Q3 2022 Exscientia PLC Earnings Call
Yes.
Hello, everyone. My name is Brent and I will be your conference operator today.
At this time I would like to welcome everyone to Accenture his business update call for the third quarter of 2022.
All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question at that time simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question again press Star one. Thank you at this.
I'd like to introduce Sara Sherman Vice President of Investor Relations, Sir you may begin.
Thank you operator, our press release and 6K were issued this morning with her third quarter 2022 financial results and business update.
These documents can be found on our website at www dot investors that Accenture has got 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.
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, Our CFO and Chief strategy Officer.
Dave Hallett, Chief operating Officer, Mike <unk>, Chief Quantitative Medicine Officer, Gary Parr to Chief Technology Officer will also be available for the Q&A session.
And with that I will now turn the call over to Andrew.
Thank you Sir.
22 has been a busy year and we are excited to update you today on our recent progress we believe that our patient first AI strategy in combination with a world class team.
Two platforms places us in a position of strength as we create value across our internal and partner portfolio, we are well capitalized.
$625 million in cash at the end of the quarter. This provides us with several years' runway to advance a near term programs.
We also invest in tandem for long term growth.
Within our platform and pipeline developments.
Meaningful progress throughout the year, which has continued to validate the technology with extensive with Samsung.
Today, we provide more insight as we get closer to treating the right patients with white dragon extremity affected.
So excited to highlight data demonstrating how we plan to innovate and increase the probability of success the clinic and the similar to what we've already achieved in discovery.
Following a topline data early this year the excess 21 546, <unk>, our HOA receptor antagonist, we remain on track to initiate a phase <unk> study by the end of the year.
Importantly, our HOA program.
If you look at the compound, but also allows us to continue to build our confidence that translational signature on how best to enrich for patients who are more likely to benefit from this treatment.
We've also made exciting progress with GTA Exa 617, or six months out our CDK inhibitor, new data that we presented actually E. N. A annual symposium in October demonstrating the potential of our precision oncology platform to SaaS targeted therapies undefined patient populations that are more <unk>.
Please respond trough Avi.
This also highlights the potential of six one side would you be less toxic compared to other therapies, such as CDK <unk> inhibitors.
We believe that our platform, which combines deep learning and functional models and multimodal all mixed data format experimental platform relevant primary human samples not only helps us better understand the potency and activity of 607, but will also lead to improved patient selection and therefore better patient outcomes.
We are on track to file a clinical trial application or Cta for this program by the end of 2022 and expect to initiate a phase one clinical trial in multiple solid tumor indications in the first half 2023.
In our partnered programs, we added an additional oncology targets into our Sanofi collaboration more generally we look forward to sharing more details on our pipeline, including additional drug candidates progressing through IND, enabling studies.
We recently shared a couple of exciting announcements, including a new strategic collaboration with the University of Texas, MD Anderson cancer Center to develop small molecule therapies in oncology and our expansion into biologics design, we can talk more about shortly.
As our clinical pipeline growth I'll now hand over to Beth to walk you through how we plan to be innovative in development as we have been in discovery.
Thank you Andrew.
Looking at the lifecycle of the drug all of its future potential is determined during the initial design phase. However, the validation of that potential only occurs during clinical testing.
Our strategic goal is to shift the economic curve of the pharmaceutical industry by improving probability of success accelerating development cycles and reducing costs.
As you will see nearly all of the tools that we use for drug discovery also apply to more effective clinical design and execution.
I'm going to spend a minute covering our clinical strategy before Andrew discusses recent advancements and precision medicine. So it's important to understand that the two are closely linked when we use real patient samples in our precision medicine platform. The guide molecular design. We are also defining our future clinical programs the phenotypic.
Genotypic and transcriptional signatures that we identify with our development candidates are the same profiles that we will target in our clinical programs.
<unk> seen us be very effective in single gene loss of function mutations and our goal is to apply that same targeting strategy to more complex biology, we have given previous guidance that we expect to initiate the next phase of our HOA molecule 546 by year end and our CDK, Kevin compound 607 will follow.
Shortly thereafter looking forward, we expect that we will have at least three compounds that are in clinical development by 2023 and four by 2024. This only includes compounds, where we maintain significant economic interest and excludes the existing clinical programs with giant Nippon Sumitomo pharma.
<unk>.
On slide seven you will see how this strategy translates into practice.
When we launched the trials for 546% and 617, we will provide you details on their individual trial designs, but this slide outlines what you can expect for all of our internal oncology programs.
We start with what we call ex vivo clinical trials using our precision medicine platform, we evaluate the drug on a multitude of heterogeneous patient samples with in depth profiling to understand why some of the samples respond better than others.
This provides us with a biomarker that we believe will correspond with the most sensitive patients for a specific drug.
We can also perform multiple iterations of these prospective ex vivo trials to create a statistically significant sample set before initiating an expensive and lengthy in vivo clinical trial.
Next we start with an initial clinical trial in a patient population, where we expect a higher incidence of the response signature.
While the trial is validating the compound characteristics, including safety and preliminary efficacy.
We're also evaluating all of the responses prospectively with a precision medicine platform to validate the biomarker once confidence in our biomarker signature has been reached and accretive find level. We can apply that biomarker signature to directly enrich the patient population in confirmatory trials.
All of this is with a specific goal of increasing the probability of success in clinical development.
We believe that many clinical trials fail because they are not treating the right patients precision medicine needs to be incorporated into clinical progression. So that you're not only define a drug's properties, but also who should receive it.
In addition, we wanted to make the trial design as efficient as possible.
Our industry, we often see novel products tested using clinical methods that are decades old even when newer methods have been proven more efficient and accepted by regulators.
Our chief Quantitative Medicine officer, Dr. Mike Crimps oversaw clinical innovation for all of J&J is portfolio and now make sure that we are adopting state of the art clinical strategy. For example, we utilized simulation guided clinical trial designed to virtually identify the best statistical models as.
Well as critical variables that may influence the success of the trial.
Another way for us to advance our clinical capabilities is to partner with a world leader in clinical Sciences University of Texas, Indiana Nursing Theres not only one of the top institutions for researching medicines. They also pioneered many of the adaptive clinical trials now define intelligent trial.
<unk>.
With this partnership we aim to combine our expertise in AI based discovery and development with their extensive experience in oncology and clinical trial execution.
Hopefully the collaboration will lead to both discovering novel targets as well as the next generation innovative clinical design.
I'll now turn the call over to Andrew to talk a bit more about our progress with patient centered precision medicine.
Thanks Pat.
We will now take a few minutes to walk through two of our ongoing programs and how we are doing a step by step perspective ex CFO analysis of patients.
Yeah to entering clinical trials highlights an important works out $5 six <unk> receptor antagonist and 607, our CDK <unk> inhibitor now on to slide 10.
For background.
No.
<unk> candidates in clinical development have seen some albeit limited clinical success with overall low response rates. We believe our clinical success can be improved through enriching and targeted for the right patients. The key to success EMEA twice deals. We believe is matching a drug to provide patients. We now have a dense.
A preliminary gene transcript signature measuring ex vivo denzin burden and primary samples, which we believe could be predictive for FIFO success pumps of $5. Six was formed signature and denizen burden school, which will be referred to as ABS.
In the heat map here, you can see ABS and <unk> 43 sample patients from six different tumor types. The signature is made up of six genes. So number five to normalized genes, which had not shown.
This gene signature was initially identified with differential expression of genes and primarily multi player being cells ex vivo put a patient with stabilized to demonstrate.
The ABS is currently set relative to biomarker levels in unexposed healthy immune cells. The more we increase adenosine seen in yellow box here.
Adenosine driven immune suppression of this.
And thus the potential FIFO six to have a real effect on blocking the dancing detection.
We see a number of samples across tumor types of high schools. We believe this signature is unlikely applicable across several cancer types as this based on the immune system.
The next two slides, we will demonstrate how ABS, we modulate it X eagle and how it relates to validated tumor inflammation signature. This work also revealed potential PD biomarker is beyond P. Crap.
We are working to consumables.
This is really important and differentiating how to think about designing the clinical trials to maximize the probability of success for these patients.
Do you have on slide 11 on the left we can see here about ABS behaves more consistently and that's expected in the presence of stabilized at that anything.
The adenosine signature from falling et al published in cancer discovery in 2020.
Score can be seen to your home Transcriptome studies conducted on for Prime rate model nuclear sample stimulated with stabilizer dancing in the presence of T cell activation.
As we increase ex vivo stabilized adenosine and takeaway FIFO six that is a progressive increase in our dennison signature score.
And any orange box.
On the very same dataset, our signature outperforms other public signatures in terms of specificity and sensitivity to detect adenosine, which Michael environments.
I'll move right. We show about signature allows the detection of adenosine, which and micro environments as presented with primary lung and RCC cancer patient samples exposed to increasing doses of stabilize the density. We believe that this ultimately show was that the immune response can be controlled by treatment.
Primary patient samples with FIFO effects as confirmed in the T cell activates it lagged mononuclear cells.
Continuing to collect again seen at $5 six specific data and look forward to sharing more about how we expect to enrich for patients in the clinic the identity and gene signature that we have developed will be further validated alongside our planned phase <unk> study starting soon.
To take this further on slide 12, as we move towards validation, we've identified a relationship between our <unk> signature.
Important public school for inflammation and checkpoint immune response, we see that as we increase the density we have also a decrease in the T. I S O.
<unk> inflammation signature.
From the chart, we show how we can increase confidence in school by mining public data sets such as of TCG a database the Ncis cancer genomics program.
So background, we already know from published research by the adenosine is alternative way by which tumors can escape immune system, even if treated with an anti PD one.
We find that the most predicted higher than seen cases amongst patients that are least likely to respond to a covenant of immunotherapy called into this.
This helps us understand potential responders to treatment with <unk> in combination with checkpoint inhibition.
Our hypothesis is that disease progression and the PD one therapy may be associated with local immune suppression mediated by higher density levels in the tumor microenvironment for a significant proportion of patients are highlighted by the specific tumor types.
This idea is further sustained by the data presented here on slide 13.
Lung cancer patient tissue cohort exposed to increasing doses of adenosine we can observe the trends for taas reduction on the same samples, notably of highest doses of adenosine underlying further the potential connection between adenosine which environment.
And checkpoint inhibitor <unk>.
Ultimately this reveals potential implications from a clinic in terms of studying <unk> in combination with a checkpoint inhibitor.
Bringing it back to the example of FIFO sticks, we mentioned above we have seen other signature is inaccurate, which means that it's very challenging to identify which patients may respond to therapy and this has been evident from data observed from current clinical candidates. The data. We've just shown highlights our highly stable biomarker behavior.
And our platform performance with ex vivo.
We've taken a similar approach with 607, our CDK inhibitor as you prepare for the clinic on slide 14.
As mentioned earlier, we recently presented data at the EAA meeting about functional precision medicine platform combined in single cell sequencing and transcriptome mix of disease relevant primary patient material to maximize understanding of the potential 607 effect.
He went to select patients, we believe will be most likely to benefit from treatment.
For the first time, we show them, how we apply multimodal machine learning to integrate data from primary human tissue samples and multi omics sequencing capabilities to predict.
Efficacy of 617.
Here, we highlight some of that important data, including data showing at 617 induces less cell death of immune cells.
Investigational CDK <unk> inhibitors, potentially indicating a differentiated clinical safety profile.
Using our deep learning powered high content imaging platform. We previously confirmed six months Avon's activity in primary human samples with example data in ovarian cancer samples shown at the AACE. Our earlier. This year. These expanded results, but you can see here on the right hand side of the slide in the waterfall plot led us to generally to.
<unk> two groups of patients samples effectively high and low responder groups and the indications tested.
The waterfall plot highlights the ability of our platform to identify from primary human tissue samples responders and non responders to CDK <unk> inhibitor across tumor types, which is really excited it's important to note that this does not show any percentage of response rates, but is helping us to understand who the right patients to study.
We can see that the type of cancer is not a determinant factor in the patient tissue response to CDK segment.
Moving on to slide 15.
Using transcriptome mix of the same patient softness we have identified a gene signature the first established in ovarian cancer for which can potentially predict an outcome for patients. We are actively working to expand our signature to other tumor types.
On the left we're showing the gene expression levels of genes that makeup our CDK segment predictive signature <unk>.
Ian cancer patient cohort using 30 samples as the training set for statistical model. We're currently undertaken single cell ex vivo functional screening combined with transcriptome mix after CDK seven great debate.
In disease relevant primary human cancer samples to refine and improve this gene signature.
We are aiming to you. This is baseline transcript data combined with a functional assessment of 617 in primary patient samples to model and then predict patient response to CDK <unk> inhibition, we economy added more indications to this model and further confirming the model biologically ahead of a trial for validation.
Alongside the trial.
So what we are doing is essentially running the clinical trial ex vivo to understand which patients will respond based on the new biomarker prior to entering the clinic, therefore, increasing the chances of treatment response on trial success.
As a reminder, the functional layer is from the same technology that we clinically validated in the separate prospective trial of <unk> in hematologic cancers in that trial, we showed that the AI driven platform was able to select the right treatment for an individual patient significantly improved progression free survival.
Finally, which is not shown here, but was presented of the Eni from single cell transcriptome mixed data collected after treatment or a sample. So CDK <unk> inhibitor. We are validated existed undefined and novel CDK seven specific Pharmacodynamic biomarkers.
May enable us to trucks six one sentence activity potentially noninvasively given our planned clinical study by combining AI based patient tissue analysis and transcriptome mixed data, we identified both a PD biomarker and the gene signature specific to CDK seven and the drug candidate 617.
Which may distinguish patients will be optimally responsive to that.
Our therapy.
This advance of using disease relevant patient tissue pre clinically to predict response of cancer cells. So CDK <unk> inhibitor means what we've taken huge strides towards the overall goal of being able to deliver the right drug to the right patients we plan to validate the state of retrospectively alongside our planned future trial our goal.
<unk> is to make precision medicine, a reality for patients for the application of AI.
Before I turn it over to Bert to kind of financials I'd like to highlight another key development the exciting progress with biologics. We've now expanded into generative AI design of novel antibodies, we've taken with potential of AI combined with automated experimentation to build a process for biologics design that we believe is more efficient.
And will lead to better results for patients.
This expansion effectively doubles, the addressable target universe of our precision medicine platform allowed us to develop the most effective drug for patients regardless of modality.
How do we actually refine how biologic developed.
And it is by design not discovery.
On slide 17 state for Cowen process, driven by experimentally discovered binders, which has many limitations by virtually generation and design and precision engineered fully human biologics. We can overcome these challenges we believe that our method can explore much boarder target universe right.
Right antibody for a specific target.
Moving to slide 18, we've already been able to show about a virtual screening methodologies of antibodies is now over three times more accurate than the published state of the art. Additionally, we can produce accurate protein modeling for antibodies up to 35 times faster have enough result.
We also generated data in new ways and analyzed using AI will allow us more complex a complete understanding of human antibody apology.
Our new automation laboratory will be up and running next year and will allow rapid assessment of essential qualities like affinity immunogenicity aggregation of stability, but feed directly back into our AI models by combining this knowledge with the ability to generate complete novel antibody designed virtually we aim to create better <unk>.
Perfect, we ticks faster and more efficiently and as we mentioned last quarter, we've already used a precision medicine platform to help select patients with biologic programs and our current collaboration with Sanofi.
I'll now turn it over to Ben to cover our financial highlights.
Thank you Andrew.
I'll now take a minute to close with highlights from our financial results for the first nine months of 2022.
Well results are detailed in our press release and form 6K.
I'll review the results using U S dollar using the September 30 constant currency exchange rate from pounds to dollars of 111.
You're converting our financials on periodic U S dollar to GBP spot rates over the last year.
There may be volatility in U S. Dollar consolidated figures. However, it is important to note that we hold our cash balances in either British pounds are U S. Dollar based on expected expenditures in that specific currency. Therefore, the actual operating impact of market exchange rate movements are greatly reduced or eliminated.
<unk>.
Our cash inflows from collaborations for the nine months ended September 32022.
Were $117 million as compared to $67 5 million in the first nine months of 2021.
We continue to expect cash inflows from collaborations to remain lumpy around milestones and business development.
For the first nine months of 2022 net operating cash outflows were $15 million in comparison to net operating cash inflows of $8 3 million in the first nine months of 2021.
This has been an important year of growth with investments in our platform, including precision medicine automation in biologics.
We will continue to pursue a strategy that balances near term monetization through partnerships with building a truly differentiated technology platform and pipeline.
This is why we can feel confident growing the business, while also protecting our cash runway.
This year, we have had a number of onetime capital expenditures associated primarily with our automation laboratories and facilities expansion.
Binding capex within our operating cash flows and a few miscellaneous items, our net cash burn for the first nine months was $37 million.
We ended the quarter with approximately $625 million in cash equivalents and bank deposits. We believe this gives us several years of cash runway and with that I will turn the call back to Andrew.
Thank you Pat.
Today, we walked you through a few examples of how we are working to transform the industry not just by bringing our AI driven drug discovery platform into new modalities, such as biologics, but also modernizing the way we select patient populations that may respond to new therapies, ultimately, allowing us to design better clinical trials.
We will open up the call for questions and answers.
At this time I would like to remind everyone in order to ask a question. Please press star followed by the number one on your telephone keypad again, if you would like to withdraw your question press.
Star one.
First question is from the line of Michael Riskin with Bank of America. Your line is open.
Great. Thanks for taking the question guys can you hear me.
Yes, Mike good to hear you.
Great. Thanks.
First of all I'll start on the biologics focus really interesting technology.
So it's not could you tell us can talk little more about how you see that ramping over time.
In terms of incremental investment going forward between the small molecule platform versus large molecule and just in general how should we think about that part of the pipeline evolving is there any particular target for you for internal in terms of small molecule large molecule or are you going to follow the target and sort of.
One by one.
That's a great question, Mike we do see if we look at the industry relative proportion of small molecules antibodies now is very significant is pretty pretty balanced across the many companies' pipelines.
That's why moving into biologics was so important to us is effectively doubles, our target universe, we can now address with our technologies.
So as we start now.
Look into really shows.
So sort of proof of concept experiments first.
Early pipeline developments in 2023, as we speak to you as we bring the automation level virtually online for our biologics platform.
We do expect it to be an important part of both our internal pipeline, but also an important part of our partnership pipeline actually and that's something which we.
I think you should expect to see in 2023, how actually we developed the biologics by owning both of those directions and we are excited by that and it increases the <unk> platform stability.
I think what's really exciting about this as well Mike is that we've already demonstrated by precision medicine platform works in biologics is already part of a deal we have with Sanofi is absolutely on good work happening right now.
So what's important about a few I think compared to maybe ever sort of.
Biologic sort of discovery.
Innovation.
So let's now if it becomes a new modality that plugs into our end to end platform that can benefit from the target discovery engine, but we're also using almost multi platform.
Then the molecules that we develop and could also that fit into our precision medicine strategy that we described today. So what I would say is we do expect this to be a major part of our pipeline going forward, but right now I wouldn't give guidance on the exact balance and small molecules.
Sure.
In biologics, but what I would say is that as <unk> seen the field developed in the rest of the industry now, but we have a wider range of topics. We can go after the biology will dictate actually which is the best modality to then go forward.
Right makes sense and then just a quick follow up on that I mean is there anything we should take in terms of.
The improvements that the platform can provide for biologics drug development and by that I mean.
You characterized the value add for the small molecule side of things should we when we think about biologics should we think about it being sort of a similar.
Benefit in terms of cost reduction time reduction of getting a drug.
<unk>.
Absolutely that's why we all taken agenda to design approach and I should give you a bit more color on the technology development that made us. So excited about development platform as you want to bring Gary CTO into this conversation Gary.
Yeah.
Yes, Thanks, Andrew Hi, Mike.
Yes.
As I said, we are Super excited about this and I think the benefits are going to come in to manifold serially. So politics by design allows us to target specific epitopes allows us to target proteins the own accessible through traditional methods and of course, we're also going to get a speed benefit and a quality benefit.
Being able to design biologics molecules.
Intrinsically more developable and more suitable for.
The treatment of patients better humanization et cetera.
If I can if I can squeeze in one one quick one for Ben.
I appreciate the color on the <unk>.
On the cash burn and cash balance.
Any.
High level guide.
Posts, we can think about for 2023.
You've got candidates progressing into the clinic in terms of how to think about R&D expense or cash burn.
Yes, we're not giving any.
Formal guidance, what I'd say is.
There probably will be a slight uptick.
It's not something dramatic.
From where we are this year.
Okay. Thanks, so much.
Your next question is from the line of Chris <unk> with Goldman Sachs. Your line is open.
Hi, Good morning, everyone. This is Charlie on for Chris. Thank you so much for taking our questions.
I just had a couple of questions on the 546 program as we're moving into the clinic and Fender trial, there with this ABS patient Sigma share that you've identified I'm just wondering how does that work on a practical from a practical standpoint into clinic, how easy is it to identify patients by this signature and as we look down the road in terms of a potential future label how are you thinking about.
What that label will look like in the context of a signature like this and how would that look in terms of if you were trying to.
After a tumor agnostic approach of a label just wondering if.
If you could provide any color there and then additionally on 546 I was just wondering as we're looking down the road towards potential checkpoint inhibitor combinations, how youre thinking about which combination partners you could potentially pursue and whether a formal partnership based around one of those combinations as possible. Thank you so much.
Good morning, Sean Thanks for your question.
Thank you again.
I'm going to actually hand over this question to Mike <unk>, Our Chief Medical Officer to guide you through how we are thinking about using our.
Biomarker signatures in our clinical trial plan Mike.
Yes, hi, Thank you very much yeah, great question. So ultimately our objective is to be a matchmaker between but probably most of the patients and the solution that we provide to achieve but it's important that we identify biomarker signatures.
Able us to match the right treatment to the right patients as we stopped all phase one two study and you will hear more about this soon.
We are going to further qualify and build confidence in the biomarker signature and the design is set up to tell us at what level, we have sufficient confidence in the biomarker signature to provide to us.
<unk> make in quality.
When <unk> starts to enrich patients. So our phase one two study will learn about the compound, but equally learn about the biomarker signature and the minute we have sufficient confidence in its being deployed as a decision making tool will switch and deployed accordingly.
I really think that the second question is absolutely critical and it speaks to offer muscle we develop drugs in a patient centric fashion. So this is not about just developing that particular compound but the question is what is this compound contributing to the overall solution to the patients in the K.
So from an <unk> receptor antagonist, we clearly need to think about combinations with checkpoint inhibitors and the entire program increased one two is indeed setup.
Sure.
T shirts.
Now too.
Think about the.
Add on strategy.
<unk> receptor antagonist onto checkpoint inhibitors and more on that story.
Okay.
Great. Thank you so much if I could just squeeze one more quick one and just for my own curiosity.
We're seeing the biologics part of the platform ramping up as well I'm wondering if you see if you can envision the possibility that you might see kind of a combination between the small molecule and biologics programs, where you might be developing adcs for very specific patient population.
That's a really interesting question Charlie actually the flexibility we have within our platform actually it gives us a wide range of possibilities of what we could go for this was most excited and actually by having these different sort of design engines at our disposal.
Is that actually such a possibility of design and Adcs is now actually we've been within our wheelhouse and it's something I'm sure I know what actually many of them sort of targeted application teams.
About the possibility now.
Modalities in combination of modality is actually can be put together with two design engines. We now have on board.
Great. Thank you so much for taking our questions.
Thank you. Our next question. Your next question is from the line of Peter Lawson with Barclays. Your line is open.
Hi, This is Jay on for Peter and congrats on the quarter.
Now you could add some color to the change in sentiment around AI from pharma companies are you seeing that there are certain pharma companies that are more likely to do collaborations and convert the slower ones. Thank you.
Hi, Sean good to meet you.
Great question actually in fact, we're seeing an.
And increased interest actually in the use of AI within big pharma companies a couple of years ago. It still felt we went into the health of mines in how we are moving forward until mid large companies.
Were signed and now actually that I believe that battle actually has been what I believe actually people now see that this is the way forward that actually drugs all going to be designed and developed using AI I think thats you proof points that we've already brought to the table show in that we already have molecules moving forward show into the kinds of patient selection.
Strategies that we talked about today and the results we've seen in things like the first AI driven sort of clinical trials of excellence.
And now coming together into a crescendo of show and potentially the direction of travel for this company.
I would say is that we continue actually to have deep discussions about the company's we hope to be unveiling in the next few months as well as sort of a third of the collaboration.
The MD Anderson collaboration with yesterday.
Deeper collaborations with pharma companies going forward.
But also what's important as well is once we start collateral into the companies also the continued deepening interest our deal with Sanofi started off actually where they have them.
One collaboration working on by specific small molecule.
Sort of in licensed lead molecule, then led to a $2 billion deal work with BMS already we've licensed then the first molecule, which would be June about hopefully in the next few months more to convert.
Ben.
Bonded with deal band originally for three projects now to eight projects all of which now.
I will go in so it gives us real confidence as well that we see a major partners. When we start working for us really come back and more than double down on they work can you give us a bit more insight into its particularly on how those relationships to develop it I just want to get Dave <unk>, Our chief operating officer actually to give his thoughts.
Dave.
Yeah.
Yes.
Thank you Angie.
Yes, I think you said the bulk of it already I think the.
What we're seeing is.
Using BMS and Sanofi have obviously has two significant examples.
Organizations.
Who.
So we engage with as many many years ago.
In terms of.
First generation of our collaborations spoke Sanofi on BMS and then based on.
On the output and the productivity that we're able to kind of show to those organizations then came back to the table to obviously to do now.
The only expanded deals, but also to gain access to the <unk>.
Expanding capabilities of our organizations.
And I think that's the best.
Ginny, we will we will continue to pursue as the.
To find kind of larger pharma organizations that.
We are open to kind of utilizing the way that we're trying to reengineer the entire drug discovery and development process.
And so kind of joining us on our journey and so I think I think we will see more of those collaborations but I think as we've reiterated on a number of occasions is that I think.
Our preference is to.
Does it go very very deep with a subset of major partners.
They're a very good operational reasons without writing to us that's the direction I think you'll see from a business development perspective over the coming years.
And just one point I wanted to add on this is Ben.
What we've really seen.
Is the commitment from the top makes a lot of difference.
If you look at.
All of the organizations across the industry. They are all convinced that.
Technology is going to change the way that we do drug discovery, they're all doing different experiments, but if you.
Look at the ones that do the larger transactions really the sort of things that we focus on because today's point, we'd like to go deeper we like to do larger deals those are really going to be deals, where they've got to the executives buying and they're making it a priority in the strategy. So that's one of the biggest items that we look for.
When we're evaluating our partners.
Just one last word from my comments as well was also.
That makes sense and pellets with question yes.
Yes, I mean, it's really just to like.
To highlight the experience that I've had coming from big pharma, joining accenture recently, what's.
Has been the interest and ability to connect the dots.
<unk> integrates AI.
His experience in the discovery platform.
<unk> expertise and experience in the translational science platform and now the ambition of applying a.
Clinical development platform, what I haven't seen before is the tight integration and the commissioning of the adults in a very strategic fashion, and then install and ambition.
Ambition.
<unk>.
Accenture here.
Rick Mono business counts in a patient centric model.
Great. Thank you so much for the color.
Your next question is from the line of Vikram <unk> with Morgan Stanley . Your line is open.
Good morning. This is Scott on for Vikram, Congrats on the quarter, we have two quick questions. So.
The recently announced collaboration with MD Anderson could you discuss the terms of the agreement on what the folks.
<unk> areas in oncology or <unk>.
Additionally, youll release notes that Youre Sanofi collaboration has progressed on additional target in oncology could.
Could you speak about the review process. This Doug went through on what the next steps are and.
Could you discuss how specifically.
<unk> is a platform was just identified as target. Thank you.
Excellent.
To speak to you again, great questions and actually the best person to answer is actually Dave Dave Holland.
Alright, Thank you Andrew.
Let's start with the question.
So as we as we announced yesterday.
The.
The collaborations.
What does that group is really predicated on.
On a couple of major concepts one is one is.
Leveraging <unk>.
Small molecule design capability, but also as translational platform.
But then marrying that with.
The significant kind of discovery and clinical scale.
It comes up to the MD Anderson so.
When we when we sat down over the course of similar to kind of put this collaboration together.
Is that what I, particularly liked about the relationship is as we're looking through potential targets that we could discuss and evaluate that would commence the collaboration.
We were we had kind of principal investigators from from MD Anderson kind of in those calls these are physicians who are interested in.
And the biological pathways that we're contemplating.
I don't think it's kind of it's highlighted in the press releases that we have a.
And more of that interest is kind of the clinical.
Sesame investigators within MD Anderson, so should we be successful collaboration I think we've already got a potential kind of pop into early stage clinical trials.
Aye.
I'm not that kind of limits to go into the kind of financial terms of the.
The collaboration but this will be a joint venture between between both parties.
I'm personally.
Very excited to be working with MD Anderson and with a very old former colleague of mine kind of affiliate Jones. So I Wonder what was kind of 20 years ago at Merck.
In terms of Sanofi.
So I'll talk.
Rather than the answer that target in particular out let me give you a sense of.
The the Broadway that we're kind of approaching that collaboration and kind of the what.
What is the additional value that <unk> brings.
To target identification.
So the kind of things that we're doing.
I'm using a central biology platform.
We're doing significant disease area landscaping within the areas of interest for Sanofi, So and the subsets specific kind of landscapes around kind of oncology indications.
Specific landscapes around areas of kind of.
Inflammation and immunology.
That's what we've been able to integrate not only their kind of culpas of kind of public data and applying the deep learning methods. The reside Darrin is that we can also integrate and have integrated with us.
Proprietary dataset slight gwas data sets for example that Sanofi have brought to the table.
So by integrating if you'd like to know how from the two organizations on top of it.
I'll kind of AI capabilities Thats allowed us to identify kind of significant kind of shortlist of kind of potential targets to explore more.
In more detail, including experimentally.
The also but parts of that exponential validation of kind of those are those kind of targets.
Is that we can.
As well as doing.
Functional genomics et cetera, and working cell lines, one of the abuses of the collaboration.
On behalf of Sanofi is that we can do further validation in primary human tissue settings on a precision medicine platform. So not only not only validates the target from a biological pathway perspective, but from the very soft of the program highlights the potential patients that we might want to go after in the clinic.
Yes.
And Mike did you want to add something as well.
Yeah.
Yeah.
I'm sorry, Mike you wanted to.
Sorry, if I if you're on mute.
I think you summarized it perfectly.
Yep.
Okay.
Alright, Thank you very much thank.
Thank you Vikram.
There are no further questions at this time I will now turn the call back to Andrew Hopkins.
Great. Thank you very much.
Great.
Yes.
Thank you to everyone on the call today. Thank you for your continued support to accept yet we'll come in months and into 2023, we look forward to advancing multiple programs going forward, including <unk> and 617.
But this work is just the beginning for us and we remain excited by the potential about AI and deep learning platforms, not a wholesale CTO drug development. Thank you again for joining us today and operator, you may now disconnect.
Ladies and gentlemen, thank you for participating. This concludes today's conference call you may now disconnect.
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