Q1 2023 Exscientia plc Earnings Call
Speaker 2: At this time I'd like to welcome everyone to Accentio's business update call for the first quarter, 2023.
Speaker 2: 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.
Speaker 2: If you'd like to ask a question during this time, simply press star then the number 1 on your telephone keypad.
Speaker 2: To withdraw your question, please press star one again.
Speaker 2: At this time, I would like to introduce Sarah Sherman, Vice President of Investor Relations. Sarah, you may begin.
Speaker 3: Thank you, operator. The press release in 6K where issued this morning with our first quarter of 2023 financial results and business update. These documents can be found on our website at www.investors.extension.ai along with the presentation for today's webcast.
Speaker 3: 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.
Speaker 3: Unless required by law, Accentia 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 and joined by Andrew Hopkins, Chief Executive Officer, Dave Hallett, Chief Scientific Officer, and Ben Taylor.
Speaker 3: CFO and Chief Strategy Officer. Gary Peridot, Chief Technology Officer, and Mike Krems, Chief Quantitative Medicine Officer will also be available for the Q&A session.
Speaker 3: And with that, I will now turn the call over to Andrew.
Speaker 4: Purslice medicine. How we use complex, primary patient tissue samples as preclinical models. Combine this with our in-house multi-omus capabilities, we can go from target identification all the way through to the clinic.
Speaker 4: 2023 is off to an exciting start as we continue to advance our pipeline and strengthen our business.
Speaker 4: We've made significant progress across our internal and partner programs, including advancing two molecules into a clinic, EXS4318 and EXS21546. An additional molecule, DSP2342, was advanced by Sumitomo Pharma.
Speaker 4: which was a result of an early collaboration with Centia that is now complete. This marks our six novel molecule created for Accentia's Genitive AI platform to enter the clinical stage.
Speaker 4: We've expanded our precision oncology pipeline by initiating IND enabling programs for EXS74539, an LSD-1 inhibitor, and EXS73565, a MULT1 protease inhibitor. More recently, we presented multiple posters of the AACR Annual Meeting.
Speaker 4: Highlighting research that continues to validate our end-to-end approach and demonstrates the potential of a platform to rapidly advance high-quality drug candidates towards a clinic. Our team's commitment to strong execution has enabled us to rapidly move programs from discovery, fruit of a clinic.
Speaker 4: We have achieved a number of milestones already this year. In March, we announced two new wholly-owned precision design molecules, an LSD-1 inhibitor, 539, and a MULT1 inhibitor, 565. Both programs continue to progress through IND-enabled studies. We expect to provide an update on clinical development plans in the second half of this year.
Speaker 4: potential first in class selective PKC FETA inhibitor. 431-8 was designed by Accentia and is currently in phase 1 clinical trials in the United States. Earlier this month, the first patient was dosed in Ignite, our phase 1-2 trial E-XS 21-546 or 546, our HWA receptor antagonist.
Speaker 4: This was the first AI designed and Munon College you drive in the clinic.
Speaker 4: And we remain on track to dose of first patient in a Phase 1-2 study of GTA, EXS 617, our precision design CDK7 inhibitor, co-owned with GTA Parion in the coming weeks.
Speaker 4: We also remain well capitalized with $553 million in cash at the end of the quarter. This provides us with several years' runway to advance our near-term programs without the need to raise external capital.
Speaker 4: On today's call, we'd like to provide more detail on our approach of combining precision design with personalized medicine.
Speaker 4: Before handing over to Dave Hallett, I'll see you so I want to highlight a recent scientific presence of Visuers AACR meeting.
Speaker 4: We presented data further validated and our ability to efficiently design high quality drug candidates and to identify and predict the right patient populations that may benefit from most from treatment. We're going to use ????? to identify people there simply by day presentation.
Speaker 4: Firstly, for 546 we presented research on our Denysine Birdon School or ABS.
Speaker 4: It showed that 546 reverses the effect of adenosine analogs, ex vivo in patient tissue samples and other complex models.
Speaker 4: The ABS has been validated in our ongoing Ignite Phase 1-2 clinical study of 546 and will be discussed further today. Ignite was designed based on extensive simulations to enable most effective continuous reassessment method settings.
Speaker 4: To predict and accurately evaluate the anti-tumormal effects of 546, in combination with checkpoint inhibition. The team also presented pre-clinical data on EXS 74539, our precision-designed LSD1 inhibitor.
Speaker 4: We designed 539 to optimally target LST1 in future oncology and hematological patient populations. These pre-tensile data demonstrated at 539 has the potential to overcome significant safety limitations of other LST1 inhibitors through its differentiated profile, combining versibility and brain penetrance.
Speaker 4: Lastly, we highlighted the benefits of using data generated with Accentia's Precision Medicine platform in combination with its proprietary methodology for multi-omics and multi-mobile mapping.
Speaker 4: By better understanding disease mechanisms, these tools combined can be leveraged to improve patient outcomes by uncovering clinically relevant drug targets already at the discovery stage. We'll go into more depth in this topic shortly.
Speaker 4: In summary, we have five programs of economics, whether either in the clinic or in IND-enabling studies. All are a testament to the power of our platform and our approach. We are thrilled in our recent advances and look forward to sharing more details of our clinical development plans in the second half of 2023.
Speaker 4: Today we would like to focus on how we advance in towards our goal of increasing probability of success within drug discovery and development for an end-to-end patient-centric approach. In our pipeline to date, we have developed precision design compounds with a patient-driven data approach in a faster and more efficient way when existing methods.
Speaker 4: Allow hand over today to walk through how we are working towards predicting clinical responses preclinically
Speaker 4: I'll now hand over to Dave to walk through how we are working towards predicting clinical responses pre-clinically. Thank you Andrew.
Speaker 5: We incorporate the concepts of patient-centric drug discovery development as early as possible in our efforts. Through the use of complex primary patient tissue samples as preclinical models, we are able to leverage our clinically predictive functional imaging platform, especially in translational research. While cell lines and organoid models are scalable and useful in design and development,
Speaker 5: They do not capture the complexity of actual disease biology. Nor do they represent the diversity of patients seen in the clinic. As you can see here on slide six, there is a clear difference in the images of the homogeneous cell line compared to the heterogeneous primary patient material we use.
Speaker 5: We believe that the heavy use of cell lines as translational models has contributed to the high rate of clinical failure we typically see in our industry. Our answer is to strategically leverage primary patient material for decision-making purposes before entering the clinic. By getting as close to the actual patient as possible,
Speaker 5: We can embrace both the heterogeneity and complexity of disease biology using our patient derived model systems coupled with AI-driven technology.
Speaker 5: In our pre-clicle studies, we utilize primary material to create complex model systems that better reflect disease and represent patient diversity.
Speaker 5: These elaborate models are deployed with the goal of identifying indications as well as subpopulations likely to respond to treatment, uncovering patient enrichment and non-invasive pharmacodynamic biomarkers, understanding the potential for resistance, combination effects and more. Depending on the program, we take advantage of our precision medicine platform.
Speaker 5: which has successfully predicted which drugs will work for a given patient as shown in the EXALT study published in Cancer Discovery in 2021. Functional endpoints in our complex systems allow us to simultaneously quantify what a drug or combination of drugs is doing to cancer.
Speaker 5: Immune and non-transformed cells at the single cell level. We can measure anything from cell size to cell death through to pathway activity depending on what we want to quantify.
Speaker 5: We then combine this functional data with omics readouts from the same patient samples, such as genetic mutations, expression, fusion, and transcriptional events.
Speaker 5: The omics state to provide a molecular understanding of the observed phenotypes.
Speaker 5: The Eugen of Technologies, Functional and Multiomics, combined with years of knowledge of how to interpret these datasets in multimodal programs, drives a deep understanding of disease biology and population heterogeneity. Thank you very much.
Speaker 5: Excentive's unique proposition is that these data are derived from primary patient samples. This provides a pre-clical understanding of how and why a drug is, or just as importantly is not working in a given patient sample.
Speaker 5: thus enabling patient enrichment hypothesis generation and the generation of molecular signatures.
Speaker 6: Today we will describe two ways in which we are combining the use of our functional precision medicine platform with our omics datasets. Once again an understanding of the effect of adenosine on the cancer micro environment ahead of the clinical trial in patients and the other for target discovery.
Speaker 7: We'll first highlight progress for our A2A receptor antagonist, 546, which specifically blocks the recognition of adenosine by immune cells within the cancer microenvironment.
Speaker 8: tumor microenvironment. Adenysine limits the functionality of multiple protective immune infiltrates, including T cells, while enhancing the activity of immunosuppressive cell types.
Speaker 9: Reversing the effects of adenosine driven through the A2A receptor with our antagonist 546 should therefore release the immune system and also help those patients who have become refractory to immune checkpoint inhibition.
Speaker 10: For patients to benefit from such an approach, two critical attributes are required to be present.
Speaker 11: One, high levels of adenosine in the microenvironment, and two, an immune system primed but suppressed by adenosine.
Speaker 12: To date, there has been no robust way to measure both immune potential and adenosine levels within the tumor microenvironment. We believe other drug candidates for this target have not achieved clinical success because they fail to enrich for those patients most likely to respond to A to A receptor pathway inhibition.
Speaker 13: Leveraging our precision medicine platform and scalable in-house obi-t's capabilities, we have identified a patient enrichment biomarker that correlates with identity levels in the CH dimension idetima My heme Uncleh
Speaker 14: We call this the Adenisine Burden score or ABS. This was found through a detailed examination of multiple primary samples at baseline and after perturbation with Adenisine pathway activation. All this work has been done in an effort to maximize the probability of success of 546 in the clinic.
Speaker 15: On this slide we show three different datasets. Two from human databases and one from mouse data. These include the Cancer Genome Atlas or TCGA and the Reactome database. TCGA is a landmark cancer genomics program from the National Cancer Institute.
Speaker 16: and National Human Genome Research Institutes that characterize at a molecular level over 20,000 primary cancer and much normal samples spanning 33 cancer types. React Home is an expertly curated database of biological pathways.
Speaker 17: At the top in the TCGA dataset, when filtering for patients with a high ABS, we observe that these same patient samples are low for public signatures related to inflammation, such as the TUMOR inflammation score or TIS.
Speaker 18: The TIS has been used to predict anti-PD1 efficacy. In the middle panel from the Reactome dataset, the ABS anti-correlates with the PD1 signaling pathway.
Speaker 19: indicating that where adenosine is high as measured by the ABS, PD1 signaling is low thereby nullifying anti PD1 effects. The last chart is an expert curated mouse dataset called TISMO or tumor immune syngene mouse dataset.
Speaker 20: This shows that mice considered resistant to checkpoint inhibitor therapy were also enriched for higher mouse ABS.
Speaker 21: highlighting the rationale for combination therapy in our 546 clinical trial.
Speaker 22: Taken together, we believe we have discovered a robust, specific and sensitive biomarker for adenosine pathway activation within the tumour microenvironment.
Speaker 23: This represents a method for enriching patients likely to respond to our selective adenosine, eight-way receptor antagonist, 546.
Speaker 24: Comparing the left and right panels, we can see that compared to other disclosed signatures, ours is much more robust and reproducible across samples.
Speaker 25: Our signature is comprised mainly of B cell genes towards the later stages of B cell and plasma cell maturation. Similar to that of data from another molecule recently presented at AACR that was discovered retrospectively after a Phase 1B clinical trial.
Speaker 26: Our work was done pre-clinically and will be validated alongside the Ignite trial.
Speaker 27: What we have shown here is that we can generate data ahead of clinical trials using primary patient samples.
Speaker 28: that our peers can only do in the clinical setting. We believe this is a key differentiator for Accenture as we advance additional programs and have implications well beyond our A2A program.
Speaker 29: Since our founding we have aimed to be a learning company with a goal to constantly increase our knowledge from and to reuse all of the data that we produce from discovery through to development.
Speaker 30: We've just shown you an example of how we can pre-clinically identify patient enrichment biomarker hypotheses using a combination of functional and omics data.
Speaker 31: I'll now take a moment to highlight how we leverage this same approach in our discovery efforts to understand more about disease biology and target discovery.
Speaker 32: Using the data sets from preclinical studies, which will be supplemented with information from our clinical and precision medicine studies when available, we can work to understand a disease computationally. I will highlight how we use functional and multi-omic data from our primary models.
to help identify novel targets and juggable pathways for future projects. Some of which we believe may help overcome resistance. Here we show an overview of some of the data inputs we use to triangulate and prioritize novel targets.
We start with our proprietary data from various programs that take advantage of our functional precision met and platform and next generation sequencing units.
All of this data is from patient tissue models and this differentiates our approach from others.
We then combine this with well annotated public data such as known drug to target annotations taking into account a drug's polypharmacology and protein-protein interactions in a custom unified and extensible computational framework.
While the use cases of a program that captures the complexity of a disease in silico are vast, the example I want to describe today is focused on target identification.
Our patient-centric multi-Omic platform has the potential to uncover targets with high clinical relevance at the discovery stage, as well as support target validation and biomarker discovery.
At the bottom of the slide we see our functional layer of data, target annotations and an interactome come together to prioritize targets using drug sensitivity and protein-protein interactions as a guide to identify convergent targets.
Here we put everything together. I want to first show you a diagram of how this data is represented.
We use our precision medicine platform to collect functional and multi-omics data from patient tissues, in combination with proprietary methodology for multi-omic and multi-modal dataset mapping.
Then we integrate it using our computational framework. The outer layer represents the standard of care drugs we use as tools to probe the potential target landscape.
Drugs are connected to their known targets, including off targets on the next layer.
Finally, known targets are embedded in a curated protein-protein interaction network, allowing us to identify novel targets at the focal points of successful therapies. More than that, we are also able to corroborate and refine our findings using a rich layer of multilomics data.
such as phosphoproteomics and single cell RNA-Seq generated under treatment conditions from the same samples.
This approach has the potential to uncover targets with high clinical relevance at the discovery stage and lead to improved patient outcomes.
What you see here is an example functional screen performed in 20 ovarian cancer patient tissue samples.
We wanted to understand the cancer-specific cytotoxic effective drugs with well-annotated targets.
You may recognize this data from one of our recent AACR posters. On the left we have identified numerous novel sensitivities to a subset of tyrosine kinase inhibitors or TKIs.
simplified by large dark purple circles within a subset of samples.
What's important to appreciate here is that the effects we observe for many drugs in patient tissues, the left panel, are not recapitulated in publicly available cell line sensitivity data indicated on the right.
This demonstrates how the use of cell lines and other cultured model systems may obscure targetable pathways. This is likely due to oversimplification of tumour biology, since the cell lines lack a complex and diverse cancer environment. Instead,
Our primary model system incorporates multiple cell types and avoids immortalization or amplification in order to better capture the complex biology of the original micro environment.
But what this does not yet tell us is why specific drugs are having an effect and what they have in common, complicated by the fact that many of them have known polypharmacologist.
and begin to reveal novel biology and target spaces.
So here we show the actual data with the targets blinded. First we use network integration of patient tissue functional data to triangulate convergent targets.
Then we add a layer of data from multiomics measurements that lets us further prioritize them by factors such as disease-specific expression, mutation profiles, or novelty. The diagram from outer to inner circle shows firstly global compound sensitivities.
then known primary targets, and finally predicted downstream targets. These targets are not impacted by community bias highlighting first-in-class potential.
Keep in mind this is data from real patient samples, grounding us in complex human biology. This means that we can combine real-time multiomics data with the functional biology readouts to directly measure drug response from multiple angles on every sample. This helps us identify novel targets who demonstrate the bleep
differentiated platform.
As I mentioned earlier, Accenture is a learning company, not just in practice but also through the reuse and redeployment of collected disease modeling datasets. Here we use functional profiling as a guide to build computational disease models for target ID. We use functional profiling as a guide to build computational disease models for target ID.
We are also working to redeploy data for target validation, faster patient enrichment biomarker discovery, and combination prediction. We've provided examples here on how complex disease relevant models combine with a smart analysis and interpretation of many levels of big data can reveal mechanisms of a denizen pathway activation.
for us to identify patients that may be sensitive to 546 treatment. We're also working on predicting combinations and identifying resistance breaking characteristics for our CDK7 inhibitor 617.
We plan to present 617 data towards the end of this year, and we'll be adding more data to these models as our pipeline grows and as we recruit patients into our clinical studies.
And with that, I will now turn the call over to Ben to walk through financial highlights.
now turn the call over to Ben to walk through financial highlights. Thank you, Dave.
I'll now take a minute to close with highlights from our financial results. Full results are detailed in our press release in Form 6K. I'll review the results in US dollars using the March 31, 2023 constant currency rate of $1.24 to the pound.
We ended the quarter with $553.3 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.
As Andrew noted earlier, we continue to successfully advance our internal and partnered projects. At the same time, we have also been executing cost efficiency programs that are expected to save over $20 million during the course of 2023 and more in 2024. This has been a combination of automation through technology.
and narrowing the focus of our operations on core activities that have a differentiated commercial profile. We remain 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. We have a robust business development dialogue and maintain our guidance of two new deals.
excitement from our potential partners, especially in our core technology such as personalized medicine in generative AI.
It is important to note that we have never stopped investing in new technologies. While we were being intelligent about burn rate, we continue to see substantial technology advancements even on a quarter-to-quarter basis. Dave discussed how we had taken a strong phenotypic translational platform and invested to add multimodal data that now can produce personalized cellular signatures.
at every stage of discovery and development. And this is only one example of our growth.
leadership position. And with that, I will turn the call back over to Andrew.
Thank you, Ben. During our presentation today, we've highlighted the progress of our clinical and preclinical programs.
We are bringing new molecules into the clinic and building out our AI-powered precision medicine platform.
We are confident that our differentiated, tech-enabled approach will yield strong outcomes. To finish, let me add just how proud I am to lead a global team this talented and determined, who help us do everything in our power to deliver on Accenture's promise, to transform the way the industry discovers and develops effective medicines, and to make the world a better place.
and to deliver the best possible outcomes for as many people as possible around the world. With that, we'll open up a call for questions.
Thank you. As a reminder, if you would like to ask a question, please press star then 1 on your telephone keypad. Our first question is from Alex Stranahan with Bank of America. Your line is open.
Hi guys, thanks for taking our questions. I have two higher level ones. I saw an interesting quote I think from Gary that by the end of this decade design of all new drug candidates will be augmented by AI. What do you see as being the key points that need to be addressed today for this future to be realized either at the basic science level programming.
pipeline assets to approval and commercialization yourself. Any directional commentary will be helpful. Thanks.
Thank you so much for your excellent questions Alex. Really great actually and very topical point as well. Actually for the first question as you did actually direct that to Gary I'm actually going to have Gary to outline as CTO what he sees actually as sort of the key further challenges to really expand AI's use in pharma for all drugs eventually to be designed by AI. Gary. Cool yeah thanks thanks Andrew. I think I mean the first thing
is a natural evolution. I think for us what's really important to us is how do we stay at the forefront of that and I think the activities that Accenture is building out at the moment particularly in linking AI design to physical automation, robotic synthesis, robotic screening is really closing the cycle.
you really want to bring medicines to patients faster and more effectively as we're demonstrating technology can do. Thank you Gary. I really want to underline Gary's answer actually in how we think about things. To answer your second part of a question Alex, the way we think about it is that we are incredibly pleased.
to see that sort of our design pro-es now and bring in six molecules of use generative AI approaches now into the clinic. As you said, actually the latest one actually being with Dynap and Surma Tomo Pharma, which was with an earlier business molecule, a business model called Design as a Service. We're always open to doing many kinds of deal structures, as you've seen actually, I think our business development prowess for the past few years has actually shown that.
But the way we see that AI is going to create real value is to think about what that product of the future looks like. What that sort of AI-enabled drug starts to look like. What we see is the hallmark of an Accenture drug.
is a drug that uses advanced compute, machine learning, AI, and physics-based methods to design precision design, a high quality molecule, but also then using our deep learning, multi-modal approaches that Dave was talking about earlier to really define the patient selection strategy. Bringing those two together in a model-driven adaptive learning approach to learn about the drug.
That's what we see. Those two pieces of key IP, the molecule being designed by AI and using AI then to design the biomarker. Both coming together is what we think is the hallmark of Accenture Drug. And that's where we believe in the long term that wealth can be created by effectively creating highly effective medicines with high response.
by actually designing the best molecule and targeting the right patients. Great, thank you. The next question is from Vietnam Period with Morgan Stanley , your line is open.
Hi, thanks for taking our question. This is Steve for VICUM. I want to ask about the A2A program. Could you discuss the prior treatment history for the patient you are enrolling into the trial and when can we expect to see the initial data and what's your expectation about the readout? Thank you.
Thank you very much Steve. For that question actually I want to hand the stage over to Mike Crambs our Chief Quantitative Medicine Officer who's actually leading our clinical development with you. Mike. Yeah thank you very much for the question. So we have recruited our first patient into this program. It's a phase one two study and we use simulation guided clinical trial design.
regimen is that we will take into a dose expansion phase.
We're going to learn about the operating characteristics of the investigational compound, but at the same time, we are qualifying the adenosine burden score, as Andrew pointed out, as our tool to identify which are the correct patients who might benefit from an A2A.
receptor antagonist in conjunction with a checkpoint inhibitor. As to when data will become available, this is a phase 1-2 study in early development in oncology as many others. So it's really very similar to other programs and we are going to provide further guidance as time progresses.
conjunction with a checkpoint inhibitor. As to when data will become available, this is a phase 1-2 study in early development in oncology as many others. So it's really very similar to other programs and we are going to provide further guidance as time progresses. Thank you.
Again, that's star one if you'd like to ask a question. The next question is from Peter Lawson with Barclays. Your line is open. Peter Lawson with Barclays, your line is open. Please go ahead. Sorry you couldn't hear me but here's part of the VoIP in chat. All right, there we go.
We will move on to the next question, which is from Chris Shibutani with Goldman Sachs. The line is open. Hi, it's Roger on for Chris. Just a quick question on 565, the MULT1 inhibitor. You're likely aware that J&J, they debuted their Phase 1 data for their MULT1 inhibitor in NHL and CLL.
question over to Dave Hallett, our Chief Scientific Officer, to give you some more color on it.
Thank you, Andrew, and thank you for the question. I think the publication of the abstract of ink is coming out.
had of a European oncology symposium was very timely. So if you recollect information that we put out very recently around the design criteria around our MALT 1 and this a bit too.
and specifically the topic of hyperbilirubinemia and driven by inhibition of UGG101. If you remember the takeaway story from that is that we strongly believe that our molecule is differentiated from J and J and most likely quite a few other competitor molecules.
and that it has little to no activity at that particular transporter and is therefore likely unlikely to drive that particular side effect. If you actually look into even into the abstract details it's pretty apparent from J&J as we would have predicted.
that they do see hyperbilirimemia in the clinic. And they've had to take account of that in that with their recommended phase two dose. I'm sure they would have preferred not to have done that. And so I think we stand by adding that that original assertion is that that was a really important differentiation criteria. I think it will, our molecule would believe should be free of that particular...
I wish J&J well. And obviously, they take that compound forward into patient studies. But I think it supported our notion about the differentiation angle of our own compound.
Thank you. The next question is from Peter Lawson with Barclays. Your line is open. Hi, this is Shae An for Peter. Thanks so much for taking my question. Just want to touch base on the biologic side of your platform and maybe some progress there and how you're thinking about balancing your biologics versus small molecule development and maybe even when we could see the first antibody program going into the clinic. Thanks so much.
Excellent. Thank you very much. I'm going to hand over this question actually to Gary who is in team has the the algorithms for developing sort of biologics by design by discovery are currently being developed.
Excellent. Thank you very much. I'm going to hand over this question actually to Gary who's in team has the the algorithms for developing sort of biologics by design by discovery are currently being developed. Gary.
Yeah, thanks and thanks for the question. And we're really excited about the way that we can introduce biologics into our AI design platform and Professor Charlotte Dean has been working to build out the algorithms and all the technology to actually drive that forward. We're still at the point where we're developing a robust process and we're starting to run our first pilot projects. So I think we're a little bit away from talking about a molecule in the clinic, right?
how then we start to map then of the antibodies, the capabilities within building actually to sort of our key of February to get a sort of focus. One exciting thing that we've already demonstrated.
is that our precision medicine platform actually also works to antibodies as well as small molecules. And that's a key thing that actually allows us then to think about how then as we head towards the clinic, we can also bring to bear precision medicine technology. And I think that's gonna bring a unique differentiator as well actually in this particular field for these modalities.
medicine platform actually also works to antibodies as well as small molecules and that's a key thing that allows us then to think about how then as we head towards the clinic we could also bring to bear precision medicine technology and I think that's going to bring a unique differentiator as well actually in this particular field for these modalities. Thank you.
I was wondering how much visibility and control do you have now into the path forward for this molecule and how it might progress through early stage development. Thank you. This is Dave Hallett. Thank you for that question. So in terms of public visibility, because...
BMS in license that particular program, they both now control the clinical development of that projects, but also obviously kind of public disclosures that are related to that. As a trusted partner in part of the GIC, we will receive kind of updates on that program ourselves. But just to reiterate to everyone is on the call, is that that
that particular asset has begun a healthy human volunteer study in the United States in the early part of this year. And we look forward to receiving updates from BMS as they progress. Thank you very much. We have no further questions at this time. We'll turn it back to the presenters for any closing remarks.
at each stage by innovative technology platforms. Our goal is to be as innovative in the clinic as we have been in discovery. Our remarkable progress to date is a testament to the strength of the company. Thank you to everyone today on a call for your continued support and on our journey.
and for joining us today, and we look forward to continuing to share our progress with you throughout the year. Operator, you may now disconnect. Thank you, ladies and gentlemen. This concludes today's conference call. Thank you for participating. You may now disconnect.