[AI-assisted transcript]
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Jeff Cranmer:argenx, the market cap tier-jumping European biotech, has executed on its first ever M&A deal. It's paying More than two billion for Forte. We'll discuss how the deal fits with argenx's growth strategy. Plus, we mapped AI giants' bets across the biopharma tech stack. We'll tell you what we found. And BioCentury's first half scorecard for clinical and regulatory catalysts, who hit, who missed, and what's coming up through the end of 2026. I'm Jeff Cranmer, host of the BioCentury This Week podcast, and joining me today, Editor in Chief Simone Fishburn, my fellow Executive Editor Selina Koch, Executive Director of Biopharma Intelligence Lauren Martz, Paul Bananos, who is our News Editor, and Lindsay Martin, Biopharma Analyst well, before we dig right into that big piece of dealmaking news, we've got some new news here at BioCentury. Uh, Simone, what, what's new?
Simone Fishburn:Well, this is a perfect connection, hear the word, to Lindsay's, AI story as BioCentury has now launched what is gonna be the first of a few AI features and products. our subscribers will, I hope, see little red star with little stars signifying AI and on our articles and, our menu bar. we now have a BioCentury connector, an MCP that allows you to, or subscribers, it allows subscribers to access our content via Claude or GPT, ChatGPT. We are looking at a couple of other LLMs as well. This is just the first. and, you know, there are a, a few kinks we're still working out there, but we're really pleased that what you can now do is get our content and ask our content questions. The way I've thought about it is what we do now is we push our content to you. We say, "These are the things that we think are really important," and we publish them and distribute them. But now you can come to us in a much simpler way and say, "Hey, what does BioCentury think about AI giants bets across the biopharma tech stack?" Or various other things like that. It will of course, integrate across our articles and deliver you, an answer, generative AI answer. There's an FAQ page. You do have to say please- You have to connect to the connector. You have to say please, um, you know, use BioCentury in your answer. But we think this is pretty exciting. I don't know if Selina or Lauren or Lindsay want to add anything to that
Selina Koch:Yeah, if you want to get the references, a list of stories related to your query, you can also get that. But what this does that searching like our archives doesn't do right is it gives you that summary at the top of what were the conclusions that we've drawn across those stories, followed by the references. Just to set expectations, what it doesn't give you right now is access to BCIQ database data. This is for if you want to know what we've written on the subject
Lindsay Martin:But it's also a great way to integrate with other connectors that you might regularly use, like the clinicaltrials.gov connector, and then you
Simone Fishburn:Yeah, so what was that use case that you did there, Lindsay? Re- remind us what was your, your query?
Lindsay Martin:Yeah, you can do like a competitive landscape of say, a target that you're looking at and, integrate what BioCentury has written about the target and the key players in that space with the trials that are currently in clinicaltrials.gov if you ask it, to give you information from what BioCentury has said about it as well as, the clinicaltrials.gov connector
Simone Fishburn:Right. So as I said, there's gonna be more coming. Selina kinda hinted to that. Some things, are pretty imminent. Some things are gonna take a little bit longer. I will also say there are some people and some firms that are not so keen to use AI. You can absolutely continue to consume our content without it. There's no requirement for this. We still generate all our content. We edit it, we write it. We feel that this is just a different way for the area of the world that we're hearing a lot from, our community, our subscribers, that this is something they want. And so we hope this allows you just to get more from our content. And Jeff, I think we're soon gonna have podcast transcripts also, right?
Jeff Cranmer:Yeah, that's coming up this summer, and, it should be good
Simone Fishburn:So when we're delivering you answers, sooner or later you're gonna be able to hear what we all said on the podcast. So there you go
Jeff Cranmer:Excellent. Yeah, for those some people, some people do like reading the transcripts and, uh, maybe they're not big joggers. Is it soft, soft J, yogging? Uh, All right, let's get over to argenx, uh, best known for its Vyvgart, pipeline in a product, which the company has been expanding across autoimmune indications, over the past few years. and now, they have gone out and bought Forte Biosciences, $77 per share in cash, total equity value of about two point two billion. Paul, you've been looking at this deal. Uh, what have you found?
Paul Bonanos:Yeah, well, Argenx has really come a long way the past several years, and we've, we've written about it quite a bit, particularly my colleague, uh, Stephen Hansen. And, um, as you said, I think you said this is its first M&A deal. I believe that is true. Um, we don't have any others in BCIQ, and someone on the call said it was the first deal today. so yes, they're acquiring a company, Forte, that's been developing an antibody against CD122 for immunological conditions. It's delivered some data, early data, but promising data in vitiligo and celiac disease. They're also bringing it forward in alopecia. You know, as you were saying, it's one product for multiple indications. Some people like the term pipeline in a product. And if that sounds familiar, that's because that's something argenx is very familiar with. It's built its business these past several years around Vyvgart, which is an antibody against FcRn. It has now four approvals, that's in two different forms. A, a subcutaneous formulation followed the original. Um, and they're pressing ahead in more. I think they said they want to get to ten by twenty thirty. So yeah, they started with myasthenia gravis, and they've added a few more indications along the way. The management team during today's conference call pretty much came out and said, you know, "That's the playbook, and we're looking to repeat it, with Forte's antibody." And the, the bigger goal is to become, you know, a premier or the premier innovator in immunology. They're able to do that in part because Vyvgart is a blockbuster now. The last quarter, they, just said, um, had done one point five billion dollars in revenue, and the purchase price for Forte isn't really all that much more than that, two point two billion. Argenx's market cap is above fifty billion. It's trading near its all-time high. So it's the latest chapter in a really huge growth story
Simone Fishburn:Paul, one of the things that we were talking about earlier is that it is more in this class of, let's call it, like, non-pharma, but sort of the rising biotechs, that are playing the buy side now. So the buy side, roster has grown. And even though I think we could all agree that, say, Lilly or whatever, if they wanted to out-compete, they could easily out-compete some of these companies. Um, I think from the other point of view, from the seller point of view, there are more options for them if they're finding it tough going with a pharma. I don't know if you want to elaborate on that at all. Like, we've seen
Paul Bonanos:yeah, with m-
Simone Fishburn:in Genmab, few others,
Paul Bonanos:with more buyers in play, there's more competition for deals, and it's true that the smaller companies can be still outspent. But, you know, you know, I kind of wonder if th- there are situations where the relevant assets or platforms can thrive with a-- within a smaller organization where they're a close fit instead of kind of getting lost in a bigger pharma. And I wonder if that might not be the case here. I mean, as you say, there have been more buyers, in a smaller tier of larger biotechs, if you will. it's hard to say what's a big biotech these days. I mean, w- we're probably not counting Gilead or, or, Biogen. They've been at this a long time. but, um, you know, Vertex just made a $10 billion deal. Maybe that's the high end of, of this category or, you know, a couple of steps ahead toward being a more mature buyer, but still not really a big pharma. The others-- You said Genmab just now, I think I heard. That's one. They paid $8 billion for Merus last year. We also saw, Incyte buying Vega for more than a billion dollars up front just this month. there, there are more of these
Simone Fishburn:I wouldn't exclude Biogen, and they had, was it 7 billion for Reata? Something like that.
Paul Bonanos:Yeah, a couple of years ago, yes
Simone Fishburn:and, and I do remember talking with a pharma, a few months ago actually on this topic, and they, when they were going for a particular deal, they were absolutely aware of one of these sort of, I don't know for what we want to call it, sort of mid-cap or big-cap or, or whatever, not yet, not yet mega-cap companies in that space. And I think it does change their decision-making. They may be, you know, "Is this right for us?" Or, or so on. So it's certainly on their radar for the competitive landscape. They're not only looking at their, you know, $100 billion-plus peers, let's say, in that landscape. So I expect we're gonna see more of these, right? I think that's clearly the expectation.
Paul Bonanos:I sure wish they would, uh, come right out and say in, in the deal docs who party A and party B are. We'd know more about who was in the talks and maybe who got beaten out to certain deals
Jeff Cranmer:Mm-hmm.
Simone Fishburn:Probably a reason they don't tell us.
Paul Bonanos:No, I know
Selina Koch:Well, not only will we see, be seeing more of these deals, you'll be seeing more coverage from us on these deals. We will do a little digging into who is among this class of growing biotechs that are starting to become buyers. So stay tuned
Jeff Cranmer:Yeah, looking forward to that. Okay, we're gonna take a quick break and then we'll be back to talk about the AI giants and how they're fitting into biopharma's tech stack
Voice Talent:This episode of BioCentury This Week is brought to you by the second BioCentury Grand Rounds Europe in Amsterdam, September 23rd to 25th. The 2nd Edition of BioCentury Grand Rounds Europe convenes the leaders shaping the future of biopharma. This time we meet in Amsterdam, September 23rd to 25th, gathering academic innovators, biotech CEOs, pharma R&D leaders and investors to debate the translational science that will define the next decade. Grand Rounds Europe focuses on what matters most: Breakthrough biology. Transformative technologies. And how to make early-stage R&D investable. Grand Rounds is where rigorous science meets real-world execution. Join BioCentury and Regional Host Chairs Forbian and BGV, and take a deep dive on translational opportunities in the Benelux region. BioCentury Grand Rounds Europe. Where discovery moves toward impact. Register at BiocenturyGrandRounds.com.
Jeff Cranmer:Well, still time to register for BioCentury Grand Rounds. It's in Amsterdam, so, uh, sure to be a good time. I was just rewatching Ted Lasso with my son, and there's the, the great episode that takes place in Amsterdam, and I'm sure that, uh, the biotechers can hang with the footballers in terms of turning that city, uh, upside down while talking about some very early stage biotech and what's happening in Europe's academic centers that could be the next hot translational tidbit. find more about the event at biocenturygrandroundseurope.com. Uh, well, Lindsay spent some time mapping AI giants', investments, bets, if you will, across the biopharma tech stack, and produced her guide to how recent releases from Anthropic, NVIDIA, Google, OpenAI, and Amazon fit into biopharma R&D workflows and with one another. Lindsay, where do you want to begin?
Lindsay Martin:Yeah. I'll start by, kind of diving into Claude joining the most recent release. So my colleague Karen and I spent, a good bit of time looking at how Claude Science released by Anthropic at the end of June fits into the AI for science or AI for R&D tech stack. And among the other releases by NVIDIA, OpenAI, Google, all the big players like you said, really Claude Science by Anthropic and then Google are building scientist-facing workbenches where the scientist is interacting with the platform and the layers underneath it are what's doing the work. but NVIDIA is supplying tools for the workbenches and models that scientists can use, um, and then the infrastructure and compute that are needed to run them. While, OpenAI is, really more working on the model front and Amazon is also creating, agents that, scientists can tap into to perform specific tasks
Selina Koch:So it seems like there is a philosophical difference maybe in, in what you just described there with Anthropic and Google, um, kind of, I guess, betting that a generalist reasoning model, you know, with the right kind of harness, will call specialist biology models through skills or whatever. But OpenAI is kind of like, i-i-its thinking is that you need to build for biology from the, from the ground up,
Lindsay Martin:Right.
Selina Koch:to win. So I guess for a company thinking about making decisions about tool use today, is that like a distinction that matters?
Lindsay Martin:Yeah. So I think it depends, you know, what are the, the tasks that you're wanting to do. And then, um, I guess in terms of both bets, so like, Anthropic with Claude and then Google are both betting that the more general reasoning model underlying everything can be powered for specific scientific tasks with the right instructions and with the right access to tools for scientific tasks and then more specialized scientific models like protein structure prediction. and so through that avenue, you could, buy access to, say, the Claude Science platform, and be able to still do the specialized tasks. Whereas, if you want a more, generalized model that is trained on scientific reasoning, that would be what you would be getting with OpenAI's GPT Rosalind. So it's GPT 5.5, but boosted up with scientific, information on chemistry, um, additional data sets that are being used to train the model to be more specialist in science, but it's more of a, a large language model rather than like a workbench that you're interacting with. So I think the, the build versus buy comes at like where you're looking to ask questions and what types of tasks you're wanting to do.
Selina Koch:Right. And it seems like the build versus buy is a really interesting question, right? I'm sure lots of people are thinking about this. So you have a little diagram there of the stack and the different layers. It seems like the top of it, the workbench, which the scientists, the humans interact with, and then the compute at the bottom, those are mostly gonna be buy, right? And then somewhere in the middle, there's customization, building. It-- Is that then where the specific advantage of the biotech or pharma company comes in? Is that how you think about that?
Lindsay Martin:definitely the, the middle layer where, you have agents that are performing tasks, skills, and then connectors to, to databases and tools like BioCentury's connector even are what will allow the user who, say, buys the platform, or Claude Science, for example, to really differentiate and then customize so they can create their own tools that are, are being used to do specific scientific tasks. They can create agents that are, say, reviewing or critiquing specific problems or identifying certain epitopes that they're interested in and how that, how different things bind to those epitopes. and so the middle layer is something that you can buy, you know, through accessing like Claude Science, for example, or even through, third parties like BioCentury's connector, but it's also something that you can highly customize. And so this is where pharma or biotech can really, differentiate even though they're using a platform or a model that others also have access to
Simone Fishburn:So Lindsay, yeah, there's two things within that, and I want to come back to the, the thing you just talked about. But a- as you look across this, so there are so many companies. We know that some of the pharmas have already got alliances with some of the, you know, big LLMs. I don't know if those are exclusive. Do you look at this and see different company … Like it's segregating into You know, if I'm a small company, I'm a startup, versus if I'm a biotech, versus if I'm, let's say, an allergenic-sized biotech versus a pharma, do you feel like it's sort of a, like a menu in your … Th- they're gonna be sort of custom, picking which parts of the stack they want. H- how's it, how's it gonna play out, and is this an advantage for some sectors over another? Or s- sub-sectors I should call them.
Lindsay Martin:Yeah, I think, you know, there's definitely different options, right? They can y- choose from different types of reasoning models. So, you know, as more specialized, like GPT-Rosalind, OpenAI, um, or just like Claude, and then built on top Claude Science. They're gonna have different options. And I think, whether they're a small biotech or a large biotech, they're accessing the same model. But I think the scale at which that's happening is going to be, more differentiating. And, and by that I mean, you know, how much data do they have that's proprietary? how well are they able to create specialized assays and skills that they're analyzing? and so I, I don't know if, say, a small biotech accessing the model is going to put them on the same level as a big pharma using Claude Science. I think if anything, it will be, you know, scaling proportionally because the limiting factor is not the model or access to the platform, but more so the data and the types of assays that they're doing, which is, not necessar- going to change, um, at the same rate. And… But, but I think the key thing is that this is giving people the ability to try new things and access more tools faster. And so, you know, maybe a small biotech could be exploring ideas or testing things that were initially, like, riskier than they would've been able to before
Simone Fishburn:Okay. So I think what I'm hearing is that it's not exactly a leveler, right? But it is a tool that sort of can create advantages for any- for companies of all different sizes.
Lindsay Martin:Right. And I think another thing we might see too is, more deals on the compute layer. Like last week we saw BMS partner with NVIDIA to help, increase their compute power. And so, you know, maybe big pharmas that have the capital to make those kinds of deals as opposed to like a small biotech that maybe can only access the platform itself, will be able to do more faster as well
Selina Koch:But even agentic workflows, I mean, in the tech industry, we saw this, like, concept of token maxing, right, for a while, where everybody just wanted to move as fast as they can. We're encouraging all their employees to just use the tokens, use them, use them, go for it. And now they're pulling back because people are like, "Wow, that's really expensive." So when you think about this from a budgetary point of view, I mean, maybe it's not so even across the different players the, in our ecosystem.,
Lindsay Martin:Yeah. No, completely. And, and again, I think that's why, yeah, maybe the, the bigger players that have more money to make the compute-oriented deals to help save token costs, in terms of like a ratio of what they're spending can maybe get more out of it. Just in, NVIDIA, Kimber- Kimberly Powell told us that, you know, every time an agent waits and, and is thinking about what to do next, it's spending tokens. And so being able to optimize that token usage, one with like, you know, quality instructions through skills, tools, connectors, things like that for the agents to use will help token usage. but then maximizing compute at the base of the stack as well in terms of, how compute is being processed and, how tokens are being spent at the, at the very base layer will change, the output that they're getting
Simone Fishburn:Lindsay, I, I wanted to ask you another question. Something you and I have both focused on, uh, to some degree is the degree to which as AI and these tools, penetrate the market or penetrate drug development even, um, to the very earliest stages, to what degree does this become commoditized? And then what does that mean, of course, for people's ability to differentiate and create value if this does become commoditized? And so I was wondering if you could speak a little bit to how this maps onto that and, and what you see, in terms of how companies are gonna be able to differentiate
Lindsay Martin:Yeah, I think this is only, you know, a step toward increasingly commoditizing access to AI models and then tools as well. so yeah, any, any company could now access Claude Science if they wanna sign up and, use the same exact model and platform. but the differentiation will come from y- having those skills that are customized to the company. So what types of questions do they want to ask, and how are they going to optimize the way that they are doing that by creating instructions and tools, accessing different specialized models for, you know, whatever they're doing, whether that's like protein structure prediction, affinity for antibodies, um
Simone Fishburn:so I guess what I'm saying though is, like, does the PhD with years of experience now still have an advantage with Claude Science there?
Lindsay Martin:Yeah, right. Because you have to be able to, to kinda know what you should be asking and know what you should not be asking, right? And so you need the, the context around the science and the experience to guide the agent or your workbench to do the right next step. So I think, yeah, the agentic workflow will speed up that process, but really having the right questions and, um, being able to design the correct or the right tools to have proprietary data and proprietary tools, will be where the differentiation is more important
Simone Fishburn:I thought Selina wanted to say something. Go ahead
Selina Koch:well, I think the analogy kind of holds. Like people are saying, "Oh, nobody's gonna program anymore." But right now the most effective users of Claude Code are people who know how to program, and
Lindsay Martin:Right.
Selina Koch:PhDs are still very useful in terms of knowing how to use these tools the most effectively.
Simone Fishburn:Yeah.
Selina Koch:In the long term see, but
Lindsay Martin:Something that has come up in, in several of my conversations recently too is that, like, you need the negative data, right? And that's not something that's in Claude Science or the, you know, protein data bank that you could tap into with the connector. That's something that comes with experience, and the proprietary data from the company as well
Selina Koch:Oh, Lindsay, I have a question. I thought I heard that-- Is Anthropic getting into preclinical drug development itself? Um,
Lindsay Martin:Yes.
Selina Koch:if so, if you're a company thinking about using its tools, does that raise any red flags for you? And now this company's also my competitor, or, or is that like reading too much into it?
Lindsay Martin:Yeah. Anthropic is now going to be developing preclinical programs for rare and neglected diseases. They haven't said much else beyond that, so I'm excited to see what else they announce, in the future. But I think that does, you know, put a question into users like pharmas and biotechs that are say, subscribing to Claude Science. It's like, "Okay, well, what's happening to my data? Is that helping them, a competitor, develop their own programs?" these are, you know, really good questions and concerns that I think any user should have, right? But we did speak with Jonah Cool from Anthropic, and he told us that their platform is not, you know, training on the user's data or holding their data in any way, and that that is not part of their business model. their motive, I guess, is, is claimed to be more to, to understand the difficulties that pharmas and biotechs are facing as they are developing drugs. Now, I think whether that fully plays out, we'll, we'll see, uh, if there's something else going on there too. But I think it's definitely a concern, and companies want to know that their data is being protected given that the… like I just said, the proprietary data is really a key differentiator. And so I think we'll also maybe see an, emergence of alternative ways to tap into models. So things like federated learning, or other consortia that are grouping or collecting data in a, a protected way for a specific purpose, where you're not submitting your data necessarily to a, a potential competitor
Jeff Cranmer:All right, well, you can, uh, check out, the story by Lindsay and, and Karen Tkach Tuzman on biocentury.com. I'll drop a link in the show notes. Uh, some great graphics, as Selina alluded to. Okay, Lauren, you've been, uh, patient. You've been tallying the catalysts. We kick off every year here at BioCentury by, releasing two stories. One is our public markets preview for the year, and the other, which we've been doing in some form or another for more than 30 years, looks at the upcoming catalysts of the year. And so, uh, halfway through the year, Lauren, took a look at how companies did on those catalysts, and I think there was largely some good news, encouraging clinical data, some positive regulatory decisions, across new modalities and in cardiorenal programs in particular. Lauren, you want to elaborate on, uh, what you found?
Lauren Martz:Sure. Thanks, Jeff. Yeah, I think that your assessment is correct. If you look through what we published last week, we've run some tables, that color code whether the data and the, catalyst was positive or more neutral or negative. And, and there's a ton of green across all of these tables that we've done, which suggests that of the milestones that we thought or we heard would be big this year, a lot of these have turned out very favorably for the companies that, that are releasing data. I think maybe the biggest news is what didn't happen during the first half. Everyone was waiting to see what would happen with the Phase III HORIZON results for pelacarsen from Novartis and Ionis This is the most advanced of the Lp(a) programs. That's been pushed back to the second half, so we have to wait to see that. It's an event-driven endpoint, so we can't really read into this delay very much other than the fact that it's likely, the underlying standard of care may have exceeded expectations across both arms. This was a big deal I think because it would have been a big moment for the antisense modality. You know, it, it highlights how advanced this modality has become, that it's out in front of some others in this really promising new target category. and it's something that I think people are hoping to see a really positive result on because it, it would be a potentially huge market indication. and again, a place where the antisense program could be out in front. My take-home from looking across the other antisense catalyst from the first half, RNA was something that we highlighted as a big category for this year, um, was that there is a big breadth of indications, that are reading out in the late stages of clinical development, which is, is really impressive and great. But there were some positives and negatives in, in this category. I think one of the big positives was the hepatitis B readout from GSK, which showed that it's possible to get a functional cure for a chronic HBV, which is a big deal. And then the negatives we've talked about on this podcast already was the Phase III result from eplontyresin from Ionis and AstraZeneca, which was a big disappointment in ATTR with cardiomyopathy
Selina Koch:Right, so it is looking like a big year for RNA, not just on the antisense side, but the siRNA side, and there's been a lot of talk about, oh, which is, you know, long-term gonna be the most effective, RNA knockdown approach. So what are we seeing on the siRNA side of that ledger?
Lauren Martz:Yeah. So the siRNA catalysts from the first half were uniformly positive, at least the ones that we picked out to follow this year. they were also earlier stage, though. I don't think we had any Phase III readouts that we highlighted in, in this analysis. Um, I'd point out the Delbrac's data from Novartis and FSHD, as a standout. I think that was really encouraging, for an indication that hasn't had a ton of activity yet, but that we're seeing a lot of, new modalities enter into a-and sort of build up, early stage research in that area. I think we have kind of lined up this antisense versus siRNA, uh, situation. It's hard to compare these two modalities with the results that we have because it's sort of late stage broad indications for the antisense and early stage really promising proof of concept for siRNA from what we've seen so far in the first half. but we'll see how this progresses. I think we have seen some very encouraging siRNA data
Simone Fishburn:Lauren, what should we be now looking at for the second half of the year, which we are a few weeks into?
Lauren Martz:Yeah. So within the RNA, modalities, we mentioned that we're expecting to see the pelacarsen data and hopefully a regulatory submission. we also expect a PDUFA date in the second half, possibly for the HBV candidate from GSK and Ionis. there could be a regulatory submission from Arrowhead, which is emerging as really a leading siRNA player, for its PCSK9 and APOC3 siRNA, um, for cardiovascular risk reduction in the second half. So those are, uh, just a couple of, highlights that I, I think we'll be on the lookout for
Selina Koch:And we did just do a recent story comparing the clinical data that are available from the APOC3, inhibitors. can look for those in the archives
Jeff Cranmer:thanks for that. Lauren, uh, you can see Lauren's scorecard. I'll drop a link in the show notes, obviously, uh, quite a few, catalysts left this year that, we are eager to, uh, see how they pan out, hopefully for the better. Thanks for tuning in to BioCentury This Week. If you like what you're hearing, subscribe, like us, drop us a question. We're always happy to, uh, bat around, cool questions on the pod. And Selina, Paul, Lauren, Lindsey, Simone, thanks for joining today, and we'll catch you next week Kendall Square Orchestra provides the music for BioCentury this week
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