do that together. I talked about the conversation being semi-structured just because you can say really what you want. We are not expecting much, really. There are some soft guardrails that I will mention. Essentially, what you put in is what you get out in a more structured manner. It is there to, to what Amanda was saying, recognize me. My name is Nathan. It will go and grab in the background a bunch of information that it knows about me, including my role, my objectives as well, just to again add a bit of flavor to what is to come.
The growth plan is about kind of that macro lens for my personal development. Here we have a pretty good overview of what we expect to put into Workday, which is the growth goal itself, some additional flavor around what is that growth goal, and a start date and a completion date. This essentially helps us put on a silver platter what is needed for an employee and a manager to have a hopefully fruitful conversation about what's to come for 2026 and sometimes beyond, given how macro sometimes these growth plans are. Here, again, it's asking me if I want to share, if they want to pull my objectives, I'll say yes. Sometimes the objectives need to be revisited. It's something that we prompt for in our quarterly connects and end-of-year cycles, for example. Here, I forgot, oh my goodness, what do I need to do?
That's right. Objective one, objective two, I'm all good. Very big objectives. I’ve refreshed my kind of my objectives, and I can go. Now starts a series of six questions. My Moderna colleagues, I hope, are within the 87% of people who went through these, so they'll know them very well. These questions have been put together so that they can offer our employees a very best-in-class type of questioning. It's a lot of self-introspection, again, leading to that manager conversation a few weeks later. We start with, like any good plan about the future, we start about a few snapshots about our current situation. Here, I will—I'm a rubbish typer. I will go and copy-paste what I have in another window. Very, very fancy. Like I said, it's not adding anything.
It is going to regurgitate what I've put in. Like Amanda said, it's got the Moderna mindsets, our corporate objectives, a lot about who we are as a company in the background. It helps to format a lot of my inputs into something that will be, again, the basis of that growth plan conversation. Again, summarizing what I've put in, I don't feel like I need to go further, but for each of the questions, it's going to probe additionally to really go and nag the user to ensure that they're giving as much information as possible to the model. I'll go and say no, and then we'll get on to the second question. Again, second question, and it's mostly about current situation. More what has been going on about my projects, what I've had maybe some troubles with. I feel pretty good at the moment.
Thank you very much. I don't need to add too much. Again, current situation snapshot, core strengths. Here, we really depend. It's a big signal for our managers to understand if our employees know themselves. It's a big point of feedback as well. That's a big important question that we've included here. I feel pretty good about that answer as well. If you remember, I've mentioned it's six questions. We are now halfway there. That was a tough one for it. All right, we're halfway there. Now, growth edge. This is where we start to slowly bring the conversation into the future. Where do I feel I can grow, really? Again, I don't expect the GPT to add anything to the mix. It has that context of my objectives. It has that context of my role.
I'm just kind of guiding it towards what I feel I could strengthen in terms of skills and kind of picks up on it. I don't feel I need to go further. Now, really, this is the we start to do a bit of soul searching here, what's coming up. Future horizon, what do I have in mind? Again, I have some I know exactly where I want to go, of course. I have exactly what I want. I'll just insert something here where you would think, easy, Nathan, I can game this and just say to my manager and include it here, I want to get promoted. Boom, 2026 promotion. Not quite as easy as that. I also want to get promoted. Rubbish typing.
Here, it's taking what I've given it in terms of my 12-month objectives, but also kind of nudging me to talk to my manager. When it comes to promotion, this is something that is, of course, part of working in a place like Moderna, but it is something that is not necessarily linked directly to my growth plan. Here, we're kind of nudging to talk to managers, to engage them one-to-one to ensure that we have something that is tangible in these growth plans. I'm good to go here. Here, we come to the final question, which is, I think, the most meaty of them all because we're really trying to make people think about the different ways that growth happens at Moderna. We have a pretty solid framework around how that happens.
It is here exploring the ways that I think I can grow, adapt my skills for today, but also for tomorrow and the HR tech stack, in my case, that is coming up. I have, of course, given it a lot of thought, and I can input that in there. At the end of the day, very, very easy output, right? Nothing mind-boggling is going to give me exactly the different elements that I need to copy-paste straight away into Workday. Workday becomes the start of the conversation for you and your manager. Your manager can consult that into Workday pretty seamlessly, and you can have that one-to-one with your manager in the moment that is in and hopefully smash it out of the park in 2026. That is all she wrote. Thank you very much.
Thank you. Yeah, could you get it going, and I'll start talking?
Yeah. While they're pulling up the slides. Hello, everyone. My name is John Ward. I'm going to hold off for a second. Hi. I'm John Ward. I have to say I'm just humbled to be up here speaking with you today. I'm really grateful for just all the great work I've seen that's been in the presentations that have preceded me. I'm going to be quick today and tell you about a GPT I created that I use every day to great results, I think. I'm the trademark attorney here at Moderna. I'm sort of a team of one, as you can tell. I didn't even bring someone to advance my slides. I deal as a trademark attorney in the pharmaceutical industry, which I've been in-house as a trademark attorney in pharma for about 18 years.
In all of that time, I've dealt primarily with creating drug names, right? I handle the legal aspect of drug name creation. It's one of my primary deliverables. There are three components to a drug name. One is commercial, one is legal, and one is regulatory. You can go back up to the original slide. The commercial part is it's the conversation that's a good name, right? That's a very broad conversation. Everyone can weigh in on that. The legal part of it is the name that has to be we can own the name, right? We can use it freely without stepping on anyone's toes. The third one is regulatory. That is, we can get permission to use the name from the health authorities.
As you go down through those three, it's a whittling down of names from a big bunch of names to a smaller bunch of names to an even smaller bunch of names. I'm going to talk about that second step briefly, how I use ChatGPT and an AI GPT I created to address the legal screening of names, right? It's search. It's called trademark search. Trademark search is the most expensive thing that I do. It's the biggest part of my budget, traditionally the biggest part of my budget throughout my career. I've spent millions and millions and millions of dollars on trademark search over the years. When I came here, I said when I was actually hired already, I was talking to the general counsel. I said, "My goal, everywhere I've worked, everywhere I go, I see waste.
There's a lot of waste in this process. In this process. Unintentionally, I mean, it's just sort of baked in. It's because you're dealing with, you're going from ambiguity to quantifiable risks. That's what you do in my space, right? And in that process, you're going forward with a lot of uncertainty. There's a lot of dead ends, but you only realize that after you spend a lot of money. That's regrettable and something I've always tried to address. I said to the general counsel, Shannon, I said, "My goal is to come to Moderna and build a virtual program that is heavily reliant on technology." Fortunately, the technology caught up with that. I was actually able to do it because I don't know if I would be able to when I actually said it. Here's the evolution of trademark search, right?
Trademark search is, again, you go from this big, you're narrowing down a pool of names, hundreds of names to get down to about 20. You try to figure out which 20 should you advance to stick into the regulatory review. The tradition in the industry, what I've done throughout my career, is you rely on outside counsel because this is really about scaling yourself, right? You have to scale yourself out. It's too much work. You can't do it as an individual. You need to send it to outside counsel. Here, we have very good outside counsel that I've built a team. It's a lot of commoditized work. I rely on counsel in Eastern Europe, very, very aggressively priced, great savings, but there's only so low you could go. The low you can go is $350,000 a project.
That is per name for search, right? We have five or more names in the pipeline, so projects in the pipeline. It is expensive. You can get 20 names in about 50 countries searched, takes you about three months. That is a very competitive price. I can tell you what Big Pharma pays for that, but I am not going to. It is a multiple of that. In 2024, we are hit with cost constraints or a constrained environment or resources. I moved this work in-house. I shrank down the number of countries from 20 to just our key countries. I could do it myself, fewer countries, 42 days. I got a subscription service that allowed me access to global databases of trademarks. I would basically do the job that here, 50 lawyers are doing this work, one in each country. That is how you do it.
I'm doing it now, but I can only do it in fewer countries because that's just impossible. When you're doing 20 names, 50 countries, do the math. That's a lot of searches. I drove hundreds of thousands in savings in 2024. This is unsatisfactory, right? Because it's a compromise in the global footprint of the search. It's not I want to have the industry standard, right? 20 names, 50 countries. This is moving forward with too much risk, which is sort of what a lot of companies will say, "We're cutting resources. We're going to have to live with this." I don't want to live with this. I created a GPT that could scale out my ability to deliver a bigger global footprint, more search, more search, right? I wrote a tool called MClear in-house, relying on commercial search database.
I got my annual subscription fee, but I can do that per project for basically nothing. This is driven—I did the math this morning—so far this year, seven-figure savings and seven-figure savings in dollars. That's all fine and good. Let's see what—let's see if I can do this. I'm going to see if I can do a demonstration of this quickly, hopefully this time. This is sort of like, unlike the projects you've seen before, which are very impressive. This is sort of like guerrilla GPT, right? I just did this at my desk. Over the course of about six weeks, I wrote this thing. Then I told my boss, "Hey, look at this. I think it can drive a lot of savings with this." So then I implemented it.
No one told me to do this or no one said it's okay. I wrote it over six weeks, and it's basically Python code, right? I just go into I use Chat04 Mini. We'll just start this. What I do when I start this, I've got these I just built this folder. This is all, again, is, I guess, what someone called synthetic data or something. The name I'm going to search is not real. What I do is I use what I call the NClear engine, which is the real GPT and some 133 lines of Python code, each one a polished gem. I just take a search. This is an example of 500 lines. What's in this? What I just uploaded is a spreadsheet. The name that I'm creating, say, hypothetically, is Syngenry. That's a name candidate.
I want to know what the risk landscape is for Syngenry. I search it in the U.S. I get 500 senior trademarks that might be a problem for Syngenry. I have to figure out what's the risk landscape within those 500 names, right? That's hard to do, right? Because you have to go through 500 lines on the spreadsheet and then rank them and then understand them. That's hard. That's what this tool solves for. That's why you outsource it to lawyers. Here's my prompt. I'm just going to copy that, paste it in here. I just say Syngenry. Guess I could have typed this in beforehand, couldn't I? The region, this is the US.A. And today. You pray because you're in front of a live audience. You hope you're connected to the internet.
You hit that, and you wait a few seconds, right? It's uploaded the spreadsheet of all those third-party rights. It's applying the Python code and the GPT. It's working in the background. It's thought for five seconds. It's analyzing. Okay. Here, it'll upload in the window the data, right? Here's a preview. It'll give me a preview. You can download the all right. Let's download it. Click the report. Comes up here, MClear Syngenry, USA. Click on that. Right. Here is the MClear report. I'll go back to the slides because it's easier to see there. There's the top row of the report that I uploaded. This is what's called, I just think of this as he raw data. This is just right out of the USPTO website, right? Right out of the USPTO.
To figure out what the to understand the risk that's the risk landscape for Syngenry in the U.S., to figure out what the problem marks are, and there's some in there, you have to read 500 lines of data and then keep track of what's a problem and what's not. That's hard, right? Do that 20 times, and you've cleared your marks in the U.S. Some of these reports are 2,500 lines long. Here's the MClear report that I just opened there a second ago. This is whatever. Data text wrapped, and the windows made the right size. The report adds seven columns that tell me what the risks are in the spreadsheet. First, I have whether it's a high-risk company, yes or no.
In the GPT, I've identified companies that I add to that list as I go forward, like CSL, major vaccine manufacturer, and so forth. This is very good to have because you can just toggle just to look at the high-risk companies because they're the ones that are most likely to create problems for you and your competitors. I have an overall similarity score that just translates medium-high. It goes from low to high. I usually put filters there. You can filter for any one of these things. Let me see what the highest and the medium-high and high are. I've got weighted high-risk score. These are the algorithms that are running. This checks for spelling, like how many letters would you have to change in order to make the words identical. These check for two and three-letter sequences.
I've got a combined overall similarity score. Wow. Okay. I've got seven different things here. These are all instantly done. As you saw, 500 lines. Each one of these things was calculated in a couple of seconds, right? This saves an enormous amount of time. Saves an enormous amount of money. I don't pay for search in any country in which is a Latin alphabet, right? It's over $1 million this year so far in savings. The money you save, money you don't spend on outside counsel, is the sweetest money you can possibly have, right? I can go to R&D and other things. That's it. It's very small. It's my job, but it's scaling myself out, which is exactly what I want to do. It's part of a larger end-to-end AI solution that I'm working on.
But where this is really the money driver, I broke it out and bolted it onto the traditional workflow that I've got. Great. That's it. Thank you very much. All right.
Last but not least, we have research. We will start with protein engineering, followed by antigen engineering.
I'm going to stand by the computer here. Just do this. Yeah. Yeah. Okay. Thanks for staying and for all the interesting AI talks we have here at Moderna. This has been actually really informative for me too, what's going on. I'm going to tell you today, my name's Dan Culpdon. I'm a senior fellow. I run a group doing computational design and in vitro selection at Moderna. That sits in the protein design department under Bill Schief. I'm going to tell you guys today about how we're using AI in protein design.
I'm going to sort of teach you a little bit about how we're doing protein design in the world of AI. And hopefully, it'll be interesting. Just as a way of background, both Bill and I have been doing protein design for decades. When I first started off in the field and I told people I wanted to design proteins on a computer, they thought I was joking. The field has grown tremendously since that time. However, both Bill and I have been able to design proteins on a computer that we've evolved into phase I clinical studies. We've been testing some of these things we've designed all the way through preclinical development into phase I. As we all know, Moderna makes mRNA medicines. The RNA goes into cells. The cells are instructed by the RNA to make proteins.
It's our job to make sure that those proteins have the right shape and the right function so the medicines actually work. We spend a lot of time crafting these proteins and trying to satisfy all the requirements of mRNA medicines for our proteins. Okay. Another way to put this is, can we choose a sequence of amino acids that help a protein fold into the shape that we want and perform the function that we need? That does not sound like a hard problem. If you start thinking about large numbers, like the number of atoms in the universe, the number of chess games, possible protein design, even on a modest protein of 100 amino acids, is a larger problem than that. We have come up with sophisticated computational algorithms to try to solve these problems in the past.
AI has helped us go even faster. Basically, designing novel biomolecules was really slow and unreliable previously. Now we can sort of rapidly create these de novo proteins with specified structures and functions. We're just moving a lot faster than we were before, basically. Another challenge had been that we'd have low experimental success. It used to be that one in 100, one in 1,000, one in 10,000 designs would actually work for us. Now, just the other day in my group, we had one in 10 that we ordered worked because we were using these new AI methods. We're basically able to go faster and build better molecules, which is pretty cool. It allows us to be very creative because we can iterate faster and over concepts and design proteins that perform better.
Another challenge had always been sort of this limited exploration of molecular structure. These AI tools allow us to build complex structures. I always think of it as it's opening this universe and allowing us to dream bigger and be bolder about what kind of proteins we actually make. I think it's really accelerating our preclinical discovery and broadening our potential therapeutic reach. I'm going to show you a few movies just to show you what the protein design cycle looks like. This is a common paradigm in the field that everybody is starting to use now for AI-designed proteins. It's this four-step sequence. Each of these steps has a number of AI tools embedded, baked into it. I'm just showing you the basic concepts here. The first thing we want to do is we want to design the fold.
We got to have the protein make the right shape. There are these new diffusion methods that take atoms that can be randomly distributed in three-dimensional space and over the course of the simulation can start folding into things that look more and more like the target protein structure that we're trying to make. You can see the simulation. If it finishes, we'll come into a structure that looks like that. At that point, you have the fold that you're looking for, the shape you're looking for, but you need to design a sequence to go on top of that. The next step in the process is a sequence design step where basically these new algorithms can just decorate these protein structures with amino acids that help it fold into that shape.
Once these simulations are done, you basically have an amino acid sequence of a protein. You could go into the lab at this point and just go try to make it if you wanted to. However, there's been a lot of innovation in the structure prediction field. Actually, the Nobel Prize in Chemistry is won for this purpose because tools like AlphaFold are so good at predicting a protein structure based on its sequence, we can use it in design. We can ask ourselves, in this design sequence that we just made in this pipeline, how well does that actually fold into our target state? Sometimes it says yes, and sometimes it says no. I'm just showing you an example here where obviously it looks like the desired protein. The last step is a structure filtering step.
This is because this pipeline generates lots and lots of designs, tens of thousands to a million designs, right? We're not going to go make all of those in the lab. I kind of think of this structure filtering as sort of the special sauce of protein design. Different companies do it differently. Different groups do it differently. Basically, you take a number of different designs and select out the ones that you want to go experimentally test. Okay. Now you're protein design experts. I'm going to show you three vignettes of how we're using this design to actually design proteins at Moderna. First is designing vaccine candidates, okay? Here's an example where we had these two proteins, this green protein and this red protein.
We thought, "Hey, these might be pretty good based on the biology proteins that we want to make a vaccine for." However, when we went and made these proteins in the lab, they just fall apart. They are not good vaccine engines. We had no way to make the vaccine. What we did is we ran that cycle that I explained to you by this movie. In the green case here, we built a de novo dimeric scaffold that actually stabilized that protein. In this case here, what we did is we actually connected two parts of the protein with a brand new piece of a protein. In both cases, when we go make these in the lab now, these proteins expressed and are good proteins, and they behave really well.
Further than just making the proteins, we've even done mRNA delivery of some of these AI designs. I'm just showing you data here where basically the gold standard in this vaccine is in these squares, and our AI design is in these triangles, and curves shifted over to the right are better vaccines. Basically, our AI design in this case was more potent than the gold standard. We're pretty excited by that. Another class of proteins that we like to try to engineer are these proteins called self-assembling nanoparticles. These are proteins that are multivalent. What's great about that is you can put your vaccine antigen onto these proteins and get multivalent display. Immune systems like proteins like this because it looks like a virus to them. They amplify their response when they start seeing multivalent proteins.
In the past, people have used lots of naturally occurring self-assembling nanoparticles. Now with AI, we can actually just build these from scratch. That's what we've been doing. We've been just building these de novo nanoparticles from scratch. I'm just showing you an example of one here. I'm showing you data confirming the assembly of this when we go make these proteins in the lab. The last vignette here is this idea that there's a lot of interesting biological molecular surfaces out there in biology, and we may want to develop binders to them. One classic example here is a peptide MHC complex. These are targets of T cells. It might be good for us to be able to design binders de novo against these types of targets. That's what we've been doing.
Here's one case where we designed what's called a mini binder. We had allowed the protein to fold into any shape it wanted to as long as it could bind the target. Where's my mouse? Oops. We diffused it just like using that pipeline. We came up with proteins that look like this. This is a three-helix bundle protein. We can also ask for antibody-like molecules. We have also been doing that, just de novo design of antibodies. We can see here that this is a case of one of our mini binders that basically this is a binding SPR experiment. You can see that the on-target binding is very strong, whereas the off-target is very low. That means a peptide MHC complex that is closely related but not the right one, we have no binding to.
This specificity is actually really important for this type of application. We're able to accomplish it through de novo design. Okay. Besides this, I think, really exciting protein design cycle that we can do, we can design proteins of all different shapes and functionalities. Our group is using AI in many different aspects of protein design. One is using these large language models as scientific intelligence systems that know all about the research that's going on around the world and about our research and can help us brainstorm about what are the possible next steps. I mean, we've made some of these proteins. What should we do next given everything that's going on out there? I think that's been a really great use of AI in our group. We've also built focused GPTs.
Both computationally and experimentally, we're generating lots of data, large amounts of data. The question is, what does the data mean, and what should we do next? These GPTs are helping us as data analysts to analyze all that data and to help us come to some conclusions. Lastly, there's agent AIs, which you've heard a little bit about these agents previously. We use it for designing the code to build these protein design pipelines. I've been coding for many, many years, and it's always been a slow process. Now with these agents, you can actually just launch them and have them generate the code, test the code, and deploy the code. It's just been amazing that it can do all these things so well. I mean, software engineering has been a really great implementation of these large language models.
What's important is that that cycle that I was telling you about on the first slide really needs to connect to a large compute infrastructure because we need a lot of computers to do what we're doing. These types of agents can actually build in that infrastructure for us. Why does everything I told you matter today? I mean, first of all, this automated coding allows us to really rapidly assess new AI tools. There's new AI tools coming out all the time that are amazing, have amazing claims. Sometimes they're great, and sometimes they're not so great. Sometimes they will be good for Moderna's applications, and sometimes they won't. We need really quickly to implement them. These agents allow us to do that. They build it into the pipeline really rapidly.
I think also what matters here is that we're able to rapidly develop really high-quality drug candidates. The proteins we're making now are just way better than the previous generation of proteins we're able to make. They're more stable, they're more specific. I think that's going to just be better for developing medicines around the company, really. The last thing is, which is I'm sort of most excited about, is this idea that you can dream bigger. It allows us to think about new concepts we could engineer going forward that we really didn't have the tools in hand to do at all. That's one of my most excited things about AI, really, for protein design. I have this tagline here. It's like a co-scientist.
It's allowing us to sort of go faster, make bolder advances, and really help biomolecular design across Moderna, and hopefully bring medicines to patients faster. That's my last slide. Thank you. There we go. Did you guys need a mic?
Yeah. Awesome.
Okay.
All right. Thank you all for sticking around. Yeah, we are last but not least, and hopefully tell you some exciting updates. I am Christine McKinney. I lead the cancer vaccines research team. These are concepts we call cancer antigen therapies externally. These are pioneering medicines, and we have many in the clinic at this point. They are all centered on and all designed by algorithms, which Wei is going to tell you a little bit about later.
Both the personalized product, the individualized product, which is in partnership with Merck, as well as the shared products, the off-the-shelf products, are all designed by these algorithms. Fundamentally, the way these medicines work, right, is by hacking into a common piece of biology, which is that all cells in the body are displaying on their surface a sort of fingerprint. There are pieces of proteins that are expressed in those cells and then displayed on the surface. What these product concepts are about, the antigen therapies, is actually trying to teach the immune system how to recognize the fingerprints that are specific to tumor cells and not normal cells. As you can imagine, making sure that you get that fingerprint right is at the heart of both the safety and the efficacy of the drug.
Although the techniques for looking for these fingerprints are getting more sophisticated, it's a huge amount of data. It's complex data, it's noisy data, and it has a lot of features that bear both on safety and efficacy. It's an amazing place for AI. We've deployed it in order to make sure we're leveraging all of those data, not only to actually discover those fingerprints so that we can design the drug, but also in terms of which peptides are present on tumors and not on normal cells, also looking forward to the next step, which is which of those fingerprints can actually be capable of being recognized by the immune system. Both of those, Wei is going to talk about in more detail. With no further ado,
I can stay here. Thank you, Christine, for the introduction. I'm Wei.
As Christine mentioned, I will walk you through this biological mechanism behind all of our cancer antigen therapies, including in T cell as well as off-the-shelf cancer antigen therapies. As Christine mentioned, the biological pathway for this antigen display and the T cell immunogenicity is very complex. First, your source protein will go through this transcription and translation process according to central dogma. That is not the end of the story. After the protein is expressed, it is actually going through a really complicated pathway depicted by this figure below to display on the cell surface, which is that peptide MHC complex Dan just mentioned. Once this peptide is displayed on the cell surface on the MHC complex, it still needs to be recognized by T cells and trigger T cell immunogenicity, which is the holy grail that activates everything that is underlying of our drug efficacy.
Today, I will talk to you about three things. First, what is the key technology that enables us to recognize these patterns on antigen display and how we're using AI tools not only to make this pattern recognition much better, data collection much accurate, but also use it to integrate the large volume of data to make predictions. Even if we don't do a very labor-intensive experiment, we can also predict antigen display very accurately. After that, I will talk about the impacts of this technology to our current pipeline programs. At the end, I will talk about some forward-looking things about new data modalities that's emerging and new models we're trying to build. First, I want to introduce this powerful technology called immunopeptidomics. Sometimes it's shorthanded as IPMS, which stands for immunoprecipitation and mass spectrometry.
Those are the two key steps used to acquire such antigen display data. After we acquire data using this instrumentation, we need to analyze this raw peptide spectra information. Right now, we are actually at a very exciting point, almost like a single sequencing to next generation sequencing inflection point over two decades ago. For this IPMS field, we're seeing the explosion of data volume. We're seeing the instrumentation getting better and better in terms of data throughput and quality. At the same time, we're working with a lot of partners using best AI tools in the field to be able to analyze this data really faster and with better quality. We actually did some quick calculations on how much better we're doing right now, just comparing to one year ago before we acquired this powerful instrument internally and deployed these AI pipelines.
These days, we can actually acquire five times more high-quality peptides that are coming through this IPMS instrumentation. We also get 65% higher quality identifications just based on some key metrics in the mass spectrometry field. We are also doing two different data acquisition modes. One is called DIA, data independent acquisition, and the layer on top was a more sensitive data dependent acquisition. By combining this and using AI to rescore the target identifications, we get 1.7 times more high-quality targets. Finally, we have integrated all these great tools that I have small logos in the bottom pink box into an in-house pipeline that is fully automated. The processing time is much, much faster. When you feed it with raw data coming from an instrumentation run, you can get the results after two hours, which is already state-of-the-art performance.
With that, I will show you two case studies of using this IPMS technology and AI pipeline. On the left, I'm showing you a preclinical cancer antigen therapy program we called LION because cancer antigen therapy is shorthanded as cat. LION is a very powerful big cat. We have very high hopes for this cancer antigen therapy. Originally, we designed a bunch of antigens to come into this drug product. However, one of the designs, as you can see from the second flow cytometry plot, does not have the optimal protein expression features as measured by flow. We quickly come up with multiple redesigns and highlighted two very promising designs, DAMD design 10 and design 12, which showed promising signals on flow cytometry. As I mentioned previously, protein expression is not the end step.
We still need this protein to be digested, peptides presented on the cell surface. That's where the IPMS really comes into play, where you can see with this orthogonal technology, we quickly proved that these peptides originated from design 10 and design 12 can be detected on the cell surface on the right MHC complex. Those are the data we collected very fast with this internal latest mass spectrometer. Three days after we acquired all the raw data from multiple designs, multiple technical replicates, we were able to comb through the data within three days and inform a locked drug candidate to go through the next stage of preclinical development. That's the power of Wet Lab and AI pipeline combined new technology. We also know that we're not the only one leveraging on this technology.
In fact, the whole field has been painstakingly collecting such data over the decades, although the instrumentation was not as good as current right now. There are a long-standing effort to try to predict what antigens can be displayed, what antigens can be displayed on certain HLA alleles on the cell surface. These kind of models are very important for antigen design. That is also this kind of public data is at the core as one component in our intrinsic algorithm. What we're trying to do here is to keep developing even better algorithms that fit our purpose. In the real application scenario, such as intrinsic, we care about the very specific feature of such models. We need to make sure that what we called as most likely to be displayed antigens, they are truly positive.
Using this specific positive predictive value metric, we benchmarked our internally developed AI model against all the state-of-the-art models, including some really well-known ones out in the field. You can see that the pink curve on top is the model we currently have developed in the research environment, trained only on the public IPMS data. I just told you that we now acquired this latest instrumentation. We have these very fast AI pipelines. We are now collecting proprietary data. We also have ongoing collaboration with Hematics, which is another expert in this field. Together, we are really trying to push this frontier for cancer antigen therapies and aiming to develop the best-performing model. The last piece is looking forward. What's next in the field?
Once we have a highly accurate antigen display model developed, how do we make sure that these antigens are really triggering T cell immunogenicity? Right now, there are many great thinkings in the field leveraging AI, leveraging different biological principles. We have seen many, many TCR agnostic immunogenicity AI models out there in the field. We are also developing our own. Right now, we have some ready-to-use research models that we are investigating. They usually rely on some antigen-level specific experimental data collected through your traditional immunogenicity assays, such as isobot or intracellular staining. However, another very exciting new emerging trend in the field is that we are seeing more and more TCR-specific data emerging, where you will have a specific peptide on a specific HLA allele binded to a specific TCR sequence. This type of binding data was very hard to come by in the old days.
Since the pandemic, we have seen adaptive depositing millions of such binding data into the public domain. That is in the infectious disease field. In the cancer field, we are also seeing this specific binding data to be collected at a rising data throughput. We are hoping that with such data volume explosion and more and more novel, innovative assays being developed, such as the ones in the gray bubble using library-on-library high-throughput screening, we will be able to collect TCR-specific training data to design even more powerful AI models to hopefully get to more accurate predictions on any therapeutics that rely on T cell immunogenicity as their mechanism of action. Internally, we are also in parallel developing our own Wet Lab assays to first be able to validate this model performance benchmark and be able to complement all the external landscape.
This is a very exciting time for our antigen selection. We are very happy to leverage AI for all of these research activities. Thank you.
To all of our presenters, I think that was very helpful for our audience. I just wanted to especially thank everyone who also helped on the technical side. Thank you very much.