Q3 2021 Exscientia PLC Earnings Call
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Hello, My name is Stuart and I will be your conference operator today.
I would like to welcome everyone to Accenture his business update for the third quarter 2021.
At this time I'd like to introduce Sara Sherman Vice President of Investor Relations you may begin.
Thank you operator, a press release on form 6K with issued yesterday after U S market closed with our third quarter 2021 financial results and business update. These documents can be found on our website at www dot investors that etc is that AI along with the presentation for today's webcast.
Before we begin I'd like to remind you on slide two that we may make forward looking statements on our call. These may include statements about our projected growth revenue business models and business performance actual results may differ materially from those indicated by these statements Accenture is not under any obligation to update these statements regarding the future.
To confirm these statements in relation to actual results unless required by law on.
On today's call I'm joined by Andrew Hopkins, Chief Executive Officer, and then Taylor CFO and Chief Strategy Officer, Dave Hallett, Chief Operations Officer, Gary Paradise, 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 Sarah and thank you to everyone who joined us today.
It's my pleasure to welcome you to our first earnings call.
Today I'll review, our recent accomplishments and then I'll be joined by Ben Taylor sort of discussion around the business models that helps fuel our pipeline.
First of all I'll start on slide three to navigate you through the progress and to give you an idea of what we're building towards.
To start with our vision of using AI to discuss a better medicines faster as we go through our results you can see how each of these accomplishments builds upon the foundation of a much bigger vision to transform the pharma industry to accelerate the creation of the best possible medicines for as many people as possible.
Cynthia is an AI driven pharmatech committed to modernize and drug discovery and development with AI and exhaust experimentation to develop drugs faster and fundamentally better for patients.
Better medicines faster.
A free simple words, but let's reflect on what I mean.
Faster for people facing serious disease time is the enemy by using AI driven drug discovery and development. We believe we can accelerate with discovery of novel molecules and improves the probability of clinical success potentially saving us some time to get the novel drug candidate approved.
More important for the cluster is a concept of better without patient first precision medicine capabilities, we're able to integrate primary human tissue samples into early drug discovery truly putting the patient at the center in practice. This means we have developing highly translatable models that can be a better fit.
Initial successes, bringing us into a realm, where we can potentially make better drugs for specific patient groups.
We've already seen in the real world. How this approach can help tangibly improve outcomes of patients our excellent one clinical trial published recently in cancer discovery demonstrated that patients who were treated with the guidance of our AI platform had significantly better outcomes and more durable responses achieved a 55% objective response rate.
Although extremely powerful for these patients. This is only one example of a use of all AI.
The real promise to what extent is doing lies in our scalability, we often refer to the company as a learning company by that we mean that every scientific idea, but we pursue every target pursued every compound design made tested we.
We own it and not just learning, but systematize and encoding those learnings so that the fed back into the platform to enable us to learn faster and do more with every subsequent project, but we tackled.
We are truly building a system that can enable our scientists and collaborators around the world to pursue more novel ideas in parallel with far greater probability of success of turning those ideas into actual medicines for patients.
Rather than the longwall to failure, but many scientists face today with less than 4% chance of a new idea successfully becoming a new medicine.
We are presenting the new way forward, where we can evolves more science more quickly and with far greater chance to reach people waiting for individual treatments.
Today, we will provide you with an overview to our achievements in the past quarter.
And what we built so far towards decision.
Throughout this I hope you can see a common thread, but sets us apart that continue to learn and nature of our AI system that powers greater precision and speed up makes it possible to execute on a greater scale than ever before imagined.
The efficiency of our platform that we built them enables us to scale that scale allows us to balance risk and then just take more opportunities for new medicine creation.
Today, we'll walk you through our balanced business model approach that is fueled by the scalability of our platform that we are building.
Volvo them, one business model will walk you through multiple approaches rather than focus on one lead acid will review our pipeline of more than 25 programs.
Even if all of these may be considered early stage biotechs parlance. Our platform has already demonstrated real world results. So the benefits in patients with that let's turn to our progress.
On slide four.
As we generate data develop new algorithms and initiate new programs our platform becomes more powerful overtime. This enables a system that is not only capable of handling many projects at once high capacity, but also high performance as it gets better and more precise system scales with new data.
We're starting to see the concepts bear fruit and execution across our pipeline over the past several years, we've done many deals across biotech and pharma, but importantly, we've begun to deliver upon reis most visibly significant expansion of a number of these relationships.
Bristol Myers Squibb's example, BMS expanded our original collaboration of three projects now to eight projects were substantially improved economic terms on the new projects.
BMS also license a first drug candidate from accent here this past quarter, demonstrating our ability to successfully discover high quality molecules in areas that are proven scientifically challenging.
In another significant expansion, we entered into our first collaboration with the Bill and Melinda Gates Foundation, adding a portfolio of antiviral therapeutics against Corona virus and other viruses with pandemic potential there is perhaps no great illustration of our promise of AI vendor with a pandemic, both because of the potential to accelerate from there.
Drug discovery, but also in the ability to do so with small molecules enable it potentially better access and distribution around the world.
We also made progress with our 50 50 joint ventures, including selection of the first two targets, while multi target deal with EQM Rex.
We've nominated our development candidate 617 for CDK seven and are actively preparing 617 for IND, enabling studies and expect to submit an IND by the end of 2022, we look forward to providing you with further updates on this important program.
We presented data on <unk> 607, using a primary patient tissue platform using ovarian cancer models and we're pleased to say, but we're also expanding our work took a breast cancer patient models and other solid tumors too we plan to share more details in the coming months.
We also continue to scale, our business with the initiation of automation apps and the expansion of our wet labs and on October 5th we successfully closed our upsized initial public offering and concurrent private placement raising over $510 million in gross proceeds given our.
<unk> business model, which will go into in more detail on our year to date operational cash burn was approximately $16 million, including the old site acquisition cash contribution.
And we have approximately 784 billion in cash and cash equivalents following our IPO and private placement, we are well positioned for several years of operating cash burn.
So now we'll take your first strategy and our business models on slide six before we open up the call for Q&A.
Alex Cynthia our strategy is to shift the curve to develop better drugs faster using our AI first approach our technology investments enable us to improve our probability of success to bring more drugs to patients. Following these three key tenants.
One increases our probability of success <unk> accelerates the time of turn in science into new medicines and number three lower costs of our processes. So that we can reset what economic model.
As I've talked before we can use technology to solve these problems and therefore, a shift the curve the whole economic lifecycle of drug development.
And what's key to our ability to deliver better drugs faster is our balanced business model as shown here on slide seven our.
Business models allow us to generate substantial cash flows where our pharma partnerships, while also creating substantial value for the company for Covid and wholly owned programs.
Our pharma partnerships provide cash upfront to cover such costs with the potential for significant milestones and royalties on average we are eligible to receive approximately $150 million per partner program and we have 10 projects ongoing and expect to increase this number in 2022.
These programs are not only important for cash generation, but we also learned from each project as a platform soles unique drug discovery problems of each new targets. The loadings created both a bus knowledge base and capabilities for the next project.
And programs will be over 50% to 100% of economics, our joint ventures and wholly owned pipeline. We are focused on creating substantial net present value or NPV, we have the ability to leverage our infrastructure and AI technologies from target identification food clinical trials of a much larger scale than traditional biotech drug development.
This scale allows us to take a portfolio approach to science spread eno risks across multiple therapeutic areas and targets.
These free models are critical to developing a robust pipeline and allowing us to balance upfront milestones strong cash flows versus equity ownership with long term potential upside.
As you can see here on slide eight we provide end to end discovery capabilities and we are responsible for years now AI and core competencies not only to evaluate the drug target, but also to design the optimized molecules all possible efforts to design better drugs faster for patients.
With our wholly owned programs currently focused on oncology immunology and Antivirals, we do everything from idea generation to patient selection for clinical trial season, a precision medicine platform.
Our patient tissue models help us, noting only to design a better drug but allow us to find the right patients that would benefit with most of our drug in the clinic.
For our partner programs for example, with BMS, we drive undelivered projects through to <unk> and our partner delivers clinical development and commercialization BMS is a great internal team and we believe that the fact that they trust us to oversee a significant proportion of our discovery portfolio speaks to the validation of our.
<unk>.
For our co owned programs, we add service also by Chevron in idea generation at the start of a project and patient selection. As we proceed to clinical development, we are able to leverage our partners know how for example, with valley Bayou in the rare disease space and share the potential and future successes.
We are an integrated and scalable pharmatech that does more than just targeted vacation or design innovation and AI are the core competencies of our company. So that can be applied throughout drug discovery and development with each new expansion of our capabilities. We have seen that our partners utilize those capabilities with enhanced <unk>.
Opex for US we believe that this trend will continue as the platform grows.
And now I hand over to Ben Taylor.
Thank you Andrew.
On slide nine you can see we have more than 25 programs in development across a multitude of therapeutic areas and collaboration structures with.
We validated our platform's capabilities by putting the first three AI design drugs into human clinical trials and several more that are advancing through preclinical development.
Other important message here is how we are able to scale. Our original pilot programs with Sumitomo allowed us to validate the complex interacting AI systems necessary to encode the drug design process.
Then in 2017, we launched our first program, where we internally oversaw both the AI design and laboratory testing.
Once we had our operating procedures down we were able to rapidly scale. Our pipeline initially with pharma collaborations than co owned projects and now with our wholly owned programs.
This scale then allows us to take a portfolio approach to both science and our business model.
We never want to be defined by a single product technology or therapeutic area, but this brings up a critical question that many of you have asked how do we determine the best balance for our different models.
On Slide 10, you can see an illustration of unadjusted cash flows under various ownership structures.
For the Holy and co owned lines, we've used publicly available data to create an example of the average cash flow profile for a drug from discovery through generic entry.
The cash flow potential is very high but requires substantial investment time and risk.
The line labeled that's pharma partnerships is a hypothetical example of cash flows from that same product if it were out licensed rather than developed internally.
In this example, the cash flows are always positive with milestones and royalties contributing to the smaller inflows throughout the drugs lifespan.
None of this should be surprising and we believe it is clear that having a mix of these business models provides a more balanced risk reward profile.
The more interesting question is actually how do we evaluate the optimal mix of models.
To do this we need to overlay expected probability of success into the cash flow profile slide 11 shows that output at three different probability of success level.
The current industry average as Andrew mentioned, it was about 4% probability of success from target identification to approval.
Even though there is a marginally higher net present value or NPV from owning a project in this scenario. It comes with a significant time and cost of risk it.
It is not surprising that in an environment, where you're almost always expect failure you would be incentivize to take your cash upfront.
If you remember from our F. One we disclosed data showing that we have demonstrated better success rates than industry averages with her first seven development candidates.
Just adjusting for this aspect would move the overall probability of success into the 10% range.
We apply a 10% probability of success to the model you can now see that the risk reward profile is more balanced which is why we are pursuing a more balanced portfolio of expansion.
You'll also note that early cash inflows from the a partnered program effectively balances out the early development costs of our wholly owned program.
Finally, if you increase the probability of overall success to 40% roughly 10 times. The current standards the risk reward profile firmly moves to keeping the economics for ourselves.
With time, we do believe that this could be an achievable benchmark, but it needs to be achieved by improving clinical trial performance. This is why we're so focused on translational systems like our precision medicine platform to potentially improve both drug design and patient selection.
Moving on to another related topic slide 12 shows how we account for expenses from our different collaboration agreements.
Our pharma partners generally provide upfront payments and associated with our expected R&D funding.
So we recognize revenues from those payments over the life of project execution.
Therefore, we also recognize the R&D costs as cost of goods sold matching the revenue.
In addition, most of the milestones in all of the royalties through our pharma partnerships will be recognized as revenue when achieved.
With all of our co owned programs, we only recognize the 50% of expenses that we are responsible for even though we are generally performing all of the discovery operations now.
The only other twist is that some of our R&D expenses associated with colon programs actually flows through a separate line item called share of loss on joint ventures. The reason why some of our co owned programs flow through that line and others not just for accounting technicalities and does not reflect an actual operate.
<unk> difference.
Our financial results are detailed in our press release and form 6K, but you can see a few highlights on slide 13.
Notably, we anticipate cash flows from collaborations between $75 million to $85 million by year end 2021, and expect our 'twenty to 'twenty two cash inflows to exceed our 21 inflows.
In addition, we expect to end 2021 with between $745 $755 million of cash on hand.
We believe this gives us several years of cash runway and the resources to continue investing in our business expansion and differentiated pipeline.
And with that we'll open it up for Q&A operator.
Thank you ladies and gentlemen at this time, we will begin the question and answer session.
Wishes to ask a question you May press star followed by one on their Touchtone telephone Jewish.
You wish to remove yourself from the question queue, you May press star followed by two.
Using speaker equipment today, please lift the handset before making your selections.
Anyone who has a question you May press star followed by one at this time one moment.
First question please.
First question is from the line of Chris Shea Battani from Goldman Sachs. Please go ahead.
Hi, guys. This is C J SaaS on for Chris. This morning, Congratulations on reporting your first quarter and thank you for taking the question.
So this is the first time I think we're hearing guidance for 2022.
In some form so.
Curious what assumptions are baked into that increase from this year to next year and related to the exercise you just walked us through.
What are you, hoping for the mix of ownership level and the portfolio to look like over the next year two years.
You increased your internal assessment of probability of success with the platform. Thank you.
Thank you C J good to speak to you.
So my question I'm actually going to hand, it over to Ben Taylor, our CFO actually to walk you through our thinking there.
Hey C J.
Great to be speaking so a couple of different things, one and looking at our 22 cash inflows guidance.
Really what factors into that is just the level of interest that we've had from outside parties as well as doing an internal analysis of where we want to put resources.
And just triangulating about that so.
So we feel comfortable that we'll be able to exceed the cash inflow levels from this year into next year.
As far as the balance of the pipeline.
Think the slides in the presentation really highlighted we're at the point right now where we feel very comfortable.
To split it between the partnerships the Jv's and wholly owned programs I think for a couple of reasons that balance will continue for the next couple of years.
One will be we are a data driven company, we look to prove things out and so just as our products advance through clinical trials.
We'll be watching and adjusting our data.
As that goes through and hopefully really driving up that probability of success over time.
Another component of that is as we build up our internal operations are on clinical trials that will give us more comfort in expanding our wholly owned pipeline as well.
We've said before.
We want to take the same principles that we applied to drug discovery and put them on to drug development.
So more coming on that over the course of the next year, but I would expect us to really focus in on how to bring data and analytics to the clinical outcomes and then that might be an opportunity for us to expand.
Greater proportion of wholly owned but near term think balanced pipeline.
Great. Thank you.
Next question is from the line of Michael <unk> from Bank of America. Please go ahead.
Hey, guys. Thanks for taking the question can you hear me.
Yes, Mike.
Okay great.
Two quick ones one I just want to follow up on the last point and go deeper into your comments on scale of the platform and ramping up overtime.
You touched on your prepared remarks, a number of times about the ability to leverage the scale of the platform to really.
Expand the number of programs expand the number of targets youre going after so given the cash balance and given your views on cash flows next year could you talk a little bit about.
What that looks like going forward as we think about 25 programs now what's the reasonable number for us to expect end of 'twenty two end of 'twenty, three and what's the opex requirements to get there. Both in terms of head count expansion building out wet labs and building out some of that automation that often just opex dollars.
Mike. Thank you very much my question is good.
Certainly as we go out now one of the key tenements to remember is that we are looking also to maintain our investments into tech alongside our investments into the of the pipeline with us partner JV on their own and that's a key important thing as the platform grows and there'll be a number.
New elements in the platform you will see us at scale, a big chunk of that has been mentioned thinking about sort of being innovative in the clinic as we think of our quantitative and learning approaches into clinical but also thinking about how we bring.
The automation technologies as well in discovery forward, but also it's about building up that internal pipeline. So I just want to introduce other Dave <unk>, Our chief operating officer, who is very much living this day to day about how then Davis building up his team and the operations behind it.
Thank you Angie.
Well the first point I'd like to make is that.
It's not just about the scale of the portfolio is critically important to us.
<unk> been made earlier about the volume of the programs that are in the overall portfolio.
I think.
Another key point would be that.
That.
As an organization, we encode and automate.
Fundamentally.
Important tenant of the organization because.
Bye bye encoding the drug discovery process, and then looking to automate as much of the experimental process as possible.
That allows us to do is to actually build and scale.
Normally human waves that we can actually manage a.
Discovery portfolio with the size of say a medium to large pharma without the requirement for having thousands of people to do that.
In terms of the internal portfolio as I mentioned I think.
Combination of that particularly that kind of more recent fund raising has allowed us to to look at that in more detail and we will continue to.
To invest in that space, particularly around oncology and anti virals.
And we'll update update you on the progress of those projects as we as we progress.
Excellent. Thank you, Dave and in terms of just how the technology expense. So I just wanted to ask Gary power duo CTO just to say if it was some backfill before your second question.
Great. Thanks, Thanks, Andre Hi, everybody.
So they've said automation is absolutely key to our thinking and we think about automation in two manifolds. So we're thinking about how we automate the design processes, how we're stringing together the in silicone processes. The generative modeling the active learning a little bit processes are key.
Our flow of design and that means we can do we can run more projects in parallel, but a really exciting development that we're working on this year as we've just released a new building 26000 square feet South of Oxford, and we're building a brand new state of the automation studio. So this is physical automation this is bringing robotics and Lin.
King those robotics to our AI processes, we see a huge synergy there. So this will be this will be synthesis. This will be purification. This will be compare management lucidly screening all integrated into one brand new facility.
Very excited about all of those developments.
Excellent.
Mike You said you had a second question as well.
Thanks for all the color a quick follow up hopefully a little bit quicker.
You talked you touched on sort of leveraging some of your AI capability is more on the development side of things such as discovery Im curious if youre alluding to something similar to what you did with exalt one just curious.
Any follow ups on that or you still got some more work going on in terms of this all too, but how should we be looking at.
News flow on that side of things going forward. What are you about we should be looking for yes, no. That's a great question, Mike and you will be seeing a lot of activity from us as we build out our precision medicine platform in 2020 to think of it in terms of as we bring into the public domain.
Lots more sort of new data on the new models that we built out in a variety of new cancer types. Further validation of the models that way already built in include and really bring into the public domain and a solid tumor data that we generated.
We've also just started building a new 50000 square foot lab space in Vienna. So that's a significant investment into building up the biobank and screening capability and the capacity then that brings us you'll be looking at us actually building out our relationships with clinicians we already have over 70 <unk>.
Right across central and Eastern Europe, which we collect in samples form while biobank, you expect to see a lot of activity from us actually as we really think about scaling up our ability to collect patient samples and go deeper into the data as well.
More of a multi omics approach as well as beyond the Sonoma approach as well, but in terms of also thinking about how we as individuals in the clinic I'm also going to ask.
Ben Taylor, our Chief strategy Officer, as well too just talk you through some I think event about how now we want to bring the same kind of individual approach.
The clinic as we have done to discovery Sharon.
Ill keep it concise because I think it's really on the principles of how we design. We are precision design company, which means that we have a precise patient population that we are designing for.
And so what we always aim to do is understand how to better target those patients.
In our clinical trials and in the future and commercialization and so if you think about what are all site platform is doing.
It's really the ideal form of personalized medicine, where we are using the patient as their own assay.
To figure out who is the right patient for a drug.
We will continue to do that both with the patient tissue platform.
As well as other ways that we can find the right biomarker the right gene signature of the REIT companion diagnostic to be able to target the right patients.
Great. Thanks, so much.
Thanks, Mike.
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Next question is from the line of Vikram purely from Morgan Stanley. Please go ahead.
Great. Good morning, Thanks for taking my question.
So I had two on the pipeline actually.
First for the first set of <unk> that we can expect to see in 2022. What do you think is the hurdle for success here. What are you looking to see and in your view what is the best way for investors to compare and contrast, this data to do other HII programs in development.
And then secondly for the translational data that you mentioned.
That we could see for the CDK <unk> inhibitor next year, what could that tell us and what are the steps forward for that program.
We have that data.
Excellent thanks, very much for calling in today, how much I. Appreciate the question. So thats actually in terms of the pipeline question, So I'm going to hand over to Dave how it's actually to walk you through.
Thank you Angie and thanks. Thank you for the Great question.
I think it was.
A common answer too.
So those two questions, but I'll start with HOA and then come on to CDK seven.
<unk>.
So in terms of data flow Nextgen and consistent with the information. We recently provided an F. One is that.
You should expect to see.
Data from the ongoing phase one study next year, so that will give us guidance on the safety and tolerability of that compound as well as a.
Our recommended starting dose for the subsequent phase one b phase III.
In terms of.
How do we think about that program and positioning.
This will be <unk> seven as well.
Critical story here is about patient selection.
Thank you.
A number of data points that I was kind of emerged from competition over the last few years, which I think have highlighted.
Mills and patients to this.
But lacking statistical significance because of our broad kind of approach to the Kansas being chosen.
In terms of how we're approaching this.
We are currently sequencing analyzing.
A significant number of kind of patient samples looking for things like my expression of Genzyme.
Ken.
Sponsor before <unk> production, but also looking for in depth gene signature as a market response.
So that would include things like lung cancer things like renal cell carcinoma.
We and others have been able to kind of demonstrates.
That.
Certain populations of those cancers won't see higher <unk> signature.
Which would indicate.
Likely response, so it sort of how we think about positioning our molecules I think.
The key thing here is that we will not go into a phase one b phase III all comers approach, we've already identified six away cancers.
Exploring in detail a gene signature, which will then prospectively guide the selection of the patients during the dose expansion phase.
Just coming off the CDK seven.
In terms of news flow.
We.
Communicated selected a development candidate.
And in the coming months.
We will initiate formal IND studies looking to open by the end of next year.
Again, it's the very same approach.
In contrast to <unk>, which is obviously, an immuno oncology indication.
With CDK seven Mechanistically, we are looking at two areas.
So this is looking at.
Oncogenic.
Impact of both retinoblastoma protein and also map kinase start then leads into looking at cancer types, which are things like triple negative breast cancer, but also a variant cancer. So again light with the HOA program. We are currently evaluating our compounds and a variety of primary patient tissues.
Looking to understand.
That compound works and just as importantly, where it doesn't so that we can identify subsets of patients and again will be during the course of next year.
When the opportunity arises we will be presenting kind of data from on those ongoing preclinical studies to help us guide patient selection.
Thanks, Rick and hope understood. That's very helpful. Thank you.
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Next question is from the line of Peter Lawson from Barclays. Please go ahead.
Great. Thank you and congratulations on the first quarter and a public company.
Just on the news today I guess from Gilead opting into August this is Dennis.
And just your thoughts on your potential combination therapies that you'd be thinking about it seems that.
There is a broader set of potential combinations for Gilead innocuous just your thoughts about how you would take Dennis.
<unk> is in a combination therapy and kind of when we could start getting <unk>.
Details around that potential data.
Thank you Peter Thank you very much for your time fluids as well about our first quarter.
And thanks for calling in today, yes, really exciting news as being for the field actually with the news of Gilead.
The work, we're doing with <unk> I think it really adds to the Testament now that.
It was actually the whole pathway and sort of being explored here and I think it shows real sort of commitment as well now to that pathway.
These mechanisms. So this is a real sort of boost we think the real field one of the key things that we are thinking about now as you go forward. This is David.
Will it just described I'm asking described again, how actually we can use the advantages of our <unk>.
Patient centric precision medicine platform to really help us potentially understand where not only potentially where the best patient saw but also potentially using that platform as well for asking those questions around sort of combination. So I'm just going to bring Dave <unk> again actually to provide a bit more color about our thinking in this space.
Thank you Andrew.
Thank you Peter for the question.
Yes.
Sorry.
Actually delighted with the Covid.
Yet announce them because.
Think it adds confidence to everyone working in the identity and kind of pathway.
Particularly two of those three lead assets kind of.
Talk to targets within that pathway.
In terms of.
We're going so we tend to explore monotherapy with our molecule.
And but we're also looking at.
Relevant kind of combination so.
Yes checkpoint inhibitors Assembly is certainly one way the best.
Tonnage carried in a number of indications so practically.
We'll have to look at those patients that particularly obviously refractory to those treatments.
<unk>.
Growing unmet need.
And the way, we will do that as I've seen and are doing at the moment is to is to evaluate monotherapy and combinations, both with small and large molecules.
And our precision medicine platform. So looking at do we do we actually deepen the respondents in say a combination with an anti PD one.
And as I mentioned earlier that those.
Those studies are ongoing we're looking to present some of that data when appropriate during the course of next year.
And there was little to kind of show you more details about.
Not only which combinations, we prefer but also which combinations.
May avoid because.
We can demonstrate that there is any benefit.
That particular combination.
Got you. Thank you.
As you saw that phase one b two.
Will you have arms in there for combination therapies and when could we see the first I guess.
Data within cancer patients is that kind of a 'twenty two event.
Does that kind of it's a little it's a little bit later.
But yes.
The current study design.
As multiple arms looking at both monotherapy and combinations.
In a variety of preset Kansas.
The first pilot study, which we hope to start next year will be.
An abbreviated dose escalation.
Because we've actually.
Deep into that.
The execution of a human volunteer studies, that's given that will should allow us to accelerate our dose escalation phase.
But I would expect to see start to see information coming out so its certainly not next year from the trial because it will only start.
Probably in the second half of next year.
Okay perfect. Thank you very much.
Okay, I'm, just going to be bad.
Just one thing I wanted to add on to your earlier question on combinations. I mean, we actually think this is a real strength of our precision medicine platform, because what we're able to do is actually <unk>.
In a laboratory setting real patient samples.
Real tumor microenvironment and test them with our drugs and multiple different combination agents.
And be able to look at the profile of how our drug might interact with those different combination agents in that human base tissue samples. So that is something that we think will differentiate us in the future, but more to come.
It's our ability to almost think about ex vivo clinical trials knowing exactly.
Perfect. Thank you take care.
No.
Thanks Peter.
No further questions at this time and I would like to hand back to Andrew Hopkins for closing comments. Please go ahead.
Thank you very much.
What I hope you'll take away from today's call is how <unk> represents a new way forward.
Im just designing drugs, where design and technology systems to design drugs. Our goal here is to have greater scale beyond the single lead asset, but also with greater precision and our probability of success than ever before.
If successful we believe this could inspire industry transformation and how new medicines are created enabling a new wave of where we can achieve the best possible medicines for as many people as possible.
Thank you all for your time today and see you next quarter.
Thank you everyone.
Ladies and gentlemen, the conference has now concluded and you may disconnect your telephone.
Thank you for joining and have a pleasant day goodbye.
Mhm.
Sure.