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Hello everyone. My name is Abby and I will be your conference operator today. At this time, I would like to welcome everyone to Exscientia's business update call for the second quarter of 2023. Today's call is being recorded. [Operator Instructions]And at this time, I would like to introduce Sara Sherman, Vice President of Investor Relations. Sara, you may begin.
Thank you, operator. A press release and 6-K were issued this morning with our first half and second quarter 2023 financial results and business updates. These documents can be found on our website at www.investors.exscientia.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, Exscientia 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 Professor Andrew Hopkins, Chief Executive Officer; Dr. Mike Krams, Chief Quantitative Medicine Officer; Dr. Nikolaus Krall, EVP Precision Medicine; and Ben Taylor, CFO and Chief Strategy Officer. Dr. Dave Hallett, Chief Scientific Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.
Thank you, Sara. The first half of 2023 have seen a major step forward in our development pipeline with 4 compounds progressing in clinical trials. We recently dosed the first patients in both elucidate, our Phase I/II trial of our CDK7 inhibitor, GTAEXS617, partnered with GT Apeiron and IGNITE, a Phase I/II trial of our A2A receptor antagonist EXS21546. Earlier this year, 2 partnered compounds at PKC-theta inhibitor, in-licensed by Bristol Myers Squibb for immunology and a bi-specific psychiatric compound, designed for Sumitomo Pharma, also started Phase I clinical trials.We've also made a significant progress in other parts of our pipeline. Notably, we initiated a prospective observational study called EXCYTE-1, evaluating the predictive power of our precision medicine platform in ovarian cancer. The study has the potential to validate further our platform from widely use across a variety of solid tumors. Building a robust pipeline and advancing our wholly-owned and partner development candidates is a testament to the strength of Exscientia's business model, our capabilities and strategic collaborations.As we continue to bring differentiated compounds to the clinic and to further strengthen our AI-led end-to-end discovery process, we are solidifying our leadership in AI-enabled drug design and development. This summer we also opened our automation lab outside Oxford, here in U.K., which will enable us to integrate AI and automation to drive faster, high-quality experimentation. We are building our own hardware and software solutions to automate a wide range of experimental laboratory processes, including chemical synthesis and biochemical and biophysical screening.We expect all our new capabilities to be online later this year and we look forward to sharing our progress. The integration of AI with automation to drive ultimately autonomous experimentation, is we believe the next frontier in improving productivity in drug discovery.I'll speak in a moment about our core capability to adapt and rapidly integrate technological advances into our broad platform. Technology is at the center of our strategy to change your way drugs invented and developed. To maintain nimble product developments in our technology platform, we have promoted 3 industry leaders to our executive committee. Professor Charlotte Deane is our new Chief AI officer. Dr. John Overington is our new Chief Data Officer and Eileen Jennings-Brown, our Chief Information Officer, all of whom are proven leaders and innovators in their fields.We also appointed Professor Franziska Michor to our Board of Directors in May. Her extensive experience in cancer research using computational and mathematical methods will be invaluable to us as we continue to advance our pipeline and platforms. We remain well-capitalized with $509 million in cash at the end of the quarter. This provides us with several years of runway to advance our near-term programs. We look forward to achieving our upcoming milestones and sharing more details of our clinical development plans and progress in the second half of the year.Our computational platform is designed to learn and solve problems that have been too complex with traditional methods. In order to make that process faster and more efficient, we must be able to generate relevant, high-quality, proprietary biological data to inform our models. The wide variety of digital data we generate reflects the complexity of biology. This integrated data is shared across our models to drive system learning and better results.Importantly, our modeling is data agnostic and our generative design technology is model agnostic. This is important as target product profiles we designed to are not defined by one data type such as a protein structure or a high content screen, but by a wide variety of data types. Some of our experimental systems are the first of a kind, such as our precision medicine technology using AI to assess live patient samples to predict patient response.We conducted a perspective clinical trial showing that was able to improve outcomes in selecting the right drug for specific patients. Other capabilities are focused on capturing the most data possible from experiments and/or integrating it with automation. This gives an advantage in both understanding the model system as well as speed and cost efficiency. The power of our computational platform is in its integration and progress. There is no single algorithm that defines our operations, but a wide variety of proprietary algorithms and datasets. Our platform is constantly evolving and our capabilities have expanded as we invent and adopt new technology. The overarching technological approach that unifies what we do from discovery to development is model-driven adaptive learning.We pioneered the use of generative AI and active learning to design drugs and we continue to build on our leadership position in both capabilities. We have now also built thousands of predictive models that can empower our workflows to evaluate novel chemistry in a virtual environment. In addition, we've now built a world-class physics-based platform that we integrate into our generative and modeling systems for structural analysis.It is a natural next step for us to integrate AI and physics-based design methods together. Over the last year, you have now seen how we apply the same methodology of model-driven adaptive learning to improve clinical trial design and execution. We utilize clinical trial simulation to better understand the most important variables in clinical trial design. We then create a statistical plan that evaluates results in real time as a trial progresses in order to make better decisions sooner on the clinical trial progress.We've also laid the groundwork to integrate our precision medicine biomarker capabilities into our clinical trial execution in the near future. We believe this can lead to far higher probabilities of success as we're able to better understand which patients will respond to our drugs. Most importantly, we have demonstrated that our platform works. Our AI-based platform has delivered 8 development candidates, in other words, novel drugs that are expected to enter clinical trials.The physical properties of these drugs can be clearly measured and that shows they have achieved complex design goals, where traditional methods did not. Importantly, our generative molecule design technology has been core to delivering the aid to development candidates so far. Exscientia pioneered the use of generative AI and active learning for better drug discovery since we first published our revolutionary approach in the leading scientific journal Nature. The combination of a deep bench of experts in both tech and drug discovery allows us to lead the field with our proprietary algorithms and deep learning models.We have also demonstrated we can rapidly adopt new technologies with customization for our platform as we learn and grow as a company. For example, we believe a physics-based modeling should be integrated into AI-driven systems to take advantage of a generative power and multi-parameterization optimization that AI allows. In a period of just about a year, we were able to deliver physics-based systems that benchmarked in line with industry leaders. However, because it's purpose built for our systems, we're able to customize it for better results within our own architecture and are even now working on how it can be seamlessly integrated with our generative algorithms. Similarly, we have industry leading protein modeling capabilities that are optimized for antibodies and other related biologics. This allows us to use generative methods for de novo biologics design at scale and we tell you more about this program later this year.Before turning over to Mike Krams and Nikolaus Krall, I'll now take a moment to highlight where we are for clinical and near clinical programs and the important progress we've made this year. As you know, by next year, Exscientia has committed to advance at least 4 molecules of meaningful economics into clinical development. We're well on our way to achieving this goal with 5 programs either in clinical stage or entering to IND-enabling studies as of now, all of which have been designed to use in our AI platform in much shorter time frames in the industry average and we believe maximum quality use now AI-led discovery platform.Profile CDK7 and A2A programs are now in Phase I/II clinical trials with patients enrolling and similar time lines. IGNITE is a Phase I/II clinical trial of 546, our A2A receptor antagonist in combination with anti-PD1 therapy for renal cell carcinoma and non-small cell lung cancer. ELUCIDATE is evaluating our novel CDK7 inhibitor 617 for the treatment of advanced solid tumors, both as monotherapy and in combination with standard of care.The ELUCIDATE Phase I/II trial will evaluate the safety, efficacy and pharmacokinetics of 617 across multiple ascending doses in 6 indications including head and neck cancers, pancreatic cancer, non-small cell lung cancer and HR positive, HER2 negative breast carcinoma and ovarian cancer. In both the ELUCIDATE and IGNITE trials, we use simulation-guided trial design to determine the operating characteristics and adaptive design to evaluate statistical results in real-time when the trial is running. Both of these programs demonstrate the true hallmark of an Exscientia drug candidate, precision design compounds using AI and ML, combined with novel patient selection strategies with the goal of identifying the right patient for the right drug.Our precision-designed PKC-theta inhibitor 4318 partnered with Bristol Myers Squibb continues to advance with Phase I clinical trials in the United States. This is another example of using our AI-driven technology to design against complex multi-parameter challenges where others have failed. Our two wholly-owned precision designs, LSD1 and MALT1 inhibitors 539 and 565, are continuing to progress with IND-enabled studies and we'll share more detail on the clinical plans later this year.I'll now turn over to Dr. Mike Krams, our Chief Quantitative Medicine Officer to talk a bit about our CDK7 program. Mike?
Thank you, Andrew. Today, we'll highlight more details of our CDK7 program, how we're optimizing our clinical development strategy and how we choose the patient population that may benefit from this molecule. Additionally, alongside the evaluation of 617 in the ELUCIDATE trial, we will partner with GT Apeiron to generate data and correlate data in response to previously collected ex vivo results to substantiate further the value of our precision medicine platform.Here, we highlight the design of the ELUCIDATE trial for 617. As Andrew mentioned, we're looking at 6 different tumor types and we'll be studying 617 both as monotherapy as well as in combination with standard of care. One thing to note is that CDK7 is a broader biological mechanism than A2A. This is why we're including more tumor types and also why we're using our platform differently in this trial.Nikolaus will talk about this more. As with IGNITE, ELUCIDATE is also following the principles of model-informed drug development. We started with simulation work to understand the key variables and structured the Phase I trial to see what dosing may be most impactful. We're moving away from traditional 3+3 designs to focus on what we believe will be more informative in learning about the investigational compound.Our precision medicine platform will help us assess the best potential combinations for dose escalation. Importantly, we don't expect to be going into specific subtypes in dose escalation as a good majority of patients are expected to be responsive to this mechanism of action. Instead, we will retrospectively assess if there are biomarkers that impact the level of response in patients to help inform the Phase II or later clinical development strategy.As I mentioned on the prior slide, CDK7 is a broad mechanism that we believe can apply to a large number of cancer types. On this slide, you can see the 6 indications we have selected for our Phase I/II trial based on experimental work we have done to date as well as peer-reviewed literature. Highlighted here is the U.S. incidents for these indications, which are all relapsed refractory patient populations.In the U.S. alone, there are 75,000 patients each year that fit the criteria for which we are enrolling in our ELUCIDATE trial. One place of potential benefit for this mechanism is within CDK4/6 refractory patients that through our platform work, we believe it could be much broader and look forward to learning more in the clinic to see which patients may benefit most from CDK7 inhibition.I will now hand the call over to Nikolaus Krall, our EVP Precision Medicine, to walk us through how we're using our precision medicine platform with this program.
Thank you Mike. Last quarter, we highlighted how we have taken functional data from our precision medicine platform and combined it with matched multi-omics data to achieve a better understanding of disease biology. This has become possible through our investment into state-of-the-art next generation sequencing capabilities at our Vienna site. Today we're expanding on this theme and showing that we have leveraged both functional and omics data to reveal potential combinations for our CDK7 inhibitor 617.Further, the NGS program will be deployed alongside our clinical trials, where we'll be profiling, for instance, peripheral cell-free DNA and blood cells to enable an understanding of target engagement and allow us to study disease mechanisms and drug activity beyond our ex vivo efforts. Specifically, here we show how we used our precision medicine platform to understand the potential effects of 617 in patient samples from various cancer indications such as breast, lung and ovarian cancer.These are 4 of our 6 indications included in ELUCIDATE. Ex vivo, nearly 75% of samples show sensitivity to the 617 inhibitor. This supports our notion that 617 will have an effect in a variety of patients and indications. Further work is ongoing to determine how the depth of response may correlate to in vivo response prediction.Using our ability to measure drug response in primary human tumor tissues, we then profile different combinations of 617 and already approved drugs for potential synergies. On this slide, we have highlighted a few examples of combinations of potential synergies that we have uncovered using our platform. We have identified 3 drugs with different mechanisms that have synergistic effects ex vivo with 617 in the reduction of cancer compartments in primary samples.Further biological mechanistic validation as well as further data from different indications ex vivo is being collected. On top of the screening shown here, we're also deploying transcriptomics and functional profiling to support the discovery of potentially clinically relevant combinations through correlation of drug activity, target and pathway modulation. This is a similar approach to the idea of using omics profiling of primary patient material combined with our functional platform to reveal disease biology and targets as presented in the last earnings call.As shown at prior medical meetings, we're highlighting that the platform has also uncovered dose-dependent PD biomarkers. Two are shown here that we believe to be non-invasive, so we can potentially show drug activity early on within the escalation study. This is an example of 2 genes uncovered through omics profiling, but one of them has also been reported by another company to CDK7 space. This data absolutely use case of our precision medicine platform in the context of biomarker discovery as we aim to bring clinical relevance to all areas of preclinical research.I'll now hand it over to Ben Taylor, our CFO, to walk us through the financials. Ben, over to you.
Thank you, Nikolaus. I'll now take a minute to close with highlights from our financial results for the second quarter 2023. Full results are detailed in our press release and Form 6-K. I'll review the results in U.S. dollars using the June 30, 2023, constant currency exchange rate of $1.27 to the pound. We ended the quarter with $509 million in cash equivalents and bank deposits. We believe this provides us with several years of cash runway and the resources to continue investing in our growth. Last quarter, we highlighted cost efficiency programs being put in place and we've already seen a significant impact on our spend. We expect to save over $30 million during the course of 2023 and more in 2024 while still delivering on the goals that we have outlined.As expected, cash inflows from existing partnerships have been limited for the first half of 2023 as we're in the middle of discovery execution on a number of pipeline programs. We expect these milestones to ramp up substantially in 2024 and beyond as we reach the development milestones for the partnerships. We are also maintaining our guidance for 2 new business development deals during 2023.Exscientia remains well-capitalized as we continue to successfully advance our internal and partnered projects. At the same time, we are cautious in the current macroeconomic environment and intend to continue our cost-control efforts through the end of the year with a focus on optimizing workflows and automation.With that, I will turn the call back over to Andrew.
Thank you, Ben. Our mission at Exscientia is to change the way drugs are made to create better drugs for patients faster. Our goal is to significantly increase the probability of success within drug discovery and development for an end-to-end patient-centric approach. The progress we've been making in precision designing important and selective drug candidates, building a clinical pipeline and advance in our owned and partner development programs, gives us great confidence in our business model, our diverse team of experts with outstanding capabilities and in the quality of our strategic partnerships. We look forward to continuing to meet our milestones and follow through on our commitments of bringing yet more differentiated compounds into the clinic and further strengthen our AI-led drug discovery capabilities.And with that, we'll open the call for questions and answers.
[Operator Instructions] And we will take our first question from Peter Lawson with Barclays.
This is [ Courtney ] on for Peter. I just had a quick one around the IGNITE trial. Can you give us a little bit more color around how enrollment is going and if it's possible to see data in the first half of '24? Or is it more of like a second half event?
Thank you, Courtney. Great for you to join us today. For that, I'm going to hand you over to Mike Krams, our Chief Quantitative Medical Officer to give you a bit more color, as you say, on the IGNITE trial. Mike?
Thank you for the question. Recruitment is going well and we are in the dose escalation part of the clinical trial. And as you know, we have a dose escalation study that will dynamically switch into the dose expansion phase. At this point, we are accruing the data and have the usual approach to looking at data through an independent data monitor committee, and so in summary, recruitment is going well. Yes.
Thanks, Mike.
We will take our next question from Alec Stranahan with Bank of America.
Just a couple from us. First on your automation laboratory, looking forward to that opening in the back half of this year it sounds like. Is the goal here to supplement your current wet lab capabilities? Or could this actually replace or amplify some of the research that you're doing? And as a follow-up, just on the R&D spend, I thought it was notable that your spend was flat year-over-year. What are sort of the puts and takes that went into this quarter, given you do have more clinical studies ongoing? And how should we kind of think about R&D spend going forward?
Thank you, Alec. Two great questions, actually. And I'm going to split the answers. I'll take the first half of it on automation and then I'm going to hand it over to Ben to give you a bit more color on the financial situation. With the automation, absolutely, we've already sort of opened up our new laboratories, still continue to build up, particularly the chemical synthesis and new technology hardware that we put in, in place there. The goal always is absolutely to increase the full experimental capability inside Exscientia.Already, as you might know, we have a very strong sort of biological laboratories here in Oxford and also in Vienna, particularly where our precision medicine, patient-centric technologies are based on the omics platforms. What we built in here in Oxfordshire and the [indiscernible] now is how we can transform the way that sort of AI-driven automation now goes to scale up, particularly sort of biochemical and biophysical screening approaches and also then to sort of bring in-house AI-driven approaches to retro services to then actually synthesize in automated fashion, many of the compounds actually that we are making in our sort of optimization design cycles.So what we are doing then actually is we see this actually as a new way then of really transforming the design cycles and the time of that takes actually for synthesis and screening to take place. Much of that work right now takes place with our CRO. So what we see then ultimately is how we start to bring more of our capability in-house because we believe this gives us a real sort of key sort of advantage now in sort of the next sort of quantum leap in productivity as we say. We're really excited about that and we look forward to showing you the labs. They're already open and hopefully, we'll be talking more about that towards the end of the year and hopefully inviting many of you to come see it.
Yes. And I'll pick up on the R&D spend. First of all, I think what Andrew just outlined is a little bit of our philosophy on how we are trying to advance the tech and the efficiency at the same time. So we are absolutely an innovation-driven company, but what we try and do is not only have that innovation drive new ideas on how we do drug discovery, but also drive new ideas on how we can be efficient. And so what we've been doing over the last 12 months and we've talked about this a bit, is really working on process, working on efficiency, looking at what are the important aspects of our spend versus things that are less important.And that's enabled us to take a lot of cost out of the system while still getting to the same results. And that's part of what you're seeing in that R&D. We are doing more. We are expanding our capabilities. We are investing a lot across the company. But because of those efficiencies we've been gaining, we actually have seen pretty flat spend. I think we expect that to continue in the near future. Where you might see that start to meaningfully change is when we get into later-stage clinical testing. Those trials obviously cost quite a bit more. But we've actually put in place a lot of the infrastructure to continue doing our discovery and early development work with what we have.
Okay. Great. And maybe one quick follow-up, if I may, just on the automation. You noted in the PR that you've built out some hardware and software solutions sort of bespoke in your process. Could you maybe highlight 1 or 2 key examples of which you've actually had to build in-house and how that feeds into the facility overall?
Absolutely, no. I mean it's a testament, I think, to the talent that we have inside Exscientia that if you look at our biology teams now, they really are sort of integration between sort of wet biologists, software developers and hardware engineers as well, really looking at our processes, getting real sort of feedback about what is the quality data that we want to now generate in an efficient manner. Give you sort of 2 examples of that actually. How we've been thinking about sort of generated and scaling out [indiscernible] is not in a traditional way you would think about high-content screening, which is usually thinking about how can I screen, say, 1 million compounds in one type of assay.We actually want to turn it around. What we're interested in is actually collecting a lot of data on actually a small number of compounds. So when we make in our precision design molecules, when we get deep inside since virology. So we turn it around it and think how do we automate them for technology we build on new hardware to allow us then to collect sort of multiplex in multiple sort of data points on many different types of assays onward compounds. So it's really changing sort of the way our view of thinking about sort of high throughput, really it is about high throughput, high-quality in terms of diversity of data that we now generate in.Also what we've built in as well in terms of how to automate techniques in sort of biophysics and where we can actually then take us to sort of a next level of really thinking of sort of modular approaches and to add analytical technologies.We're really excited about sort of showcasing some of that later in the year, et cetera as we have started to reveal it. And I think it actually then outlines the depth of innovation that Exscientia has now taken. This is actually part of a bigger picture we see. The bigger frontier now is how AI now starts to help and control and drive experimentation and that's where automation is key then linking those 2 worlds together between experimentation and automated design coming together and that's for the automation platforms.
And we will take our next question from Gaurav Goparaju from Berenberg Capital Markets.
First, especially since you have 2 preclinical programs on deck, what's your bandwidth in terms of how many programs you believe you can internally conduct clinical trials on simultaneously? Just trying to think about what your capacity is on how many internal programs you're able to have in the clinic at the same time?
That's a great question, actually. And we're going to answer it in sort of partly in terms of the capabilities within our discovery and development capabilities, then we are building a very flexible pipeline. That's why if you look at the number of projects we're running pre-clinically across it, we have significant bandwidth there. The automation platform is also increasing our capacity long-term in that space as well.In terms of how we run in development as well, I'll have Mike to step in here as well. We have run in actually a very tight ship. And that some of the key advantages, which are actually model-informed drug development and adaptive technologies also allow us to do. Mike, do you want to give a bit of color on how you're thinking around the capacity in your clinical department?
Yes, absolutely. Thank you for the question. So Andrew, you mentioned the keyword and it is model-informed drug developments. Before we ever start going into the clinic, we really try to dive deep into our understanding of the underlying biology, integrate information in a quantitative fashion and then create development strategies that allow us to make the correct decision at the earliest time point in the most efficient manner.The way we do this is to currently plan for approximately 4 programs at any one time to undergo this process. But there is an opportunity to borrow information across. So in our efforts to apply the model-informed drug development thinking, we have the intention to leverage learnings that are applicable from one program to the next. And in terms of pure numbers at any one time point on compounds we progress is approximately 4.
Got it. And then just one more follow-up from me. On capital allocation, given the current macro environment, are you prioritizing later stage programs and preclinical programs versus let's say starting new programs or emerging discovery programs? Or do you feel you have the resources to tackle both simultaneously?
We are well-capitalized. And I believe as a team we have the capabilities and resources actually to both develop the pipeline, as you see actually this year. Actually, we've been incredibly proud the development we've seen in the clinical space of our pipeline. But also in terms of where we are and as Ben sort of explained in the last answer as well, how we manage inefficiencies in discovery is that we now start to see that how our technologies can allow us actually to potentially do more good resources we have. That's actually for a key part of our philosophy in terms of automation. Ben, I don't know if you want to add actually some extra color to that.
No, that's absolutely on from my perspective. So we keep a very balanced portfolio. I think what it has required us to do because remember we are investing behind target ID with our Sanofi collaboration and our internal efforts. We continue to move things up through our partnerships and internal pipeline, but obviously have our clinical stage portfolio. So what we have to do is to be really disciplined about management decision-making and say from the beginning, is this a patient population that needs a drug? Is this an area where we can design something that's differentiated?And if it's not, we need to be replacing it with something that can be. So we use our operating efficiency to be able to maintain the scale that we have, but we also need the management discipline so that we don't get distracted in lower value projects.
And we will take our next question from Chris Shibutani with Goldman Sachs.
This is [ Roger ] on for Chris. Just 2 quick questions from us. One is, could you elaborate a little bit on the milestone payments you've previously talked about? I just want to better understand, is it going to be more early near term in the second half of this year or more latter weighted towards 2025? And then our second question is on 617. Just want to learn a little bit more about the deep learning component for the study from the ELUCIDATE study. Will you be prioritizing any of the 6 indications to maybe build upon training data sets before looking at others and then is the goal here to really just see outcomes from an initial set of patients before moving to the dose expansion phase?
Thank you, Roger. Thanks for your questions today. I'm actually going to split your 2 questions. First one to Ben on the milestone payments progression and the second piece on 617 precision medicine strategy to Nikolaus. Ben?
Yes, sure. So on the milestones, we expect that 2024, 2025 and the beyond years are going to be much more heavily weighted towards the milestones from the BMS and Sanofi collaboration. So if you think about the time period, when we entered BMS and Sanofi, it takes a couple of years basically to start up the program to advance it and reach the initial milestones. And so from the timing of when we entered that, you would expect 2023 to be probably the lightest year and then building up as you go into the future, and you start to hit the more meaningful milestones moving forward.That being said, we still see several hundred million of potential milestones over the next handful of years and are really excited about that relationship even with all of the craziness in the macro environment. Sanofi and BMS have continued to be great partners and we both continue to really invest a lot of time and resources behind those partnerships.
Thank you, Ben. And in terms of the 617 precision medicine strategy and how we play in the technology to explore that program. Nikolaus, would you like to describe it in a bit more detail. Build upon I think some of it, your comments earlier.
Yes. Thank you so much for your question. So in our preclinical work using our primary human tissue platform, we have shown that our molecule actually has activity across a broad range of tumor indications, some of which we are exploring in our clinical studies. Maybe Mike can comment about this as well. In terms of prioritization, we are currently exploring different hypotheses of how we could further whittle this down and stratify patients further alongside our clinical trial and then really be guided by our data to go from the exploration that we're doing at the moment to potentially focusing on a few more indications.
The other thing I'd add to that as well is, of course, as Nikolaus presented earlier, of course, using the platform help us to understand and prioritize potential combinations as well where we hope to move into, but certainly see that as part of a key long-term strategy. Mike, anything to add?
No, I think, Nikolaus, you said it all. Two decision problems. One, which indications to pursue, the second, which combinations within those. And we're going to integrate across all information, both on empirical outcomes and biomarker outcomes to make the correct decision.
And we will take our final question from Vikram Purohit with Morgan Stanley.
We had two. One, just to follow-up on the topic of partnerships. We would just like to learn a bit more about how you're thinking about the potential for more partnerships over the next, call it, 6-12 months? And how your views on BD might have evolved, if at all, over the past several months? And what you'd be looking for in new potential partners as you have those discussions?And then secondly, on the topic of biologics. Was just wondering if you could provide us an update on how those efforts are progressing internally and when you might be ready to discuss the biologics capability you've been building a bit more externally?
Excellent. Thank you for your questions today, Vikram. On the first one, on partnerships actually, I want to introduce Dave Hallett, our CSO, to talk in a bit more depth about how those are developing because Dave is obviously intimately involved with the ongoing projects and the new projects under discussion. Dave, do you want to give Vikram a bit a bit more color on where you see the partnership position right now?
Thank you, Andrew. With the [indiscernible] we have in progress, the Sanofi collaboration and the BMS, that potential kind of collaborations kind of across different therapy areas...
Hey, Dave, I think we're having some trouble hearing you, Dave. So maybe I'll just jump in really quickly on the partnership side.
Okay then.
Thank you though. So on the partnerships, obviously, we continue to give guidance that we expect 2 new partnerships by the end of the year. And so we absolutely feel confident that there is a good business development environment out there. Now that has evolved quite a bit over the last 18 months. And I think we talked about this a little bit before the IRA and some of the other macroeconomics had a chilling effect not on necessarily pharma evaluating opportunities, but really on decision-making inside of the pharma is, I think, what we've seen is a real reengagement from the pharma community.And so a lot of those discussions that were ongoing have, over the last several months, really gained traction at the higher levels inside of the organizations. We've seen a lot of engagement out of the senior leaders of large pharma and new inbounds coming in to us.Now interestingly, I think the scope of where those partnerships may go has expanded quite a bit. From 2 years ago, 3 years ago, when almost all of the discussions were really about how can we have a number of pipeline programs coming through, I think what we're seeing now is really a lot of the pharma are interested in more advanced technologies and capabilities, which we love because that plays right into our strong suit of having an integrated platform.Obviously, I'd be remiss if I didn't comment on the tech side as well. We've seen some really interesting movements from big tech and that's also a very interesting potential partner base for us. They have a different set of priorities than large pharma do, which makes them potentially open to different types of partnerships than a large pharma would. And we will see where all of that goes over time, but I think we feel much better about the business development outlook as a whole from where it was even 6 months ago. We've really seen a lot more excitement.
Thank you, Ben. And Vikram, just to add some thoughts on where we are with the biologics platform. If you've been following sort of the conference circuit, we've actually been making quite a few sort of presentations and posters on the tech development of the algorithms and how they are performing in benchmarking [indiscernible] and incredibly progress actually in how the algorithms are now coming together in that space. We've also been building out automation as well for how we think kind of generating data for our biologics platform.As I sort of hinted at earlier, we'll be sort of telling you further about some of the sort of automation lab approaches and new ways of conducting sort of biophysical experiments that we've been building and hopefully to be unveiling that towards the end of the year. Importantly as well, we've also been undertaking sort of the first sort of proof concept projects as well and bringing all this technology together. That we hope to bring you sort of news on that as well in the second half of this year now.And to give -- building up new capabilities, building up skills and [indiscernible] actually is to guide or sort of design within that. So we're incredibly excited. I do think that from 2024 onwards, you will start to see biologics start to contribute to the Exscientia pipeline as our confidence grows now in this space.
And ladies and gentlemen, there are no further questions at this time. So I will now turn the call back to Mr. Andrew Hopkins for closing remarks.
Thank you, operator, and thank you to everyone on the call today for your continued support of Exscientia. We hope you've seen today how our technology platform can accelerate emerging science to create new therapeutic opportunities. We believe by controlling the intersection between generative molecular AI, predictive modeling, where that's by machine learning or physics-based approaches and automated experimentation with patient-relevant systems that we can produce better drugs faster. We look forward to updating you on our progress throughout the rest of the year. And operator, you may now disconnect.
Thank you. Ladies and gentlemen, this concludes today's call. We thank you for your participation. You may now disconnect.