Exscientia Plc

Hello everyone, my name is Chris and I'll be your conference operator today. At this time, I'd like to welcome everyone to Accenture's business update call for the full year ended 2022. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you'd like to ask a question during this time, simply press star, then the number one on your telephone keypad. To withdraw your question, please press star one again. At this time, I'd like to introduce Sarah Sherman, Vice President of Investor Relations. Sarah, you may begin.

Thank you, Operator. A press release and our 20F were issued this morning with our full year 2022 financial results and business update. These documents can be found on our website at www.investors.accentia.ai, along with a presentation for today's webcast. Before we begin, I'd like to remind you that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, preclinical and clinical results, and business performance. Actual results may differ materially from those indicated by these statements. Unless required by law, Accenture does not undertake any obligation to update these statements regarding the future or to confirm these statements in relation to actual results. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer, and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Scientific Officer, Gary Paradue, Chief Technology Officer, and Mike Krems, Chief Quantitative Medicine Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.

Thank you, Sarah. 2022 was another transformational year for Accenture. We continue to validate our AI-driven precision medicine platform and strengthen our business. Accenture's goal is to fundamentally transform the way our industry designs and develops drugs. We believe our unique pairing of excellent science with advanced computational experimental capabilities at every step of the R&D process differentiates our patient-first precision medicine approach. Our approach of model-driven adaptive learning as an overarching technological principle enables us to innovate from discovery and into development. To that end, our remarkable progress to date is a testament to the strength of our company. We are well capitalized with $611 million in cash at the end of the year. This provides us with several years' runway to advance our near-term programs, deepen in our pipeline, whilst also investing the long-term growth. To that end, we marked several significant milestones throughout the year across our internal and partner programs, which we will provide more insight on today. We will also highlight data that illustrates how we're working on designing better drugs for the right patient by combining precision engineering with personalized medicine. At a high level, last year was significant for the trajectory of our business. We started here by signing a collaboration with Sanofi for up to 15 targets. and end of the year, showcasing the value of our multimodal gene signature data for immuno-oncology patient selection at ESMO-IO. Importantly, we've advanced our pipeline. We highlight five programs across oncology and immunology and inflammation, either in clinical stage or IND-enabled studies. We have presented important data from our design and translational platforms across these programs, and we are pleased we can now share these targets and how they combine precision design with personalized medicine. For our A2A candidate, EXS21546 or 546, last year we reported top-line healthy volunteer data in June. The data confirmed our target product profile design, including portency, high receptor selectivity, and expected low brain exposure, with no CNS adverse events reported. This data provided support to move into our patient trial. Late last year, we also received CTA approval to initiate our Phase 1-2 trial. The IGNITE trial will examine the safety, pharmacokinetics, pharmacodynamics, and efficacy of 546 when used in combination with anti-PD-1 therapy in relapsed refractory renal cell carcinoma and non-small cell lung cancer. During the trial, we will be observationally validating our patient selection biomarker aimed at enriching patients more likely to respond to 546 discovered preclinically. The trial will enroll up to 110 patients and we continue to expect the first patient to be dosed in the first half of this year. A vital component of our A2A program is a robust biomarker strategy, as we believe the key to a successful A2A inhibitor is enriching for the right patients with high adenosine in the tumor microenvironment. We presented relevant data at ESMO Immune Oncology Annual Congress in December, identifying a novel patient selection multi-gene transcript signature the adenosine burden score, or ABS. The biomarker was discovered preclinically using our patient tissue platform and multi-omics dataset integration from complex disease-relevant model systems. The IGNITE trial will evaluate this gene signature to identify patients most likely to respond to 506. We look forward to presenting additional data on biological validation of the ABS and our 506 program at the upcoming AACR meeting next month in Orlando. With our CDK7 inhibitor, GTA-EXS617, developed in partnership with GTA Perion, we remain on track to involve a first patient in our planned Phase 1-2 study in the first half this year. This program also showcases what truly makes Nixenti a drug unique. Precision design aiming to transform patient benefit and patient selection strategies. We highlighted some of this patient selection data at the ENA Congress in October to maximize understanding of the effect of 617 using primary patient material. For the first time, we showed how we integrate machine learning, data from primary human tumor samples, and multi-omic sequencing capabilities to predict tumor efficacy of 617. Using our deep learning AI and high content imaging platform, we've previously confirmed 617 activity in primary human samples Data presented at AACR led us to generally define two groups of patient samples, effectively high and low responder groups, when focusing on ovarian cancer. We believe that leveraging this information will enable us to identify responders and non-responders to 617 across tumor types. This is a key component of how we designed our Phase 1-2 study. We look forward to sharing more detail on the tumor types we'll investigate in shortly. With both our CDK7 and 8-way programs, we can now begin to see the hallmark of what an Accenture drug looks like. AI and machine learning is applied not just in the process of how we design a drug, but also how we identify the right patients for that drug. Accenture invented the first AI-designed drug to ever enter the clinic. Since then, we have made significant advancements in our technology and AI capabilities. We have developed a comprehensive physics-based platform encompassing molecular dynamics and quantum mechanics, which is combined with our AI, generative, and active learning capabilities. We have also significantly progressed our engineering and data platforms, enabling scalability and robustness. As you may have seen, over the past several weeks, we have highlighted three new targets that are progressing. EXS4318, our clinical stage PKC theta compound that was in-licensed by BMS. EXS-74539, our LSD-1 inhibitor, and EXS-73565, our MALT-1 protease inhibitor. These embody what Accenture can do in terms of AI-generated molecular design, where we truly lead the field. These molecules are great examples of how our drug hunters using our AI can solve complex problems such as kinase selectivity in the case of PKC-beta, brain penetration coupled with reversibility in the case of LSD1, and allosteric inhibition in the case of MALT1. I'll now spend a few minutes on each of these important programs, showing you how we designed these compounds and what we have seen to date. In February of this year, Bristol Myers Squibb initiated a first-in-human study of AXS4318, a potential first-in-class selective PKC fetal inhibitor. BMS will oversee clinical and commercial development, and Xcentia is eligible for milestone payments and, if approved, tiered royalties on the net product sales. PKC-FETA is an attractive immune-modulating target, which plays a critical role in the control of T-cell function and is a key driver of several highly common autoimmune diseases, but has proved challenging to dose. The target product profile was particularly challenging due to the need to balance the stain, high levels of target inhibition to drive efficacy, with low daily dosing in humans, PKC-FETA is structurally similar to several related kinases, making it difficult to achieve the high levels of selectivity required to avoid off-target effects. Our team of experts, leveraging our AI design platform, delivered a balanced candidate with potent on-target activity while maintaining high selectivity and a favorable fabric index, as demonstrated in the IND enabling studies. As you can see here, Previous molecules have failed at the challenge to design a candidate with required potency as well as selectivity against other closely related kinases. We believe a well-balanced molecule meets required properties to potentially provide benefit in patients. This molecule is our first immunology and inflammation candidate to enter into a clinic. This is a significant milestone, illustrating Accenture's strength, efficiency, and flexibility to precision design high-quality therapeutic candidates. Last week, we shared an update on our next generation LSD1 and MALT1 inhibitors. I'll highlight a few details, but encourage everyone to visit our website and watch the video which detail these candidates further. 539 is a differentiated lysine demethylase 1 or LSD1 inhibitor, precision designed to improve patient benefit and solve challenging design objectives. It promises strong potential in both hematology and oncology. To date, Other LSD1 inhibitors in development elsewhere have failed to achieve the combination of appropriate pharmacokinetics, good brain penetrance, and reversible mechanism of action. Our candidate has been designed to achieve suitable CNS penetration to target brain metastases common in certain cancer subtypes. In vivo studies have shown favorable activity also in small cell lung cancer xenograft models with dose-dependent inhibition of tumor growth. in vivo studies have also shown a favorable absorption, distribution, metabolism, and excretion profile with shorter predicted human half-life than some LSD1 inhibitors currently in clinical trials. We believe this may benefit on-target tox management, allowing for platelets to recover following dosing given the reversible nature of 539. We believe the exquisite control of LSD1 inhibition and the superior management of platelets will be a critical differentiator for 539 in the clinic, particularly in combination with a standard of care that often has negative effects on platelets. The flexibility to genuinely explore intermittent dosing regimens in the clinic and thus maximize the therapeutic window is another reason we believe that 539 is differentiated from other compounds in development. Here you can see the properties of 539 against two other LSD1 candidates, specifically looking at factors such as CNS penetration, mechanism of action, and predicted, as in the case of 539, or published clinical dosing regimens. As you can see, only 539 achieves a unique combination of a reversible mechanism, suitable CNS penetration to target brain metastases, and a predicted human half-life aligned with once-daily dosing. Our molecule, also met a long list of other criteria such as selectivity against related enzymes, high bioavailability in preclinical species, and in vivo efficacy in relevant models of SCLC, a potential indication where 539 may have benefit. Importantly, we were able to use AI to find this highly differentiated molecule with a specific target product profile ultimately exploring new chemical space. Using our 3D mapping algorithms, we identified targetable features of each region of a protein. We then used our 3D generative AI design algorithms to produce prioritized populations of molecules, meeting specific optimization criteria. Machine learning models efficiently scored the compounds of CNS penetrants, alongside optimizing multiple parameters, including portency and admin properties. By then applying active learning methods, we were able to select the most information-rich molecules to make and test at each design cycle, usually around 10 to 20 compounds. This allowed us to find novel molecules outside the established domain of applicability that were counterintuitive, which enabled us to find a new starting point for design, and covering this chemotype ultimately led to 539. The result was that we were able to create molecules which achieved a target product profile that no competitor molecule had exhibited. We believe 539 has the potential to become the first potent, selective, reversible, and brain-penetrant LSD-1 inhibitor to meet significant unmet need in a range of oncological and hematological indications. We look forward to highlighting the precision design of this compound, as well as the latest in vivo data at the AACR next month. I'll now highlight another candidate we've recently unveiled, our MALT-1 inhibitor EXS-73565 or 565. MALT-1 or mucosa-associated lymphoid tissue lymphoma translocation protein 1, is an important protease target with potential applications in hematology. It aims to inhibit the uncontrolled proliferation of malignant T and B cells in hematological cancers. Accentia's AI-driven precision design approach was able to optimize the safety profile of agents targeting MOT1, whilst also generating potency and selectivity. When considering an optimal target product profile, the team took into account the likely use of a MALT1 inhibitor in combination therapies such as BTK inhibitors. Therefore, in addition to portency, selectivity, and a balanced set of overall properties, we were mindful of potential drug-drug interactions. In these two charts here, you can see that in vivo studies of 565 have shown anti-tumor activity in mouse models and favorable pharmacokinetics both as monotherapy and in combination with abutranib. Toxicology studies have also shown an acceptable therapeutic index with the ability to maintain high levels of potency, selectivity, and safety benchmarks, whilst avoiding meaningful inhibition of UGT1A1, which can lead to excessive levels of bilirubin and is a known cause of drug-drug interactions. This chart here compares 565 directly with published and patented MALT1 scaffolds from various groups. 565 compares very favorably across all parameters, examining potency, cellular activity, and drug-like properties. 565 has very little activity at UGT1A1 and is highly differentiated in this respect. We would predict that many of the other compounds would likely inhibit UGT1A1 to a meaningful degree and thus present challenges in clinical development. It's important to note, in designing this compound, it was the first time that we merged molecular dynamics with AI at Accenture. Molecular dynamics, a physics-based method, is just one of the tools that we have in our tech stack today. In fact, molecular dynamics simulations provided additional insights into critical binding interactions within the allosteric site. Using hotspot analyses allowed us to understand the allosteric binding pocket, highlighting key interactions needed for design. Molecular dynamics then enable us to understand the dynamic motion of the binding pocket and develop a design strategy to improve the potency and broader properties of our compounds. Using data and knowledge of other allosteric multiple inhibitors, our generative design algorithm, Gambit, was used to evolve novel molecules. This resulted in a suite of promising compounds. We believe that 565 can be developed to meet the significant medical need that exists today. through potent and selective MALT1 protease inhibition with the potential for a meaningful safety differentiation. In summary, these compounds demonstrate the potential of the Accenture platform to efficiently deliver precision design compounds that may provide substantial benefit to patients. Better design, we believe, improves the probability of success of reaching patients. And while the average industry timeline to discover a development candidate takes around four and a half years, and synthesizing between 2,500 to 5,000 compounds, it is remarkable that these two candidates were designed in 15 to 20 months respectively, and from synthesizing 344 and 414 precision design compounds. These stats underscore how our AI-driven approach is not only differentiated in terms of precision design, but also faster and more efficient than conventional methods. IND enabling studies are underway for both these inhibitors, and we expect to provide an update on clinical development plans, leveraging Accenture's personalized medicine platform in the second half of 2023. These compounds have potential broad application in oncology and hematology. Overall, we are thrilled with our recent advancements and look forward to sharing more details on our progress. I'll hand over now to Ben to walk through our financials.

Thanks, Andrew. I'll now take a minute to close with highlights from our financial results. Full results are detailed in our press release in 20F. I'll review the results in U.S. dollars using the December 31, 2022 constant currency rate of $1.2077 to the pound. We ended the year with $611 million in cash, equivalents, and bank deposits. We believe this provides us with several years of cash runway continue investing in our growth. At the same time, we believe that the recent macroeconomic factors, including bank defaults, political trends, and large pharma announcements, will cause 2023 to be a year of economic conservatism in the biopharma industry. As a side note, Accentia does not have any banking exposure to SVB or Credit Suisse. From a business model perspective, we are well positioned to respond to the current market environment We have now repeatedly demonstrated that we can achieve better drug discovery outcomes faster and with less cost than traditional methods. In order for the pharmaceutical industry to improve ROI in the face of growing price and competitive pressures, it needs the quality and efficiency that we bring. We also believe that our personalized medicine platform will help improve the probability of success in the clinic, which in turn will further improve return on investment. At Accenture, our partners continue to invest substantial resources in our projects. Our existing partnerships alone could contribute several hundred million in milestones over the next three years. We expect a number of earlier milestones during 2023, with a majority of the milestones occurring in 2024 and 2025 as we achieve development candidate goals. We are also seeing an active interest in new business development and are reiterating our guidance of at least two deals this year. We have seen a focus on innovative technologies and specific pipeline candidates from potential pharma partners. And as a result, we have begun to adjust some of our operations to focus on areas that we believe will have the highest near-term impact and return. In addition, we believe it is important to respond to the macroeconomic environment by keeping our own operations as efficient as possible. Over the last six months, we have reduced costs with several of our CRO relationships and believe there is additional room for improvement without a loss of quality. In addition, we are evaluating multiple ways to apply technology or streamline our internal operations in order to drive greater efficiency. We estimate the combination of these efforts will save tens of millions of dollars in operating costs over the course of 2023. I also wanted to be clear that our guidance of several years cash runway allows us to take all four of our disclosed later stage internal programs, including A2A, CDK7, LSD1, and MULT1 through initial proof of concept clinical trials if and when the clinical evidence and strategic business rationale support that decision. With that, I will turn the call back over to Andrew.

Thank you, Ben. Today, we walked you through several examples of how we are working to produce better drugs faster by innovating in both discovery and in development. We believe that our differentiated approach and our advancements this year further validate our end-to-end platform and distinguish our company as leaders in the field of AI-based drug discovery. As you can see, we have another important year ahead to best position us for the future. With that, we'll open up a call for questions and answers.

Thank you. If you'd like to ask a question at this time, please press star then one on your telephone keypad. The first question is from Vikram Parikhit with Morgan Stanley. Your line is open.

Hi, good morning. Thanks for taking our question. Two from our side. First, for 539 and 565, your slides mentioned that the molecule's were developed in, I believe, 15 to 20 months versus a much longer industry average development timeline. And I was wondering if you could just walk us through which components of early stage development you believe your platform helped cut timelines for the most. And then secondly, on the topic of partnerships and business development, I mean, going forward, what would you be looking for in your next set of partnerships? What kind of capabilities would be most additive to seek out and Conversely, what also do you think you could be bringing your partners with the next set of partnerships that you weren't able to with the earlier set of partnerships?

Thanks. Vikram, good to speak to you again. This is Andrew. Actually, for that first question, I want to introduce Gary Parado, CTU, actually, because I think Gary's unique background of being a drug hunter and a technologist actually will highlight and understand where actually Accenture's platform really brings value to the drug discovery and drug design process.

Sure. Thanks, Andrew. And thanks, Vikram. So the timelines we put out are the time from starting the project with a hit molecule to identifying the candidate molecule. So that's the molecule we take forward into development. Typically, industry average times are around about four years for that period. The reason why our platform is so much more effective is because we're harnessing the full power of using artificial intelligence, using multi-parameter optimization and using generative design to explore chemical space much more rapidly. widely what that means in a nutshell is every optimization cycle we're exploring more possibilities and we're effectively taking larger jumps at each step which is what's allowing us to cut the time down so dramatically and we've seen this across all of our programs excellent thank gary and dave do you want to add a bit more about the broader set of capabilities we have now yes thank andrew um

I think it's important to realize that Accenture operates that interface between kind of experiment and artificial intelligence. And I think it's the components of each that when they're brought together, that actually make a significant kind of contribution to both timelines. It's very much in our interest to actually generate high-quality data that can actually be then utilized in a machine learning kind of environment. And so that explains why we've created and continue to grow our experimental footprint. But it's ultimately kind of placing experts with the right tools they need and with the right technology. And that's kind of where you see the acceleration of the timelines.

Dickram. I'll take your second part of the question as well about partnerships. I think what's exciting about Accenture's development, particularly over the past couple of years, is how our end-to-end platform has really expanded. We've gone upstream, thinking about now how we think about target selection, particularly incorporating patient tissue, patient-derived approaches in target selection, and combining that then with data science approaches and deep learning approaches that really think about how to integrate the wealth of external knowledge into those experiments. And going all the way downstream now to thinking about how we design precision medicine biomarkers for patient selection using a whole range of machining approaches to multimodal omics. You can see that with the Tennessee Burden School and the work we're doing on CDK7 and expect to see more of that coming forward each year. We start on the scene now of actually the hallmark of an exentio drug is one where we combine both precision design with personalized medicine going forward. It's not just a process of how we design the drug, but also how we think about embodying that kind of technology into the kind of labels we are thinking about. But that means then we now have a much broader offering. And I think actually as we think about the developments and advances now we're thinking about in clinical development and precision medicine, I think we're in a very strong position actually to offer a broad range of solutions to the pharmaceutical industry who are themselves facing a number of challenges right now. So we are very excited right now, a lot of discussions we're having, which regard how we think about applying precision medicine with partners, how we think about applying actually the model informed model driven adaptive learning processes that actually we've also created in discovery how they also now apply in development in the design of our trials so we're really excited actually as accentia grows i think we're in a very strong position actually to continue to develop and strengthen the offering of solutions we can bring to our pharma partners

The next question is from Michael Ryskin with Bank of America. Your line is open.

Hi, this is Wolf on for Mike. Thanks for taking the questions. So starting off the announcement of 539 and 565 are obviously quite exciting. I'm just wondering how to think about the incremental spend associated with these and other IMD enabling programs given the continued refinement and scale of your platform and kind of building off of Vikram's question. And then as a follow-up, how are you thinking about your current capacity for parallel IND enabling studies? What do you see as the primary limiting factor to the number of studies that you can have ongoing once? Is it just a headcount thing or are there technical issues as well?

Excellent. Good to speak to you again, Wolfram. I'm going to introduce Sir Ben Taylor, actually, to answer this question.

Hey, Wolf. Nice to talk to you again. So a couple of things on just thinking about our budget and going ahead and operating expenses. So As we think about 2022 versus 2023, 2022 is really the year of scaling and putting a lot of the infrastructure in place that we would need to be able to execute on a broader pipeline, both in the discovery phase as well as in development. So when we look ahead at 2023, even though we are initiating a number of clinical trials, we actually don't see that scaling of cost continuing and would expect 2023 to be much more level with what we saw in the fourth quarter. So I think we've actually achieved a lot of that scale and infrastructure to be able to execute. And I think that goes into your question of how we can handle clinical programs moving forward, the actual additional expense internally that we would need to do that is not substantial. I think what we would look at is as those programs continue to get into later stage development, that's where you really see the scaling in the expense. And so I think both on the discovery and the development side, we're at a pretty good place right now. I also mentioned where we are finding a number of good efficiencies with our CRO relationships. That's been a real change for us as well, because we're now of a scale and doing enough projects where we can actually get economies of scale out of our CRO relationships. We can push that pricing dialogue without losing quality. And so that's something that has been very powerful for us recently. And I think you'll see that impact in 2023.

Got it. Much appreciated.

Thank you. The next question is from Peter Lawson with Barclays. Your line is open. Thanks for taking the questions.

I guess a question for Ben, just on the back of your comment about the, potential air of kind of conservativeness around a farmer and just how does that help or hinder collaborations? Kind of what's your analysis around that?

Yeah, so to be clear, we still see a lot of interest out of pharma partners. I think what we've seen is a bit of change in the focus of the pharma partners. So back in 2020, a lot of the dialogue was around how can I scale my pipeline? How can I do the really large pipeline deals? I think what we're seeing in this sort of environment is a real focus on specific technologies and specific identified programs. So from our perspective, we can actually handle either. And in fact, sometimes the specific partnerships can be more profitable for us than the broad pipeline deals because there's less infrastructure required to execute on them. I think also we are in a different position than we were two years ago because we've advanced a number of programs and technologies and platforms that we didn't have then. And we've seen a lot of the pharma partners have interest in those later stage programs as well. So I think with this economically conservative environment, the fact that we're still seeing a lot of interest out of pharma partners, and hopefully this is a economic cycle. So if we're seeing this level of interest right now, we'd feel pretty good if the economic cycle improved.

Thank you. And then just a question on timing for the data. I know both of your adenosine and CDK7 patients kind of in trials enrolled the first patients in the first half. Just your expectations for timing around those data sets.

Thanks, Peter. First, I'm going to introduce Mike Rams, who leads our development efforts. Mike.

Yeah, hi. Thank you for the question. First of all, we are using model-informed drug development, simulation-guided clinical trial design, and experiments where we accrue in real time all the data and look at the data at all times. However, the time at which we will actually announce major findings will probably coincide with the movement from the dose escalation phase to the the dose expansion phase in the phase 1-2 trials. We haven't given guidance as to the exact timing of that. However, it will be similar to any of the phase 1-2 trials that are running in this field. But importantly, the trials that we're running are based on model-informed approaches where we accrue and analyze the data at all times.

Could we expect data in the second half, or is that too tight a timeline?

We haven't given guidance on the exact timing, but I would think that the second half of 2023 is particularly early, given that the first patients will come in the first half. You can just go through the time frame. Thank you so much.

Thank you, Peter. Again, as a reminder, that's star one. If you'd like to ask a question, the next question is from Chris Shibutani with Goldman Sachs. Your line is open.

Hi, this is Roger on for Chris. So you've noted that you plan on moving towards antibiotics as the company pivots towards using action learning techniques to develop biologics. Just wanted to kind of understand, you know, since that's such an area that's challenging from both development and economics You know, what are the variables that make you believe this kind of investment going forward is worthwhile? Is it the, you know, the plethora of data that kind of feeds into the closed loop model, lower competitive dynamics? You just kind of want to understand the rationale there. Thanks.

Hi, Roger. How are you doing? No, we have a relatively small effort in antivirals and pandemic preparedness. We have no plans at all to move into antibiotics as a field right now. We have a great collaboration in antivirals with the Gates Foundation. That right now is focused on small molecules. And we move forward. That's led by Professor Ian Goodfellow of the University of Cambridge. But an important element we brought up actually is Accenture's development of a biologics platform. We are, as we announced last year, developing a biologics design engine. That currently we are testing a proof of concept as we speak. We're also building out a new automated biologics lab actually to really speed up sort of make and test cycle for the generation of data. So one of the key areas that we see here actually is the ability actually to generate high quality data to drive machine learning models. And I'll give you an example of that. We currently have a collaboration with Oxford University where we're looking to generate a lot of paired sequence data. We believe actually we can create some of the largest databases in the world and understand in the observable human antibody space. And that actually then gives us sort of the priors in defining our models. So when evolving and designing molecules then by AI, we can then design into where human antibody space actually is. We're incredibly excited by that, actually. It's led by Professor Charlotte Dean, who works here at Accenture, also holds the chair at Oxford University. And later this year, we look forward to bringing you sort of more news and information about how our biologics platform is developing. But importantly, I think we have a key advantage here as well. We've already shown that our patient-centric precision medicine platform works equally well for antibodies as it does for small molecules. So we can there also think about downstream, how we can start to think about applying precision medicine also to biologics and bringing those two fields together. And I think that's going to be a unique set of attributes actually in that field. Got it. Thank you.

Thanks, Roger. There are no further questions at this time. I'll turn it over to Andrew Hopkins for any closing remarks.

Thank you, Operator. And thank you to everyone for the call today and for your continued support of Accenture. Overcoming months and into 2024, we look forward to advancing multiple programs going forward, bringing new molecules into the clinic, unveiling incredibly important new projects and programs, and building up our clinical development innovations to bring truly personalized medicine to patients. I want to thank you again for joining us today and have a good day. Thank you, Colin.

Ladies and gentlemen, this concludes today's conference call. Thank you for participating. You may now disconnect.