Q1 2022 Exscientia PLC Earnings Call
[music].
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 first quarter of 2022.
At this time 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 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.
This time I'd like to introduce Sara Sherman Vice President of Investor Relations Sara Please begin.
Thank you operator, our press release and 6K was issued yesterday after market close of our first quarter 2022 financial results and business update. These documents can be found on our website at www dot investor that extent Joe's Saudi I, along with the presentation for today's webcast before we.
Again, I'd like to remind you that we may make forward looking statements on our call. These neighborhood statements about our projected growth revenue business models and business performance, including with respect to product development preclinical and clinical progress and a precision medicine platform.
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, Nicholas crowds C. P precision medicine, Ben Taylor, CFO and Chief strategy Officer.
And Dave Hallett, Chief Operations Officer will also be available for the Q&A session.
With that I will now turn the call over to Andrew.
Thank you Sarah and thank you to everyone joining us today.
<unk> is off to a strong start in 2022. This is led by several factors.
And that's been our strategic collaboration with Sanofi to develop a pipeline of AI design medicines utilizing the breadth of our end to end platform. Our teams have commenced target identification and validation work and we'll share updates as this progresses.
We are also advancing our pipeline.
Near term milestones anticipated.
<unk> 607, our Colo program with GT, a python, but which we plan to submit a cta by the end of the year. We also expect topline data from our phase one healthy volunteer study for <unk> 21 to five or six in the first half of this year.
What is also critically important to us as a management team and heightened in today's environment is that we've maintained a strong balance sheet and cash runway.
As of March 31, 2022, our cash balance was approximately $720 million with an additional $100 million upfront payment from Sanofi received in April 2022.
Do you have in the first quarter, our operating cash burn was only approximately $10 $4 million.
This gives us confidence in being able to execute on our near term business plan with our current cash on hand.
Lastly.
And the focus of today's call advancement in building out our proprietary precision medicine platform, including new data generated by the platform that we presented at this year's ACR annual meeting.
On our previous quarterly calls, we took you through our balanced business model and our AI drug design technology platform.
Today, we are focusing on deep learning driven precision medicine, and I'm delighted to be joined by Nicolas <unk>, Our vice president of precision medicine.
Maybe first one share more about our work on this front and how we integrate and primary tumor sample data throughout our end to end system in a way not previously possible for drug discovery and development.
As we sold the groundbreaking excellence one clinical trial recently published in the peer reviewed journal cancer discovery.
Our AI driven precision medicine platform allows each patient serves as their own models to understand potential treatment effects. This can yield better treatment recommendations personalized to the individual and.
<unk> this technology demonstrates that the ultimate impact of significantly better clinical outcomes.
In essence, the long sort of personalized medicine.
I think Cynthia we imagine the impact we can make for patients. If we take this approach from the start of discovering a new medicine integrated patient led insights for each stage to yield more precise better informed decision, making and design.
This is what we call patient first AI and has the potential to significantly improve the probability of success of Jordan.
<unk> into new medicines for patients.
The will be presented at this year's AAC, Amit and demonstrates tangible ways in which we've been able to bring this tibet.
Firstly with Exa X 21, 540, <unk>, our clinical stage <unk> antagonists, we show how a deep learning approach to reveal the gnomic endpoints can be integrated with single cell sequencing.
This helps us to move forwards towards identifying the patient signatures on potential biomarkers that will help us understand if a treatment is likely to work best for a specific patient, hence potentially enriching our clinical strategy.
The CDK seven we apply to the platform to assess our novel AI and design development candidate GTA E X 607 through the application of deep learning driven image analysis, we detailed some molecules effects on primary tumor samples from patients at single cell resolution a far more complex dynamics.
But what's possible with patient derived cell lines.
Common in the industry.
And by using models built from our proprietary human tissue Biobank, we were able to reveal ultra related pathway inhibition as a novel mechanism of action in a subset of ovarian cancers. This means that with the use of patient focus models, which have higher translate ability and can better capture patient heterogeneity as opposed to a traditional.
We'll move on models.
We were able to recover clinical relevance at the targeted discovery stage.
A common thread throughout this work is the potential to increase our probability of success within drug discovery and development. If you recall improving probably success. In addition to accelerating the time after the new science into new medicines, and lowering costs underlies our goal to shift the curve and the.
Economic lifecycle of drug development for the industry.
Clearly a relevant and pressing concepts in today's environment and with that let me turn to today's topic and how we are progressing on this mission.
As you can see here drug discovery as we know it today is it linear or sequential process, one that starts with identifying target progression for molecular design and into clinical development in Ms. Many years later potentially with other drugs.
And by the time this drug is commercially available it's being likely been over 10 years of all.
The sequential approach is not on the inefficient the flaws in either design or other factors may not be realized until the drugs in late stage clinical trials.
We believe the structure of its process contributes to the low probability of success, we see across the industry.
Sure.
We believe for the actual drug creation is best described as a patient first learning problem.
What you see here is how we have taken a a conventional sequential way of problem solving.
I think thats, our discovery as aluminum problem or a learning loop, where all aspects are fundamentally connected you may recognize this from a prior presentations, where we talk about our full end to end process last quarter, we touched on AI driven molecular design highlights in our tech for generative design and active loaded today.
We are just focusing on a key aspect of this with a precision medicine platform.
Today, the Biopharma industry uses mostly cell lines and organize that have limited translate <unk> into the clinic.
And we believe this is why the industry faces EMEA, 96% failure rate.
Our goal is to increase the probability of success of new drug candidates by using the closest representation of an actual patient to help and form every stage of drug discovery and development.
What's different about what we do is that we actually take the lives patient tissues, which we believe is a higher clinical relevance given it is close to the actual patient as you can get our England is when to assess from a life patient tissue, which molecules would likely work from that patient. We can then bring that data into how.
We think about selecting targets and use a real patient data driven approach to AI drug discovery.
Using our algorithms and generating high quality relevant data to design the best balanced molecule.
We have taken any insights would be excellent one trial further by placing the patient at the center of OLED loop, where we can then work to enable and inform our platform to potentially identify the best drugs, but given patient across a range of indications. This is driven by our AI platform, which learns from every new piece of data and covers everything that we do from Todd.
Vacation.
The potential for identifying the patients who we believe will most likely respond in a given clinical trial.
We take what was done conventionally in the clinic upstream into drug discovery. So many years before direct historically would be tested in patients. We can measure is potential impact and to reiterate we showed a lot of it at the ACR included in how we can identify novel targets evaluated modulation of a complex.
Microenvironment and work to better understand patient enrichment, who biomarkers.
I'll now turn the call over to Nicholas who will walk us through how we have developed this first of a kind technology validated in the clinic and how we are using this tech to inform drug discovery for projects going forward.
Thank you Andrew and thank you everyone for joining us today, it's my pleasure to walk you through our unique patient tumor tissue based precision medicine platform and show you how we use it to drive patient SaaS drug discovery and development I would also like to detail the evidenced that our concepts are springing to patient into preclinical research may actually support the development of more.
Better design medicines.
Precision medicine, a personalized medicine has been around for a while we talk about precision medicine is given the right drug to the right patient at the right time. Traditionally this is based on dividing patient populations into ever smaller subgroups based on Biomarkers.
Or given the same treatment importantly, this is a concept that is usually not well addressed onto drugs reached a clinic.
By contrast here, we show our approach to precision medicine based on deep understanding of the treatment effect on in the individual patient by patient level.
Further we used the knowledge of how patients would likely respond already the preclinical drug discovery and development stage.
This is possible because we can measure drug response, Directv in life human tumor tissues.
So what evidence do we have that drug response measured in mice human cancer tissues contains clinically relevant information.
As Andrew mentioned, we conducted a study called the gold one where our aim was to determine if it could prioritize effective therapies, but testing activity of drugs and patients very own tumor tissues.
The study was a prospective single arm open label Basket study of 7% to six patients with a variety of aggressive hematologic cancers and is the SaaS ever prospective clinical study demonstrating the clinical impact of AI driven functional precision medicine.
On the left you can see the setup of a function of precision medicine platform. As you can see we take primary tissues from patients such as blood or bone marrow and lymph nodes and expose these tumor tissues to over 100 different drugs, but then use our AI enabled high content single cell image analysis to assess drug effects on these samples.
This process is soft with a turnaround of less than five days.
So this study drugs were ranked by the ability to selectively kill cancer cells are not to kill off target cells based on table, then shared with physicians, who chose individualized therapies for these patients.
And on this slide is one of the SaaS patients. They sold one trial in elderly late stage diffuse large b cell lymphoma patient with no standard of care available.
So what you can see here is a sample of the patient's tumor that was taken expense to library of over 100 drugs to the time in which had the most selective activity against his specific cancer cells into.
In the chart you can see the test the drug's ranked in order of the selective anti cancer activity.
And then integrating a ranked list at the overall state of the patients tumor board selected the second drug from the top patient was treated and able to go into complete remission. Most strikingly stayed in complete remission for two years.
Now when we look at the full study. The results are also clear patients had a significantly longer progression free survival or PFS compared to the prior line of therapy were taken the individualized therapy prioritized by a function of precision medicine platform. By contrast, when patients were treated with drugs that were not prioritize but a platform there's no <unk>.
<unk> and PFS compared to the prior line.
Interestingly pretreatment performance influence outcomes in patients with an <unk> score of less than or equal to one we're more likely to experience improvement in PFS showing the potential for greater benefits to be realized if this platform was to be used earlier in the treatment paradigm.
What this shows is that drug filed to be active in primary tumor tissues have a high chance of being effective in the clinic. We believe supports the use of primary tumor tissues is highly patient relevant model systems and preclinical research.
This means that before drugs that you've been tested on patients we could use a precision medicine platform in drug discovery with the aim of identifying drug candidates that have a higher chance of being effective in the clinic and potentially increasing the probability of success in terms of finding the right drug for the right patient.
Based on these encouraging results, we intend to expand our clinical research efforts into solid tumors and are planning to conduct the observational study to better understand the potential of our platform and prioritizing effective therapies outside hematology.
As we'll talk about later in the presentation, we've already been using solid tumor samples for drug discovery efforts.
It's important to note that as some precedence for using this platform in solid tumors as there has been some exploratory work from professor Barron's Tonight at Eth Zurich small observational study of 14 patients with Glioblastoma showed statistically significantly longer PFS and overall survival OS for patients, whose tumor tissues with Tim was although might sensitive.
As compared to insensitive ones.
An observational study we plan to take patient tissue and send these to our lapse analysis separately the physician chooses a therapy.
He referred to a physician's choice without using the assay.
At the same drug has been tested for activity in the patient's life tissues, and we compare the response to the clinic the ex vivo drug response.
Our hypothesis is that drug responds measured in patient's tumor tissues will correlate with clinical outcome.
We look forward to updating you on our progress for this exciting study.
I'll now spend some time focusing on what makes a patient SaaS AI screening technology truly unique in how we screen for drug effects. The highly scalable way primary tumor tissues, using microscopy and quantifying siblings that outcomes, such as cell death and cell morphology, using AI powered image analysis.
Approach is used in the past, but many of our peers today, a focus on measuring global cell death and bulk tissues.
Human tissues, however, Randy comprise only of cancer cells. They contain a plethora of immune and stromal cells. This means that you can't distinguish between the toxic drugs that make her lung cancer cells one.
<unk> and selectively kills only cancer cells.
Relying on measurement of bulk tissue death may in fact prioritize molecules that are not effective against cancer cells at all.
As a consequence these approaches often not translatable in the clinic with <unk> provides patient benefits.
Whats needed is a drug that has a balanced must be potent but also selective in other words. It must have a good therapeutic window developing balance drugs. As you may remember from previous presentations is an important focus of exane, Sir through all aspects of discovery design and development to be able to distinguish between broader toxic and selected.
Active anti cancer drugs require a single cell resolution, which is what we do in our platform. It is this single cell resolution that has allowed us to be clinically translatable deliver patient benefit and differentiation in the industry.
So how do we achieve this as I alluded to tumors are inherently complex that made up of various tumor cells and immune cells thats growing three D. Our automated microscopy tools allow us to analyze drug responses at least complex specimens with unprecedented speed in single cell resolution just key as outlined on the previous slide.
Fortunately, we can do this without fully dissociating of tissue into individual cells, thus maintaining vital parts of the tumor microenvironment and cell viability.
In brief we take tissues minimally processed them to make them imager bull exposed to drugs for no more than four days, which avoids culture adaptation and colonial selection bias fix and stained fluorescently labeled dies that'll allow us to visualize the effects of interest for example, selective killing of cancer cells.
We'll then take high resolution images, which generate many gigabytes worth of data of patients and then process them using computation of image analysis.
My computation of image analysis has existed for decades, our use of AI in our proprietary tech stack makes it possible to process a complex images that we are generating accurately and at scale across many different tumor indications.
Here you can see an example, three D microscopic image of a solid tumor sample as an animated stack comprise.
Comprises many cell types growing very closer together, which makes it difficult to analyze using conventional image analysis technology to.
To give just a few examples to overcome these analytical challenge we have built highly specialized deep learning models to find nuclei cytoplasm measure staining intensity classify life is that cancer vessels immune cells.
With these models, we can analyze microscopic imaging data foster more reproducible and accurate than humans could ever do this is yet another.
Example, where AI helps us to work better in phosphate extends here.
Our proprietary platform specialized with tools highly optimized for given tosco problem.
Having us to not only provide more accurate outputs compared to what general purpose third party software systems are able to do but also to be faster and work with more precision.
The aforementioned readouts allow us to count and characterize cells based on well established features such as cell surface marker expression. This is sufficient for many translational questions about other applications, we need to and can extract much more information from our images.
And our data sets, we see sales of many different mythologies sizes and shapes, we believe differences in sell appearance and code important information about drug response going beyond traditional cell death activation readouts that morphological changes in Lasalle actually relate to functional changes.
In order to quantify sell appearance, we have developed deep learning networks to generate a mathematical representation of what to say it looks like we speak embedding the visual appearance in the feature vector.
<unk> has many more than two dimensions, but for simplicity, we can map them to duty space.
We talked on the map as a set of unique appearance closer to all together the more similar they look.
Using this feature appearance map, we can for example quantify wholesales change in appearance as we treat them with drugs.
On the right hand side, you can see a plot of how different is that looks like a decreasing concentrations of drug compact with no drug treatment.
We believe these advanced technical capabilities will be essential when looking at drugs that elicit more self casino types that just killing cancer cells.
Importantly, we are developing patient derive model systems across different tumor tissues of tumor indications, including blood and bone marrow for leukemia lymph nodes for lymphoma, and malignant pleural effusion scientists as well as solid samples for solid tumors.
These patient centric models are used across the drug discovery pipeline is extensive.
As I've outlined we can use these models to measure therapeutic effect not only on tumor cells directly but also on immune cells. So not just cell death, but it was the impact of the tumor microenvironment.
Going back to the loop Andrew described I'd now like to highlight a few examples of how we use the platform across our value chain to put the patient at the center of all applications ranging from pockets elucidation to biomarker discovery.
So here, we have an illustration of how we can use small molecule compounds of drugs as tools to identify biology of disease relevance we.
We use knowledge about the target spectrum and the fact that small molecules typically have multiple targets to our advantage. So for example, supposedly have observed that molecule <unk> hits targets wanted to molecule be hits targets, two and three molecule fee hits, Todd three and four.
Further we see that molecule A&P, but not see you have an effect on the patient tissues, what does that show us based on this observation, we can assume that target too will be relevant to explore further this is just a concept.
If you go to the next slide we show you how we applied this with actual data of course Theres a more complicated now, but the same basic principle applies.
On the left is a panel of over.
H drugs assayed, and seven point concentrations and triplet kits. The heat map shows how strongly and selectively these different drugs kill cancer cells in primary tissue samples of 20 patients diagnosed with ovarian cancer.
Socket of color the greater the drug effect.
Looking at the data in more detail, we found a cluster of tyrosine kinase inhibitors that we're active across the diverse range of patients.
He then observed is that these molecules SaaS some concern targets out in IGF, one R as well as some downstream once.
This led to the hypothesis that this pathway space, maybe therapeutically interesting.
The process of evaluating this hypothesis was complementary omics approaches.
We can also use our platform for selecting candidate molecules and indications the left we show what it looks like if a certain molecule works in certain disease and Hasnt effect.
Cancer cells are selective depleted over immune cells. This would be a molecule to consider studying further tested indication.
On the right, there's no effect on cancer and immune cell viability and no responses have been depicted for these compounds. So this would not be a molecule to study further tested indication.
Okay.
Based on this approach, we can determine which molecules actually have activity in SaaS medications in the preclinical setting and models that are much closer to the actual patient on cell lines at which may make sense to develop further in the clinic.
We'll now turn the focus to Biomarkers and how we believe our platform can help us develop better signatures and biomarkers that are clinically relevant for patients.
So here, we have an illustration of how we use our ability to measure drug response and primary tissue samples in conjunction with Omics analysis to identify biomarkers that.
At our precision Medicine Center of excellence in Vienna, We are currently building out all makes capabilities for this purpose even further.
In this example, we can see the patients who respond to sudden drug enriched for <unk> mutation gene. One we look at gene too. There is no such expectation typically the association between real World genetics of drug responses really starting to be evaluated in clinical trials with our approach. We can do this much earlier ideally going into the clinic already with the hypothesis for patient.
Richmond biomarker in mind.
And this is what it looks like in practice here, we can see patients treated with our CDK <unk> inhibitor GTA E 617, a stratified clearly into two response groups distinguished but dose response costs starting to falloff Ali does this plan.
Plateau effect.
The next step will now work towards correlating these responses to genetics to identify signatures that potentially allow us to enrich patients with higher chance of response in future clinical studies.
In the previous example looked at a drug directly targeted cancer cells. However, working in primary human tumor samples that contained not only tumor but also immune cells allows us to look at immune modulation as well. This is an important advantage of our approach of a conventional approaches using tumor organoid, which students contain immune cells. This is a key differentiator of our.
Platform.
Left hand side, you can see how we apply this approach to our <unk> antagonist currently in clinical development. When we treat patients samples within <unk> receptor agonist, we see that a key receptor involved in cellular activity unknown Association with adenosine sensing CD 71 is down regulated.
Treated without <unk> antagonist, we see the reversal of this phenomenon that CD 71 expression goes up again.
71 here with experimented as an exit from a number of receptors based on its robust association with pharmacological modulation of <unk> signaling.
We are now investigating correlating dysfunctional readout with genetic transcriptome make data to understand differences between patients and their ability to respond to <unk> inhibition.
Based on this we intend to refine existing signatures of adenosine mediated immune suppression for the selection of patients in a future studies of Exs 21546 that have a higher likelihood of responding to <unk> inhibition.
With that I'll turn it back to Andrew.
Yeah.
Thanks Nicholas.
So what does this all mean.
We believe our precision medicine patient enrichment capabilities combined with modeling for drug development and real time, Ronan will lead to a more efficient clinical development strategy by selecting the patients who are most likely to respond.
When a drug reaches the clinic, we have used actual patient data to inform nearly every stage of positive edification drug discovery research and development in an effort to ensure medicines reach a clinic or potentially more effective.
The integration of our AI platform with novel clinical strategies will be led by our new Chief Quantitative Medicine officer.
Mike crops, Dr. Cramps joins us with 25 years experience in <unk>.
Til clinical trial design at J&J and adviser.
With this our goal is to extend our learning approach.
To drug discovery into the clinic.
Ultimately by creating a learning loop system made up of breakthrough technologies in science, we aspire to a day when the therapies that work best for each of US are available to all of us.
And with that we'll now open up the call to question and answers.
At this time I would like to remind everyone in order to ask a question press star followed by the number one on your telephone keypad.
Your first question comes from the line of Chris Shea with Johnny with Goldman Sachs. Your line is open.
Yeah.
Thank you and good morning.
Appreciate the background and the additional update.
The inner workings of your precision based approaches and congratulations on bringing aboard a chief Medical officer can you talk to us about how youre thinking about.
Structuring, perhaps more specifically to your lead program. These early clinical studies.
In terms of <unk>.
Comment in general about having quicker faster cheaper better kinds of responses more targeted but maybe specifically for the programs that are on this cost of entering the clinic, maybe a little bit more information will be helpful. About how youre thinking about designing those studies. Thank you.
Hi, Chris.
Wonderful to speak to you again and thanks for joining us today are.
Great question actually it's key it's at the heart of what <unk> is building with our end to end platform here, it's about how we actually bring in the patient tissue models into every aspect of what we do target identification work use these models as part of our drug design process, particularly select in our late stage drug candidates.
And then moving that into preclinical studies for biomarker selection of patient stratification.
Due in vessel of IND, enabling stage. So that we are ready to start our phase ones wave actually a patient enrichment strategy right from the beginning we hope it to do that actually with the work we do non CDK seven Nicolas talked about so not just now I'm going to ask Dave highlighted in a few minutes to elaborate a bit more as well.
The key thing about it now is that as I went to a platform is coming together is that our programs are that we now driving forward, particularly a precision oncology are actually now so native to our platform. So we do see that a lot of our new <unk>.
Oncology programs coming forward actually this is a strategy that we're following to bring this forward.
I, just what I mentioned that before I open it up to Dave is the use of its precision medicine platform.
And bringing the patient into target selection.
<unk> patients selection is actually a key part of work we're doing to Sanofi, It's actually one of the key reasons I think why that.
It's such an ambitious collaboration they are choosing to use our whole platform.
But what I wanted to do in our Chris is open the floor to David Paletot CFO just to give you a bit more color on how his team is really use of the patient based assays <unk> late stage target selection and clinical candidate selection processes.
Thank you Andrew.
I think I'll stop by.
As a concept.
That we discuss internally, which is the clinical studies should be confirmatory and not exploratory.
Well actually means is that you need to generate.
Kind of politically relevant data, so using a patient tissue platform.
Kind of years ahead of us before we even think about going into a clinical study.
The way that I see then looks in practice. If you think it was it was.
Manifest within Nicholas's presentation, So and also our recent posters ACI, when we talked about <unk> and CDK seven.
This is about kind of at an ex vivo preclinical setting is identifying.
Yeah.
Both are relevant and clinically practical kind of.
Other biomarkers these COVID-19 gene signatures that.
Derived from testing of all of our molecules and combinations of molecules on relevant cancer samples. So that when we actually go into our clinical studies that we can actually prospectively select those patients that are likely to respond to our drug rather than actually doing a clinical study and then retrospectively analyzing who all who didn't respond.
And Chris This is Ben just one thing to add on there certainly will also be the quantitative medicine portion to that so how are we able to use innovative clinical trials structures innovative statistical models.
Both to drive home that patient selection point, but also make the trial more efficient.
Mike has been doing that for years he ran a.
A large division at J&J that I was doing that for all of J&J portfolio.
So just brings a wealth of experience and in that aspect as well.
Great. Thanks for the thorough answer I'll get back in the queue.
Your next question is from the line of Peter Lawson with Barclays. Your line is open.
Great. Thank you so much just one clarifying question initiatives just around the R&D for the CDK <unk> inhibitor. When is that expected is that year end along with the Cta filing.
Hi.
Good to speak to you today.
Looking to open a cta.
CDK seven with an IMD later in 2020 free and actually with full structure of that and how we're thinking of the early stage plans into humans I'm going to ask.
Dave actually to give you a bit more detail on that.
Hi, Peter.
So you're obviously correct is that.
For a reason so for practical reasons.
As much as anything else.
Our current strength in clinical operations as in Europe .
Our intention is to open a global multinational trial for CDK, seven, but we will actually start off.
With the dose escalation phase in Europe under a Cta and as Andre points, so still on track to be.
Josh can be executed in the second half of this year.
And then simultaneously that we would look to then open in India and the United States in 2003, so actually allowed us to access U S clinical sites for the cohort expansion, so that kind of practical outlines of.
Of how we think about running the study pizza.
Got you.
The reason for the <unk> in 'twenty three is more of a <unk>.
Nicole component on your side is not because of.
More stringent requirements from the U S.
No it's purely practical law.
The actual the actual preclinical work that we're doing around toxicity etcetera is identical so the actual the packages of the savings is really around.
But our strength in that visit the moment with our clinical kind of our clinical team.
Got it. Thank you and then on the the primary tensor tissue platform.
For the ability to kind of select approved drugs is that something you think about that you would monetize for approved drugs or this is kind of an internal tool that's key to select and your own drugs.
It's around that.
Sure.
It's a great question and the XO one clinic.
Clinical trial data.
Incredibly excited by shows the real potential of this technology.
Our primary focus right now is to use this technology to design and develop better drugs.
That's why we are expanding in a range of indications as Nicholas showed in this trial about it means expanding our links networks through collections as we expand the availability of both tissue samples as we build out our biobanks.
Looking to see how a clinician networks expand for central and Eastern Europe into Asia, and hopefully later say, even announced an expansion into the U S. As well so that's the <unk>.
That's a key component to it as well build out those relationships with the clinicians as well, but right now our focus really is to use this technology to have.
To design better drugs.
Applied was not just for our platform by potential partners as well as we work away such as Sanofi, who we mentioned, but we absolutely keep idle.
Open mind as we see this developing and as we think about sort of way.
We're potentially these concepts of digital therapies of diagnostics could go it is certainly something that's on our mind to thinking about.
We're holistic strategy fix it but I just want to reassure you right now our primary focus today is to use technology to develop better drugs.
Thank you and just on the.
Phase one data for the adenosine inhibitor.
We plan on presenting that is that would we get press release or is that going to be preserved for medical meeting just your ideas about.
Venue and how you release the data for the identity.
Thanks Peter.
So in today's or thoughts on how we want to communicate the HOA results sure.
No.
Peter as you know that will come out before the end of this current quarter.
And I would expect.
Normal top line readout.
During that time period, where we will give.
Some indication along the lines of what we've talked about before so PK PD and safety.
And so we'll.
We will give you some results on it this quarter.
And we may save some of the more detailed readout for a medical meeting to be presented later as would be normal that you will get some data.
Perfect. Okay. Thank you so much thanks for taking the question.
Thanks Peter.
Your next question is from the line of Vikram Pearl hit with Morgan Stanley .
Your line is open.
Yeah.
Great. Good morning, Thanks for taking my questions.
I had one on the precision medicine platform going back to the data you've highlighted for exalt one could.
Could you remind us which solid tumor types of the platform is currently able to work with than.
What are some of the capabilities you need to build out to be able to accommodate an even broader set of tumor types.
Thank you very much for this question Nicholas Carl here.
So the exalt, one study was run and Hematological cancers leukemia.
But also lymphoma, so solid tumors that are <unk>.
Hematological.
Background.
We are currently.
<unk> the platform in a range of.
Solid tumor indications, including ovarian cancer lung cancer, we highlighted this on slide 21 of our.
Presentation, and really we follow a very structured approach here of how we go about this coming really from assay development.
As a SaaS kind of milestone, making sure that we can measure dose dependent direct responses, then really gathering biological data understanding whether mechanistically, what we're seeing in these primary tissues. It makes sense all the way to going into clinical testing and really building on our kind of philosophy as a learning company. The more we do this.
The more technology, we can reuse for example of image analysis capabilities. We are now increasingly seeing that they can really be transferred from one tumor type to the next the more economy of scale, we're going to pick up and really move. This forward. So it's really it's really a question of timing the lining wheel and continue.
And our path that we have really fast chartered out with exalt one.
Okay.
Yeah.
Okay understood. Thank you and then a follow up question.
To an earlier one on 607, so once that study desk startup.
Dose escalation and into the dose expansion portions.
Could you just give us some color on what what the study could look like.
Some of the initial indications of focus could be in.
What some of your initial thoughts are about the number of sites enrolled the number of patients to enroll et cetera.
Sure.
So I think in terms of I'll give you a high level view of the.
So there is going to look like we still obviously flushing through the precise details. So I won't be able to give you precise studied numbers and kind of patients here.
What do you think about this is.
First of all stop start and the level of the mechanism.
So reminding ourselves what CDK seven inefficient dose within a cellular context and so from a.
Mystic perspective, this is also where the.
Biomarker work is being done at the moment is that we will be looking for signatures.
So all hygienic alterations in either the retinoblastoma.
Blastoma or the map kinase pathway.
That's where the mechanism that we will be looking.
If you then overlay that with well, okay, well, how does that tie into the solid tumor indications is that.
The solid tumors are likely to respond to CDK <unk> inhibitor or aligned as a biomarker strategy of things like lung and breast and ovarian.
So what we'll be doing over the over the course of the second half EBITDA, who will provide you with a lot more detail about about the study design and also.
Yes, the kind of the <unk>.
Questions you just asked which is kind of precise patient numbers study centers etcetera, but hopefully that gives you a flavor of our direction of travel.
The other important component to layer on to that is there will be a lot of prospective biomarker evaluation.
From the initiation of the first study.
This will be something that's built into it we wont be looking at patient populations afterwards and trying to do.
AD hoc retrospective analysis. So these are going to be a biomarker informed from the start and well also be using our <unk>.
<unk> platform from the start.
Understood very helpful. Thank you.
Your next question is from the line of Michael <unk> with Bank of America. Your line is open.
Great.
Sure.
Hello.
Alright.
Of course on the Sanofi collaboration is focused on early stage indicators is all inclusive.
Jason target validation.
Any early update on how that's going any expectations for further updates from that how should we think about catalysts.
Coming out of that at some of those things move through and then waited well my second question would be on Opex burn.
Both as it relates to some of the collaboration but also for your internal programs, you've got phase one readouts in Cta submissions coming up how should we think about the ramp spend given your balance sheet.
And related to that.
On Europe .
Expectations for the balance sheet quite out of them.
Thanks.
Thanks, Mike and good to speak to you.
Two great questions actually and also both by relevance in today's environment.
The first one on the Sanofi collaboration.
As you absolutely pointed out it's.
We're really excited about this collaboration because it really uses the full end to end nature of our platform and in fact, we currently.
<unk>, our central apologist deep learning knowledge graph technology to really prioritize.
Drug targets, a wealth of information so far on a range of oncology and dermatological indications.
Sanofi and in fact been taken a <unk> event into our new target validation platform that we built it out as well.
So we already been prioritize had several projects of the 15, which we've signed up for to move forward and give me a little bit more color what that I'm going to ask Dave highlights actually to two.
To take the question further.
Andrew.
So thank you for the question.
Right. So we're really excited about this collaboration.
And the audience that it was only signed and announced.
<unk> of 22.
There are only a few months into the <unk>.
We're already we've made significant progress in identifying and starting to validate targets to kind of come in to that.
Operations so.
At this point in time, we're not actively designing novel molecules per se.
Working collaboratively with our colleagues at Sanofi to really understand.
Essentially creating a sandbox if you like in terms of.
Im doing experimental validation leveraging.
Particularly leverage our capabilities in Vienna, two ads at further credentials, if you'd like to the to those targets.
I look forward to updating everybody on this call over the coming months and years as we as we transition projects out of the exploratory phase into into active design. Thanks, Dave I don't know.
Question of Opex.
It really is a one it's very close to the management as half actually it's it always has been from the day, we actually founded a company to think very carefully.
About how we balance the ambitions, we have in our company with operating a solid business as well and I think actually that's why you see such a strong focus on business development inside extensive as well as well as new technology development and to give me more detail on that actually I want to open the floor to Ben Taylor CFO .
Hey, Mike it's great to be catching up so couple of thoughts on the numbers first of all I don't want to I don't want.
Kash this is sanofi on its own because remember we did enter into partnerships with BMS and <unk> and others last year that are actually going at full bore right now and do you have.
A number of those programs in the design phase right now so.
We actually did do a lot of scaling already.
And even with that our cash flow from operations. This quarter was negative $10 4 million, that's actually a slight decline over our cash burn from operations last year.
But remember the right way to look at it is over longer periods of time so.
Looking at cash flows from our partnerships as a total this year, we've had about $116 4 million coming in from different partnerships.
Free cash hit the balance sheet in the second quarter, rather than the first so you won't see that in the financials.
But we've continued to bring in a lot of cash from those partnerships.
Balancing off our our operational burn so.
We'll continue to give guidance that our operating burn will increase year over year. So last year, we had about $9 million in that operating burn will continue to see that.
Increase from this year, but we are we are absolutely trying to be.
Very pragmatic.
And sensitive, especially in this environment on how we spend money, we want to drive forward our business model, but we don't want to do it in a way where we ever feel like we have to make business decisions based on making it to the next financing.
Your next question comes from the line of Christopher <unk> with Goldman Sachs. Your line is open.
Hi, Thanks for taking the question this morning.
As you talk about.
Innovative and efficient trial designs, one thing that might come to mind as early surrogate endpoints that could give you. The early signal of efficacy. When you look through <unk> abstracts. This year Youll see that Theres, a lot of interest and circulating tumor DNA.
Is that something that you think about looking at in these early trials or are there other surrogate endpoints that you might be able to maybe leverage your <unk>.
Preclinical platforms to identify early signals of efficacy and then I have one more for Nicolas after that.
Okay. Thank.
Thank you C J great to speak to you again.
One of the key things.
Happened this year in the first quarter was actually bringing by comparable we are incredibly excited to have.
Some of the mics.
Stature as an innovator in the field, it's actually he won and develop one of the very first adaptive clinical trials when he was at Haifa.
When he joined US he was running at a 500 personal organization that J&J quantitative science, so to bring that kind of intellectual firepower now to extend to you.
I would us to really think about how to be as innovative trial design as we have been a drug design moving forward is.
Really exciting new chapter of how the company is developing exciting.
Exciting thing as well about the platform build at Vienna, as a precision medicine platform grows as Nicholas hinted in his slides the range of technologies that way now develop it goes far beyond high content for Alex We're already building out our genomics capability looking at single cell sequencing and looking at transcript old mix as well.
So we certainly will be looking at a wide range of potential genetic and other circulated markets.
Which when would hopefully allow us to position our drug for the best patients possible, it's far more than just high content imager that we know work at all but to give you a bit of a flavor about how our platform is developing into a more of a multi omics platform that's going to have this over to Nicholas <unk>.
Thanks very much for this question and I also followed these presentations with a lot of interest.
<unk>.
So certainly in modern trial designs.
This is well understood you need biomarkers, both for selecting patients to.
To increase the appropriate if success, but also to understand is the earliest possible who will respond and.
Who not so these are all things that we are exploring.
The way how the Vienna platform can really over how we are using it at the moment is as we have.
Shown that the ACR poster.
For example for biomarker selection for biomarker discovery patient selection, where really we are trying to understand in the close to patient setting.
Patient tissues respond to drugs.
In a close to patient setting and then analyze all the genomics of these patient settings, I understanding a clinically relevant genomic breath.
And kind of setup correlating these together to really get.
That gets.
Biomarker hypotheses for patient selection that we can then later studied in the clinic rather than doing this once we are in the clinic, we can actually already go into the clinic with hypothesis in mind.
And one thing that I'd add T J.
Is just whenever you think about ctc's or Cte DNA or radio mix or a lot of the.
Alternate or alternative or more sophisticated ways of trying to measure response.
They are providing really important data and potentially elucidating mechanisms. However, we also need to think about how is the drug to get approved and how is it going to move through.
And so we are a data driven company, we are focused on making the right clinical decisions as early as possible and really.
That goes into if Mike was here. He would have just said that all of the quantitative medicine that we're doing.
But you also have to expect that.
These trials will be structured to drive towards a normal regulatory approval and.
And right now the guidelines for that are pretty clear.
Okay.
Thanks, Dan.
Exactly where I was going next with the question is do you.
Preclinical platforms seem like they are an excellent way to get a good sense of sort of the very precise immediate impacts of the drug on the tumor biology.
As you are thinking about.
Transit into the clinic, how are you leveraging these platforms to kind of get a sense of either resistance challenges through adaptation or sort of acquired mutations or.
Designing for durability and response as you said the approval endpoints are going to be a much longer term than you might see it with just our initial CTO DNA decrease.
<unk>.
So one of the key ways, we think about race and it is an important to think about how one response.
One of the interesting things actually with some of the studies Nicholas's team has been undertaken is actually thinking about and looking at actually how.
We can observe how a patient response actually changes.
With therapy. Good example would be we've been looking an example of how cisplatin sort of resistance has been occurring exempting some ovarian patients and it allows US then to really understand actually that once a day.
<unk> is given to a patient it actually that does change the actual tuna.
And then actually some of these longitudinal studies and allow us to understand that and Steve analytic drug is given later actually has a different effect and actually just going to open the floor here, Nick let's maybe just tell us a bit more about some of the longitudinal studies, we're looking at with the platform as well.
I guess, what's what.
We can do here by having the access to the primary tissue is really stimulate some of these approaches that you have described ex vivo again at ACR. There was a lot of talk about emerging tumor targets adaptive tumor response, which I find very exciting.
And.
Really what is being done that is to test to analyze how tumors respond by sequential biopsies across clinical studies. These are things that one can incorporate into clinical studies, but as we are already sourcing tissues of many different points in the patients treatment. We can also.
Do some of these functional analyses ex vivo prior to going into a clinical trial. So I think what you're describing here really very nicely integrates with the way how we think about leveraging primary tissues to advantage to really understand tumor biology, and a patient rather than setting as early as possible in the <unk>.
And development.
Yes.
And at the end of its a C. J, we consider ourselves alluded organization. So as we see these more innovative ways about how we can use the platform. The platform actually has a wide variety of ways. We can exploit it everything formal Peter was talking about with the possibility how you could use it.
To help patients directly to a possibility of matters Nicholas is talking about how we can then think about the use of this actually to understand more longitudinal responses as well and Thats why for US now the benefits of the iPhone systems, you patient focused systems really allow us to build within the company. These rapidly loops, that's what drives actually innovation.
I think we've seen in <unk>.
The more we do the more we can learn.
Great. Thank you.
Okay.
There are no further questions at this time I will now turn the call back over to Mr. Andrew Hopkins.
Thank you Brent I will make a few final remarks in closing.
As a company we continue to use data reformed approach to carefully directing our resources towards areas of we believe we have our strongest probability of success.
These include continuing to build our differentiated AI powered precision medicine platform and expand into solid tumors with high unmet medical need.
And we are focusing our pipeline on areas, we have a platform and I alluded systems are powered to make the most difference for patients. This involves leverage on a full end to end system and strong patient based model systems in areas such as oncology.
Thank you all today for joining us and we look forward to sharing our continued progress with you throughout the year. Brent you may now disconnect.
Ladies and gentlemen, thank you for your participation. This concludes today's conference call.
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