IP Group Plc (IPO) Earnings Call Transcript & Summary

All right. Good morning, everyone. Welcome to another IP Group investor engagement event. This one is one that we've done to coincide with our AGM. The AGM -- formal AGM happens later at 11:00 this morning, but we thought we'd take the opportunity to engage with shareholders both here in the room. So welcome, everybody. Great to see so many familiar faces, a few new phases, which is definitely something that we're really trying very hard as a management team to attract new buyers to the IP group stock and very helpful to have our friends from Berenberg, from Numis, from Bank of America here, who are obviously helping us on that journey. I had 5 minutes for a quick introduction, but we sort of started a couple of minutes late. I currently have about a minute in 10 seconds for which I've got about 4 slides. So I'll go sort of a swift as I can. The job really I've got to do is to welcome everybody to this. But I wanted to just remind everyone about our investor engagement. So this is a slightly updated slide from the one that we had. You'll remember if you dialed in or came to our full year results presentation back in March. We said, look, we've got a really active program of IR and sitting engagement and that we're going to take everybody through over the course of this year. And we highlighted a whole number of them that have little asterisks by them, which we are doing for all shareholders, and we're doing those both physically and online via the Investor Meet Company platform. It is obviously what we're using today. Thanks very much to Mark and team, as always, for hosting. So professionally, you can see we've already done a few of those, and we had a very successful in-person event, our flagship, our first annual flagship science event, which is really about showcasing the amazing businesses and the amazing entrepreneurs and the amazing co-investors that we've got in our portfolio and in our wider ecosystem. It was a fantastic event that we held at the Science Museum, a few members of the IP Group team, particularly Joyce pulled that together for us. And it was part of our efforts at IP Group. We like to do things a little bit differently. So rather than having a whole bunch of companies present, we had a panel discussion about what the U.K. can do to both nurture, but fundamentally and importantly, scale up companies that we've become very good at creating and there's many, many examples in the IP Group portfolio that can benefit from that trend. So there are a number more to come. We'll keep ticking these off. You can see there some for the second half around cleantech and around ESG. I thought I'd just refresh everybody's memory. Again, this is a slide from the full year results presentation as to why do you hold the share in IP group? What's the point? What do we offer you? And there are 4 particular strengths or skill sets that we have within IP Group, which we highlight here. We focus everything that we do around a deep expertise in our sectors that we operate in. We operate in 3 sectors. As you will probably all know, we look for businesses that contribute to a healthier future, regenerative future and a tech and rich future. In sort of English, I guess, that's cleantech, Life Sciences and deeptech. We really focus everything that we do on impact. We're looking for companies that address a big societal need, a big market opportunity that are overlaid with a deep technical solution to that. And we pride ourselves on being able to understand and price and take technology risk and that's where we fit into the ecosystem. But we've done work with Berenberg over the years and with others to map our portfolio on to the SDGs and to help people who want to get impactful returns alongside financial returns to be able to understand and hold IP Group. And you'll notice, if you look at our share register, the number of people who are impact focus is increasing. And so that's something that we think will be a trend to continue. And we're an international group. Some of the questions that came in on the AGM, which I'll cover later as part of the AGM around what's going on in Australia, what's going on in the U.S., I can talk to that. The reason we have an international group is really threefold. One is for access to deal flow and innovation, so we can compare the best stuff that's happening here in the U.K., that's happening in the U.S. and happening in Australia. The second is around talent, so moving top teams and individuals around those countries, particularly in the English-speaking world. And the third, and I think we've had probably most success on this front is around accessing international capital particularly in Australia, where we have a partnership with Hostplus, we now manage about AUD 300 million for them, and they're interested in investing in the portfolio internationally, and that's something that the U.K. is trying to have its position in the world as being the most international capital markets. IP Group sort of plays that role for our portfolio companies. And then the fourth is this permanent capital structure. This has its benefits, and it definitely has its challenges. But we're trying to do things and have tried to do things differently. And we think that offers significant benefits over fixed life funds, particularly for the duration of investment that we are making. And of course, it means we have to try and balance our capital allocation between shareholder returns in a cash form and shareholder returns in a capital form, and we're trying to do that in a sustainable way. And that's led to us having a portfolio which is sort of diverse but focused on those 3 themes. And as I set out at the full year, each of those 3 themes has very clear value creation milestones over the coming 1 to 2 years. And I know this is as written about them, and we've covered them. I won't go back over them again. So these are the 3 sectors, the point of today, and you'll remember in the slide that we showed, we did have in Life Sciences investor event earlier this year that was planned. We had to move that for personal reasons, and so we thought we take the opportunity to focus today on Life Sciences. I'm delighted that we've got Sam Williams, who is our Managing Partner of our Life Sciences team here in the U.K. And I'm also delighted that we've got a professors to Peter Donnelly, I think that's the right order of the titles, who heads up Genomics plc and Dr. Gordon Sanghera, who will be having a Q&A who leads Oxford Nanopore. Just a reminder, these are some of the inflection points we highlighted and at the full year, trying to just sort of focus down progress on a handful of companies in the 3 sectors in which we operate. And we may well come back to these in Q&A. But today, you're going to be hearing about both Oxford Nanopore, Genomics and Sam will definitely update on progress at Istesso. So with that, thanks, everyone, for joining, and I'll hand over to Sam to update on the Life Sciences portfolio.

Thanks, Greg. So yes, good morning. So I'm Sam Williams, Head of Life Sciences, IP Group. And well how does this work. There we go. Right. So I want to talk about a simple principle that underpins our approach to investment in Life Sciences and it's illustrated by this graph here, which plots the cost of treating disease as a function of the time at which we start to treat disease. And it might not surprise you to see that when we intervene in disease early on, start treating it early, the cost of that treatment is low. And the longer we leave disease to develop before we start treating it, the cost goes up. And that is obviously because the disease becomes more severe, develops complications, all of which also have to be treated. Patient might require hospitalization or even a visit to the ICU, which is all very expensive. And in the worst case, we may actually end up treating the patient towards an inevitable mortality event, which then, of course, the cost is not just about dollars but also about lives. So you might think, well, obviously, the way to treat and the way to practice health care would be on the left-hand side of this curve. But that's not the way the Western world manages health care. At the moment, largely our approach to health care is reactive, i.e., we treat disease once it's diagnosed, once it's established and once it's become chronic. And that's what the medics would call in more technical terms, episodic treatment of established chronic disease. And it's a very expensive undertaking. It requires huge hospitals, for example because patients with developed disease and chronic disease, they need a large inpatient and outpatient facilities. That's why we have these enormous regional hospitals up and down the country, and it's very expensive. It's an incredibly inefficient, so I'll give you some examples. Currently, there are thousands of people every day being treated in the U.K. for late stage or stage 4 cancer. And typically, to give them another 3 or 4 months of life. Now those 3 or 4 months of life are priceless and we should absolutely pay to give those people that -- those extra months. But it's an incredibly inefficient use of money. If you imagine how much more efficient it would be used, where it would be to use that money to treat those patients early on in the development of the cancer before their cancer has really even started developing and then you could not just treat one patient for the cost of treating one patient at a later stage, you could treat several patients very early on. Another example is in mental health. There are currently 20,000 NHS beds occupied by people with mental health issues, where arguably, we could have diagnosed their mental health issue much earlier and provided the counseling and therapy needed to provide resolution before they have to be hospitalized and put in a bed. Probably the most striking example is in cardiovascular disease. So every year in the United States, several hundred thousand people suffer from a heart attack. And a number of those have to be hospitalized. And the average cost of treating someone after heart attack for the first 90 days is $38,000. So if you do the math, it's no surprise that the annual cost of the U.S. health care system of heart attack is $120 billion. So you might ask, well, how is this happening? Why are we treating disease so late on -- in its cycle and its life cycle? And particularly in cardiovascular disease, where we know there are drugs, for example, that can reduce the risk of a heart attack or a stroke, the statins, the newer class of PCSK9 inhibitors, for example, both of which have been clinically proven to reduce risk, and yet they're clearly not being used as widely as they should be because people are still having heart attacks. And I just want to give you an explanation or some part of the explanation of that. So this is the standard protocol that is used in the United States for determining if someone is at risk of a heart attack or stroke, a major adverse cardiovascular event. And you can see it's quite a basic set of questions, 9 questions, what sex are you? What age? At what race? Do you have diabetes? What's your blood pressure? What's your cholesterol? So -- and this has been in force for quite a long time and it hasn't changed that much. And what's really striking is despite everything we read every day about the advent of genetics and new discoveries of genes and disease and so on. There's not a single piece of genetic information here. And that's despite the fact that we know DNA underlies all biological activity and probably most of the disease. We've also had the human genome for more than 20 years now. There are people in this room who have spent their lives analyzing, identifying and publishing in peer-reviewed journals, genetic risk factors that are associated with cardiovascular disease. There are also people in this room who've developed techniques for analyzing DNA with handheld devices that can be done anywhere, can be done in a GP surgery, could be done in a hospital, could be done in this room on a laptop or even probably soon your phone. And yet none of that is included here. So this isn't to blame anyone. There's nobody -- a single person, you could point the finger at. But what it just tells you is there's a huge amount of improvement that could be made here and thereby, in the case of cardiovascular, it is reducing the number of heart attacks, reducing mortality and reducing cost. So clearly, what we want to do is move to the left-hand side of the curve and into what we would call proactive health care. And this isn't a new phenomenon. This isn't a new concept. This is the world is waking up to this. The U.K. is particularly forward thinking in this area. So this is the motivation behind, for example, the availability now of the GRAIL genetic test for cancer, which is available through the NHS and allows for the very early detection of cancer so that treatment can start in an appropriate fashion. Also, it's the motivation behind our future health, which is a U.K. government NHS back initiative to genotype over 5 million U.K. individuals over the next 5 years and to correlate their genotype with their health care progression. So how do we IP Group play a part here? So we are focused on 4 pillars to our investment strategy and if you want to develop products and devices and diagnostics and drugs that allow you to intervene very early in disease and potentially stop disease even from occurring in the first place, you need a fundamental understanding of the biology underlying disease. So we are investing as a first part of our strategy in companies that are understanding fundamental processes underlying disease. So genomics, for example, and Peter will talk shortly, but genomics is involved in identifying genetic risk factors behind a number of diseases, cardiovascular, autoimmunity cancer and diabetes. Microbiotica is identifying strains of bacteria in the human gut that are associated with predisposition to cancer and inflammatory bowel disease, for example. And then IESO in the mental health space is identifying patterns of speech that can be associated with the development of mental health issues. And when you have all of that sort of information what you would try to do is develop drugs that therefore can -- and products that can intervene a disease at an early stage. But you need to do something that conventional drugs don't do. You need to have drugs that will change the way that cells and tissues behave almost reprogram their behavior so that you can intervene in a more sensitive manner, and you can intervene at an earlier stage in a disease. And we are doing that through, for example, our investment in Istesso, Istesso has a new technology, which reprograms the way the immune system behaves, has a Phase IIb drug, a first-in-class, which will read out from a Phase IIb study in the second -- in the first half of next year. We have Mission Therapeutics, which is applying a similar approach across a whole range of different disease areas. We have companies like Akamis Bio in the gene therapy space and Crescendo Biologics in the monoclonal antibody space, who are developing products that help recondition the way that tissues behave. And then finally, we like to think about redirecting people's behavior away from a disease state. So coming back to genomics and IESO, genomics is applying its genetic analysis to providing algorithms and tools that can be used so that people can avoid developing disease in the first place. Similarly, IESO algorithms and tools that will allow people to avoid a more serious mental health state. And then when I think about Oxford Nanopore, Oxford Nanopore is providing the methods, the hardware and the software and the techniques that can be used in every setting to understand DNA. And of course, DNA is fundamental to all of this. So this may all sound quite a long way off, but in fact, it's actually going to -- we're going to have quite a few results from this approach over the next couple of years. So this is the therapeutics part of our Life Science portfolio, 12 of our companies now have clinical stage products and 8 of those are going to read out over the next couple of years. So those will be data from Phase I, Phase II and Phase III studies but each of those will be very important in terms of determining the fate of those companies. And if positive, we believe, will be very significant for those companies in terms of the ability to transact with the pharmaceutical industry, potentially, IPO if the markets are there or raise money at a significantly increased valuation. So it's a very exciting time to be exposed to our portfolio. So with that, I am going to hand over to Peter, I just want to say a few words about Peter. Peter -- worked with him now in Genomics plc for a few years. He's great colleague, fantastic entrepreneur. He is though, one of the world's preeminent geneticist. He's dedicated his life to the understanding of genes and genetics in disease, and he's applying his life's work to practicing that in health care space through genomics. So Peter, over to you.

Thanks very much, Sam, and it's a great pleasure to be here and to have the chance to tell you a little bit about what we're doing in genomics and nicely follows on from a number of the key points that Sam made. Just to set the context in terms of genetics and health care. Genetics closed into health care currently largely through serious rare conditions. So people often, very ill, usually after a long diagnostic odyssey, often young kids where it sought that the cause of their condition -- finally sought the cause of their condition might be genetic. And genetic technologies like the one Gordon and his colleagues at Oxford Nanopore have developed, now allow us to read their DNA, their entire genome to find the 1 or maybe 2 positions that are causing that set of symptoms. So that's what we call genomic medicine. It feeds into clinical genetics within health care. It is, as I said, about late stage after a long diagnostic odyssey when you find the mutations, it closes the diagnostic odyssey, you have a diagnosis for the condition sometimes, but not very often, it will also inform on treatment. The other area in which genetics close into health care currently is in sequencing of tumors. Again, in people who are already very ill with cancer, trying to find the mutation or mutations, which are driving the growth of their particular tumor to 2 therapies. So that's what I think that was the first wave of genetics in health care. What we're focused on is the second wave, and it's all about meeting the challenge that Sam said, which is how do we move health care upstream instead of looking out to people late stage when they're very ill into prevention. So it's different in many ways from the way genetics is currently being used. First of all, there's not rare diseases. It's all of the common diseases. It's heart disease, diabetes, the common cancers, breast cancer, bowel cancer, prostate cancer. The diseases which use 70% of health care budgets and which are responsible for most mortality and morbidity in our populations. So it's common diseases, is not rare diseases. It's about doing the genetics when people are healthy, not when they're ill. And it's about using the genetics to understand for each of us what our particular risks are in order that we and our health care systems can funnel us into the right pathways in the health care side and lifestyle changes on our own path. And finally, you don't need -- I mean, in time, we'll all be using technologies like Nanopore or other sequencing technologies but in the short term, you can use a much cheaper technology to get the genetic information. Why is this important? It's important because genetics is a major risk factor for all of those common diseases. We've known that for many, many years as I'll explain in a minute, it's only recently we've been able to measure it. So genetics is a major risk factor for some diseases, it's the major risk factor. And for some, it's really all you need to measure. So the figure on the right here relates to breast cancer, each row is a known risk factor for a woman for breast cancer. So her age, her BMI, whether she's had hormone replacement therapy and so on. And the size of the buyout and the number is a measure of how predictive that risk factor is in terms of the woman developing breast cancer. The third bar from the top is how powerful a predictor you can make if you use all known risk factors. The second bar from the top is how powerful you can get in terms of prediction if you only use genetics. So genetics by itself is much more powerful at prediction than all of the other known risk factors we have. And the top bar says, actually, if you add in all the known risk factors to genetics, you improve prediction, but only by a tiny amount. So the population scale, if you want to understand risk for breast cancer, for example, and this is true for many of the other cancers, genetics is effectively all you need to understand. I said until recently, we didn't have a way of quantifying this, we do now buy something called the polygenic risk score. So the simple idea here is that we've learned from 20 years of genetic studies that for a disease like heart disease, any of the common diseases, there's not 1 gene that matters. There aren't 2 genes that matters. There are something like 1 million places in our DNA. Each one of those million places affects our risk of heart disease, but any one of them affects our risk to a tiny degree. So we now have enough data for each of the diseases to be able to work out what are the million positions, which matter for heart disease, what are the million which matter for breast cancer, they will be different and a different million for diabetes and so on. To measure them, and we can do that with one test, which is actually very inexpensive. And it needs to be done once in a person's life. And then to use sophisticated algorithms using machine learning, which combine that information to get a single overall score of someone's risk of the disease. And that's what a polygenic risk score is. So the figure on the right gives a sense of the impact of this. We've locked down the score using our data and our proprietary methods. We take that score into an independent set, not used for training the algorithm an independent set of 100,000 individuals in U.K. buyback, in this case, men and we're focused on heart disease. And we just look at their health outcomes. The red curve are the men in the top of the polygenic risk distribution, so those with the highest genetic risk. The blue curve are those at the bottom of the distribution, the lowest genetic risk and the green curve of those with medium risk. So what you can see is that for the high-risk men shown by the red curve, their lifetime risk of heart disease is over 40%. For the low-risk men based on genetics, their lifetime risk is 2% or 3%, massive differences in risk. And the horizontal line shows you that if you're a high-risk man, your risk in your mid-40s is about the same as the typical man at risk in his mid-60s. So that risk accrues much earlier in life. This is a slide that illustrates predictive power across many diseases. So each row here is a disease. This is to the right, is a measure of how powerful the polygenic risk score is as a predictor and each dot is a polygenic risk score. The gray dots are the ones developed by the specialist academic groups, the colored belts, which aren't green are the ones developed by other companies. And the green belts are all the ones we've developed. So 2 things for you to know this. The first one is that there are differences across disease in our ability to predict. And the second one, a commercial point from our perspective is we've got the best technology. For every single disease, our prediction is more powerful than all of the alternatives. How does that impact on health outcomes. So I said a minute ago for heart disease, and this is the 20x in the top of -- the middle of the top row, people with high risk, men who have high risk are about 20x more likely to develop a part disease by age 70 than men who are low risk. For prostate cancer, that differential is 40-fold. Men with high polygenic risk score for prostate cancer are 40 times more likely to develop it than men who are low risk. For diabetes, and atrial fibrillation, it's about 30-fold, for breast and bowel cancer, it's 10- or 15-fold, really big differences in risk when you use the right powerful predictors. So that's a snapshot of the technology. I want to talk a little bit now about opportunities, how we commercialize it and how we can get this to have an impact. And we see 3 different customer segments, each of which builds on the same underlying technology. And I'll say just a little bit about each of them. The first is what we think of as enterprise. So they were selling into large corporations. Our first application there is in life insurance. It's not what you'd first thing. It's not about using genetics to decide whether a life insurer should insure someone or how much they should charge. It's once someone has a life insurance policy. So if you have a life insurance policy, and I'm your life insurer, I want you to live as long as possible. Of course, I care about you. But the longer you live, the more premiums you pay and the longer for which I have been investing in your early premiums before I pay out. So there's real actuarial value for me as an insurer, a life insurer, if I can do something to improve your health outcomes and improve your life span. So that leads to the idea of the insurer paying for you to have a test from genomics. And that test will tell you individually, what your personalized risk. So across this set of diseases, we're not likely to be in the top few percent for heart disease. Some of this will be, but most won't be. But across 8 or 10 diseases will -- we're very likely to be at high risk for one of them. So you can think of this as for each of us giving a personalized risk plan and understanding where those risks are. And for all of the diseases we look at, health systems already have pathways, screening programs for the cancers, prevention programs for diseases like diabetes, treatment, as Sam mentioned, the heart disease through statins and PCSK9. So there's value to the insurer, and they calculated this value in us giving health information to the policyholder, it's obvious that was value to the policyholder, and it's great for us, in fact, at the price we charge, we had a 70% margin and the insurer has a 40% ROI. They get more value than it cost them to pay for the test. So that's one opportunity in enterprise in life insurance, you can do similar things with employer programs. In Life Sciences, so that's about using these technologies to improve the way of pharma and biotech companies through clinical trials and also range expansion of drugs. Down the stream, we also work upstream in using genetics to find new drug habits. But the downstream piece, I think, is really interesting, and I think this can transform the way we do clinical trials. It wasn't obvious, but it's now clear that individuals who are on statins, who have a high polygenic risk score have a larger therapeutic response. So the statins reduce their risk by more than a typical person. The same is true of the next generation of lipid-lowering therapies to PCSK9 inhibitors. Individuals with high polygenic risk scores have a larger response to therapy. And there are reasons why that's probably more general. And if it is, it means that in advance, we could choose individuals to participate in clinical trials who would increase the chance of the trial being successful or reduce the sample size you would need or reduce the time for which you need to run the trial. So all of those, I mean, as you'll be well aware, potentially very valuable. That's something where we've just signed a contract with one of the major global pharma companies to use our technology in their clinical trials to assess that potential. As a related opportunity, imagine a situation where a company has an approved drug for a small subset of individuals, for example, those with the genetic condition. We could use our polygenic risk score to identify a group in the general population who might actually have a similar phenotype just because they're in the tail of the normal distribution. So that means that our technology could be a companion diagnostic to expand the patient population for a drug that's already approved and safe to a much, much larger group. So they are 2 interesting segments where we have commercial traction now. The big opportunity here when it comes back to Sam's team is in health care. So let me tell you about the work we're doing there. Actually this picked up exactly on Sam's example around heart disease, cardiovascular disease, we ran a trial with the NHS in a number of GP practices last year. And the trial was all about adding the genetic component of risk by polygenic risk score into what GPs already do in the U.K. in estimating risk for cardiovascular disease. So Sam showed a screenshot of the tools used in the U.S. The tool used in the U.K. is called QRISK. It combines an individual's age, sex, blood pressure, BMI, cholesterol level, family history, smoking history and so on. And it gives us an estimate of their 10-year risk of cardiovascular disease. That's over 10% the GP will talk to the individual about statins and life style changes. Genetics is a big risk factor for men between 45 and 55, for example, the genetic affinitive risk, the polygenic risk score captures as much risk by itself. As all of those clinical risk factors together in the current score that use. And it's independent of them. So it's an additional source of risk. So what we did with the trial was to add in the polygenic risk score. The scatter plot here shows for each trial participant. On the x-axis, what their score was just based on the clinical risk factors and on the y-axis, what the score was when you added in genetics. Genetics made no difference. All of those points would be on the diagonal. In fact, you can see there's quite a lot of variation. Genetics can change the risk quite a lot. A bunch of individuals who are at high risk, so over 10% who were invisible previously to the NHS. They can now be identified and put on statins. There are other individuals who are known to be high risk, but they're really high risk. So the GP thought -- they and the GP thought the risk was 13% or something, and it might be 30% also really impactful. We wanted to check to see how this fitted in with standard GP workflows, and the trial showed that does really well. How does it land on the patients. They were extremely positive about it, 98.5% said they had it really helpful. And in 13% of cases, a change clinical management. That actually makes a difference in practice. It had a huge impact, both among [indiscernible] leaders and quite a lot of attention in the U.K. press. So it's an important first step in showing that the hope Sam expressed about using genetics to make the difference can be realized. The next thing in the U.K., Sam has already mentioned the our future health program. It plans to recruit 5 million individuals in part to build the next-generation research results, a resource in which drugs companies can do clinical trials where they recruit on the basis of health information or genetics. So there's a research platform component, but the other part of our future health is that it plans to give risk information back to individuals. Genomics, our company is partnering with Our Future Health, we're providing those risk scores, both for the platform and back to individuals. So this will be the first population scale program in the world where risk information that includes the genetic component will be given to individuals and to their doctors. It's an incredibly exciting development. It's another example where the U.K. is forward thinking in the genomics and health care will, I think, be globally. And just let me finish, come back to Sam's point to give you a sense of impact. So if you use these technologies, the natural thing to do is to do that genetic test once, maybe for individuals when they get to about age 40. To then work out their risk across multiple diseases, using genetics and where available and relevant other clinical risk factors. And then we can do a much better job of identifying who are the high-risk individuals for each disease. So in terms of cardiovascular disease, there would be a whole lot of people, in fact, about 750,000 of them in England who meet the clinical threshold, the 10% guideline of statins, who are currently invisible to the NHS. If they're identified and put on statins, it would save and avoid almost 20,000 acute events, strokes and heart attacks over the next 10 years. For breast cancer, where I said genetics was almost all the story for risk by polygenic risk, NICE have a number of threshold, a number of tiers of risk for women. Their guidelines are that for women in the top 2 tiers of risk. Instead of waiting until the U.K.'s program -- national program, which offers screening to all over age 50 kicks in, NICE guidelines are that those women should have mammograms from age 40. 20% of women fall in that risk category, 1 in 5 women, they are completely invisible to the NHS. What happens currently is the part of through 40s some of them sadly will develop cancer, which could have been prevented if we'd started screening them earlier and others will go told they get to the national program. So it's much better for the women involved, obviously, to catch cancer earlier in terms of outcomes and for health system point of view costs. Actually, if you think from an NHS point of view, the NHS is paying for those mammograms. If you give the mammograms to people who actually need them who are at higher risk, the chance of a mammogram detecting a cancer, so as it were the bang for your buck per mammogram goes up, and it almost doubles in this case. Similarly for bowel cancer in the U.K. colonoscopy capacity is a bottleneck. And if we use genetics in the right way, we would increase the effectiveness of each colonoscopy by about 20%. And as a last example, osteoporosis, bone fragility that affects older individuals, particularly older women. Again, if you use genetics in clever ways you'd need fewer scans, DEXA scan, but you'd identify more than twice as many individuals who are at high risk for which there are cheap therapies, bisphosphonates, which are [indiscernible] . So I hope that gives you a sense, and I hope you'll get an idea of how we can meet the challenges that Sam put. This is about moving health care upstream. We have the capability now of being much more sophisticated about understanding disease risk for individuals across all the major diseases, all of the killers and source of mortality. When we do that, we can get the right people into existing screening programs, treatment programs and prevention programs, better for the individuals, more effective use of those screening programs, better for the health system, both in terms of efficiency now, but in terms of long-term outcomes. We're really excited about the possibilities. We think it could make a massive difference to health and health care globally. Thanks very much.

Come back here, so those are like and here. So we're just going to switch sort of pace a little bit now, and we're going to -- I'm going to ask Gordon who's the Chief Exec of Oxford Nanopore to come up, and we're going to do Q&A here.

I've got a number of questions. There are probably a few online. We'll try and spend about sort of 20 minutes, half an hour talking to them. There will be a chance for questions from the floor. And then at the end, I'll also ask Sam and Peter to come up, and we can just add 5, 10 minutes on general questions on the Life Sciences space. If you've got questions for the broader business or some of the AGM matters, if you could keep those for the AGM section of 11:00, and we'll cover the Q&A and the many questions we've had through the IMC platform as well for them. So I'll...

Morning.

So we'll use handheld mics because apparently, this works better for those online. So for many people, Gordon Sanghera will need no introduction. I'll do a very brief one, though, for those who don't know Gordon. Gordon is one of those people that has been -- that were looking to back through IP Group. He is a true pioneering entrepreneur, somebody and who has set out on a mission to create a genuinely well changing business based on fundamental underlying science and to do that based here in the U.K. And there's a number of things Gordon has done sort of differently that we can probably learn from in terms of role modeling. And this is a really important thing and to have confidence in our ability to do this more broadly and to create value and to create impact. And Gordon, I hope I'm saying was recognized for his efforts with the CBE. I thought it was really interesting that one of the role models Gordon thought of [indiscernible] grandfather and his grandfather's journey and it's a very touching story. For those in the room know and if you've worked with Gordon, one of his other role models, is coffee which maybe gives you an idea if you follow football of some elements of Gordon's management style. So I thought I'd start just quickly that many people will be familiar with the Oxford Nanopore story for those who aren't, can you just sort of summarize who you are, what you do for a couple of minutes, please?

Morning, everyone. I thought I'd go back a bit because it's important -- IP Group have been backers from the [indiscernible] , first take Norwood and then Ireland. We fought many wars and money funding rounds together. But if you go back 1 step further in the mid-80s, I did a [indiscernible] with Professor Allan Hill, passed away last year. And he pioneered a bioelectronic device blood glucose measurement. So that company was combining [indiscernible] Electronics, which is what I did from my PhD. And I was obsessed with joining the company he had set up. So I did a post-stop with him for 2 years. But that company went on, and this was before tech transfer groups even existed, raised a lot of money from the East Coast U.S.A. It went -- it floated on NASDAQ in '94. It was sold to other for $900 million and really a British technology that could have dominated and become a $10 million company as part of Abbott Diabetes Care these days. And the vision that Allan Hill had which is finally achieved by other about 5 years ago was a continuous glucose monitor, measuring real-time glucose all the time. And that is important and critical for type 1 diabetics who have to control the glucose all the time, but is now becoming used in routine health care. In lifestyle, in preventing type 2 diabetes, which Peter talked about. I'm not going to talk about genetics. It's always intimidated to talk about it after Peter been on stage. But that was disappointing because Albert acquired the company. And yet again, another British technology escaped. And so early 40s, midlife crisis. I really was not enjoying being part of Abbott but I did 7 years in the States, and I'm just not really very good with authority, except investors who I'm fabulous with, just so as you know. So fast forward, in 2005, I was still -- we kept in touch with ISIS innovations at Walzem, came across single molecule, Nanopore sensing, stochastics in the molecule sensing, have to go and line up in the dictionary. But it was label-free, easy and you can make electronic devices. And so I thought, actually, all the things we learned at medicine and all the mistakes we made in those 14 years could be corrected. That was a 14-year MBA in entrepreneurship that I then plastered onto the 18 years we've been doing Oxford Nanopore. And Peter has eloquently talked about the power of genomics and DNA/RNA sequencing. What we are doing is looking to bring about the same revolution that we saw in mainframe computing. So everything Peter has done has to be done in supercomputer. So he's got a fabulous facility and it's millions of dollars to buy the capital, multimillion-dollar infrastructures, brilliant people like Peter and his teams to really unpick this hard stuff. What we're doing is bringing simple, cheap, affordable, accessible DNA/RNA information into everybody's biology life every day. And that was beautifully embodied during a pandemic when over 2 million COVID genomes sequence on Nanopore sequencing in 85 countries and predominantly larger middle income countries who do not have sequencing capacity. You have to be a super power to do that. And that's all changing now. And I always bring a sequencer with me. I'll pass it over to you. Greg will explain how it all works because he can. But suffice to say, the revolution and the parallel is with computing. When we went from mainframe batch base to desktop handheld real-time streaming and we can get into use cases and applications later, but that is the difference. These instruments are really low cost to make. That machine there can perform the same as $0.25 million alumina machine, which is the market leader, owns 90% of the market today. So this is really a step change and a disruptive innovation. And that comes with challenges, which I'm sure we'll talk about.

Okay. I don't to -- pass the seatings around for those who haven't -- they're very ubiquitous, but not everyone will have seen one yet. Okay. So I thought let's start off on sort of technology and commercial progress. You keep the market pretty well updated and those of us certainly IP group. Me, Sam, we follow things like London calling where you do lots of the technical updates and you also did a tech update for the investor community following that event. But since the IPO, and sort of commercially and technically, you've made great progress. You've grown revenues faster than your peers. You've rerated those revenues and continue to grow strongly. And there's been a number of these sort of technology updates. Can you just sort of summarize that sort of 12, 18 months progress sort of on the tech and commercial side?

What is London Tech? You can't get away without saying something about ChatGPT and augmented machine learning and AI. We started doing that 10 years ago. There's a signal that comes out of that device and all the genomic information and much richer content than the single point mutations that Peter and people like him brilliantly garnered, medical insights too are in the software. The problem is the software just wasn't good enough 10 years ago. And in the last 2 to 3 years since IPO, the exponential growth in machine learning the quality of voice recognition, that same word learning we've applied to nanopore sequencing, we've now reached a point where we can add to everything you can do with existing traditional systems and just to give you a context there, they are photocopied and you take beautiful high-definition color biology and make black and white versions. So is it all to fall of brilliance for people like Peter to be able to garner all the insights they do. What we do is keep that clean copy because this is a single molecule, and that's all you need to know about that and we provide rich content and high definition, which will ultimately accelerate the kind of research that Peter does, gives you much more insight and content. And the -- we launched a chemistry 9 months ago. And that is now out there with customers, and we are equivalent to the existing systems plus much, much more content and really empowering that change. And that technology, reaching that point is important in terms of driving revenue growth in the research markets.

Very good. And on that point on revenue growth, one of the things that I think has been quite noticeable is the number of collaborations and partnerships, be it with governments, with corporations by Mary and others, particularly in the last sort of 6, 12 months. Any of those you'd particularly call out for both impacts in commercial relevance.

Yes. I think strategically, we always get asked, okay, so this is better. It's really cheap. Why don't people just switch and buy it straight away? The incumbent has been there since 2005. A lot of clinical workflows have been set up and validated. And it's the switching cost is very challenging, and they are a monopolizing market leader with billions in revenue. So strategically, how do we do things and get the technology adopted quickly, we have picked things that only you can do with our technology and try to look to jumps from research markets directly into applied testing. The form factors we have from the small handheld you see to the desktop devices. There's no upfront capital. You only pay for consumables. It's a razor-razorblade model commercially. They're very attractive to applied market applications. So bioMérieux or a world-leading infectious disease company, we have shown time and time again rapid insights in critical care, and I'll give the example here in London at [ Geysers and Thomas ]. First is Jonathan Edgeworth has been looking at respiratory infections in the ICU. And in that setting, hours are critical. It takes 4 days to find out what infection somebody has got in ICU. We've got that down to 4 hours using MinION, which is a power of a new technology in that real-time streaming. We've done 350 patients. We're doing 1,000 patient proof-of-concept trial over half were on the wrong antibiotics. So there was a change in treatment and it's already groundbreaking in this proof-of-concept stage. There's still a long way to go to get that into routine clinical care but our deal with bioMérieux as a potential channel partner is how we -- and I'll talk about lots of different things we can do, how we will not become an infectious disease company but a channel partner such as bioMérieux will sell market and distribute and be domain experts, we will provide the underlying sequencing platform, which is ubiquitous for many, many application areas.

Great. Right. I've got a whole bunch of questions on the future and the opportunities, but there have obviously been some challenges that you've had to face. What are the key ones that you're grappling with, where are you spending your time as CEO, share price?

Share price. I think it's a double-edged sword. We were private for 18 years, brilliant long-term shareholders, but every party has to end at some point. And it was the right moment, October 21, there was -- I thought things were opening up, we're going to see a nice, good boom. And the first quarter, while we were public was good, but then the markets collapsed. Money was sucked out of Life Sciences and high risk, high reward companies. And so that's been challenging. But we did raise GBP 500 million in capital. And we always said we wouldn't need more capital to get to profitability. So we filled our boots. And I feel for companies out there trying to raise money in this environment. We are well financed, well capitalized. It does put pressure on us because we did float in London. We -- I still think that was the right thing to do. And at the time of IPO, we did a very thorough analysis with our advisers, and it was pretty marginal. You always get a higher return on NASDAQ, but only having to report every 6 months when you're going through an explosive growth phase and the focus has to be that path to profitability, we felt and we're all British. We're an Oxford-based company. London is just down the road. It was the right place to start from being private for 18 years to becoming public.

And reflections now?

I think notwithstanding the share price, I'm very happy with the way things have gone so far, and I tell everybody at work don't look at it on a daily basis. This recession will end. I mean, I always say the full horsemen of the apocalypse will disappear at some point. The wall will end. The supply chains will ease and this recession will shift and our focus is to make good on the promises we made in October 21, which is greater than the 30% year-on-year growth with that path to profitability and we're capitalized to achieve that. So I'm not comfortable with the share price, but other than that, we're very comfortable with the progress we've made and the cash we have to really execute on some of these really big opportunities that are coming quickly towards us.

Great. And similar sentiment for IP Group really happy with the progress that the portfolio is making and share price is deeply frustrated. So it's a shared thing. The other one, which I know it's sort of frustrating because I've seen the progress, and we talk about it at every sort of technical event that you do the old [indiscernible] accuracy. You mentioned earlier that you're now at least equivalent and you get color readout rather than black and white in sort of -- do you want to just talk a little bit about that and the pathway, some of the stuff [ Clive ] was talking about for the existing chemistry, the new chips, et cetera.

Sure. So I think somewhat foolishly, we thought if you give somebody grainy black color, they'd watch a sneaker on it, but it's just not that simple because they can't just -- it's not like a consumer product, a sequencing center is 10 million plus. So they just want you to give them what they've already got to the accuracy that they get today. So they want high definition in black and white and then they look at the grainy color. And it's taken us until last year to reach that point. But now that we've done that, we're really making inroads into particular areas that are hard to diagnose and Peter talked about rare diseases. So there's something like 8% of the genome is unmapped, which doesn't seem like very much with existing short-read alumina sequencing but that codes for over 1/3 of hard-to-diagnose diseases. And we're really seeing commercial traction in newborn screening for -- and doing whole genome for hard to diagnose patients on a diagnostic odyssey. And the speed, the real-time speed and pace at which we can do this, I'll just frame that for you. So Professor [ Nashley ] actually at Stanford, managed to sequence a newborn, 3-month old with severe epilepsy from heelstick, to answer in 7 hours. That's 2 hours prepping a sample, 2.5 hours sequence -- 2.5 hour of data analysis. It almost broke the servers at Stanford to do that. It's just a lot of data, but they did that in 7 hours. In the middle there, the sequencing was done on our supercomputer. That's the handheld desktop use the MinION, which is a [indiscernible] And instead of putting 48 samples on and getting 48 genomes in 3 days, which is as good as any alumina top end machine, you put that 1 sample of that infant across 48 channels. He read that genome 60 times. And Peter mentioned 10 years -- 20 years ago, the first genome was mapped, I think Sam actually. We were reading those genomes, single copies every 90 seconds. So when people talk about machine learning AI and how the big data is going to come at you, having affordable, accessible, ubiquitous access to DNA information will change from the brilliant, superb centers of excellence we have today, took hundreds and thousands of biologists elucidating these structures. And that will be just like the information age [indiscernible] be the genomic information age and era that we're entering into now.

And not only did you nearly break the servers you also broke the Guinness World Record with that.

I did indeed. And by the way, they diagnose the type of epilepsy the newborn had. The PCR gold standard where you look at regions where you think you know what you're looking at or you don't know what you're doing in football [indiscernible] came back 2 weeks later and conclusive. So looking at the whole genome, it's a good illustration of why looking at much more detail gives you so much more insight.

And you and I have spoken a lot about how do we build on this relationship that we've had for 15 years between IP Group and Oxford Nanopore and it's been the journey financially so far has been great. We've been taken all of our cost off the table. We remain the largest shareholder in the business. But I also see a future in collaboration, and that could lead to spin out opportunities for us or new investment opportunities. There's not one for today, but I was just sort of thinking in terms of where are you seeing the most exciting, interesting opportunities for the use cases for DNA sequencing or RNA or protein.

So I think 2 things before I articulate that. I talk about how we've set ourselves up strategically to be able to enable our goal, which is the analysis of any living thing by anyone anywhere. And we've set this platform up to have an open source developer license. So we are not content creators. We merely and it's not trivial, we create fast, cheap, rapid, high content biology sequencing. Customers then can come up with applications, and I'll pick something away from medical, which is related to RNA. So all RNA vaccines have a piece of RNA inserted into the vaccine and they put certain chemical modifications on that RNA. We're the only company in the world, this speaks to this richness of content and the fact that we don't photocopy the biology, we read it native. We redirect RNA. And what RNA manufacturers like Moderna, BioNTech, AstraZeneca, Pfizer want to do is to check that the thing they wanted to put in was properly made. And then they inserted it correctly into the vaccine, and they want to check that that's working correctly. Today, that requires a multitude, 10 to 12 different proxy-based assays and we can remove all of that and give them rapid real-time insights in their biological manufacture. That has resulted in a year ago as a paper at London calling our customer conference. This year, a company has been set up at the University of Queensland spin-up and they're starting to work through the workflow to take that from an academically curious potential application to a workflow that biologics manufacturers will be interested in. And it really illustrates the power of real-time DNA/RNA information near the point of origin of the DNA, which you cannot do today. And that's just one of what is essentially a handful of startups, some in infectious disease, some in cancer, some in biopharma manufacturing. And that's not -- we're not creating incubators. This is just naturally happening. And that will happen more. And I think there will be some good opportunities for follow-on investment in the next few years as we kind of get that message out there and the first few demonstrate potential and opportunities in the applied markets.

Very good. And to paint a picture on a sort of 5-, 10-year view how you're thinking about the scale of the market evolving and the sort of switch from the research markets to the applied markets, the difference in the potential scale of the applied markets. There's lots of research we've read about this by and people in the room over the years. What's your view on that sort of on a 5-, 10-year basis? And what does it mean for Nanopore?

I'll draw historical parallel. I think the applied market applications do run into tens of billions. Alumina, I said that, JPMorgan reports says lots of applications in cancer, human health, ag bio, environmental, food safety, security, [indiscernible] manufacturing. But there's been very little penetration of those, and I'm quoting a 2014 JPMorgan report, where they said alumina by 2018 to 2020 would dominate and be in all these markets. And it's the same reason why mainframes never really became ubiquitous. It's not affordable. It's not accessible. It's too complicated. It's the preserve of the brilliant genomic specialist in the world today. And that will only happen as you make it affordable, accessible and simpler. We expect in the 2- to 3-year time frame to demonstrate those value-creating applied market use cases, whether that's rapid real-time insights in ICU, in antimicrobial resistance for respiratory metagenomics or early detection of cancer in liquid biopsy or pharma manufacturing. And one other we have is a collaboration with Sirigen, which is a world leader in pregenetic screening, carrier screening. And all of these demonstrate how this technology affordable, accessible, real-time can cross the chasm into those applied markets. And then we can start to think about and talk about how we would address those markets that run into something like 50 billion to 100 billion depending on how you add up that whole space.

Very good, right. I could talk to you all morning, I'm very conscious that we've overrun the 10:30. For those in the room hopefully, we can continue sort of 5, 10 minutes of questions and those online, feel free to stay and we'll obviously put this recording out so that you can see anything if you had to step off it at sort of around 10:30. So sort of I guess kind of one final question, and then we'll maybe ask and see if there's any questions from the platform, and then we can take some questions from the room, and I'll get Peter come up in a second. I suppose the last one is, we've spoken a little about the share price, we've spoken about the risk off and the cancer, et cetera. Are there any -- there are 1 or 2 things that you think the market is particularly missed about the story that you'd like to have the opportunity to highlight here other than growing faster than peers, differentiated platform, colored technology and dot, dot, dot anything you really want people to take home about the value creation opportunity.

I think I can't talk about whether this is a good investment or not. But the share price is somewhat suppressed versus our peers. And yet in last year, for example, alumina and Pacific Biosciences had 0 growth. We still hit our 30% year-on-year growth. And that's probably something to do with the fact that you don't need millions of dollars. So budgets that suppress CapEx goes first. We're a CapEx-free system. I think there's huge potential here. And if you read up on it and particularly focus on the applied market use cases that we're starting to put partnerships together with, I'm sure you might come to an opinion I have, which is biased. It's a good investment opportunity.

Very good. We agree. Right. So to summarize that a little bit. Differentiated technology, GBP 0.5 million in cash, part of profitability, we now need to raise money, massive market opportunity, inspiring leader that's done it before with a medical diagnostics platform. So these are the sorts of businesses that we're trying to create with our shareholders' capital both for the greater good and to generate investment returns. And so excellent, really enjoyed that, Thanks a lot Gordon. And maybe if I just ask Peter and Sam to come and join us up here and we can... Given this is the morning and so we're not between anybody in drinks that are may be more attractive than a coffee, we'll spend sort of 5, 10 minutes up until quarter 2, then we'll give 15 minutes for people to mingle have a coffee and we'll come back at 11:00 for the AGM. So in the spirit of inclusion diversity we'll ask for some questions from outside the room. First, Dave...

I think you come right actually is that the impact of AR in the industry was really the main one came up and what other opportunities I think [indiscernible].

When are we going to have a pour in on our iPhones.

We found from our market and so we have one, we have an ASIC, an application-specific circuit that we can now plug into an iPhone. It's called this [ smidgen ]. It's a very small -- but actually, what customers want is fewer channels than the MinION, so a lower-cost device. So we're going to do that first. It's code-named pebble, we haven't come up with the name for it yet, but we could do. But it's just, I think, a smaller handheld device is what the market is screaming for. So rather than the MinION starter package of $1000 if that were $200 to $300 as a startup pack, it opens up a whole new range of customers we can do. So we've kind of diverted our smidgen program, which for those who don't know, was actually a little consumable that would plug into an iPhone and you could sequence on an iPhone, which we can do. We've diverted that and we're putting it on an iPad because we're now also generating even ever increasing amounts of data and the machine learning algorithms quite complicated. So an iPad version is coming next because that's -- it's actually what our customers want and we will get there, and there will be a consumer-based iPhone device at some point.

Thanks, Sam. Questions from the room. One here in the front next to Chris. It's going to keep you fit, Mark normally when you're doing video up and down the stairs.

Sam, if you're moving from reactive to proactive, how big a challenge is the existing colossal pharmaceutical industry and business model?

Well, the pharmaceutical industry is recognizing this challenge as well, and it's moving and it's always been the case in part but a huge amount of the pharma industry R&D budget is external in-licensing, acquiring products from the biotech industry, which is probably slightly more rapidly moving towards that left-hand side of the curve for addressing that left-hand side of the curve than the pharma industry. So I don't think the pharma industry is a challenge. And I think what you're saying is, is it a barrier to doing things in a unconventional revolutionary rate. I mean the pharma industry more than ever needs to get drugs and products on board that will get reimbursed and you're more likely to get reimbursed now if you're revolutionary, you're treating earlier and you're saving a lot of health care dollars.

Thank you. More from the floor. [indiscernible] Next to the poster markets. I'll try -- I'll take them as far away as possible to...

I have one for Gordon, please. So I've heard a lot of instances of people wrongly comparing you to competition, which I'm sure you have as well. Many reasons for that, but simply, and I think it's not an easy market for the average investor, particularly given how different to your platform, is your native approach to reading DNA, but also different market focus at times. So there is oversimplifying this, what's the single most significant driver that, in your view, will trigger a step change from either an R&D or like market proof-of-concept perspective into people finally making this comparison in better terms?

Well, there's so many, but real -- I mean there's like the technology is unbelievable as hours talking about how fabulous it is and how native DNA is going to be in the future. But simple thing is rapid insights in real time in critical care is kind of emerging or something only we can do. And whether that's in hard to diagnose newborns or in-intensive care or even, and this is still the coolest thing I've seen apart from the NASA space station. Real-time tracking liquid biopsy. So this is circular in tumor DNA during brain surgery. You really don't want to hack out more than the brain than you actually need to. So I don't know if you guys are familiar with circulating tumor DNA. That's before you see a lump. So stage 1, stage 2. The cancer cells are in new proliferating. And you can see fragments called tumor, circulating tumor DNA. And those fragments give you early indication. You can use that same liquid biopsy play to do live real-time surgery -- brain surgery and see the circulating tumor DNA drop in real time. So you're actually getting real-time feedback. So I mean, that's not going to be a huge market, but it's the real-time feedback loop that you get in critical care with the richness of content that we provide which is really emerging as sort of the thing that will really catapult this and kind of transform us from an interesting life science research tools business, which is very important, and our revenues in the short term will come from that. So it's a great market, GBP 6.2 billion, compound only grow 20% plus per year. But that will really catapult us into 1 or 2 of these applied markets, which really changes the needle quite dramatically in terms of other creation.

Thanks, Gordon. Charles?

The Charles Weston from RBC. A question for Gordon and Peter, please. Over the last few years, there's been sort of a race to create data, DNA data, specifically to understand biology better and get more insights that we can apply for drugs or diagnostics. And perhaps because of the risk of nature of the market, that seems to be sort of considered less valuable now or less dollars are going -- fewer dollars are going into that. But given the different -- the color aspect of the technology that Nanopore produces around epigenetics or RNA or long read or whatever, there's a whole different set of dimensions of data that can be generated at scale to create different biological insights. So do you think there will be sort of another step, another race to create some of these additional data sets for this more colorful technology, colorful information. And Peter, companies like Genomics plc set up to use that additional color to generate even better biological insights.

I mean, I would just say we're ready and waiting, Peter.

I'll give a slightly longer answer. I think there's huge potential for the kind of richer set of information biologically to be used in our world in prediction. I think what I'd say is the prediction we do at the moment based on DNA is robust. It's been tested in hundreds of thousands of individuals. It's ready for prime time now in health care. The prediction problems using other omics are much harder. First of all, there are multiple omics. Secondly, if you take any one of them, that readout differs. It's one of the reasons particularly interesting, but it differs in different types of cells in our bodies. It differs in the same cell type over time in our bodies, and it differs in response to similars in our body. So instead of predicting from DNA, which is effectively the same in all of our cells, already a hard problem, but I think as I said, ready for prime time, the vastness of the data space with omics, cell types, age, external similars makes that a much harder prediction problem. In time, that information will be incredibly useful and Charles, absolutely, we want to be at the forefront of using that for prediction for health care and for individuals, but it's some way away from prime time yet. Technologies like Gordon's will be a key driver in getting the data, which allows us to develop the prediction method.

Yes. I think the biology needs to be rewritten from that discovery translational phase and that's some way behind, but the good news is that's revenue generating for us and people are beginning to go back to the beginning of looking at some of these things and really rebuilding that biology with the high definition that we offer.

Thank you, Charles. Right. It is quarter 2. I'm sure we could have plenty more questions, but I'd just like to take a second for us all to thank very much, Sam, Peter and Gordon for a fascinating discussion. I hope has given you a flavor of what we're doing in Life Sciences, the way we're looking to create value, the types of companies that we're backing and trying to create and why we are so confident in value creation for our shareholders and genuine impact along that pathway. So thank you very much, and thanks very much for attending, everyone.