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spk07: Hello, everyone. My name is Chris, and I'll be your conference operator today. At this time, I'd like to welcome everyone to Accenture's business update call for the fourth quarter and full year end of 2021. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you'd like to ask a question during this time, simply press star then the number one on your telephone keypad. To withdraw your question, please press star one again. At this time, I'd like to introduce Sarah Sherman, Vice President, Investor Relations. Sarah, you may begin.
spk01: Thank you, Operator. A press release in Form 20F was issued yesterday after U.S. market closed with our fourth quarter and full year 2021 financial results and business updates. These documents can be found on our website at www.investors.accentures.ai, along with a presentation for today's webcast. Before we begin, I'd like to remind you on slide two that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, and business performance, including with respect to our technology platform and pandemic preparedness program. Actual results may differ materially from those indicated by these statements. Unless required by law, Accenture does not undertake any obligation to update these statements regarding the future or to confirm these statements in relation to actual results. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer, and Gary Pardo, Chief Technology Officer. Ben Taylor, CFO and Chief Strategy Officer, and Gabe Hallett, Chief Operations Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.
spk06: Thank you, Sarah. And thank you to everyone who joined us today. 2021 was a remarkable year for Accenture. We strategically scaled the company and we expanded our capabilities. As you can see on slide three, we have significantly grown our pipeline year over year, adding 11 programs and advancing two programs into late discovery and three into IND enabling studies. The press release issued last night included an exhaustive review of our 2021 accomplishments. Let me recap a few of the most notable and a few recent highlights. Hiring key talent and expertise, tripling the size of our global workforce, and adding to our U.S. footprint with a new Boston office and office expansion in Miami. Completing our acquisition of AllSite, integrating the world's leading patient tissue platform into our end-to-end system, and gaining a tremendously talented team. listed on NASDAQ and raised in over $510 million in gross proceeds from our IPO and private placement. We ended 2021 with approximately $759 million in cash or cash equivalents, and we are well positioned to deliver on our strategic imperatives. Announcing one of the industry's largest AI-powered drug discovery and development deals to date. We've got a $5.2 billion collaboration with Sanofi with a $100 million upfront payment. Successfully executing our partnerships, as we've seen by the expansion in work with three of our major partners, BMS, Sanofi, and with Bill and Melinda Gates Foundation. With BMS in licensing and AI-designed immunomodulated drug candidates. The successful application of artificial intelligence and machine learning to reduce our industry's failure rate and produce better, more effective medicines has long been recognized as transformative potential. We are now working to put that promise into practice. In the last several months, we've seen some of the world's largest drug makers announce the largest deal to date in AI-powered drug discovery. The back-to-back announcements by Titans and Biotech and Pharma represent the industry's fullest embrace of AI to date. And we think of an inflection point in the evolution of AI-powered drug discovery in Iran. It should come as no surprise that there's mountains of satisfaction with the time it takes to deliver new medicine, particularly when we are faced with urgent health crises such as the global pandemic. Even more frustrating is that most of this time is spent trying to fix problems as they arise, It was currently a lengthy, step-by-step process, drawn out over the course of 10 years. It's striking when you consider that no other consumer products are made by this way. By the time a drug reaches the patient, the underlying research is dated by 10 years, and the science is likely significantly advanced. Can you imagine if any other technology products were made in this way? The founding team and I set out to build a completely new type of company, to re-engineer the drug discovery and design process. Today, that's best illustrated in the near equal split in our team between drug discovery scientists and technologists, which you might be surprised is an anomaly in our industry. By bringing together these two seemingly desperate disciplines, our scientists are able to tackle new problems with the power of our AI systems, while our technologists encode these learnings, working towards a day when we can achieve full automation. Today, Our chief technology officer, Gary Paradue, will talk more about our technology and how we're using it to design and develop better molecules and the deep investments we've made in technology. What I find incredible about this is how our underlying technology and AI platforms may have the potential to achieve feats that were never seen before in drug discovery. Our AI platforms can make decisions based on analyzing thousands of different parameters in parallel, enhancing creativity with generative algorithms, working in a computational space far beyond the ability of any one scientist or team of scientists to consider. Our drug design process, from the AI generation of the first novel molecules to the design of a development candidate, has averaged about one year versus industry standards of four and a half. Our AI-driven methods lead to the nomination of drug candidates after the synthesis, on average, of less than a tenth of the number of compounds versus the industry average. This efficiency enables us to concurrently advance more than 30 programs. I'd like to think of AI as supercharging our amazingly talented drug discovery teams. This is a combination of human and machine that's enabled us to begin to crack forms and areas such as truly personalized medicine, an area that our industry has been talking about for more than 25 years. as seen by results published in Cancer Discovery, where a platform was the first to successfully guide treatment outcomes for late-stage cancer patients, achieving a 55% ORR. This gives us confidence that the models we're developing may translate to potential patient benefit in the clinic. This is an area that I'm personally very enthusiastic about, and I look forward to seeing where we can take the platform next, including ovarian, lung, and breast cancers. And as we look at what's ahead in 2022, we're driven by the possibility of how much we can advance, powered by this AI-led approach. We anticipate continued expansion of our pipeline by not only adding new discovery programs, but by also continuing to nominate new drug development candidates and progress them towards the clinic. So the ability to have clinical capabilities and infrastructure Increasing validation of a platform for additional data, including data on our pipeline programs EXS21456 and GTAEX617 that will be presented in April at the upcoming AACR Congress and throughout 2022. Further, our mission is to fully automate drug creation for the opening of our laboratory automation suite in Oxford. Today, Gary, our CTO, will be focusing on just one aspect of our tech. How do we design better drugs? The technology team is up to some incredible work this year, including opening and operationalizing a new 26,000 square foot automation suite that will bring us one step closer to what's fully automated in the chemical synthesis and analysis of our small molecules and our drug discovery programs. And I'll now turn over the call to Gary to walk through our technology platform. Thank you, Andrew. Today, I would like to give you a high-level overview of our technology platform so that we can bring to light how the underlying technology at Excientia is differentiated from what others in the industry are doing. There are several fundamental ways in which I believe we stand apart, but perhaps the easiest way to explain it is where we start, with the patient, as you can see on slide six. We think about drug discovery as a learning cycle. A cycle that begins with the patient, fueled by our AI platforms, that enables us to learn from every new piece of data and bring more information to bear through every step of drug creation. In a conventional drug discovery project, it may take years before a potential new drug candidate is tested in humans. With our AI precision medicine platform, we're able to bring this process much, much earlier into the discovery phase. On the next slide, we show how we are identifying the bright target. This is possibly the most important decision for a drug discovery program. We use Stentor Biologist, which integrates literature along with genomic and transcriptomic data into our knowledge graph to identify connections and predict target to disease associations. This process is disease area agnostic, with application today across oncology, immunology, immuno-oncology, and rare diseases. Our precision medicine platform utilizes primary human tissue samples, and we align our early target identification activities to leverage this platform, capturing the insights from drug action on patient cells, along with transcriptomic and genomic data, All of this gives us increased confidence in the relevance of our targets to actually make a meaningful difference in improving the outcomes for patients and having the ability to better understand the potential impact long before we reach the clinic. Once we've established the desired target, we rigorously define our objective. the target product profile, or TPP, which describes in detail the properties we desire in our optimized drug molecule. Once we have rigorously defined the target product profile on slide eight, we now take this set of objectives and encode them as a reward bundle for our algorithms to optimize towards. This enables our design systems to create structures meeting those criteria. For example, we may want to design a brain penetrant drug that has a low human dose, good selectivity, but in particular avoids having efflux issues. We can encode that specific set of objectives, potency, selectivity, efflux, et cetera, so that normal structures generated drive towards these criteria. As you might imagine, we use and generate a lot of data when doing this, illustrated on slide nine. For each project, we generate the initial hit structures algorithmically from integrating any public data with proprietary data from fragment or focus screening, which is developed in-house. As you've heard us talk about, half of our company are drug discovery scientists, generating proprietary assays and data at our Vienna and Oxford labs that we can bring to bear within our projects. In addition to that priority data, the platform can also scour existing data, going back years to search for anything that might be relevant. For example, data extracted from a 20-year-old patent or a recent Nature paper can all be integrated with data generated in our labs this morning to help serve the models. One of the great powers of our AI platform design is that we can use any type of data to drive the design process, meaning it does not require a specific data type like 3D crystal structures or high content images, but we can use any and all of these types of data, plus many others that will enable us to triangulate towards designing drugs that meet complex design requirements. This diversity of data is required to precision engineer a novel chemical series that we anticipate will have a robust treatment effect in patients. In order for our systems to generate potential molecules, we need models to predict all of the properties that we require. This could include potency, acne, selectivity, physical properties, and many, many more. We have extensive model-building capabilities that span the full range of skills and technologies, from quantum mechanics and molecular dynamics to exploit structural information, to machine learning and computer vision to interpret pharmacology and cellular imaging. Going back to our earlier example, where we highlighted that we are trying to design molecules that meet specific project requirements, for example, the right level of selectivity and potency, but that doesn't have unwanted issues. We are now at the stage where we have identified the desired TPP, and we have an initial set of models that will help guide us on that journey, as you can see on slide 10. We can now apply generative design, which is an AI-driven process of molecular ideation. Our system is exploring nearly the entirety of chemical space and creating molecules to meet our desired criteria, scoring them and learning. learning from the scores how to create better molecules. Using evolutionary algorithms or reinforcement learning, the system rapidly and efficiently explores chemical space, creates a population of novel molecules that are predicted to meet our criteria. At the end of each iteration, usually a population of tens to hundreds of thousands of molecules are created. These molecules are driving towards the criteria that we desire. On the next slide, from this large population, we apply a detailed filtering process that may involve more sophisticated and compute-intensive models to reduce the set. And then we apply a process called active learning. We want to make as few molecules as possible because it's time consuming and expensive. Usually, we make 10 to 20 molecules per design cycle. Therefore, we want to synthesize and test the compounds that will help us learn fastest to improve our models and to take us forward towards our objectives. It is by learning faster to navigate across a potentially vast chemical landscape that gives us the industry-leading productivity metrics that we have been demonstrating. Our active learning algorithms ask which molecules will provide us with the most information to improve our models in a certain dimension. In short, what should we do next in order to learn the most and to select this set of molecules in an unbiased and mathematically rigorous way so that they enable us to learn the most at each cycle? Now on slide 12, the selected molecules are synthesized and tested. We profile each molecule in detail so that we can update our models with new information and learn the maximum amount from the laboratory work. We have extensive biology capabilities in our labs in Oxford, including structural biology, biophysics, and pharmacology screening. We can then visualize the project telemetry, the progress of the project, in an unbiased way using what we call a merit score as a representation of the desired target product profile, as you can see on slide 13. Each dot is a novel compound synthesized and tested. The x-axis is the sequential progress of the project in terms of compound numbers, and the y-axis is the multi-parameter optimization score with one being the ideal score across multiple objectives. Each design cycle is colored from red through to blue. As the project progresses, the system moves from exploration, where we are exploring a range of different chemotypes. Once the most promising series is identified, we move into an exploitation phase, focusing on a particular area of chemical space. At this stage, molecules are consistently fulfilling most of the key project goals and will rapidly close down on a candidate molecule suitable for preclinical testing. As we learn through each cycle, we can track the learning as the project progresses towards its desired criteria. On the next slide, you can see that the AI algorithms are refining the final designs in order to achieve the project's potency, selectivity, bioavailability, and safety requirements in a final candidate molecule. Hopefully, I've shown you how we design differentiated molecules. It's one aspect of our end-to-end platform. On slide 15 is our learning loop. By starting and ending with the patient we can apply the platform to produce new candidate medicines with attributes that we predict will lead to better treatment benefits. We are also using our precision medicine platform in biomarker discovery and in patient stratification as we move forward. You will hear more about this later in the year. So there's no better way to showcase the true value of our design capabilities than with an example.
spk03: I now turn the call over to Andrew to talk more about our design process with one of our programs in development as part of our pandemic preparedness efforts.
spk06: Thank you, Gary. Today, we want to highlight how our platform can truly overcome complexities and design challenges in efforts to create molecules that fit the desired properties we are seeking. I'll start on slide 17. We are showcasing our objectives for designing the drug against MPRO, a critical virus protease enzyme target for SARS-CoV-2, the coronavirus responsible for COVID-19. mPro is a key enzyme of coronaviruses and has a pivotal role in mediating viral replication, making it an attractive drug target. Importantly, we started this project less than nine months ago, in the summer of 2021, And with a clear target product profile, we've been able to design and synthesize promising compounds that are starting to meet our objectives in in vitro studies. We entered into a collaboration with the Bill and Melinda Gates Foundation in September 2021, and we've accelerated our efforts in pandemic preparedness. We've not yet nominated our development candidate for this target. but thought this was an important example to showcase our design capabilities and share some emerging early discovery data coming from our platform. So here you can see what our design objectives are, namely to develop a once daily, orally bioavailable covalent protease inhibitors with pan-coronavirus activity. Turning to slide 18. We've highlighted our process to design a potential candidate. This process is still ongoing, and the in vitro data we will be highlighting today is illustrative of our design capabilities for an important target. Our design cycle utilizes generative design, as Gary mentioned, with a focus on improving key parameters and prioritizing the most promising compounds for synthesis and testing. Importantly, we recently brought on Professor Ian Goodfellow, Professor of Virology at the University of Cambridge, as our new Vice President of Antivirals. Ian is a leader in the field, and to advance our efforts in developing a wholly-owned antiviral platform, including pandemic preparedness, Ian has already provided invaluable insight, and we're pleased to welcome him to Accenture. Here on slide 19, you can see we're looking at the potency of two of our lead molecules, as measured by equilibrium dissociation constant by surface plasmid resonance, or SPR. Compared to nirvatrol beer, the M-prone inhibitor given in combination with ritonibir to form Paxlovid, the first approved SARS-CoV-2 protein inhibitor. In our head-to-head pre-central study, we are comparing the portability of two of our designed and synthesized compounds that have emerged from our AI environment process. To be clear, we believe Paxlovid is an incredibly important drug that provides benefit to patients suffering from COVID-19. Our focus today is on how we can design an optimal antiviral with the potential to be built once daily or without the need to be co-administered with ritonavir, which can result in adverse events and reduce the metabolism of other medications a patient may be taking. What we're showing here is the progression of our design cycle, how we can continue to learn and improve. Compound EXF68 is a 68 compound synthesizer. and an early lead that was designed to show superior enzyme binding affinity based on SPR biophysical assays, compared to nonatural there, with an 11-fold improvement in portency, as measured by enzyme binding affinity and a potential for improved global bioavailability. One of our latest compounds that's still undergoing profiling, compound 161, has shown a marked improvement in activity, being the most potent compound in our series at a Kb of only 3 picomolar, about 200-fold more potent than the natural risk, as seen in the graph in this in vitro assay head-to-head. As part of our pandemic preparedness efforts, we have focused not only importantly against SARS-CoV-2 that causes COVID-19, that other variants on coronaviruses, to be able to design a molecule to have a potential to be useful in a future pandemic. So on the next slide, in the chart, the lower the fold variation, the more potent the molecule is against other coronaviruses in the legend, as tested in the functional enzyme assay. The higher the bar, the more likely the compound is to lose effectiveness and eat higher dose than against these other coronaviruses. For some background. we wanted to look at coronaviruses that were identified to cause severe disease, such as SARS-1 and MERS, both beta coronaviruses, with mortalities of approximately 10% and 34% respectively, as well as common respiratory viruses, as illustrated by 229E and NL63, both alpha coronaviruses, and HKU1 and OC43, both beta coronaviruses. The Accenture compounds, importantly, showed broad-spectrum activity in vitro across diverse coronaviruses. We believe that this activity, combined with the biophysical potency on target observed in vitro in SPR, will be critical property necessary to retain antiviral activity against emerging coronaviruses. Turning to slide 21, this ties to what Gary walked us through earlier. Our approach has designed them against multiple objectives and allowed us to create a molecule that balances potency with our desirable properties. We were cognizant of a need of our compounds to be designed to avoid an off-target impact given their potency. We have been able to design compounds where the increase in potency against the viral proteases did not come at the cost of inhibiting human cysteine proteases, with EXS161 showing greater than 1,000-fold selectivity in in vitro biochemical assays against human proteases. So to summarize, on slide 22, we have already been able to use most of what we set out in our target product profile. In our efforts to develop a one-state leak over antiviral, A platform that's able to integrate viral target protease analysis without state-of-the-art biophysical screening capabilities to design portents, SARS, COV2, and pro-inhibitors with selectivity over human proteases while still showing pan-coronavirus activity. We have designed and simplified compounds with good drug-like properties, including promising antiviral activity and pre-clinical pharmacokinetics. We believe we have designed a molecule that shows, based on data involved on human cell lines, better potency compared to the natural base, potential broad spectrum coverage, and the ability to be dosed on its own, that with the properties that will allow it for co-dosing in the face of resistance. We look forward to continuing to synthesize and design against our target product profile and to share with more data on this important program later in 2022. And with that, we look forward to questions.
spk07: Thank you. As a reminder, if you'd like to ask a question, please press star then one on your telephone keypad. Our first question is from Chris Shibutani with Goldman Sachs. Your line is open.
spk02: Thank you. Good morning. This is CJ on for Christmas morning. Congratulations on all the results and progress of the last quarter and year. I was wondering if you could give us a sense of whether we should expect the AACR presentation for the adenosine receptor antagonist to give us a sense more of what the patient-specific expansions are going to look like when we get to the patient phase of the trials, or should we wait for the top line or the more detailed healthy volunteer data to have visibility to that? maybe could you also give us a sense of sort of business development priorities for the year? I saw that you've promoted a business development chief at this point. So how should we think about priorities there? Will there be more deals like the Sanofi deal, or is there going to be a shift in some way? Thank you.
spk06: Thank you, CJ. Thank you very much as well for the comments on the quarter. In terms of answering the question on AACR data, I'm going to hand that over to Dave Hallett, our Chief Operating Officer, to explore that. And then I'll come back on and talk about business development strategy for 2022. Dave, do you want to introduce what we're thinking about introducing to the world at the AACR?
spk05: Sure. Thank you, Andrew. So the All three posters that we're going to present at AACR are really focusing on translational aspects and patient selection. And also one of the posters is actually touching on the design aspects that Gary outlined, how they were applied to CDK7. So going back to A to A specifically, think about timing of information this year. The AATR poster itself will focus on ongoing functional and multiomic work, which is to identify both novel and robust patient stratification methods ahead of a forthcoming clinical study that we're anticipating will start in patients in the second half this year. The phase one information that you referred to, we've been looking to release that towards the end of the first half. And that will cover information such as pharmacokinetics, safety and tolerability, but also recommended phase two dose based on a pharmacodynamic biomarker that we have in place. And I'll pass it back to Andrew to address the question around business development. Thank you.
spk06: CJ, yes, we've been incredibly active in business development, as you might have noticed, over the past six months or so. And that expansion for extra work, not just with joint ventures, but with CMS and Sanofi, we're keen as well also to make sure we do balance out our business model. And the key to that, actually, is how do we think about business development, particularly for 2022, where what we are thinking about also is ensuring that we build out our capabilities and showing that as we expand new ways of doing things, that we're able also to bring along partners to do that. And in fact, you've already seen elements of that with the Sanofi deal. A big difference between that collaboration and the BMS collaboration was the inclusion of a precision medicine platform. And I think that gives an example that as we develop new technologies, we then look to see how we can also work with partners At an early stage, actually, to ensure that all technologies are on the right track in terms of understanding real patient needs and real needs in the marketplace. So one thing I would expect this year, actually, is to think about how we do technology deals as well as doing pipeline deals. And also, I'd like to bring our chief strategist, Ben Thaler, as well, CJ, just to add some more color to that.
spk07: Hey, CJ. So just a quick note on the AACR poster. I think although we'll probably save most of the enrichment data for around when we're starting the actual clinical trial in the next phase, what is really exciting about what we think is really exciting about the A2A poster is you'll see some evidence of how you can have a functional ex vivo IL model. And remember, A2A is not a direct cytotoxic agent, so we really have to have that immune interaction, which you're not going to see in most all of the current translational models. And that's why IO has really suffered from having good translational models. So this is a really exciting potential model that could be used not only for A2A, but hopefully other IO agents in the future. that might be more directly relevant to the patient environment.
spk06: Absolutely, Ben. And that really underlines the importance of these models more generally to the company. And the way we think about it, CJ, is that it's not just the human data from the Phase Ia on the molecules. It's actually the work being done in parallel on the ex vivo human data in defining the patient selection approach which we are taking. And those two bits of work coming together then into designing the Phase Ib2.
spk02: Great, thank you. That ex vivo assay has certainly been a big gap, so I'm looking forward to seeing that data. Thanks.
spk07: Our next question is from Michael Ricekin with Bank of America. Your line is open. Great, thanks for taking the question. I want to start on the MPRO inhibitors you talked about, just because you spent a decent amount of time on that. Given how quickly the COVID pandemic is evolving and given the presence of Paxilid and other agents out there. I'm just wondering if you could talk about the potential to accelerate the development of a final candidate. And could you talk a little bit more about your commercialization strategy or sort of the next steps beyond that, assuming you're able to develop a solid candidate?
spk06: Thanks, Paul. Thanks, Mike. I'll give a bit of introduction to the question, and then I'm going to hand it over to Dave again, actually, to give you a lot more detail on how we're thinking about it. The first thing, of course, is this project that we showcase today, I think, is a really good example of the ability of a company to rapidly design and develop high-quality growth molecules. And I think the data we're getting through now actually really places that in context. The other thing that's important to take on board is that all this started with our collaboration with the Gates Foundation and the private placements that they came in at the IPO. And it's a case then of really giving us that as a key partner going forward and ensuring that we are ambitious in sort of the target product profiles that we're going after, you know, particularly compared to some of the competitor molecules that are out there. And when we look at markets, the ideal TPP we think about is how do you identify something that could be low-dose and long-acting that really has the protection against future variants that we're seeing. And to give you some more context now about how our program is developing and how we're thinking about it, I want to introduce Dave now to the table.
spk05: Thank you, Andrew. In terms of the first question around specific timing, yeah, it's important to note that we continue to kind of synthesize molecules and explore the lead series we have in place. And we're looking to select a development candidate in the second half of this year, obviously clearly aware of the the timing and the need for additional agents. I think that's your first question about PaxLibid in the wider market. I think it's interesting that the data around both vaccines and small molecules tells us a lot of things around that there is waning resistance following vaccinations. We continue to live in a global environment where vaccine uptake around the world differs by geographies. In some areas, vaccination uptake, particularly in high-risk areas, is still very low. And even more recently, there's a Nature paper that's just come out showing how when people are actually infected with the Omicron variant, actually that generates a really low immune response and is unlikely to generate this kind of herd immunity that I guess everybody's hoping for. So I think it highlights, as is often the case with viruses, is that the combination of the vaccines, the potential for resistance to agents on the market, I think it just highlights once again the importance of having multiple new therapy options available for everyone, not just developed nations. And also thinking about how you might apply these in combinations as we saw successfully applied. For example, think about HIV therapies and the importance of multiple therapies but against different mechanisms of action. So they're the kind of things that we're looking for over the next couple of years.
spk06: And that's why, first, as well, Mike, it was important to design an agent that doesn't need co-dosing with a metabolism inhibitor such as ritonavir. And if we're going to create combinations of co-dosing, we think it's, we've got a strategy where ultimately you're combining, you know, two agents acting on COVID, hitting at different mechanisms. And as Dave says, we've seen with HIV to be a very successful approach in the long term. Okay.
spk07: That's awful. And then a follow-up just sort of on investment priorities for 2022. You've got a very healthy balance sheet exiting a year, and you also have the upfront payment from the Sanofi collaboration from January. So how do you think about expanding investment priorities this year. You know, given the relatively neutral cash flow from operations last year, it seems like you should be able to support a lot more investment and expand programs. I mean, you kind of indicated, I think, 30 programs concurrently is what you want to be able to run. But as you move into, like, Discovery 9, the enabling, you know, where should we expect the incremental spend to come in and sort of what's a good runway as we think about, you know, progressing through 2022 for that?
spk06: Excellent. Thanks, Mike. I want to introduce here Ben Taylor, actually, to take that question. I was CFO and Chief Strategy Officer. Ben.
spk07: Hey, Mike. So if you saw our financials for this year, we had an operational cash burn of about $9 million. A lot of that is because we can offset so many of our expenses with cash flows from partnerships. So we brought in a little over $85 million, so just a little bit above the guidance that we'd given earlier on in the year for our cash flows from our partnerships. I would expect that to continue into the coming year. So we've already had the $100 million up front from Sanofi come in. That will probably hit the actual balance sheet in the second quarter. But we did sign the contract in the beginning of this year. We've had a number of other smaller milestones come in as well. So we're going to have a nice cash flow from collaborations this year again. and have meaningful growth over the cash flow from collaborations last year. So even though we will be growing our operations significantly, and in a second we can talk about what that means, there should also be a nice balancing from those inflows so that we'll maintain a very balanced business profile of growing our business in a way that matches our growth in our partnerships as well. So just a quick note on some of the areas that you asked about investment. We continue to grow our platform capabilities, and I'm going to turn this over to Gary in a second to talk about it, along with all of our projects. The project growth, you'll see our pipeline grow as well. But remember, our partner programs pay for themselves ahead of time. So that actually reduces the net burn considerably. And then we will continue to make some capital investments. We're growing some of our offices around precision medicine out in Vienna. We opened up a 50,000-square-foot facility. We've got an automation lab that Gary can talk about in Washington. in the Oxford area as well. So we will have some increase in cash outflows, but I'd imagine that it will stay in a very reasonable neighborhood. Gary, are you going to pick it up from there?
spk03: sure thanks thanks ben i think i mean you've hit on two key expansions for us i mean obviously we're building out our technology platform um going really deep into the ai capabilities that that we described earlier and that cover our kind of end-to-end platform but two things i call out that we're really excited about is obviously the automation lab that i think we've mentioned previously 26 000 square foot just south of Oxford, and we're deep into the design and specification and building all the equipment to go into that. So that's going to be fantastic. End-to-end synthesis, purification, screening capability, which can really bring a transformational benefit to timelines and drug discovery. And then the other new exciting area that we're starting to look at is we recently announced that we hired Professor Charlotte Dean, which is a super exciting hire into the tech group and into the organization. And she's going to be looking at developing our biologics capability and how we can apply. It's a really tight synergy with the sort of work we're doing at the moment and how we're going to apply AI to the design of biologics.
spk07: Thanks. Great. Thanks. Just to confirm a quick one. So are you still sort of projecting about five to six years of cash runway? I mean, some of you commented on earlier. Yeah, we haven't given specific guidance, but I think we feel very comfortable with a number of years of cash flow runway. So part of that is also remember under our control as we determine the flow between internal pipeline and partnerships, but our business model expectations would certainly meet around what you're talking about. Great. Thanks so much. Our next question is from Peter Lawson with Barclays.
spk04: Your line is open. Hey, thanks for the update and the Maybe just a follow-up question for Ben. Just as we think about the build-out you're undergoing at the moment, how should we think about your needs for expanding when you move into Phase II and Phase III clinical trials as well?
spk07: So we've actually factored that in to a lot of our current thinking on the growth. So we're building up our clinical team. and doing it in what we think is a very balanced, data-driven way. But I wouldn't expect that side of the business to really become the major cost center until some of the drugs get into late phase two or phase three, and then obviously the clinical trials get more expensive. In the near term, it's not going to have a dramatic impact on our overall cash expenses, and we're able to manage that much more. So I don't see that as being a substantial line item or a driving line item.
spk04: Is there anything in that kind of later stage development that you can improve on as well, whether it's from the AI side of things, or is that kind of almost like a bolt-on of existing approaches?
spk07: Well, now, Peter, you're getting to where we get really excited. So we hope so is the right answer. We're intending to take a very data-driven approach to clinical trials, too. So as we mentioned earlier, A lot of what we design for is actually better clinical trials. And so what we need to do is match those clinical trials to the drugs that we're producing. So if you think about that, any clinical trial, whether it's phase one or phase three, is all about statistics. And so the more powerful you can make your statistical analysis, the smaller the trial, the faster the trial, the better the results that you can get to. So by designing more targeted clinical trials, it actually has the follow-on effect of potentially making them smaller and faster and less expensive.
spk04: Thank you so much. And just on the kind of the near term, on AECR, what should we be looking
spk06: I'd like to take that question, Peter.
spk05: So the key information you'll see in New Orleans is ongoing work around, as well as the design of the molecule and some in vitro data and in vivo data showcasing the kind of qualities of the development candidate we have is ongoing data we're generating in primary patient tissue, which is helping us to identify not only which cancer types, but also within those kind of specific cancer types, so for example, ovarian, which patients are likely to respond better and why, and so producing kind of signatures that we can then use prospectively in a future patient study to guide exactly what Ben's pointed out is to highlight which patients are likely to respond to our drug and which aren't and understand why, and ultimately that will drive very specific recruitment, should allow us to actually run smaller clinical studies and therefore actually get earlier and more successful readouts.
spk04: Thank you. And then I guess the final question just around A2AR, just that as a single agent, you get a sense of what percentage of patients could show a response with a single agent A2AR?
spk05: I think so. I think you'll see some of that in the poster. I think it also depends on the cancer type. So if you look at the data that others have published and that we're building upon, is that particularly looking at which subjects do you see this high adenosine signature and also where do you see kind of high expression of important enzymes like CD73 and other components that are likely to respond. It varies across cancer types. It can be as high as, say, 15%, 20% in some areas, but it can be much lower than that in other areas. And so as part of the work that we'll present at AACR, you'll start to get a sense of the cancer types that we're focusing on that will actually form the basis of both the dose escalation and the expansion. So again, with this kind of concept of... narrowing down on a smaller patient kind of subset as we go into the clinical trial. Again, identifying which patients like to respond. So it depends is the answer to your question. But I think the key thing is actually is knowing and having data available and more important kind of biomarkers and tools to actually identify those patients before you take them into the clinical trial.
spk04: Great. Thanks so much. Thanks for the update.
spk07: Our next question is from Vikram Purhit with Morgan Stanley. Your line is open. Great. Good morning. Thanks for taking my question. So I had two, both kind of on the platform. So first, is there any color you could provide at this point on the targets that have been identified through the collaboration with Sanofi? I understand it's early days and you may not be able to share much about targets in particular, but any context you might be able to give around the process for identifying these targets and then prioritizing them, that would be very helpful. And then secondly, for the precision medicine platform highlighted by the EXALT-1 data, where specifically do you think you could apply this functionality next, and how do you see it being weaved through your current pipeline programs over the coming months and years?
spk06: Thank you much, Vikram. Great question, actually. It gives us a chance to talk about the expansion of our end-to-end platform. And that's actually a real key feature of the Sanofi deal. In fact, it's moving upstream into using our target ID approaches and downstream into using precision medicine into patient stratification. But in fact, Those two things do come together in how we're thinking about identifying new targets for Sanofi. To give you a bit more color on that, I'm going to bring Dave into the conversation as his team has been identifying targets in ANGA using the platform.
spk05: Thank you, Andrew. The key components to this is that the first thing to appreciate is that the Felipe Aries kind of cover oncology inflammation. And so what we're able to do there is kind of a few ways of approaching target selection and target validation. One of the critical ones, and it's kind of at the heart of one of the reasons that Sanofi did a collaboration, is to kind of give them access to our patient-driven approach to target identification, which comes back to this kind of critical story of placing the patient at the center of both the target discovery but also the kind of translational aspect. And so what that looks like in practice is that we're assembling data sets that are provided to us by Sanofi, because their therapy area heads have obviously been thinking about this for a while and the kind of targets they want to work on. So they give us access to their proprietary data. We can actually then add that on, as Gary described earlier, in terms of the the rich history of public literature, both patents and peer-reviewed information from the last 20 plus years. And then add on to that the information that we're getting from our platform in Vienna, which again is kind of experimental data both at a functional level but also genetic and transcriptional. And we basically bring all that information together and to ask questions around how strong is the relationship between a particular target and a disease of interest and then able to kind of narrow that further down into kind of subtypes of cancer. So it's still in its early phases but I think I think the power that we have from the kind of proprietary data that Sanofi have brought, coupled to our own, it's kind of, it will stand in good stead in terms of both identifying novel targets, but also kind of prosecuting them as we go over the next few years.
spk06: A good example of this pipeline of target discovery, Vikram, actually will be presented at one of the AACR posters. which is how we can show that a deep learning approach to primary patient tissues is actually being used to discover novel necrosis of action, and that particular one is ovarian cancer, which I'll talk about in a few minutes. But it gives, I think, a textbook example of how we're using the platform then to start off with the patient tissue material and then use it then for novel target discovery. So once that post is out, actually, we'll be able to tell you more details about that. But I would recommend looking at it. In terms of then the broader application beyond EXALT-1 and the precision platform, firstly, we are incredibly pleased with the EXALT-1 paper that was published in Cancer Discovery and the results of that trial. It's the first time that an AI-based system has shown improved outcomes in oncology. That's an important thing to note. The other really important thing to note was, of course, the results of our trial. We have a hazard ratio of 0.53 and an ORR of 55%. And if you look at sort of where the patients of ECOG1 or less they had even seen more significant benefits within that by this sort of assay AI-guided sort of therapeutic approach. So that was, of course, a trial in hematological cancers. And, of course, we are now got a clinically validated approach to that, and hematological cancers are things we are exploring now in terms of our wider internal precision oncology sort of pipeline as we go forward. But what we are doing now is looking to rapidly expand the range of cancers that we can then apply the same methodology to in developing lab-based, high-content, AI-driven assays, and also then looking to run both sort of observational and other investigational trials in other cancers along the lines of EXALT-1. We are, you know, advanced in those stages now. We're looking at ovarian cancer, breast cancer, lung cancer. We're actively developing. You'll see developments on bioblastoma taking place and evidence of that already gathering. So what we're looking to do really is build this out for a range of different cancers. The key to doing that really is also the other part of our precision medicine platform, which we talk about in a future earnings call, which is how we expand our clinical network and our biobanks. The range of clinicians that we can interact with, you'd be hearing about a lot more sort of collaborations in that space as that network expands, forms of central and eastern Europe at the moment, across to other continents, hopefully including the U.S. and Asia as well. And what we see from that then is that expanded network of patients providing then underlying material and data from those patients, which allows us then to expand our biobank. And what's really exciting, of course, is the depth of analysis we're extending to, not just high-content approaches, but also a much wider range of omics approaches. now taking place, including transcriptomics, single-cell sequencing, and basically trying to extract as much deep information as we can and deep profile it on every hard-won biobank sample that we've managed to gather. And that's what's really exciting about this. And that then also provides new data into the target validation platforms, as well, of course, as providing us with much more sophisticated approaches to thinking about patient stratification when you consider then the multiomics approach far beyond euthanomics.
spk05: Let me just kind of wind up in that even though it's important, even though we just signed a really exciting kind of collaboration snuff, we've actually already identified a few targets. and we're just initiating the operational relationship around that. So we'll keep you informed as to the progress of that over the coming months and years.
spk07: Great. Thank you. Very helpful. We have no further questions at this time. I'll turn the call over to Andrew Hopkins for any closing remarks.
spk06: Thank you, Chris. And thank you to everyone who joined us today. As a scientist by training, it can be easy to solely focus on the exciting new chemistry and biology in the creation of a new medicine. However, I hope that today we've illuminated how it's our technology systems that can truly take the best of science and accelerate it, helping to move us towards a world where we see that all medicines might be designed with the extraordinary computing power of artificial intelligence and machine learning. enabling all of us in industry to achieve more in advancing new medicines for patients. And with that, thank you for your time today, and it's been a pleasure.
spk07: Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now disconnect.
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