So, good morning, ladies and gentlemen. Welcome to our Capital Market Day here at our headquarters in Hamburg. Pleasure to have you here, and I think we are on time. Thank you for taking the journey, and hopefully this will be a very interesting day, a lot of science, and obviously the opportunity to raise any questions you may like in the afternoon. So the overall setup is there will be presentations in the morning, on particularly on PanOmics and on iPSC therapy. We will go through the agenda later on. Take your notes. There should be a PDF version on your laptop now, which hopefully makes your life a bit easier. And, then in the afternoon, there will be two breakout sessions, one hour each, and I think that's sufficient time then for you, that all your questions hopefully will be answered by the team. And with that, I would like to lead over to Claudia Karnbach, Co-Site Head of Hamburg. Yeah, good morning, ladies and gentlemen. My name is Claudia Karnbach, on behalf of Gabriele Hansen and myself in our roles as site heads here for Hamburg. I would like to extend a warm welcome to Evotec's second Capital Market Day in 2023 here at our site in Hamburg. We felt what better way to start this day is providing some interesting facts about our Hamburg site that you may or may not be aware of yet. Our company was established 30 years ago here in Hamburg, and at this point in time, Evotec was known for their groundbreaking work in high-throughput screening, a technology that was originally established by Manfred Eigen, who had this fantastic, groundbreaking vision about evolutionary processes at the molecular level. That vision is actually reflected in the name of our company, Evotec, evolution and technology. And another fact, and I saw some of you actually saw the machine downstairs already, in this building, in the lobby, in the entrance of this building, we are showcasing the very first, Ultra-HTS screening machine that we developed and commercialized with Novartis and SmithKline Beecham. So take a look. If you go downstairs in the lobby, you will see that beautiful testament to our history. Since then, Evotec has come a very long way when we moved in 2011 from a smaller site near a famous Hamburger soccer stadium to this particular building, which was actually the former Eli Lilly research site. And that unveils the clue behind the big red cube that you saw while entering into the building. Another actually very interesting fact is that Manfred Eigen, the Nobel laureate himself, did inaugurate this building at the inception and of the site in 2012. Over the last couple of years, Evotec has experienced a tremendous exponential growth. And what does it mean for our site here in Hamburg? Our workforce since then has tripled, and actually one building turned into four, with the iPSC Lighthouse, which is now the construction site to the left of this building and will be fully operational in 2025, being a true testimony to Evotec's vision on discovering and developing groundbreaking new medicines for high unmet medical need conditions for future patients. And last but not least, some of our core capabilities that we are hosting here in Hamburg. We have cradle-to-grave in vitro and in vivo biology, supported by our end-to-end Shared R&D capabilities, from target identification to lead optimization. We are the proud host of our CRISPR-based target identification and validation team. We do small molecule RNA work here, actually tackling these untackled so far problem of making undruggable targets druggable. We are working on biomarkers, supporting the rest of Evotec in identifying and developing biomarkers to enhance the patient stratification. We are actually hosting the iPSC-supported Drug Discovery team, led by Sandra, who actually will talk about her work later today. When you talk about what is the therapeutic focus of our Hamburg site, it's clearly that we are very strong in neurodegenerative diseases, an area that is very much on everyone's mind with regards to the aging population and the incredibly unmet high medical need in this particular area. But we are also working in pain and in women's healthcare, as well as in rare diseases. And, of course, this is actually a rendering of our iPSC Lighthouse, and if you come back in 2025, this is, I mean, the right side of the building, the left is still construction what you will actually see, and hopefully this will be something that actually will show you how much Evotec is actually working on innovation and being on the forefront of. Having said that, the Hamburg site is certainly one puzzle of this beautiful innovation ecosystem of Evotec in support of discovering and developing medicines that matter. Without any further ado, I would like to hand over to Werner Lanthaler, CEO of Evotec. Thank you much. Good morning. My name is Werner. I also work for Evotec, and it's great that you are here. Why do I say this? Because taking today the time to talk about what we are doing and how we are doing it, but most importantly, also hopefully getting across why we are doing it, is so much better than in person than via a video conference. And that's why let me first bring you into who is doing this. Many of you have not met our CFO, Laetitia, who is here, and maybe you just stand up for a second. This is Laetitia. Laetitia joined us about six months ago, and I remember very clearly when Laetitia said, "The big idea of this company must be to have as much money available for the science to bring it to the patients and not for anything else." So that's probably also a guiding thought of what capital efficiency should mean, that the money has to go to patients and to science that actually goes to the patient. And that's also why I think I start here with the, the mission of a CFO to bring us then into the mission of who will present today. Claudia, you've seen before. Claudia and Gabi together being the site head, and let me use this opportunity to thank the communications team and the investor relations team. And by the way, the tall guy there, this was Volker. Volker and the communication team, they do what has to happen in the background of such a day. There's more background that very often we think is done, and really big thank you for doing all of this. When you then go forward, we have the privilege of a CSO who understands what long means. Because when Cord and I started many of the initiatives in 2011 together, it was clear to us, if we go for omics-driven drug discovery, we have to go long. If we go for iPSCs, we have to go long. But if you go long, then you can win, because if you want to go fast, you go alone. If you go long, then you go with a team, and then you can win, and that's the spirit, how we have built many of our platforms together. Bhushan will bring you into this world of what is AI doing in all of this? And then you will see that for us it's a tool, but not more. It's not magic. It's something that allows us to do something faster and better, but it's not magic. It might come across as magic because Bhushan is so good at it, but when you then question him, you will find out it's not magic. Sandra is driving our Induced Pluripotent Stem Cell platform for many years now, so she also knows what it means to go long, but every day, a little piece of success makes us more excited in going long. Christiane will bring you into the world of what molecular understanding of a disease means. And when you today think about the ancient world of diagnostics and the ancient world of disease understanding, then after today, I hope that after Christiane's talk, you will see that in the future it will be so much clearer from what we are suffering and how we can intervene than ever before. Olivier is the master of kidney diseases in a broader sense, and metabolic diseases in a broader sense. Andreas, Christiane, Christine, and Markus will then bring you into what applied iPSC drug discovery, and more importantly, not only drug discovery, but also going the full path to cure with cells can mean and will mean to us. I wanted to show you a bit more what they are doing, because that they have many titles and that they study a lot, and that they have been at all pharma places on the planet and all drug discovery places. You should assume this, because you are part of a company now where 86% of our total population, where the average age is thirty-eight years, have at least one academic degree or more academic degrees. So that's why you're in a zero bullshit place, where you have more science concentrated than anywhere else on this planet, and where more know-how and academic translation is happening here than anywhere else when it comes to understanding diseases, and with this, building pipelines. That's also where I wanna go with you now at the beginning. What does it mean to go from disease understanding to pipeline building? This is so important to me because shaping new markets is something where others are following existing markets. What we are doing is we are following science that leads us into new markets. And if you think the really big markets in all industries, they are opened by industrializing science into usable products. And that's why shaping new markets is what I will bring you into, and that's why I always like to refer back to the Nobel Prize laureate, Manfred Eigen, who, on the 8th of December in 2012, was here on a little speech, making a very, very beautiful remark, where he said, "This is about not one individual. This is about mankind when we want to do something together." And here you have to see that he was the first professor who said, "If I'm inventing something in my institute," in, by the way, Göttingen, the city of science, "it's not good enough. I have to start to share my realization of a topic with other academics." That's how he was wired. "Because why should another professor build something what I have already built?" So he didn't know it, but he was Jeff Bezos' idea of the sharing economy already in the year 1972, applied into research and development. And on that path, I really think that a core theme also for today should be: why should we all invent the same, if ultimately everyone gains by sharing to come to the patient faster and more cost-efficient? That's the key thought, and that's why creating the future together is possible, but it will be possible only with companies like Evotec, who do this in a networked world. So many often ask us: What are you doing? Then some of you often say to us: This is complex. It's not complex. It's very simple. We are building pipelines together with our partners. So we are a research and development biotech that offers accelerated, high-value pipeline building with services and solutions. In the construction industry, we would say we are building houses together with partners, and if you build a house, you need a plumber, you need someone who brings carpets, you need someone who does that. Bringing all of this together to make a pipeline, that's what we are doing, because what is more needed than anything else in this industry? It's highly cost-efficient pipelines. There are 4,000 scientists who do this. We are co-creating pipelines. We are not a fee-for-service shop, where others are telling us what we have to do in order to make their experiments happen. Yes, we also do this, but we have the end in mind, and that's why educated experiments are happening here together with our partners, and that's why we have this track record of data integrity and the highest quality experiments that are done here. That's so simple and so much needed, and when I say needed, you should see here that the Health Access and Quality Index analysis tells us that even in highly developed countries, there's a huge unmet medical need, especially for many smaller indications, where so far it didn't pay off to go there, or so far we didn't have the market understanding to go there, so-called orphans. Of course, there is 40% of the global disease burden in lower- to middle-income countries. But we are addressing both, and that's why this access seems to novel diseases, novel solution for diseases, and cost-efficient solutions is key to what we are doing. Following science has many different aspects, and I really like this chart a lot, because what you should see is that the demand for differentiated innovation is expanding. What you should also see is that you have a quite interesting evolving trend in the industry, and I only showed you here from 2013 to 2022, so 10 years, where, for example, once we said, "Okay, oncology is totally underfunded, and it's undercrowded," 10 years later, people say oncology is overcrowded. If you look at patients, and if you look at what we see out there, 30% of the world will have a cancer diagnosis in the future. No one of them thinks it's overcrowded because they don't have a solution. If you look at infectious diseases, that goes a bit into this direction, yeah? If you think about COVID, it should have gone totally into other directions upfront, yeah? It's good to see, it's good to monitor, but at the end, science is guiding us, and data points is guiding us where success is. Talking about success... It's the best time in this industry ever if you think about what is possible. This is just a very-- and everyone talks about it, even newspapers now have found their way into, that big innovation is happening. But of course, big innovation also is raising more questions. When you now just take this one example, what does it mean to have all of a sudden an obesity cure? Can you say that? An obesity intervention, yeah, for 140 million Americans, and that's just a number to be put in there for the prevalence in the U.S., with 10,000-15,000 costs. All of a sudden, 10% of the U.S. economy is spent on obesity. That's a lot. Yeah? And now think about this intervention, and think about the so many other diseases where we can go to and where solutions will be possible. These solutions should all be made, but they have to be made on IRRs that are sustainable for this industry. Cord will go into this in a bit more depth, but what you should see here, we have to turn this internal rate of return around, and that's again, if you think about silos who are doing something alone, that's not the solution. If you come to more a cost-efficient world where you can take these IRRs around by bringing probabilities of success up, then the intervention that Evotec is doing with our partners makes absolutely sense, and you will never turn around from doing it that way. What you're seeing here is that higher spent with lower IRRs is not sustainable, clear to everyone. If you see that the IRA, the Inflation Reduction Act, is reality, then this already quite grim picture has another acceleration factor in there, because what is effectively happening, the R&D costs are the same, but the costs and innovation premiums on the market go down because your lifeline for innovation premiums is getting smaller. The only way to react here is either bring R&D costs down or probabilities of success up to fully exploit that market. What will not happen through the IRA, that innovation stops. Clearly not. There will be an adoption phase, but then people will, of course, go for the most cost-efficient solution on the market and go forward here. But it's a new reality, and it will not go away for many years now. What you should also see is, yes, some companies are turning around this IRA now into higher profitabilities, and some of them, with new solutions, have really a situation where for the first time in many years, a pharma company shows the turnaround. Lilly and Novo Nordisk, two of them. But of course, that's not the full problem solved. That's only the starting point where many others have to do this, and new tools and also new disease areas that we're tackling have to come into this. When I say new tools, why do we need new tools? Because better pipeline building needs better precision. Today, 50% of all the drugs that we are using are not precise. Why should you pay for something that is not precise? We shouldn't do this. So that's why this precision claim is so important. 30% have a situation in oncology where we still don't understand the cause of the diseases. 30% no disease understanding, that cannot be sustainable and shouldn't be sustainable. And another thing is when it comes to probabilities of success, why are we failing so late? Failure is a fine thing, but only if you don't do it too late in this industry. So yes, if we have to fail, it's always bad, but 60% of all the drugs only in phase I finding out that they are toxic after already spending way beyond EUR 10 million, is way too late. And why? The excess is clear. So going here into more efficient business models, that's the claim. We can because the toolbox is better filled than ever before, and the beauty of Evotec, that basically everyone who works with us has, at every moment in time, access to the full toolbox. That's why you partner with us or someone partners with us, it's not that you have a one-to-one individual relationship, you have access to the whole platform. With this, you have access to all these technologies that you see here, because we have come to critical size to really hold, master, and nurture this toolbox that is visible here. Also for the discussion, if you're interested in any of these technologies, it is so beautiful that there will be, within the 4,000 experts in our company, always someone who is the key expert in the field, who has more experience than anyone else, and by sharing this across multiple partnerships, yeah, it gets really, really powerful. That's why these four focus areas is what we have built, and we will today focus on PanOmics and on iPSCs for drug discovery and for cell therapy. I'll give you a little snapshot also into our Just - Evotec Biologics business that we are building. Now, let me come back to the thought from the beginning. We are following science.... Science has never been more beautiful than it is right now. But when you think about how long it takes to industrialize visionary thoughts, it's sometimes quite amazing. With induced pluripotent stem cells, for example, with Shinya Yamanaka, sometime we'll come to that, we are the place where iPSCs are now industrialized a decade after the Nobel Prize was awarded for that. It might sound long, on the other hand, it's just the beginning of a total revolution happening. If you think about how long it took the thought of Emil Behring to industrialize, yeah, you talk about 100 years. Now we are there. Now, industrialized vaccines, industrialized antibodies, it's clear. In a bit more complex, we are trying to show that for PanOmics, the scientific world is coming together from many different angles, because it's the contributions of Frederick Sanger, it's the contribution of Leroy Hood, it's the contribution of Paul Berg, and it's the contribution of all the gentlemen that you see here and all the ladies that you see here, that ultimately makes us standing on the shoulders of giants, scientifically, to really put this in an industrial format in place. It feels good to stand on the shoulder of science if you can building if you're building markets. What you should see here is on PanOmics, iPSC, cell therapies, and biologics, these are massive growth markets that are outpacing all these 5%-10% growing industries in industry out there, because science is opening new markets. That's what's happening when you look at the driver behind precision medicines and diagnostics leading into patient stratification. That's what's happening when you look at the driver going away from donor dependency to scalable solutions in cell therapy. That's what's happening when you look at the driver of coming to fully flexible and agile manufacturing systems, for example, in biologics. If you're building these platforms, there are, of course, many people acting in a scattered world of technologies. What you should appreciate that the integration that we have built of these platforms is unbeatable. And I always hate it when we claim world-leading in all of this, but let's just be analytic for a moment, and that's what we've done here. You map out about 15 of your competitors, and then you map out how is data integrated, and then you see that the degree of integration of these capabilities is by far the best on our platform. And why is this so important? Because if the data flow can be guaranteed in an integrated way, then today you are winning not only speed, but most importantly, you are winning quality of what you're doing. And that's why I think these markets are ripe for massive growth, and that's why, for example, PanOmics, it's not a clear, one identified market. There are many elements in PanOmics coming together. But you see here in 2022, a starting point that will quadruple in only a decade by applying, all of a sudden, biology that you can measure into your drug discovery processes. If you look at the CAGRs here, and if you look at the disease areas here, then, in the technologies here, then, of course, genomics will grow. But most importantly, transcriptomics, metabolomics, and proteomics will outpace even genomics, what you have seen as a growth area here. What I already mentioned before is we are totally aware that we are not alone, and we are happy to partner with everyone, but we are also aware that a lot of progress in many industries is happening at many different places, and that's why Evotec being an open ecosystem and applying always the best solution for our partners, is the message that you should get from this slide. It's not a full competitive picture, but it just gives you a flavor that, of course, we are observing the market here. If you look at the next area where science is opening a new market, iPSCs is something that we really, really want to bring very close to your heart. And why is here the claim of science opening new markets so much clearer than anywhere else? Because, yes, we still have to see these clinical validations. Yes, we still have to see these patients that are cured on a cost-affordable basis. It's not there yet, but the science is there already. The processes are there already. And that's then the triggering moments in our industry, where all of a sudden, an industry will not only grow in a linear way, but where an industry will grow in exponential ways. It can take a bit of time, and it needs these data points, but we are one of these places where we are preparing these tipping points to happen, and these tipping points to the upside, especially on iPSCs, will come. Also, here, we are not alone, and we like it that we are not alone, because whenever you would be alone in a technology field, you would have to ask the question: Am I the one who is the ghost driver, or are the others the ghost drivers? We are not ghost drivers in this. This is going to come, and the planet is really waiting for these solutions... Talking about access and talking about the planet, providing cost-efficient technologies is key in so many areas, and that's why, as a third area where novel science is opening markets and gives more access, and where this access North Star is so important for us, is very clear when it comes to underserved populations, underserved regions, and underserved indications when it comes to biologics. What we did here, we didn't invent biologics, we didn't make the first antibodies, but what we are doing, we are applying now precision technologies and paradigm-shifting manufacturing technologies into a world where especially continuous manufacturing represents this technology paradigm shift to bring costs of goods down, and with this, open markets that so far were not possible to be opened. If you look back in the last 10 years of biosimilars, it was not a big success. Why? Because if a biosimilar is not massively cheaper than an originator, why should it be a big success? But if of all of a sudden, yeah, you can bring prices down massively by over 50%, up to 75%, markets will open, and that's why these paradigm-shifting technologies are so important. Another area that you should appreciate here, the growth indications will come now with a lot of biologics. You will see a lot of biologics going, of course, into oncology, going, of course, into eye and eye indications, and that's where you will see many of these technologies now really building platforms where it's not about capacity, it's the right technology for the right indication that you have to build. That's why Evotec didn't enter into a capacity competition in biologics. That would be madness. We entered into a technology paradigm shift, where all of a sudden, for these indications, where precise, cost-efficient solutions is clearly the solution, that's where we are going, and that's what Just - Evotec Biologics stands for. That's just the market environment of the players that are mainly driven by capacity. We are not driven by capacity, we are driven by a technology shift where this continuous platform allows us, especially for more complex biologics, to provide optimal solutions. And many of you have heard about bispecific, trispecific, multispecific biologics. That's the world where, with a fully continuous, precise technology, you will win, and we clearly see our pipeline that we're discussing now coming from more complex antibodies than from purely capacity-driven, quote-unquote, "simple" antibodies. So it's really this multispecific and the conjugated antibodies that will be a big driver here. Let me repeat and also highlight that Just - Evotec Biologics is not about, quote-unquote, "only access via lower costs," it's also access for novel biologics, like you see it here with Alpine going into phase III using our platform, and with this, you have partners that are sticky on your platform and will really have cost advantages going to the market on our platform. We have signed more than EUR 850 million of committed sales into Just - Evotec Biologics in only from 2019 to now, four years. There are not many people on the planet who have done this with any industry, in any industry, but it is possible, if science is so convincingly translated into industrialized technologies, that people will see this as irresistible technologies when you have access to open J.PODs in Seattle, Redmond, and very soon in Toulouse, in France, where we are on track to open our second J.POD in the year 2024, by being fully operational then in 2025. Why is this so important? Because then in the two Western places where we want to also nearshore biologics, we will be able to deliver. So science is opening new markets. We are applying these technologies, and we can prove that it works. And this gives you only a snapshot from 2015 to 2023, so that's eight years, and if within eight years... Of course, the pie overall is bigger because our market is bigger. But when you see that the proportion, for example, of PanOmics, yeah, is growing from 8%- 29% on that pie chart, then you see that precision technologies have a huge impact, a huge impact, and that's why this differentiated platforms makes people work with Evotec, and that's why whoever in this room, and I know that no one of you does, has ever called us a CRO, latest by now, you should see that PanOmics is a totally different concept than reacting to the orders of others. We are educating experiments with technologies, and by educating experiments with technologies, these paradigm-shifting platforms are key growth drivers.... That's behind our aspiration level to say we go to beyond EUR 1 billion in sales in 2025, because we saw it in 2020 already, and we saw it in 2021, and we saw it in 2022. That's why if we are portraying, yeah, an aspiration level to go to EUR 300 million in EBITDA, we also see that with novel technologies and with increasing milestones that are already signed up, profitability will go faster than our revenue growth in the next years. What you will see once Just - Evotec Biologics turns from an investment case, because it's a startup business so far, into a delivering case, then it's a very long business with very sticky partners that are going to be with us here. With this, let me confirm Action Plan 25, what you see here, driven by differentiated platforms, by following science and industrialized science, and how this works will be best shown to you by Cord Dohrmann. Thank you. Good morning, and welcome to Hamburg, also from my end. It's a great privilege to have you all here today. As Werner just eloquently showed you guys, we are living really in privileged times, where new technologies are driving our world and are changing it constantly, and not just in social media, but also in the pharmaceutical industry. Even though we can make the argument that in the pharmaceutical industry, we are sometimes a bit slower to adopt new technologies, but not here at Evotec. We're doing this constantly, and today's Capital Markets Day will be focused on two key topics that we want to discuss. One key topic will be PanOmics-driven drug discovery, which addresses a fundamental challenge in the pharmaceutical industry, which is really poor productivity. And then we will also talk about a second platform, which is our iPSC-based cell therapy platform, which also addresses a very important challenge in the industry, which is the challenge of delivering on the promise of cell therapies, which have shown to be really game changers for many patients, life-saving treatments. They can be. They can cure patients, but currently, they cannot be delivered to the vast majority of people in the world, and this has to do with a paradigm that is currently focused on autologous cell therapy treatments, and that has to be changed to allogeneic and off-the-shelf therapies. We will be starting today with the topic of PanOmics with our PanOmics drug discovery platform. And here, what you will hear about today is, in particular from Olivier and Christiane, they will talk about molecular patient databases. They will talk about molecular patient databases and how they will influence target selection, target validation, and how they influence biomarker ID, how they will influence the stratification of patient populations. Then, we will move forward to the world of iPSC-based disease models, and here Sandra will talk about this in great detail. iPSC-based disease models or patient-derived disease models are the only way to really make sure that in the pre-clinic, you are not only, you know, curing animals, but that you are actually doing something that is relevant for patients. And it is still something, a technology that is increasingly used but still has not really taken hold in the industry. Evotec is a leader in this, and I think we have shown and proven that these technologies are superior to the conventional models, and we will talk about one example here in greater detail. Then we will end this session with Bhushan, and Bhushan will talk about the fact that we at Evotec, we use AI machine learning tools, essentially throughout the value chain, to improve and increase efficiency and efficacy in the delivery of drug candidates, from the very early stages through the pre-clinic, and the clinic, and all the way to the market. Before we dive into great detail, I would like to have a quick look so at what is happening in the industry. What's happening in the industry is good and bad news. The good news is that the R&D spend in the industry is at an all-time high, with almost EUR 140 billion being spent by the 15 biggest pharma companies alone. This is an absolutely massive amount, and it doesn't even account for all the biotechs and et cetera, etc. So a lot of funding is available in the industry, but the bad news is, and there's also bad news, the bad news is that this funding translates very poorly in novel product opportunities. On average, the pharma companies spend over EUR 6 billion to generate a single drug that makes it all the way into the market. This is, this is an unsustainable situation... essentially, most pharma companies don't have enough funding to even generate one drug per year into the market. And so a lot of things need to change to address this. But before we look at that, we want to look at what are the, what are the key drivers here, of this situation? And this is, of course, the huge, absolutely humongous attrition rates in the preclinical. So in the preclinical, we are essentially losing over 95% of all candidates. But what's really shocking is that, even in the clinic, we are losing over 93% of the candidates along the value chain. So from moving from phase I to phase II, to phase III, and into the market, which means that we simply are not doing a good job in the preclinical. The problem is that in the preclinical, you are selecting a drug candidate, and once you have selected your drug candidate and you're moving it forward into the clinic, there's very little you can change then anymore. It's essentially the cards are dealt and these now you have to play them. And if the candidate is not good enough, it will fail. It will fail either in phase I because of safety issues, or because you're not demonstrating enough efficacy in phase II, or you learn later on that the benefit ratio of between efficacy and safety in phase III is simply not good enough, or it's not competitive, and then you also fail. And so in order to change this, we need to really change something in the drug discovery setting, in the early stages of drug discovery, and how we're selecting targets and how we are selecting compounds. And this is also essentially exemplified on this slide. So if you're looking at this, once again, what you can see here is that the overall composite clinical success rates are not improving. Despite the fact that we're using and pouring more money into the system, we are still essentially where we were many years ago, and actually, you could argue that the success rates are actually dropping and are getting worse. They're actually at an all-time low currently. Once again, you have to keep in mind that the clinic, this is the area in the clinic, this is where we spend most of the time in terms of development. So if you're thinking about a 15-year product development cycle in the industry, on average, you spend about five-six years in preclinic, and then you spend 10 years in the, in the clinic. On top of that, the vast majority of funding for develop a drug successfully, of course, is also spent in the clinic. So the failure rates in the clinic are simply unacceptable, and the only way to fix this is to do a better job in the preclinical. This is our mission. One way to address this, or the key way, I would say, and probably the best way to address this, is through omics technologies. The fact that this is actually starting to take hold in the industry or generally in the life science industry is the best evidence here: if you're looking at the rate at which genomics or omics data is being generated. What you can see on this chart is that, most importantly, omics data generation is not only a function of genome sequencing. That's just. The genome is essentially a constant and only tells you something about the predisposition of disease. But what's more, much more important is actually looking at transcriptomics and proteomics because these are the parameters that tell you something about disease, disease progression, and how diseases are progressing for patients, and that you can measure the, the status of a patient in terms of disease progression. And you can see here that in the last two years, essentially from 2019 to 2022, we have, or in the public domain, we have generated more omics data combined than in the last 10 years combined. And that is absolutely stunning because this is only a chart that describes the publicly available omics data. If you think about what is being generated in privately held companies, it is probably just as steep the curve. And here, just a sort of a number for Evotec. When at Evotec, over the last two years, we have essentially doubled the amount of omics data generation every year. That means that within a four-year span, we have tenfold increased the output of omics data in Evotec alone. In order to leverage omics, we have built a very unique platform at Evotec. This platform, which we call our PanOmics-driven drug discovery platform, is put together through various components, so a number of components. The most important component is our molecular patient database, which really is like a lighthouse tower that tells us about what is disease, what does disease look like on a molecular level, and how does this compare on a molecular level to healthy people? Only if we really understand on a molecular level what disease look like, we can identify the most relevant target starting points, molecular starting points, to then address them through drug discovery process via small molecules, antibodies, cell therapy, et cetera. Once we have understood that, you know, in a healthy patients, a transcriptome or proteome looks like this, and a diseased patient, it looks more like it sits over here, we can then definitively say, if we are able to move the transcriptome and proteome of a diseased patient to a healthy patient, closer to a healthy patient, that then we are approaching something like a cure. And this is exactly what we are trying to do with our platform here, and the iPSC platform that Sandra will talk about, is a key tool, because with the iPSC platform, we can model the diseased patient, a disease in a dish, and we can directly screen against it using our omics technology platforms, and here in particular, our PanOmics platform. These are high performance platforms that are very industrialized platforms, that allow us to do things that were impossible to do just a few years ago. That is, to screen these assay systems in essentially a high throughput performant format using omics technologies. That means that we are able to screen cell-based assays, and you see that we screen hundreds of thousands of compounds using transcriptomics as the primary readout. So this is, in general, it's an enormous amount of datasets, and it tells you for every single compound you screen, the complete biological profile. So this is absolutely unique, and there's no other company in the world that can currently do this. Similarly, for proteomics, we are able to screen compound libraries using proteomics as the primary readout. This is also a first. No other company in the world has done that so far. Evotec is screening compounds at a clip of currently about 500 compounds a day using proteomics as a primary readout. This is absolutely unique in the industry. We used to do this maybe in exceptional cases for one or two compounds here or there in the drug discovery process, but using it as a primary screening tool is, once again, absolutely unique. And if you're generating all of these datasets in molecular patient data, in pharmacology data, in screening data, you need AI machine learning tools in order to be able to analyze and interpret the data. The human mind cannot interpret 25,000 variables in parallel. That is impossible to do. So for us, it is if we would be looking at the spreadsheets, we would be completely lost. The only way you can analyze this is using AI and machine learning tools, and for us, this is a constant. But you can only use these tools effectively if you have solid data points, solid datasets, databases, that you can rely on, that they are accurate, and then train your algorithms accordingly. And Bhushan will talk about this in more detail later in the setting. So this is our solution to. Well, it's a fundamental change on how drug discovery is done. It is really PanOmics driven, not just genomics, but pan-genomics, transcriptomics, proteomics driven. And that this platform is actually also starting to resonate in the industry is shown on the next slide. So we have invested about EUR 450 million in the last 10 years in building these platforms, testing it, refining it constantly. And looking back, we have already signed over 40 deals that, to some extent, are using the platform, but increasingly using all of the platform. If you look at this chart here, you can see that we really signed deals here with key leaders in the industry. We've signed deals with Novo and Lilly in metabolic disease that are accessing our molecular patient databases. We have signed deals with J&J in the field of cell therapy. We have signed also with Sanofi and Pfizer and Bayer in the field of oncology, women's health, and ultimately, Bristol Myers Squibb, in the field of neurodegeneration in particular, but also oncology. Through the signings of these deals, we have already generated more than EUR 1 billion in revenues on these platforms. We have already generated more than EUR 500 million upfronts included in these revenues. Most importantly, we have a huge financial upside, which currently accumulates to over EUR 15 billion in potential milestones and performance payments that we can achieve in these collaborations. I think if you have followed our press release, you can see that we constantly, some of these are coming in. On top of that, there are, of course, royalties that come on top of all of this. So these two platforms, the PanOmics-driven platform and the cell therapy platform, are both platforms that are key components of building Evotec's co-owned pipeline, together with strategic partners, where we hold a humongous upsides, financial upsides in terms of milestones, but also royalties. I want to dive a little deeper into two particular deals we have signed. One deal is shown here, which is a deal we have signed earlier in the year with Bristol Myers Squibb in neurodegeneration. It's really an extension, actually, that tells you a lot about the power of the platform. It's an extension of a deal that we originally signed in, you know, by now six, seven years ago. It's an extension by eight years where we will use the entire platform that I just described in the field of neurodegeneration together with BMS, to generate or to advance and generate the further pipeline in this space, and it in particular uses our iPSC platform and the PanOmics platform that I just described. We received EUR 50 million upfront, and already this year, we have garnered EUR 40 million in milestones in this deal since signing in March. So close to EUR 100 million have already come in, and we stand to benefit from this collaboration on the order of EUR 4 billion in total over the lifetime of the collaboration. So this is an example where a already sizable deal we had, actually, after five, six years of running that, translated an even bigger follow-up deal, which tells you that people who have seen and worked with the platform just cannot imagine going back to a world where they don't have access to this. Yeah. These are unique type of deals. A second example is the oncology deal we signed with BMS in the targeted protein degradation space. And here, we're using the platform slightly differently. We're using, in particular, a key tool here is our proteomics or mass spec-based platform, where we are screening a compound library using proteomics as the primary readout. And once again, I challenge any of you, have you ever heard of this? I'm pretty sure, no. Nobody else has done that before. It's only possible here at Evotec today, and we, like I said, we are screening thousands of compounds annually, using this platform, generating huge datasets, and once again, in the targeted protein degradation space, where you're looking at molecules, potential drug molecules to degrade certain targets. If you're looking at these targets or molecules using proteomics, you know, in those volcano plots, you can see usually every change, right? And when you throw a compound on a cell, with this technology, you can see exactly that only one or two or 10 molecules are being degraded, and that helps you to select the best possible molecules at the very early stage. You don't have to go through essentially a long-winded process where you have a molecule that then you don't have to optimize for extended period of times, and therefore, we can accelerate the pipeline building process here substantially. And, this deal originally garnered, about EUR 200 million in upfront payments, and we have already received, this year, EUR 75 million in milestones, performance-based payments. And, we believe that this deal will, in the first year of this deal alone, we will generate on the order of EUR 1 billion in revenues through this deal alone. So these are really very unique deals in terms of length, in terms of breadth, in terms of pipeline building breadth, and in terms of the financial upside that is coming. And so with this, I come to my last slide, where I basically just want to give an introduction once again to the topics that we will hear about in the following, following me. And we will talk about the Molecular Patient Database, MPD, up here, and its effect on the drug discovery process and how essential it is to have molecular patient data if you want to improve success rates in the industry. Then, we will talk about the iPSC platform again and how it will affect essentially translating what you learn in the molecular patient database about the disease and molecular parts of the disease, and how to translate that into the drug discovery process, and to constantly test and make sure that the compounds that we are identifying and optimizing are really compounds that affect the disease process in the right way on a molecular level. And then we will talk, of course, about our machine learning and AI-based efforts or tools that we apply throughout the entire value chain and throughout the entire process to actually just be able to work with the data. It's like I said, for us, it's not an option to do these things; we have to have it. With this, I'd like to thank you for your attention, and I will now hand over to Olivier, who will bring you into molecular patient databases. Thank you. Thank you very much. So, building a pipeline, specifically in kidney disease, in my case, I'm leading the kidney disease research at Evotec, starts with indeed, we described it, a better disease understanding. This better disease understanding finds its source is rooted in the EMPD, which stands for Evotec Molecular Patient Databases, and it's a collection. Oh, sorry, that's the other one. And it's a collection of patients in a chronic kidney disease. And really here, you have to focus on the light blue part of the circle. You have here 12,000 patients. 12,000, it's still a lot, all suffering from chronic kidney disease, many indication, many etiologies. These patients are documented with a full medical record, and most importantly, for the understanding of the disease pathology at a molecular level, we have the full PanOmics profile of these patients. So, you heard before about the proteomics, the genomics, the transcriptomics, all of that data, metabolomics, all of that data is included and attached for each and every one of these 12,000 chronic kidney patients. I will just put the number of 12,000 in perspective with the numbers of patients that are typically recruited for clinical trials in kidney disease. And over 90% of the clinical trials run in chronic kidney disease enroll a number of patients that fits around 1,000. That's the typical. More than 90% of the clinical trials focus on 1,000 patients. Here we have 12,000. So you can imagine the depth of understanding that we have regarding all the disease progression stages of these patients along the trajectory from being more or less healthy to ultimately needing kidney replacement therapies. Chronic kidney disease is the bigger part of this cohort, but also includes immune-mediated disease, and other types of metabolic disease. So it's a whole syndromal conglomerate of disease, the cardiorenal and metabolic disease, and we are developing it in liver fibrosis. You have heard of the standards of care that have efficacy across these poles of the metabolic disease syndrome, and of course, including into the database, we have the healthy control that are an essential part of it. Just a glimpse in the future, if you look at the right part of the slide, is the next step for us in kidney disease, the next unmet medical need, uncharted territory, acute kidney injury. Essentially, a very important market and also a very important indication for the patient to have suffered from cardiovascular disease, have been seen at the ICU. Most of these patients, not most, 30% of these patients had a chance to develop cessation, complete cessation of the kidney function in the weeks following their admission in the ICU. So there again, no drugs available on the market. There is nothing for them except the hope that upon eventually dialysis, the kidney will recover its function, its function. And if it does not, then they are on an unfortunate track of getting back into the chronic kidney cycle, because this is a very important predisposing factor for developing, after an episode of AKI, chronic, some type of chronic kidney disease. So you may have heard of different molecular patient databases out there. There are not many, but there are some that are known in the public domain, and we differentiate ourselves from these public domain datasets by essentially three aspects. One is that we are very much involved in the inception of these studies. So we participate in the design, in the recruitment, in the frequency of the biopsy uptake, how much, for what purpose, how are they stored? With one objective in mind is to do drug discovery. So it's not about observationally, it's very much designing these cohorts and the recruitment together with the physicians to get to the objective, the final use of this MPD, which is enable drug discovery. The clinical data that are associated with these patients also is by design and by choice from Evotec. It's very much what we are focusing on. We want it to be as complete as possible. Where you typically would find five medical and clinical indications associated with each of the patients in the public databases, we collect from 50 up to 500 indications. So everything from the blood analysis is in there, all the anthropometric data, but also some social data, so BMI, smoking habits, everything. Everything is built in the medical record and comes in addition to those molecular description of the kidney biopsy, so the tissue themselves, the blood analysis, but also biofluids like urine, which are essential, of course, for the in the case of kidney disease. So we have these tens of thousands of patients, and we have these enormous data sets, either from the clinical data themselves or from the PanOmics, and overall, it totals into no less than billions of data points. And making sense of all these billions of data points in the purpose of developing drugs that that address unmet medical need requires some of the tools that you will hear from later on today requires AI, requires machine learning systems, and all of this is embedded in a system that we use on a daily basis in my team, and not just in my team, across the company, and it's called the PanHunter. So with PanHunter, you can go much beyond the correlation. You can go and get into the realm of causality, because by superimposing the different omics together with the clinical data, you pass the simple correlation to get into how those gene change translate into protein change, how those protein change translate into pathway modification, and ultimately affect the tissue. This is an essential tool, and the conclusion are also essential, and it's recognized by us, and it's also recognized by our partners. So we use these information to do different things. Certainly, the most important one is to develop first-in-class treatment. And thereby, I mean, with this molecular description, we also have an understanding of how the standards of care are acting and how the new therapies are acting in complementarity or in additivity or in synergy to them. So we built in very much the next generation of drugs that have an increased efficacy over the treatment that the patient will already be taking when our drugs will hit the clinic and will be tested in these patients. That's the most important part for me as a professional in drug discovery. But also, you will hear right after the beautiful work that is done in the development of new molecular marker of disease progression and the future of diagnosis in those indications where classic diagnostic at this point in time is insufficient or leads to potential misdiagnosis and suboptimal trajectory of the patient because they lack the right—they're not being taken care of in the right way. That's the EVOgnostics part. So improving the probability of success in the clinical trial, as has been said before, is essential because that failure at that moment is a failure that is the most expensive for all companies. The failing early is still cheap, but in the clinic, we're talking about billions. That's essentially where the money is invested, is in the clinic. So for us to use these database and for our collaborator to use these databases to select the right patient that enters the clinical trial, making sure these will be responding to the therapy because we have their profile, and we know how the therapy works molecularly, is essential, and this is understood by our partners as well. So with these tools and all these datasets, we are set to improve together with our partner, the design and the, and the probability of success of those clinical trials. Then we have this asset, PanOmics EMPD. How do we do drug discovery today with these? How do we create value out of these? As I mentioned, the pipeline of drug discovery, that's the most essential. The first thing that we would think about doing, bringing value to the patients. And we do this through two different axes. One is developing our own internal R&D pipeline, so we have some liberty and some freedom to select and progress an internal pipeline of projects up to an inflection point at which we will be able to discuss with partner in order to collaborate on the future development of these drugs, or simply to out-license, and in some cases, we have molecules already that we own fully, out-license the molecule. So this is internal R&D. EVOgnostics, I will not enter into the details. As I said, it will be covered later on. And the most important thing, and probably also the most revenue-generating, but the one that will bring a change to the patients the fastest, is the partner program that we have with pharma companies. Whether they be big or smaller, we have, and you will see that in an instant, signed a number of collaboration drug discovery alliances with the big player in the field of kidney disease and metabolic diseases. The idea here is to develop a joint discovery pipeline, bring it from the target identification to the validation, hit to lead, lead optimization in the clinic and in the market. And of course, it's associated with incentive, financial incentive, but it certainly, as a scientist, would be an enormous reward to see that the project that we are working on collaboratively finally make a difference in everyday lives of people. So our partners share the same vision as ours in terms of the importance of Panomics and the molecular patient databases to really root all these hypotheses and the development of these of this project. And we also have a shared vision on the clinical development and certainly the value creation for the patients, meaning, again, bringing value on top of the existing standard of care. So these are the companies we are working with, seven companies, well-known names. Chinook has been recently bought out by Novartis, so AstraZeneca, Bayer, Pfizer, Novo, Lilly, Chinook, and so on. You see here marked the time at which the EMPD really came into play and played into the success of this collaboration. We are developing, we have developed these EMPD for kidney disease. As you see, we are developing them for liver inflammation and the associated cardiovascular fibrosis. Of course, other areas of unmet medical need, like oncology, neuroinflammation, infectious disease, benefit from the same knowledge at a molecular level and are areas of expansion, for the company. In kidney, this is a snapshot of our internal R&D program, so the targets are masked. But the important thing on this slide is, one, the positioning of these, of this asset, meaning really as a complement to what already exists or what will exist in the coming two years. You will see some of these projects, like the first three ones, have hit the mark of hit-to-lead or lead optimization. Meaning that is in these three cases, what we have is actual molecules. We have actual molecules, or in the case of the third one, an antisense oligonucleotide, so a type of biologic, that we can discuss with our partner in order to again optimize them or out-license them as they are. In the grand scheme of things. This is how the pipeline in metabolic disease looks like. We have just recently first, I need to describe the dark blue lines refers to the partner project. So this is with some of the big pharmas and smaller pharmas that were on the previous slide. And the light blue are internal projects. Not all in kidney disease, but mostly in kidney disease. But then the important thing on this slide is the success that we just met, and we talk about the difficulty of entering the clinical development phase, but with a project that we run in collaboration with Bayer. And just in June of this year, Bayer decided, and we were very happy about that, to progress the project in first time in men. The second important thing on that slide is that you will see that the other projects are not that far from the first time in men dosing. So we expect in the next two to three years to have this, the same, to see the same success with at least part of the program that you see on that slide. And then on my final slide, what I would like to conclude and to leave you with is that, of course, we know, we believe we are doing the right thing. Our partners from the industry understand that we are doing the right thing. But what about the nephrologist in the field? And it was a very good. And it still is because we presented; we had five communications just earlier this month at ASN in Philadelphia. And this is a comment that was shared by a nephrologist in Paris last year about the molecular signatures that we have derived from these patient databases. And for him and others, the future is already here. And I would say for us, because this is our daily bread, the future is already now. So with this window open in the future, I will leave the floor to the next speaker. Thank you. Thank you, Olivier. So I will take it to the next level. I will take it to that, in order to accelerate pipeline building, we really need to understand the difference, what Cord said, between health and disease. We need to understand, even on this side, on disease, we need to understand the difference between a disease, between not only cancer, but within cancer, between different types of cancer. And I will share some examples in autoimmune diseases. And to get there, we need to stratify patients better. And that's what we do in EVOgnostics. We have recently added this, value chain to the pipeline, which starts really at the front of target ID, all the way to clinical trials to better stratify patients. When I joined Evotec 3 years ago, with a background of 20 years in diagnostics, coming from a company which is the leading provider of next-generation sequencing, which sequences today every cancer patient, which brought out the first prenatal diagnostic test to market, I was intrigued by this data set. I'm intrigued by these platforms, which we use at Evotec to drive it from the genomics level to the transcriptomics level, which really measures disease development, which measures how we treat patients and how they react to these treatments. What we have started building is a tool set, which is a PanOmics-driven tool set for diagnostics, for stratifying patients, to apply the right drug to the right patient at the right time in their disease journey. And the center of this, and you see this on this slide, the center of this is our Evotec Molecular Patient Database, which are data derived from patients with these diseases, and where we, all the way going to the left, developing tools now, which we can use in collaborations with pharma to stratify patients, to find that individual patient in dark blue, which we can treat with the disease, or where we can start doing target identification, which we then can take all the way to the clinical trials, select the patients which respond to that specific disease, and be more successful. We do this, on these transcriptomics data, in my case. In a single blood sample, in one test, we get about 35,000 observations per patient, per disease. And to bring that down to diagnostic platforms, to then monitor and also stratify, we have to bring that complexity down to, let's say, 100 or five, to be able to go on these diagnostic platforms, to partner with the diagnostics industry and clinical laboratories, to then run these tests for patients. So we provide, in addition to applying the tools to pharma, we will provide these tests further to partners in the diagnostics environment. So I'm going to give you examples now. We work in kidney disease, nicely described the datasets by Olivier, but we also go into autoimmune diseases, and these diseases can affect all of us. The triggers are vastly unknown. It is a state where our immune system attacks our own cells within our own bodies. And there's these diseases are heterogeneous. They're heterogeneous populations. We have similar symptoms in these diseases, very similar disease drivers, and very similar immune mechanisms. So identifying drugs for the right patient at an early stage of disease is very, very essential, to avoid patients getting into organ failure, described by Olivier, embarking like getting kidney failures, failures of their lung, cardiovascular impact. We look into rheumatoid arthritis, we look into systemic lupus, Sjögren's syndrome, and ANCA vasculitis. All of these diseases are diseases which have connective tissue involvement. Rheumatoid arthritis, the joints are impacted by the immune system, and that's the most common immune-mediated disease. And we are going all the way down to the left to ANCA-associated vasculitis, which is really a rare disease, but is in the complex of these connective tissue diseases, where blood vessels get attacked by autoantibodies, and blood vessels we have everywhere. So this can very rapidly lead to kidney failure. So very severe impact, and we don't know what triggers this. So what's essential in terms of diagnostics here is the early diagnosis and the accurate diagnosis. So putting the patient into the right bucket to apply the right treatment at the right time, and to monitor these patients accurately, to intervene, and intervene early, because these patients can get into relapse. They seem cured, but they can fall back into the disease. So applying this extensive PanOmics toolset, which we developed, is essential to find new targets, but also to manage these diseases and to stratify the patients in the right way. I'm going to give you two examples. These are case studies which are showcases to show you the impact of these diseases and the difference we can make with the technology we apply. The first example is this rare form of vasculitis, which is ANCA-associated vasculitis. Harold Ramis, we might know him as one of the actors in Ghostbusters, passed away of ANCA-associated vasculitis due to a complication within four years after diagnosis. This disease, the trigger is unknown. We see this disease coming up in winter season, more so than throughout the rest of the year. It's a multifactorial disease, and the tricky part in this is the symptoms are really, really vague. It starts with chest pain, malaise, anorexia. There is a lung involvement, so you can have a suspicion of a pneumonia, of an infectious disease, which then could get treated with antibiotics, but you see this treatment fail. That takes the patient really rapidly, in this case, on the path of organ failure. In the case of when the kidney is impacted, the diagnostic paradigm is biopsy. It's an invasive biopsy which risks that these patients bleed to death. The treatment is harsh. It's corticosteroids, and it's chemotherapy to try to attack the own cells in your body, to deplete them and stop these antibodies from acting, on the blood cells. So what are we doing here? How are we trying to stratify this population from the rest of kidney diseases? In the middle, you see these health to disease maps, which we generate. So each dot on this map is one blood test from one patient. The blue ones are, on the very far left, healthy individuals. The other blue ones are kidney disease patients, and to the far right ones in orange, you see ANCA-associated vasculitis patients. So with a single blood test now, we can position these patients on the map here. The map is constructed of patient data. The map is constructed on molecular data of transcriptomics data, and we take the same measurement and position the patient on the map and follow them through the progression of disease on these maps. We can then zoom into these blood samples, even down to a cellular level, and that's what you see on the far right. On the top, you see the cell molecular level in the blood with, in different colors, different blood cells, and in red, in this small square there, you see the B cells. Those are the cells which produce the antibodies, which go against the blood vessels, and that's at diagnosis. And then at treatment with chemotherapy, you see in this patient down there, this patient is now moving from the far right into the blue area, and these B cells are depleted. And we can follow this, we can track this more accurately, we can discover it earlier, and we can save lives, like the life of Harold Ramis. Let me give you another example. Very famous individual, Serena Williams, was diagnosed with lupus, an autoimmune disease, which really has symptoms like fatigue, weakened immunity. Very, very hard to diagnose. Again, diagnosis is essential to happen early, to intervene. She was diagnosed in 2014 and got a kidney transplant in 2017. Again, this disease can happen to all of us. Monitoring the disease in this case is very essential because. You have flares of the disease, and you need to intervene to avoid organ transplantation. This is done by, today, by a diagnostics which is really archaic. It's 24 different components, which the physician has to combine to a so-called SLEDAI score, to a disease progression score. And you see in the middle part, the visits of the patient to the doctor, in this example, 13 visits. You see the SLEDAI score, where they have to go extensive diagnostics, is showing flares. The valleys are a well-being of the patient, and the hills and the peaks are really the relapses of the disease. And as I said, we take one blood sample, and we position this patient on the map here, and then we can follow the patient moving through the map, and we get to a similar profile of these patients. I'm going to show you this in a little animation, where we can actually, with single blood tests, follow this patient through these health to disease maps. So in summary, we take the EMPD, the molecular patient database, and the data in there to stratify patients. We currently work with three university clinics to have the immediate interaction with the physician on these data. On the samples we get, we apply machine learning to create these health to disease maps, and then we take these large, rich data points down to biomarker sets, which are informative, to put them on diagnostic platforms and carry this approach on existing diagnostic platforms in clinical laboratories. This is really applied at Evotec from the start of target identification all the way to clinical trials, to more accurately stratify patients and apply treatment to the patient at the right time, but also accelerate the probability of success to bring out medications which are needed, especially for those patients. And with that, I hand over to my colleague, Sandra Lubitz, because we take these disease signatures, which we get, and take them forward into human models, to translate this further. Sandra. Thank you, Christiane. Good morning, everyone. My name is Sandra Lubitz. I'm the head of the iPSC team based here in Hamburg, and as you've heard earlier by Cord, iPS cell stands for induced pluripotent stem cells. And when I look back at my scientific career, stem cells run like a common thread throughout all my career steps. And from the very beginning, during my PhD, I got fascinated by the amazing potential that these cells have for drug discovery, and even more so in the more than 12 years that I've now been here at Evotec, we are using such models to really drive drug discovery forward. And with this, I would like to introduce you to what is motivating me every single day to come to work and start new projects, and this is by introducing Leah. Leah is a young woman who, at her middle twenties, realized that something is wrong. She was often tired, her legs were aching, and she thought, well, she's young. This will go away. Life is great. And she just continued as it was. But then, at the age of 27, she got the devastating diagnosis that she has ALS, and just one year later, she was already bound to a wheelchair. And it is really ALS is a very severe disease with a very poor prognosis, so Leah only has a life expectancy of two-five years. And despite the fact that ALS usually affects people at the age of 60 or above, also, younger people like Leah can be very severely affected by this disease. And she's formed a not-for-profit network organization. I really encourage you to check out her website. It's really impressive what she does. To those of you who now think, "Hmm, ALS, amyotrophic lateral sclerosis sounds familiar to me," you might remember the Ice Bucket Challenge that really went viral in 2014, where basically people poured ice buckets, and we participated in this as well, over their heads in order to raise awareness for this, very fatal and, devastating disease to, secure more research funding to actually find a cure for this disease. And with this, let me introduce you what ALS actually is. So amyotrophic lateral sclerosis. Amyotrophic, this is a Greek word, and basically what it means is, a means no, myo refers to the muscle, and trophic means nourishment. So amyotrophic means no muscle nourishment, and that's what's happening in disease. So the muscles, in our body, they are all connected to neurons. They are called motor neurons. We have motor neurons in our head. The motor neurons reach out to our spinal cord, and then from the spinal cord, the motor neurons reach out and connect with every single muscle that we have in our body. What happens in disease is, so normally a nerve cell would sense a signal. If we, for example, decide we want to lift our arm, then this signal is being transferred through the motor neurons, and we know—our muscle knows I now have to lift my arm. Whereas in sclerosis, this signaling cascade is disturbed. So now, this diseased motor neuron cannot send the signal, so the muscle doesn't know that it's supposed to contract, and that, in the end, leads, first of all, that the muscles waste away, and in addition, the motor neurons also die. So in the end, what you end up with is that basically you cannot actively control your muscles anymore, and because your lung is also a giant muscle in your body, in the end, you lose the capability to actually breathe. And this is something that, yeah, affects a lot of people worldwide. It's predicted to even increase further, and the most important to remember is there's very limited treatment options, and so far, there's no cure available to stop or reverse the disease. And we believe, and Cord has already alluded to this in the beginning, we believe that the main reason why we still don't have a drug is that we've been looking in the wrong models. People have been using mouse models to study a human disease, and we do not want to treat mice, we want to treat the patients. This is why we feel we really are in a strong need to develop better human models in order to be able to study disease. That's where the iPS cells come into play. With iPS cells, we now have the chance to go to an ALS patient, request a skin biopsy or a blood draw from this patient, and those cells are programmed. They know they are a skin cell or a blood cell. But what we can do now, and this is what Shinya Yamanaka has discovered, we can reprogram such cells into a pluripotent state, and this is shown here. This is the iPS cell state, where we can then push the cells through the addition of certain growth factors. We basically mimic embryonic development, and we can push them into the cell type that is affected by a specific disease. In this case, this is the motor neurons. Now we can take cells from an ALS patient, and via iPS cells, generate motor neurons from that very patient. With this, we now have the opportunity to build a disease in a dish model that we can then use for high-throughput screening to identify better drugs that will ultimately have an effect for such patients. That's what we do, and that's what we use our, our iPS cells for. It was actually the very first program that we started in this area. When I joined, this was the first project I was working on. We did this in collaboration with the Harvard Stem Cell Institute, two well-known professors, Professor Kevin Eggan and Professor Lee Rubin, and they had established a protocol to derive motor neurons from the induced pluripotent stem cells. And then we optimized this protocol further, and here, if you look at this, this is a beautiful network of neurons. They're nicely spread out. If you look at the green dots, this is cell bodies of these neurons, and then you have these nice red projections, so they're all interconnected. They talk to each other, and this is cells... You have to imagine, this is cells from an ALS patient you're looking at here, but they were derived from the iPS cells from this patient. So now you might ask, "Well, but they already had the protocol, so what did Evotec do in the game?" And our goal was really to bring this to an industrial standard, because it's one thing to do a small experiment in a small well to get your paper out, but it's a completely different story to take this to a scale that we need, because we need billions of cells if we think about high-throughput screening, and that's actually what we've been focusing on. So we took this protocol, optimized it further, checked. You can see down here, there's bioreactors. So we always check what is the best way to produce these cells. Is it 2D or a 3D system, like the bioreactors, for example? And we apply very strict quality control criteria to our cell production processes because we feel that this is really essential. We need to make sure that every time we start such a production run, we get the same results out at the end. And this then, in combination with the automation workflows that we also established, allows us to culture these cells for a long time, and it also allows us to culture the cells in an amount that is needed to actually perform drug discovery on those cells. And that's what we do. We currently have more than 20 different drug discovery programs ongoing, and in September 2021, we had the first major breakthrough, because from the very first project I showed you, we determined and investigated a new drug that is currently in phase I testing and will enter phase II very shortly next year. And let me explain to you what this drug does. So in a nutshell, this drug is impacting the integrated stress response. So this is a response that we all have in all our cells, and what this response does, it basically checks out if a protein that we generate in our cells has been misfolded. This can happen by accident, and for this, we have a mechanism that would sense this misfolded protein, and then a component of this so-called eIF2 complex, it's called eIF2α, gets phosphorylated. And then when this is being phosphorylated, this is actually a good thing to happen because it tells the cell: "Okay, now I, I shut down protein synthesis, I increase the expression of a few stress response genes." And with this, the cell has time to get rid of all these misfolded proteins, and once this is done, it will then reinitiate the protein synthesis, and everything is back to normal. The problem is, if you overstimulate this pathway, which is happening in disease because there's tons of misfolded proteins occurring every single day, then all of a sudden, you enter into a state where the cell actually is totally overwhelmed and starts to die. And this is what's happening in ALS. So our compound that we identified actually manages to keep the equilibrium between these two states. So it gives the cell some time to not get hung up in this vicious cycle, but to dephosphorylate eIF2α again, to bring it back to the normal state, so that the cell can produce its proteins and continue its cellular homeostasis. And this is really, really important because it brings the cells back into balance and prevents the cell death from happening. And so this drug, BMS-986419, or as we call it, Evotec-8683, was really the first compound that we identified on our iPS cell models from an ALS patient. And this is now, it, it has shown very good results in the phase I study in healthy volunteers that we performed. And as I said, we will move into phase II testing in ALS individuals. But the important thing is, because the integrated stress response pathway that I've just described not only plays a role in ALS, but in multiple other neurodegenerative disorders as well, such as frontotemporal dementia that you may have heard about, or Alzheimer's disease, there's a very high potential that this drug can then ultimately also be applied to those patients. So, as I said, this was just one tiny example of what is going on. We have this very successful collaboration ongoing with Bristol Myers, from which also this drug I just described originated from. We have another successful partnership in the area of eye diseases with Boehringer Ingelheim. But as you can see here, there's multiple additional opportunities with all the models that we have established using our own internal R&D funding, that allow us to tackle psychiatric diseases, more eye diseases, the kidney diseases that Olivier has described, also metabolic diseases, all the diseases that affect our liver cells, for example, cardiovascular, or this is a very recent topic, but very hot currently, inflammation and immunology. And what you need to remember is we have all the right cell types in place that allow us to also tackle these diseases, and we look for strong partners who really embark on this journey together with us. And what makes us really unique in this field is, and this has been reflected by many partners we work with and we talk to, is that we combine all the platform expertise that we have in-house, including the iPS cell models, with our disease expertise that we have in-house that allows us to develop novel approaches. And in a nutshell, what the future looks like, and this is, this is how all the upcoming future opportunities will be designed, is, first of all, as you've heard, we need to get a better understanding of disease on a molecular level. What is really going wrong here? And then we use our iPS models to actually recapitulate what is going wrong in disease. And for this, we use the, the iPS models that I've described. The beauty is we are entirely modality agnostic, so depending on the question we ask ourselves, we can check, do we want to make a small molecule approach? Do we want to discover an antibody for this question? We have all these capabilities in-house, and we can apply them accordingly. And just one last note on the iPS cell models, of course, you would all agree, disease is much more complex than just a single cell type. So we really also need to look at cells in combination, and this is also something that our iPS models allow us. So if we again focus on neurodegeneration, we can now study the neurons in combination with the glial cells that are usually around the neurons, protecting them, and even in combination with microglia to study the inflammation of the brain. So a lot of different combinations we can use and study in future. And last but not least, what it also allows us is, we cannot only look at one patient in our culture dish, but at multiple different patients. And with this, we have the unique opportunity, before we even go into clinical testing, to already check in vitro what is going on. Are they all responding the same to the drug that we've identified, or are there potential differences? And this we can then use for the design of the clinical study that will follow. So in a nutshell, the way how we fuel our program pipelines is the very first question we ask ourselves is: is there a high unmet medical need, or are there a lot of compounds in the clinical pipeline already? And if the answer is yes, then we ask ourselves: is this something novel that we can discover in the patient data that we analyze? And again, if the answer is yes, then we ask ourselves: do we have the appropriate model? And if there is no good model available, we believe that our iPS cells are the perfect tool to actually develop better models for drug discovery that will then reveal relevant drugs for the patients. And with this, I will hand over to Bhushan, who will explain you more how we actually use artificial intelligence to determine these signatures that can then ultimately fuel our programs. Thanks, Sandra. So my name is Bhushan Bonde. I head a small group in Evotec, which leads the AI and machine learning models. I'm sure there is nobody in this group who doesn't know what AI and ML is, the jargons at least, you know. That should be fair. My previous work was done in pharma industry for 15 years, building these models and deploying them at scale to a pharma pipeline across the drug discovery. What is mean by AI/ML? Let's go through a little bit of a basics here and understand what these models are doing. When we say linear AI/ML machine learning models, these are small models, had been there in the industry, had proven it very successful. They have a high precision rate, they have scalability, they have a speed to detect. These models allow us to go from qualitative models into quantitative. They know what signal means. So instead of saying, "Yes, I see the drug is acting," one says: "How much is there?" And that is what I mean by linear. These models have relatively small variables. They are really proven very important in a milestone in drug discovery phases. But when we talk about non-linear model, and these are the eureka models that we have been seeing, that has been entered into the drug discovery. And one of the example is protein folding model from AlphaFold Google. I mean, luckily, we have here a protein folding expert there, Lawrence there, and I was speaking to him yesterday in a discussion, and he said, "If you ask me, Bhushan, that somebody can crack a protein folding problem, I'm a PhD in protein folding, and I would have bet at saying no, it is impossible." That is the eureka moment I'm talking about. 30 years, scientist has been telling us that it is impossible to get a 3D protein structure just from a 2D sequence, a sequence. These models are revolutionizing the pharma industry, and we will also jump, jump into these, the, the impact of those models into drug discovery. With this, we are not just talking about models, or we are not just building about the models, we are actually publishing them with our other pharma colleagues into peer review. As we saw it, that the work we do is not just to keep it up with ourselves, so we want it to be in peer-reviewed journal. Two key books published in the last two years has been seminal in bringing these AI-based models into drug discovery, and also how can we deploy these AI tools in pharma industry? We will really need to delve deep into the high-performance compute needs that enables those models. I mean, AlphaFold wouldn't have been possible without the use of CPU, GPU, and TPU. That has been the compute capacity that has been available to companies like Google. And these are the books which has been similarly... The first book was like accessed more than 115,000 copies, and the second book, which just came a month ago, already has been 8,000x copied down. We are publishing them so that it can be, the work can be reviewed by our peers across the industry. Having said that, one question we want to ask: Does AI live the hype around in delivering drugs to the pharma industry? The first graph we can see, since the entry of the rise of the machines in pharma industries, you know, machine learning models in industry from 2010- 2021, we can see there is a significant increase, and the increase is more in obviously the light blue color, which is a discovery and preclinical phase of the pipelines. And this also translated a little bit into slowly into phase I, phase II, and phase III. But the proportionality is not, is not kind of, let's say, one to one there. And also we could see with the top pharma compared to the AI companies, the impact is in the area where the availability of the quality datasets were more. Kinases, ion channels, GPCRs, catalytic receptors, where you can see large amount of quality data was available, where the AI companies could make an impact. But we need to be cautious. If we see on the, on the right-hand side there, that the drugs which has been, you know, enabled by AI, had already shown to reduce the time to, from bench to clinic as a median compared to the before the AI companies, we need to be cautious there because it is early days, actually. We now know that three of them are already pulled over in a short time period. The publication came in early 2022, within less than 1.5 year. So the question here is that it's not by numbers we need to judge it, it's the quality that we really need to judge whether AI is going to deliver this or not. Across the pharma chevron, and this is a classical pharma drug discovery pipeline you had been coming across. We have a target validation, hit identification, then we identify leads, and then do some tox studies to make sure the drug is safe. Then we start into clinical trials, various phases. What we had been doing at Evotec is apply those across the pipeline. It's not only the models are not impacting at one particular stage, and we will deep dive into these four key stages or four key examples that we can look into. The first example is the identification of right target, and the target is really a critical role. I mean, it plays a critical role. 30% of the attrition in pharma industry is attributed to the target-right target selections. So at Evotec, what we had been doing is to mine all the available literature, public datasets, around 4.4 million abstracts, patents, chemical databases, you know, profiling the disease datasets and all, build them into one central graph database, a graph neural. Knowledge graph, we call it. And the knowledge graph can be mined using the machine learning and then AI-based graph neural network approaches. I mean, these are the approaches where you would like to find something like social networks, where we want to find the closest link to some famous personality, and exactly that's what we want to find from this. Where is the closest link to disease from the drug candidate or the features that we want to extract? Based on that, we can take it down the target into a machine learning approach and validate those targets into the lab using the omics profile, you know, transcriptomics, metabolomics profiling, and then make that screening and do a lead into the target validation by using the right assay development. You have to identify whether it is right or not by providing the assay development. And this whole pipeline can be driven to aid then to identify a new target, which is rapidly delivered to the pipeline, and it reduces the time by 22, I mean, it's only 22, 16-20 weeks of the work that we can be doing. And this is an end-to-end pipeline that can lead to regeneration. The second example, we can delve into a generative AI. The classical problem we had, we had a compound series which came from one of our partner, and then the compounds were not right. Somebody had tried to do combinatorial, you know, explosion of the compounds, and they were totally not in the right space. They had not been in a druggable space, and they had a few challenges into absorption, metabolism, basically, and toxicity issues. And this compound series was totally wrongly selected, and it came to us and say: "Can we fix this?" Within 12 weeks of time, we trained the reinforcement learning. Reinforcement learning models are, again, deep learning models. They don't need a prior knowledge. They learn from the mistakes that you do. Learning from those mistakes, we can actually see these are mistakes done from the earlier experiments in identifying the right hits for the target. Learn from that, in addition to that, identify the synthetic capability of this. Can this compound series be synthesized in the lab? And that was the added benefit that we gave it. With end-to-end pipeline, we could deliver a new series of a compound with increased potency and fix the related ADMET problem by the design, make, test, and analyze cycle. It was a cyclic approach, and you can see the red compound series was all junk in the earlier version of the target sets, and what the green set was the actual hit, which increases the probability of success by 90%, and the whole pipeline just took 12 weeks, including the lab validation of the target sets. Going forward, the third example, and we have seen this particular thing in the earlier presentations, that we had a PanOmics and PanHunter datasets. They had a huge omics datasets. Even for a single cell line, we have 35,000 outputs coming out for transcriptomics and omics datasets. We have millions of such data points coming out of that. We want to churn them using the machine learning model, which are specific to organs. Organ-specific models are there. We can identify, for example, drug-induced liver toxicity is one of the key challenge for the compound series. And our model, which is basically compared to the baseline, open model at the moment, which performs 70%, this model was constantly retrained every year, and it improved now to 87%. So not only we can give for the compound series the safety labeling, how much safe it is, how much DILI index it has, but along with that, the added benefit of all this modeling approach was we'll get a mechanism of action predicted, we'll get a hints on what is the potential dose selection in the human will be, and that improves your compound safety matches. So you can have a series of compound predicted, saying these are much safer compounds. And this whole thing then really improves this chance of probability of success at failing at a later stage for the pipeline. And the last case we will see is our EMPD databases. Again, 19,000 patients, and it is increasing. We have multiple data sets there, millions of data points coming from transcriptomics, metabolomics, even from the clinical, and phenotypic data sets like histopathology, organ and function biology. What we did here was train these signals using the LSTM. These are the type of, again, deep learning models. We have long and short-term memory models to identify a right cohort for a given therapeutics area. And then these models again, were validated using the expert, domain expert. They had a prediction scores. What will be the score of selecting a right patient cohort for a given clinical trial to become more successful? And that lead to selection of a right patient cohort, and that reduces the time to select the clinical trials delivery for the program. All this is possible because of the key essence that we had been using, these machine learning models, and they are not magic, by the way. They are really the challenges that we have been doing to the science and technology, enabling them via the ability. To enable this, we need to have a platform. At our end, as we've seen, we have a PanHunter as one platform, which kind of takes where PanOmics has been building the data sets for us: genomics, transcriptomics, proteomics, metabolomics, even the compound data sets, even the clinical data sets we have been bringing on. And then we have metadata sets, which is coming from experimental data set, assay catalogs, from making a lot of assays in the lab and pharmacology labs, bringing them together into one centralized platform, delivering that as, as integration, data storage, curation, governance, and making sure that the quality of the data stays in it. We can then do a lot of machine learning models, which is already pre-trained there, stored for the layman. The scientist doesn't have to build this model. They can use this. At the moment, as we speak, more than 300 projects has been using this tool, our platform, internally and externally with our collaborators. Also, they can bring external models. If somebody had better models, superior to a. It can be brought in in-house or into the PanHunter team, which will deliver us and increase your probability of success by leading to that actionable outcomes. And with these actionable outcomes, you can see in each and every examples we have shown is that the model end up in producing actionable outcome. In the lab, either it delivers a assay, a compound series, a strategic decision for selection of patients, populations, and that actionable outcome is really important. I would like to end here with one final message that for AI/ML to be successful, at the evolution of AI/ML, if we believe it, it's not one model, but the ensemble of the models that we need to bring in, the best-in-class models that need to be brought in to make sure that success stays. But along with that, what is important is that the model is not just going to change. What we need is the wet lab support, the model, the validation again, back in wet lab, and the cyclic manner, the DMTA cycle, is what AI is going to make impact into the drug discovery. Thank you. Thank you, Bhushan. Thank you, team PanOmics, for these passionate presentations and time-efficient presentations, I have to say. We didn't have to manage it. You are on time. Thank you very much. We have now 50 minutes break for coffee and taking fresh air, and then afterwards, we will hear, I guess, similarly passionate presentations about iPSC-based cell therapies. Thank you. Thank you. Ladies and gentlemen, welcome back to the second part of the presentations. Introducing the team running the iPSC-based cell therapy business at Evotec, starting off with Christine Günther, Andreas Scheel, Markus Dangl, and they will guide you through our programs, our platform. With that, I would like to hand over to Christine. Maybe we give the audience another two minutes to settle, but then we can start. Thank you. Two minutes, yeah. Yes. Let's get. Okay, so welcome back to our next session, focusing on more the translation to the therapeutic side based on the iPSC stem cell technology. My name is, Christine Günther, and we will share the presentation, Andreas and Markus, so it's one presentation. My background is, a medical one. I'm a physician, not a pure scientist. I'm a hematologist, oncologist, and, my mission is really to bring beautiful science into therapies that can be applied, not only to one patient, but to many patients, and that's, that's driving me here. So, as you heard already, cell therapy and iPSC therapy plays a role in drug discovery and, drug screening, and now we are moving to the therapeutic side. So as well known, cells can be used also to treat or to cure diseases, and the best example is the stem cell transplantation already performed for decades to treat incurable leukemic patients and to cure them. And that not only showed the potential of cell therapy, but also showed the immunologic power of immune cells of the donors to eradicate highly malignant leukemia cells. So the next, or even the next next step, is to use induced pluripotent stem cells, so the Nobel Prize-awarded technology now moving into the clinic, and it allows us to get ethically approved cells out of somatic cells by dedifferentiation into pluripotent cells, and then it's possible to differentiate again into cells that we need, and Sandra highlighted that already in quite detail. Our aim is to cure patients, of course, and as many patients as possible. And just to show the tremendous potential of cell therapy in the last decade, I have here some examples, and many of these patients I had in my clinical career in the hospital in the past as well. So on the left side, it's a tumor patient with a melanoma, and by applying tumor-infiltrating lymphocytes of the patient, it was possible to induce a remission, in this patient. But this is a good example in oncology, but this is not the routine case, and that's why, we are working on that. Only a minority of patients are responding to such, treatment in solid tumors. And, this is one of the challenges we will tackle and where Markus will explain a little bit more. A beautiful example in the middle is the use of the pioneering CAR T cells. These cells, so genetically modified T immune cells, they really opened the door to treat patients in a with a curative potential. It was possible to induce remissions or even cure in patients without any chance to survive their disease. This is Emily Whitehead. She was the first pediatric patient receiving CAR T-cells already 10 years ago, and last year, she celebrated her 10-year survival, and also founded a foundation. It's really interesting to look into that. It's remarkable because at that time, CAR T-cells had a lot of risky side effects, and the field has evolved quite a lot in the meantime, and as said, opened the door for more cell therapies. But it's not only oncology, hematology, but cell therapy has also a place in regeneration and in non-oncology diseases, and we will focus today also a little bit on diabetes. So, we know from, type 1 diabetic patient, where the gold standard is to perform an islet transplantation, that most of these patients can get insulin-free for quite a long time. But of course, the limit here is that, islet donors, so these are cadaveric donors, this is really extremely limited to a very small proportion of patients. And this shows already where we have to go to overcome some limits in efficacy and to make it accessible in an affordable way for more patients. To give an overview what's going on, and I'm aware that you can't read it, but it doesn't matter here. So this is only a snapshot of the pipeline of products waiting for authorization in 2023, and authorized products. And it shows that we are in an area where the cell and gene therapies are rising with market approval, and overall, we have in the U.S. and Europe, about 45 authorized products, mainly in hematology, so leukemia, lymphoma, and so on, but also in the inherited genetic disease, where it is possible to replace the damaged gene through cell and gene therapy. Most of these therapies are still autologous, and I will come to that in more detail, meaning that the cells are derived from patients with all the limits. It's not only the limit of quality, because patients have received chemotherapy or other therapy, but it's also the limit of commercial success. Although the products are in the market, it's difficult for companies to really get high revenues, and that's also the feedback we get from big pharma. The next generation is clearly moving to off-the-shelf therapies and larger indications to really enter the market. New indications on the horizon are in the cardiovascular disease, metabolics, and neurology. I think most of that you heard already in the screening iPSC platform from Sandra. iPSC-based therapies are still not marketed, but they are entering the clinical phase, and so far proved to be as efficacious as other therapies and also safe. This is now the right time point to consider to switch from autologous, which is not sustainable, to real allogeneic using the iPSC cell technology. It's also shown here in a graphic why it is so important to move from autologous to allogeneic. On the top, you see the current standard in cell therapy is one patient, one batch. This needed a completely new infrastructure also for pharma companies, and it's not really sustainable for multiple reasons. The patient being part of the manufacturing process, this is not for high numbers. Moving to donor-derived, so voluntary donor-derived, is an option, but it's still limited. We and many others are quite sure that the iPSC-derived model provides a solution for this limit, to have not only unlimited starting material, but also homogeneous quality. What is even more important, it's possible to gene edit these products deriving from iPSC, and the gene edits have just to be performed in one process, but not for every patient. So this is really crucial to move it into the clinic, and I now move to Andreas, and we'll come back. Good morning, everyone. My name is Andreas. I'll be talking a little bit on how do we translate this into reality, actually, this, you know, this vision for off-the-shelf therapies. And this like, it's an overview of what we have been doing and where we are today. And so we started actually very opportunistically in 2015, in a diabetes collaboration with Sanofi that you may have heard about. And of course, at the time already, we were building on the long history of iPSC science within the company, and we're trying to translate that into therapies. And maybe that's also a point where I can introduce myself. So I've actually been with the company a very long time, and I, I'm a biochemist, and I've spent most of my time on, many years of my career in small molecule drug discovery. But then I started moving into cell therapies in 2015. I've literally been hooked ever since, and I've been doing that as my main, job ever since because this, this vision of a, a patient goes to a clinic, and the doctor pulls out a frozen bag of cells from the fridge, and it's applied to that patient, and the patient goes away and is healthy for five years or is maybe cured for life, that- that's really such a revolution that, that drives us all to, to figure out how we can create, that, a, a reality a- around that. So we started several years ago to do this a bit more strategically, and I think we focused on two areas. Since this is such a new technology for the industry, using iPSC-derived cells for therapeutics, it required building what we call an end-to-end platform, so the ability to take project from inception to the clinic, including manufacturing of clinical material. This is, as of today, still one of a big technical challenge, and it really fits into our strengths to actually build that internally. Then secondly, we started to branch out from diabetes into oncology, into other areas, building early and innovative starting point from projects to drive partnerships to help us move projects to the clinic. And you can see on the right-hand side that we've, we're active in two partnerships already. That kind of validates the model also that other companies seeing the same trend and trying to capitalize on this new paradigm. And the next slide is a little bit complicated, but I think it's worth spending some time on it, on this. This is our current pipeline of projects in the iPSC-based therapeutic space. On top, you can see the two partnerships, the one in diabetes with Sanofi, you will have heard about, and Christine will explain it a little bit more detail on the science behind this. I'm glad to be able to announce that we actually have a collaboration with Janssen, that we now also can disclose publicly. I think this is something that we started nine months ago, and I think this illustrates a number of things. First of all, of course, partnerships are our currency for success in helping us drive projects to the clinic without having to carry the financial burden, but retaining some upside. But then also, of course, Janssen is a big pharma company that actually have an autologous cell therapeutic on the market, yet they're investing in iPSC-based therapeutics, which is, again, a validation of the model that off-the-shelf therapies are probably gonna be the way to go in future. And then thirdly, of course, you know, before the partnership happened, you know, there was extensive due diligence by Janssen on the know-how on our side, the work that we had done on this specific cell type that they're, they are pursuing, which is a subtype of T lymphocyte called γδ T cells, on the platform that we've built to drive iPSC-based projects. So for us, this is just a fantastic validation of all the work that we have done internally. And then beyond that, you can see that we've invested, as I just described, in a whole range of different areas, different disease areas, but also different cell types. And it's the beauty of iPSCs, that they can be used to create a whole range of different cell types using the same platform. So we're working on immune cells, we're working on heart cells, and in diabetes, and others. And you could—the arrow is actually meant to indicate that each cell type can actually give rise to multiple therapeutic product, depending, for example, in the area of oncology, depending on what targeting moiety you include, introduce or what genetic modifications you introduce. And so overall, this creates a universe of project opportunities and partnering opportunities, that would, that we feel will really have an impact, on the field and that we put a lot of focus on. So just a few slides on this element of the platform and how do you actually take projects into the clinic, and it's actually fair to say that there is a good handful of early clinical trials going on with iPSC therapeutics. So it's only a very small number of companies on the planet who actually have such a platform and are actually able to do the translation into the clinic. And before I describe our platform, I just wanna show this slide. I know it's a bit complex. I don't know whether you can read all the details, but it clarifies a little bit on what we actually mean with off-the-shelf therapies. I mean, how that differentiates from the autologous space, where you literally start in the manufacturing procedure with an iPS line. It needs to be clinical grade, obviously, because you're manufacturing clinical material. You can do gene editing, depending on what you want to do. You can introduce a CAR. If you want to make a CAR T type of therapy, you would typically do that with a CRISPR-Cas approach. And then you would essentially select a single cell that has the transgene in, and you would create, on the bottom right, a master cell bank that is essentially the starting material for all the manufacturing activities of such a product for the lifetime of the product. So you do that process once for all the clinical phases, all the entire life, commercial lifetime of such a product. And then the manufacturing procedure at the bottom of the drug product resembles actually the manufacturing procedure for an antibody therapeutic. You have a cell bank, you take a vial with a milliliter of cells in it, you expand, you differentiate in a specific cell type, and then you freeze it down. So that's the vision. It's not the reality yet, but it illustrates the huge benefits, as Christine was saying, compared to autologous therapies, because the gene editing is very precise, because every cell in the drug product will have the, the gene edit in it, every single cell. That's not achievable with autologous methods. The gene editing method will only have to be developed to be done once in the lifetime of a product. Not for every patient, only just once. And then, of course, the scalability of, of the production is what, in the end, hopefully will deliver the off-the-shelf character and the, the off-the-shelf nature for a large number of patients. So the platform that we've built, it essentially contains all those elements. And so we can go from the very early start and the exploratory phase, and where we figure out how do we make a T lymphocyte out of iPSCs, how do we gene edit, what gene edits do we make, all the way to the right-hand side, including in the manufacturing part, which is really a key element in the cell therapy space, manufacturing clinical-grade material, and also in a scalable format, so that this idea of off-the-shelf therapy is not just a statement on a piece of paper, but it actually allows you to manufacture thousands of doses, and that's really a key element. And we've actually acquired a company in Italy last summer, and I'll show that to you in a second, a cell therapy manufacturing specialist company that exactly allows us to do that. We've also built quite a significant team. This platform is actually very different from what you would need in a small molecule world. In the antibody world, the expertise is very different, and we've built a dedicated team of about 120 scientists that only do cell therapy, only do iPSC-based cell therapy, actually. And so this is now by now one of the biggest therapeutics team on the planet in the iPSC space. Genetic engineering, we touched on that. This is really a key element of any of those therapies, and there are so many different things that can be achieved through gene editing, whether it's preventing rejection of cells. You need to remember that the, you know, the cells actually come from a single human donor, so when they get transplanted to thousands of patients, they are recognized as foreign, and they may get rejected, depending on what the cell type is. This can be prevented through genetic modifications. When people hear therapies and stem cells, they think about safety risks and tumor risks. There is, there's ways to actually build in a very elegant way to prevent that from happening. And then Markus will talk on the—from the oncology space, if you want to introduce two targeting moieties, or if you want to make genetic modifications, that will especially, especially be important to enabling targeting solid tumors, which is still one of the biggest hurdle in the oncology space. All of that can be accomplished in a very elegant way in stem cell-based therapies. And you can see at the bottom, for every indication, the gene editing strategy can actually be made—can be tailored towards the specific cell type and the need of that indication. And so this is just a summary of what I just described. We're really proud to have that manufacturing facility actually in Modena or just outside of Modena. It's a spin-out from the university. It's an expert group in cell therapy manufacturing, and in the past year they've already been working on multiple iPSC-based Evotec projects. And we're now moving very close to GMP manufacturing of starting material or drug substance in a number of different projects. And this is an absolute enabling technology and group because the translation into the clinic is the biggest hurdle, and we're really glad to have them on board. And with that, we want to actually move to Christine back again and to the diabetes world. I just want to close with this slide, and just saying we feel we're actually quite uniquely positioned in this new world of iPSC-based therapeutics because we have the platform and the people, but we have a whole range of innovative starting points for partnerships that help us to develop the value of those therapies over the coming years. A few examples are now being shown by Christine and Markus. Yeah. So let's come to a concrete, translational effort to a pipeline project in the field of diabetes, as mentioned before. And just a few words on diabetes. Of course, you are all aware this is one of the biggest challenges in healthcare for the next decade, 20, 30 years. It's closely related also to chronic kidney injury, heart disease, so it's really relevant. We are focusing currently on type 1 diabetes, which is a diabetes form where insulin has to be applied to the patient because their beta cells are not able to produce the hormones, anymore. And this is really important, also to know that it's not only adult patients, but increasingly adolescents and pediatric patients. And it's really-... difficult and a pain for the family to have such a child with all these interventional measures to be done. So one subtype of type one with a specific high risk is patients with hypoglycemic unawareness. So these patients do not notice when they do not have enough blood sugar, and they faint, and have a higher risk to die suddenly. So this is the first patient group we would target here. And comparing to the gold standard treatment in type one diabetic patients, which is the islet replacement therapy, and I already mentioned that in the beginning, this is using cadaveric islets from deceased donors to be transplanted to patients. They need immunosuppression, of course, and as you know, organ donors, these are really rare, so it's not possible to really take care of all the patients who would need that. Type 1 is a part of the huge diabetes universe, but of course, also type 2 diabetes patients may develop into insulin-dependent, and we have in mind this really big market for our project. This slide illustrates how we are going to target diabetes with iPSC-derived human beta cells. So we are able to create multiple product or commercial opportunities. We have an ongoing cooperation with the company Sernova to bring together iPSC-derived beta cells in their device to the clinic in a specific subgroup of patients. So there are two projects ongoing, but the first one is the first also to move into the clinic. Sernova is mainly funding that effort, and also taking responsibility for the clinic. The next step is to move to genetically modified iPSC-derived beta cells to tackle some of the hurdles we still have. So for example, to make the cells tolerated by the patient, and yeah, that's the main goal of that. So these are opportunities to partner with industry. And although it's the same drug product, in the end, human-derived beta cells, they can be applied in a device or can be applied directly into the liver, so this creates more opportunity. As Andreas mentioned, Evotec has a long history of diabetes expertise, also in the iPSC field. And just to show you some of the work which has been done. So the process, the quality testing, has been built up in a robust process for these beta cells you see in the upper panel. And this has been translated also in the preclinical murine models. So these are a couple of animal studies. And in this case, and in this disease, the animal model is quite suitable to test these cells. And on the lower side, you see the blood glucose level in diabetic animals to be very high before the treatment. So these animals would all die within a short time without insulin, and they get implantation of our human derived beta cells. And what you can see that the blood glucose is normalizing to a human normal blood glucose. What is really remarkable that these mice are completely healthy for more than a year. So it's stable, it's functional, and also, the tests we are using in the clinic for diabetic patients, they are all normal here. Most probably, the cells would persist longer, but the animal permits do not allow to test beyond a year. So that's the natural limit of this experiment. Just a few words on our cooperation with Sernova. So we selected Sernova as a partner because they have already a device as a home for the cells, and this device has been tested clinically and has provided very good clinical data. So we know that cadaveric islet cells are persistent and functional in this device, and many patients have got insulin independent already. So this is a clear advantage to have something validated. And just as a side note, there is no real device available so far. It's really difficult to do that. Our part is the more complex one and the more difficult one, bringing these iPSC-derived beta cells into the clinic, into a real drug product, which then can be applied into this device. So we started this cooperation mid of last year. We are now moving into CMC and clinical manufacturing. We are not there yet, but working very hardly on that. On the right side, you see just a short snapshot how this device looks like. So it's implanted under the skin. It has the size of a credit card. You need several of them, and the specific topic is here that the device is vascularized, and only after that, the cells get implanted so that they find a perfect home. And this principle could be translated also in the preclinical model again, and this was really a real milestone. So you recognize the pictures already. So on the right side, you have the beta cells, but also the vascularization. It's a preclinical animal model again, and you see again that diabetic mice, after getting the implantation of beta cells into the Cell Pouch of Sernova, they are getting normoglycemic. And again, we can reach a year of functionality and stable blood glucose levels. It's also shown on the lower part. This illustrates that we are moving from preclinical research, exploratory platform, now on the right side, direction drug substance, drug product, into preclinical development, clinical. As Andreas already mentioned, the CMC part, the regulatory part, but also the clinical, this is really challenging for cell therapy, and we are now also addressing this part and managing to get everything in GMP grade, including the gene editing technology. As mentioned before, gene editing is the next step. So the current project is iPSC-derived beta cells without any additional gene editing. But if we are looking into the diabetes indication, the next step is that we don't want to use immunosuppression in patients to tolerate the transplanted cells, but they should, the cells should be accepted and not recognized. This is shown on the left side. We call it cloaking. So, by gene editing the cells, they are no longer really visible for the patient's immune system, and should be tolerated and persisting for a long time. And there are different methods to do that, so it's a very, very simplified cartoon, but obviously, it's necessary to inactivate the signal pathways which which render allo recognition and rejection. But the signal pathways are also important because they they are relevant for the inflammatory response we have in diabetes. And the next gene editing approach, which is also relevant for this diabetes project, then if they are genetically engineered to include a safety switch so that it's possible to inactivate and eliminate the cells. This is important for regulatory reasons also, because it, of course, regulatory authorities ask you to provide something to inactivate cells if they are genetically modified. So this is as well implemented here in our toolbox. And by presenting these two concepts, so the non-modified beta cells on the way to the clinic, and the genetically modified cells, not directly on the way, but in the making, I would say, I hand over to Markus to move into the oncology space. Thank you very much, Christine. Hello, everybody. My name is Markus, and I have the pleasure to oversee Evotec's internal R&D, areas in oncology and I&I. In the next 15 minutes or so, I want to tell you two things. First, how we now apply this fantastic iPSC technology in oncology and I&I. Second, I also want to tell you about our strategy in this space, a strategy we believe will put Evotec really in a position to shape the future of cancer treatment, which is, of course, a bold statement, but you will see what I mean in the next couple of slides. Let's start maybe with, some molecular background, why cell therapy is so highly efficacious in cancer treatment, and why you can deliver cures with this approach, as we already heard. So what you see here is just one cartoon illustrating the mode of action. So you have an immune effector cell, for instance, a T cell, and those cells can now really attach to the tumor cells, and what they do is they secrete now their toxic payload. So literally, they really perforate the tumor cell and kill the tumor cells. Now, the beauty is that these are really living therapies. So what they can do is they can kill a tumor cell, recharge their toxic payload, and kill the next tumor cells. So they are really serial killers, which is very good in this space, right? In addition, being living therapies, they can also proliferate after administration. So they can proliferate and generate an army of cells, really killing off the tumors. Furthermore, you can also genetically modify those cells, which is super important, for instance, to have a targeting approach. On the right-hand side, now you see that currently there are 6 autologous cell therapies marketed in oncology, and they were approved to, well, some people say, due to heroic clinical efficacy. So what does this mean? Here's just one example, Carvykti, that's from, from Janssen, J&J, and what they, what they saw in their pivotal trial was a 75% complete response rate. So what does this mean? That means that 75% of the terminally ill patients, they were cancer-free after the treatment. Of course, it depends on how long and durable this response is, but what's also clear is that such a response is the basis for a cure. That's, that's what it is. So there are already six marketed drugs, but there are still many, many challenges with those drugs that are already on the market. One of the big challenges is that the patient is part of the manufacturing process, and the patient journey is not a nice one. Second, the logistics attached to this, logistics are a nightmare, and also the manufacturing is a nightmare. It's highly costly, and manufacturing fails, then the patient cannot be treated, and that is a death sentence for them because they don't have any other treatment options. In addition, the companies are having to charge up to $400,000 for a single treatment, and we heard it from Werner. This is not sustainable, especially not in parts of the world that you know where the economy is not as strong as here. And also, in addition, all of those therapies only, if I may say so, focus so far on blood cancer. They only focus on blood cancer so far. So our strategy now, in a nutshell, is to use this promising iPSC technology to really overcome all of those shortcomings and produce drugs that are much more affordable and also efficacious in solid tumors. So that's what we want to do. This slide now shows that this is really badly needed because also a lot of our, I call it now, competitors, that are in late-stage clinical development, still focus on the liquid tumors and not on solid tumors. And the reason, of course, is that to treat solid tumors with this highly efficacious therapy is very hard. If it would be easy, we would already have cracked the nut, right? So this is very challenging, but of course, this is the way to go. To fully unlock the potential of cell therapy in oncology, we have to go into solid tumors. And the slide, or the cartoon on the right, shows you why. Because 90% of all cancer patients globally are solid tumor patients. And now this is where we, at Evotec, with our iPSC technology, will go to. We will go, and see to really cure solid tumor patients in the future. That's our goal, that's our strategy. Talking again about strategy, I tried to put it here in one slide, what we do. I already told you, the basis for our strategy going forward is really the iPSC technology, with all the advantages Andreas and Christine already told you. And then basically, our path forward is based on three pillars. We have multiple of those immune effector cells. You might know the T cells, the αβ T cells, but we have three more cell types. In addition, we at Evotec, we have also multiple targeting moieties. What I said is this is what you need to bring these highly potent cells to the tumor and spare the normal organs. And then, this is also what the Christine already told us, the gene editing. The beauty of the iPSC technology is also that you can, well, don't tell this to the people in the lab, but relatively straightforward, do multiple gene edits. Not just one for targeting, but more. And this now brings us in a position to really tackle solid tumors. And what we now do, we have the platforms in place, and now we start with our internal own product candidate development. As you will see, with all of this, what we have established, we are now also leveraging this in the R &D, I&I areas to even boost our portfolio. This is now an important slide because it now visualizes what I said, and going a little bit more into detail. What we built, and I think it's coming all back what Werner said, is a real toolbox. We generated a toolbox, and I will explain in a second why a toolbox is important. A toolbox that will be able to create a universe of products or product candidates. Of course, you know, we are limited by resources, but in theory, you can create a universe out of this toolbox of product candidates. I already told you the basis are our iPSC-derived immune effector cells. So we at Evotec cannot only make iPSC-derived NK cells, we can also make αβ T cells, where all the marketed drugs are based on right now, but we can also make γδ T cells, and we can make iPSC-derived macrophages. So we are the only company, as far as I know, that has four different cell types in the pipeline, and the same holds true for the tumor-targeting moieties. We cannot only do the classical CARs, so the chimeric antigen receptors, we can also do TCRs and use immune cell engagers for targeting, and we even have an artificial docking receptor. Same story, our competitors have one or at least maximum two of those targeting moieties. We have all four and can handle all four and can use all four to create dedicated medicines for the future. Last point is the gene editing. We already heard about this, how important this is. We can do knock-ins to bring function into the cell. We can do knock out to knock something out, and this provides us with the opportunity to really build product candidates that are dedicated for a disease area, dedicated for a patient population. And this differentiates us also from a lot of competitors. Why is this now so important, this full flexibility? Two things. For our internal pipeline build-up that we then will partner, this is important because now we can really follow the science. We at Evotec can follow the latest science and also the latest results that come out of the clinic. Because if suddenly one of the cell types wouldn't work, okay, we have three alternatives. Others, they are stuck. But let's be more positive. If now there are results, let's say macrophages,... You will later hear why I'm using this as an example. Macrophages work extraordinarily well in solid tumors. Well, we at Evotec will already have the next generation of these drugs in the making. It will be iPSC-derived, with all the advantages, being much cheaper and so on and so forth. So the same holds true for the other areas, and of course, this is now not only important for us internally, it is also important for our partners. Maybe let me tell you what we also experience in our daily life when we talk to big pharma companies. Talk to a big pharma company that has a global leading KOL, and they tell me, "Okay, Markus, this is your portfolio. Yeah, it's all fine, but Markus, you know, we want to hear about αβ T cells, but the rest, just forget about it, right? We, we are not interested. They will never work. Okay. Then, of course, we talk about αβ T cells and go forward. Next day, we talk with another big pharma partner, with another KOL, and guess what they tell us? They will tell us, "Markus, the future is γδ T cells. Just forget about the alpha-betas. They will never work." And we say, "Okay, sure, right. Let's continue," because we also have gamma -deltas. So also from a partnering perspective, this of course gives us a lot of flexibility to really fulfill the needs or the wishes of different partners. Internal pipeline build-up. I think let's go back to our partnered pipeline we have so far with J&J, with Janssen. That's why I also took the example of the marketed drug, what Andreas already said. They have an autologous CAR T cell already on the market, and now they have partnered up with us to really develop the next generation of therapies. So it's not only me, it's not only us believing in this, it's also now big pharma that have an autologous product on the market that goes in this direction. And of course, the partnership in gamma -deltas also validates our broad approach, our broad portfolio. There are not many people out there on this globe who can make iPSC-derived gamma -deltas. There are very few. And of course, the partnership with J&J was only possible because we have such a broad portfolio, because they did exactly what I told you. They said: "What do you have?" And then we said: "Well, you know, this is what we have." And they said: "Oh, you have gamma -deltas? Fantastic!" That led to a deal. And of course, that was also important for us, this external validation, to convince our management that it is worth investing in this area. I have to say that was also good from this perspective. So this is now the internal pipeline we have, we are building, and you see here the different cell types. And again, what is so important here is this little blue triangle, because this is not our pipeline of product candidates, this is our pipeline of programs. As Andreas already mentioned, each of those programs can deliver multiple product candidates. How does this work? Very simple. You just, sorry for the just again, need to exchange the targeting moiety, and then you have multiple projects, that you can bring forward. Same approach that we of course now do with Janssen. Last thing, and I think this is a really a super good move, is we now have established all of this, and some of you might have seen that the recently high-ranking papers were published that you can use those immune effector cells also in inflammation, for instance. And now a lot of people are jumping on this boat with autologous approaches, for instance, in SLE, systemic lupus erythematosus, because the paper showed-... Wow, with this approach, you can heal also those patients. So we already, from the very beginning, have the next generation, so we have iPSC-derived T cells, for instance. But if you, if you need less side effects, which for an SLE patient population might be the case, we can also use immediately iPSC-derived NK cells. For various reasons, they are not, they don't have that many side effects. So we hired now a fantastic scientist manager from Bayer to also drive this one forward. So you see, basically overnight, without investing any more resources, we have also an early portfolio in I&I. And hopefully, this also showcases the beauty of this toolbox approach, giving us maximum flexibility going forward. Out of the portfolio, now, the challenge for me was: What can I show you to demonstrate that it's not only glossy slides we have, but of course, this is all backed up by data? And at the end, I selected to show you something really innovative, really cool and new. What you see here is iPSC is macrophages killing tumor cells. Now, a lot of you will say, "Well, Markus, what is excited about macrophages killing tumor cells?" I'll tell you. First, what you see here is, it is iPSC-derived macrophages killing tumor cells, and not blood-derived. So it's our own iPSC-derived macrophages that doing the job. Second, we did not use cancer cell lines that grew on plastic for 50 years. They are relatively easy to kill. What you see here is our iPSC-derived macrophages killing primary patient tumor cells, cells from a patient that underwent multiple lines of chemo and whatnot. They are hard to treat, and as you can see here, maybe without going into the details, when you add an antibody, an approved antibody as targeting moiety, you will kill 60% of this hard-to-treat primary patient cells in a couple of hours. And that's not only happening in one patient, we have a second patient, you see the very same. And this is now very impressive. So the killing capacity of the macrophages is close to the killing capacity of the αβ T cells a lot of people use. So this is already very good, but of course, the macrophages have a couple of additional features that make them so attractive to treat solid tumor patients. Remember what I told you? I told you that our vision is to crack the solid tumor nut, and with this cell type, we believe we are close. Well, we have the opportunity to do this. Because macrophages, some of you might know, the primary focus of macrophages is to engulf things. So what they can do is they can really eat up tumor cells. I know this sounds a little bit strange, but that's what they do. And the beauty of them is they eat the tumor cells, and then, now, it's a little bit complex, but then they present the antigens of the tumor on the surface and activate the immune system of the patient. So they can do two things: They can directly kill the tumor cells, and they can present the antigens of the tumor to the immune system. My last slide. Why do we believe that this is now the right thing to do for solid tumors? So this is a therapy that will be dedicated to solid tumors. Here, you have the cartoon, how the product candidates look like. They will have a targeting moiety. They will have two knockouts, one, to lock the macrophages in the right, in the right, form, so to speak. And also, we have a knockout to block the "do not eat me" signal that tumor cells have established to not being eaten by macrophages. We just block it. With this approach, now, let me walk you through why we believe that this will be able to overcome the limitations of the current therapies. First, T cells, they really poorly infiltrate into solid tumors. They don't like it there. They are not supposed to be there. The hypoxic environment is deadly for them. Macrophages, well, they are in the tumor. They have a natural ability to penetrate into the tumor. Now, I also have to tell you, there are two forms of macrophages. The good guys, they are called M1 macrophages, and the bad guys, they are called M2 macrophages. The good ones, we produce, they kill the tumor cells. The bad things, they protect the tumor cells and kill off other immune cells. So the good thing is, both can penetrate into the tumor. Then, another thing is, you know about this hostile tumor microenvironment that will not allow other immune cells to come into the tumor. So our macrophages can even reprogram the environment to make it easier, accessible for other immune cells. Then, I already told you that our macrophages, you might know about these checkpoints. You might have heard about the PD-1, PD-L1 axis, and this is also how it, you know, how T cells are limited in their efficacy. Our macrophages don't care because they don't have PD-1, and the 'don't eat me' signal we're gonna knock out, so they will be highly efficacious. And then the last thing is really, we have already iPSC version here, so the cost will come down, gene editing is possible, and also, we will overcome the complex logistics and have an off-the-shelf treatment for solid tumors established. So maybe closing the loop with Werner, he said at the very beginning, which, again, I very much like, that science will shape new markets. And I hope I can convince you that with this science, we will be able to shape new markets. We will be able to tackle solid tumors with cell therapies. With this, I want to stop here. Thank you very much, and hand over to Volker then. Thank you, Christine. Thank you, Andreas. Thank you, Markus. That was really an interesting day, the first three hours that you experienced with us. Since we are in time, a few, yeah, maybe remarks on the summary, and I'm following on Markus. We hope that you got a sense on where we really want to shape new markets. We are not entering in markets that are fast-growing. We are also moving into areas where there is no appropriate approach today, but with these technologies, we can actually open up markets. And I hope this was a nice illustration, and hopefully, then also in the discussions in the afternoon, you will get further insights into this. We thought of inviting you to a brief, yeah, window tour in our labs. We won't go into the labs, but we have at least the option to show you the, yeah, what we call the 42. I don't know who of you has read The Hitchhiker's Guide to the Galaxy, but the answer to all questions in the universe turned out to be 42, and the machine that is really managing our iPS cells is called 42, and we invite you to take a look there. Sandra will lead you there during the lunch break, which is comfortable 75 minutes. We will be starting with the roundtable sessions at 1:30 P.M. A bit of logistics, some of you have blue dots on their badges. Not the Evotec people, but some in the audience. You are invited to bring your laptops with you because the breakout room will be in MEC 3, where we will have lunch, so you will stay there after lunch. We will come back afterwards here, so the rest of the luggage can stay here, but I think if you want to have, take your notes, take your stuff with you. And, the first session there will be then the Team iPSC, I call it for the moment. And, after 60 minutes, the Evotec teams will switch. You can stay. I think that's more efficient than, the other way around. And, then we have the second session, and we'll meet here again shortly before 4:00 P.M. But the only question is left for me. Three thirty. Three thirty? Yeah. Okay. Let's see how many questions we get. Only question for me to Sandra is she's... And would you, would we do it now before lunch or afterwards? Whatever you... Yeah? Okay, then let's go for a window shopping tour.