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spk02: Hello, everyone. My name is Stuart, and I will be your conference operator today. This time, I would like to welcome everyone to Accenture's business update for the third quarter 2021. At this time, I'd like to introduce Sarah Sherman, Vice President of Investor Relations. You may begin.
spk01: Thank you, Operator. A press release in Form 6K was issued yesterday after U.S. market closed with our third quarter 2021 financial results and business updates. These documents can be found on our website at www.investors.Xentia.ai, along with the 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. Actual results may differ materially from those indicated by these statements. Accenture is not under any obligation to update these statements regarding the future or to confirm these statements in relation to actual results unless required by law. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer, and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Operations 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.
spk08: Thank you, Sarah. And thank you to everyone who joined us today. It's my pleasure to welcome you to our first earnings call. Today, I'll review our recent accomplishments, and then I'll be joined by Ben Taylor for a discussion around the business models that help fuel our pipeline. First of all, I'll start on slide three to navigate you through the progress and to give you an idea of what we're building towards. I'd like to start with our vision of using AI to discover better medicines faster. As we go through the results, you can see how each of these accomplishments builds upon the foundation of a much bigger vision to transform the pharma industry to accelerate the creation of the best possible medicines for as many people as possible. Accenture is an AI-driven pharmatech committed to modernizing drug discovery and development with AI and advanced experimentation to develop drugs faster and fundamentally better for patients. Better medicines faster. These are three simple words, but let's reflect on what I mean. FASTA. For people facing serious disease, time is the enemy. By using AI-driven drug discovery and development, we believe we can accelerate the discovery of novel molecules and improve the probability of clinical success, potentially saving years of time to get a novel drug candidate approved. More important than FASTA is the concept of better. With our patient-first precision medicine capabilities, we're able to integrate primary human tissue samples into early drug discovery, truly putting the patient at the center. In practice, this means we are developing highly translatable models that can yield better clinical successes, bringing us into a realm where we can potentially make better drugs for specific patient groups. We've already seen in the real world how this approach can help tangibly improve the outcomes of patients. Our EXALT-1 clinical trial, published recently in Cancer Discovery, demonstrated that patients who were treated with the guidance of our AI platform had significantly better outcomes and more durable responses, achieving a 55% objective response rate. Although extremely powerful for these patients, this is only one example of the use of our AI. The real promise of what Accenture is doing lies in our scalability. We often refer to the company as a learning company. By that, we mean that every scientific idea that we pursue Every target is saved, every compound designed and tested, we are learning. And not just learning, but systematizing and encoding those learnings so that they fed back into the platform to enable us to learn faster and do more with every subsequent project that we tackled. We are truly building the system that can enable us scientists and collaborators around the world to pursue more novel ideas in parallel, with far greater probability of success of turning those ideas into actual medicines for patients. Rather than the long road to failure that many scientists face today, with less than 4% chance of a new idea successfully becoming a new medicine, we are presenting a new way forward, where we can advance more science, more quickly, and with far greater chance to reach people waiting for innovative treatments. Today, we will provide you with an overview to our achievements in the past quarter and what we've built so far towards this vision. Throughout this, I hope you can see a common thread that sets us apart, the continual learning nature of our AI system that powers greater precision and speed that makes it possible to execute on a greater scale than ever before imagined. The efficiency of a platform that we're building enables us to scale. That scale allows us to balance risk, and then to take more opportunities for new medicine creation. Today, we walk you through our balanced business model approach that is fueled by the scalability of the platform that we are building. Rather than one business model, we walk you through multiple approaches. Rather than focus on one lead asset, we review a pipeline of more than 25 programs. And even though these may be considered early stage in biotech's parlance, our platform is already demonstrating real-world results that are benefiting patients. And with that, let's turn to a progress. On slide four. As we generate data, develop new algorithms, and initiate new programs, our platform becomes more powerful. Over time, this enables a system that is not only capable of handling many projects at once, high capacity, but also high performance as it gets better and more precise as the system scales with new data. We're starting to see the concept bear fruit in execution across our pipeline. Over the past several years, we've done many deals across biotech and pharma, but importantly, we've begun to deliver upon these, most visibly in a significant expansion of a number of these relationships. Bristol-Myers Squibb, for example, BMS, expanded our original collaboration of three projects now to eight projects. We've substantially improved economic terms on the new projects. BMS also licensed their first drug candidate from Accenture this past quarter, demonstrating our ability to successfully discover high-quality molecules in areas that are proven scientifically challenging. In another significant expansion, we entered into our third collaboration with the Bill and Melinda Gates Foundation, adding a portfolio of antiviral therapeutics against coronavirus and other viruses with pandemic potential. There is perhaps no greater illustration of the promise of AI than with a pandemic. both because of the potential to accelerate the nature of drug discovery, but also in the ability to do so with small molecules, enabling potentially better access and distribution around the world. We also made progress with our 50-50 joint ventures, including selection of the first two targets for our multi-target deal with EQRX. We've nominated our development candidate 617 for CDK7 and are actively preparing 617 for IND-enabling studies and expect to submit our IND by the end of 2022. We look forward to providing you with further updates on this important program. We presented data on EXS617 using our primary patient tissue platform using ovarian cancer models, and we're pleased to say that we're also expanding our work to look at breast cancer patient models and other solid tumors too. We plan to share more details in the coming months. We also continue to scale our business with the initiation of automation labs and the expansion of our wet labs. And on October 5th, we successfully closed our upsized initial public offering and concurrent private placement, raising over $510 million in gross proceeds. Given our diversified business model, which we'll go into in more detail on, Our year-to-date operational cash burn was approximately $16 million, including the all-site acquisition cash contribution. And with approximately $784 million in cash and cash equivalents following our IPO and private placement, we are well-positioned for several years of operating cash burn. So now, we'll take you through our strategy and our business models on slide six before we open up a call for Q&A. At Accenture, our strategy is to shift the curve to develop better drugs faster using our AI-first approach. Our technology investments enable us to improve the probability of success to bring more drugs to patients following these three key tenets. Number one, increase your probability of success. Number two, accelerate the time of turning science into new medicines. And number three, lower the costs of our processes so that we can reset the economic model. As I've talked before, we can use technology to solve these problems and therefore shift the curve in the whole economic life cycle of drug development. And what's key to our ability to deliver better drugs faster is our balanced business model, as shown here on slide 7. Our business models allow us to generate substantial cash flows with our pharma partnerships while also creating substantial value for the company through our co-owned and wholly owned programs. Our pharma partnerships provide cash up front to cover search costs, with the potential for significant milestones and royalties. On average, we're eligible to receive approximately $115 million per partner program, and we have 10 projects ongoing and expect to increase this number in 2022. These programs are not only important for cash generation, but we also learn from each project. As the platform solves unique drug discovery problems of each new target, the learnings create a more robust knowledge base and capabilities for the next project. In programs where we own 50 to 100% of economics, our joint ventures, and wholly owned pipeline, we are focused on creating substantial net present value, or NPV. We have the ability to leverage our infrastructure and AI technologies from targeted identification through clinical trials at a much larger scale than traditional biotech drug development. This scale allows us to take a portfolio approach to science, spreading our risks across multiple therapeutic areas and targets. These three models are critical to developing a robust pipeline and allowing us to balance upfront milestones and strong cash flows versus equity ownership with long-term potential upside. As you can see here on slide eight, we provide end-to-end discovery capabilities, and we are responsible for using our AI and core competencies, not only to evaluate a drug target, but also to design the optimized molecules all part of our effort to design better drugs faster for patients. With our wholly-owned programs currently focused on oncology, immunology, and antivirals, we do everything from idea generation to patient selection for clinical trials using our precision medicine platform. Our patient tissue models help us not only to design a better drug, but allow us to find the right patients that will benefit the most from that drug in the clinic. For our partner programs, for example, with BMS, we drive and deliver projects through to IND, and our partner delivers clinical development and commercialization. BMS has a great internal team, and we believe that the fact that they trust us to oversee a significant proportion of a discovery portfolio speaks to the validation of our capabilities. For our co-owned programs, we add to this also by sharing an idea generation at the start of the project. and patient selection as we proceed to clinical development. We are able to leverage our partners' know-how, for example, with Valley Bio in a rare disease space, and share the potential in future successes. We are an integrated and scalable pharma tech that does more than just target identification or design. Innovation and AI are the core competencies of our company that can be applied throughout drug discovery and development. With each new expansion of our capabilities, we have seen that our partners utilize those capabilities with enhanced economics risk. We believe that this trend will continue as the platform grows. And now I hand over to Ben Taylor.
spk03: Thank you, Andrew. On slide nine, you can see we have more than 25 programs in development across a multitude of therapeutic areas and collaboration structures. We validated our platform's capabilities by putting the first three AI-designed drugs into human clinical trials and several more that are advancing through preclinical development. The other important message here is how we are able to scale. Our original pilot programs with Sumitomo allowed us to validate the complex interacting AI systems necessary to encode the drug design process. Then in 2017, we launched our first program where we internally oversaw both the AI design and laboratory testing. Once we had our operating procedures down, we were able to rapidly scale our pipeline initially with pharma collaborations, then co-owned projects, and now with our wholly owned programs. This scale then allows us to take a portfolio approach to both science and our business models. We never want to be defined by a single product technology or therapeutic area, but this brings up a critical question that many of you have asked. How do we determine the best balance for our different models? On slide 10, you can see an illustration of unadjusted cash flows under various ownership structures. For the wholly and co-owned lines, we've used publicly available data to create an example of the average cash flow profile for a drug from discovery through generic entry. The cash flow potential is very high, but requires substantial investment, time, and risk. The line labeled as pharma partnerships is a hypothetical example of cash flows from that same product if it were outlicensed rather than developed internally. In this example, the cash flows are always positive with milestones and royalties contributing to the smaller inflows throughout the drug's lifespan. none of this should be surprising and we believe that it's clear that having a mix of these business models provides a more balanced risk reward profile however the more interesting question is actually how do we evaluate the optimal mix of models to do this we need to overlay expected probability of success into the cash flow profile slide 11 shows that output at three different probability of success levels The current industry average, as Andrew mentioned, is about 4% probability of success from target identification to approval. Even though there is a marginally higher net present value, or MPV, from owning a project in this scenario, it comes with a significant time and cost risk. It is not surprising that in an environment where you almost always expect failure, you would be incentivized to take your cash up front. If you remember from RF1, we disclosed data showing that we have demonstrated better success rates than industry averages with our first seven development candidates. Just adjusting for this aspect would move the overall probability of success into the 10% range. If we apply 10% probability of success to the model, you can now see that the risk-reward profile is more balanced, which is why we are pursuing a more balanced portfolio expansion. You will also note that the early cash inflows from a partner program effectively balances out the early development costs of a wholly owned program. Finally, if you increase the probability of overall success to 40%, roughly 10 times the current standards, the risk-reward profile firmly moves to keeping the economics for ourselves. With time, we do believe that this could be an achievable benchmark, but it needs to be achieved by improving clinical trial performance. This is why we're so focused on translational systems, like our precision medicine platform, to potentially improve both drug design and patient selection. Moving on to another related topic, slide 12 shows how we account for expenses from our different collaboration agreements. Our pharma partners generally provide upfront payments in associated with our expected R&D funding. So we recognize revenues from those payments over the life of project execution. Therefore, we also recognize the R&D costs as cost of goods sold matching the revenue. In addition, most of the milestones and all of the royalties through pharma partnerships will be recognized as revenue when achieved. With all of our co-owned programs, we only recognize the 50% of expenses that we are responsible for, even though we are generally performing all of the discovery operations. The only other twist is that some of our R&D expenses associated with co-owned programs actually flows through a separate line item called share of loss on joint ventures. The reason why some of our co-owned programs flow through that line and others not is just for accounting technicalities and does not reflect an actual operational difference. Our financial results are detailed in our press release in form 6K, but you can see a few highlights on slide 13. Notably, we anticipate cash flows from collaborations between $75 to $85 million by year in 2021 and expect our to exceed our 21 inflows. In addition, we expect to end 2021 with between $745 and $755 million of cash on hand. We believe this gives us several years of cash runway and the resources to continue investing in our business expansion and differentiated pipeline. And with that, we'll open it up for Q&A. Operator?
spk02: Thank you. Ladies and gentlemen, at this time, we will begin the question and answer session. Anyone who wishes to ask a question may press star followed by one on their touchstone telephone. If you wish to remove yourself from the question queue, you may press star followed by two. If you're using speaker equipment today, please lift the handset before making your selections. Anyone who has a question may press star followed by one at this time. One moment for the first question, please. First question is from the line of Chris Shibutani from Goldman Sachs. Please go ahead.
spk09: Hi, guys. This is CJ Zafon for Chris this morning. Congratulations on reporting your first quarter, and thank you for taking the question. So this is the first time I think we're hearing guidance for 2022 in some form. So I'm curious, what assumptions are baked into that increase from this year to next year? And related to the exercise you just walked us through, What are you hoping for the mix of ownership level in the portfolio to look like over the next year, two years, as you increase your internal assessment of probability success with the platform?
spk08: Thank you. Excellent. Thank you, CJ. Good to speak to you. For that question, I'm actually going to hand it over to Ben Taylor, our CFO, actually to walk you through our thinking there.
spk03: Hey, CJ. Great to be speaking. So a couple of different things. One, in looking at our 22 cash inflows guidance, really what factors into that is just the level of interest that we've had from outside parties as well as doing an internal analysis of where we want to put resources and just triangulating about that. So we feel comfortable that we'll be able to exceed the cash inflow levels from this year into next year. As far as the balance of the pipeline, I think the slides in the presentation really highlighted we're at the point right now where we feel very comfortable to split it between the partnerships, the JVs, and the wholly owned programs. I think for a couple of reasons that balance will continue for the next couple of years. One will be we are a data-driven company. We love to prove things out. And so just as our products advance through clinical trials, we'll be watching and adjusting our data as that goes through and hopefully really driving up that probability of success over time. I think another component of that is as we build up our internal operations around clinical trials, that will give us more comfort in expanding our wholly-owned pipeline as well. You know, we've said before we want to take the same principles that we applied to drug discovery and put them on to drug development. So more coming on that over the course of the next year, but I would expect us to really focus in on how to bring data and analytics to the clinical outcomes, and then that might be an opportunity for us to expand a greater proportion of wholly owned. But near term, think balanced pipeline.
spk02: Great. Thank you. Next question is from the line of Michael Reiskin from Bank of America. Please go ahead.
spk04: Hey, guys. Thanks for taking the question. Can you hear me? Yes, Mike. Hey, great. I got two quick ones. One, I just want to follow up on the last point and go deeper into your comments on scale of the platform and ramping up over time. You touched on your prepare market number of times about the ability to leverage the scale of the platform to really expand the number of programs, expand the number of targets you're looking after. So given the cash balance and given your views on cash flows next year, can you talk a little bit about what that looks like going forward. You know, as we think about 25 programs now, what's a reasonable number for us to expect, you know, end of 22, end of 23, and what's the OPEX requirements to get there, both in terms of headcount expansion, you know, building out wet labs, building out some of that automation, and also just OPEX dollars.
spk08: Mike, thank you very much for that question. Good. Certainly as we go out now, one of the key tenements to remember is that We are looking also to maintain our investments into tech alongside our investments into the pipeline, whether that's partner, JV, and our own. And that's a key important thing as the platform grows. And there'll be a number of new elements in the platform you'll see as that scale up come. A big chunk of that was Ben mentioned, thinking about sort of being innovative in the clinic as we think about quantitative and learning approaches and into clinical, but also thinking about how we bring our automation technologies as well in discovery forward. But also it's about building up that internal pipeline. So I just want to introduce Dave Hallett, our chief operation officer, who's very much living this day-to-day, about how then Dave's building up his team and the operations behind it.
spk05: Thank you, Andrew. The first point I'd like to make is that It's not just about the scale of the portfolio. It's critical, I think, coming back to a point Ben made earlier about the value of the programs that are in the overall portfolio. I think another key point would be that as an organization, we encode and automate, and that's a fundamentally important tenet of the organization because, by encoding the drug discovery process and then looking to automate as much of the experimental process as possible. What that allows us to do is to actually build and scale in a non-linear human way so that we can actually manage a discovery portfolio that's the size of, say, a medium to large pharma without the requirement for having thousands of people to do that. In terms of the internal portfolio, as Ben mentioned, I think a combination of the particular kind of more recent fundraising has allowed us to look at that in more detail. And we'll continue to invest in that space, particularly around oncology and antivirals. And we'll update you on the progress of those projects as we progress.
spk08: Excellent. Thank you, Dave. And in terms then of just how the technology expands, I just want to ask Gary Parado, our CTO, just to say a few words on that, Chris, before your second question.
spk06: Brilliant. Thanks, Andrew. Hi, everybody. So Dave said, automation is absolutely key to our thinking. And we think about automation in two manifolds. So we're thinking about how we automate the design processes, how we're stringing together the in silico processes, the generative modeling, the active learning, all of the processes that are key to our flow of design. And that means we can run more projects in parallel. But a really exciting development that we're working on this year is we've just leased a new building, 26,000 square feet south of Oxford, and we're building a brand new state-of-the-art automation studio. So this is physical automation. This is bringing robotics and linking those robotics to our AI processes. And we see a huge synergy there. So this will be synthesis, this will be purification, this will be compound management, and it will also be screening, all integrated into one brand new facility. So very excited about all of those developments.
spk08: Excellent. Mike, you said you had a second question as well.
spk04: Yeah, thanks for all that color. Quick follow-up, hopefully a little bit quicker. You touched on sort of leveraging some of your AI capabilities more on the development side of things, especially discovery. I'm curious if you're alluding to something similar to what you did with Exalt 1. I'm just curious, you know, any follow-ups on that. Again, you've still got some more work going on in terms of Exalt 2. But how should we be looking at news flow on that side of things going forward? What are key events we should be looking for?
spk08: Yeah, no, that's a great question, Mike. And you will be seeing a lot of activity from us as we build out our precision medicine platform in 2022. Think of it in terms of as we bring into the public domain a lot more sort of new data on the new models that we're building out in a variety of new cancer types. further validation of the models that we are already building, including really bringing to the public domain a lot of the solid tumor data that we generate in. We've also just started building a new 50,000-square-foot lab space in Vienna. So that's a significant investment into building up the biobank and the screening capability and the capacity then that brings us. You'll be looking at us actually building out our relationships with clinicians. We already have over 70 sites across Central and Eastern Europe, which we're collecting samples from, our biobank. You expect to see a lot of activity from us actually as we really think about scaling up our ability to collect patient samples and go deeper into the data as well, more of a multi-omics approach as well as beyond the phenomics approach as well. But in terms of also thinking about how we as innovative in a clinic, I'm also going to ask Ben Taylor, Chief Strategy Officer as well, to just talk you through some of our thinking, Ben, about how now we want to bring, you know, the same kind of innovative approach to the clinic as we have done to discovery. Thank you.
spk03: Sure, and I'll keep it concise because I think it's really on the principles of how we design. We are a precision design company, which means that we have a precise patient population that we are designing for. And so what we always aim to do is understand how to better target those patients and in our clinical trials and in the future in commercialization. And so if you think about what our all-site platform is doing, it's really the ideal form of personalized medicine where we are using the patient as their own assay to figure out who is the right patient for a drug. We will continue to do that both with the patient tissue platform as well as other ways that we can find the right biomarker, the right gene signature, the right companion diagnostic to be able to target the right patient.
spk04: Great. Thanks so much. Thanks, Mike.
spk02: As a reminder, if you'd like to ask a question, please press star followed by one on your touchstone telephone. Next question. from the line of Vikram Piroit from Morgan Stanley. Please go ahead.
spk09: Great. Good morning. Thanks for taking my question. So I had two on the pipeline, actually. The first for the first set of A2A data that we can expect to see in 2022, what do you think is the hurdle for success here? What are you looking to see? And in your view, what is the best way for investors to compare and contrast this data to to other A2A programs in development? And then secondly, for the translational data that you mentioned that we could see for the CDK7 inhibitor next year, what could that tell us, and what are the steps forward for that program once we have that data?
spk08: Excellent. Vikram, thank you very much for calling in today. I much appreciate your question. For that, actually, in terms of the pipeline questions, I'm going to hand it over to Dave Hallett, actually, to walk you through that.
spk05: Thank you, Andrew, and thank you for the great question. I think there's a common answer to those two questions, but I'll start with A2A and then come on to CDK7 second. So in terms of data flow next year and consistent with the information we recently provided in the F1 is that you should expect to see data from the ongoing Phase 1s to be next year. So that will give us guidance on the safety and tolerability of that compound, as well as a recommended starting dose for the subsequent Phase 1B, Phase 2. In terms of how we think about that program and positioning, and this will be true for CDK7 as well, I think the critical story here is about patient selection. I think a number of data points that have kind of emerged from competition over the last few years, which I think have highlighted signals in patient studies, but lacking statistical significance because of a broad kind of approach to the cancers being chosen. In terms of how we're approaching this is that we are currently sequencing, analyzing a significant number of patient samples. looking for things like expression of a key enzyme that's responsible for adenosine production, but also looking for in-depth gene signatures and markers of response. So that would include things like lung cancer, things like renal cell carcinoma, where we and others have been able to kind of demonstrate that certain populations of those cancers, one sees a high adenosine signature, which would indicate a likely response. So in terms of how we think about positioning our molecules, I think the key thing here is that we will not go into a phase 1B, phase 2 with an all-comers approach. We've already identified six-to-weight cancers, and we are exploring in detail a gene signature, which will then prospectively guide the selection of patients during the dose expansion phase. Just coming on to CDK7. In terms of news flow, we have communicated and selected a development candidate. And in the coming months, we will initiate formal IND studies looking to open IND by the end of next year. Again, it's the same approach. In contrast to A2A, which is obviously an immuno-oncology medication, With CDK7 mechanistically, we're looking at two areas. So this is looking at oncogenic impact of both retinoblastoma protein and also MAP kinase. That then leads into looking at cancer types, which are things like triple negative breast cancer, but also ovarian cancer. So again, like with the A2A program, we're currently evaluating our compound in a variety of primary patient tissues. looking to understand where that compound works, and just as importantly, where it doesn't, so that we can identify that subset of patients. And, again, we'll be, during the course of next year, when the opportunity arises, we'll be presenting kind of data on those ongoing preclinical studies to help us guide patient selection.
spk09: Thanks, Rick. I hope that helps. That's very helpful. Thank you.
spk02: As a reminder, if you'd like to ask a question, please press star followed by one on your touchtone telephone. Next question is from the line of Peter Lawson from Barclays. Please go ahead.
spk07: Great. Thank you, and congratulations on the first quarter in a public company. Just on the news today, I guess, from Gilead opting in to Arcusys at Denison, and just your thoughts on your potential combination therapies that you'll be thinking about. It seems that, you know, there's a broader set of potential combinations for, you know, Gilead and Arcus. Just your thoughts about how you'll take adenosine forward in a combination therapy and kind of when we could start getting details around that and potential data.
spk08: Thank you, Peter. Thank you very much for your kind words as well about our first quarter. And thanks for calling in today. Yes, really exciting news of things for the field, actually, with the news of Gilead and the work they're doing with Arcus. I think it really adds to the testament now that it was actually the whole pathway is sort of being explored here and I think it shows real sort of commitment as well now to that pathway and these mechanisms. So this is a real sort of boost, we think, for the real field. One of the key things, then, that we are thinking about now as we go forward is, as David will just describe, and I'm not going to describe it again, how actually we can use the advantages of our patient-centric physician-medicine platform to really help us potentially understand where, not only potentially where the best patients are, but also potentially using that platform as well for asking those questions around sort of combinations. I'm just going to bring Dave Hallett in here again, actually, to provide a bit more color about our thinking in this space.
spk05: Thank you, Andrew, and thank you, Peter, for the question. Yes, like Andrew, I'm actually delighted with the Gilead announcement because I think it adds confidence to everyone working in the adenosine kind of pathway, particularly two of those three lead assets kind of talk to targets within that pathway. In terms of where we're going, so we tend to explore monotherapy with our molecule, but we're also looking at relevant kind of combinations. So, yeah, checkpoint inhibitors is certainly one way. They're standard of care in a number of indications, so practically. We'll have to look at those patients that are particularly, obviously, refractory to those treatments because that is a growing and unmet need. And the way we will do that is actually, and are doing at the moment, is to evaluate monotherapies and combinations both with small analyzed molecules in our precision medicine platform. So looking at do we actually deepen the response in, say, a combination with an anti-PD-1 molecule? And as I mentioned earlier, those studies are ongoing. We're looking to present some of that data when appropriate in the course of next year. And then we'll also kind of show you more details about not only which combinations we prefer, but also which combinations we may avoid because we can't demonstrate that there's any benefit of that particular combination.
spk07: Thank you. And as you start that phase 1B2, will you have arms in there for combination therapies? And when could we see the first, I guess, data within cancer patients? Is that kind of a 22 event, or is that kind of?
spk05: It's a little bit later than that. But, yes, so the current study design has multiple arms looking at both monotherapy and combinations in a variety of preset cancers. The first part of the study, which we hope to start next year, will be an abbreviated dose escalation because we've actually kind of deep into the the execution of a human volunteer studies that should allow us to accelerate that dose escalation phase. But I would expect to actually start to police the information coming out. So it's certainly not next year from that trial because it will only start probably in the second half of next year.
spk08: Hey, perfect. Thank you. Sorry, Peter. I just want to bring Ben in here as well.
spk03: Just one thing I wanted to add on to your earlier question on combinations. I mean, we actually think this is a real strength of our precision medicine platform because what we're able to do is actually take, in a laboratory setting, real patient samples, real tumor microenvironments, and test them with our drugs and multiple different combination agents and be able to look at the profile of how our drug might interact with those different combination agents in that human-based tissue sample. So that is something that we think will differentiate us in the future, but more to come. Absolutely.
spk08: It's our ability to almost think about them as ex-evil central trolls in a way. Exactly. Thank you. Take care. Thanks, Peter.
spk02: There are no further questions at this time, and I would like to hand back to Andrew Hopkins for closing comments. Please go ahead.
spk08: Thank you very much. What I hope you take away from today's call is how Accenture represents a new way forward. We aren't just designing drugs. We are designing technology systems to design drugs. Our goal here is to have greater scale beyond a single lead asset, but also a greater precision and a probability of success than ever before. If successful, we believe this could inspire industry transformation in how new medicines are created, enabling a new era where we can achieve the best possible medicines for as many people as possible. I want to thank you all for your time today and see you next quarter.
spk02: Thank you, everyone. Ladies and gentlemen, the conference is now concluded. You may disconnect your telephone. Thank you for joining and have a pleasant day. Goodbye.
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