Q4 2021 Exscientia PLC Earnings Call
Hello, everyone. My name is Chris and I'll be your conference operator today.
At this time I would like to welcome everyone to accentuate his business update call for the fourth quarter and full year ended 2021.
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'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 Sara Sherman Vice President Investor Relations, Sir you may begin.
Thank you operator, a press release on form 20-F issued yesterday after market close with our fourth quarter and full year 2021 financial results and business update. These documents can be found on our website at www dot investor satisfaction is that AI.
Along with a presentation for today's webcast before we begin I'd like to remind you on slide two we may make forward looking statements on our call. These may include statements about our projected growth revenue is this model that they prefer.
Formats, including with respect to our technology platform and systemic preparedness program.
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.
On today's call I'm joined by Andrew Hopkins, Chief Executive Officer, Gary Carano, Chief Technology Officer, Beth Taylor, CFO , and Chief strategy Officer, and Gabe Hallett Chief Operations Officer will also be available for the Q&A session.
I will now turn the call over to Andrew.
Thank you Sarah and thank you to everyone who joined us today.
2021 was a remarkable year for accenture.
Strategically scaling the company and we expanded our capabilities as you can see on slide three we are significantly growing our pipeline.
Adam a life in programs and advancing two programs into late discovery, absolutely into IND, enabling studies.
The press release issued last night.
I'll stick with you about 2021 accomplishments, let me recap a favorable notable.
Recent highlights.
Hiring key talent expertise tripling the size of our global workforce and adds into our U S footprint with a new Boston office and office expansion in Miami.
The acquisition of Oakmont integrating patient.
Patients with each of.
Enjoy it.
David a tremendously talented team.
Listing on NASDAQ and raising over $510 million in gross proceeds from our IPO and private placement. We ended 2021 of approximately $759 billion in cash or cash equivalents, we are well positioned to deliver on our strategic imperatives.
And I would say one of the industry.
Largest AI powered drug discovery and development deals to date, we've got five 2 billion dollar collaboration with Sanofi with $100 million upfront payments.
Successfully executing our partnerships as we've seen by the expansion of work with three of our major partners.
Yes.
Rafi available indicates foundation with BMS in licensing them AI designs immune modulating drug candidates.
The successful application of artificial intelligence and machine learning to reduce our industry speculate.
Produce better more effective medicines has long been recognized as transformative potential.
We are now working to put that performance into practice.
In the last several months, we've seen some of the world's largest drug makers and that was the largest deals to date, an AI powered drug discovery.
If I can I was spun spike items in biotech and pharma represents the industry's bullets embrace of AI to date, we think an inflection point in the evolution of AI powered drug discovery and development. It should come as no surprise, that's why it's lumped into satisfaction.
And what it takes to deliver new medicines.
Particularly when we are faced with urgent health crises, such as the global pandemic.
Even more of a breakdown is that most of his time is spent trying to fix problems as they arise.
Currently our lead free step by step process rolling out over the course of 10 years, it's quite good when you can say, but no other consumer products are made by this way.
By the time the drug reaches a patient the underlying researches data by 10 years and appliances likely significantly advanced can you imagine any other technology products were made in this way.
Our founding team and I set out to build a completely new type of company to reengineer.
And design process.
To date, that's best illustrated in the near equal split and that team between drug discovery scientists.
Technologists, which you might be surprised is an anomaly in our industry.
By bringing together these two seemingly desperate disciplines, our scientists are able to tap a new problems with the power of our AI systems.
Whilst our technologists and code. These living is working towards the day when we can achieve full estimation.
To date, our Chief Technology Officer, Gary Power, Joe will talk more about our technology and how we use that to design develop better molecules.
Deep investments we've made in technology.
What I find incredible about this is how our underlying technology and AI platforms may have the potential to achieve peace will never seen before and drug discovery.
AI platforms can make decisions based on analyzing thousands of different parameters in parallel in holliston creativity with generative algorithms working at a conversational space far beyond the ability of any one scientist team of scientists to consider.
Our drug design process.
Generation of a first novel molecules to the design of a development candidate has averaged about one year versus industry standard affordable.
AI driven methods leads for nomination of drug candidates after an average of less than a 10th of a number of compounds versus the industry average.
This efficiency enables us to concurrently at Baltimore 30 programs.
I'd like to think of AI turbocharging, our amazingly talented drug discovery team. This was a combination of human and machine. That's enabled us to begin to crack Columbus areas, such a truly personalized medicine and area, but our industry has been talking about it's more 25 years today as seen by our results published in cancer discovery.
Our platform was the first to successfully guide treatment outcomes for late stage cancer patients achieving a 55%. All this gives us confidence that our models. We are developing may translate to potential patient benefits in the clinic. This is an area that I'm personally very enthusiastic about and I look forward to seeing what they can take.
Our next included ovarian lung and breast cancers.
And as we look at what's ahead in 2022 were driven by the possibility of how much we can advance powered by AI led approach.
We anticipate continued expansion of our pipeline but.
Not only as a new discovery programs.
Continuing to nominate new drug development candidates and progressing towards the clinic.
So the ability that our clinical capabilities and infrastructure.
Increasing validation of our platform through additional data, including data on our pipeline programs.
<unk> 21 to $4 six and GTA 617 that will be presented in April at the upcoming AACE All Congress and.
2022.
Good machine to fully automate drug creation to the opening of our laboratory automation suite and Oxford.
Today, Gary our CTO will be focusing on just one less visible tech how do we design.
Thanks.
The technology team is up to some incredible work. This year included opening and operationalized and a new 26000 square foot automation suite that will bring us one step closer towards fully automated the chemical synthesis and analysis of our small molecules in our drug discovery programs.
I'll now turn over the call Gavin to walk through our technology platform.
Thank you Andrew.
Today, I would like to give you a high level overview of our technology platform.
We can bring to life, how the underlying technology is differentiated from others in the industry are doing.
There are several fundamental ways, which I believe we stand apart, but perhaps the easiest way to explain it is where we start with the patients as you can see on slide six.
We think about drug discovery as a learning cycle.
Michael that begins with the patient fueled by our AI platforms that enables us to learn from every new piece of data and bring more information to bear through every step of job creation.
In a conventional drug discovery project. It may take years before a potential new drug candidate is tested in humans without AI precision medicine platform, we are able to bring this process much much earlier into the discovery phase.
On the next slide we show how we identify the right target. This is possibly the most important decision for drug discovery program.
We use them to biologists, which integrates literature, along with genomic transcriptome Nic data into a knowledge graph to identify connections and predict target disease associations.
This process is disease area agnostic with application to date across oncology immunology.
You know oncology and rare diseases.
A precision medicine platform utilize these primary human tissue samples.
Line early target identification activities to leverage this platform capturing the insights from drug action on patient sales along with transcriptome genomic data.
All of this gives us increased confidence in the relevance of that target to actually make a meaningful difference in improving the outcomes for patients.
And having the ability to better understand the potential impact loan before we reach the clinic.
Once we've established the desired target we've rigorously define our objective.
Target product profile or TPP, which describes in detail the properties, we desire optimized drug molecule. Once we've rigorously defined the target product profile on slide eight.
Now take this set of objectives and encode them rewards bundle for algorithms to optimize towards.
This enables our design systems to create structures meeting those criteria. For example, we may want to design a brain penetrant drug that has a low human dose good selectivity within particular avoids having eplex issues. We can encourage that specific set of objectives potent facing activity.
Eplex et cetera.
Structures generated drive towards these criteria.
As you might imagine.
We use and generate lots of data when doing this illustrated on slide nine.
For each project, we generate the initial hit structures algorithmically from integrating any public data with proprietary data from Franklin or focused screening.
Which is developed in house.
<unk> heard us talk about half of our company, our drug discovery scientists generating proprietary assays and data I talked Vienna, and Oxford labs that we can bring to bear within our projects.
In addition to that proprietary data the platform can also scour existing data going back years to search for anything that might be relevant for example data extracted from our <unk> patent or recent nature paper can all be integrated with data generated in our labs. This morning to help serve the models.
One of the great talents of our AI platform design is that we can use any type of data drive the design process, meaning it does not require a specific data type like three D crystal structures or high content images, but we can use any and all of these types of data plus.
Many others that will enable us to triangulate towards designing drugs that meet complex design requirement. This diverse Steve data is required to precision engineered a novel chemical series that we anticipate will have a robust treatment effect in patients.
In order for our systems to generate.
<unk> molecules, we need models to predict all of the properties that we require this could include potency at many selectivity physical properties and many many more we have extensive model building capabilities.
The spend that full range of skills and technologies from quantum mechanics, and molecular dynamics to exploit structural information to machine learning and computer vision to interpret pharmacology and cellular imaging.
Turning back to our earlier example, where we highlighted that we are trying to design molecules meet specific project requirements. For example, the right level of selectivity potency, but it doesn't have unwanted issues we.
We are now at the stage, where we have identified the desired TPP and we have an initial set of models that will help guide us on that journey as you can see on slide 10.
We can now applied generative design.
Annualized driven process molecular ideation.
Tim is exploring nearly the entirety of chemical space and creating molecules.
Side criteria, calling them and learning learning from the school is how to create better molecules.
Using evolutionary algorithms reinforcement learning the system rapidly and efficiently explore chemical space creates a population of novel molecules.
To meet our criteria at the end of each situation, usually a population of tens to hundreds of thousands of molecules are created.
These molecules are driving towards the criteria that we desire.
On the next slide.
Large population, we apply a detailed filtering process.
Angle more sophisticated and compute intensive model to reduce debt and then we apply a price that's called active learning.
We want to make a few molecules as possible because it is time consuming and expensive usually we make 10 to 20 molecule per design cycle.
Therefore, we want to from current and past the compounds that will help us to improve our models and to take us forward towards our objectives.
It is Brian learning faster to navigate across a potentially past chemical landscape that gives us the industry, leading productivity metrics that we have been demonstrating.
Active learning algorithms.
Which molecules, who will provide us with the most information to improve our models in a certain dimension ensure what should we do next in order to determine the mud.
And to select the set of molecules in an unbiased and mathematically rigorous way since they enable us to learn the most of each cycle.
Now reflect 12 selected molecules synthesized and tested we profile each molecule in detail. So that we can update our models with new information and learn the maximum amount from the laboratory work, we have extensive biology capabilities in our labs, and Oxford, including structural biology, biophysics and pharmacology.
Screening.
We can then visualize the project telemetry the progress of the project in an unbiased way using what we call <unk> score as a representation of the <unk> target product profile as you can see on slide 13.
Each dot is a novel compounds synthesized and tested the X axis is the sequential progress of the project in terms of compound numbers and the Y axis is the multi parameter optimization score with one being the ideal score across multiple objectives.
These design cycle is covered from risk through to blue.
As the project progresses, the system lease exploration, where we're exploring a range of different king the types.
Once those promising series has identified as we move into an exploitation place focusing on a particular area of chemical space.
At this stage molecule consistently fulfilling most of the key protect Kohl's and we.
Rapidly closed down on a candidate molecule suitable for preclinical testing.
As we learn through each cycle, we can track to learning as the project progresses towards.
Criteria.
On the next slide you can see that the AI algorithms refining the final designs in order to achieve the project's potency selectivity bioavailability and safety requirements and a final candidate molecule.
Hopefully I've shown you how we design differentiated molecules.
One aspect of our end to end platform.
On slide 15, it's learning loop.
And ending with the patients we can apply the platform to produce new candidate medicines with attributes that we predict will lead to better treatment benefits.
We are also using a precision medicine platform in biomarker discovery and in patient stratification as we move forward you will hear more about this later in the year.
So there is no better way to showcase the true value of our design capabilities with an example.
Now I'll turn the call over to Andrew to talk more about the design process with one of our programs in development as part of our pandemic preparedness efforts.
Thank you Gary.
Today, we want to highlight how our platform can truly overcome complexities of design challenges in efforts to create molecules that fit the desired properties we are seeking.
I'll start on slide 17.
We are showcasing our objectives for designing the drug against envelope are critical of Vivus protease enzyme targeting the Sars cov, two but corona virus responsible for COVID-19.
<unk> is a key and final corona viruses and as a pivotal vote immediate and viral replication, making it an attractive drug target in.
Unfortunately, we started this project less than nine months ago in the summer of 2021.
With a clear target product profile, we have been able to design and simplify promising compounds, but are starting to meet their objectives and in vitro studies.
We entered into a collaboration with available indicates foundation in September 2021, and we accelerated our efforts in pandemic preparedness.
We've not yet nominated a development candidate focused target, but this was an important example to showcase our design capabilities and share some emergent early discovery data coming from our platform.
So here you can see without defined objectives, namely.
Namely to develop a once daily orally bio available covalent protease inhibitors with patent Corona virus activity.
Turning to slide 18, we've highlighted our process to design a potential candidates. This process is still ongoing abbvie vitro data we will be highlighted today is illustrative of our design capabilities for an important target.
Our design cycle utilizes generative design as Gerry mentioned with a focus on improving key parameters are prioritizing who most promising compounds both synthesis and test it.
Importantly, we recently bought a professor in Goodfellow Professor <unk> at the University of Cambridge, as our new Vice President of Antivirals, Ian is a leader in the field and to advance the efforts and develop at our wholly owned antiviral platform, including pandemic preparedness and has already provided invaluable insights and we're pleased to welcome him.
<unk>.
Here on Slide 19, you can see we are looking at the potency of two of our lead molecules as measured by equilibrium dissociation constant by surface plasma residents OSP.
Compared to <unk>, the <unk> inhibitor given in combination with all of that to fall back loaded. The first approved solid called two protease inhibitor.
No head to head preclinical study we are comparing importantly, two about design simplifies sort of emerged from our AIP buying process.
We believe <unk> is an incredibly important drug could provide benefit to patients suffering from COVID-19.
Our focus today on how we can design, an optimal antiviral with a potential to be dosed once daily oral without the need to be co administered with holiday, which tell without an adverse events that reduced the metabolism of.
Other medications that patients will be taken.
What we're showing here is the progression of our defined cycle. How we can continue to learn and improve compound nyx at 68 68 components in place.
We designed to show superiority.
And blayne bodies independently based on SPL up IP traffic.
Compared to actual there with an 11 fold improvement in potency as measured by anybody binding affinity and the potential for improved oral bioavailability.
One of our latest compounds still undergoing profiling compounds 161.
We marked improvements of activity being the most potent compounds in OCD at the Cape.
Katy if only fleet picomolar.
200 fold more potent than <unk>.
Sure.
Seen in the graph.
Hi.
As part of our pandemic preparedness efforts, we are focused not only importantly, a gain solid pulp too that causes COVID-19, but I lived there we intend to award the viruses to be able to design a molecule to have a potential to be useful in the future pandemic.
So on the next slide in the chart below the full aviation, but more.
Optum to molecule is against applicable to viruses and related as tested in the functional Adelaide assay, the higher the ball, but more likely the compound is to lose effectiveness I need higher dosing against these other corona viruses.
For some background, we wanted to look at one of viruses that we identify a cold severe disease, such as Sars, one adverse both particular viruses with mortality of approximately 10% and 34% respectively.
As well as common respiratory viruses as illustrated by two to <unk> 63, both Alfa Cola viruses.
<unk>, one and <unk> 43, both beta Kona viruses.
The extent of your compounds importantly showed broad spectrum activity in vitro diagnostic Ebola viruses, we believe that this activity combined with a biophysical potently on target observed in vitro and SPL will be critical properties necessary to attain antiviral activity against emerging towards the viruses.
Turning to slide 21, this ties toward Gavin walked us through earlier, our approach of designing and against multiple objectives allows us to create a molecule by molecule potency with our desirable properties.
We were cognizant of the need of our compounds to be designed to avoid an off target impacted given very potently.
We have been able to design compounds, where the increase in potency against the viral proteases did not come at the cost of inhibiting Cumulus <unk> protease with Exs 161 show a great deal of a thousand fold selectivity and in vitro biochemical assay against tumor proteases.
So to summarize on slide 22, we have already been able to meet most of what we set out to our target core pump and our efforts to develop a once daily oral <unk>.
Anti viral.
Platform was able to integrate viable target protein analysis without stated to be honest by physical screening capability to design a quarter solid color to <unk> inhibitors with selectivity of acumen corrugated while still showing Pam Cohen the virus activity.
We have designed by compound.
Good drug like properties, including Columbus, and antiviral activity and preclinical pharmacokinetics.
We have designed a molecule that shows based on states are involved in human cell lines, better potency compared to the macro base.
<unk> broad spectrum coverage and the ability to be dosed only itself but.
The property has allowed for co dosing in the face of resistance.
We look forward to continuing to simplify the design and the case. So it's hard to put a quote share with more data on this important program later in 2022.
With that we'll open the call for questions.
Yeah.
Thank you.
As a reminder, if you'd like to ask a question. Please press Star then one on your telephone keypad.
Our first question is from Chris <unk> with Goldman Sachs. Your line is open.
Thank you and good morning. This is CJ on for Chris This morning.
Congratulations on the results and progress in the last quarter and year.
I was wondering if you could give us a sense of whether we should expect the ACR presentation for the adenosine receptor antagonist to.
To give us a sense more of what the patient.
Specific expansion is going to look like when we get to the <unk>.
Patient phase of the trials.
Or should we wait for the kind of the top line.
The more detailed healthy volunteer data to have visibility to that and.
Maybe could you also give us a sense of sort of business development priorities for the year.
I saw that you've <unk>.
Promoted business development Chief at this point so.
How should we think about priorities there will be more deals like the Sanofi deal or is it going to be shifts in some way. Thank you.
Thank you C J. Thank.
Thank you much as well for comments on the quarter.
In terms of answering the question on AAC, all data I'm going to hand that over to Dave <unk>, Our chief operating officer to explore that and then I'll come back on and talk about business development strategy for 2020 to date.
Dave do you want to introduce how what we are thinking about introducing.
Introducing them to the World Cup the AAC sure. Thank you Andrew.
So they all.
All three posters that were going to present the AAC.
Really focusing on translational aspects and patient selection.
And also one of the posters is actually touching on the design aspects that can that Gary outlined but how they would apply to two CDK seven.
So coming back to HOA, specifically think about.
Timing of information this year and the.
<unk> poster itself and we will focus on ongoing functional and multi homing worked which is to identify.
Both novel and robust patient stratification methods are ahead of us.
Walt I mean clinical study.
Are you anticipating we will start in patients in the second half this year.
<unk>.
The phase one information that you referred to.
We'll be looking to release that towards the end of the first half and that will cover information such as.
Pharmacokinetics safety and Tolerability.
Also recommended phase two dose based on a pharmacodynamic biomarker that we have in place.
And I'll pass you back to Andrew to address the question around business development.
Yes, we've been incredibly active in business development as you might have noticed or for the past six months or so and that expansion.
Extra work not with just with joint ventures, but with BMS and Sanofi, we came as well also to make sure we do balance out our business model.
Keith's about actually how are we thinking about business development, particularly for 2022, what we are thinking about also is ensuring that we build out.
Our capabilities and showing that as we expand.
New ways of doing things that we are able also to bring along partners to do that and in fact, you already seen elements of that with the Sanofi deal.
A big difference to invest collaboration and the BMS collaboration was the inclusion of a precision medicine platform and I think that gives you. An example of that as we develop new technologies.
We then look to see how we can also work with partners.
On an early stage actually to ensure that both technologies are on right track in terms of understanding real patient needs in real needs in the marketplace. So one thing I would expect this year actually is to think about how we do technology deals as well as doing pipeline deals.
And also I'd like to bring in our Chief strategy Officer, Ben Taylor Boswell C. J just to add some color to that.
Hey, C. J. So just a quick note on the ACR poster I think although we'll probably save most of the enrichment data.
For Roundwood, we're starting the actual clinical trial in the next phase what is really exciting about.
What we think is really exciting about the HOA poster is youll see some evidence of how you can have a functional ex vivo Io model.
And remember <unk> is not a direct cytotoxic agent. So we really have to have that immune interaction.
Which youre not going to see in most all of that the current translational models and that's why it's really suffered from having good translational.
Our model. So this is this is a really exciting potential model that can be used not only for HOA, but hopefully other io agents in the future.
That might be more directly relevant to the patient environment.
Absolutely Pat and not really underlines the importance of these models more generally to the company and the way we think about the CJ is that it's not just me of them.
The human data from the phase <unk> on the molecules.
The work being done in parallel on the ex vivo human data in defining the patient.
Patient selection approach, which we are taking on those two bits of work coming together then into designing the phase <unk>.
Great. Thank you that ex vivo assay certainly been a big gap, so looking forward to seeing that data. Thanks.
Thanks.
Our next question is from Michael <unk> with Bank of America. Your line is open.
Great. Thanks for taking the question.
I wanted to start on the <unk> inhibitor that you talked about just can be spend this amount of time on that.
Given how quickly the COVID-19 pandemic.
Evolving and given.
The presence of.
<unk> and <unk>.
Other agents out there I'm just wondering if you could talk about the potential to accelerate the development of <unk>.
Anil candidate and could you talk a little bit more about your commercialization strategy.
The next steps beyond that.
Assuming you're able to develop a solid candidates.
Excellent. Thanks, Mike I'll give a bit of introduction to the question and then I'm going to hand, it over to Dave again actually to give you a lot more detail on how we're thinking about it. The first thing of course is this project that we showcased today I think is a really good example of the ability of the company to rapidly design and develop high quality.
Both molecules.
I think the data we get into and now actually really places us in context. The other thing is.
Important take onboard to a dot OLED started with our collaboration with the Gates Foundation and the private placements that they came in.
And it's a case span all really given us fast of key.
Key key partner going forward and ensure that.
We are ambitious in sort of a target product profile. So I would go enough to.
Particularly compared to some of the competitor molecules that are out there and when we look at that market. The ideal TPP, we think about is.
How would you identify something that could be low dose and low back then that really has the protection against future variance that we've seen.
I'll just give you some context now about how our program is developing and how we're thinking about it I want to introduce Dave notes the table. Thank you Angie.
In terms of the first question around specific timing yes.
Yes, it's important to note that we will.
Continue to kind of synthesize molecules and explore the elite series, we have in place.
Looking to select a development candidate in.
In the second half of this year, obviously clearly aware of.
The timing and the need for additional agents.
Your first question about Pac limited and the wider market.
I think it's interesting in that.
The current climate.
The data is around both vaccines are small molecules tells us a lot of things around.
There is waiting resistance kind of following vaccinations.
We continue to live in a global environment.
We've seen uptake around the world differs by geographies. So in some areas vaccination uptake, particularly in high risk areas is still very low.
And even more recent data the nature paper.
Showing.
How.
When people actually.
So you effected with the omicron variant.
Actually that generates a really low immune response.
Likely to kind of generate kind of a pretty muted to the I guess everybody is hoping for.
So I think it highlights as is often the case with <unk>.
<unk>.
<unk> is that the.
The combination of the vaccine has the potential for resistance to.
Greg Great agents on the market I think it.
Just highlight once again the importance of having multiple new therapy options available for everyone.
Just developed nations.
But also thinking about how you might apply these combinations as we saw successfully applied for example think about HIV therapies on.
I think importance of multiple therapies.
Again, it's different mechanism of action, so that kind of things that we're looking for over the over the next couple of years.
And that's why for us as well Mark It was important to design an agent that doesn't need co docent within metabolism inhibitors, such as <unk> and if we are going to create combinations of co dosing.
We don't have a strategy, where ultimately you're combining two.
<unk> agents.
Acting on Covid is not different mechanisms and as Dave says, we've seen with HIV to be a very successful approach in the long term.
Okay. That's helpful and then a follow up.
Just sort of on.
Investment priorities for 2022, you've got a very healthy balance sheet.
Exiting the year and you also have the upfront payment from Sanofi collaboration from January So how do you think about expanding.
Investment priorities this year.
Given the relatively neutral cash flow.
From operations last year. It seems like you should be able to support a lot more investment and expand upon your comment you indicated I think 30 programs concurrently what did you want to be able to run, but as you move into a discovery 90, enabling.
Where should we where should we expect the incremental spend to come in and sort of.
What's a good run rate because we think about progressing through 2022.
Thanks, Mike I want to introduce Ben Taylor actually to take that question, our CFO and Chief strategy Officer.
Hey, Mike So.
If you saw our financials for this year, we had an operational cash burn of about $9 million.
A lot of that is because we can offset so many of our expenses with cash flows from partnerships. So we brought in a little over $85 million. So just a little bit about the guidance that we had given earlier on tier four.
Our cash flows from our partnerships I would expect that to continue into the coming year. So we've already had.
The $100 million upfront from Sanofi come in.
That will probably hit the actual balance sheet in the second quarter.
But we did sign the contract and the beginning of this year, we've had a number of other smaller milestones come in as well so.
We're going to have a nice cash flow from collaborations this year again.
Hum.
Meaningful growth over the cash flow from collaborations last year, so even though we will be growing our operations significantly and then the second we can talk about what that means there should also be a nice balancing front those inflows.
That will maintain a very balanced business profile.
Growing our business in a way that matches, our growth and our partnerships as well.
So just a quick note on some of the areas that you asked about investment.
We continue to grow our platform capabilities and I'm going to turn this over to Gary in a second to talk about it along with all of our projects. The project growth, you'll see our pipeline grow as well, but remember our partnered programs pay for themselves ahead of time.
So that actually reduces the net burn considerably.
And then we will continue to make some capital investments we're growing some of our offices around precision medicine out in Vienna, We opened up the 50000 square foot facility. We've got an automation lab that Gary can talk about.
And the.
The Oxford area as well so we will have some increase in cash outflows, but I imagine that it will vary.
Very reasonable neighborhood, Gary you want to pick it up from there.
Sure. Thanks, Thanks, Brian I think you've hit on two key expansions for US I mean, obviously, we are building out our technology platform going really deep into the AI capabilities that we described earlier.
Kind of end to end platform, but two things I called out and we're really excited about is obviously the automation lab. So I think we've mentioned previously 26000 square foot.
Just south of Oxford, and we're deep into the design and specification ability will be equipment to go into that so.
That's going to be fantastic end to end synthesis purification screening capability.
Can really bring a transformational benefit timelines in drug discovery and then the other new new exciting area that we.
We're starting to look at as we recently announced sweetheart Professor Charlotte Bain wishes a super exciting.
Into the Tech group into the organization.
And she is going to be looking at developing.
<unk> capability and how we can apply so really tight synergy with the sort of what we're doing at the moment and we can work how it is going to apply AI to the design of biologics.
Thanks, Greg Thanks.
Just to confirm a quick one so are you still sort of projecting about five to six years.
Cash runway I think some of you commented on earlier.
Yeah.
We haven't given specific guidance, but I think we feel very comfortable with.
A number of years of cash flow runway.
So part of that is also remember under our control.
We are determined to flow between internal pipeline and partnerships, but.
Our business model expectations with certain white meat around what you're talking about.
Great. Thanks, so much.
Our next question is from Peter Lawson with Barclays. Your line is open.
Hey, Thanks for the update and the detail on the call maybe just a follow up question for Ben just because we think about that.
The build out.
Undergoing at the moment, how should we think about.
Needs.
Expanding when you move into phase II and phase III clinical trials as well.
So we've actually factored that in.
A lot of our current thinking on the growth. So we're building up our clinical team.
And doing it in and what we think is a very balanced data driven way, but I wouldn't expect.
That side of the.
Business to really become the major cost center until some of the drugs get into.
Late phase II or phase III, and then obviously the clinical trials get more expensive so.
In the near term, it's not going to have a dramatic impact.
On our overall cash expenses, and we're able to manage that much more so.
I don't see that as being.
Substantial line item alright, driving line items.
Is there anything in that kind of late stage development.
You can improve on.
Well, whether it's from the AI side of things or is that kind of almost like a bolt on or existing approaches.
Well now Peter Youre getting to where we get really excited so we hope so.
Is the right answer.
We're intending to take a very data driven.
Our approach to clinical trials do so.
As we mentioned earlier.
A lot of what we designed for is actually better clinical trials and so what we need to do is match those clinical trials to do.
Drugs that were producing so if you think about that.
Any clinical trial, whether it's phase one phase III is all about statistics and so the more powerful you can make your statistical analysis. The smaller the trial faster that trial the better the results that you can get two so by designing more targeted clinical trials. It actually has the follow on.
Correct.
Potentially making them smaller and faster and less expensive.
Great. Thank you so much and just on the kind of the near term.
What should we be looking for in the CDK seven preclinical data.
Yeah.
I'd like to add.
David will take that question Peter.
So the key information Youll see in New Orleans.
His ongoing work around.
As well as the design of the molecule and some in vitro data and in vivo data just showcasing the kind of qualities of the development that we have.
As ongoing data that we're generating and primary patient tissue.
Which is helping us to identify.
Not only which cancer types, but also within within those kind of specific kind of site. So for example, a varian.
Which which patients are likely to respond better and why and so producing kind of signatures that we can then use prospectively.
<unk> patient study to kind of to guide exactly what that is as pointed out.
To highlight which patients are likely to respond to our drug and with Johns and understand why.
And ultimately that will drive.
Very specific recruitment should allow us to actually run smaller clinical studies and therefore actually get early are more successful readouts.
Got you. Thank you and then I guess the final question just around.
<unk> just that as a single agent you gain a sense of what percentage of patients could share response.
With a single agent <unk>.
I think so I think youll see some of that in the poster I think.
But it also depends on the cancer type.
So.
If you look at the data that others have published on that.
Building upon.
Is that so.
If youre looking at.
Which subjects do you see in this high <unk> signature.
And also where do you see kind of how.
High expression of important enzymes like <unk> 73, and other components that are likely to respond a varies across cancer types. So it can be as high as say 15, 20% in some areas.
It can be much lower than that in other areas and so.
As part of the work.
We'll present, the ACI, you'll start to get a sense of the other cancer types that we're focusing on that Steve.
Steve form the basis of.
Both the dose escalation and the expansion so again with this kind of concept of.
Narrowing down on a smaller patient kind of subsets as we go into the clinical trial again identifying.
Which patients respond.
So it depends is the answer to your question, but I think the key thing is that C is knowing and having having data available and more importantly kind of biomarker because until you.
Actually identify those patients before you take them into the clinical trial.
Great. Thanks, so much thanks will be updated.
Our next question is from Vikram <unk> with Morgan Stanley . Your line is open.
Great. Good morning, Thanks for taking my question. So I had two both kind of on the platform. So.
First.
Is there any color you can provide at this point on the targets that have been identified through the collaboration with Sanofi I understand it's early days and you may not be able to share much about targets in particular, but any context, you might be able to give around the process for identifying these targets and then prioritizing them.
That would be very helpful. And then secondly for the precision medicine platform highlighted by the exalt one data.
Where specifically do you think you could apply this.
<unk> next and how do you see it.
B, we have through your current pipeline programs over the coming months and years.
Thank you Mr. Vikram, great great questions actually it gives us a chance to talk about the expansion of our end to end platform.
That's actually a real key feature of the Sanofi deal in fact, that's moving upstream into using our target I'd approaches and downstream and to use a precision medicine into patient stratification, but in fact, those two things do come together in how we thinking about identifying new targets of Sanofi, So to give me a bit more color on.
I'm going to bring Dave into the conversation as it has been his team was being.
Identifying targets and anger using the platform.
Thank you Angie.
I think.
Some key components to this is that.
So the first thing to appreciate is that the <unk> spaces.
Kind of cover.
<unk> inflammation and so.
What we're able to do there is kind of.
A few a few ways of approaching kind of target selection in target validation.
One of the obviously the critical ones and it's kind of at the heart of the one of the reasons that Sanofi did the collaboration.
We give them access to a patient driven approach to target identification.
It goes back to just kind of critical story of.
Placing the patient at the center of both the target discovery, but also the kind of translational aspect and so.
We are well that looks like in practices.
So.
With assembling datasets.
But it is by Sanofi.
Because that therapy therapy area had some obviously be thinking about this for a while and the kind of targets at once are welcome.
So they give us access to that proprietary data.
We can actually that either aren't as Gary described daily in terms of the.
The rich history of kind of.
Published literature of Parkinson's and peer reviewed information from the last 20 plus years.
And then I don't know that the information that we can gain from.
Our platform in Vienna.
So again, it's kind of kind of experimental data both at a functional level, but also genetic and transcriptional and we basically bring all that if it makes it together and to ask questions around.
How strong is the.
Is there a relationship between a particular target on the diseases of interest and then it also kind of narrow that further down into kind of subtypes of cancer. So.
It's still in its early still early phases, but I think.
I think the power that we have from the proprietary data that Sanofi abroad.
To our own.
Just kind of.
Well sizing good steady in terms of both identifying.
Novel targets, but also kind of prosecuting them as we as we go over the next few years I'd say a good example of this pipeline of total discovery.
It's actually will be presented at one of the ACL posters, which is how we can show that.
Deep learning approach to prime mutation tissues is actually being used to discover novel mechanism of action and that particular, one that's ovarian cancer. She will talk about in a few minutes, but it gives us I think.
A textbook example of how we're using our platform then to start off with the patient tissue material and then use it in for novel target discovery. So once our posters out actually we were able to tell you more details, but I would recommend looking at it.
In terms of Inver boards and application beyond excellence one of our precision platform. Firstly, we are incredibly pleased with the excellent one peso.
<unk> was published in cancer discovery and the results of our trial is the first time path Dave.
An AI based system that has shown improved outcomes of oncology, that's an important thing to note.
We have a really important thing to note was of course of the results of our trial a hazard ratio of <unk> five free in all 55%.
If you look at sort of where we have paid.
Patients have equaled one unless they had even signaled significant benefits moving back by vessel.
Assay AI guided sort of therapeutic approach.
That was of course, a trial in Hematological cancers and of course, we are now got a clinically validated approach event on hematologic cancers. All things we are exploring now in terms of the wider internal precision oncology. So the pipeline as we go forward, but what we are doing now is looking to rapidly expand the range.
In cancers that we can then.
Apply the same methodology to in developing lab based high content AI driven assays and also then looking to run both sort of observational end of investigational trials of accounts of along the lines of excellent one we.
Advanced those stages now looking at ovarian cancer breast cancer lung cancer, we're actively developing youll.
Youll see developments around Glioblastoma, taking place and evidence of that over the GAAP rent. So what we're looking to do really is build this out for a range of different cancers.
The key to doing that really is also the other parts of our precision medicine platform, which we've talked about in the future earnings call, which is how we expand our clinical network and are biobanks. The range of clinicians that we can interact with.
You'll be hearing about a lot most of the collaborations enough space as the network expands.
Form sort of central and eastern Europe at the moment across 12, a call to them.
Hopefully it included in the U S and Asia as well and what we see from that then is that expanded network of solutions provide and then underlying.
Material and data from our patients, which allows us to expand our biobank I was really excited of course is the depth of analysis, we expanded extend into not just high content approaches, but also a much wider range of <unk> approaches now taken place, including transcriptome mix.
Single cell sequencing and.
Basically trying to extract as much deep information as we can and deep profiling on every hard won biobank samples that we've managed together and that's what's really exciting about this and back that also provides new data into the target validation platforms as well of course is provides us with much more sophisticated approaches to thinking about.
Patient stratification, where we consider that as a multi omics approach far beyond D with Pheno mix.
Let me just kind of wind up in that.
Even though we're just as importantly, just sign a really exciting collaboration with Sanofi, we've actually already identified a few targets.
And we're just initiating the operational relationship around that so well.
We'll keep you informed as to the progress over the coming months and years.
Great. Thank you very helpful.
We have no further questions at this time I will turn the call over to Andrew Hopkins for any closing remarks.
Thank you Chris.
Thank you to everyone who joined us today.
As a scientist by training it can be easy to solely focus on the exciting new chemistry and biology in the creation of a new medicine.
However, I hope that today, we've illuminated how it's our technology systems that can truly take the best of science and accelerators, helping to move us towards a world. When we see that all basins might be designed with an extra ordinary computing power of artificial intelligence and machine learning, enabling all of us in industry to achieve more and.
Advancing new medicines for patients and with that thank you for your time today.
It's been a pleasure.
Okay.
Ladies and gentlemen, this concludes today's conference call. Thank you for participating you may now disconnect.
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