Aptamer Group PLC (APTA) Earnings Call Transcript & Summary

Good afternoon, and welcome to the Aptamer Group plc Investor Presentation. [Operator Instructions] Before we begin, I'd like to submit the following poll. I'd now like to hand you over to the management team. Arron, good afternoon, sir.

Good afternoon. And I'd like to thank everyone for attending the meeting, and welcome people to Aptamer Group's full year results for the 12 months to June '26. So I'm Arron Tolley, Chief Executive, and I'm joined by Andrew Rapson, our Chief Financial Officer. So I'd just like to start out by saying this has really been a year of 2 halves. The first half, we signed our first licensing deals and set out a clear plan for the next 3 years. The second half, we've expanded our lab offering and pipeline, setting the store out for the next several years. So today's presentation aims to update you on the progress, show you what we said we would do with the funding, what we've actually done in the 6 months since we received it. We'll broadly cover 4 areas: a brief overview to the business and the year's milestones, the financials, which Andrew will take you through and then operational progress across the commercial platform, licensing and progress on our artificial intelligence work and our 2 therapeutic programs. And finally, a brief update on things that have happened since year-end, our post-period highlights and where we're going to go from here. So we're happy to take any questions at the end, so please submit them as you go. Right. So on to the next slide to the presentation team. So I'm Arron Tolley. I'm Co-Founder and Chief Exec. My background is I have a PhD in structural and molecular biology. I've spent the last 18 years building the technology in the company, taking it from a company started in the basement of my house many years ago to one that now works with the majority of the world's top 20 pharmaceutical companies. I run the day-to-day side of the business. I sit on the Board, the Scientific Advisory Board and remain reasonably hands-on with both the science and commercial strategy. In particular, I've taken responsibility for the AI and machine learning strategy that formed a reasonable part of the April fundraise, which I'll update on later. With me is Andrew Rapson, and I'll pass over to him to do a quick introduction.

Thanks, Arron. Good afternoon, everyone, and thank you for joining us today. I'm Andrew Rapson. I'm Chief Financial Officer of Aptamer Group. I joined the business in August '22 and became CFO in August '23. I'm a chartered accountant with more than 20 years' experience in finance and have worked with [ AIM listed ] companies for over a decade now. Today, I'll talk you through the group's financial performance for FY '26 and how our financial position supports the next phase of our commercial growth.

Thank you. So for anyone new to the story, I figured we just do like a brief orientation. So Aptamer Group is a York-based AIM listed company, a recognized global player in aptamer/optimer development. We discover and manufacture optimer binders. These are short nucleic acid molecules that essentially do the job of antibodies, but they can be made faster, they can be made chemically and they can be made against targets where antibodies struggle or fail entirely. And we do this on our proprietary Optimer discovery platform, which David and myself have spent the past 18 years building and refining. And the core of this is essentially liquid handling robotics, running highly parallel selection. So this is multiple different selection projects in parallel. And we believe this outperforms anything comparable out there in the market. Our customers are generally pharmaceutical companies and large biotechs. And today, we can still say that we work with 85% of the top 20 pharma companies and a good proportion of that is now repeat business, which I'll come on to. We are a revenue-generating company. We've moved into the next phase of growth, which is looking to license and build an asset portfolio. This particular phase is taking these assets that our platform produces and converting them into licensed products that earn recurring royalties and supply income alongside our fee-for-service revenue. So over the last 12 months, in particular, the last 6, these have been, I would say, the most technically dense in the company's history. We've spent some time building the platform out for the next phase of growth. And in that period, we've managed to grow revenue by 28% to GBP 1.5 million and importantly, a 36% increase in repeat business from large pharma. We've signed 2 licensing deals with Twist Bioscience and Alphazyme, set up manufacturing internally to supply the materials for these deals and delivered the products and received our first licensing income. We've set out and delivered an AI strategy, hired a team, built and trained our first in-house models on our own proprietary data and put the first AI design candidates into our lab, and we've done this all within a 6-month period. We've also bought, commissioned, installed new automation that has cut discovery time lines by half and added much needed capacity to the system. And I'll come on to explaining how this ties into the artificial intelligence program a bit later. We've launched a targeted radiotherapy program, signed our first radioligand contract with a top 3 pharma company and initiated separate animal studies with consultancy company who are expert in the handling of radio isotopes. We've designed and manufactured and tested Aptamer siRNA conjugates, which is effectively a drug molecule for our planned fibrosis preclinical animal studies. And we finished the year with GBP 4.4 million of cash, a sales pipeline that has grown from GBP 4.8 million to GBP 7 million on account of repeat business converting potentially into multi-target material transfer agreements, which I'll explain in a bit more detail. So I'll come back to each one of these in the operational review. But first, I'll pass over to Andrew to take you through the financials.

Thanks, Arron. So on to the income statement and cash flow. FY '26 was a year of significant commercial and strategic progress for Aptamer Group as we continue to grow our customer base, we established our first licensing revenues and strengthened the balance sheet to support future growth. Revenues increased 28% to GBP 1.5 million compared with GBP 1.2 million in the prior year. Of this total, GBP 1.4 million related to fee-for-service contracts, while approximately GBP 0.1 million was generated from licensing agreements. Whilst licensing revenue remains a relatively small portion of total revenue today, strategically, it represents an important milestone for the business. The licensing agreements signed during the year validate both the quality of our assets and our ability to generate value from intellectual property beyond traditional service revenues. Over time, our objective is to increase the contribution from licensing, creating a more predictable, scalable and higher-margin revenue mix while continuing to leverage our fee-for-service activities to develop new commercial opportunities. Commercial momentum remained strong throughout the year. We secured GBP 2.2 million of new fee-for-service contracts and carried a further GBP 0.3 million of laboratory work into the financial year. So that resulted in an opportunity pool of GBP 2.5 million of contracts. We recognized approximately GBP 1.4 million of that revenue during the year, and we've carried forward another GBP 0.5 million into FY '27. In addition, our sales pipeline has grown to approximately GBP 7 million. We believe this reflects both increasing market recognition of our platform and the success of our strategy to structure projects in a way that reduces technical risk for customers while maintaining attractive economics for the group. Whilst investing for growth, we remain disciplined on costs. Administrative expenses increased modestly to GBP 3.2 million from GBP 2.9 million in the prior year. Much of this increase related to people with headcount increasing from 31 to 34 employees as we strengthen scientific, commercial and strategic capabilities across the business. Importantly, the group also increased investment in internal innovation. Research and development expenditure included within admin expenses increased from GBP 0.4 million to GBP 0.6 million. This reflects targeted investment in our fibrosis delivery program, our AI platform and our radionuclide delivery program following the successful financing completed in April. As a result, adjusted EBITDA loss was GBP 2.3 million, broadly in line with the prior year's loss of GBP 2.2 million despite the increased investment activity undertaken during the year. One notable feature of the income statement is the increase in the R&D tax credit benefit. The group recognized a tax credit of GBP 0.9 million compared with GBP 0.1 million in FY '25. During the year, we undertook a detailed review of qualifying R&D expenditure and submitted amended claims relating to prior periods. That was for FY '25 and FY '24. This resulted in an increased tax receivable at year-end with approximately GBP 0.5 million subsequently received shortly after the reporting date. This demonstrates the continuing value of innovation activities and provides a meaningful source of nondilutive funding for the business. Just turn on to the cash flow. As expected, for a business at our stage of development, we continue to invest in growth during FY '26 whilst maintaining disciplined control over cash utilization. Net cash used in operating activities was GBP 2.1 million compared with GBP 1.9 million in the prior year. This reflects ongoing investment in commercial expansion, platform development and the advancement of our strategic programs while continuing to drive revenue growth across the business. We invested a further GBP 0.25 million in capital expenditure and intellectual property. These investments support increased automation, manufacturing capability and development capacity, helping position the business for future scale. The most significant cash flow event during the year was financing activity. That was across the July '25 and April '26 fundraises. The group secured approximately GBP 6 million of cash proceeds. These funds are enabling us to accelerate development of our AI initiatives, progress therapeutic assets towards key value inflection points and expand capabilities that support future licensing opportunities. As a result, year-end cash increased from GBP 1.1 million to GBP 4.4 million. I turn to the balance sheet now. Our financial position strengthened significantly due to the fundraise in April. Net assets increased to GBP 5.7 million from GBP 1.4 million. Cash at year-end, as I've just mentioned, was GBP 4.4 million compared with GBP 1.1 million, and the group received GBP 0.5 million of R&D tax credit shortly after the year-end. So that gives us a position where our runway now extends through to calendar year 2028. Working capital balances increased broadly in line with activity levels across the business, reflecting the higher volume of customer projects and growing commercial engagement. The increase in tax receivables reflects the R&D tax credit that I just mentioned. And overall, the balance sheet is substantially stronger than a year ago, provides a solid platform for the next stage of growth. So the next slide, I want to touch on the fundraise that we've just completed in April. And importantly, how these investments support our strategy to create long-term value for shareholders and deliver a route to EBITDA breakeven. Our strategy is centered on transforming Aptamer from a predominantly fee-for-service business into a platform company built around proprietary technology, differentiated therapeutic assets and recurring licensing revenues. The first area of investment is artificial intelligence and machine learning. So over the last 15 years, we've generated substantial proprietary data through our Optimer discovery programs. We believe this data set represents a significant competitive advantage. By investing in AI, we're not simply improving operational efficiency, but creating a discovery engine that can accelerate candidate identification, enhance success rates, generate new intellectual property and potentially create an additional revenue stream through AI-enabled services and future licensing opportunities. The second area of investment is advancing our therapeutic programs towards in vivo proof of concept. This is a particularly important objective because animal validation is often the milestone that allows pharmaceutical companies to meaningfully evaluate partnering opportunities. Our aim is to generate high-quality preclinical data that demonstrates the value of our platform and supports larger licensing discussions. Third, we're targeting some of the most attractive and fastest-growing areas of pharmaceutical innovation. Specifically, we are investing in technologies that address what the industry often describes as the undeliverable and the undruggable. This includes therapeutic oligonucleotide delivery, targeted radiopharmaceutical delivery and accessing difficult biological targets such as transcription factors and RNA binding proteins. These are areas attracting significant pharmaceutical investment, while existing technologies continue to face limitations. The reason this focus is important is that successful technologies in these markets can support very substantial commercial partnerships. We believe Aptamer's platform is well positioned to participate in these opportunities and create multiple future value inflection points. The fourth area of investment is commercial expansion. Our objective is to increase the proportion of revenues generated from licensing, milestones, supply agreements and royalties. These revenue streams are typically higher margin, more scalable and less dependent on continuous headcount growth than traditional service revenues. And finally, we're strengthening our manufacturing and operational capabilities. This ensures we can support current partners, fulfill future supply obligations and participate more fully in downstream value generated from programs that progress towards commercialization. Bringing all of this together, our route to EBITDA breakeven is supported by 3 key drivers: firstly, continued growth in fee-for-service revenues from our expanding partner base, supported by an increased sales pipeline; second, increasing contributions from higher-margin licensing revenues, including upfront payments, milestones, supply revenues and royalties; and third, improved operating leverage through AI-enabled development and automation, allowing revenue growth to outpace growth in our cost base. Importantly, we're not investing this capital simply to grow the current business and investing to build a stronger platform business with multiple opportunities to create value. Success over the next 12 to 36 months will be measured by progress in our AI platform, the generation of in vivo proof-of-concept data, further licensing agreements, expansion of our therapeutic pipeline and continued progress towards sustainable profitability. We believe this strategy positions Aptamer to capture greater long-term value from its technology while building more predictable and scalable revenue for shareholders. Alongside the latest fundraise, we launched a new share option scheme, which was approved by shareholders at the April '26 General Meeting. The total scheme represents just under 18% of the fully diluted share capital and was designed to align management and employee rewards with long-term shareholder value creation. The first tranche represents 4% of the fully diluted share capital invested immediately. This was intended to recognize past performance while preserving cash for investment in the business. The second tranche represents slightly under 5% of the fully diluted share capital and vests over 3 years and is subject to a minimum share price hurdle of 0.65p, supporting retention and continuity as we execute our strategy. And importantly, over half of the option pool relates to tranche 3, which is performance-based. And this tranche only vest if the share price delivers significant appreciation from the fundraise price, vesting linked to share price performance between 1.5x and 3.25x that level. Including all options and warrants, the fully diluted share capital will be approximately 4.8 billion shares. So we believe the structure strikes an appropriate balance between managing dilution and ensuring management's rewards are overwhelmingly tied to delivering value for shareholders. I'll now hand back over to Arron to take us through the operational review.

Thank you, Andrew. So turning to the operations. So this year has essentially got 2 jobs. One is to secure the funding that lets us build and two is to use that to remove bottlenecks between growing pipeline and the revenue it should produce. So let's start with the commercial numbers. So our fee-for-service revenue grew by 28%. And for context, the pharma R&D services market is currently forecast to grow roughly 8% to 10% a year, and we grew at 28%, which is 3x the market rate, which we believe is good progress against that background. More importantly, repeat business from large pharma grew 36%. And in this industry, repeat orders are not generally given lightly. Repeat business is given when our binders are generally shown to be functional in the customers' hands having solved the problem and then we get asked to develop a solution to another problem and so on. So the 36% in repeat business is really the clearest validation that the technology is being taken up that we can show ahead of larger revenue and bigger deals being brought in. It's the indicator that usually precedes this happening. It's worth at this point as well being clear about revenue timing and why revenue lags the work in a business like ours. This is not a company like Amazon. Growth in the pipeline and repeat business doesn't necessarily immediately translate into an uptick in revenue within a few weeks or a few months. And there are a couple of reasons why. And I figured it would be a good point to explain this. Firstly, a pharma customer or one of our bigger biotech customers will test the platform on a couple of opportunities, a couple of deals anywhere between 100,000, 300,000. They need to validate those binders in their own assays internally. Generally, that can take between 6 to 12 months. And then at this point, once they're demonstrated to be successful, they expand their interest. We go through another negotiated discussion, and then we bring in more opportunities in the repeat business. The revenue line effectively stays relatively modest through that validation period while the relationship and the data accumulate. So the 36% rise in repeat business is generally what we would consider the evidence that the expansion phase is beginning to happen. So we've made 4 main changes ahead of this year. Automation, new liquid handling systems installed and running. They let us do several large pharmaceutical programs in parallel and they add physical manufacturing capacity to the company. We've reengineered the core discovery process and cut the time to identify binders by roughly half. We've increased our chemistry capability, standardizing conjugations, adding broader range of functional groups into the molecules that we can now manufacture, including the ability to make our own siRNA conjugates, which will be critical for the therapeutic delivery programs that we'll talk about later. We can also use the in-house capacity to support licensing by making our own products further increasing margins. So in terms of manufacturing, a lot of work that we have previously outsourced, we're now doing in York. That removes vendor dependency, shortens further time lines for customers. And it's one of the reasons our gross margins have improved from 48% to 53%. So simply put, a bigger pipeline is only useful if the lab can deliver it, and that's where some of the funding has gone to enable us to do that. So the summary really is the platform is now faster, higher capacity than it was 6 months ago. Our margins are improving and getting higher. Our largest customers are buying more products, and that's effectively the operational base that we needed to have installed to begin converting the pipeline that's growing as we speak. So moving on to the next slide. So the Optimer assets. So the next step in the model, once we can develop binders is turning the products into assets that we own and we can license and generate revenue from. So if we think of fee-for-service as the gateway pays for the work, it puts our binders in front of customers in the industry, minimizes our investment needs and it generates the assets that we can then turn into recurring royalties and it supplies us with additional income streams. So the win on the left effectively shows why this matters for a company of our nature and particularly us as a business. And we're not a single asset biotech where one trial result decides the outcome of the progress of our business. The same platform generates intellectual property across 5 independent markets. We've got life sciences, tools, diagnostics, therapeutics, vaccines, cosmetics. Each of these is a separate route to revenue. None of them depend on the others. And this year, we've demonstrated that by producing its first results. We've licensed the enzyme modulating optimers to Twist and Alphazyme. Both products are launched. They're being used in the market. We're receiving licensing income. We've had further orders from these customers as it appears that they're growing their sales themselves. We are also talking to 4 further global life sciences companies who are evaluating the same binders under material transfer agreements. This has led -- news of this has led to further interest in our pipeline in enzyme modulation from potential customers, and we're also building internally our own assets in that particular area. So moving on to update on the AI side of things. So the logic here is fairly straightforward. Optimer discovery is becoming quite a crowded field. Much of the new competition is computational in nature. Our advantage is not that we can write algorithms as a company, but we have 18 years of fee-for-service work. We have data sets that nobody else has. So on the table here, we've got on the left-hand side shows the gap. The published models are typically trained on 1 to 4 targets or on sparse publicly available data. So our model is trained on nearly 660,000 or over 663,000 sequences across 223 protein targets, which is a bit different from the 4 or so that general models are trained on. That number has grown sharply since June. We reported 450,000 sequences and 42 targets. And this is because we've now loaded our complete historical archive into the training set, and we are now adding to that with every project that we run. This gives us depth and breadth at the same time, which is what you can't get from the public data sets. And just to add there, we've taken all the publicly available data, and we've added that into our model as well. So in the 12 months since we set the strategy, we've hired a machine learning team, built the model architecture, trained it to a working state and manufactured the first batch of AI-designed Optimer candidates. Some companies are spending millions to do this and taking years to get there. Some competitors have raised millions to do this and have also taken years. We've started and completed within 6 months with GBP 4.4 million left in cash at the end of the year. And we think this is a really good positive thing to kind of update the market with. Last week, we doubled our training capacity with a second GPU, which is now installed and operational. So what the model we've built is generally aimed to do is to design new candidates computationally from a target structure. This is something that we're aiming towards. Take the billions and trillions of possible sequences and narrow them to a focus set that we can actually test in our labs and refining our existing binders by identifying the parts of the molecule that drive binding, and this should essentially shorten our time frames for delivery and enable us to deliver better quality products for our customers. So where are we today? The first candidates are being validated in our wet labs. Now we're not going to jump the gun and claim performance before we've got the data. But what I can say is the model is running. The validation is underway, and we expect to report the results in coming months. That's a milestone, I think, that we'll be looking forward to presenting to the market because it will tell us and it will tell you guys how much of the discovery process can move effectively from wet labs through to an in silico or computer-based system. The automation is important to mention here as well because this is the other half. The robots that we have installed and we've increased the performance on run effectively a loop with the artificial intelligence engine. The robots design the [ Optimer ] get manufactured, they get sequenced, data goes back into the model, and it creates a kind of virtuous cycle of data generation and refinement and improvement within the model. So each time we go around this process, the model gets stronger and we get better quality data. And it gets us much closer to what's referred to as a generalized model where Aptamer sequences will be possible to generate what's referred to as in a de novo setting or without actually doing any lab work. So that's why the lab automation and the artificial intelligence were funded together. And it's what will allow a team of 30 people plus to handle multi-target and multiyear accounts without the head growing -- the headcount growing in step with that. And again, I would like to point out that this has all been done within the first 6 months. I would argue that that's what good operational delivery looks like within a company of this nature. So moving on to an update on the targeted therapeutic side of the company. So the AI engine and the automation are effectively tools they operate and work within our fee-for-service side. But what we have to look at is what we point the technology towards that's a very high value. So the next 2 slides will aim to show you what we're actually pointing the technology towards. So we're looking at 2 therapeutic areas, both of which are delivery focused, which is getting molecules to the right cells within the body and where our binders have properties that peptides, antibodies and protein technologies lack. So firstly, we'll talk about the targeted radiotherapy. So radioligand therapy is essentially using a targeting molecule, in this case, an Aptamer or an Optimer to carry a radioactive isotope directly to a tumor cell. So it's one of the fastest-growing fields in oncology at the moment where big pharmaceutical companies are actually acquiring assets quite aggressively. So it's important that we get on the front foot here. So we entered into this space in October '25 with a [indiscernible] discovery contract with a top 3 pharma company. We're looking forward to update the market on that as we move that forward. Building on that with confidence internally, in March this year, we launched our own programs in partnership with a company called Radiopharmium. This is a consultancy that works with radioisotopes. We just can't handle radioactive material in Europe. So we have to partner this in order to get the data. We've identified 3 high-value targets of our own, taking the pipeline to 4 assets, one of which is partnered and 3 where we hold fully the intellectual property. And I think it's critical to explain our progress in this area, is not particularly slow going. We're actually doing really well. It takes time to do the work properly and get things ready for animal studies. There's a lot of precursor work that needs to be done. The technical progress that we've made here really does matter, so I'll take the time to explain. So stability historically is a weakness of radioligands. If you imagine that these molecules have highly radioactive particles attached to them, then they're obviously designed to damage cancer cells and damage DNA in cells and proteins and so on and so forth. So the actual carrier molecules are very important for them to have high levels of stability. Our team has established standardized radio labeling methods, which is effectively adding a small cage on the end of the molecule that enables the radioactive particles to be conjugated to them. And we've managed to achieve a 99% radiochemical purity at clinical cell activity levels. We've done some head-to-head in vitro, which means in test tube studies with radiolabeled optimers, and we've shown 76% to 90% less radiolytic degradation than protein and peptide comparatives. So what this means is the aptamers that we have tested are much more stable than their protein counterparts. If that holds in an in vivo setting, so if that translates through to an animal model, it means a more stable therapeutic molecule with a more useful longer therapeutic window. And that testing is currently underway. So in March, we told you that we would have some in vivo data across our therapeutic programs within 12 to 18 months. Material has been shipped to Radiopharmium and the first in vivo studies are in progress, and we expect some results by the end of this calendar year, if not early into next calendar year. The second area applies the same idea, but really to a different payload. So this is the delivery of genetic medicines to the right cells. So the second program is what's referred to as therapeutic oligonucleotide delivery. So this is a market growing at close to 20%, and it has one defining problem as there are a lot of technologies currently, which is delivery. So siRNAs and antisense [indiscernible] work incredibly well. but almost all of them struggle with delivery outside of the liver and in fact, targeting very selective types of cells within the liver, such as the cells responsible for fibrosis, which is one of the things that we've looked to do as a company. Getting them to be delivered to any other tissue is an industry unsolved challenge. Now we believe that conjugating aptamers or optimers to these siRNAs and [ antisense oligos ] can represent a way to overcome that unsolved challenge. So optimers can be generated to bind selectively to cell surface markers and can effectively act as a GPS coordinate to guide the therapeutics to the tissue of interest. So we have 3 delivery vehicles in development. So we have our fibrosis/liver vehicle, which is the lead that we're working on. It's in preclinical characterization. It's shown no toxicity as of yet, good stability, no markers of immunogenicity in the tests that have been done. And these are all 3 things that typically end or kill a delivery candidate early in a discovery program. The in vivo study design, which is how we're going to test these molecules for efficacy in animals is finalized. The study partner has been selected, and we're currently and have made the siRNA conjugates in-house for the study. The kidney vehicle has been identified and shown to be selective for a specific type of kidney cell that would allow siRNA delivery for the treatment of chronic kidney disease, and we're moving through that process in the same way as the fibrosis delivery vehicle, although we're just slightly behind pace on that. This year, we started a third program. We're looking at a shuttle to carry therapeutics across the blood-brain barrier. This is a very early-stage project. It's still in target ID characterization and discovery, but we expect initial results to come over in the next few months. So beyond these 3, we're exploring further tissue targets through partnerships that can provide preclinical animal data. And we've got a particular interest in cardiovascular delivery and skeletal muscle, which are currently also unmet needs. So across these 2 slides, the picture is 4 radioligand assets with one with the top 3 pharma and the preliminary in vivo data due on the others by calendar year-end, a fibrosis vehicle entering animal studies, a kidney vehicle in optimization and a new brain delivery program started and discussions underway on extra targets in different areas. So moving on to the post period review. So everything so far covers the year to June. We didn't obviously pause at the end of the year. So I'd like to bring you up to date with things that we've done. To start with the commercial pipeline, that stood at GBP 4.8 million, and it is now today over GBP 7 million. And to be clear by what I mean when I refer to pipeline, I mean a qualified opportunity that we're actively quoting against or in legal negotiations. Nothing earlier than that gets quoted in our pipelines when we put any news out. So the growth since June mainly comes from the shift that I alluded to earlier. Customers who are currently spending on one-off development projects in discussions now to move towards multiyear, multi-target master service agreements, several of which are now in active negotiation with top pharma companies. If these convert, they will give us contracted volume and visibility rather than revenue on a project-by-project basis. And this is the foundation that we wish to lay to get a path towards EBITDA breakeven. I can't give exact time lines on these deals, but we expect them to start closing shortly. And these protracted negotiations have largely been behind the slower rate of RNS releases as existing deals get wrapped into these larger agreements. Technically, several important things have happened. Folic acid binders have gone to Imperial College London for integration into lateral flow assays. The fibrosis vehicle in vivo study design has been finalized, conjugates have been made and purified in-house, and this work will start soon. The radioligand delivery optimers have been synthesized and shipped for animal studies. And on licensing, we're now developing our own assets and enzymes and looking at applications in cosmetic fields to build on the success that we've seen with Twist and Alphazyme deals. These are self-funded, so the group would keep all of the IP rather than sharing them with a development partner. And each one that we can successfully generate and develop adds to the pool of assets available for out-licensing. We're also continuing to explore extrahepatic delivery targets through partnerships. So this is different tissue types I mentioned in the last slide. We're looking to partner with companies to get additional preclinical animal data packs that are required to engage larger pharma partners. This is a more cost-effective model by partnership because it allows us to gain access to animal models and the data that's required across a broader range of opportunities and target types than if we were using our investment money to do that. So we're already looking at liver, kidney, blood-brain barrier internally with our siRNA delivery work. We aim to update the market on partnerships for cardiovascular and skeletal muscle, which are hot areas of interest, and we will update as we progress through signature on those partnerships. So over the last several months, we've focused on AI and automation, expanding the pipeline with repeat business contracts, passive income generation via expanding opportunities for licensing, generating molecules from animal studies and expanding our partnerships for additional delivery system, animal data validation. So I'll close off with the roadmap that we set out in April and where we are against it. So this is the roadmap that we published in April, and I want to report where we are against progress. So we set out a 36-month plan in 3 phases. We are 6 months into the first phase, integration and discovery. The milestones for this phase were to get the expanded manufacturing capacity operational, to have wet lab data flowing into an AI model, to have the model trained on our historic data set. All of these are done, and they're ticked on the slide. The second phase, the 12 to 18 months phase, is a preclinical validation and scaling. It's 2 headline deliverables effectively our in vivo proof-of-concept data across 2 therapeutic programs and the launch of the AI enhanced fee-for-service offering. On the first one, the radioligand in vivo data is expected, as I said earlier, by the end of the year, and the fibrosis study will be following shortly after due to the length of time difference between the 2 types of study. On the second, the first AI design candidates are now in validation and the AI enhanced service offering follows those results. We could probably launch that now, but we wish to be cautious and wait for internal validation on the candidates before doing so. The third phase, which is 18 to 36 months out, is effectively the commercialization phase. The aim here is to out-license at least 2 validated therapeutic assets, and we aim to do that by diversification and partnership that I described earlier to get multiple shots on goal with respect to getting animal validation data for delivery across a range of different tissue types. To make the AI model itself available for licensing, this one is probably the more difficult one to predict the exact time line for because it will rely on the model generalizing, which may happen sooner rather than later. Generalization effectively means that we would be able to do what's referred to as de novo discovery. So that's discover sequences without having to do any lab work, and that's obviously the holy grail of the discovery for these kinds of molecules. I would also add and it's not on the slide that we will be doing this alongside the growing base of tools and cosmetic assets that we've talked about on the asset wheel a few slides back. So to close, in April, we set out a specific plan 6 months further on. The manufacturing capacity is operational. The AI model is built and in validation. Radiotherapy and delivery programs are entering animal studies. We have our first licensing income. We've got repeat business from large pharma, up 36%. We've grown the pipeline to GBP 7 million, and we're on the cusp of multi-target deals. We ended the year with GBP 4.4 million of cash, which funds the plan and the company into 2028 and enables us to make good headway towards our EBITDA breakeven position. So I think the main take home from the slide is that within the first 6 months, the team have delivered the first 12 months' worth of work. So thank you for your time, and that concludes the formal part of the presentation, and Andrew and I will now take your questions.

[Operator Instructions] As you can see, we have received a number of questions throughout today's presentation. And if I may just start off with the first question here, which reads as follows. Can you please let shareholders know why the patent applied for by Aptamer and Unilever has not been published?

Well, I think it has been published. The application went in early '25, February, March '25 and was published later that year, around October, I think. So it won't have been granted yet. So it's still in that phase, but I believe we should be able to find that online now.

Just turning to the next question here. How are the Twist and Alphazyme licenses going? And when can we expect to see the first revenues?

So generally speaking, they're going well. We have seen the first revenue. So we've reported GBP 0.1 million of licensing revenue in this period, which came from those 2 contracts. Both customers there have launched new products, both in the public domain, so you can find their product on their websites with our Aptamer included. And particularly Twist, I think, have progressed fairly well so far. It's going through a customer validation phase. We knew that when they get to product launch that they'll be looking to push this out to new customers, and that can take some time. But so far, so good. It seems to be progressing quite well. So yes, we expect to receive supply agreement payments and royalties through this financial period.

Just moving on here. How is the fortified foods rapid testing program progressing?

Yes, I think I can take that one. So we've delivered the folic acid binders to Imperial College London for integration into a lateral flow test. So we've developed sample preparation methods for 4 different food types. So this is where we extract material from the food substances in order to add the samples to the lateral flow device. So that was the first part of the program prior to developing the lateral flow. So the program aims really to produce an affordable field test of monitoring folic acid levels in fortified foods.

We just got a question here around AI. How will you prove that AI model is better than your existing platform?

That's a good question. How will we prove that? I think we'll prove it by taking existing aptamers that have been generated from our standard platform, adding the data into our AI model that will then give us additional molecules that it suggests could perform better. We would manufacture those and test them and compare the results. So do the aptamers that come out of the AI model perform better than aptamers that come out of our standard service offering. And we believe that, that will be the case. The first AI selected candidates are currently undergoing lab validation. We do need to demonstrate they perform against the intended targets before we launch the service, as I alluded to in my presentation. But the aim really will be faster discovery, improving the performance of the molecules. So as we validate that, we'll provide updates to the market.

Can you update us on the next milestone in the Unilever program?

Do you want to take that one, Andrew?

Can do. Yes. So Unilever, I think the last update we gave was going to on-person testing. So it's still in that phase of work with Unilever. Obviously, we're not allowed to report too much on the detail there, but Unilever is still progressing with that and seems to be progressing well so far. It's going through scientific development. Obviously, testing it on person is quite a different environment to testing it in the lab. So we're working with Unilever on that process.

Just moving one here. With programs running across diagnostics, cosmetics, food testing, radiopharmaceuticals and [indiscernible] delivery, is the company spread too thin?