GSK plc (GSK) Earnings Call Transcript
September 23, 2026
Earnings Call Speaker Segments
Hi, everyone. Thank you so much for being with us here today. My name is Courtney Breen. I am the U.S. biopharma analyst here at Bernstein. I am thrilled to be sharing the stage today with Kaivan Khavandi. He is the SVP and Global Head of Translational and Development Sciences at GSK. I know you have a focus and is responsible -- and are responsible for respiratory, for hepatology, for immunology, inflammation.
Can you give us a little bit of context about the scope of your role and the things that you focus on in the company because it sounds quite vast.
Yes. Great. Well, first of all, Corey, thanks for having me. So the role, I guess, has 2 aspects. One is the end-to-end accountability for R&D for respiratory, immunology and inflammation. So that's a fully integrated unit from discovery, target choice through to translation, clinical development, assets, leadership through to product approval. So the full life cycle of assets as you described, it currently represents a lot of activity through to the late stage in respiratory medicine and hepatology, but also, I think, some very promising programs in what we're increasingly thinking of just as comorbid disease that's the consequence of chronic inflammatory pathology. The other half is the Translational Development Sciences, which is a relatively recent organization, which integrated 3 key components of the organization across categories, so oncology, specialty and vaccines ID, and that was Asia R&D for reasons that probably are obvious and the opportunity that we see represented by China, in particular, perhaps the unrealized opportunity to partner with China for translational medicine. The second is genetics, genomics and single cell technologies, which we see as 1 of the most high confidence areas to be able to build an understanding of target trade pairings and the third being real world evidence or Development Sciences, which integrates epidemiology and causal inference from large-scale.
Fantastic. It's a broad scope. And I think the seat that you have covers -- it means that you must have to think about some of the topics that we've been discussing throughout the day to day in terms of the role of China and kind of innovation that's happening there as well as AI and drug discovery and drug development, and these are critically important themes that I think keep popping up in so many of our conversations. So I might squeeze in a couple of questions on those as well. But perhaps to start with your kind of therapeutic area orientation, respiratory has been a long-standing strength for GSK. And you have had leadership in the COPD space and among others. How do you see the franchise evolving from here? And where do you see the biggest opportunities for growth over the coming years?
Yes. So you're right to point out that GSK has got what I would really say is a unique heritage in respiratory medicine. But I think to pioneer is to be forward-looking and to continue to think about what's coming next rather than reflecting backwards. And so whilst that heritage and health therapeutics and I guess, the first wave of targeted biologics with the IL-5 franchise continues to be really important to the portfolio, Trelegy, Nicola are incredibly important products. I think what we're seeing is the majority of how we think about asthma is not necessarily true for a disease like COPD. And so in asthma, we know that there are very effective therapeutics. And part of the reason is that we understand that the majority of that disease process is driven by Th2-driven inflammation. And so a number of now monoclonal antibodies have been developed that are effective. One of the key challenges is bio penetration and persistence. So there's an unusual level of poor persistence to biologics in asthma. And in fact, the majority of patients come off short-acting biologics within a year despite the fact that they are safe, well tolerated, and the efficacy is unequivocal. And so that's led us, of course, to think about what could enable penetration and persistence. The 2 things that we've identified is, one, a deliberate approach to the comorbid profile in the indication mix for any mechanism, and we're seeing the different Biologics have a slightly different flavor of where they're effective across different disease processes, a number of which overlap in patients with asthma. And then the second is how do you reduce the burden of the management and administration. And obviously, that's led us to have confidence that an ultra long-acting biologic, which almost completely relieves the patients of any burden of engaging their management with just 2 injections a year is going to be 1 of the key enablers. In contrast, in COPD, it's more a case of understanding the underlying drivers of risk and so it's a much more scientific mechanistic proposition that we see. The scale of the problems as in COPD. So it's 300 million patients affected globally, a life-threatening disease -- and as of today, only 2 approved advanced therapies with DUPIXENT and Nacala. And both of those are reserved for 1 segment of the disease. And so we really have invested time to understand what's driving different segments of COPD and then what's the rational approach trying to interrupt those disease processes with the right mechanism. And so that led us to diversify alongside IL-5, which remains a very important mechanism in obstructive lung disease to acquiring a ultra-long-acting TSLP and also progressing our program for a long-acting IL-33 as well as a number of other approaches including a PD 34 inhaled approach and also what could end up being the first oligonucleotide in respiratory medicine. We have a program that's currently in Phase Ib, which is an siRNA.
Fantastic. How do you think about positioning some of these assets relative to 1 another? There's obviously a lot of potential innovation. There's also a lot of competitive pressure in some of these places. And so -- can you just help contextualize the relative positioning that you're contemplating at least at this point in the development process? .
So the simple framework that's been extrapolated from the asthma field is 1 that stratifies based on eosinophils as a surrogate of Th2-driven disease. So you've got these thresholds of patients with over 300, an intermediate level of Th2-driven risk, which is eosinophils have 150, where we acknowledge there's going to be other pools of risk. And then estates under 150, which is kind of considered to be T2 low. And what we understand, of course, is that IL-4 as an example, is restricted to those with acitiphils over 300. Nacala was able to generate an evidence base that showed clinically meaningful effects over 150, enhanced effects over 300 and again, the TSLP mechanism appears to be, although obviously being tested in pivotal studies now effective in those with eosinophils over 150. But now we starting to gain a more sophisticated view, there are certain mechanisms that are effective in improving FEV1, lung functional spirometry, there are certain mechanisms that make the patient feel better. And those may be discordant with approaches that improve exacerbations or even improved survival. And we know this from other settings. The analogy I always draw is to heart failure, where diuretics make you feel great. They don't make you live longer. -- beta blockers make you live longer and don't improve how you feel and exactly. And ACE inhibitors are the most prognostic. And I think in COPD, we're going to start seeing this more sophisticated understanding of what mechanisms deliver what. And that, again, will likely lead to combination approaches as well. And of course, IL-33 now, and we predicted this and we initiated programs, for example, testing IL-33 in non-CF bronchiectasis, which is a disease process that's largely not TH2 driven. It's neutrophilic inflammation that drives that disease process and obviously, at the ERS congress that we've just come from last month, we've now seen that the IL-33 mechanism is effective in patients without elopatideosinophols or TH2 drivers of risk.
Fantastic. And continuing on the IL-33 mechanism. We've seen some mixed results for some of your competitors in this space. And I'm thinking here of kind of Sonofi and Regeneron and I'd love to get your thoughts on what did you learn from some of those studies? And what gives you confidence in the GSK 995 asset relative to what we've seen play out elsewhere?
Yes. Yes, it's been a kind of a storied history for IL-33. Look, I think there's probably a clear molecular differentiation between ST2 receptor monoclanal antibodies which might limit the mechanism to 1 potential pathway and then everything else. So people might be aware of the hypothesis that there's a redo dependent set of activity that IL-33 signals through -- it starts off in its reduced form that can signal through the ST2 receptor, but there's also an oxidized form which again is oxidized from the reduced form that can signal independently of ST2 potentially through the rage pathway. So if you're an T2 receptor blocker only, you may miss a potentially relevant signaling pathway through rage. If you're a potent neutralizer of the ligand, which is what our program is, then you're going to neutralize the entirety of that reduced form it doesn't have a chance to become oxidized. The sort of black and white differentiation of our program is that it's long-acting, so every 3 months. But what we really invested time in, as you say, was to, first of all, understand from competitor pharmacology and clinical data reading out, where we're seeing evidence of a differential effect based on patient characteristics. And we've also got a universe of data internally in respiratory medicine, genetics, genomics, clinical data as well as a Phase II study with IL-33 to also integrate into those data sets and understand what's the correct population that's going to be responsive to IL-33. Based on what I've just described, the astegilumab data from Roche at 15% annualized exacerbation reduction is understandable if the SDG receptor was limited in terms of what it could do. The confusing data was itepekimab. The ARIF studies from Sanofi. But we do also recognize that those studies were challenging. And 1 of the studies there was effectively almost there's very little exacerbation. So I think you have to separate the operating characteristics of a study to be able to demonstrate a treatment group difference from biological enrichments. Tosorakumab then took forward a Phase III population where they mandated the patients answered yes to a CAT score domain, where they said, do you have a particular problem with cough from your COPD? And do you have a particular problem with productive mucus from that cough. And that was because in the Phase II study, they saw an enhanced effect in patients that had imaging evidence of a mucus plug. That's a surrogate for neutrophilic inflammation. And so we take those insights, astagilimab just published an Elance, pool data from across their pivotal program. You can see, for example, in patients with more severe lung function decline, the mechanism appears less able to provide a benefit. That might not be surprising when you consider -- this isn't a circulating cytokine is expressed on the lung epithelium. And in patients with advanced lung dysfunction emphysema, they've lost that cell type in the lung. And then internally, the data set that really is proprietary to us was based on the observation that IL-33 is largely expressed in the lung epithelium, but the vascular endothelium. And IL-33 has clear evidence of causing endothelial dysfunction. And so we signaled a couple of months ago that our intent is to take forward a more conventional pivotal program to demonstrate the benefits of exacerbation reduction. But for patients with COPD, they have an incredibly high comorbid burden. And what you really want is to keep them alive and out of hospital. And so you don't really want to be separating whether the reason they've decompensated and arrived in Host August because of a degree of heart failure or their COPD or their metabolic profile -- and so we've been waiting for the right mechanism to be able to evaluate a study that says, actually, we're going to evaluate the benefit on CV hospitalizations, respiratory hospitalizations and mortality. And so that's the cardiopulmonary outcome study that we have shared. We plan to start next year as the third pivotal program there.
Fantastic. And I know you've got a the slip, the long-acting test also in development. This is already a pretty clinically validated mechanism. What do you believe your asset needs to show to demonstrate differentiation going forward, particularly recognizing pipelines are kind of long-actings are kind of coming in a few different places.
Yes. So increasingly, we want to bag the benefits of the modality being ultra long-acting and that is not true as I described, because most patients come off short-acting biologics. So I think that the long-acting components, if it replicated a Tepalike data sets would be a highly successful product. We know that Tespo is effective in asthma, irrespective of the osinophil levels. it was demonstrated what I consider to probably be best in disease activity in chronic rhinosinusitis when the zopolyps with the Waypoint data. COPD, not confirmed efficacy yet, but being studied, but the core states have suggested that it's efficacious in patients with eosinophils over 150 and 300. So if you were able to replicate that and provide a product that patients could persist on, then I think that's going to be highly successful. However, the modality plus component comes in with what do we understand from the mechanism, how can we bring our innovation to trial design to the benefit of the product profile. And so for example, for that program, the PERSIST asthma study is recognizing that if you want to have long-term benefits of treatments and achieve clinical remission type benefits, you really need to try and intervene earlier in the disease process. Now that makes sense. But from a trial point of view, if you don't have enough substrate of events to demonstrate a treatment group difference, that's very difficult. And so we have 1 pivotal study that's kind of more classical for asthma with TSLP, which is these patients have exacerbated twice in the prior year, but we have another study where the patients are only required to exasper once. And we've got some other criteria to make sure that we are able to simulate an event rate that's going to give us confidence that we can show a treatment group difference. So that's a great example where we've kind of borrowed from competitor data. We've got an understanding ourselves of how the mechanism links to the disease process. And then we've got this wealth of understanding around clinical trial design to be able to ultimately achieve a product profile that's going to allow for eligibility of patients at a less advanced stage of their disease. And we're doing exactly the same thing with extension for COPD.
Absolutely. And you're kind of across those studies that you referenced, I think that 6 in total that you're advancing 283 and 2 in Phase II. The conviction required in the shape, there is all kind of in parallel in some respects rather than sequentially. Does that come purely from this kind of very well understood biology and some of these other pieces you were speaking to? Or kind of have you seen something internally that kind of makes you say, "All right, this is the right time to go both assets.
So all of the above and we did have a Phase II study in Aspa, the NASA study, we're going to share those data next year, but they were very, very clear that we achieve the pharmacology necessary for twice yearly dosing. That was obviously something that we -- we weren't going to take a risk on and we wanted to make sure we had empirical data in our hands. We did, of course, benefit again from our partners in China and Hungary who also evaluated the same molecule in patients with nasal polyps. So actually, we've got a wealth of data. We had a GSK Phase II confirming the pharmacology necessary. The data was very strong indeed, actually. We've got competitor data. We've got our China partner data in nasal polyps. And so I would describe that as an extremely high confidence that a pivotal study starts.
Great. Yes. That makes a huge amount of sense. -- to move forward with initiating all these studies. I want to kind of continue in this space but go beyond to pulmonary hypertension. You've got HS 235, I believe, which is an active in trap in development. Can you talk a little bit about what gives you conviction in this mechanism? And kind of why do you believe that this has the right balance of safety and efficacy to advance at this time point?
Yes. So acknowledging that this program is relatively earlier than everything else we've spoken about. I think that, that mechanism is extremely exciting. Again, it builds on the evidence base that's been generated by Wind River sotatercept, which I think everyone in the field acknowledges as being transformative to pulmonary arterial hypertension, Group I pulmonary hypertension. The first mechanism of many, but the first mechanism that's demonstrated remodeling of the pulmonary vasculature that provides evidence of disease modification. And that product has been very successful post launch, but has some very real liabilities. And so even actually as recently as yesterday, their label was updated to reflect the Hyperion study from an efficacy perspective, but marketing data that's led now to new language around GI bleeding. So pericoifusions, telangictazia, potentially GI bleeds, -- and because of the unmet need and because of the efficacy, the product is still successful and penetrating well in PAH. But if you can overcome that, then you have the products which the benefit risk profile becomes pretty remarkable really. And so the proposition was if you can be more selective and not inhibit the ligands that are signaling through to the bleeding and the adverse events, but preserve the efficacy, then that will be a very clear proposition. And so 35 pharma came with a real heritage in understanding medicine design that modulates the TGF-beta superfamily of signaling. And in that instance, recognize that active in A and B are key to inhibit to modulate the efficacy, but BMP9 inhibition is probably leading to the bleeding risk. And 1 of the reasons that we recognize that is that if you have a genetic loss of function of BMP9, you literally develop the phenotype of the adverse events. You can develop a hereditary hemorrhagic telangiectasia and so this medicine was designed to spare BMP9, inhibit active in A and B. But the really exciting bit on top of all of that is that GDF-8 myostatin is a protein that we know leads to skeletal muscle dysfunction, insulin resistance and inflammation. And so if you're not dose limited like sotatercept is, you're able to unlock additional metabolic pharmacology and benefits and anti-inflammatory benefits, which then if you think about group 2 polar hypertension, which again cetacept has a signal of efficacy from pulmonary vascular resistance in the CADENCE study, but those patients have an average BMI of over 30. A lot of them have metabolic perturbations, diabetes, comorbid risk. So imagine if you're able to treat the pulmonary vascular remodeling, and offload the pressures and treats the patient more holistically in terms of the metabolic risk then that's quite tantalizing. And of course, we have clinical data. The point of the deal, we had multiple ascending dose stayed up to 20 weeks that did empirically observe actually 0 of the adverse events of concern.
Well, that's really exciting because it is a patient population with so much unmet need and with kind of adverse events they do have today. We began pivoting towards kind of the metabolic angle, and I think you've got a mesh product in development. Can you hear -- can you share -- sorry, some thoughts on the potential differentiation of your asset versus kind of some of the experimental other FGF tubes in development or Redivra? And how do you think about the mesh sub groups relative to the opportunity for your assets?
Yes. So I'll start off with what attracted us to acquire Boston Pharma and access that program was the observations of what was achieved across the class with FGF21 analogs. And that was unequivocally transformative observations in cirrhotic F4 mash, where something that was thought to be impossible just a few years ago has demonstrated that you could actually reverse the patients and shift them from cirrhotic disease to noncirrhotic disease, and that's enabled based on the clear benefits on fibrosis and reversing fibrosis -- and that's in a setting where none of the other mechanisms you mentioned have had any degree of efficacy. So for example, semaglutide has been tested in F4 cirrhotic MASH and what was observed was an effect that favored the placebo arm over GLP-1. So I think FGF21 has a very clear, compelling and important proposition in cirrhotic mash, and we know that these patients have an incredibly poor outcome. In F23 MASH, again, fibrosis improvement is the single most important factor that predicts improvements in liver-related outcomes. But actually, the mechanism is also a pretty potent metabolic modulator. So it actually was initially developed for severe hypertriglyceridemia. So it does improve the lipids alongside best-in-class fibrosis improvement. And collectively, what that means is you've got highly competitive data for mass resolution and then you've got best-in-disease data fibrosis improvements. And importantly, a mechanism that's probably entirely additive to agonists or as you say, resmetirom. And actually, there was a clinical data set that was really important for us when we did the deal, which was a study undertaken by Steve Harrison and Oxford, I think, in collaboration with Phil Newsome where they had patients, they were on stable GLP-1s and then they were given an FGF21 analog and what you saw there was the benefits of the FGF21 were completely additive on top of the GLP-1. And then our product, we were the first major pharma to transact on a deal for an FGF21 agonist. There was a number of factors that relate to that, the most simple being that it's a monthly dosing proposition in the portfolio that, as you have highlighted in this chat has significant conviction around the benefits of long-acting injectables. There was other attributes we saw the ability to scale manufacturing better with Mammalian like isolation, very low antidrug antibodies observed. But really, it's the class that we found incredibly exciting. And then finally, I described this completely unique proposition in cirrhotic MASH. That pathology is quite adjacent to all correlated liver disease, which is actually the main driver of liver transplants in developed countries. And so if it's effective in F4 cirrhosis, we think that derisks a completely open area for a number of reasons, stigma and otherwise, a prevalent life-threatening disease in alcohol-related liver disease. So the proposition then is if you're a clinician, do you really want to be trying to weigh up the impossible task of is this patient's disease driven mostly because of metabolic risk, mostly because of alcohol intake, which, of course, fluctuates as well? Or do you want a product that's effective in steatotic liver disease, irrespective of etiology or stage. And so I think that proposition is why we think it could be an incredibly important product.
Fantastic Super helpful in understanding that scope and ability to apply that asset across the range of liver issues there. As we've been discussing kind of some of these assets, many of them have come in through external acquisitions, external innovation. I think 35 pharma reps, new Valent even recently, how do you think about the role of M&A or licensing in building the pipeline from here? And perhaps if you can make some comments, given the breadth of your role and the contemplation of early research in China and how you're assessing that external versus internal trade-offs?
Yes. So I think that there's now an appreciation that the conventional model of you do some target finding, you operate in a discovery setting Five years later, you look to test whether that's relevant in humans. You then have a sort of transition to development product mindset. You might have not generated evidence that actually would be really important for trial design you might have generated evidence and delayed the product for things that don't -- aren't material to how you're going to develop the product. That world has changed now. And so for us, it's really a case of how do you build the translational confidence that circumvent that. So can you build an understanding of a mechanism in a way that allows you to apply it directly to the patient population study. And in doing so, therefore, short circuit the long runway of discovery. I mean a good example of that is what we've just discussed. IL-33 has been around for a while. The first-in-class players weren't the ones that were successful. It was trying to deconvolute the target to trade pairing and sometimes just tweaking the population and eligibility criteria in a trial is enough to overcome the heterogeneity of that disease. And so then when you think about that in the context of BD we don't want to be testing for the relevance of the mechanism in biology with a potential 0 value proposition. And our CEO, Luke Miles has been very clear on that, that he wants to take forward derisked biology and immediately test for differentiation and the product proposition rather than whether this matters at all in the disease process. That doesn't necessarily mean the bolge has been derisked for everyone. It means can GSK bring the best of our translational understanding and apply it to data sets, ideally to have a proprietary interpretation of that and interpret it is derisked and take it forward into late-stage development. HS2 35, which is the polyhetension program is a good example of that. It's an early stage program, relatively small data set in Phase I but enough to understand the relevance of the biology and the mechanism of the disease process, the potential differentiation proposition and then a relatively tractable small study to be able to confirm that advance into product development. China is the same enabler, right? So you're able -- you don't have to undertake 10 years of discovery and biology activity. you can circumvent that. We take our translational data sets and understanding around human causal confidence. And then we're able to test that at a pace that is unmatched today and probably will remain so. And so we've established something that we've labeled the China -- the global China translational hub. We're in partnership with companies like Hungry were able to again teller biology teams, you've got the privilege of taking that understanding of human disease and applying it in a way where you can truncate that the distance from insights into trial design into product application.
Yes, absolutely. And kind of 1 of the other things that came to mind as you were speaking through those elements is where AI might be helping you and your teams in the most material ways I mean we've had some various players on the stage here today, some that focus on applying AI in the discovery space. some that focus on applying AI in the development space and kind of perhaps with a more operational orientation but can be equally as important in some respects in terms of outcomes. You've got obviously a pretty long purview in the company from that discovery all the way through in some of these indications. And so -- can you just speak to what you're seeing at the moment in terms of the potential for application, the potential for real impact today versus the hooks for the future.
Yes. So what's established and now business as usual is the application of AI to efficiencies, and those efficiencies range from real-time intel trial enrollment across the global footprint, integrating that with competitive intel and insights. -- automated development of regulatory documents. So some pretty high stakes activity. So we've been able to reduce cycle times from completing a study to developing first draft of modules for submission to regulators based on automated developments of these documents. Medicine design, again, I think every company has to be using AI for both small and large molecule design. But the GRail that I think everyone is focused on is can you use AI to reason over novel. I'm not going to call it discovery. I don't not even going to call it target discovery, but target trade pairings. That's really our job is how do you understand whether your target is interrupting a disease process in a precise patient phenotype. And that's where we do have examples that I'm hoping will be substantiated shortly where if you're able to integrate multimodal data sets. And you hear that phrase, but just to kind of highlight what does that really mean? A multimodal day set could be, for example, how do you relate to an observation from a cell phenotype to imaging trades to a clinical functional outcome in a manner in which it informs how you match that target as I say, to a patient population. That's quite difficult to test through conventional a-priori statistical methods. And we're seeing this more and more that, for example, you can learn something about for example, what's happening in your lung alveolar macrophages based on what's happening in your bone marrow, where you can understand a fibrotic pathology through a nonfibrotic cell type. And so I label that as these latent spaces of information that have been untapped based on conventional methods. -- and then query it with the right clinical and biological insights so that you're not unbiased because the universe of biology is too vast, but you're able to skew those queries. And frankly, curate the right data so that it has a higher yield and likelihood of surfacing something meaningful, then I think that gets us to that grow. And we've got an example of that. I shared with someone earlier today that 2 years ago, we had to meet the management events. And 1 such environment where we're able to integrate genetics, genomics, imaging data, clinical trial data was in COPD. And whilst I think some of our competitors were empirically testing bispecifics through trial and error, we systematically look at every single combination in that multimodal data environment, AI-enabled and simulated which of the combinations is likely to be redundant, additive or synergistic and we disclosed at the time, and we were the first to describe this combination that TSLP 33 was the combination that would give you additivity and potentially synergy. And at the time, that wasn't very well understood external to us why that could be the case because the jury was out on -- and now after last month's data readout at the ERS, it becomes very clear that -- and logical and I think now we're going to see a wave of activity around that combination approach. And we apply that, of course, to a pipeline that's developing approach with that combination. We're actually just started a Phase II study where we're co-administering our 2 monospecific antibodies. And actually, we're using our presence in China to expedite that through to demonstration of additive efficacy.
Fantastic. And 1 of the kind of practical question about this. How much is this changing the way that your teams work or the way that you're thinking about your organization kind of over the coming years? And are you needing to kind of build things internally? Or are you able to kind of get solutions off the shelf from vendors that are able -- enabling you to kind of make some of these advances? I'm thinking all the way through development.
Well, I think the magic happens, as I described in the integration of those different enablers and domains. So the good thing is GSK wasn't early recognized very early the influence that AI was going to have. And so we do have 150 AI engineers internally in a dedicated group in R&D who have built proprietary tools. And so we're not dependent necessarily on external frontier models. Again, we invested time to make sure that we are accessing data sets that were likely to have yields when applied to these technologies and hosting them in the correct environments. And we -- again, we're a company that made an early recognition that those had to be human data sets rather than animal models that don't translate. And now having the correct end-to-end mindsets and increasingly, physician scientists are leading discovery and translation through this kind of reverse translational approach where you take a clinical observation that might come from a large routine health care data, you qualify the causal association you might have observed with high-confidence human causal instruments like genetics, you design the right pharmacological perturbation and clinical experiments to be able to test that, and then it seamlessly transitions to product development. So what does that mean for an organization design? It means this kind of siloed chronology of sequential, as I said earlier, Discovery Biology going through the motions of IND-enabling, start to think about trial design, start to think about medicine per far, it's collapsed into basically a single effort that happens frankly, a single time point. And what that requires from a people point of view is the right experts that can think across that life cycle of an asset, the right data sets to be able to access and query and the right tools to be able to enable these queries to inform actionable insights that inform product development.
It sounds like there's a lot of opportunity ahead, both with the pipeline you already have in hand and the work that you're doing, they continue to build that over time. And it sounds like there is some exciting innovation not just on the kind of biology or pipeline, but innovation in the way that you're doing things as well. So thank you so much for your time today. It's been an absolute pleasure to have this conversation with you.
Great. Thank you, Courtney.
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