Exscientia plc (EXAI) on Q3 2021 Results - Earnings Call Transcript
Operator: Hello, everyone. My name is Stuart and I will be your conference operator today. At this time, I would like to welcome everyone to Exscientia’s Business Update for the Third Quarter 2021. At this time, I’d like to introduce Sara Sherman, Vice President of Investor Relations. You may begin.
Sara Sherman: Thank you, operator. A press release and Form 6-K was issued yesterday after U.S. market close with our third quarter 2021 financial results and business update. These documents can be found on our website at www.investors.exscientia.ai along with a presentation for today’s webcast. Before we begin, I would like to remind you, on Slide 2, 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. Exscientia 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 am joined by Andrew Hopkins, Chief Executive Officer and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Operations Officer, and Garry Pairaudeau, Chief Technology Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.
Andrew Hopkins: Thank you, Sara and thank you to everyone who joined us today. It’s my pleasure to welcome you to our first earnings call. Today, I will review our recent accomplishments and then I will be joined by Ben Taylor for a discussion around the business models that help fuel our pipeline. First of all, I will start on Slide 3 to navigate you through the progress and to give you an idea of what we are building towards. I’d like to start with our vision of using AI to discover better medicines faster. As we go through our 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. Exscientia is an AI-driven pharma tech 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 they mean. Faster, 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 faster is the concept of better. With our patient first precision medicine capabilities we are able to integrate primarily human tissue samples into early drug discovery, truly putting the patient at the center. In practice, this means we have developed highly translatable models that can lead to better clinical successes, bringing us into a realm where we can potentially make better drugs for specific patient groups. We have 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 have 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 use of our AI. The real promise of what Exscientia 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 pursued, every compound design made and tested we are learning. And not just learning but systematizing and encoding those learnings so that they are fed back into the platform to enable us to learn faster and do more with every subsequent project that we tackle. We are truly building a system that can enable our 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 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 the platform that we have built in enables us to scale, that scale allows us to balance risk and then to take more opportunities for new medicine creation. Today, we will walk you through our balanced business model approach that is fueled by the scalability of a platform that we are building. Rather than one business model, we will walk you through multiple approaches. Rather than focus on one lead asset, we will review a pipeline of more than 25 programs. And even though, these may be considered early stage in biotech’s parlance, our platform has already demonstrated real world results that are benefiting patients. And with that, let’s turn to the progress. On Slide 4, 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 performing as it gets better and more precise as the system scales with new data. We are starting to see the concept to bear fruit in execution across our pipeline. Over the past several years we have 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 3 projects, now to 8 projects, with substantially improved economic terms on the new projects. BMS also licensed their first drug candidate from Exscientia this past quarter, demonstrating our ability to successfully discover high-quality molecules in areas that have proven scientifically challenging. In another significant expansion, we entered into our third collaboration with the Bill & Melinda Gates Foundation, adding a portfolio of antiviral therapeutics against coronavirus and other viruses with pandemic potential. There is perhaps no greater illustration of performance 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 of our multi-target deal with EQRx. We have 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 EXS-617 using our primary patient tissue platform using ovarian cancer models and we are pleased to say that we are 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 5, 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 will go into in more detail on, our year-to-date operational cash burn was approximately $16 million, including the Allcyte 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 will take you through our strategy and our business models on Slide 6 before we open up the call for Q&A. At Exscientia, 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 the probability of success; number two, accelerate the time of turning science into new medicines; and number three, lower the cost of our processes so that we can reset the economic model. As I have talked before, we can use technology to solve these problems and therefore shift the curve in the whole economic lifecycle 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 upfront to cover research costs, with the potential for significant milestones and royalties. On average, we are eligible to receive approximately $150 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 owned 50% to 100% of economics our joint ventures and wholly owned pipeline, we are focused on creating a substantial net present value, or NPV. We have the ability to leverage our infrastructure and AI technologies from targeted implication through clinical trials that are 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 8, 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 anti-virals, 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 the fact that they trust us to oversee a significant proportion of their 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 RallyBio in the 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 for us. We believe that this trend will continue as the platform grows. And now, I hand over to Ben Taylor.
Ben Taylor: Thank you, Andrew. On Slide 9, 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 design 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 have 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 out-licensed 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 it is 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 NPV, 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 our F1, we disclosed data showing we 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 partnered 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 10x the current standard, 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 are so focused on translational systems like our precision medicine platform to potentially improve 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 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 and Form 6-K, but you can see a few highlights on Slide 13. Notably we anticipate cash flows from collaborations between $75 million to $85 million by year end 2021, and expect our 2022 cash inflows to exceed our 2021 inflows. In addition, we expect to end 2021 with between $745 million 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 will open it up for Q&A. Operator?
Operator: Thank you. First question is from the line of Chris Shibutani from Goldman Sachs. Please go ahead.
Unidentified Analyst: Hi, guys. This is on 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, 2 years as you increase your internal assessment and probability of success with the platform? Thank you.
Andrew Hopkins: 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.
Ben Taylor: Hey, CJ. Great to be speaking. So, a couple of different things. One, in looking at our 2022 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 will 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 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 will 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. We’ve said before we want to take the same principles that we apply 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.
Unidentified Analyst: Great. Thank you.
Operator: Next question is from the line of Michael Ryskin from Bank of America. Please go ahead.
Michael Ryskin: Hi, guys. Thanks for taking the questions. Can you hear me?
Andrew Hopkins: Yes, Mike.
Michael Ryskin: Great. I’ve 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 in your prepared remarks a 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 are 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? As we think about 25 programs now, what’s a reasonable number for us to expect end of 2022, end of 2023? And what is the OpEx requirement to get there both in terms of headcount expansion, building out wet labs, building out some of that automation and also just OpEx dollars?
Andrew Hopkins: Mike, thanks very much for that question. Good. I am certainly as we grow up 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 partnered, JV and our own. And that’s a key important thing as the platform grows. And there will be a number of new elements in the platform you will see as that scale up – big chunk of that has been mentioned, thinking about being invested in the clinic as we think about quantitative and learning approaches into clinical. But also thinking about how we bring our automation technology 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 Operating Officer, who’s very much living this day to day about how then Dave is building up his team and the operations behind it.
Dave Hallett: Thank you, Andrew. The first point I’d like to make is that it’s not just about the scale of the portfolios. It’s critical, I think going 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-really 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. And in terms of having the internal portfolio, as Ben mentioned, I think a combination of particularly the more recent fundraising has allowed us to look at that in more detail. And we will continue to invest in that space particularly around oncology and antivirals. And we will update you on the progress of those projects as we progress.
Andrew Hopkins: Awesome. Thank you, Dave. And in terms then of just how the technology expands, I just want to ask Garry Pairaudeau, our CTO, just to say a few words on that first before your second question.
Garry Pairaudeau: Thanks, Andrew. Hi, everybody. So, as Dave said, automation is absolutely key to our thinking and we think about automation in two manifolds. So, we are thinking about how we automate the design processes, how we are stringing together the in silico processes, the generative modeling, the active learning, all 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 are working on this year is we’ve just leased a new building, 26,000 square feet south of Oxford, and we are 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. 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.
Andrew Hopkins: Excellent. Mike, you said you had a second question as well.
Michael Ryskin: Yes, thanks for all that color. Quick follow-up – hopefully quick, a little bit quicker. You touched on leveraging some of your AI capability more on the development side of things versus discovery. I’m curious if you’re alluding to something similar to what you did with EXALT-1. Just curious any follow-ups on that, again, still got some more work going on in terms of EXALT-2. But how should we be looking at the news flow on that side of things going forward? What are key events we should be looking for?
Andrew Hopkins: Yes, that’s a great question, Mike. 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 are building out in a variety of new cancer types through a validation of the models that we are already building, including really bringing to the public domain solid tumor data that we generated. 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 a bio bank and streaming 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 for our biobank. You expect to see a lot of activity from us, actually, as we really think about scale and that probability to collect patient samples and go deeper into the data as well, more of a multiomics approach as well as beyond the approach as well. But in terms of also thinking about how we are investing in the clinic, I’m also going to ask Ben Taylor, our Chief Strategy Officer, as well to just talk you through some of our thinking then about how now we want to bring the same kind of innovative approach to the clinic as we have done to discovery.
Ben Taylor: Sure, and I’ll keep it concise because I think it is 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 in our clinical trials and in the future in commercialization. And so, if you think about what our Allcyte 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.
Michael Ryskin: Great. Thanks so much.
Andrew Hopkins: Thanks Mike.
Operator: Next question is from the line of Vikram Purohit from Morgan Stanley. Please go ahead.
Vikram Purohit: Great. Good morning. Thanks for taking my question. So, I had a few 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 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?
Andrew Hopkins: Excellent. Vikram, thanks very much for calling in today. Much appreciate the question. For that actually in terms of the pipeline questions, I am going to handed over to Dave Hallett actually to walk you through that.
Dave Hallett: Thanks, Andrew. And thank you for the great questions. I think there is a common answer to those two questions, but I will start with A2a and then come on to CDK7 second. So, in terms of dataflow next year, and consistent with the information we recently provided in the F-1, is that you should expect to see data from the ongoing Phase 1 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 our program and positioning – and this will be true for CDK7 as well. I think the critical story here is about patient selection. A number of data points that have kind of emerged from competition over the last few years, which I think have highlights 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 are 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 identity and production, but also looking for in-depth gene synapses 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 demonstrate that certain populations of those cancers, one sees a higher gene 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 have already identified six to eight 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 – as was communicated, have selected a development candidate. And in the coming months we will initiate formal IND studies looking to open an IND by the end of next year. Again, it’s the same approach. In contrast to A2a, which is obviously an immune oncology indication, with CDK7 mechanistically we are looking at two areas. So, this is looking at oncogenic mutated 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 are currently evaluating our compound in a variety of the 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 will be – during the course of next year when the opportunity arises, we will be presenting kind of data on those ongoing preclinical studies to help us guide patient selection.
Andrew Hopkins: Thank you, Vikram.
Vikram Purohit: That’s very helpful. Thank you.
Operator: Next question is from the line of Peter Lawson from Barclays. Please go ahead.
Peter Lawson: Great. Thank you, and congratulations on the first quarter being a public company. Just on the news today from Gilead opting into Arcus’ adenosine, just your thoughts on your potential combination therapies that you would be thinking about. It seems that there is a broader set of potential combinations for Gilead and Arcus. Just your thoughts about how you will take adenosine forwards in a combination therapy. And kind of when we could start getting details around that and potential data?
Andrew Hopkins: Thank you, Peter. Thanks very much for your kind words as well about our first quarter. And thanks for calling in today. Yes, really excited news I think from the field actually, revenues of Gilead and the work they are doing with Arcus. I think it’s really a testament now that it was actually the whole pathway 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 boost we think for the real field. One of the key things then that we are thinking about now as we go forward, as David will – just described and I will ask him to describe again, how actually we can use the advantages of our patient centric positioned medicine platform to really help us potentially understand where – not only potentially where the best patients are, but also potentially news in that platform as well for asking those questions around combinations. I am just going to bring Dave Hallett in here again actually to provide a bit more color about our thinking in this space.
Dave Hallett: Thank you, Andrew, and thank you, Peter, for the question. Yes, like Andrew, I am actually delighted with the Gilead announcement, because I think it adds confidence to everyone working in the adenosine pathway. Particularly two of those three lead assets kind of talk to targets within that pathway. And in terms of where we are going, so we tend to explore monotherapy with our molecule. But we are also looking at relevant kind of combinations. So, our checkpoint inhibitor is certainly one way that started care in a number of indications so practically, whilst a lot of those patients are particularly, obviously, refractory to those treatments because that is a growing 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 and large molecules in our precision medicine platform. So, looking at do we actually deepen the response into combination with an anti-PD-1. And as I mentioned earlier, those studies are ongoing. We are looking to present some of that data when appropriate in the course of next year and then we will obviously show you more detail about not only which combinations we prefer, but also which combinations we may avoid because we can’t demonstrate that there is any benefit of that particular combination.
Peter Lawson: Got it. Thank you. And as you start that Phase 1b/2, 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 2022 event or is that kind of…?
Andrew Hopkins: It’s a little bit more than that, so – 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 have actually cut deep into the execution of treatment volunteers studies that that’s given – that should allow us to accelerate our dose escalation phase. But I would expect actually to start to fully see information coming out – certainly not next year from that trial, because it only starts probably in the second half of next year. Sorry, Peter, but I just want to bring Ben in here.
Ben Taylor: 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 are able to do is actually take in a laboratory setting real patient samples, real tumor micro environments and test them with our drug in multiple different combination agents and to 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 we think will differentiate us in the future but more to come.
Andrew Hopkins: It’s that ability to almost think about them as an ex vivo in terms of trials in a way.
Ben Taylor: Exactly.
Peter Lawson: Perfect. Thank you. Take care.
Operator: There are no further questions at this time and I would like to hand back to Andrew Hopkins for closing comments. Please go ahead.
Andrew Hopkins: Thank you very much. What I hope you take away from today’s call is how Exscientia 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 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 way of 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.
Dave Hallett: Thanks everyone.
Operator: Ladies and gentlemen, the conference has now concluded. You may disconnect your telephone. Thank you for joining and have a pleasant day. Goodbye.
