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spk01: 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 second quarter of 2022. 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 of Investor Relations. Sarah, you may begin.
spk05: Thank you, Operator. A press release in 6K were issued yesterday after U.S. market closed with our first half and second quarter 2022 financial results and business updates. These documents can be found on our website at www.investors.extension.ai, along with the presentation for today's webcast. Before we begin, I'd like to remind you that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, preclinical and clinical results, and business performance. 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, Rich Law, Chief Business Officer, and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Operating Officer, and Gary Paradue, Chief Technology Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.
spk09: Thank you, Sarah. In the first half of 2022, we made significant advances across the business to scale our team and operations. We have continued to deliver progress across our collaborations and pipeline, positioning us for long-term growth. A detailed overview of our pipeline progress is included in our press release. We believe the decisive actions we've taken to continuously refine and focus our strategy have put us in a strong position to deliver across our portfolio of drug projects. Whilst remaining agile, and entrepreneurial in our approach. We are well capitalized with $732 million in cash at the end of the quarter, providing several years' runway, which is highly pertinent in today's market. On the operational front, our unique, nearly equal balance of talent between drug discovery scientists and technologists continues to be an area of strength. It's what underlines our ability to combine expert drug hunter knowledge with tech scalability. In the first half of this year, we continue to add new talent for our team in key areas of growth for the company, including Dr. Mike Crams as our Chief Quantitative Medicine Officer, whose expertise is invaluable as we begin to plan for clinical development in the coming years, and Professor Charlotte Dean, who joined to lead Biologics AI in January. Charlotte is a renowned leader in the field and was recently awarded an appointment to the most excellent order of the British Empire, for leading the UK Research and Innovation's rapid response call for projects addressing issues arising from the pandemic. And also, Eileen Jennings-Brown has joined us as our Chief Information Officer from her previous position as Head of Technology at the Wellcome Trust. We've also gone through a period of transformative growth. At the time of our IPO in October 2021, our team was around 200 employees. By the end of June in 2022, we had grown to more than 400 employees at locations across the world, building new areas of expertise and growth for the company, such as our clinical and precision medicine infrastructure. During this time, we've also made considerable progress in our pipeline, adding additional programs and progressing existing programs to later stages of discovery. We started the year by announcing our historic deal with Sanofi to develop a portfolio of AI, precision engineering, and medicines. Together with Sanofi, we have formally accepted two programs into the collaboration, one in oncology and the other in immunology and inflammation. We are also exploring the potential role of dozens of additional targets identified using a central biologist AI platform in these therapeutic areas, and we look forward to providing additional updates this year. Beyond small molecules, you may recall that our collaboration with Sanofi includes leveraging our translational platform for biologics. We're pleased to say that we have initiated our first project to help Sanofi select patients for one of the important antibody discovery programs. By leveraging our translational platform for our partners, we aim to potentially increase the probability of success for the patients we are seeking to help. Our journey to achieve automation in drug discovery development is well underway. We're working towards alternating not just design, but also some of the biggest bottlenecks in the design-make-test cycle, such as compound synthesis and molecular profiling. Our state-of-the-art automation suite will become operational next year, and we're excited about the potential to usher in a new era of what's possible in accelerated productivity. And with the announcement of top-line data in our A2A program, EXS 21506, one of our first-ever AI-designed molecules to enter clinical trials, We demonstrated the ability of our AI platform to create novel molecules based upon defined design objectives. We're engaged in ongoing translational work to establish a predictive biomarker to enable targeting patients most likely to benefit from a molecule. This has the potential to unlock the key challenge in effectively treating patients with high adenosine signature cancers. We look forward to sharing more details on our progress. We're using simulation-guided clinical trial design to plan our clinical trial for EXS21546, which we're calling Ignite AI. Identifying the right patients remains core to our approach. We're also using our translation platform and innovative approach to trial design as we plan our CDK7 CTA submission by the end of the year. We expect to start a trial in patients in the first half of 2023. Overall, We look forward to nominating additional drug candidates and reporting further pipeline progress in the second half of the year. And now, I want to take a deeper dive into the interplay between our business models and our technology strategy and how we're able to achieve productivity and why we are so confident in our ability to achieve success in the future. In short, the more we do, the more we learn. Accenture's overarching mission is to create better drugs for patients by applying AI and novel technologies, which allows us to be faster and more scalable than traditional methods. We apply the same tech-enabled strategy to all our programs, whether we're developing new medicines for our own pipeline or for a partner. A fundamental aspect of what we do is based on the scalability of our technology platform. In previous earnings calls, we described how different aspects of our pioneering approach to AI-first drug discovery and precision medicine and why we differentiate it. It is not simply our differentiated technology platform that is key to success, but also the opportunity tech scaling gives to a strategic interplay across our drug discovery pipeline, between our partnerships and our own internal pipeline. The scalability and systems of a technology company means we can create a different way to balance risk compared to a traditional biotech. In our internal pipeline, our focus hones in on areas where we have an established expertise and where we believe our precision medicine platform can provide the greatest probability of success. Our CFO, Ben Taylor, will provide more details on this in a few minutes. Our partnerships not only generate substantial standalone value, but also have the practical application of allowing us to scale. The more we do, the more our platform learns and improves. By applying our tech to new challenges, we're expanding our knowledge base, and more than that, increasing the impact that we can bring to the world. So how does this translate into the actual pipeline? Because of our technology capabilities, today we're able to progress a multitude of discovery programs in our pipeline, balanced between internal and partner programs. We have a phase one asset about to enter a phase one B2. three programs in IND enabling studies, news of which we expect to announce in the coming months, and a rich pipeline of early drug discovery programs that we've highlighted here. Furthermore, beyond what we have presented here, we have an active program of early stage portfolio of target implication and validation projects. We continue to generate targets for our partners, including Sanofi, as mentioned earlier, and have two approved programs moving into design. Focus is critical for us. We have prioritized our resources in those areas where we can have the greatest impact on better drug development, translation, and patient selection. This has resulted in deciding to de-prioritize a few early stage programs so that those resources can be moved to our core areas where we believe our patient-based translational platform can be applied to increase the overall probability of success in selecting the right patients. Our message today is one of focus. and how we can leverage all our platforms and truly impact the probability of success in the clinic going forward. In previous earnings calls, we've talked about Accenture's AI-first end-to-end process to drug creation. We originally pioneered AI-driven drug design, but as the business has grown, we have grown downstream into clinical development and upstream into target identification. We have a number of parallel and complementary approaches to generate new projects for our pipeline and our partnerships. New projects can come via our center bulges, which applies deep learning to genome scale knowledge graphs to identify connections and predict target disease associations. This technology is being actively used to prioritize targets for validation as part of our collaboration with Sanofi. We can also use our patient tissue translational platform to identify new drug targets. You may recall at the AACR earlier this year, we presented an example of how we discovered ALK as a potential new target for ovarian cancer using our human tissue precision medicine platform. We are also sourcing new assays, models, and targets from outside Accenture with programs such as Exelomics, where we're seeking to accelerate early stage research with phenotypic screening of novel disease models with academic labs. I'd like to highlight an example where using our capabilities, in just one quarter, we were able to identify around 20 novel target hypotheses with high confidence from an initial genome-wide search in a collaboration with one of our partners for an inflammation and immunology indication. This is a nice example of the power of our operations, technology, and platform, and we continue to improve them. We plan to present a deep dive of our experimental and AI-driven approaches to target identification and validation at a future earnings goal. I'm now happy to hand over to Richard Law, our Chief Business Officer, who will walk us through how we think about our platform and business development strategies tied together. As evidenced by our news flow, Richard and the business development team are quite prolific and create exceptional value for the company.
spk08: Richard. Thank you, Andrew. I'll now take a few minutes to walk through our approach to business development at Accenture and how this has evolved over time. Since the founding of the company, we've continued to expand our capabilities, and in turn, this has increased the value of partnering with Accenture over time. Using the combination of our AI tech platform and the human tissue platform Andrew mentioned earlier, our approach can validate targets in ways that others can't. Rather than relying on the limitations of the traditional industry-dominant, human expertise-led approach to selecting targets for new drug discovery projects, one which is a long and sequential process, but then often relying on a single key academic publication. We're using AI to bring to bear a new level of scale and connectivity to turn publication overload and biological complexity into a data-driven confidence builder on which to base the start of new drug discovery projects. Another area to highlight is how we have integrated advanced molecular dynamics and quantum mechanics into our approach to help us fully utilize and understand the potential for a protein structure, whether it is determined or AI predicted, to guide molecular design. Molecular dynamics allows the quality of a structure to be assessed, but also provides a movie-like insight into the target's flexibility instead of just a snapshot. This is just one example. As we continue to build and scale, These enhancements and builds are value add, not just for us, as we can improve on our efficiency, quality and probability of success, but also for our partners. And so the deal terms, milestones and royalties have reflected this build out as well. We've seen a shift over the past few years. The more end-to-end, fully externalized drug discovery programs with all of our pharma partners. such as BMS and Sanofi. As we expand our capabilities, so the breadth and value of what we can bring to partners will grow. Going forward, within our partnered programs, our aim is not just to do more. It is to have increasing ownership of the partnered projects within our pipeline, principally by enabling a match of drug to patients in the clinic. As an example, our collaboration with Sanofi includes an option for clinical co-investment with a royalty rate up to 21 percent on net sales of co-funded projects without any reduction in milestones for the delivery of the asset by the ai discovery engine there are several potential ways in which we can deliver value for partners leveraging our end-to-end process our partnerships with pharma are often focused on the ability to design a better molecule from an idea, whether that's for a known or relatively novel target. Novel targets are often the least well understood or studied, but the value may be the highest here, and where we can provide differentiated expertise, since well-known targets will already be well known by others in the industry. It's a balance, but with a combination of biology AI and patient tissue data, we believe we can break the dogma of novel targets being riskier. We can predict who the patients are going to be even before we start the discovery project. This is key to altering the probability of success equation in the industry in an end-to-end fashion. For known targets and or molecules, the platform can assess molecules to select the patients and indications for which they are best suited. in a way that others can't as we are testing against actual patient tissue. Thirdly, we have the potential to match the right drug candidates or combinations to the right subsets of patients with our translational work. This is something Andrew mentioned earlier that we are doing for Sanofi. Consider the number of molecules that may have been pushed to the side that may have unrealized potential for patients. For instance, certain drugs that successfully treated patients in our EXALT-1 trial that had previously failed in clinical trials for that same indication. As we work with partners, we're often generating a number of high-value potential targets, not all of which will be selected to advance. As the internal pipeline advances, some assets will need to be monetized to manage cash flow. And this will mean finding partners for those molecules that will best shepherd them in the clinic, with our help of course. And while there are many opportunities to add value for our partners, our partners are increasingly tapping the full extent of our capabilities from early target discovery all the way through to eventual patient selection. Put simply, the real potential lies in a re-engineered process of drug discovery and development. which is the vision and track record our business development team aims to articulate. That is, how when implemented alongside the re-engineered process, AI can drive a step change in the probability of advancing new precise medicines to exactly the right patients. I'll now turn the call over to Ben to walk us through how we think about scale and impact business development has on our platform.
spk04: Thank you, Rich. We are often asked two important questions. First, how do we determine if a new target should be developed internally or through partnership? Second, how do we think about the value of a partnership program? In the following section, we'll address those questions from a strategic and economic perspective. Before we talk about our business strategy, it's important to spend a moment on the operational value of scale. Economies of scale certainly can benefit us. But much more importantly, increasing our scale can help us design better medicines. The more pipeline programs we work on, the more data we generate and the more design problems we solve. This data and learning can then feed back into the platform to design better drugs more efficiently in the future. Traditionally in biotech, developing two pipeline programs in parallel requires approximately twice the number of people or CRO services as developing one. However, by using a combination of AI based technologies and novel processes, we have repeatedly demonstrated exceptional productivity gains on both the time and costs compared to traditional benchmarks while maintaining a relatively small infrastructure. So for us, scaling is not just about developing a bigger pipeline. It is actually helping us develop a better pipeline. This is exactly why we continue to expand our closed loop operating model. from target ID through the clinic. The more aspects of development and data we control, the more robust our system can become. Not only do we already have a broad pipeline, but our capacity allows us to initiate another five to 10 programs per year. This means that we can continue to expand both our internal pipeline as well as our partnerships into the foreseeable future. We would expect the majority of new programs to be initiated in the partnership model going forward, with a few focused candidates chosen every year for internal development. Here we can see the valuation impact of our partnership terms by comparing the net present value received in a partnered program versus the same drug as a wholly owned internal program. The chart shows risk-adjusted discounted cash flows over the life of a drug candidate. In this case, at a 10% overall probability of success to reflect a slightly better rate than the industry average of 4%. For the orange wholly owned line, we have used publicly available data to create an example of the average cash flow profile for a drug from discovery through commercialization. The cash flow potential is very high, but requires substantial investment, time, and risk. In other words, This is a typical biotech model, and you may recall we shared the assumptions behind this model last November as well. For the TEAL partnership line, we have used the general terms of our 15-target Sanofi agreement from earlier this year. That partnership uses a broad range of our capabilities, including our target identification, AI-based design, and precision medicine platforms, resulting in per project economics of $343 million in potential milestones as well as royalty rates from the high single digits up to 21%. In this example, we are using a low teens royalty that would be appropriate for the average product described. To sum the wholly owned scenario, it results in about twice the discounted cash flows as a partnership, but that only comes after several years of large investment with substantial binary risk. By comparison, the partnership is always cash flow positive from a direct cost perspective, which strategically makes up for some of the lower MPV. To be clear, we would always prefer twice the value. However, our capacity allows us the flexibility to do both. We can select a focused group of wholly owned targets where our internal capabilities may improve the probability of success while still capturing a large portion of the additional pipeline value with lower risk through partnerships. In addition, our partnerships are providing us with an operational benefit by building out and improving the technology platform. Now let's take a step back and look at what our current partnership business means in terms of value. If you run all of our BMS and Sanofi programs through the same MPV analysis, it would equal just under half the MPV as if we were to internally develop those candidates as wholly owned programs. Hopefully, this gives a sense of the importance our partnership business contributes to our long-term business model. On this slide, you can see how the model's net present value changes based on our assumption of overall probability of success. The greater the probability of success in the model, the greater the magnitude of value we may achieve from a wholly-owned program as compared to a partnered asset. Importantly, even at 100% probability of success, the wholly-owned program is only about three times the partnered MPV, highlighting the importance of having a combined strategy. What this shows is that if we believe our internal capabilities can significantly increase our probability of success, pursuing a wholly-owned strategy becomes more valuable. On the other hand, if we think we can increase the probability of success with a partner's capabilities, the partnership model will become closer to or even surpass the wholly owned value. And so we are thoughtful in evaluating each target on our internal ability to improve probability of success. It needs to not only be a target where AI design will create a differentiated drug, but we also need to know that our precision medicine platform will guide our clinical development and we will have the internal expertise to execute and potentially commercialize the program. You can also see why we would invest in better translational systems that enable patient enrichment and improved data-driven clinical trials. Both these investments are directly tied to increasing the probability of success at critical points of development. Our approach also provides us benefits in risk diversification. As I just mentioned, We focus our operations on improving probability of success by targeting the most common points of failure in discovery and development. However, on a very basic level, we target binary valuation risk by having a broad portfolio. We believe that having dozens of pipeline candidates increases our chances of having commercial products. As you've seen from our financial results, our partnership business has brought in over 200 million over the last 18 months. This allows us to continue rapidly expanding our platform and pipeline with substantially less dilutive financing required. Finally, we also believe it is underestimated that many biotech companies have a pipeline based on a single scientific hypothesis. If that hypothesis fails, it may undercut all of the products in the pipeline. At Accentia, We execute projects across therapeutic areas, target classes, and types of small molecules, which provides a high level of scientific risk diversification. Andrew already spoke about our strategy and the two different pipelines. However, it's important to note that operationally, these two models look very similar until the point of entering the clinic. This has actually provided us substantial flexibility in adjusting our pipeline to fit changes in our long-term goals. We have moved programs from our internal pipeline into partnerships, as well as converted partnerships into joint ventures. Our core focus is to successfully launch new medicines, so we will continue to be nimble and rational in how we achieve that. Now I'll take a minute to close with highlights from our financial results. Full results are detailed in our press release in Form 6K. We ended the quarter with $732 million in cash, equivalents, and bank deposits. We believe this gives us several years of cash runway and the resources to continue investing in our business expansion and differentiated pipeline. Our cash inflows from collaborations for the six months ended June 30, 2022, were $117 million, which included a $100 million upfront payment from Sanofi. Importantly, our strategy, which we have just reviewed, creates a potential stream of non-dilutive capital inflows, helping maintain a strong balance sheet to fund our business. As an example, our operating cash flow in the first half of 2022 was positive $63 million. It is important to note that we do not expect to be operating cash flow positive for the full year as we continue to invest in our pipeline and platform to best position ourselves for the future. With that, I'll turn the call back over to Andrew. Thank you, Ben.
spk09: Ten years ago, Accenture was founded by a small team in Dundee, Scotland, nearly all of whom are still with us today. Since that time, we've forged our way as a leader using AI-driven approaches to modernize how new medicines are created. We've pushed the field by bringing the first AI-designed molecules to clinical trials, and we were the first to improve clinical outcomes in oncology using AI. We are truly transitioning from rapid growth and scaling to now entering a period focused on stability and execution against our long-term vision. And with that, we'll open up a call for questions and answers.
spk01: Thank you. As a reminder, if you would like to ask a question, please press star then 1 on your telephone keypad. Our first question is from Michael Reskin with Bank of America. Your line is open.
spk11: Great. Thanks. Thanks for taking the question, guys. And I appreciate the overview. I was just wondering on that, some of the last points you kind of touched on, the ability to execute and leverage the platform across multiple programs. Obviously, with a $700 million cash balance, you're really well positioned for a number of years. So could you give us a little bit more color on your hiring plans, your investment plans, both in R&D? and the rest of the organization over the next six to 12 months? How should we think about deploying some of that capital?
spk09: Excellent. Mike, good to hear from you. Thanks for the question. So Ben and I will actually give an answer to this question. So we're well positioned, actually. We've been through rapid scaling over the past few years. We doubled in size year-on-year for the past four years now. And at the time of the IPO, we're around 200 people and we're just over 400 people at the moment. But we do expect to see now, we can start to see how now the business starts to sort of stabilize around the capacity which Ben started to describe in his slides and the ambition we have then for the partnership business. But at the same time, we do continue to invest in technology areas where we believe actually give us a continuous advantage both for developing our own molecules and our own programs, particularly focusing now in the areas of precision oncology, and also areas where we see that where the next generation of productivity enhance is going to come from, whether that's from investments in automation, investments in biologics designed by AI as well. So we do see that the investments put in place are actually allowing us then to grow a company which we believe actually will create a portfolio of agents which will be designed with translational biology and precision medicine in mind right from the start, but also then a platform which we believe actually will continue to be one of the most productive drug discovery engines in the industry. So that's the core sort of areas where our strategic plans for next years are leading to. But to give a bit more color on that, I want to introduce Ben, actually, whose team has been doing a lot of work actually in preparing the plans behind this.
spk04: Hey, Mike. Always great to hang out.
spk09: Hi, Ben.
spk04: So, well, I think what hopefully you picked up from the presentation is both of the business models have a great value proposition. So I think what this really highlights is, no question, we've got the balance sheet capital to make investments. However, what we want to continue doing is make smart investments. So where we start everything is how is this either going to benefit our long-term platform or how can we improve the probability of success on a drug that we're designing? And so that's really where we start. It's not about, hey, we're going to set out and invest X number of dollars this year. It's more of a question of how can we make investments that have a lot of impact to them. If we can come up with a great translational strategy and do a patient enrichment scheme around the clinical trials where we're really impacting the potential to get to market and get to patients, that's something that we're much more likely to do internally. However, we've got a lot of great partners who come in with their own technologies and viewpoints and capabilities, and sometimes they have a way to do it, and those should be partnering programs. So I think... When we take a step back, what we want to do is make sure that we are always in control of the business. And so we have the flexibility to continue investing, but also to make good business decisions. We never want to be held to outside influences. And so I would expect us to have a continuing balanced business model. And to be honest, I kind of like having a low burn rate. So, you know, I don't think we're going to flip that the other way around.
spk11: Okay, great. No, that's helpful. And if I could ask a follow-up, on the 546 Phase 1B2 start, could you remind us any details you provided on sort of the format of that burn? of that trial, sort of how many patients are you looking at, the duration, when should we expect the next data point there, and if there are any other things we should be looking for in the clinic in the next six to 12 months. Thanks.
spk09: Excellent. Mike, so yes, we'll be giving further details of the 21-546 trial design in the next few months. We're really excited by what Mike Crams, our new Chief Quantitative Medicine Officer, has been bringing to the organization, in particularly thinking about how we're using simulation-driven clinical trial design in VAT, and how that has been integrated then with our work on using the precision medicine platform for thinking about translational biomarkers with that. So we'd be really excited to bring that forward to you in the next few months within it. Just to sort of recap on the timings and how we're thinking about the practicalities of running a trial, I just want to hand this over to Dave Hallett, OCO.
spk06: Yeah, thanks, Andrew. Just to reiterate what Andrew said is that the precise details about study designs for both 21.546 and the CDK7 asset will be forthcoming in the second half of the year. So watch out for that news flow. But as Andrew said, one of the beauties of Mike Crams joining us, who's a world expert in creative clinical trial design, reminding that he was the first person ever to lead an effort to use a Bayesian statistics approach to running clinical trials. So what he's brought to the team, not just for the HOA asset, but for other ones, is that We're running a lot of simulations, kind of hundreds of simulations in the last few weeks and months, just looking at how best to kind of optimize that forthcoming study, which obviously will be in patients, looking at kind of interactions with kind of standard of care. So can't give you precise details today about patient numbers, about cancer types, but be patient. That will be forthcoming in the second half of the year. And in addition to that, clearly a key part of that is the work that we're doing with our translational platform at the moment in terms of both kind of drug response biomarkers but also kind of patient response markers. So, yeah, that will all be forthcoming in a lot of detail during the second half of this year.
spk09: And, Mike, just to the second part of your question, and I hear the word sort of for the catalyst coming through as well, and... I just want to open it up to Ben, who's been looking at the range of potential events we've got flowing down over the next few months.
spk04: I would think about it as three different areas where you might see things. One is pipeline, two is operations, and three is business development. On the pipeline front, obviously, we've got a lot of candidates that could be moving forward over the course of the next six and 12 months. Certainly, our CDK7 and A2A drugs are looking to start the next phase of clinical trials soon for both of those. and more updates coming on that, as you noted. But we have a number of other pipeline programs that are moving forward, both internally and that have been in-licensed by some of our partners. Obviously, we don't have control over the timing of events for our partners, but we will let you know when that happens. And then, in addition, I think operationally we're looking at a lot of new platform developments coming on, We actually have a lot of technology development going on internally. I think we've probably got one of the biggest and best technology teams in the industry, and they're being incredibly productive right now. So we'll continue to keep you updated as those things come forward. And then finally, Richard has obviously been busy on business development, and we hope he's not going to coast for the next 12 months and we'll be very busy as well.
spk03: Great. Appreciate all that color. Thanks again, guys.
spk01: Thanks, Mike. The next question is from Vikram Parohit with Morgan Stanley. Your line is open.
spk02: Good morning. Thanks for taking my question. My first one is on the A2A program. So I know that we're waiting to see more details about We designed that study over the next couple of months here, but on the simulation-guided design principles that you've mentioned in the release and throughout the call, is there any more color available on particularly what kinds of design features you may be thinking about adding in how these design principles might impact timelines to initial data and the parameters of data we might initially see? And then lastly, how transferable are some of these design features from the A2A program to other pipeline compounds that you're going to be looking at and that are going to be moving forward in the next one to two years?
spk09: Hi, Vikram. Good to hear from you. And thanks for the question. That's a great question, actually. And it touches upon one of the reasons why it was so important for us to bring someone with myCRAM skill set into the organization, because it's fundamentally for us about we want to be as innovative in the clinic as we have been as innovative in developing new technologies in discovery. So the way we're thinking then about sort of computational-driven, simulation-driven clinical trial design in all these approaches is, as you hinted at, this isn't actually just a technology and approach for the A2A molecule. This is actually a philosophy which we're looking to apply to all our clinical trial design approaches as we go forward. And we've got a number of other projects right now which are also actively working on in thinking about sort of trial design. So what we're looking at then is fundamentally the goal then of how do we identify the correct dose and the correct treatment regime as efficiently as possible in the trial design to do that. So that will be potentially, so obviously you can think some of the key parameters then we'll be trying to do that. So as David just hinted at, you know, the simulations allow us to go, you know, not just hundreds but actually thousands of variations then on trial design to be run and the simulations that allowed us then to think where we can find sort of a maximum tolerated dose and specific oncology indications. And also really think about how then we design that trial. So we test in A to A then with standard of care and what are the right sort of indications, if there's a combination of standard of care is best applied. As I said, there'll be more details to come on that trial design. particularly how our trial design sits alongside how we're thinking about developing and validating translational efficacy biomarkers, which is also key, and that's what we use now human tissue patient-based platform for as well. And actually then bringing those two fields together, I actually think creates an almost unique approach for Accenture in how we start to think about clinical innovation. And that actually is something we really look forward to telling you about and future audience calls and talking you through that. It's about how do we bring out innovation, apply some of the really greatest thinking we've seen in this field start to emerge. So you don't expect a traditional clinical trial approach from Accenture. We do want to think about how we bring these two leading approaches to adaptive trial design and precision medicine together in how we think about our trials, not just for A2A, but actually for all our programs.
spk02: Got it. That's helpful. And then a quick follow-up on going back to capital deployment and business development. So how does the unwinding of the Bayer partnership and the thought process that went into that mutual decision, and then also the lessons learned that you've accumulated through the rest of your partnerships that you have ongoing, how does all of that impact your perspective of what future partnerships could and should look like looking out over the next couple of years?
spk09: Thanks. Good question, Vikram. And it's a good opportunity now to introduce you for the first time at our earnings call to Richard Law, our Chief Business Officer. Richard.
spk08: Thanks, Andrew. Thanks, Vikram. Yeah, as you heard in the press release, you know, we've sunsetted the collaboration with Bayer. And I think what you've seen is an evolution of the company over the last few years. We're much more end-to-end in what we're doing. But most importantly, we've created a system and a process that is now in place with every other partner of ours, whereby we drive everything. This is really vital to both making the best use of how AI works but also in how we create value from the platform. So, you know, we ended on a high with Bayer. We delivered the lead candidate to the design criteria of the collaboration. And, you know, we wish them luck with that in the future.
spk03: Yeah, and maybe I'll just add in one thing on that, Vikram.
spk04: As far as I would expect more in the vein of the BMS and Sanofi type of deals on the partnership side going forward. And, you know, obviously that has a great economic return to us. But what we really think about is we want to be a partner on everything that we do across at least through the end of discovery. And our partners often may pick it up in clinical, but it's really got to be something strategic and a partnership before that.
spk09: Absolutely. And Rick, in many ways, the reason why we sort of ended on a high with Bayer, after getting the first milestone with them, was actually to do with real development of how do you apply AI? And that's exactly how we've been building our end-to-end platform. What we find sort of is, These older type of deals, which are sort of AI only sort of design deals, are not the optimal way, which is why we built out Accenture now so we can really control the whole process of how we generate experimental data, how we add on upstream and downstream capabilities of target ID and precision medicine and integrate all these into a new process. And that's fundamentally the advantage we see. And that's why now all our deal structures, all our partnerships are built that way.
spk03: Understood. Very helpful. Thank you.
spk01: The next question is from Chris Shibutani with Goldman Sachs. Your line is open.
spk07: Good morning, and thank you. With really a foundational tenet being the company's ability to continuously learn and weave into your efforts that which you are learning, I think when you think about your internal pipeline, I always think about your focus as being on small molecules and in oncology. And yet you made some comments about leveraging this towards biologics with some of your partnerships, et cetera. Can you talk about how you were thinking about this sort of therapeutic and modality focus as far as circling what you think might be priorities from an internal pipeline standpoint? And then I have a follow-up.
spk09: Excellent. Thank you, Chris. Good to hear from Aaron. Thanks for joining today. So, yes, we're still developing these new areas, and you can see from some announcement we've made where you might think direction of travel is going. I mean, in January, we hired Professor Charlotte Dean from Oxford to head up and build our new Biologics AI design group. That's obviously a strong signal, and that has been going incredibly well. I'm very pleased to see Charlotte join the company. you know, really apply her talents and also win her MBE actually earlier this year. The other thing as well, of course, is there's some important elements around our precision medicine platform, which we think actually opens up a really interesting possibility in this whole biologic space, which is we've already validated our precision medicine platform using human patient tissue for use in both small molecules and antibodies. And in fact, that's already built into the Sanofi deal. And in fact, we've already started our first precision medicine projects with Sanofi where actually we're bringing in their antibodies discovered by Sanofi into our sort of precision medicine sort of pipeline and is a whole set then of milestone-based success fees then based on how we can help Sanofi select patients. You'll be hearing more for us in future. I think Chris once we've actually sort of ready for prime time to talk about these new developments. But I think you can start to see, you know, things taking shape where, you know, we are, you know, it's public knowledge actively interested in how we can design biologics using AI. And importantly, have already validated how we can use precision medicine downstream to test biologics. And we think those two things coming together creates something very interesting.
spk04: Yeah, I think the other really important aspect is we're actually building a very modular system. The way that we do it and how we remodel the process actually allows us to slot things in and out. And so if you think about our five areas of capability, so we do target ID. We can do the drug design using our AI systems. We do a lot of experimental biology. We do our translational technologies, and we're expanding into the clinic. So if we put another modality into design, for example, that would really just be slotting out the small molecule design and putting in a different modality. But the rest of those modules that I talked about would still function in the exact same way. And so we are designing from the foundation to be able to build and grow our business over time and be very flexible in how we advance medicines to patients.
spk09: That's a great point, Ben. It's about an engineering of that process and how we can bring in new technologies and really get synergies of that new technology across the rest of the platform. Chris, there was another part to your question I just wanted to follow up, actually. so right today you know sorry sorry which was uh you know oncology focus yes so it is certainly uh we do see that for the internal pipeline as of today you know it is a small molecule focused and more and more so now focusing on where we can see we can gain an advantage of increasing our confidence or probability of success in the clinic where we believe we can build validated and high-confidence translational models. And right now that's in the area of precision oncology. That's where we believe our precision medicine platform, which we already got the first sort of clinical validation of, is giving us confidence. And our goal then is to align our sort of target, validation, therapeutic area strategy towards areas which really align then to the clinic and areas where we believe we have higher confidence and probabilities of success based on these platforms.
spk07: Right. And that leads exactly to my second question. I guess there was a discussion earlier about 546. You're other near to approaching the clinic asset 617, the CDK7 inhibitor, I believe with Ocarion. So I think it is that challenge, doing the preclinical into the clinic transition with confidence and enhancing the probability of success. I suppose a standard question would be what type 616? what remains to be seen in order for you to feel that you can transition to the clinic just from a housekeeping standpoint, but what might be the first signs that you can demonstrate clinically that 617 is differentiated, and what you need to learn in humans to inform the next stages, or can you already set the clinical path based on your preclinical work alone? Thank you.
spk09: Thanks, Chris. I'm going to open this one up to Dave Hallett, who's been leading the 617 program.
spk06: Hi again, Chris. Good to meet you virtually. Really good question. The housekeeping piece, let's deal with that one first. So the regulatory talks, kind of clinical formulation piece, all the kind of boring but necessary pieces that you have to do to get a compound into patients. They're nearing kind of successful completion. So that kind of regulatory, the IMPD piece and the tox package that kind of goes with that molecule is there. So that's why we remain confident about giving guidance about opening the CTA later this year. In terms of differentiation, where we go next, the things that we'll update you on, kind of during the second half of this year and probably at medical meetings into next, the things we're looking at at the moment are what's the, particularly for CDK7, what's the kind of optimal dosing regimen to kind of maximize the therapeutic index? So pre-clinically, we're doing work at the moment looking at different dosing schedules to maximize that. In addition to that, we've released some data, and we'll release some more in the next six or nine months, most likely around, so I refer back to the AACR poster, where we started to kind of gain an understanding at both a phenotypic level using our platform, but now looking at more molecular levels. What is, kind of, so I'm referring to the ovarian cancer work that we did. We're now getting, starting to get an understanding, and we look forward to releasing that data to why certain subgroups of patients in that ovarian cancer setting, what were the molecular markers that underpinned the differential response to the drug? So in summary, the things we're looking for will be to show differentiation and progress. The early part of the clinical study will really be looking not so much for efficacy but maximizing therapeutic index, making sure that we find the right dosing regimen in patients so that we properly test the mechanism. And then we're just narrowing down at the moment, and we'll release that information in due course about the specific kind of cancer subtypes that we're going to look at. So I can't give you any more precision on that at the moment, but like 21546, Chris, we're pretty close in very advanced discussions about the kind of clinical trial design. So I look forward to sharing that with you and the rest of the world in the second half of the year.
spk09: Yeah. Sorry, Ben, I was going to add one more, but you go first.
spk04: Okay. I was just going to say, Chris, this goes back to something that is very close to our heart and how we think about operations. When you were talking about what can we do before we start the trial. So throughout our company, starting from target idea all the way through the clinic, we always try and use a data-driven modeling approach. tech-forward approach to doing things. However, you have to balance that with experimental validation. And so the clinical trials will be no different. We intend to start our clinical trials with a tech-driven thesis that has been backed up by the best experimental data that we can get. However, if you think about the heart of an adaptive trial, for example, and that's just one way to do it, there's different ways to do it, That is actually validating that hypothesis in the clinic. You're using Bayesian statistics to do it much more efficiently on much smaller patient populations, but you're basically validating that hypothesis and then designing that so that the trial can continue once that's been validated. And so we'll continue to use methods like that that are familiar to the FDA and that the FDA actually really appreciates, but You have to go through and validate these things in the early stage trials. I think one of the differences is we can do it a lot earlier than other people would. Most of the time you see people running phase one and phase two trials doing a post hoc analysis and trying to figure out what the right strategy is. We're able to do it from the beginning with the early design and then factoring that into even the phase one trials.
spk09: Chris, there was just one final point I just wanted to add about, particularly around Ben's point on validation, which is as well as running trials on our drug assets, we continue to look to clinically validate a precision medicine platform. So as Ben and Dave said, we get our validation as well alongside the innovative trial design that we run in interlacing the biomarkers that we're developing from our platform But also we're looking for further clinical validation of our precision medicine platform beyond blood cancers, beyond the EXALT-1 trial. And, you know, we are looking to, you know, be initiating, you know, shortly further sort of studies where we start to think about how to validate a precision medicine platform in its own right in other solid cancers as well.
spk03: Thank you. Appreciate the bountiful answers. Thank you, Chris.
spk01: The next question is from Peter Lawson with Barclays. Your line is open.
spk10: Great. Thanks for taking the questions. Just kind of dovetailing into Chris's question, just on CDK7, just the number of PM molecules and whether you've kind of synthesized those and where you think you could potentially have differentiation based upon whether the structure of those molecules or the way they're kind of targeted. Any details around that? CDK7 differentiation would be great.
spk06: Hi, nice to meet you again, Peter. I think probably just a reminder to refer back to information that was generated and disclosed at the time of the IPO. In the case of CDK7, differentiation against competition. Against most of the molecules that we're aware of that are in the clinic at the moment, and that we have either synthesized the company itself or patented examples from those companies, potency selectivity is something that kind of stands out with our molecule. There are molecules that are a little bit ahead of us in the clinic, but in our hands, those molecules are either less potent against ADK7 or are less selective against the wider kinome. And we expect that to have kind of manifested several kind of negatives against that approach in that CDK7 itself is a promising mechanism, but it does come with mechanism-based toxicity. If you then add on kind of other kinase activity, you're going to erode that therapeutic index even further. So we're optimistic that we've designed a molecule that can push the therapeutic index as far as it will go in human studies. I think there are other balanced properties of the molecules as well. So we kind of factored that into designs. One of the key aspects is that one of the competitive compounds we looked at, we know for a fact, actually, is that the molecule is experimentally a substrate for a number of key transporters in humans. That itself is likely to kind of cause problems in terms of variation in exposure in humans. And just as importantly, one of those transporters is actually present in tumors that are overexpressed and polarized such that it's trying to push drugs out of a tumor rather than in. So I guess in summary, differentiation from potency, selectivity, and balanced properties allowing us to dose a molecule or really test the CDK mechanism. Yeah, we are, as we go into the clinic, doing simulations around dosing regimens to maximize that therapeutic index, again, using data and simulations. As we'll allude to, using simulations as well, actually, on CDK7 to look at
spk10: kind of likely effects that will happen together with standard of care so a very more data-driven methodology taking place at this moment which we look forward to sharing with you in the next six months thank you and then just around the the bear collaboration or determination of the program just any further details around that and also i think you mentioned the potential to develop one of those targets any details around the target and potential indications
spk08: Yeah, I can speak to that a little bit. So yeah, so we successfully delivered the lead on the first target that we worked on with Bayer. We're really happy about that. We looked at several other targets. So we used the central biology AI platform and other elements of our target evaluation. We decided on one other target to start work on. We also pushed back on several other targets that we felt like the target validation wasn't there for them. So we did start on another target, but essentially at the point of deciding to exit, we agreed with Bayer that we could get full freedom to operate back on that target. So, you know, we'll potentially look to start to work on that either ourselves or maybe even with another partner.
spk04: And one other thing to add on there, Peter, just to be clear, this was in many ways an economically driven decision. Even though we've got wonderful scalability, it's not infinite. And so we need to be really disciplined managers of how we use our resources. So we showed the MPV analysis during our call for this no fee deal in comparison to wholly owned. I won't go into the details on Bayer, but it didn't look as good. And so if we're thinking about where we are going to have our team spending time, we would rather have them spend time on programs where we have better economics. You've heard it from several of us already. We had a very nice relationship with Bayer and would do business with him again in the future, certainly, but that deal itself wasn't the right deal for the way that we do business now.
spk10: Richard, thanks for the details around that. Any details around the target that you would develop out of that relationship?
spk03: I'll
spk06: Olly, it's a very interesting kind of eye-on-eye target. Again, just to kind of link back to kind of one of the key themes of today's presentation is really around what we're looking at at the moment is, again, being very critical about, okay, what is the validation around that target? How does it fit with our precision medicine? Is it a target that we feel either ourselves or a partner that may have a a different kind of pharma partner that may have a kind of technology that changes a view on a probability of success. So it's scientifically very interesting, but scientifically interesting only gets you so far. So we're spending a lot of time looking at, yeah, what are the right patients for that target? What's the landscape? And then figuring out, is this one for us to take on internally or one that's actually best within a partnership?
spk10: Good job. Thank you. And just a final question, I guess, for Ben or Andrew, just on the ideas of any bolt-on acquisitions.
spk09: Good question, Peter. Accenture actually, I think, has been very successful how we've carefully acquired and integrated companies to grow. From our early days, acquiring Connected Discovery would help us build experimental biology capabilities, which then actually allowed us to really, truly run in our own projects. That allowed us then to really enable the Celgene deal that we signed because we can both have a first new deals of a new type we've run in where we can actually then manage the whole thing. One of the experiments to doing a project management. And then of course last year when we integrated Precision Medicine into our platform with the acquisition of AllSight which has been an incredible integration into the company and the team has been absolutely fantastic. It's as if we formed Accenture together actually I think the feeling of the team is. And so where we think about it now is how do we then position any sort of new acquisitions into our core strategy? And I think hopefully what came across today, you can see there's a focus strategy emerging now. One that is, you know, think about the platform. How do we increase productivity continuously? And how do we increase our probability of success? And that's why for us, uh building in precision medicine uh using sort of the the high content approach was so important but what was more important was actually the network of uh biobank samples collected from you know dozens and dozens of hospitals that network expanding as we speak so what's important for us now would be to look at any acquisition to think about how does increase our probability of success moving forward to the clinic does it potentially allow us to maybe expand into new indications with a greater degree of probability of success as we've seen with oncology, with our current platform. Those are the ways we think about something, actually. Those are ways we try to sort of position it, etc. And so that's a way to think about it, Peter, as we go forward. Ben, anything to add to that?
spk04: Yeah, I'd say we're always going to be data-driven. We're not a dogmatic team. So we're just looking at it and saying, if we're going to invest anything in an external technology, it's got to exceed our internal ROI capability in that area. And we've got a very productive operating team, so that's a pretty high bar. Typically, That's not going to be something that is tech standalone. We are more interested in things that can integrate with experimental operations, like how are you making experimental data better? The focus of what would probably be interesting, but again, we've got plenty to do internally. We don't worry about doing M&A as a growth driver. But if we find something that makes a lot of sense, we're not opposed to it either.
spk09: Absolutely. So Peter, just to reemphasize what Ben just said, coming back to our concept of modularity, as we build out new technologies, whether we build them ourselves or whether it's an acquisition, are we getting synergy? Is it truly something that we can see could be plug and play that would also synergize with the rest of a platform, whether it's target identification, drug design, different types of modalities, design engines, or downstream into sort of precision medicine and into clinical sort of innovation. And that's a way to think about it, actually. We don't want standalone technologies. We want things that actually really give us synergy. So as we plug something in, we can really see that that new modality concept gains from actually the rest of the platform. And I would say about precision medicine, actually. Precision medicine has gained so much more by being part of an end-to-end AI-driven drug discovery engine as well.
spk03: Great. Thank you so much.
spk01: We have no further questions at this time. I'll turn it over to Andrew Hopkins for any closing remarks.
spk09: Thank you, Chris. We now have a growing pipeline that is progressing towards and actually into the clinic now. And we aim to be as innovative in the clinic as we've been put into our efforts into developing drug discovery and design. And we start to see that come to fruition and come to life as we advance our pipeline. We believe there are many more firsts and achievements that lie ahead of us. Thank you all for joining us today. And operator, you may now disconnect.
spk01: Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now disconnect.
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