Hello everyone and a warm welcome to those of you joining us today. On behalf of Norstella, thank you for joining this webinar on how integrated real world data is reshaping GLP-1 commercial strategy. GLP-1s are amongst the most consequential drug classes ever developed. Now, they’re transforming the treatments of cardiometabolic diseases for millions of patients worldwide while reshaping the commercial fortunes of the leaders of the PAC, which is Nova Nordisk and Eli Lilly. Their impact is extending beyond pharma as well, influencing healthcare delivery models and consumer behavior, even adjacent industries. Over the last six months, we’ve also seen the US launches of novel oral GLP-1s introducing a further major shift in this market dynamic. These launches are amongst the most closely watched launches in history, yet surprisingly little information on their real world uptake has been disclosed publicly. Those of you who’ve listened to Novos and Lilly’s Q1 earnings calls may have come away wanting a bit more detail on revenues, patient mix, persistence, channel dynamics, et cetera.
So that’s my scene setting and my segue to introductions. My name is Daniel Chancellor. I’m rather generously listed as our expert, one of the experts on this slide, but I’m going to be playing the role of moderator and facilitator today and I’m delighted to be joined by two actual experts, Ilan Behm and Alison Perry. They are our leaders within Norstella’s real-world data commercial strategy and innovation team. Welcome to you both. I’m shortly going to be handing over control. We have five slides today that will frame this conversation. Before I do so, please note we are recording today so you will be able to grab an on- demand version of this webinar. If you have any questions as well, our closing slide contains contact information for Ilan and Allison. But if you can’t wait that long, our email format is firstname.lastname@norstella.com. So over to you guys.
Awesome. Thank you for the introduction. And as Daniel already alluded to, there really is a massive shift in the market that we’ve seen in the last few years with GLP-1s, and especially in the last six months with the launch of oral GLP-1s. And we’re really hearing from not only the major players in the field now, but also new and emerging life sciences companies, how important this area is, really it transforms everything. So it’s thinking about the immediate with obesity and weight loss, but there are really so many other downstream health impacts to patients and to the market in general that extend beyond direct weight loss. Things like sleep apnea and other comorbidities, pain that really are going to be impacted by this market and the shifts. Quality of life is another big one. And so we’ll really want to focus on that today and where we’re hearing from the market interest and the need for more data, especially when there’s so many players in this field, I think it’s really important to think about what is helping inform business strategy for our clients and life sciences in general and what kinds of data they need, what they feel like their gaps are today and what’s missing.
So I think it all really does start with the patient and in particular who these patients are that would be eligible for these GLP-1s. Obviously the most immediate is that related to weight and the need to understand where these patients are and where there’s market potential that’s untapped today. So Elon, maybe you can just chat a little bit about what we’re hearing and seeing in terms of where life sciences manufacturers have typically been looking to find these patients and where we think there are gaps that are actually potentially really well addressed with some of our North StellaLink data.
Yeah, happy to. So traditionally, especially in a commercial chronic market like this, the go- to would be open claims. And within open claims, trying to understand patients that have been diagnosed with obesity or have ICD codes pertaining to their BMI status, that’s typically where you would look. But what we’re seeing within the data is that these are not consistently coded for patients. So patients who are obese, if you look at their BMI, may never show up with an obesity diagnosis code. And there may be patient reasons for that or physician reasons why that code is never being used, but the fact is it’s not used from a billing perspective. And then the second thing we’re seeing is that when the BMI codes themselves, the ICD BMI codes are being used, they’re not consistently being updated over time. So as a patient gains weight, is a different BMI ICD code being used?
We see the answer is no.
And sometimes we’re also seeing when we have the actual BMI that the wrong ICD code for their BMI category is being used. And that’s where something like EMR that has actual BMI readings where you have the height, the weight of the patient, you can calculate BMI accurately, you can track it longitudinally over time is really important. And so using EMR data for such a large chronic market I think is a bit of a shift in the paradigm because like I said, traditionally you’d go to your claims data for that and it’s just not meeting the need here is what we’re saying.
And I think that’s a good point around the fact that we can follow these patients over time. And if there are shifts in their height, weight, their BMI, we’d see those. I think we’ll talk a little bit more later around how clients can start measuring success of their product too. So it’s not just about patients gaining weight, but also being able to track the effectiveness of the products too. So if we’re seeing that these patients are losing weight over time or what kinds of trajectories they have in their BMI, it can help with understanding more around the market positioning of the different products and what’s more or less effective in the market. But I think the main thing here is that we’re really hearing from clients this need to understand where the untapped market is and that untapped market is really going to be reliant on identifying exact height, exact weight, being able to see where these patients are coming from.
And also if there are differences in oral GLP-1s versus the non-orals in different brands related to BMI as well, because that can maybe help with some of the strategy for clients. Ilan, do you have any more thoughts on how a life sciences company might strategize as it pertains to what they’re seeing when it comes to BMI and brand selection? For sure.
So there are a couple things. So one, before we even get to the brand piece, just understanding the market and patients getting on therapy within this market, once you identify a patient that is eligible for therapy based off BMI, you can then tie those patients to their physicians to the health systems and outpatient offices where they’re getting therapy and also the payers that cover these patients. And if there’s an inconsistency in how BMI is being used and recorded, obviously you can go and educate and approach physicians and healthcare systems to try and improve that consistency. There’s also the payer dynamic to it too. A lot of the payers where it is covered, and obviously access continues to be a challenge for this market, but where it is covered, BMI and obesity requirements are usually part of that prior authorization. If they’re inconsistently being used or not being recorded, it’s going to be even harder to gain coverage for these patients even where their payers would pay for it, which then could lead to downstream choices of switching to another brand where maybe there’s less of an access barrier or moving to a compound or outside of the market even and trying something more cost-effective if they’re going to be hit with a high cost sharing because their payer’s not going to cover it.
It’s a lot of implications here and it’s not just about finding the right patients, but making sure that clinically and documentation-wide, the right things are being recorded so they can ultimately get on the therapy once you’ve identified them.
So it sounds like also a potential opportunity to educate medical community related to documentation and how that can help with.
And just a comment from me looking at the call-out box that we have at the bottom of this slide, how the winners of this market as it evolves were all about being, how can we tap into this untapped patient pool? You published some research a couple months ago now about how the launch of the first, well, oral Wegavy was tapping into about three quarters of those patients were previously untreated via injectable GLP-1. So that’s very much surfacing patients that were perhaps either not known because there’s no prior claims or perhaps not recorded because their BMIs weren’t diagnosed as such. If you look at Novo’s Q1 results, that three quarters has since been risen to 80% of patients that are initiating on orals are treatment naive and the same extends to that of EDI Lilix drugs. So the good signs for the market is overall that it’s not just fragmenting, but it’s growing and certainly tapping into the large numbers of untreated patients and indeed invisible patients is enabling that.
Yeah, I agree. And I think that what you’re speaking to too is there’s more choice in the market now. There’s more choice in terms of the route of administration and there are going to be reasons why patients may choose an oral versus an injectable. The dosing regimen is also different. So instead of there’s weekly dosing, now there’s daily dosing available with some of the orals as well. I think again, when you look at claims data alone, you don’t understand and appreciate how that patient sentiment, physician sentiment fits into what treatment a patient may ultimately lean towards. But when you have more robust data, specifically the notes, the EMR, the clinician notes with sentiment, both the patient reported sentiment as well as the physician reported sentiment, you can start understanding that and using that to strategize of, okay, in this area, in this part of the country or in this health system or in this socioeconomic status, these are the levers I need to pull on to make sure that more patients get on therapy.
And it might be more of the oral play with the daily dosing. In this other market, it might be more of the injectable play with the weekly dosing. Those things matter and you don’t get that from if you go that traditional open claims route.
Okay. Well, we’re starting to get quite a bit more tactical here, so I’m going to take the opportunity to move to slide on one.
Yeah, I think that brings us to something we already alluded to a little bit, which is really around this launch. So there has been this shift in the last six months with the introduction of oral GLP-1s. And I think as we really think about where we begin to get information and we touched on our EMR data as an important player in identifying who these patients are that are eligible. Something else, I think it’ll really be, we’re seeing this shift and a need in the market to really focus on integrated data. So it’s not just one source, it’s a kind of accumulation of multiple sources trangulated together. And so one thing that’s been really interesting is to be able to use this electronic prescription data. It’s a daily prescription feed that we have at Norstella that’s really allowed us to be able to track and monitor launches as they’re happening.
So in your traditional claims EMR sources, there is a lag in terms of what we see, how much completeness we see each day, week as they come in. With this daily e-prescription data, we’re really seeing the scripts that are written by the providers as soon as they’re written and sent to the pharmacy through that electronic system. That’s been a really big shift too that we’ve seen and as we’ve worked with some of life sciences companies, manufacturers, really strong interest here in being able to understand and track and monitor launch. As more players come into this space, understanding everything as it’s happening, understanding the diverse channels of care so we’re able to see not just your traditional channels that might come through a traditional claim source or an EMR, hospital or health system. Here we’re really able to see and track retail, specialty, grocery. We’re able to see the cash pay market a lot more and we’ll show that on a subsequent slide as well, but there are so many places where these GLP-1s are being prescribed and received by patients.
And so being able to understand where the market’s been and where it’s going I think is really important in this space.
Yeah, I fully agree, especially in this market, it’s just a very unique market. It’s not just always traditionally going to your pharmacy down the street, the brick and mortar pharmacy and getting your therapy, but it’s really going through these various dispensing channels, which could be the provider dispensing it themselves. It could be going through specialty, it could be going through retail, but also a lot of it is going directly through the manufacturer and you really do need a robust way to track that and understand, again, which providers and which parts of the country are these other channels being used and what does that mean for you and how can you leverage that to get one leg up on your competitors? Because now we have the injectables, we have the orals, we have multiple manufacturers competing in both. We have at least a handful of manufacturers that are waiting with their drugs in the pipeline to come into this market.
It’s how do you get that competitive edge and something timely like this e-prescribing data where you can get daily insights on what’s happening today, yesterday, two days ago is the only way to do that. So I think something like this is going to be key even in a large chronic market like this.
Yeah. And I think that- Sorry. Oh no, I was just going to say, I think the next slide does a really good job of showing some of what we’re seeing. So maybe we can shift there too. I don’t know, Daniel, if you were going to add anything, but this is just-
Oh, I was just going to comment that, just further alluded to what Alan was saying, just the depth of the pipeline for GLP-1s is just so big that there are going to be numerous entrants coming online over the next couple of years and just the rate of change in the market is going to be so high that anything you can do to reduce the latency of your insights is just going to support your decision-making tremendously.
Yeah, absolutely. And I think the timeliness is super important. I think also this visibility that we’re seeing now is really novel. So with this novel way to source data and these daily scripts that are being written in the oral market, we’re really able to see different pharmacy NPIs in the data and really understand where are these patients coming from. So here you’re seeing some of that in terms of what’s coming direct from a Lilly or a Novo, what’s coming through mail order, what’s coming through cash pay versus an insurance channel to really understand where the market share is, how these patients are accessing the drugs and then being able to understand and triangulate that with patient demographics, provider information, you can really begin to understand what’s driving these things. So some of these patients might feel like it’s just an easier path of less resistance to be able to go do cash pay and you can look at their socioeconomic status.
You can look at what providers are saying for these patients to really understand where they’re coming from versus your traditional claim source where you can really only see certain channels of access. So this really begins to tell you more about the market and give you more than just the tip of the iceberg when it comes to patients receiving these GLP-1s through these different diverse channels.
So if I can come in with a question, which I suppose is probably quite a naive question, but more of a layperson. So before the advent of direct to consumer and these new channels that we’re seeing, how would you surface these patients and these trends within real-world data or indeed could you surface them or are they again, invisible patients?
Yeah, I think that’s a great question. So again, if you’re going traditionally in a market like this, you would use open claims and open claims for these higher cost drugs that are going through very specific distribution channels, there would be a lot of just blockage and they’d be invisible to you. These patients would be lost. You wouldn’t be seeing the direct manufacturer. You also wouldn’t be seeing a lot of the specialty pharmacy and large retail pharmacies being used. By using a data source like this, all of a sudden you do have visibility into them. And then to Allison’s point, you can start leveraging that tactically to understand what does this cash pay market look like? Who are they? But then also is there an opportunity to move them out of the cash pay market? If you combine this with some of the other unique data that we have here at Norstella through MMIT, for instance, with the payer policy data, are some of these cash pay patients, do they actually have coverage now?
Is there coverage improving and is there an option to move them out of that cash pay market back into that traditional commercial payer market and have payers start to contribute up to some of the cost of these drugs, which I think is, again, an opportunity for both these manufacturers. And
Presumably with the connected data elements of this, then you’d actually be able to segment, well, what does a typical cash pay patient look like? How does that differ to coming through insurers? And you might be able to build a strategy or product positioning that depends on that kind of route.
Absolutely. And if you look at both of these manufacturers, their cash pay programs have evolved year over year over year. I think as they think about designing what next year’s program is going to look like, it should meet the needs of the patients who are sitting in that channel today. I think this dataset would really allow you to better design that and make sure that it’s not giving away too much, but also meeting the needs of the patients where they are.
Okay. Well, should we move on, Allison?
Yeah, I think that actually brings us to, you started to touch on the why and the insights going beyond just what we’re seeing in the data, but understanding the qualitative elements that might lead patients or providers to go a certain route or another when there’s so many different options out there now and so many more coming. And then also to understand where there are barriers to care, channel mix shifts, et cetera. So here what we’re seeing is really an ability to tie together our multiple data modalities, especially these clinical notes that come from our EMR or electronic medical record data. So being able to understand not only what’s in the data for yes or no, did a patient receive this treatment and where did it come from? We’re now also able to see and surface some of the insights that providers are documenting directly in the notes when they see these patients and talk to them and being able to … We’re seeing a ton of different insights.
They’re super rich and deep and very diverse. So we can see things related to providers talking about affordability concerns, side effect concerns, questions around access, questions around can this patient be on this GLP-1 with other treatments? Again, a lot of insurance and access is what’s coming out of this. And really being able to start telling a story around not only the channels that these patients are going through to fill a GLP-1 if they are, but also seeing what providers are saying about patient sentiment, provider sentiment is super helpful when it comes to commercial intelligence and actionability. And we could tie that back to the patient and see who these patients are as I started referencing related to their sociodemographic information. If they have comorbidities, we can see that. We can really start to not just see one dimension of these patients in the story, but multidimensions about who these patients are and that can really help with brand adoption and market strategy.
Any comments about how we can scale the insights within these notes? Because obviously they’re incredibly rich, but each node is one patient and we’re talking about a market that is millions of patients. How are you tackling that problem?
Yeah, that’s a great question. Again, these notes are rich. Every time you look at an individual note, you just appreciate just how much information is there. But to your point there, that’s where you start extracting the information in a scalable way across the notes and then you start rolling them up to the various levels where a life science company can affect change and what are those levers they can. So you can roll up notes to a specific physician or provider and understand if there are any macro themes there of they prefer compounding, they’re referencing or suggesting that the patient move out of the channel into a compounded therapy. You can also roll up information then from that provider level to the health system level. Is there more of an issue here? Is there perhaps some sort of guideline that the health system is pushing down on the physicians that are causing them to prescribe or treat this disease area a specific way?
And then you can go again as a manufacturer to send your CAM team out to the health system level. Is there education and awareness needs to happen at a class level? Is there an opportunity for your MSLs to go into interact here and raise education awareness about the class, not at that brand level? And then of course being able to tie these patients to their payers and understand how much is that payer policy impacting what we’re seeing physicians do? How attuned are the physicians to the payer dynamics? How much are they trying to avoid the onerous PA process or approval process that might exist? And then in this market specifically, it’s also really understanding at a geographic level, what kind of employers are active in this market? Because employer coverage and employer carve outs are so prevalent in this market and trying to get employers to cover these therapies is just as important, if not more important than getting payers in this market to cover them.
So understanding the patients and what employers have a large footprint in this market is really important as well. So those are the different levels at which you could roll up these insights and then as a manufacturer, go and bring about the change you want to see.
Yeah. And I’ll just add to that too. I think one of the things that we’re doing here that we’re seeing more and more is needed to roll these things up is our AI capabilities. So being able to take the different notes that are super rich, diverse, every note looks different. And if you can imagine the notes, your excerpts you’re seeing here, but hundreds or thousands of them for a given patient and then tens of thousands, if not hundreds of thousands of patients that we’re seeing in a given segment. We’re really able to leverage and lean into AI capabilities to extract the relevant information and patterns and themes using large language models from these notes along with the additional data elements that we’ve been alluding to, to roll them up to the patient, provider, system, payer, employer level. So I think that’s been a really interesting and opportune resource that’s come at the same time as this market has shifted is the ability to really lean into not just the different data modalities we have to tell this story, but also AI to really make sense of it and make it into something actionable for manufacturers.
Great. So let’s move on to our final slide of today.
So I think this kind of ties a lot of what we’ve talked about together. It’s really just another way to look at some of the different patterns and themes we’re able to pull out of our data. So as we started talking about in order to really identify who these patients are who are eligible, BMI is super important and accuracy of BMI, but also the longitudinally of MI is important. So manufacturers aren’t just going to want to know who these patients are and where they are, but they’re also going to want to be able to start telling a story around where they have a competitive edge and how they perform as compared to others in the market. And so being able to tell stories around persistence, are patients able to stay on treatment or are there issues with side effects or access over time is important.
And then of course, also effectiveness. So being able to understand are these patients losing weight, to put it simply and being able to leverage that those BMI metrics over time in an accurate, reliable, and longitudinal way is super important and something that really allows our manufacturers to go beyond just understanding where to penetrate the market for new patients, but also how to keep these patients on their treatment and have that loyalty in the brands.
Yeah. And I think in this market specifically as manufacturers are looking to make their value propositions to payers, to the physicians, to the employers, having a data-driven approach to that is super important. So obviously showing the reduction in weight loss and then being able to tie that to a reduction in comorbidities and a reduction in healthcare events, an increase in days of productivity, a reduction in days lost to work is obviously going to be super important. But I think this also highlights here, these medications only work if you’re adherent and persistent and using them as they’re intended to be used as indicated, which is either weekly dosing or daily dosing and staying on them for the long haul to see that change in weight loss. And I think here too, what’s unique in this market is that there’s so many barriers to that continued and sustained use of these medications and those are going to be the cost piece of it.
They’re going to be side effects and adverse events and how manufacturers use this pieces of data to understand where patients may be dropping out of therapy and going to message their providers or having direct to patient advertising and messaging that’s going to promote continued use is just really, really important. And I think that’s another point where these types of data points just are so important.
Yeah, I’m thinking as this market scales, the amount of data that’s generated in the post-approval setting is just going to increase exponentially relative to what these manufacturers are doing during phase one, two, and three and just the amount of information that’s going to be out there certainly as the numbers of market entrance increases, as the experience with patients over a longer period of time increases, this is just going to be such a rich source of information for evidence. However, you’re wanting to use that evidence, whether that’s with payers, whether that’s with providers and reimbursement policies and treatment pathways. But yeah, this is going to be an incredibly useful resource And not to mention the potential application of GLP-1s outside of the core cardiometabolic indications are going to get some really strong evidence here for broader clinical benefits if it exists, which will then allow and inform the next generation of clinical development strategies.
Yeah, absolutely. All right, unless there’s anything more from you guys, I think that concludes our webinar. So my thanks goes to Allison and Ilan for sharing the excellent work they are doing in deciphering how GLP-1 patients are showing up or indeed not showing up in real-world data sources. This is all built on the NorstellaLinQ data platform and is validated by the clients that we are currently working with. If you’d like to find out more about how Norstella can support your GLP-1 commercial strategy, please do get in touch. Our contact information is below. Of course, GLP-1s is not the only show in town as well. So if in listening to this, you are seeing analogs with other markets that you might be launching into. Then much of what we are speaking about today does translate and we would love to support you in building your commercial strategy.
Any final words from you, Alan, Alison, before we wrap up?
No, just that this is such a fascinating market. It’s changing. I think it requires really robust real-world data, integrated data across modalities and looking forward to continue tracking this over time.
And I second that and we’ll also say as we’re hearing about the data and the need for more data and integrated data, there will be more and more of that. And I think something that we really have heard from our partners in the industry too is going beyond data, our technology, our AI and our services, our people really help our clients make sense of that data. So the more data points we have, the more noise there might be and being able to figure out how to create a unified commercial strategy out of that is super important and I think that’s where Restella is really excited to lean in.
Okay, very well said. Well, thank you to you both again and thank you to the audience for joining us. Please do get in touch. Bye now.
