IQVIA Holdings Inc. (IQV) Earnings Call Transcript & Summary

Hi, everyone. Thank you for joining our IQVIA hosted event, unleashing the power of precision medicine in oncology. Throughout the webinar, we encourage you to submit questions in the Q&A chat box, located on the left-hand side of your screen. We will hold time for Q&A at the end of our presentation. If you are having any connectivity issues, please try a different browser. We recommend you use Chrome or Firefox. I would like to take a moment to introduce our speakers. Our first speaker is Vladimir, who is an Associate Director, Global Oncology Product and Strategy at IQVIA. Vladimir has more than 22 years of pharmaceutical industry and related commercial experience, including various oncology-focused sales, marketing and business development positions. Vladimir holds a Master of Pharmacy degree from Zagreb University and a Master's degree in Health Economics and Pharmacoeconomics from Pompeu Fabra University in Barcelona. Our next speaker is Kriti. She is a client service manager on the Global Oncology team within IQVIA based in London. Kriti is currently a specialist in IQVIA syndicated oncology patient data. She is responsible for understanding project requirements, developing fit-for-purpose solutions, and executing and delivering end-to-end projects using IQVIA's syndicated offering oncology dynamics. Her previous experience includes market research, bespoke consulting knowledge of consumer good markets and wet lab R&D. Kriti holds a Master's degree in Pharmaceutical Analysis from NIPER India. We are also very pleased to have Lien, CEO of Persomed, joining us today. Lien studied Pharmaceutical Sciences, after which she did a PhD where she focused on the design of semi-personalized cell-based cancer vaccines. Lien is acting as Interim CEO of Persomed, where she manages and coordinates the investment round and the preparation for the clinical trial Phase I in metastatic colorectal cancer patients. Lien is also the Scientific and Clinical Alliance Manager at myNEO, where she is the link between the bioinformatics team, the collaborating laboratories, hospitals and universities, as well as the partners of myNEO. I will hand it off to Vladimir to get us started.

Thank you, Alexis, for the introduction. I would like to once again welcome you all to IQVIA webinar that will focus on precision medicine in oncology. But before we start, let's take a moment to outline the agenda for today's session. So you can see the agenda that we have planned for today. We will be starting with navigating the complex realities of biomarkers in oncology, where we will provide a short overview of the targeted therapies and biomarker dynamics at the global level, followed by biomarker landscape in IQVIA oncology dynamics, which is actually an overview of biomarker tracking in our syndicated secondary market research. At the end, we will go through genomics in the immuno-oncology landscape presented by Lien Lybaert from the Persomed and myNEO. At the end of the webinar, we will try to answer as much of your questions during the Q&A session. So let's start with the first presentation. So in the following 15 minutes, I will be covering a short overview of IQVIA precision medicine capabilities, dynamics of the global oncology medicine market. We'll also provide an overview of biomarker testing differences, and at the end, challenges associated with biomarker usage and clinical success factors. So before we deep dive into today's topic, I would like to give you some details about the IQVIA capabilities in precision medicine area, which can be divided basically in 3 main categories: first is data, technology and research services. With those 3 categories, we cover end-to-end solutions to support the discovery to launch needs of precision oncology products. We are particularly proud of the data solutions designed to support molecule-to-market needs, which includes retrospective clinical genomic data, access to [ bio ] samples and the opportunity to collect data prospectively. To support processing the wealth of the data that we collect, we have also developed a suite of technology, including privacy and de-identification technology, artificial intelligence, machine learning, natural language processing, analytics and biobanking software and many more. So through our global presence, we have developed also a data partner network across U.S., Europe, APAC and recently expanding to Africa and India. We have multiple case studies as to how we help pharma companies, payers and regulators. And we would be happy to do a deeper dive on our wider precision oncology topic. So when we speak about the global oncology market, it is important to notice that oncology special -- spectacularly increased its value over the last decade. So oncology became the leading therapy area back in 2010. And since then, it has continued its rapid growth. Within last decade, global oncology medicine spend rose from $67 billion to $182 billion, with a global compounded annual growth rate of 12%. At the same time, oncology increased its share within total drug spend by 6 percentage points from 8% to 14%, which represents a tremendous growth, actually. It is also expected that global oncology market will reach $300 billion by year 2026. In parallel with oncology drug growth, oncology has also seen biomarker test growth, and not just entering the mainstream, but also shifting the way decisions are made. If we take a look into the oncology drug spend structure, we can observe an interesting trend. The largest portion of the oncology budget is dedicated to targeted therapies, which, together with biomarkers, represent a core of precision medicine in oncology. A decade ago, targeted therapies accounted for 55% of the total oncology budget in the audited markets. But now in 2022, this expenditure reached 76%, showing compounded annual growth rate of 16%. So this data is actually showing that advanced targeted therapies, although more costly compared to cytotoxic therapies, have become a mainstream treatment option. And consequently, 3/4 of the global oncology drug budget is allocated to the targeted therapies. So the question is, what is actually fueling this growth? The answer is innovation. Actually, there are several factors, but innovation is the most important factor behind this phenomenal growth. With the sustained launch of diverse medicine throughout the decade, most of which are targeted therapies with various mechanisms of action. So the chart is actually showing the number of approved targeted therapies by FDA for the past decade. It is apparent from the chart that the number of new targeted therapies launched has been increasing with a compounded annual growth rate of 16%. This growth rate actually correlates with the rising spend allocated to the targeted therapies globally, which we saw on the previous slide. The increasing number of the FDA-approved targeted therapies demonstrates actually the continuous advancements in oncology research and development, leading to a broader range of treatment options for patients. But there is another question, is -- what share of patients do we actually reach with targeted therapies? So in order to answer this question, we have reached out to oncology dynamics, which is a syndicated secondary market research that provides a comprehensive understanding of the oncology landscape and patient treatment offers. So the chart on the left-hand side is showing the structure of oncology spend for EU4, plus U.K., U.S. and APAC region. The bars to the right-hand side are showing the percentage of patients treated with specific types of oncology drugs, considering that a single patient may receive 1 or more different oncology therapy types. So it is evident that on average, 76% of the budget is allocated to the targeted therapies in the EU4, U.K., U.S. and APAC regions, while only 48% of the patients within the same regions have received targeted therapy, either as a stand-alone treatment or in combination with other therapies. On the other hand, the expenditure on cytotoxic drugs account for 12% of the total spend, while 53% of the patients receiving treatment have been prescribed cytotoxic drugs, either as a monotherapy or in combination with other treatments. So these numbers actually reflect the differences, huge differences in pricing between those 2 mostly utilized categories of oncology therapies, and actually emphasize the need to carefully consider the balance between patient access, therapeutic efficacy and financial sustainability. The move towards precision medicine with oncology has had also a big effect on the number of relevant biomarkers and the tests. So the level of complexity and the availability of biomarker tests and products that patients receive have been increasing, but also, they vary across tumor types. For example, breast cancer, AML and non-small cell lung cancer are some of the highest for the both number of tests, but also for percentage of patient population tested. We can also notice that in EU4 plus U.K. countries, top tumors like breast cancer, CML, non-small cell lung cancer, AML have very high percentage of tested patients for at least 1 biomarker, which basically indicates that biomarkers are an essential part of the treatment paradigm. Even more extensive use of biomarkers is not only to reassure that patients will receive the right targeted therapy, but also due to the limited drug budgets. So in a global health care environment where we see widespread cost containment measures, it is important for payers also to identify patient subgroups who will benefit from certain drugs. Besides differences in biomarker testing among different tumor types, there are evident differences among geographies as well. So the [indiscernible] table is showing the percentage of patients tested for specific biomarker across different tumor types and geographies. It is obvious that U.S. is leading. If we take a look at the first column, we can see that it's mostly green, because the green color is indicating high percentage of tested patients, while yellow and red color are indicating lower percentage. So it is -- at first line, it's obvious that U.S. is leading in terms of percentage of tested patients. Large geographic variation in testing rates suggest that patients may not yet have full access to diagnostics or the novel medicine for which a positive test would support the treatment or they are lacking guidelines that could regulate the use of biomarkers. For example, in case of non-small cell lung cancer, we can see from the table, guidelines actually recommend testing non-small cell lung cancer patients for EGFR, ALK and PD-L1. So as a result, we have more than 70% testing rate for those biomarkers across all geographies, except for China, which we can see in the table. Another example is coming from colorectal cancer. Biomarker testing actually differs significantly across countries. And this is probably due to differences in guidelines, where national comprehensive cancer network guidelines recommend MSI testing in all patients and [ KRAS ] and [ NRAS ] testing in patients with metastatic disease. So this is the reason why we have the U.S. having tested more patients than other -- compared to other countries. So testing rates in EU4 and U.K. for TMB and MSI, which are tissue-agnostic checkpoint inhibitors, have lagged behind the rates in the U.S., as there have not been guidelines and the drugs have received a tissue-agnostic indication approvals later compared to the U.S. So there is also another interesting example which explains another reason for high biomarker testing rates. And it is coming from the breast cancer, where we can see the testing for HER2 patients is very high across all geographies. So we can conclude that there is a correlation between how long the therapy and the biomarker testing were available for the clients with the level of percentage of tested patients at the moment. So the use of biomarkers is a -- is at the core of the precision medicine. But there are some -- there are actually multiple challenges that come with that. We can summarize those in 3 categories: ones that refer to oncologists, to hospitals and laboratories. So we all know that oncologists are facing increasingly complicated treatment options and decision-making. So for example, new targeted therapies with biomarkers do not always fit in with the current clinical practices. They can make the treatment landscape more complex, and that's overwhelming the health care professional. Another challenge is staying informed with the numerous innovative products which are being launched frequently with the new mechanisms of actions, and for which actually oncologists are having hard time to keep up and adopt not just the new products, but also related biomarker tests. When we speak about the hospitals, they have challenges in testing capacity and infrastructure, but also with funding and other issues like data management and data integration and increasing level of coordination and so on. As far as laboratories are concerned, they're facing a need to -- they're actually faced with the increased load of biomarker tests. They need to implement new laboratory technologies, and also they have a need for a greater in-house expertise. Despite the challenges, targeted products supported by biomarkers still present an attractive commercial opportunity. To overcome the challenges, companies need to ensure the following is addressed. So first, ensuring the readiness of diagnostic infrastructure, which means that rapid uptake of biomarker-supported therapies require that appropriate testing infrastructure is in place well before the launch. This actually ensures that capacity issues or funding bottlenecks are addressed in time. Secondly, selecting the right in-vitro diagnostics [ path ]. The success of the targeted therapy is directly linked to the 1 of the [ IVD ]. So pharmaceutical companies need to make the necessary investments and decisions to develop the right [ IVD ] for their assets. Third is building the product and [ IVD ] value proposition. This actually means generating compelling evidence through clinical trials and real-world studies to support assets which is crucial for regulatory approval and gaining market access. Then integrated launch readiness. So pharmaceutical companies need to operate closely with diagnostic companies, not just through the development process, but for the essential launch preparation and commercialization activities. Pharmaceutical company also need to make an additional effort and investment to operate as a holistic oncology players to be able to assist oncologists and nurses as well as other key stakeholders in today's oncology environment. So we have numerous examples of pharmaceutical companies transferring to this holistic approach. So that will be all from my side. Thank you very much for your attention. I will pass on to Kriti now to present biomarker landscape in onco dynamics. Kriti, over to you.

Thanks, Vladimir. Hi, everyone. My name is Kriti Jindal. I'm a client service manager with the IQVIA Global Oncology team, which is responsible for IQVIA's standardized commercial insight offerings in oncology. And in this section, I'm going to talk about the coverage of biomarker landscape in our flagship syndicated offering called Oncology Dynamics. So to give you a background on what Oncology Dynamics really is, Oncology Dynamics, or OD, as we call it in short, is a web-based cross-sectional physician survey that collects anonymized patient-level data in oncology. So it works in the following manner. Every time there is a patient-physician consultation, a patient record gets generated or updated. The participating physicians are instructed to refer to these patient record forms to fill information about drug-treated oncology patients in the online OD survey. This data is then processed at IQVIA, which involves stringent quality checks as well as projection of the data to national drug-treated prevalence. And then, of course, this data is analyzed to derive meaningful insights and actionable recommendations for our clients. So we have a wide, global coverage in OD that includes 10 countries in Europe. It also includes Asian countries such as China, Japan, South Korea and Saudi Arabia. And we have also recently launched in India, Australia and Turkey. And we're also providing U.S. oncology data since last year. Now to give a quick little background on biomarkers. As many of you may already know, biomarkers are biological molecules found in blood, tissues or other body fluids, and they act as a sign of an abnormal or a normal process in the body or as a sign of a condition or disease in the body. Now they're very useful in cancer, because they can help in estimating a patient's stage of cancer, the prognosis, informed treatment selection and even detect any residual disease. So biomarkers can be broadly categorized into 3 main types based on usage. The first type is diagnostic biomarkers, which enable diagnosis of cancer, and that too, in noninvasive ways. So prostate-specific antigen, or PSA, is 1 such example in prostate cancer. The second type, predictive biomarkers, predict the probability of a patient's response to a clinical intervention. For example, ALK mutation in non-small cell lung cancer patients allow for the identification of patient populations that are most likely to respond to ALK inhibitor therapies. Lastly, the prognostic biomarker provides information on the expected prognosis that is the expected health outcome of a patient. So PIK3CA mutation in HER2-positive breast cancer patient is 1 such example, where the presence of this mutation is an indication of poor prognosis. So in OD, we collect information on a comprehensive list of biomarkers across many different cancers. Here at the top, you can see a list of solid cancers collected in OD. They're listed in alphabetical orders from A to L. And on the left, we can see the different biomarkers that are collected in OD, along with which tumors they are collected for. So as you can see, we collect data on predictive biomarkers such as ALK, EGFR, MSI, and also prognostic biomarkers such as PIK3CA, which you will see that we collect for breast and colorectal cancer. Similarly, you can also see the biomarkers collected for other solid cancers, status cancers from M to Z. And you can see that we also collect information on relevant diagnostic biomarkers such as the PSA, prostate-specific antigen, in prostate cancer. Additionally, we also collect a wide list of biomarkers in hematological cancers, which you can see on this slide. Now there are various ways in which we can use the biomarker data collected in Oncology Dynamics to study the oncology market. For example, it allows you to observe how treatment decisions are made in routine clinical practice, depending on the biomarker status of the population. So to illustrate, let's look at the total NSCLC population across U.S., EU4 plus U.K. and Asian markets like China, Japan and South Korea. We observed that around 7% of the total NSCLC patients present ALK mutations, which are mainly ALK fusions or rearrangements. We can see that more than 95% of ALK-positive patients in Europe, Japan and Korea received ALK inhibitors. In the U.S. and China however, the use of ALK inhibitors in ALK-positive population is between 75% and 79%. And as for the ALK-negative population, they're mainly treated with products that target biomarkers such as EGFR, VEGF or PD-1, PD-L1 or with chemotherapy. And this is because, as we discussed previously, ALK mutation is a positive predictor for ALK-targeted therapies. The study can also help you understand the population that is ALK-positive but not being treated with an ALK inhibitor. To put another example, you can also track the uptake of a biomarker testing using OD. And if we compare the rate of BRCA testing in ovarian cancer patients, with the approvals of different BRCA-associated PARP inhibitors. We can see that olaparib was approved in BRCA mutant patients around the same time in both U.S. and Europe. However, Rucaparib received an approval much earlier in Europe -- sorry, in the U.S. compared to Europe. And understandably, U.S. shows a much early uptake of BRCA testing compared to Europe. China, however, is lagging the most in BRCA testing as was also pointed out by Vladimir in his presentation. We do see a progressive increase in testing rate from 2020 onwards. And this could be because of the approvals of PARP inhibitors like olaparib, fuzuloparib and pamiparib from 2019 onwards in China. In Japan, we see a spike in BRCA testing between 2018 and 2019, which can be explained by the approval of olaparib in mid of 2019. But overall, it's promising to see an upward trend in all markets with respect to testing for BRCA and ovarian cancer. Now we also ensure continuous enrichment of our survey to keep it relevant to the rapidly evolving market, and we do this by tracking developments in oncology through secondary research and conference coverage. And we also make updates based on client requests. This is evidence -- evident from the universe of biomarker-related data that we collect in OD from its inception in 2019, when we were collecting only 46 biomarkers, and its evolution across the years to reach 103 biomarker-related information by 2023. Some notable additions include the collection of information on KRAS G12C, which we started in 2019, and MET exon 14 skipping alterations, which we started collecting from 2021. And these data have been very useful, especially in the light of the recent approval of KRAS-G12C inhibitor in lung cancer, and also a MET exon 14 targeted treatment which was in lung cancer as well. So let me show you some examples of how we ensure enhancement of the survey by tracking advancements in oncology. Recently, a new technology called radioligands have been developed, which allow targeted delivery of radiotherapy to cancer cells. These products comprise of 2 components: a radioisotope that emits radiation to damaged cells and a target ligand that binds selectively to markers on cancer cells, thus bringing the radioisotope in close proximity to the cancer cells. And it's innovative mechanisms such as these that form important pillars of precision medicine. So Lutetium PSMA is a product that recently became the first radioligand to be approved and is indicated for PSMA-positive metastatic castration-resistant prostate cancer. And based on these findings from our coverage of the ESMO Congress in 2022, we have updated the OD survey to collect PSMA status of prostate cancer patients. Another important development are the results from the Phase II MOUNTAINEER trial, which showed good activity of the combination of 2 HER2-targeted treatments, tucatinib and trastuzumab in HER2-positive metastatic colorectal cancer patients. These results signify the actionability of the HER2 biomarker in this patient population, based on which we have updated the survey to collect HER2 alterations among CRC patients. We have also prepared posters for congresses such as ESMO and ASH using biomarker data from OD. One such example is the poster we presented in ESMO last year, which provided insights into the real-world MSI testing practices in colorectal cancer patients across European and Asian markets. And as you might be aware, the biomarker MSI plays an important role in predicting the susceptibility of the patients towards certain immuno-oncology drugs. And with that in mind, I would now like to hand it over to Lien for a stimulating discussion around the immuno-oncology landscape. Over to you, Lien.

Thank you, Kriti. My name is Lien Lybaert, and I'm working both for Persomed and myNEO. I will talk about the genomics in the immuno-oncology landscape. More specifically, I'll talk about personalized immunotherapy and then also detail on the technology of both myNEO and Persomed. So if you look at immuno-oncology fields, the checkpoint inhibitors and also the CAR T-cell therapy are well known now and have been approved for multiple cancer indications. However, still many patients do not benefit. For instance, for the checkpoint inhibitor therapy, we know that approximately 12% to 15% of the treated patients do respond. And if you look at CAR T-cell therapy, these show very high response rates up to 80% or more percent in hematological cancer. However in solid tumors, they do not provide benefit yet. So there's still a clear need for platforms that are feasible for these hard-to-treat solid cancers that do not respond well to these checkpoint inhibitors to the CAR T-cells, but also to the standards of care. The hard-to-treat solid cancers are usually cancers that have a low number of mutations, so that are low tumor mutational burden. And this -- or this results in the fact that the immune system is not able to recognize these tumor cells, as the tumor cells are not that very different from healthy cells. So that results in the fact that tumors that remain under the immune radar. So for these cancers, we need a new type of therapy, and cancer vaccination is 1 of the new therapeutic approaches that are being explored. Cancer vaccination can activate your immune system against the cancer cells so you can really kickstart the dormant immune system in the patient and redirect it to recognize and then kill the cancer cells. And what's, of course, ideal about cancer vaccination approach is that it causes minor side effects. So it's an ideal therapy to combine with other therapies. There are 2 types of cancer vaccine designs. On the 1 hand, we have the ex vivo vaccination approach. And then we also have the in vivo vaccination approach. The ex vivo vaccination approach is a cell-based approach where we start from the immune cells of the patients, from which we isolate the naive dendritic cells. These dendritic cells are unloaded with tumor antigens and with adjuvants to give rise to mature dendritic cells. We need these mature dendritic cells, as they are able to or should be able to activate your T-cells to recognize the tumor. So after expansion, these mature dendritic cells are reinfused into the patients, and then those should educate naive T-cells to become killer T-cells that are then able to recognize the tumor. On the other hand, we also have the in vivo vaccination approach. Those resemble the approaches that are used for the corona vaccine, for instance, or the influenza vaccines. And with these vaccines, particles are injected into the patients. These particles are designed so that they resemble a virus or a bacteria so that it triggers the dendritic cells. And these dendritic cells then take these particles up so that they mature again, and then they can again exert their function, as I already explained before. Now if you look at the immunotherapeutic strategies, if we compare the cancer vaccines with the CAR T-cells and immune checkpoint inhibitors, we see and we know that the immune checkpoint inhibitors and also CAR T-cells have been approved already for several cancer indications. However, cancer vaccines, there's only just 1 approved, which is called Provend -- Provenge, sorry, for prostate cancer. So why is it that we only have 1 cancer vaccine approved? And that is mainly -- or something went wrong with this slide, I apologize, but this is mainly because of the fact that the older cancer vaccine approaches, similar to the 1 that was approved, targets tumor-associated cell antigens. Those tumor-associated cell antigens are [ self-antigens ] that are expressed by healthy cells, but become overexpressed onto tumors. So that makes them an interesting target, of course, if we design a vaccine that targets these [ self antigens ], we should be able to destroy tumors that presents these targets. However, we're talking about [ self-antigens ]. And your immune system is actually instructed to not react to self antigens, otherwise, we would all have autoimmune reactions. So that is basically the explanation why a lot of these tumor-associated targeting vaccines have failed, because they are just not [ effective ] enough. And on the other hand, there is also a risk of having off-targets and side effects. What is a better approach is actually targeting the neoantigens, which are tumor-specific antigens, and they are a result of tumor-specific mutations that have occurred in the tumor. So they are tumor-specific, they are not present on healthy cells, so we don't have any off-target toxicity risk. And also the efficiency of activating your immune system will be a lot higher because these neoantigens are [ nonself ], and so it's a lot easier to direct your immune system against these targets. And so the field is more and more focusing on these personalized cancer vaccination approaches. And since 2015, we have seen multiple clinical successes, both in Phase I and in Phase II, showing that personalized therapeutic approaches really have -- can provide clinical benefits in patients. However, the field is still early, of course, and there's also quite some challenges that need to be tackled. For instance, if we -- once you do -- or want to identify new antigens that are present on these hard to treat solid tumors, so these tumors that have a low tumor mutational burden, there, it's hard sometimes to find enough targets because these tumors do not mutate a lot. So you need very efficient AI language models to be able to find enough targets in any type of tumor. On the other hand, we also see that the neoantigen prioritization and actionability can still be improved. If we look to the clinical trials that have been performed with personalized cancer vaccination, we see that only a fraction of these neoantigens are found to be [ immunogenic, ] so are found to be able to induce a neoantigen-specific T-cell response. So this shows that also the prioritization can really be improved a lot. And then aside from the genetic heterogeneity challenges, we also have immune heterogeneity that we need to take into account. We know that within the same tumor and within different patients with the same tumor, different immune types can occur. For instance, we have the immune-inflamed immune type, we also have immune excluded and also immune deserted tumors. And depending on which immune status the patient or the tumor has, we should select another therapy. For instance, if we would have an immune-inflamed tumor, this means that the tumor is fully inflamed with a lot of T-cells and other immune cells. However, the T-cells that are there, they are not able to recognize and kill the tumor cells. This is likely because the tumor is expressing the immune checkpoints. And so these tumors are likely to very well react against immune checkpoint inhibitor therapies. So for these patients immune checkpoint inhibitor therapy would be the best option. However, if we would have a patient with an immune deserted tumor, so those are the hard to treat tumors with the low tumor mutational burden, there, the immune system is just dormant. It's not active. So if you would treat that patient with an immune checkpoint inhibitor therapy, it will not do anything. Here, this patient should be treated with a therapy that kickstarts the immune system. And there, for instance, the personalized cancer vaccination can offer a solution. So if we look at the future, we think we will move towards more and more a personalized therapy approach. So not only looking at personalized targets and designing a personalized, tailor-made cancer vaccine or therapy, but also look at a personalized treatment plan. So really look very broadly, what does the tumor look like both on a genetic level, but also on an immunological level so that we are able to select the best combination therapy for that specific patient. And then even a step further, it will also become more and more important to follow up the patients and to adapt the treatment if we see that, for instance, the tumor acquires resistance against specific therapy because then, it doesn't make sense to continue to treat the patient with that therapy. And also, you want to avoid any unwanted side effects, of course. All of that, all these personalized immunotherapeutic innovations and approaches have been really been driven by the new developments in sequencing technology. So the importance of data of genomics is becoming more and more important, and we'll continue to do that, of course. And that brings me to the next part of the presentation, where I will dive deeper into the technology of myNEO and also Persomed. So myNEO is a biotech company situated in Ghent in Belgium, and it's really focusing on improving personalized immunotherapies by data-driven innovation. So we use sequencing data to improve the personalized immunotherapies. For that, we have developed our own technologies called the ImmunoEngine. The ImmunoEngine comprises of 4 parts, and is the most comprehensive neoantigen discovery pipeline that is out there so far. First of all, we focus or we start with variant calling. So how does that work? We start from patient blood, from which we extract a healthy DNA. On the other hand, we also start from a tumor biopsy, from which we expect tumor DNA and RNA. Next, the tumor DNA and the healthy DNA is compared so that we can explore the tumor-specific variants, and we then check in the RNA which of these variants are expressed. And then a second step or a third step is where we look at the translation and where we select only those mutations that give rise to tumor-specific peptides. How do we -- or from what data do we start with? Where most of the players in the field use whole exome sequencing, myNEO has always focused on whole genome sequencing. Why do we do that? Because if you also look at the noncoding part of the genome, we know that there are also some parts that become coding and become interesting parts to explore because they give rise to more or better targets and also novel targets. And especially in the lower tumor mutational burden cancers, where it's sometimes hard to find enough targets, we really see the benefit of whole genome sequencing, as we have seen that up to 50% of all the actionable targets come from this dark noncoding genome. And so that's why we continue to focus on whole genome sequencing. The next part in the ImmunoEngine is presentation prediction. So once we selected the tumor-specific peptides, we narrow them down. We try to prioritize the best peptides. So of course, first of all, we want and need to be sure that the peptide is presented on the surface of the tumor cell. Otherwise, it doesn't make sense to direct the therapy against it. Where the state-of-the-art usually only focuses on MHC binding affinity and/or stability, we have developed an algorithm that actually takes into account every step of the complete antigen presentation process. This algorithm is called [indiscernible] and because it takes into account all these steps, we do outperform the current state of the art. And we have a very good prediction efficacy. Then we go a step further, because even if an antigen is predicted to be presented, it is not set that antigen is going to be immunogenic and going to be able to be -- or to activate T-cells. So that's why we also focus on immunogenicity prediction, especially because we see or we have seen, as I said before, in clinical trials, that there's only a small fraction of these neoantigens that are selected that appear to be immunogenic. So that's why we wanted to also develop a immunogenicity predictor. And if we compare again at the state of the art, immunogenicity predictors aren't really developed yet. There are very few, and those that do perform quite poorly. You can see this also here, where you see the [indiscernible] indeed outperforms the current state of the art. We have, of course, validated our approach and we see, actually consistently, very strong immunogenic responses with our neoantigens that are similar to even higher than the positive control peptides that are used, and that's something we had never seen before at the state-of-the-art prediction pipelines. And then the last part of our ImmunoEngine is then -- even more detailed and more stringent neoantigen prioritization to really have the best target for that specific patient. So what does that entail? So we, first of all, focus on the genetic heterogeneity. So we want to select or we prioritize on neoantigens that are clonal. So we want to avoid to select neoantigens that are only presented by subclone of the tumor cells. We always prioritize on neoantigens that are present on most or on all tumor cells, of course. Next to that, we also focus on functional relevance. If a neoantigen is a result of a gene that is functionally relevant for that tumor, then we will also prioritize that antigen. And then next to that, we also focus on epitopes pressing across the HLA alleles to avoid that if a patient silences 1 -- or if the tumor silences 1 of the alleles, we still have therapeutic efficacy. And all of that would then, of course, reduce the risk of [ immunoscape ] of the tumor. MyNEO collaborates with different partners to bring the neoantigens to the clinics. And 1 of these partners is Persomed. And Persomed specifically has developed a cell therapy, more particularly a dendritic cell therapy platform for hard-to-treat cancer, so for cancers that have a low tumor mutational burden. The Persomed approach is as follows. So we start from the tumor biopsy of that patient that is then being sequenced, which allows us then to select the best neoantigens that are present on the tumor cell surface of the patient. In parallel, we also start with a leukapheresis, from which we isolate the monocytes or the dendritic cells of the patient, and then those dendritic cells are loaded with neoantigen and coding mRNA are also activated with an adjuvant mix. And those then -- or this results in mature dendritic cells that are expanded. And then this potent dendritic cell therapy is reinfused into the patient to hopefully then induce a neoantigen-specific T-cell response. We have already performed several clinical studies. We have done 4 clinical studies in melanoma cancer patients with the dendritic cell platform, but using nonpersonalized antigens, so the self antigens that I've explained before. With that approach, we were able to get an increased clinical benefit for those patients. For instance, here, you see a comparison with competitor platforms, where we compare the dendritic cell therapy combined with checkpoint inhibitor therapy versus checkpoint inhibitor therapy given as a monotherapy, and we see that, indeed, the combination of the therapies results in a higher clinical benefit for the patients. In addition, we also did an indirect comparison with the FixVac platform from BioNTech, which is an in vivo vaccine also targeting tumor-associated cell antigens. And there, we also saw an increased clinical benefit with the dendritic cell platform. So that is really -- that formed the basis of Persomed. But as I said before, personalized targeting of the neoantigens is a lot more efficient. And so that's why we are now moving towards a fully personalized dendritic cell vaccine. To allow the personalized antigen targeting, we have a partnership with myNEO. As I explained before, myNEO is really a leader in the field because they focus on whole genome sequencing, which specifically allows us to find always enough targets in these hard-to-treat cancers because they explore the dark genome of the tumor. On the other hand, also the prioritization and the selection of the neoantigens is highly efficient because we use the best presentation prediction algorithms and also the immunogenicity prediction algorithms. I'm not going to go too much into detail into the technology itself, but I just wanted to also explain that we have developed our own synthetic mRNA synthesis methodology, which allows us to faster synthesize the mRNAs if you compare it with the plasma-based mRNA technologies that takes weeks, whereas ours -- or our manufacturing only takes a few days. So that's, of course, of interest if we're talking about a personalized approach. Next to that, we also developed our own adjuvant to activate other dendritic cells, which is a proprietary 3-component mixture. And then very importantly, especially for personalized immunotherapy, we have put a lot of efforts in optimizing our manufacturing to lower the turnaround time from biopsy up until the treatments of the patients. As of now, it takes 10 weeks from biopsy up until treatment, which is definitely okay. However, of course, we want to do better, and we are now still improving our process, and we want to move to a fully closed automated system. And our aim is to even -- to go to 5 weeks of manufacturing. We also have done already a single-patient proof-of-concept study, where we treated the patient's pancreatic Stage IV cancer patient with the dendritic cell vaccine targeting 5 neoantigens that we predicted from the tumor of that patient. After the treatment, we collected the blood of that patient and we looked at whether the neoantigens were indeed able to induce a specific T-cell response. And this was indeed true. We saw very high T-cell responses for 4 out of the 5 neoantigens. And what was also very interesting is that the level of response was, for some of these neoantigens, similar to an antiviral positive immune response, which is a level that we have never seen before with the nonpersonalized approach. So that really shows, yes, the benefit of going personalized. Also of note, the expected prognosis for the patient was 6 to 9 months. However, patient still survived for 2 years after vaccination. And then where are we now? We want to start the first clinical Phase I in metastatic colorectal cancer patients. So the patients will be treated with the standard of care, which is chemo induction therapy and then afterwards, start with a maintenance therapy. And at that point, the dendritic cell therapy will be given to the patients in 6 cycles over a period of approximately 2 months. The primary objectives will, of course, be toxicity and logistic feasibility, but then also secondary, we will do detailed immuno-monitoring to really dive deep into, okay, is the vaccine able to induce immune cell response? And what is the depth and the duration of that T-cell response? So we have a very ambitious mission. We really want to become a leading cell therapy company in Europe. We have all the assets now in place, the first-generation products was clinically validated in the nonpersonalized approach. We also saw very good responses in that 1 proof-of-concept patient case. So now we are ready to move towards the clinic. We have acquired all the regulatory approvals. And it's a typo here in the slide, but we aim for a first patient in -- by the end of this year, and we're currently finishing off our financing round. And that brought me to the last -- to my last slide, and I'll now give it to -- give it back for some questions. Thank you.

Thank you so much. That was wonderful. I really appreciate that detailed presentation for -- from all 3 of you. We've received a number of great questions in the chat. And to the audience, please continue to enter any additional questions you may have because we'll probably only have time to address 1, but a member of our team will be in touch directly to answer your questions off-line following this discussion. So Vlad, would you like to take the first question that we have time for?

Yes, sure. So let me see. We have a question for Lien. From a logistics point of view, how different is supplying personalized therapies compared to the standard ones?