Aptamer Group PLC (P0J.SG) Earnings Call Transcript & Summary

Hello, everyone, and welcome to the Aptamer Group Technical Investor Update Presentation. I'm Dave Bunka, I'm the Chief Scientific Officer here at Aptamer Group, and I'll be taking you through today's presentation. So to start off with the usual disclaimers about forward-looking statements and so on. So I'll just give you a second to read that and then we'll move on to the presentation. So I'll start with a quick snapshot of the company for anyone who's new to the story. So Aptamer Group are a biotech company based in York in the North of England. We started operations in 2012 and have grown to the point where we listed on the AIM market in 2021 and moved to our new facilities here in 2022. You can see a picture of those in the new building on the right-hand side of the slide. So as a business, we see ourselves as an enabler of technologies, offering our development services to other companies across the life sciences industries, helping them to develop bespoke binding reagents called Optimers for any application where a customer has an unmet binder need. I'll explain more about this throughout the presentation. So the need for our binder development services is seen right away across the life sciences. So this binder market covers approximately $210 billion market, spanning everything from diagnostic tests through to general research reagents, all the way up to things like drug development and many more applications. So the broad applicability of technologies like ours has led us to have a global customer base with 75% of the top 20 pharma companies and has allowed us to horizon scan. So what I mean by that is use customer inquiries that come into us to look for opportunities to develop a platform technology, develop specific binders that we can then keep the IP around and license out for applications. So this lets us look for technology niches where our platform really excels and then develop those licensing opportunities to give us long-term revenues. And we'll talk more about those in the coming slides. So as mentioned on the previous slide, the need for binders, that's a molecule like an aptamer that sticks to another molecule of interest like a customer's target protein is seen in all areas of the life sciences, spanning everything from diagnostic test development, that's at the bottom in purple, through general research reagents, that's on the right-hand side in gray through to drug development that's on the left-hand side of the circle here shown in blue. We've also had a few projects in areas that have come very much left of field through our regular fee-for-service work. Those are shown at the top of this circle there. Again, this speaks to the broad applicability of our platform and the benefits of this horizon scanning approach. So again, work that comes in through our fee-for-service, we can look for areas where the technology particularly excels and push those forward. Over the last year, we've continued our Optimer development programs, and in doing so, have developed many potentially licensable assets. These efforts have expanded our pipeline of license opportunities from 4 active licensing negotiations last year to 11 assets in licensing negotiations at the year as it ended in June. So I'll be updating you on the science behind four key examples on this today. So four projects that I'll be updating you on today are summarized on the slide here. The first project that I'll talk about involves the use of our Optimers to modulate the activity of an enzyme reagent, which is widely used in research and diagnostics. This represents a significant opportunity where we've demonstrated the utility of our Optimers in lab-based testings, both in-house and with two independent external laboratories. We're now in active negotiations with two parties to non-exclusively license an asset from a single project. So one of these parties has already engaged us in large-scale reproducibility studies with a view to launching the project -- the product in the coming months. The second party is currently reviewing licensing terms, and we've recently been approached by a third party who have engaged us to begin their own evaluations with a view to using the same Optimer in their reagent offering. The second project that I'll talk about involves the use of our Optimers in noninvasive prenatal testing. So this is an asset that's currently under evaluation with a large multinational biotech life sciences company and is generating some really promising and very reproducible data. The third project that I'll update on is our ongoing collaborative project with the team at Neuro-Bio, developing Optimer binders to detect their Alzheimer's disease associated biomarker. And finally, I'll update you on our project developing Optimer binders as drug delivery vehicles for liver fibrosis, demonstrating the selective delivery of several potential gene therapy cargoes and the functional therapeutic effect in cell culture models. So the first project that we're going to talk about falls into the reagents and tools part of the wheel on the right-hand side. So the first project that we're going to discuss today, we were engaged to develop Optimers that bind and block the activity of an enzyme. So for those who don't know, an enzyme is a protein that generally speeds up a biological reaction. So for example, enzymes that are used in washing powder are put in there to help break down stains. So enzymes can be involved in either making something or breaking it down. In this example, the enzyme is widely used in research and development. It's also used in a lot of diagnostic kits. So for example, the early tests that were involved in COVID diagnosis, involve the use of an enzyme of this type. So that was done actually before and then alongside the lateral flow testing. Now to make this enzyme useful, it's important to be able to turn the enzyme on and off at the appropriate time to prevent unwanted activity that might lead to inaccurate results. So you can see in the diagram on the slide here, the enzyme represented by the yellow circle, having an activity when the aptamer is not bound. But as soon as the aptamer binds to that in the upper panel, you can see that the enzyme is turned off and doesn't have its activity. So the Optimers that we've developed has a number of advantages that make it attractive. Firstly, our Optimers can be developed to have property specific to the end application. So in this case, the Optimers were developed to bind to the target but also to release them under customer specified conditions. This is critically important for this application as the Optimer must bind and inactivate the enzyme under one set of conditions, but then must release it to reactivate the enzyme under another set of conditions. So in this way, the Optimer acts as a precise inhibitor keeping the enzyme inactive until it's actually needed. So you can see that in the data on the slide on the left-hand side. So what we're measuring on this graph is the enzyme activity. Now what you can see in our negative control Optimer sequence is that when the negative control is incubated with the enzyme under binding conditions, there's no effect. The enzyme is still very, very active. When we take that same control and put it under the release conditions, there's very little difference. So this control sequence isn't having an impact on this enzyme. However, the Optimer that we've developed, shown in the middle here, under binding conditions, you can see that the enzyme activity is very significantly reduced. But then when we change those buffer conditions to the release conditions, the enzyme is then active again. So we can turn that enzyme off and turn it back on again by modulating when the aptamer is able to bind. And obviously, if we put no enzyme in, we see no activity whatsoever. So that's good. The aptamer is able to modulate the productive activity of this enzyme. But this enzyme actually has two important activities. So one a productive or processing activity and it also has a degrading activity. Both of these need to be inactivated and activated at the appropriate times. So you can see on the right-hand side of the slide here, the results of our degrading assay. So again, using the control sequence, now we're looking at the production of a fluorescent product as the template is degraded. So a high signal means lots of degradation. So again, using our control Optimer here, this has no inhibitory properties on the enzyme because we've got a lot of degradation. Similarly, when we have no Optimer at all, we see a lot of degradation here. So the enzyme is active and chewing up the template. Now when we add our Optimer, you can see here it's binding to the enzyme and it's turning off that degrading activity. So the signal here is very, very low. And in parallel, the no enzyme control, again, has a very, very low signal. So what this shows is that our Optimer inhibits both the productive enzyme activity and the degrading enzyme activity. Now this is actually really important because current products actually require two separate enzymes, one to bind to each of the regions of the protein associated with each activity. So that means that there are two antibodies that need to be licensed to make that product. Our Optimer binds to the enzyme and inhibits both activities from a single binder. So this simplifies licensing and the associated costs for any manufacturers making this enzyme. Thirdly, a very important point here is that our Optimers are synthetic. So they're not made using plants or animals or bacterial cultures or anything like that. They're made on a machine. So they are readily and much more reliably manufactured on a large scale, offering a significant performance and cost advantage over other existing technologies. Now it's important to point out that as a standard in our business model, Aptamer Group retained the IP covering the binders that we've developed. So this has given us the opportunity to license that developed asset to the clients. And as they were only interested in a nonexclusive license, we have the ability to engage other parties and license the asset to them as well. And as I mentioned on the earlier slide, one of those parties has already engaged us in large-scale manufacturer with a view to launching the product in the coming months. A second part is reviewing the terms and conditions on their license agreement and a third party has recently engaged us to undertake their own evaluations and put the Optimer into their service offering. So this highlights the fact that this enzyme is sold in many, many companies for a wide variety of different applications across the life sciences. So the global market estimates for an enzyme of this nature, so not just this enzyme, but enzymes of this type, the market is estimated at about $14.5 billion so that includes enzymes of this type and the kits that involve it. And there are a lot of enzymes related to this. So based on our performance with this Optimer, how well this actually works, we've already been engaged by the client to develop a second inhibitory binder against another target. And the licensing terms for that second project are already underway and under legal review. So this is obviously a great niche application for our platform, and we'll be looking to further expand our reach in this area. The second project that we'll talk about falls into the diagnostics part of the wheel. This project involves the use of Optimers to aid in noninvasive prenatal testing. This is a market with a estimated value of over $4.5 billion. As you probably know, it's common to undergo routine screening during pregnancy to check on the health of the fetus. Now testing may include an amniocentesis, which involves taking a sample of fluid that surrounds the fetus and isolating the fetal derived cells, so you can test them for signs of conditions like Down syndrome or cystic fibrosis and many, many others. Now an amniocentesis is a very invasive procedure. As you can see on the diagram here, it essentially involves putting a large needle into the sac surrounding the fetus and collecting some of the cells. This is a risky procedure. Estimates suggest that 1 in 200 amniocentesis procedures result in a miscarriage. And importantly, they can't be carried out before 15 weeks without significantly increasing that risk. Now it's known that some of these fetal cells actually pass across the placenta into the maternal blood. So this gives an opportunity to solve this problem. We've developed a technology where we can isolate these fetal cells from the maternal blood for testing. Incidentally, tests of type involve the type of enzyme that we mentioned on the previous slide, again, highlighting the broad applicability of that process. So the data on the slide here shows that our Optimer binder is able to recognize these fetal cells, the blue bar in the graph there and shows minimal binding to other cells in the maternal blood. So this ability to isolate these fetal cells gives us the basis of an isolation kit that can then be used to safely collect fetal cells for genetic screening. Again, we retain the IP covering these binders, and this has given us the opportunity to further develop and license this asset with third parties. So we have an ongoing collaboration with a multinational biotech and life sciences company who've shown in their hands the Optimer can be used to capture and recover as few as 5 to 10 fetal cells, which is enough to form the basis of a commercial test. So we're continuing that work at the moment. And Arron, our CEO, has already started commercial conversations with their team. The third project that we're going to talk about also falls into this diagnostics sector. So this project is our ongoing collaboration with the team at Neuro-Bio. Now many of us will have family members or at least know somebody who's been affected by Alzheimer's disease. This is a terrible neurodegenerative condition, which robs people of their memory and cognitive abilities. Now currently, there is no cure for Alzheimer's disease, but there are several approved treatments available, which have been shown to slow disease progression. Now obviously, in order to maximize the effect of those treatments, it's really important to identify potentially affected individuals as early as possible so that you can get them onto treatment ideally before symptoms even appear and allow that drug to have the maximum impact and improve the quality of life. So there are other diagnostic tests in development. Several of those focus around a protein called tau. As I mentioned, there are several tests in development for that. The test that we're working with the team at Neuro-Bio detects a different protein that they've identified as an early indicator of disease. So the Neuro-Bio data looks really compelling and suggest that their target could actually be used to detect the disease even before the tau is seen, meaning that this could actually detect the disease even earlier. The protein that they've identified has also been shown to have diagnostic capabilities with mood disorders, such as depression, which is not associated with the detection of tau. So this makes the Neuro-Bio target a particularly exciting opportunity as it presents a dual function test. As a bonus, this test will be based on saliva rather than on blood or spinal fluid, which makes testing compliance a lot easier because a lot of people don't like needles. I'm one of them. So there are a few reasons here that we're excited by this opportunity with the team at Neuro-Bio as an alternative to tau. So as mentioned, the team at Neuro-Bio approached us to develop binders against their protein after their antibodies fail to perform. The team at Aptamer Group have developed several Optimers against this protein, and then together with the team at Neuro-Bio, we've tested our Optimer on clinical samples from several sources. And we've shown the basis of a quantifiable test in saliva. So you can see with the data on the left-hand side of the slide here, in the control group, there's very high signals for their protein, but then in the Alzheimer's group, you can see that protein is very, very much reduced. So there's a very clear difference between the two. And actually, this difference gets gradually lower as the disease progresses. So there's an opportunity here to monitor disease progression. So this data is really exciting. We're actually coauthoring a scientific publication describing this work with the team at Neuro-Bio. So our team have taken this Optimer binder and demonstrated its use in a different assay format called an ELISA. You can see that on the right-hand side of the slide here. What we've done here is enabled a broader clinical evaluation of the Optimer because an ELISA is a standard laboratory research tool. So this allows this test to be carried out in a lot more laboratories to get additional data. So the data on the right-hand side of the slide is our ELISA data showing a really good concentration-dependent detection of the Neuro-Bio protein. Importantly, we've shown that this detection range spans everything from the healthy clinical concentrations downwards. So correlating with the conditions here as the level of this protein drops, the disease becomes more severe. So this assay has now been handed over to the team at Neuro-Bio, who are continuing to evaluate it with clinical samples. The final project that we will be discussing today falls into the therapeutic section. This product is our Optimer development-based drug delivery system aimed at treating fibrotic liver disease. So when looking at chronic liver disease because it remains a massive problem. There's approximately 2 million deaths per year. And despite this massive societal impact, at present, there's only one approved drug and this only shows mild fibrosis reversal. This is also one of the only diseases that continues to rise. You can see this on the graph on the right-hand side. So this translates into a massive potential for shareholder returns because there's limited number of drugs currently available, and our platform solves a current clinically unmet need. It's also a good differentiator for us as a business as other platforms seem unable to match the selectivity that we've achieved. I'll show you some data for that in a few slides' time. And this also plays to the strength of our platform, solving unmet needs. So you can just see on the slide there, a diagram showing progression of the disease from healthy state through fibrotic tissue, all the way up to cancer. And we'll talk about this more on the next slide. So essentially, fibrosis is the excessive accumulation of fibrotic connective tissue in and around a damaged tissue. So in simple terms, it's the formation of a scar in the liver. Now permanent scarring can lead to organ malfunction and ultimately, liver failure and death. So I'm going to use this diagram to describe exactly what's happening in the various stages in the liver as it progresses through disease. So first of all, at the top, you can see the active metabolic cells in the liver. These are the hepatocytes. So these are the cells that carry out the normal functions like digestion, detoxification and so on. Now these cells can become damaged through a number of different mechanisms, including alcohol consumption, viral infections such as the virus that causes hepatitis and even fat buildup through poor diet. Now in response to these various injuries, the healthy liver cells essentially start to release a variety of signaling proteins and small molecules, for example, cytokines, reactive oxygen and so on and so forth is not really important to understand what these are. Essentially, these injured cells are crying out for help. Now other cells in the liver called hepatic stellate cells, you can see these here, play an active role in liver development and repair and maintenance. They also respond to these cries for help. So the normally dormant cells sit in the liver and maintain its function until an injury occurs. Then in response to that injury, these cells become activated. And when they're activated, they then start producing all sorts of things to help repair the damage such as proteins such as collagen. Now this repair process helps at first, so it heals the damage. But like a lot of things, too much of a good thing is bad. So you can think of that repair laying down this tissue like forming a scar on your skin. So the damage has been repaired, but the scar is not the same as regular skin. And the same thing happens in the liver. It's repaired, but it isn't as good as new, okay? Now to add to this, the immune system can also respond to these cries for help from the hepatocytes, enhancing this hepatic stellate cell activation, so making that situation worse. Now if these HSCs, the hepatic stellate cells remain active through repeated injury, for example, or increased stress signaling, it can lead to the formation of excessive scars within the liver. This essentially means your liver has billions and billions of scars that just keep building on top of each other until your liver essentially becomes one big scar and that makes it very hard for the liver to function properly. So then you have issues with all of those normal liver functions such as blood filtration, digestion, immune functions, et cetera, they don't work properly in the scarred liver. And this can then lead to further injury essentially perpetuating the cycle, and that can result in cancer, liver failure and ultimately, death. So we've shown on the previous slide how liver fibrosis occurs. What we want to do here is specifically target those activated hepatic stellate cells and control their ability to produce a scar tissue. So there's many mechanisms through which the disease can be treated. Most of them involve administration of a drug at high doses, which goes all around the body or at least most of the body, so if we use chemotherapy as an example that a lot of people will have heard of and have witnessed. Most chemotherapies work by circulating around the body and being taken up by all active cells, including the cancer cells. Now this works because the highly toxic drug then indiscriminately kills those active cells, both cancerous and healthy. And this leads to all the side effects that we know also hair loss, nausea, vomiting increased risk of infection through depressed immune systems and so on. So it would be much better if the chemotherapeutic drug could be selectively delivered just to the cancer. So it has its beneficial effects in the cancer, but it doesn't have the so-called side effects. So targeting of drugs to a disease has three broad benefits. Firstly, you can increase the drug efficacy. So how well it produces the required effect with less time, less effort and less resources. So there's a benefit there. It reduces the amount of drug that you actually need because it goes to the site that it's required. It isn't essentially wasted by being taken up by cells where it isn't needed. And obviously, if you can stop it being taken up by cells where it's not needed, you can reduce the patient's side effects because the drug isn't going where you don't need it to go. Now like a tumor, the liver is no different. There's an unmet need to specifically target the cells that cause liver fibrosis and deliver drugs to control that excessive scar formation. So in this project, we've developed an Optimer binder against those activated hepatic stellate cells. That's the ones that cause the scar formation and allowed us to demonstrate the power of the Optimer platform to specifically target a disease cell type. Now the only currently available delivery technology for the liver is a molecule called GalNAc, but this does not specifically target those activated HSCs. So there remains this unmet need for a specific targeting molecule. So to solve this problem, we have developed an Optimer molecule that can deliver a genetic medicine to essentially turn off the ability of those cells to produce the scar tissue. So the Optimer delivery vehicle that we've developed essentially consists of three parts, and you can see them on the slide here. So first off is obviously the Optimer. This is generated against the specific disease associated cell type, in this case, the hepatic stellate cells. So you can see the Optimer at the top of the molecule here. Now in the Optimer development, we can also include counter selection in this case against the healthy hepatocytes to make sure that we don't isolate a binder that binds to the healthy tissue. So this gives us the selectivity that we require. You can't do that during traditional antibody generation. It must be screened for in the materials afterwards. And that can be a problem for antibodies as the cell activity is really important to minimize the off-target effects. So the fact that we can build this into our Optimer development program is a big advantage. The second part of the molecule is a linker. You can see that in the chain representation here. So this is used to attach a therapeutic payload to the Optimer. There's a huge range of these available with well-defined chemistries for attachment to all sorts of different molecules and payloads. Many of these linkers also have functional groups that allow them to be cleaved, so cut once they get to the site of action, releasing the drug cargo when it's been taken where it needs to go. And then finally, at the bottom of the diagram, there's the payload itself. So this is our drug molecule. In this case, this is a gene therapy called an siRNA. We don't need to worry about what that is today. I'll just show you the use of it. Now that siRNA can actually be used to turn off the production of proteins associated with the scar formation. In actual fact, that drug molecule could be any drug of interest. So it could be a small molecule like a chemotherapeutic. It could also be a radio therapeutic or a drug-loaded nanoparticle, it could even be a functional protein. We're actually in conversation with a number of partners about different drug delivery systems. So we're particularly interested in use of our Optimers for radioligand delivery, but that's a topic for another day. So let's take a closer look at a snapshot of the data that we've generated in this project. So in this work, we took our same Optimer binder and added a pinky red fluorescent dye. So you can see that in the diagram on the left-hand side. So we have our Optimer binder recognizing these hepatic stellate cells using similar linkers and now we've attached this fluorophore that allows us to visualize the Optimer and where it's binding. So here, you can see the Optimer binding to the activated stellate cells. If you remember back to the earlier slide, these are the cells that are associated in causing liver fibrosis. We do also see some binding to the nonactivated HSCs, which you can see here. That's obviously to be expected. These are the same cell type, but these are the cells that are dormant, this is what happens when they become active. So you can see there's a lot more binding of the Optimer to these activated cells. Importantly, we see no interaction with the healthy hepatocyte. This is the cells on the right-hand side. Now our Optimer control, this is the molecule across the bottom here shows no binding to any of the cell types. So this is really important because it shows that this is a functional effect of our Optimer binder. It shows our Optimer is binding to the cells of interest. Now this is important as it reduces the potential for these off-target effects that might cause side effects with other non-targeted therapeutics. We then looked at a range of different cell types representing other tissues across the body to look for potential binding in other tissues. Again, in the top left, we can see the Optimer binding to the hepatic stellate cells that drive liver fibrosis, but when we look across the rest of the cell types, you can see the Optimer shows little to no interaction with any of these other cell types. So that's other cells from the liver shown here or cells from the kidney, the lung, blood cells or prostate cells. So this data shows that our Optimer doesn't interact with any of these other cell types, suggesting that this is specific for the intended target of liver fibrosis. We then set out to prove that our Optimer is functional as a delivery system. So we took these Optimers attached to the siRNA molecules that I showed you on the previous slide and treated diseased cells with that molecule at different doses. The idea being to see if the Optimer delivered the drug in active form to the target cells. So success in this experiment will be seen as a reduction in the level of a gene product here measured against the baseline amount of expression in the target cells using our Optimer. So in the data set on the left-hand side, so we'll just look at this, first of all, you can see that the cells on their own, represented by the black bar here have a normal level of this gene product. Cells treated with the Optimer alone, so this is the Optimer without the drug conjugate shown in the yellow bar here, have no significant effect. So that's good. It means that the Optimer is not having an effect on these cells. Importantly, the drug on its own, the siRNA, shown here in purple, at this high dose also doesn't have an effect, okay? So essentially, this drug on its own can't get into the cells in sufficient amounts to have an effect on its own. We've then shown four different doses of the Optimer attached to that drug, as I showed you on the previous slide. And what you can see here is essentially as you put more Optimer with this drug into the cells, you see an increasing effect. So more and more of this gene product is turned off, so you get a reduced amount of it formed. So the key take-home message is that our Optimer is taking the active siRNA gene therapy into these disease causing liver cells and having the effect of reducing the gene product. But the siRNA on its own can't do that. So comparison between here for the siRNA and here for our Optimer drug delivery system. Now to confirm that this is selective and only goes to the activated hepatic stellate cells, not to the healthy hepatocytes, we also carried out that same test in the hepatocytes, which is what you can see in the middle data set. Importantly, what you can see here is there is no significant change in the level of this gene product at all in any of these situations. So we know that the Optimer is not binding to these cells and is not taking the gene therapy into those cells. And then there's a final control on the right-hand side of the slide shown here. We checked to see that this was a genuine result by essentially punching little holes in the healthy hepatocytes to allow the Optimer siRNA drug into those cells and show that if we allow it in, we can actually reduce the amount of that gene product. So you can see the Optimer with the drug here and importantly, the siRNA on its own, having this functional effect. So you can see there is a significant effect -- a significant drop in the gene product, demonstrating that the siRNA is functional, but it cannot get into those cells because the Optimer is not taking it there. So we've demonstrated here, selective delivery of a drug molecule and the targeted reduction in a gene product in specifically the disease-associated cells. So that's great. That's really exciting. But what happens when we replace that gene therapy siRNA with another one that actually reduces a protein that's associated with fibrosis? That's what you can see here. So before we progress this to animal testing, there are a couple of things left to demonstrate to see if the Optimer with its siRNA payload has the potential to function as a therapeutic candidate. The first thing we need to show is the reduction of a gene protein that's associated with liver fibrosis and then to show that, that same Optimer siRNA drug product can control processes associated with scar formation. So first, let's look at reducing a fibrosis-associated protein. So on the left-hand side of the slide here, you'll see that in the untreated cells, when the cells are activated, so go from the dormant state here in yellow to the activated state here in blue, we see an increase in the production of this fibrosis-associated protein. So that's essentially the cells responding to this cry for help. Now when we treat the cells with the Optimer siRNA that's shown here, we stopped the production of that fibrosis-associated protein reverting it back to the same level as the dormant cells. So you can see here the active cells in blue and the dormant cells in yellow now have the same level of this protein. So we've essentially switched it back off. Secondly, we wanted to test if the Optimer siRNA is functional in an assay shown to affect the cell behavior. So in the data on the right-hand side, you'll see the results of what's called a cell proliferation and migration assay. So first of all, let's explain how that assay works. So cells are grown in a dish. So these are the hepatic stellate cells that we mentioned previously. These have grown essentially to create a lawn like grass on the lawn. A scratch is then made across the middle of those lawns to remove the cells from there. So we essentially create a scratch region shown here. This is like removing the strip of turf from your lawn. The cells are then allowed to grow and move. So if they repopulate that scratch, they're obviously regrowing much like the grass would do on your lawn. However, if that growth is prevented or slowed, you'd see a reduction in the repopulation of cells within that scratch region. So that's what we're looking to do here. Now remember that in fibrosis, these fibrotic cells grow rapidly and move around leading to worse scarring and higher fibrosis. So essentially, in this experiment, slowing down the repopulation of cells means less movement into that space. That reduction in cell growth and migration essentially leads to less scar formation. So what does that mean? Essentially, if our Optimer siRNA can slow the repopulation of that scratch region, it can reduce scar formation. So as you can see in the data at the bottom, these are actual images of cells. As you can see in the untreated activated hepatic stellate cells, the cells have repopulated this scratch region. That's the region between the dotted lines. So the cells are growing and moving and repopulating this region. Similarly, cells that are treated with our Optimer on its own. So this is the Optimer without that siRNA payload have repopulated that scratch region. So the Optimer alone is having no impact on the sales growth or motility. So that shows it's not having a detrimental effect on the cells. In contrast, cells that are treated with the Optimer with that siRNA payload that's shown to be able to reduce the amount of protein needed for this repopulation, you can see there is a reduction in the amount of cells in that scratch region. So the Optimer siRNA has actually reduced the ability of those cells to grow and repopulate in that region. So we're now in discussions with several top 20 pharma companies who are interested and really excited by this data and the platform as a whole, so looking at the platform to deliver other things. But that's not all. So we asked ourselves, okay, if the Optimers are able to bind to these fibrotic disease cells, could we use the Optimer to target all the disease fibrotic conditions, such as kidney fibrosis or fibrosis associated with cancers. And that's what you can see on the slide here. Essentially, we've tested our Optimer for binding to other fibrosis-associated cell lines. When the Optimer is tested against fibrotic cell models, we saw a significant binding event, especially in cell types where the cells have been activated. So in the case of lung fibrosis, you can see at the top left here, there is binding in the nonactivated cells, but that's significantly increased when these cells are activated when they become fibrotic. Now this is not really surprising as hepatic stellate cells are themselves a particular type of cell called a fibroblast. Now these fibroblasts share a common ancestry across all of these tissues, so the fact that we see binding in all of these other tissues is not really surprising. It actually suggests a potential to use our Optimer to treat a much wider variety of fibrosis associated conditions with a range of different treatment types. For example, targeted chemotherapeutics, targeted radio therapies, et cetera, et cetera. And not surprisingly, we've had interest in these applications as well. So while our primary focus will remain on liver fibrosis to address that unmet need, there is the potential to address other tissue associated fibrosis using this same Optimer. So that's all great. We've shown that the Optimer is able to bind to the target cells and deliver a therapeutic payload selectively and not touch the target -- the healthy cells that would surround it. But what we haven't talked about so far is what the Optimer is actually binding to on those hepatic stellate cells. So in the remaining section of the presentation, we'll talk about the processes that we've undertaken to identify that biomarker. So firstly, it's important to understand what a biomarker actually is. Well, simply put, a biomarker is something in your body that can be measured as a way to check on your health. So changes in the level of a biomarker can be used as a warning sign that something is not quite right in your body. It can also be used to show how severe the problem is or even how well you're responding to your treatments. So you can kind of think of this like the engine warning light on the car dashboard. Just like that light that warns you when something is wrong with your engine, a biomarker can signal that something is wrong or something is changing in your body. So again, this could be whether you've got a disease, how progressed that disease is or how you're responding to treatments for that disease. An example of a biomarker that you may have heard of is a protein called prostate-specific membrane antigen, or PSMA. This is the biomarker that's highly expressed on the surface of prostate cancer cells. So elevated PSMA levels are used as a biomarker for prostate cancer. Now as you can imagine, biomarkers are really useful in disease diagnosis and treatment planning, but they're also very useful in drug development as they allow pharmaceutical companies to monitor the effects of their drug. The biomarker itself may also be useful as a new target for future drug developments. So as you can imagine, isolating novel biomarkers is big business. And you can see a few examples on the slide here of deals that have been done in the biomarker discovery space. Now the biomarker discovery process is usually broken down into a series of steps going from broad exploration to rigorous testing and validation. So I'll just outline some of those steps here. So the first thing to do is work out what your goal is, decide what the health problem or the challenges that you'd like to look at. And what sort of clue or biomarker might help a doctor to diagnose. You then gather samples, so collect blood samples or tissue samples, saliva samples, something like that, from both sick patients but also from healthy patients. You would then compare those two data sets and look for differences between them to see what's different between healthy and sick patients. You can then narrow down and pick and choose the different markers that seem to be most linked to the disease of interest and then test that to see if that is indeed a biomarker that works over a much broader, much more varied sample set and then obviously make a test or a drug associated with it. Now the challenges with identifying biomarkers using this approach are as follows: well, first off, many diseases are associated with the change in protein level. So it's not a straight yes or no. It has this protein gone up or down. Now that in itself presents another challenge because changes in the biomarker level can differ from patient to patient. So what might be perfectly healthy for you might be disease associated for me or vice versa. So the level of that biomarker can be important in a patient. Now once you've identified a list of potential candidates to look at for your biomarker, you then have to narrow that down to a few to actually test. Now that narrowing process involves a lot of data analysis, pattern recognition, and it also relies on a good understanding of the disease biology. Now any discrepancies in any of those could lead to inclusion of an incorrect biomarker or indeed discounting of a useful one. So there's potential sources of error there. Now assuming that a candidate biomarker is identified, it can then enter the drug development pipeline, which is in and itself a very expensive process. Any point in that process could lead to failure of the project, leading to a loss of all the work done so far and obviously, the time and the money. Now our Optimer development process is a little bit different. So we use our hypothesis-free or cell-based selection approach that we've used in the fibrotic liver disease project to identify binders to that cell. We don't need to identify the biomarker upfront. So that's really important. Here, we isolate the Optimer binders based on their ability to discriminate between the healthy and the disease associated cells. We don't need to know what it is that they're sticking to just that it's able to tell the difference between those two cell types. We then screen the Optimers for the ability to bind selectively to the disease-associated cells and to have the desired effect. So in the case of the fibrosis project to bind to and to deliver that therapeutic molecule and have the desired knockdown effect that we showed. Once we've demonstrated all of these properties from our Optimer binder, then we can actually use that same Optimer to isolate the biomarker and analyze it and identify it. This is summarized on the next slide. So here briefly, we'll describe that biomarker identification process. So we start with the same Optimer binder that we've already isolated and characterized. So you can see that here. The same Optimer is prepared with a tag that's instead of being fluorescent or having a drug molecule attached to it, that tag allows us to capture that Optimer and pull it out of the sample. So you can see here the Optimer binding to the target protein on the surface of these activated stellate cells. The Optimer is then pulled out of that sample and pulls the bound biomarker with it. This is then recovered, purified and analyzed. You can see that here to identify where that protein actually is, and then it can be sent off for analysis by standard proteomic approaches like mass spectrometry. And finally, the biomarker is tested for Optimer interaction. So positive binding to that biomarker means that we've identified the correct one. And that's what you can see here. So finally, we confirm that the biomarker candidate that we identified through the processes on the previous slide is the right one. Essentially, we purchased a sample of the candidate biomarker as a purified recombinant protein in order to test the ability of our Optimers to stick to that target protein. So here, we measure how well the Optimer binds to the protein often referred to as its binding affinity. As you can see on the data on the left-hand side, the Optimer binds to its target, so you can see binding curves here, showing that the Optimer binds to its target with what's generally considered to be a high affinity, this value here, about 20 nanomolar. Now to make sure that this interaction is specific, we take our Optimer sequence and jumble it up. So we scramble it to disrupt all of that sequence and then retest it to make sure that the scrambled sequence is not able to bind to the target, essentially demonstrating that the sequence of the Optimer is important. And what you can see in the data on the right-hand side of the slide here is that our scrambled control shows no binding to the target whatsoever. So this confirms that the Optimer interaction is specific and that the sequence of the Optimer is necessary for binding and that we have identified the correct biomarker. So that's great. We've developed a new process for biomarker identification, and we're actually in the process of preparing to launch that biomarker identification service. So watch this space. So briefly to summarize, we've talked today about four key projects. Our enzyme regulation project, a project where we're developing noninvasive cell sampling methods for fetal diagnostics, the ongoing project with Neuro-Bio to detect their Alzheimer's linked protein and our fibrosis liver disease delivery project. All of these are progressing well and have licensing conversations underway already and are at various stages. We've also developed our biomarker discovery service that we'll be launching imminently. So I hope you've enjoyed today's webinar. I hope you found it useful. We'll obviously be keeping you posted on any future exciting developments. So please do watch this space. So I thank you for your time and your attention, and wish you a good day.