Diaceutics PLC (DXRX) Earnings Call Transcript & Summary

Hello, everyone, and welcome to Diaceutics Capital Markets Day. My name is Deborah Davis. I'm non-Executive Chair of Diaceutics. In the next couple of hours, we're looking forward to sharing with you the opportunities that we see at Diaceutics and our DXRX platform to play a pivotal role in the rapidly growing industry of precision medicine. In the next couple of hours, we will cover a number of things. I'm joined today by 4 members of our Board and Executive team. Peter Keeling, Ryan Keeling, Susanne Munksted and Philip White. Our agenda for today will start with Peter providing an overview of Diaceutics and the precision medicine diagnostic landscape. He will be followed by Ryan, who will take us through a series of demonstrations of DXRX capability. We're delighted to be joined by 3 experts from the diagnostics and precision medicine world and Susanne will introduce our special guests when she facilitates the panel discussion, before opening the floor to questions. If you do have questions on the DXRX platform or the topics raised during the panel discussion, then please raise them here in this first Q&A session when Ryan and the experts will be present. In the second part of our presentation today, Peter and Philip will take us through the strategy, the business model and the key performance indicators we use to measure our progress. We'll then open the floor up for questions to Peter and Philip on these topics. Of course, while there are just 5 of us here from the business today, we do have a far larger team behind the scenes with over 100 staff across 3 continents, together with an experienced board and an expert advisory panel, all helping to drive the business forward. Now I was asked in this introduction to provide some insight into why I joined Diaceutics as Chair in January of this year. Essentially, there are 5 key reasons I was drawn to this company. Fundamentally, it has a compelling purpose to get every patient the precision medicine they deserve. It's developed a well-engineered core platform and data capability that can transform the diagnostics commercialization industry. There's a strong leadership team in place led by Peter. It already has an impressive client base comprising of many of the world's largest pharma companies and is operating within this super exciting growth market of precision medicine. I also thought that I had some experience that could be additive to the team and to the company. I spent the last few years in my full-time executive career in global leadership roles in the pure-play Internet platform businesses, PayPal and eBay. I've experienced the growth and the complexities of developing platform ecosystems and the need for compelling propositions that engage and delight platform participants. I've also spent my entire career in technology-related businesses spanning software, telecoms, technology-based data and system solutions, which required a well-versed understanding of scaling platform businesses, developing data assets and data insights, building strategic product roadmaps, evaluating and implementing software business models, including subscription and recurring revenues and managing essential human capital across the globe. So I'm sure you're asking how should Diaceutics approach this massive opportunity and take itself forward? I'm reminded of the time when I was at PayPal some 8 years ago now, when it had a market cap of circa $3 billion. Today, that's over $300 billion. The company was enjoying high double-digit growth, was faced with a huge raft of opportunities, new products, new geographies, different customer types and the ability to enter adjacent markets. It's very easy for us to become distracted and dilute our efforts and end up being suboptimal in many areas. Our approach to avoid that was to ruthlessly prioritize and focus our relatively constrained resources on the critical few areas that were fundamental to our success, while experimenting and piloting in areas that would allow us to test and learn, but not detract from the delivery of our key priorities. I control parallels to our business. We've launched our DXRX platform on time and on budget. Our key performance indicators are tracking ahead of plan today. We have a solid runway upon which we can build. We still have significant scope to deepen our relationships with our current clients and increase customer lifetime value and also to attract new clients to the network and the propositions that are already launched. We then have more to do to build out the ecosystem, to attract new participants with propositions that will engage and delight them, and that will stimulate that flywheel effect. This challenge reminds me of eBay's platform ecosystem as we transition from a consumer-to-consumer auction platform to face off against the competition by owning the platform to business sellers, including large scale merchants, and then balancing engagement, monetization and customer satisfaction across all platforms, such that we could benefit from that virtuous circle of buyers accessing long-tail inventory from a full range of sellers, thereby attracting more buyers and in turn attracting more sellers. So move towards take our share price from its lowest point to 500% increase over 5 years. I think the takeaway is that at this stage of our business, we plan to employ our resources to build critical mass and a strong base from which to expand. We believe we have all the right ingredients to accomplish this. And I hope that by the end of this session today, you'll believe in our ability to deliver and will want to come on this exciting journey with us. So without further ado, I'd now like to hand over to our Founder and CEO, Peter Keeling, who will start today with an overview of Diaceutics and the diagnostic landscape. Thanks, Peter, and please all enjoy.

Thank you very much indeed, Deborah, and indeed welcome all of you to today's meeting. And as Deborah suggested in the agenda, I wanted to use this next session to provide an overview of Diaceutics and our business model. I'm aware that most of you joining us today are existing shareholders, but our audience also consists of followers of what I would describe as the Diaceutics trajectory. So I want to provide an up-to-date perspective on our business as it sits today, whilst refreshing on the key investor economics underpinning our business. As Deborah showed in her earlier slide, our goal is eliminating the many diagnostic hurdles to precision medicine for treatments. This is a largely unmet need within the precision medicine ecosystem. And to deliver on this, Diaceutics is in its first year of rolling out a purpose-built diagnostic commercialization platform. Development of this platform and the proprietary business method upon which it is based is the endeavor of a decade of on-the-job experience with the world's leading pharma companies and laboratories. A significant part of today's presentation will be given over to demonstrating that platform of work. And subsequently, to expert comments from Peter Hoehn, Head of Commercial Strategy at J&J; Professor Pruneri, who's Head in Department of Pathology and Laboratory Medicine at the University of Milan; and from Avi Kulkarni, who's Senior VP and Head of Life Sciences and R&D at Cognizant. As you can see on this slide, a plus of time invested as a first mover dedicated precision medicine has enabled us to understand and design a method to address a significant diagnostic problem and then use technology to convert what was otherwise a 50% gross margin consulting business pre-IPO into a high-growth, high-margin, high-tech-enabled business. Critically, we have understood the role which laboratory is playing as the change agents of diagnostic practice in the frontline of precision medicine. We've ensured that our platform not only delivers a specialized diagnostic commercialization solution to our pharma clients, but includes a unique way to harness the collaboration of a globally distributed laboratory network. In fact, today, we hold data on over 11,000 laboratories across the globe and via our work have selected 2,500 of those labs to incorporate into what we believe will be the optimal launch network for precision testing. And our platform adoption can now take advantage of our year-on-year track with the most important companies advancing precision management. Today, precision medicine in itself is a bit like the electrification of the car. It's an unstoppable force to change the way that we are treating patients. The metrics speak for themselves. Only 10 years ago, there was not 1 precision drug in the world's top 10 drugs. But as we entered into the 2020s, that has already changed, fueling a $60 billion market that no one was calling a niche any longer, but it is to the future where the most important message of this slide lies. We can clearly identify almost 500 drugs already within the lines or a drug will be highly dependent upon a diagnostic ecosystem. Put that in perspective, in 2020, Diaceutics worked on 53 therapy brands. In front of us, therefore, is a 10-fold lift-in opportunity, and that is within those same clients with whom we have invested so heavily to build trust and confidence. For the pharma industry, many of these drugs will come to market faster and cheaper than the one-size-fits-all drug being replaced. Frankly, the economics driving precision medicine today are highly compelling. However, the headlines surrounding precision medicine deservedly speak to the success of the pharmaceutical industry as it transforms to precision medicine. However, underneath the surface lies a significant fissure in the precision medicine business model. Namely that the diagnostic marketplace is not fit for purpose here. In fact, it operates on a fundamentally different set of market principles. It's hugely siloed, it's slow to change and economically, it's the per relation at the table, earning less than 3% of the global rep of the drug market enables, which is why we call it broken. But the pressing issue for pharma launch teams is that, that same broken diagnostic ecosystem means that they're losing up to 50% of patients in the first and critical years of launch. As many of you know, a commercial pharma team's success is measured on speed to peak sales. And in an increasingly competitive pharma marketplace, the first 18 months are critical, the first 36 months determine the lifetime success of that drug. But if that same drug is dependent upon a diagnostic, which like the tortoise is really only getting going after 36 months, then life and treatment is profound. In truth, our decade-plus of experience has helped us here to: one, understand what the real hurdles are at a lab level; and two, build a commercialization method, which can eliminate them. In many ways, the simplest of slide is probably the most important one in this deck. It calls out the real-world hurdles diagnostics face as they commercialize. And they span from the test not even being asked for by the doctor, all the way to the turnaround time on the test taking too long before the doctor needs to make a treatment decision. Sadly, the complexity is considerable here as each biomarker in each market in each disease will have a different mix of hurdles. There is simply no one-size-fits-all solution for a broken diagnostic ecosystem drenched in practice over 50 years. However, our experience and our data have combined to allow us to predict and measure with significant accuracy the loss of patients and revenue in any new drug launch dependent upon overcoming these mix of hurdles. As you might imagine, when you get this right or wrong, the return on investment impact to pharma has started. We were the first external agency to call this out way back in 2010 when we ourselves reverse modeled the patient leakage and loss of revenue to Herceptin, then called the poster challenge for precision medicine. Experts were already calling out on the front page of The Times the profound patient impact in breast cancer of a broken diagnostic ecosystem, but our analysis showed that with only a 10% reduction in the hurdles, we could deliver a $3 billion gain to the lifetime revenues for that drug. Anecdotally, we did, of course, speak to our friends at Roche before we published this. However, they told us that our numbers were wrong. They had done a simulation and their our estimate was the loss was closer to $7 billion. We've continued to update the investment impact on key therapies of fixing the diagnostic ecosystem. You can see here on this slide that we continue to see smart investments will eliminate a lot of the patient leakage and loss of revenue, which for the brands mentioned here, could be worth up to $8 billion per year on those products alone. But what is different today for Diaceutics is that now we're no longer just pointing out to our clients the predicted loss of revenue, but rather, we're showing them how we can take $1 of their drug launch budgets and via our platform turn it into $30 to $60 more treatment revenues and to do that for multiple therapies at the same time. So at this junction, I'm going to hand over to Ryan to showcase the DXRX platform through the lens of 3 use examples, and we will introduce via a high-level explanation of the model underpinning the platform. and I will chat again to you later.

Good afternoon. My name is Ryan Keeling. I'm Chief Innovation Officer for Diaceutics. And my intention today is to walk you through our DXRX platform and demo some of the capabilities of the platform as it exists today. I wanted to start by walking through this schematic of how we think about the data flow, the network effects and indeed the revenue cycle within the platform. DXRX provides digital services that streamline the commercialization of diagnostics. That's the primary reason why a pharmaceutical customer would come to use this platform because they are ultimately wanting to commercialize a diagnostic, typically one that identifies patients eligible for a therapeutic treatment. User interaction on platform generates data. And the data is combined when it's combined across stakeholders if it had significant value. This value is realized through market analytics services, again, provided on the platform and the more analysis of the market that we provide, the more investment in platform services are acquired. So there's a network effect here that the platform activity generates data. That data that helps us understand what's going on in the market. And we feed that back to our customers to ultimately require them to invest more into the market. I will assure you that in action through 3 different use cases. The first is with a pharmaceutical client who wanted to build a lab network for a new biomarker in breast cancer. The second, a client in the United States who wanted to optimize their sales force using some of our testing data to do this. And the last is, again, in the United States, where we identified through our market data, a particular biomarker test that was being performed in a suboptimal way. The first use case focuses on the third barrier. You heard Peter talk earlier about the different barriers that we focus on trying to remove through the DXRX platform. And this one is focused on testing not being offered by the preferred lab. And what we wanted to do here was enable our pharma clients to have a lab network available at the launch of their drug, which would ensure patients got tested. So here's our scenario. It was a top 5 pharma. They were first to market launching a new targeted therapy in advanced breast cancer. The therapy is targeting a subset of these breast cancer patients identifiable through the results of a new biomarker test called PIK3CA. Remember, the pharma company needs to make that test available because the test is in the label of the therapeutic and the physician must test before putting patient on treatment. This company has 18 months on market before their competitors arrive and therefore are keen to get a head start and not have the diagnostic slow down adoption of the therapeutic. This biomarker PIK3CA is not widely available nor routinely tested for in breast cancer. It is in other therapeutic areas, but it's novel in breast cancer. And the pharma company fears lack of access to testing will impact therapeutic sales. So I'm going to move from here to our platform. I'll show you what this use case looks like in reality. So I've logged into the platform now. And this is typically the screen that our clients would see when they log in. And the first thing this customer wanted to do and we enabled them to do was to get some initial messaging out to the lab ecosystem around this new biomarker. So this particular client worked for us to create some messaging. We used our lab broadcast capability and we basically created for them a broadcast that was sent out to the entire network. Remember, our network here has over 2,000 laboratories, a multiple of that in terms of actual health care professionals working within the lab, and we're sending that message out at the click of a button to potentially thousands of stakeholders. So through the magic of the Internet, I've been able to populate this message for us. And you can see here the type of information that was relevant. And this is a general message, non-branded, that will be sent out to the labs. PIK3CA mutation testing is a critical component, starting to build the clinical awareness and understanding of the biomarker and description. So the labs can understand the importance. 40% of patients, HER-positive patients are PIK3CA mutated. That's a significant thing for the labs to understand because this is likely to be highly adopted by physicians. And then there's more technical information. We talked about particular gene coverage, et cetera, that was needed. And we talked about some of the gold standard testing that's available. Again, technical information a lab needs to know in order to provide a testing service to its physicians. This is step one in helping the laboratories to get up and running with testing. Let's, first of all, tell them about the test. And as I said previously, this is the first-of-its-kind mechanism. In order to push that message to labs, previously, you'd have to put boots on the ground, have reps, MSLs, et cetera, calling on the labs that deliver these sorts of messages. So we'll take the next step here. Ultimately, what's happening is that message would get sent out to all of the laboratories in our network. You'll see here the 53 different countries where we have a lab partner presence. And if I go and look at any country, say Norway, for instance, you'll see the list of laboratories, you have 13 labs in Norway, a mix of academic hospitals, public health hospitals. These are the laboratories that are testing breast cancer today. They've just received the message to say, PIK3CA is coming. It's on the horizon. Let's start to think about it. Now that we've sent that message out to our network, the next thing we want to do with our clients is to enable them to build the optimized lab network for the launch of this drug. They don't need every single lab offering the test, but they do need the right labs to at least understand what to do whenever a physician requests PIK3CA testing for advanced breast cancer. So we worked in multiple countries here. But for today, we're just going to focus on Spain. What we provided was the current understanding based on the platform data of who is providing testing in breast cancer in Spain. So I'm just going to go here and I'm going to do a query on our data for PIK3CA testing in breast in Spain. And I want to look very recently, let's look and see who's been doing it over the last 18 months. So what I get back here is what we call a lab mapping. And that lab mapping has a list of laboratories in Spain performing breast cancer testing. Totally, we have visibility over here over 1,700 patients. That would get bigger if we increase the time frame on the biomarker, but we can see straightaway some of the laboratories and wherever they currently provide PIK3CA testing. So here you have the top lab in Spain from breast cancer testing based on its market share. This lab has 21% of the market, but yet doesn't do PIK3CA testing. There's no assay details available. If we go on and look for instances at Vall d'Hebron in Barcelona, a significant lab, fourth on the list, 12%. They already do have a PIK3CA test using Thermo Fisher Oncomine Focus Assay, but it's validated for lung. It's still a conversation we can have with them to validate for breast. That's a normal thing for them to do. They may have already done it. But we can now start to piece together with the client based on all of this sort of information, the biomarker that the lab tests for, the method, the specimen types, the turnaround time, sensitivity of the assay, the reporting format, all of those things that need to be optimized. We have that data already locked in for these labs so that we can basically design a network that is going to be up and running from day 1. And we know, for instance, that this lab in Girona doesn't have an assay, but we want to make sure that they are sending out to Vall d'Hebron in Barcelona, probably a likely thing for them to do. This lab we'd have to work with in order to get testing up and running. Let's go and look at how we might want to do that. So once we have selected those laboratories. Remember, they are on the DXRX network. We've already recruited them. We've already done the heavy lift for the pharma client. We're not going to start a conversation from scratch. They're there. That's the advantage of using us because we've built all of this ahead of time. And these labs are here for the sole purpose of being engaged in a program like this one to bring on a new biomarker test. So it's usually something they're very willing to do and there are advantages for them. There's a value proposition to getting that head start on the biomarker. So we bring them into a location like this. This is now closed off for the rest of the network. We would have our labs just in Spain, or in this instance, we actually combined all the countries so that there was a cross pollination across the different labs, but we can work with them with our pharma clients with the laboratories in a collaborative way to bring on the biomarker test in time for launch. And this area basically has ways for us to communicate with the labs. We created some events around this. They were virtual at that time, various tasks. And we invite different partners in to help the labs bring on the new biomarker in a timely way. The last piece of this program once we got the network up and running was to monitor that over time. We use our lab monitor tool to basically, again, keep a watching brief on the laboratories, as their assay got developed, as they brought in a new kit and verified it, et cetera, the data was updated here. So at any one point in time, we could see the information coming in from the lab. And then of particular interest to the customer is we're enabling them to start to track the volume of testing through that lab. So we have our lab here, our histopathology lab, and you can see different testing. But you can see then through the different quarters of 2020, how their breast cancer disease volume started to build. And indeed, we can narrow that down specifically to PIK3CA. So the pharma client can start to see where there's some ROI for that test in that particular lab. Again, information that's not available easily today, but information that is procured through this lab engaging in the platform, receiving the services that it gets through participating in the PIK3CA lab network, and this is the exhaust data from that interaction. So to summarize what we've done here, we work with a top 5 global pharma company to launch a new diagnostic into European markets over the course of 5 months. We first broadcast a message in thousands of labs to immediately bolster the understanding of PIK3CA testing in those markets. We've selected a number of those labs to participate in the lab network. We've got them up and running with testing. And then we have subscribed them to our lab monitor service, so that over time we monitor the data and overall performance of those labs, alerting our pharma client if one of those labs drops out of optimal service. The next use case is focused on sales force optimization and providing data to form a sales force to allow them to have a more optimized approach to targeting their physician audience. This use case is based on a U.S. client, and we're going to focus on the first barrier in our framework, which is test not requested, typically the starting point to investing in that is with a physician. So our scenario, with a top 10 pharma. They launched last year a therapeutic in lung cancer, targeting EGFR exon 20 mutations. EGFR-targeted therapies are not new. We saw the first one around 2009, but EGFR exon 20 is new. There was unmet need there and we have recently seen new therapeutics coming to market targeting this particular mutation. The therapy is targeting a subset of metastatic lung patients that have this EGFR exon 20 mutation. Lung cancer is a highly competitive market. There are many companies competing to convince an oncologist to prescribe with their therapies. You have competition from other EGFR therapies, other biomarker targeted immunotherapies, et cetera. So very dynamic, very competitive. And anything that could be really reached to try to be more targeted, more specific and more timely is of extreme value and interest to our former customers. This particular company wants to use our data to target its sales efforts by using a data-enabled strategy to only target oncologists who are currently testing for EGFR exon 20. So what was the solution that we provided. This pharma company leveraged DXRX data analytics tools to provide its sales force weekly data on the testing habits of physicians. The following DXRX services were provided: physician mapping, where we provided 18 months of baseline data on the testing and laboratory interactions of some 24,000 physicians. And then we provided a subscription to our patient signal service, where we provide weekly alerts, providing anonymized positive EGFR patients utilizing data from our DXRX lab network. So this company availed of a data subscription and the current project duration is 24 months. As before, I'm not going to jump out of here and show you what this looks like on the platform. So we've kicked on our physician mapping tool, and I'm going to select the subscription available to this customer. Customer can have many subscriptions. And then I'm going to select the disease for this particular program. And here, we have non-small cell lung cancer. We have some options. We can select the overall disease, an active cohort or a metastatic cohort. And there's significant difference between each of these. And actually, I'm going to take a moment now just to explain what is going on behind this. Because ultimately, what these are doing are reading from our diagnostic deductive pathways. And if I jump on and show you a quick slide on this. We have taken our data, effectively our raw data. We've analyzed the coding, the diagnoses, the procedures, the treatments, the lab data, matched it all together and ultimately developed AI algorithms that are transforming that data into understandable and recognizable disease pathways. And we use expert labeling and standardization of that data on DXRX to allow our plans to identify the best possible testing journey or, as I say, diagnostic deductive pathway for patients. And through that level, we can get to very specific disease cohorts. Again, that's a really crucial thing for our clients. They don't just want to target a disease very broadly, they want to get very specific on the types of patients. And our data can support that, but you need to build one of these pathways for each different disease. And that then enables the real-world business application for this data. The raw has value, but until you apply the AI and ML on top of it to be able to read that data and deduce what's going on. Remember, our data continues to grow day-on-day. We've marked 54% of it so far, and we have created DDPs for 49 diseases. So if we go back here, you'll see the diseases we have mapped and we're adding to this all the time. But for today, we're going to focus on non-small cell lung cancer, metastatic, and we're going to look at EGFR testing. I'm going to pull in my 18 months. So the data is lower than 9. And what we have pulled back here are data on the testing and treating behavior of some 23,598 physicians who have been managing non-small cell lung cancer, metastatic, patients over this time period. And you'll see this list here, there's 131,000 patients in total represented here. And that is a very high percentage, approximately 65% of the overall metastatic lung cancer population in the U.S. over this time, so very highly representative and potentially highly insightful for the customer. So what have we got here. Start with looking at Rule 1. We have a physician identifier. This is their public identifier, their MPI. We know the hospital that physician is associated with. This is Moffit Cancer Center in Tampa, Florida. And if we go to the right, we can get additional information about the physician. But really, what we want to know is how much testing is this physician doing. So this 290 patients at this time, quite prolific in terms of number of patients. And what is great news is that 289 of those patients were tested for EGFR. We should expect that. It's in the guidelines. For metastatic lung cancer patients, all of them, if possible, should be getting an EGFR mutation test. Other oncologists, they're not all testing to the same rate. Some were only testing 1/4 of their patients, some 1/2, some 2/3, but there is variability here. And ultimately, what we're saying to our pharma client is, for now, while you have the resources available to you that you do, focus on those physicians who are testing for your EGFR biomarker a high percentage of their patients. So we can start to segment this and rank them based on let's take a cut of the 23,598 patients who are testing 70% and above. And we can use our tools here to basically filter and shift this data, so that we get a targeted list of maybe 2,000 physicians who are testing and treating a high percentage of their patients. That's the lower hanging fruit and a bigger opportunity for this company to go after. In time, we can look at how we convert some of the lower testers, the ones with a lower testing rate, to test more. And we can do that through education and awareness. We're building tools to help do that. But for now, these are the guys who are likely managing your patients today, make sure you're out there talking to them about your therapeutic. I'll remind us that all the data within DXRX is anonymized data, it's HIPAA compliant, GDPR compliant. The information you see about physicians and in a moment about patients is allowable information that has been passed through an expert determination that gives us the ability to spread to our customers. As we go beyond the physician, we can actually get normally to a patient level, where we cannot just see which patients got tested with an exon 20 mutation test, but actually those who are positive for the mutation. And that's the data I'd like to show you now. Now I'm actually seeing our patient level data. So I've gone from physician level to patient level. And each row here represents a test performed on a patient. You'll see multiple rows for some patients who got multiple tests. So we have here an 80-plus-year-old male from Florida. Obviously, it's anonymized, and we have to group by age, et cetera. But we have a male who had a mass on their left gluteal. They were diagnosed with non-small cell lung cancer. We can move across here and see a lot of valuable information about this patient from the physician who ordered the test and is managing that patient, but all I'm really interested in is those who got an EGFR test. So now we have sorted my patients and filtered out those who didn't get EGFR and I'll now adjust the patients who had an EGFR test. And again, I can go further than this and say, actually, I'm only interested in those who got an EGFR exon 20 and where they were positive. So if we scroll down through these patients, just looking for an EGFR exon 20, you'll see, okay, there we have one. We have a patient test for EGFR exon 20 P768i mutation. We scroll over, we will see this patient was a 60 to 69-year-old female from Arkansas; non-small cell lung cancer in the right upper lung nodule and we can see the physician at center who managed our patient. Now they have an opportunity to engage with physician not just knowing that they are managing and treating non-small cell lung cancer patients, not just knowing that they test those patients for EGFR, but actually knowing that in the last week they had a patient who was positive. They don't know anything about that patient other than the age and some high-level information like we just showed you, but crucially, there could be a timing there where the sales rep calls on the physician just as they are making decision around the therapeutic and the timely education and understanding of the drug efficacy could be very useful. Remember, one of these patients going on that therapy could be $100,000 plus for the pharma company. So every patient going on treatment is a big deal. So to summarize then what we've done here is we've taken a very congested market, EGFR in particular, and allowed our pharma company to leverage the sales resources that it has in a very targeted way. First of all, we benchmark all of their physicians to identify those who are most likely to be seeing patients that are positive for EGFR exon 20. And then we've gone a step further and provide weekly updates for that data to say, this physician has just seen a positive patient and is, therefore, right place, right time for some targeted education on your therapeutic and a higher likelihood that you'll be able to convert a patient onto drug. This brings me to our third and final case study for today where we're looking at best-in-class test quality implementation in the U.S. for a biomarker called NTRK, and this is specific to our barrier 4. So our scenario, again, top 10 pharma. There's a third year market with a pan-cancer therapy targeting NTRK gene fusions, pan-cancer, relatively novel, not a specific type of cancer, but all cancers, if they are able to detect an NTRK gene fusion. Therapy is very rare in most lung cancers. You may need to test 1,000 patients to find 1 positive. So it's imperative that NTRK resides alongside other biomarkers that are more prevalent on next-generation sequencing panels that are typically performed now in oncology. And therefore, if you're testing for a multitude of biomarkers at once, while NTRK may not be your focus, because it's so rare, if it's present, then there is a positive result that can go back to the physician. And the company that we're working for wants to assess the ability of laboratories to detect and successfully report a positive NTRK gene fusion. So what's the solution we provided? The pharma company reached DXRX data analytics tools to gain an understanding of laboratories that are able to detect an NTRK gene fusion and the following services were reached. We provided the lab validation service alongside our partner, SeraCare. We'll talk about that in a moment. But effectively, we sent on samples that had a non-NTRK gene fusion to these NGS testing labs to assess the ability to detect the fusion. We then used our lab monitor tool. The labs on to DXRX provided the results of their validation, and we then presented that data back to the pharma company. So the first thing I wanted to touch on here is as part of DXRX, we have a rich partner framework. And we partnered with this company, SeraCare, who have a rich toolkit as it pertains to manufactured clinical samples that can be sent out to laboratories in order to assess the capabilities of the test. This is one of the many uses. And SeraCare have an NTRK fusion sample material that we use for this particular program, working alongside them in the United States. So the samples were sent out to the labs and they then performed testing and they came all to DXRX to provide that test result back to us, which we then reached our lab monitoring service to help the pharma company interpret the data. So as we look at our lab monitor tool, we are now showing all of the labs in the U.S. who perform next-generation sequencing panels. And we are able to provide a view of each of these labs to the pharma company so they can understand does that lab offer a test that detects NTRK fusion. And then we provided some customer analytics to the former to really understand the capabilities on a lab level and indeed how they performed whenever they tested the sample sent out by SeraCare. And you'll see in the analysis here, some of those performed well, others less well, and a sense of an opportunity for the pharma company to then reengage with the laboratories. They can do that through DXRX as many of these labs onboarded as part of this program, or they can do it offline if that's a choice they make. Obviously, it's preferential for us to do through DXRX. we're currently in discussions with this pharma company as to the next steps. So that brings us to the end of our third use case today. And if you'll allow me one more moment. I just want to summarize what I feel DXRX brings to the market. It's a large source solution. Pharma can outsource their entire DX commercialization to Diaceutics using DXRX or the services we build today and the continued services we'll build going forward. It's digital post COVID-19, in an era of rapidly accelerated drug development and commercialization. DXRX technology provides a digital solution, which can meet this market need. Scalable. The number of new precision medicine therapies is increasing exponentially. The complexity is increasing. DXRX can provide Diaceutics with a scale to deliver on that growth. It's a multisite marketplace. You've seen we're friends with SeraCare and other lab partners. We bring all these key stakeholders together in one commercialization supply chain to create an enablement for collaboration and launching of these tests. And that's data rich. If anything comes across from today, I hope you can see how the combination of the platform generated exhaust data, the DDPs, and the world's largest precision medicine focused lab network offers best-in-class insights to pharma's key markets. Thank you for your time today. I'd now like to pass to my colleague, Susanne Munksted, who will walk us through our Q&A. And I will join her there shortly. Thank you.

Thank you, Ryan, and thank you for walking us through some powerful use cases for the DXRX platform. I will invite you back to join a Q&A session shortly. But initially, allow me to introduce myself. My name is Susanne Munksted, and I am the Chief Precision Officer at Diaceutics. And it's likely a title you might not be very familiar with. Basically, I'm leading a team of experts responsible for helping clients understand the critical relationship and the dependency between testing and treatment using the DXRX platform. I joined Diaceutics after spending years in the pharmaceutical and diagnostic industry, working with precision medicine in various commercial roles. Today, I'm very delighted to host a panel session with 3 strong professionals with working experience of the issues of precision medicine and the interface of diagnostics. We have representation from Pharma: Peter Hoehn, Head of Commercial Strategy at Johnson & Johnson, with a long and diverse career in precision diagnostics and whom we've had the privilege to work with over many years. Second, Professor Pruneri, Head of Department of Pathology and Laboratory Medicine at the University of Milan. And he is a leader within the laboratory in Europe. He has a passion for establishing a better connection between the various stakeholders and diagnostic ecosystem. And Professor Pruneri is also an early laboratory partner on the DXRX platform. And last but not the means least, we have Avi Kulkarni, Senior Vice President and Head of Life Sciences, R&D at Cognizant, a NASDAQ 100 consultancy company. Avi has, in various consultant roles, observed firsthand the trajectory of precision medicine from its early days in the mid-90s, and he understands, from a pan-industry perspective, why efficient drop diagnostic commercialization remains a challenge. Avi also is on the Advisory Board for Diaceutics and has been influential in helping design DXRX for the scale needed in the industry. Gentlemen, thank you very much for joining us today and for sharing your valuable insights.

Peter, I would like to start with you. Could you please share with us some perspectives of the hurdles you see presented when launching a new therapy alongside a novel diagnostic.

Sure, absolutely. First of all, thank you very much for the opportunity to join today. And I can build upon some of the comments that both Peter and Ryan said in their presentations. If you think about this growth of precision medicine, companion diagnostics, it really is a significant change within the pharma organization. Our commercial models obviously have been set up to really focus on physicians and changing physician behavior. But for companion diagnostics, you really need to think about what happens in the back end. And for us, if we have a companion diagnostic launch, there is a significant opportunity to lose patients who would be eligible for our therapies. I can maybe build upon 1 recent example, and it's in the EGFR class and exon 20 product that we launched recently. We did an analysis based on supported data from Diaceutics that showed if we did nothing in the lab world, in the diagnostic testing world, we would lose up to 40% of eligible patients for treatment. So if we did everything else right, we would miss 40% of our patients. That's because even though testing for EGFR is high, it wasn't complete, as Ryan said. But also we know a lot of labs were not testing for the EGFR exon 20 component of EGFR. So that by itself is a significant barrier that we needed to address. And that is for every single companion diagnostic launch that we have. I think the other thing that's really important to understand is this notion of seamless access to testing. Our world is a competitive one. And if we present a copy for our physicians and our pay are problematic, then they may not want to use our therapy the next time. So it's not only enough that our physicians and patients can get access to a new companion diagnostic, but that is done in a way that doesn't present hurdles or barriers to make that experience as easy and as seamless as possible so that it becomes embedded into their practice and becomes routine. So those are barriers that we're facing when we go launch a new diagnostic. And as I think you've said, we've worked with and partnered with Diaceutics over the years to help, first of all, understand those barriers and to put strategies to commercial plans in place to address them.

Thank you, Peter. And in your experience, is there a particular area or hurdle that you have experienced that you have learned here is a place for you to hone in or to sell in?

If you think about Peter's comments about the growth of companion diagnostics and precision medicine, the more that growth the practices are already embedded. As a pharmaceutical company, it would be highly unlikely for us to come in and change how a particular physician or provider group does their testing. So how that testing is done is also a bit of a black hole for pharmaceutical companies. There are a million labs out there. They all have little different protocols and methodologies. And to understand what they're doing and how that could impact our drug is critical. So that's really the most important piece. It's really understanding what labs are doing, what the potential miss is, right? So what their practices are and how we may potentially miss patients based on those practices, but maybe even more critically, how to change their practices in an efficient and timely way. And as Peter said, the launch is critical to become self-providing. If you don't have a successful launch, you lose investment in that particular brand and it's really difficult to recover. So to be able to be ahead of that and work with labs early on, to understand how they can change to support your drug and what you need to do to help support that change is really critical.

Thank you, Peter. And that, your reflections here on the seamless testing in the lab, that naturally leads me on to my next question for Professor Pruneri. And in many ways, I will ask you the same question, but obviously, to look at it with your pathology lab lens. So professor, when you need to introduce a new test related to a drug in your lab, what are your considerations and the challenges you may face?

Thank you for this question. The answer is not that easy because, of course, as you know, there are a couple of hurdles that you must overcome before introducing a new test in your lab. I would say that we could divide this answer in at least 3 main sections. The first is organizational. Of course, I have to get an analysis of the costs including reagents and personnel. Personnel is particularly important nowadays because the figures of professionals we are working with, they are changing very rapidly. We were familiar in work with pathologists, molecular biologists, and biotechnicians. Now we are working in strict relationship with bioinformaticians, biostatisticians and physicians. So the cost of this very complex analysis has to be taken into account. Then, of course, there is the cost of the platforms and the maintenance. And we have to take all these issues into account before deciding whether it is better to keep the analysis in-house or externalize it. Let me just give you an example. I'm running quite a big lab doing basically all kinds of molecular testing, but we recently decided to keep in-house next-generation sequencing. Whenever this kind of analysis is carried out by targeted sequencing, so the panel has 50 to 500 genes. And we decided to externalize to another academic institution the analysis of all exome sequencing and all genome sequences. This is why we would not be able to get this kind of complex analysis within the turnaround time that is absolutely compelling for a clinical practice. So the organizational issue is very important. Then there is the reimbursement. And of course, every country is different with regard to the reimbursement. I'm based in Italy. And in Italy, the reimbursement is based in a health system that has a universalistic approach. So basically, my system dictates that only tests for drugs which are registered by the national drug agency would be reimbursed. So this means, for example, that in the examples made before, in the earlier presentation, the analysis for PIK3CA by RTPCR or NGS has become reimbursable just 3 or 4 months ago. And for example, the analysis for microsatellite instability for the prescription of immune checkpoint inhibitor has not been decided to be reimbursed until now. So we could not do that by using our budget. And lastly, there is a technical issue. Let me just tell you that, of course, we have to decide whether or not to get a single test for the biomarker included in a panel, for example, NGS or NanoString. And then, of course, we have to decide if the test is a companion test. In this case, we are forced to use a specific kit for a complementary test. In this case, we have to choose according to specificity, sensitivity and so on. Let me finally just comment on the fact that the pre-analytical phase is very important. And this is, in my opinion, an important point because, of course, technology is, of course, very important as well. But let me tell you that as we have seen in the earlier presentations, all the systems used in the collection of clinical samples in the pre-analytical phases are very, very, very critical. Very complex tests like target NGS, they could be affected just by the precision. So this is why, in my opinion, the pharma companies rulers and academic institution must work to get in order to ensure that all the phases are run in a proper way.

Thank you, Professor. And thank you for sharing those insights and helping us to understand all the complexity that you're facing. So Avi, I hope you don't mind me describing you as one of the veterans of precision medicine and someone who has been observing the transformation underway within the industry. Could you help our audience zoom out for a second and give your perspective on how industry-wide the issues described here by Peter and Professor Pruneri are.

So thank you. Thank you, Susanne. Thank you, Diaceutics team. Thank you, distinguished panel, Peter Hoehn and Professor Pruneri. Let's first conclude with the statement and then we'll build up to it. Absolutely, yes, industry-wide, multiregional, so there's no easy answer in any 1 particular region. And now let's explain why, right? There's many ways of picking this up. But let's start where our good professor just ended, right? He was talking about the pre-analytical components and for purpose of consistency, we are generating a sample appropriately, the analytical and then there's a post-analytical, there's all of the, what does it mean? Let's interpret it? Let's allow for an actionable lab result to be reported and produced and then, of course, billing and all of that sort of a wonderful commercial stuff, which is also important for the longevity of the lab. What this actually was alluding to was the incredible variation that the system has that comes from first, biology and chemistry. As in every disease, in every patient, has a multitude of reasons why there could be variances either in the kinds of biomarkers expressed, the levels of biomarkers and for certain additional sort of chronologic progressive variances in patterns. So we start with a biological problem, labs start, physicians start, pharma companies start with that. Let's assume for the sake of simplicity that they can solve for that. They can say either the 1 biomarker or the mix of biomarkers that definitively with this special reading can give us an actionable analytical answer is provided. Then comes the, well, is the industry, the lab industry, able to provide this answer with any degree of consistency? And it's a brilliant cottage industry of high-level experts, right? So the cottage industry allusions when it does a disservice. But what it is, is, again, to the point Peter made, it's like a million odd total number of labs in the world, all of which, because of what they do, can produce variation. And so for any 1 decision maker, if it is a pharma company exec or a scientist, it is, how do I pick this lab to give me the answer that allows me to then move this assay through and produce them for patients who just have to have the right prescription decision and then get to the right outcome, right? And then following all of that is a huge amount of variance around reimbursement and reimbursement coding, and for that, let's not just yet [Audio Gap]. It is a gigantic problem, which till recently, we have said to ourselves, wouldn't it be nice if somehow all of these data could be aggregated in some logical fashion and insight engines be overlaid onto the data to start answering the sorts of questions that have bedeviled us for decades. And not to make this just a pure-play pitch for DXRX, but that is exactly what DXRX is doing, right? So to turn this now to sort of what Ryan said as he was presenting the case, the use cases, in effect we have taken 1 of the industry's most difficult challenges, a long operation of data, which was a very hard thing to do, cleaning the data so that it could actually lend itself to analysis, and then the AI techniques, and especially AI, because you want to have a hypothesis that you want to bake into the analytical tool that you'll build, start to draw an insight generation engine. And so that's both the problem and the solution that I think we're talking about today. Thanks, Susanne.

Thank you. And I think we hear some common themes here on variations, on complexity and the need for standardization. So let me move to the next topic, which essentially is how do we address these issues. And you are, from different perspectives, familiar with, as you've [indiscernible] the recent launch of the DXRX network as an implementation platform and data-rich solution. So Peter, if I turn to you again, when talking precision medicine and introducing a novel diagnostic test, we've heard various challenges via the panel. In your view, what needs to be the key focus in an end-to-end solution for diagnostic commercialization?

Great. Thanks, Susanne. I think from my perspective, unfortunately, I don't think there's just 1 thing. But the important point that you mentioned is end-to-end solution. So for us, it starts with who our diagnostic partner is going to be. We live in this kind of weird dichotomy that we need a regulatory approved companion diagnostic. So we want to pick a partner that has capabilities of that, but that they also have some footprint in the world, so that we know that, that 1 diagnostic test is available in many places around the world. But we also know that in most countries that companion diagnostic will not be the majority test used. That they're laboratory developed tests that will be used. So it starts with picking the right partner, but then it quickly goes to understanding that lab environment, as I was talking about before. And this is why I think Diaceutics' capabilities and, in particular, DXRX is still important for pharmaceutical companies. We have no real capabilities focused on that lab customer. There are some pharma companies who have built up some small capabilities. But that's really just the tip of the iceberg. So really understanding that lab environment, what labs will be that, kind of the 80-20 rule, the top 80% of labs that are going to be servicing your test. And what are they doing, what do you have to do to help them either adopt a new test, change what they're doing, be a partner in helping to kind of validate a new test. So to me, it shifts quickly from, we have this companion diagnostic partner to, there's this whole lot of world out there that we have to get our hands around and doing it in an efficient and effective way, so we can meet that test uptake and product uptake requirements that we have.

Thank you, Peter, and some very interesting reflections. And I'll direct my next question to Professor Pruneri. As you know, 1 of our ambitions with DXRX is to support a stronger local and global lab network to facilitate the collaboration between multiple stakeholders in the diagnostic ecosystem and to shift that better testing for doctors and their patients. So Professor, can you give us your early view on the advantages of this type of platform and how this may support you and all the laboratories?

Thanks for this question. I mean, I remember the first time I saw this platform. I mean, I thought that it could be an extraordinary chance to build networks focused around specific tasks and procedures, especially in area of [indiscernible] related to accreditation, regulation, and of course, good clinical practice. This was the first impression. Then I think the second important issue that is related to a platform like that, it's the chance for an institution. This is quite new in my opinion, because we are not very familiar to this kind of activity, but it's a very good chance for an institution to let the other institutions, the biotech companies, the pharma companies, how we are dealing with specific problems. It's a kind of -- I would not say advertisement, because it will not be correct, but it's just -- in order to let the others know what we are doing. And I think that it will be very, very important. Let me tell you that, for example, in my institution, we are trying to integrate and improve and implement the platform, the profiling platform that we have here, and we launched the molecular tumor board 1 year ago. And as soon as we completed this project, we will for sure try to present a project in your platform because it will be very important, in my opinion, trying, for example, to get it in Italy or throughout Europe with regard to the molecular tumor boards, how they are working, the procedures they are following and so on. For the time being, we have just been involved in a project that is very important, in my opinion, that's been chosen properly. That is the assessment of the HER2 low in the clinical practice. This is a very important task for the future. And with regard to the laboratory and the pathology labs, this is quite a critical area because we must still discuss how to evaluate the cases, we have to register our reproducibility and so on. So I'm very excited to be working in this project. And lastly, I think that we should keep in mind that the future, in my opinion, will be awareness, culture, and data. And I think that the platform like yours is fulfilling all these activities and this issue. So I think a platform like that could be faster and endorse a more close network between institutions. And I was also thinking about the fact that we will be faced in the next future with the IVD regulation at least in Europe and we need help. We need to work together in order to get this kind of regulation real in our labs and, again, procedures. Working together, I think that you are creating a tool that is very important to get these kind of objectives.

Thank you. So time is running and there are so many questions I would like to ask. But before we go to the Q&A, Avi, I would be interested in hearing from you, your perspectives on the digitalization, which digitalization is a word we cannot avoid, but it seems that digital commercialization is still relatively new to the pharma business model. What do you see as the advantages of DXRX as precision medicine and the need for timely access to new diagnostics tests continues to evolve?

Thanks, Susanne. And I think this is a key question in today's times, right? We accept, for today's discussion, especially, that our definition of digitization is that all of the data related to the tests, the labs, the performance specs, and the network and the outcome is digitized and is available. And then added to that, over time we start to put in social determinants of behavior. What we have here then is the ability to, in 1 set of key strokes, start answering some of the most important questions that we have grappled with for a long time. Is this test or assay available? Where is it available? What are the performance specs? Which are the technology platforms that support this particular performance? How can we roll this out in to meet the disease burden? What kinds of variances come about because of idiosyncrasies with patients and providers and all of the things around guidelines that do vary from around the world? And then how can it be coded for appropriate usage, for appropriate pricing. And that list does go on, right? So the advantages of digital is that it allows for this step change where it is not a 1 problem, 1 analysis, 1 answer to a multitude of repeatable and fast analyses that, well, I think, break the log jam in this business of how do we generate valuable and perfect information that then results in action. So I think this is critical and we are on the cusp of it now.

Thank you so much for sharing your insights. And at this point, I would like to bring Ryan back in and open the floor to a Q&A session and to any of our guests for any questions and indeed also questions to Ryan on the platform. And while we're waiting for those first questions to come in, I would like to maybe point attention to a recent article in The Economist where the CEO of Roche talks about the Insights business as potentially bigger than Pharma and Diagnostics. Avi, I'll point to you again and interested in hearing if you agree to this statement? And if so, what is the opportunity at DXRX?

Let's break this up into the following components and then answer it. Pharma produces a drug just as surgery produces an intervention, right? So it's an action that is taken. And in that sense, it's no different than, let's say, there's a device like a knife that can be used to cut something. Diagnostics produce information on the state of the patient that inform you on what action you can take. Each of these steps have their own inherent value. But it is when you combine these 2 that you actually generate the best value so that you can have the best outcome. So Severin Schwan, the CEO of Roche, who was in this article that came out, I think, in last week's Economist, actually alluded to something very, very important, which is, if you only participate in part of this, if you only participate in explaining what the disease status is, biomarker-based patient segmentation strategies and so on, that's part of the equation. If you only can [indiscernible] I know what to do, I have drugs to take care of that. Well, that's very good. But if I can put it all together, I can start getting to genuine outcomes, I can posit what the outcomes will be, and then I can participate in the health care system as both value creation and value capture on an outcomes basis, and I can get back to a concept which, if I was in his place, I'd be thinking about when do I get to participate in disease management and start thinking about things like capitation models and so on, which are all the rage, especially with personalized medicine and some immune therapies coming to the fore. So this is a very powerful statement that he's made. I think we're going to see this play out over the next several years.

Thank you for sharing that. Do we have?

We do have some questions, yes. Precision medicine has so far only really been widely prevalent in oncology. What are some other therapy areas that should see an increase in the use of precision medicine in the near term, say, 3 to 5 years?

Peter, can I direct this question to you?

Sure, absolutely. In my role when I was leading the strategy group for precision medicine, we worked across all therapeutic areas. And admittedly, a majority of our projects were in oncology and still are. But what we had projects across areas such as neuroscience, immunology, cardiovascular, and in our new gene therapy unit. So we definitely see the science and the market behind in other therapeutic areas, but in some respects it's catching up quickly. The science is obviously different, oncology, targeted therapies is already mutationally driven. What we are seeing in most of these other areas is that there are biomarker determined discovery efforts that are leading to drugs. One area, in particular, is Alzheimer's disease. We saw a recent somewhat controversial approval. And that's also based on a diagnostic test around beta amyloid. We worked in that space significantly as well. So this is a trend. The precision medicine trend is not limited to oncology. It is definitely the leader. But in our business, we see it across all areas. Every single 1 of our therapeutic areas have biomarker leads, diagnostic experts and commercial strategy to help really understand and identify where the precision medicine opportunities are and to bring them to life.

Many thanks. And if many new diagnostics take years to reach patients, how much more quickly could laboratories offer new diagnostics if they're able to collaborate and network together?

So I would be very interested, Professor Pruneri, to hear your opinion on this question.

I think that working together would be absolutely necessary, especially in this group. We could not just keep working separately and trying to get in each lab all the diagnostic procedures. I already mentioned the fact that even if we are a quite big lab, we decided to externalize whole exome sequencing and whole genome sequencing analysis. Let me tell you that in a more pragmatical view, there are data coming from ASCO telling us that patients with advanced lung cancer, they have an extensive and sufficient analysis of their disease just in half of the cases. And this is why we are not working together. I think that the future, I'm just using the example of Italy, I'm based in the north of Italy, but I think that it will be the same in Europe and possibly in other countries, we must try to set hub-and-spoke networks very closely interrelated, possibly working together by using digital pathology as the first analysis. So we could work together with regard to the first diagnosis by digital pathology and then sending in central labs tissues or nucleoid analysis for second and third analysis like next-generation sequencing. So patients getting in very local and small centers, they could have the same analysis they could receive in very big hospital, very big centers. And this would, for sure, speed up the process of diagnosis and the translation from research, clinical trials and real-world data. This is, in my opinion, 1 of the most important challenge we will face in the future. There are a lot of new drugs, new tests coming from the research and clinical trials area. What it is difficult is trying to integrate these new biomarkers within the real life of our patients.

And do you expect the budget spend per brand on improving the testing landscape of therapies to increase?

Avi, can I ask your opinion first, but I would also like Peter, if you could give your opinion after.

Well, I can just point to the industry trend, right, which as we have seen over time, as it becomes clearer to industry and to the commercial and brand and the lifecycle teams, what the value of diagnostics and segmentation strategies are. They are putting more money into it. And as they put more money into it, it's a nice virtual circle. When they put more money into it, better assets get developed, price points go up, and so we start to see, in general, a larger spend going towards companion diagnostics, enabling diagnostics, that general area of assays. Peter, I'll defer to you. You're probably on the side where you get to make these decisions.

Yes. So I would say absolutely, yes. And there's a couple of components to it that I think are really important to understand. So first of all, unfortunately, we as big organizations, sometimes tend to learn things after the fact. We have the Herceptin story that Peter talked about. But I think everybody in every company that has any experience with launching companion diagnostics realizes that the things that we could have done sooner that we could have done better to really optimize patient identification. So that's a learning that's now embedded in our organization and I think in every organization. The other thing that's really important to understand as you have this trend towards more companion diagnostics, particularly in oncology, where testing becomes the standard, oncologists are going to test a patient. So if you have a patient with non-small cell lung cancer, most likely, that physician is going to do a broad panel test for that patient. That test will determine how that patient will be treated. So by definition, that lessens the impact of any particular representative. It's not about, hey, doctor, please use my drug, it's please test and do what the test says. And those testing practices are already determined. So as I was saying before, it's really critical to understand how that physician tests, how the test is done, and does it appropriately call out a positive patient. So that will shift budget from more traditional pharma selling and marketing towards commercialization around the tests. And I used the term commercialization broadly because it's not just about a sales rep, but it's about really helping to enable our lab network that could start years before launch to do things like concordance studies, bring studies, getting labs up and running. So that shift is starting and I believe it's going to continue as you see less impact in traditional selling and marketing and more impact, more consequence around getting the lab world, the lab networks ready to test appropriately.

And how much manual input from the lab is needed to keep all data up to date. How onerous is it basically? And how much can be automated?

Ryan, do you want to address this question?

Sure. Thank you, Susanne. I would say we're halfway through our full automation of data extraction from the lab. So today, there is a level of commitment they need to make to provide that data, but we provide significant value to them as well where that level of commitment they need to make seems to be justified. So through that value proposition through giving the data back to them when they put it in, there is a general view that it's worth it from their side. And as we continue to develop the platform, we're making it easier and easier to get that data and allow the labs to interact with us.

And a question for Peter. When you looked at your diagnostic testing for EGFR exon 20, what were the other options considered for addressing this issue? Other data providers' in-house capability, et cetera. Why will a lab engage with the lab broadcast message? And what's the engagement depth?

So a couple of things. So there are 2 companies that have launched exon 20 drugs. So I'll speak to our experiences and our work with Diaceutics. Once we understood the issue at hand. And again, you have to really be in this space to understand it. Even our pharm colleagues didn't really appreciate the fact that even though EGFR testing is standard, being able to identify and call out exon 20 was a nuance that not every lab was doing. So first of all, really understanding that and getting the organization to understand that was critical. One response potentially to it was to build our own lab sales force and start, once we identified the key labs, going out and working with them significantly before launch to help get them on board that exon 20 was coming and make sure they were testing appropriately. That potential solution was actually reviewed, but it was not accepted because that's a high-cost solution to build, even if it's a small sales force, to be able to do that. It's also spending significantly at risk because that would be long before the drug was approved. So we opted to work with the Diaceutics team and the lab specialists within Diaceutics to be able to identify and call on the right labs to help support the development of the exon 20 test within that particular lab. So that was our response to that particular question. And working with Diaceutics, in some ways, I wouldn't say it was the only option, but it was clearly the most effective and efficient option, and that was in the U.S. specifically where we've launched that with our U.S. team.

And the integration of data from the lab matters. Could you talk a little about the general state of the labs' IT systems and how the integration processes work and how much of the average labs testing is actually being captured in the data and how much manual data entry, et cetera?

I could take this. And I'd like to maybe pass to Professor Pruneri. It varies possibly and it's not just regionally, but by lab type, by type of testing. Some laboratories are very ready to engage and their data is in a very ready state to work with and we can engage and integrate very quickly. Others are at a different stage in their journey. We have built our model to be compatible with labs across the entire spectrum. It's not just the bigger labs or better prepared. As I said, it's incredibly variable. It is improving. And as a level of standardization comes in, we are benefiting from that. But in many ways, a large part of our onboarding and integration is working through that data integration with the laboratory, which, as I said, is a variable depending on the market type of lab, et cetera.

And we have a question for Professor Pruneri. How many of the service companies do you provide testing data to? And what do you believe the benefits will be to you from being a part of a larger DXRX marketplace that brings together other labs and pharma companies?

We are working in close partnership, especially with biotech companies. Approximately 30% of our, for example, the results of next generation sequencing are provided in our institution by FoundationOne. This is because they are providing tests which are companion tests in clinical trials. And so what we have done is externalized our cases to FoundationOne and centralized the results, so that all the data are discussed within our molecular tumor board. Then, of course, most of our patients are included in clinical trials, which may be pharma driven or academic. And lastly, let me just comment on the fact that data are very important. And this is a problem in academic institutions. We just licensed a project for the implementation of the informatic infrastructure in our institution and the funding was approximately EUR 1 million just for the implementation. And this is because we need safe data, data which can be integrated with clinical outcome data and also the biorepository data. Lastly, with regard to the chance that we have to work together with the pharma companies, I think that, in my opinion, academic centers will be an ideal partner for pharma companies and also from biotech companies. And in the next year, I would bet that, we will be working in closer contact that earlier because at the end of the day, we have the same objectives. And I think that we could address all the issues working in close partnership with the pharma companies.

And that's the end of the questions. Susanne, back to you.

Thank you so much. And I would like to thank the entire panel and especially thank you to Peter, Professor Pruneri and Avi for taking the time to join us and share your expertise and insights. That's been really helpful and fascinating. I would like now to pass back to Peter to talk about the strategic road map underpinning the platform.

Indeed, thank you, Susanne, and I echo my thanks to Peter, Professor Pruneri and Avi. It's interesting just listening to each of you breathing life into a problem, which for many, even in the industrial world, has been opaque for far too long. And I think you've given voice to that and indeed described quite well the importance that this will have within the future pharmaceutical business model. So I really appreciate you taking the time to do that. And in a few minutes, I'm going to ask Philip, who is our CFO, to speak to the financial flows underpinning our evolving business model. And I suspect there will be a question from many of you. But I want to take a few minutes to speak to the strategic road map and where are we going with the platform and the progress along it. So let me first deal with competition. And I think as Peter and others spoke about, the pharmaceutical industry is already spending money to understand and address the diagnostic hurdles. But this budget is not yet centralized. In fact, by our own view, it's spread over as many as 15 different buckets within the same brand team around the globe. It's not yet pulled and integrated into 1 location. Consequently, we see competitors falling into a number of the buckets, beginning to target the nonrecognized diagnostic problem. In areas of data, for example, we have competitors like Prognos, which is U.S.-centric, it's non-oncology focused by and large and provides largely generic data sets to pharma for their own teams to interpret. In contrast, our data insights and the diagnostic to Dr. Pathways, which Ryan mentioned. And these are focused on identifying and eliminating the hurdles that we've described. There's also competition in the lab network space. Companies like Roche's own Foundation Medicine, which Professor Pruneri mentioned. They are promoting a highly centralized testing model, very effective within research and development. In our model, at the commercial end, we do not impose a disruptive lab infrastructure initially. But instead, we've sought to create a network which we hope to leverage their experience over several thousand labs, all those labs with existing doctor relationships. And of course, we also see consultants in pharma services augment their own businesses with the precision medicine offering. Companies like LEK or IQVIA, names that will be familiar to you. We, of course, absolutely recognize the importance of understanding our competitive landscape itself. It's a function of a precision medicine market that's on the move. But our focus really is building a comprehensive and dedicated offering to pharma and to our lab partners. And in our view, it combines all the strengths of those competitors, but we go 1 step further. We go 1 step to deliver a single commercialization service dedicated to delivering a superior return on investment and getting more patients on the treatment. Many talk, in the precision medicine business, about the highly competitive and mature services market, which is focused on the R&D side of the pharma business. But today, our market opportunity on the less well-served commercial side is now clearly marked out for us. More test dependent therapies will continue to need a seamless diagnostic commercial solution. More pharma wallet will be released and managed to ensure each of these important drug launches do not lose patient opportunities. And these budgets themselves will increase. They'll become centralized within pharma. And we can also see an accelerating trend towards seeking digital and indeed cost-effective solutions here. Again, Peter already alluded to that in his own example. Based on our track record, our position with all the important players in the precision medicine alongside now a scalable platform, we believe that we're optimally placed to capitalize on the first-mover advantage in the space that we built and we think it's a space that's worth over $3 billion in the next 3 to 5 years. So what do we see as the strategic road map for those 3 years? In essence, it's an aggressive pursuit of market share in this space. Let's start with 2021. Platform adoption, as you know, is all about bringing the most important partners onto the platform and driving hands-on use. That's our focus for this year. Using the measurable impact of those first platform-based projects to quantify an investment to the clients and give them the proof of their ability to actually control in the team this diagnostic ecosystem. And to do that predictably is our focus in 2022. And by 2023, we will illustrate the benefits to each of our clients of migrating many or all of their diagnostic commercialization needs onto a single platform. Consider this. Salesforce.com has become the ubiquitous tool in managing the time and metrics of sales force optimization. We aim to provide that same enterprise-wide solution for our clients when it comes to diagnostic commercialization, and we believe we're really well placed to do that. So in 2021, it's all about partner acquisition and hands-on experience, watch our progress after 6 months on the market. We're genuinely excited by the traction in this foundational year. And whilst we were budgeting to convert 20% of our business onto the platform, as we sit today in the kind of first 6 months, the run rate is closer to 40%. But it's also unlocking benefits to value coming from margin and price differentials versus the same time last year. And as you can see by the chart, all the adoption indices that we're building for ourselves are all trending in the right direction, with only 6 months into a 3-year conversion of our business model. Interestingly, our multisided platform is already generating interest as an integrator in this broken diagnostic ecosystem even beyond our historic focus on pharma and lab stakeholders, something we hope to report on in future sessions. However, our goal for platform conversion was, as you know, not just about projects, not just about patients and not just about disrupting and changing our business model. It was also about evolving our underlying financial architecture away from our consulting-esque, project-centric approach to predictable long-term revenues and high margins enjoyed by platform business models. And here, I'd like to hand over to Philip to just refresh where are we in our journey and progress there?

Thank you, Peter. You can see why we're hugely excited by the power of the platform. Ryan demonstrated some of the granularity around the data and also the activity, as Peter described in the KPIs to do it. So over the next 3 slides, I really want to set out the DXRX business model. There are some dynamics within this business model I want to bring to life. We then want to look at the revenue streams that underpin the business model and then bring that all together into the financial impact and our aspirations as we move towards $1 billion business. But firstly, let's look at the DXRX business model. And if you look at the graphic, starting from left to right, the platform itself leverages off the data repository and the insights that we've developed over many years, and Ryan has demonstrated how the data can flow back many years. A key question for us is how do we monetize the platform and how do we increase our revenue capture. We monetize the platform by selling 16 modules, which come off 5 different revenue streams, network access, data license, data subscription, tech-enabled services and professional services, and we'll come to these in detail on the next slide. These 16 modules, and Ryan has demonstrated some of those, make up our full end-to-end commercialization offering. Some modules are point-in-time revenue, some are our subscription based. And our sales strategy is to land and expand, landing 1 module within our clients and expanding into many modules over many years, effectively selling more to our existing clients and winning new clients. So to summarize, monetizing via our 16 modules, increasing our total contract value as we layer in our data, our subscription, and our tech-enabled services over a longer engagement period, as Deborah stated at the start of the presentation, increasing the customer lifetime value. Looking at another positive financial impact for the platform is our laboratory engagement and the financial benefit as we bring laboratories onto the platform is the reduction in labor associated with our own laboratory engagement teams. Ryan touched on communication channel within the platform. That was all manually done at free platform. Another important financial impact of the platform is that we have access to the data flows from the laboratories within the platform. Laboratories get the benefit of being on the platform and we received data in return. We call this exhaust data, and this effectively reduces the acquisition cost of our data. So as we increase activity and engagement on the platform, it generates stronger data and insights for us. It creates and increases our offering in real-time data and insights, opening up more pharma brand teams for our BD folks to target. And just to close off this loop, as more pharma brand teams come on to the platform, the stronger it becomes via the data and the laboratory network and the cycle continues. So the platform in itself lends itself to a subscription revenue model because it's data led. It's a scalable platform with high gross margins. The data alone represents a gross margin of 90%. I touched on the network effect efficiencies within the platform in data capture and the reduction in labor costs around our laboratory engagement teams. And this potential for short-term to medium-term upsides in non-pharma revenue channels that has data companies, laboratories and diagnostic companies. So I've mentioned the 5 revenue streams. Each of the 16 modules we sell within the platform fit within these revenue streams. Again, some are point-in-time revenue, network access and data license. We also have data subscription, which is a 12- to 36-month subscription to data. Tech-enabled services and professional services are broadly milestone-based revenue. And we see data and tech-enabled services making up 90% of revenue. Our objective is to land the client into network access and data license, transition into data subscription, and then layer in the tech-enabled services modules. And if we take a recent example of a top 5 pharma company firstly engaged via data license to really in 1 country, 1 biomarker to understand the market. Within 3 months that had moved to $0.5 million data subscription and multiple tech-enabled service modules covering multiple countries. The output of this was an understanding of what Peter described earlier as the barrier to prescribing 1 of those key barriers. And we are exploring the opportunity of a further $0.5 million contract to understand the laboratory testing. And we can see this engagement for this 1 brand team expanding globally with the need for data and tech-enabled services addressing those barriers that Peter touched on earlier. So within this real world example, we can see really a runway for us providing $10 million of value to this 1 brand team over multiple years. And we don't want to stop there. We want to provide that type of value to all of the brand teams within our clients, really embedding as an enterprise-wide license and the platform allows us to achieve this. Capturing a meaningful market share will transform our key financial results and it is our aspiration to do so. And I'd refer to Stifel's recent note on our immediate outlook. We have a clear path to revenue growth with our DXRX platform and the ability to capture the increasing demands from our clients. The platform is delivering high gross margins. We have this network effect efficiency within that resulting in a strong EBITDA moving from 15% to 25% and into a cash-generative position. So we have a platform to become embedded within our clients underpinned by a high percentage of subscription revenue. And of course, we will continue to invest in the platform and continue to invest in our data. The aspirations that I have presented are highly achievable and we are passionate and excited about delivering the next phase of growth for our business. I'll hand you back to Peter to round out the presentation.

Indeed, thank you, Philip. This is the last slide, I think, before we open up to any final Q&As and you couldn't read the key investment points for yourself here. As I reflect really on the decade-plus journey that we've been on at Diaceutics, and I told my team many years ago that, in essence, we were placing a bet. We're going to bet to be in the right place with the right company at the right time. And as most of you know, today, I think precision medicine is the right place to be in health care. I hope that what we've demonstrated today is that we build a strong beachhead in precision testing with leading clients, and we're in the process of converting that to a scalable platform. And that makes us the right company to serve the complex on that need here. But of course, what I did not say to my team was that the last of these, in other words, to get the timing right, it's not actually in our control. It's more in the hands of the marketplace. But we can hold our heads high and speak to the team and say that we really are in the right place with the right company at exactly the right time. We're never complacent about the challenges in front of us. And I can say that across the company every day, there is an entry of a team that knows the impact that we can make and very much focused on the road map and is ready to take our place alongside the input company shaping precision medicine today. So with that, I'm going to open the floor to any final questions you might have for myself and the team.

And the first question directed at Ryan, what is the lead time to build a lab mapping and network for a pharma client. And how many of your 2,500 labs need to sign up to DXRX in order to have a critical mass i.e., versus greater than 200 labs today? And what is the potential issue, if a pharma client decides to go direct to a lab identified on DXRX versus trying to keep the engagement with the platform.

Okay. Thank you. I'll try and deconstruct that question. First of all, the lead time is, we can put those lab mappings out effectively instantly. We've maintain the data across those 2,000 labs. And just to correct that, while we have 200 and increasing labs on the network recruited, we still have profile information and data on the additional labs. So we can still provide lab mapping, but where we want to interact with those labs, we're limited to those that are actually already recruited onto the platform. So the lab mapping is still done in its entirety. And where there's a recruitment step or an involvement in the tech-enabled service, that's limited to the laboratories that are onboarding today. And that process is very quick from a pharma perspective. As the labs onboard, they sign an agreement with us as to the types of services that they are enrolling in. One of the part of that agreement is that they will be exposed to pharma. That's actually an advantage to the laboratories for the most part. They see that as a good thing to get closer to industry and to get closer to the pharma company. And indeed, where a client wants to take that information and build upon it themselves, that's perfectly appropriate to do so. We don't restrict it in any way. We're trying to build a collaborative model where we believe all stakeholders should be allowed to interact. In fact, we promote that. Ideally, it's done on DXRX. And what we believe is we're building a channel that makes it easier to do that on platform rather than off platform, but we don't limit that. Ultimately, it's about doing the right thing for patients and we make sure that, that lab and pharma thing can happen if it needs to.

And have you had any pushback from doctors about being targeted around prescribing decisions, even though it's based on anonymous data.

Again, I'll take that. There are mechanisms by which physician can opt out of any direct interaction with sales teams, et cetera. That is well established as a mechanism. Ultimately, where we are leveraging the data, it's typically to provide education and awareness. And what we are hearing consistently from physicians is this is of benefit to them. This is an incredibly complex space. We see through market research that anything we can do to support the understanding of biomarkers and when they should test and how they should test and where they should test is generally well received. And the information we have is powerful, but also it needs to be used in a way that is appropriate for these. And there's a high bar as it pertains to compliance and a regulatory framework on which that data can be used, which keeps all stakeholders somewhat protected here. And as you say, it is anonymized from a patient perspective. So they're absolutely anonymized and the physician relationship with pharma is a regulated one.

And what are the greatest risks to Diaceutics platform being broadly and reasonably rapidly accepted from a structural industry and medical practice perspective. What costs do Diaceutics need to bear to break down resistance?

Let me take that. One of the things I would say, we cite in many of our documents, the experience that we've had over the last decade or more. And we're not just saying that as kind of a lie to really boast. We're really saying it because we have spent many, many years on-the-job experience building the right product. It started with building a method, which we call the Diaceutics method. And as you know, we've been converting that method, the data of the lab network into the shape of the platform as it sits today. I think from our point of view, we recognize that the platform is both a smarter way to deliver what in essence are a series of very disparate and fragmented existing services. So it's providing an efficiency and driving on top of that efficiency, we've already seen some of our pharma clients already starting to move and adopt that because of the efficiencies that it brings to the table. And again, Peter and Avi mentioned that. But of course, we're introducing a platform business model into an area which is siloed and fragmented, and we shouldn't be complacent about, I think, the challenges, both the educational challenges that, that requires. What we do know is that if you want to drive a modern smart platform in health care today, you need to do 2 things really well. One, you need to give your clients and your lab hands-on experience. You need to let them go and get dirty with the product itself, roll up their sleeves, see what that experience is. And we're absolutely in the midst of that process. And the second thing we need to do is to give them the confidence that the impact of that platform is real. And here, I think we have an absolutely huge advantage. We're not just changing practice. We're measuring all the time. We're quantifying. We're quantifying going into projects, what is the state of play today. We're changing practice and we're measuring how that practice is changing. And there's nothing that is going to give confidence more to our clients and to our labs and to other stakeholders than to see in front of their eyes, but something that has normally taken 3 to 7 years, to get a new biomarker into the hands of right labs is happening in 10 months. That's the sort of impact that will drive change in the market. And that's why I said in our strategic roadmap is this year, get hands-on experience, get our clients and our labs rolling up their sleeves and joining us. Next year as we bring some of those impact studies through, we will make sure that we shout loud and clear at how we are acting, turning a broken diagnostic ecosystem into something that is of benefit to patients where the outcomes to those patients will radically alter. They will get tested faster and they'll get on to drugs faster. That's the sort of impact that we will be bringing back to our world. In my view, other hurdles like cost, et cetera, will fall away because we'd be bringing a bespoke and tailored solution to a marketplace that frankly, over 15 years, if not 20 years, has been neglected.

And we'll squeeze 1 final question in. Revenues are currently relatively modest, especially set against the immense value you create for pharmas given their high return on investment. Can the model shift to take a share of the incremental drug revenues the platform seems to create?

The platform allows us the runway to provide $10 million of benefit or value to our clients and we see that transitioning through 2023. But the question raises a good point. And we do have our eye on what we call milestone payments and our pharma clients do not really like the word royalty. So it would have to be in the form of a milestone payment and I think within a mature platform. Post 2023, that certainly we could look on pivoting the platform towards a milestone payment, really extracting more of that value that our clients see and really rebalancing the sort of the value shift there.

Peter, back to you. Do you have any closing remarks?

Well, just 1, I know that everybody who has joined us today from the investor side does so in the midst of an extremely busy period for you all. So look, really grateful that you've come along and spent the time with us today. And as [ Thompson ] mentioned, all the questions that you've asked will be answered. And for those of you who have already invested in our journey, hopefully, you can see some of the fruits of that investment coming through. And we genuinely look forward to reporting forecast back in the months and in the years ahead. So thank you very much, indeed, for joining. And again, to our panel for breathing life into kind of a very opaque issue. Thank you very much, indeed.