Absci Presents at J.P. Morgan 40th Annual Healthcare Conference 2022

Absci founder & CEO Sean McClain presented at the J.P. Morgan 40th Annual Healthcare Conference 2022 and discussed how Absci is harnessing deep learning AI and synthetic biology to expand the therapeutic potential of proteins. Watch a recording of this presentation above and read the transcript below.

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Presentation Transcript

Casey Woodring:
Hi, my name is Casey Woodring from the Life Science Tools Equity Research team here at J.P. Morgan. Welcome to our Annual Healthcare Conference. I’m pleased to introduce our next company here, Absci. Just a quick reminder, before I hand it off to the management team here, if you’d like to ask a question during the Q&A portion of the presentation, there’s an option to do so on the conference website. So with that, I’ll turn it over to Sean. Thanks.

Sean McClain:
Awesome. Casey, thanks so much for the introduction.

Casey Woodring:
Yeah, no problem. I’ll let you get to it.

Sean McClain:
Awesome. Thanks so much. So here at Absci, we are merging AI and synthetic biology to translate ideas into drugs and to get better protein-based drugs to patients at really truly unprecedented speeds. I will be making four looking throughout this presentation. So as a lot of you know, one of the biggest breakthroughs this last year in biotech came from AI. AlphaFold, being able to predict protein structure from the amino acid sequence. And this is really where the industry is headed, merging AI and biology together to transform patients lives. And in our case, we’re leveraging really exciting proprietary wet lab data here at Absci, along with cutting-edge AI to ultimately get better protein-based therapies to patients.

Sean McClain:
So why is AI important for protein-based drug discovery? Well, if you look at an antibody sequence, there are more sequence variance in an antibody, then there are atoms in the universe. And only a subset of these sequences are actually biologically viable. And the current high throughput wet labs screening techniques that are available today, only screen a very small portion of the possible universe. It’s like a drop in the ocean. And not only that, many of them are not suitable for drug candidates, so you’re not actually hitting the sequence window that is necessary. And this is seen by the success rate from going from idea to approved drug product of about 4%. But what we’re doing here at Absci is leveraging our proprietary wet lab data along with our cutting-edge AI to enable Absci to explore more of the right sequences, ultimately getting better drugs to patients at truly unprecedented speeds.

Sean McClain:
So how do we do all this? Well, we do this through our integrated drug creation platform. We are a data-centric company, and our data engine all starts with our synbio Platform, which I know a lot of you are familiar with. So this starts off with our SoluPro^® E. coli strain capable of producing large complex mammalian proteins. And then that is paired with our proprietary high throughput screening technology that allows us in a single experiment to screen billions of different drug candidates or protein-based drug candidates, looking at their functionality and manufacture ability, all in a single experiment within a few hours. Now, this is compared to screening in the tens of thousands with within a couple of weeks. And so the throughput that we have, the quantity as well as the quality of data, allows us to leverage AI, to allow us to design and predict better protein-based biologics, but it all starts with the data.

Sean McClain:
The data is key, and we have the technology that allows us to generate that data. The reason why Google is never going to be displaced is because they have that pipeline of data. And that’s exactly what our synbio platform allows us to do. Now, our second data engine is our Tuscan target engine. Now, if you look at the approved biologics today, a majority of them are going after the same 12 targets and in order to solve and be able to help prevent these diseases or cure these diseases from happening, we don’t really have to look at the drug candidate itself, but we also have to look at the target and we have a technology here that allows us to take patient samples, extract out the antibody sequences, reconstruct those, and then screen them in the lab against a whole proteom and find the antigen that pairs with the antibody, allowing us to discover brand new targets.

Sean McClain:
And we’re feeding in all of this data into our AI. We’re showing our AI what a human antibody should look like. We’re also throwing in all the protein interaction data as well for what antibody binds to what target. And so this is our second source of our data. So we have our synbio platform, and then our Tuscan target engine. Now, what is this allowing us to do? This is allowing us to see our big vision through, really the holy grail. Being able to go from patient sample, predict the target, the protein-based therapy, as well as the associated cell line, all fully in silico. And this is all because we are able to generate the data necessary to feed our cutting-edge AI models.

Sean McClain:
Now, we’re not there yet, but we are creating the bleeding edge. And I’d like to walk you through three case studies to show you how this is real and how we are on the way to going fully in silico. The first example that I’ll be talking to you about is deep contextual language models that generate antibodies with specific target affinity. The second one is on manufacturability. AI functional embedding to identify novel chaperones that increase tighter and quality of a protein biologic. And the third one is our non-standard amino acid technology that allows us to continue to out evolve nature and design, better drug candidates for patients. So the first case study Absci’s AI-enabled drug discovery platform.

Sean McClain:
As I mentioned previously, we are creating the bleeding edge and we have just had a huge breakthrough here at Absci with AI for protein-based drug discovery. And what we’ve done here is we have figured out a way using our AI to be able to predict affinities that have high correlation with values measured in the wet lab. And so the previous state of the art was being able to take a trastuzumab sequence and be able to predict whether it would bind to the HER2 target. Now, this was binary, did it bind or did it not? And what we’ve done here at Absci is be able to take a trastuzumab sequence and be able to predict its affinity with very high accuracy and correlation. And you can see this from the plot on the right-hand side, that AI predicted versus the actual, and we have very high confidence and accuracy and this is the first time that this has ever been done.

Sean McClain:
Again, this is showing you that we are indeed on the bleeding edge and we are on the quest to be the first to go fully in silico the second case study is on manufacturability. So one of the dirty secrets in pharma is that you discover really exciting novel biologics, but a lot of times they can’t get to the clinic because you can’t manufacture them. So how can we leverage AI to help us enable cell line development? So not only discover the best drug candidate, but ensure that you can manufacture it. One of the exciting studies here is where Absci leveraged our Denovium AI engine to discover a novel chaperone that increased our antibody fab yield by two X. And for those of you that don’t know what a chaperone is, a chaperone is a protein that helps make and fold another protein. And in this case, our target molecule.

Sean McClain:
And if you take this chaperone that our AI predicted and you blast it in a public database, you end up finding out that this is a protein of unknown function. Our AI was actually able to predict that this was indeed a chaperone and not only a chaperone, but it increased our overall yields by two X. And this is what I’m really excited about with AI, it’s exponentially increasing our understanding of biology as seen from this case study. Now, the third case study is on bionic proteins and bionic SoluPro^® incorporating in non-standard amino acids into proteins to allow our partners to really out all nature and design the best biologics to meet their needs. So protein or non-standard amino acid technology has been around for quite some time. And one of the issues that companies have run into is being able to commercially scale this.

Sean McClain:
We’ve figured out a way to do that and so now that it is commercially viable, what are applications that can be used? Well, first off you can incorporate in a non-standard amino acid into a particular site of a protein, and then attach an ADC warhead, or maybe you want to attach a peg to increase the overall half-life. So the data here really illustrates how we have been able to make non-standard amino acid incorporation into proteins a reality, and commercially viable. We’ve shown when we incorporate in our non-standard amino acid into trastuzumab, we can produce it at right around a gram per liter and we have very low misincorporation rates, less than 1%. And again, this is allowing our partners to be able to attach different modalities to proteins to have them achieve the biology that they want or out evolve nature.

Sean McClain:
So I’ve laid out how we are on the bleeding edge or creating the bleeding edge and we are on the quest to go fully in silico. So how do we monetize this? Well, we are not a traditional biotech company developing our own drug assets. We’re also not a service company or a fee-for-service, we are a technology company that is sharing in the upside with the drug candidates and cell lines that we discover. And so with a single program that we work on with a partner, we receive upfront payments for the work and they pay us for the work, and then we tech transfer, we get an exercise license fee, and then we get milestones as the drug progresses through the clinic, and then ultimately royalties on net product sales. And the milestones and royalties is really where the long-term value lies.

Sean McClain:
And we like to look at this on an MPV basis. So how many programs are we doing each year and what is the MPV associated with that? And so the MPV that we normally get for cell line development and drug discovery ranges between 10 and 20 million, and this is fully risk-adjusted. So for discovery, we assume a 4% success rate throughout the clinic and with cell line development it’s 8%. And with the other assumptions seen here, cell line development ranges anywhere from 10 to 15 million MPV, and the drug discovery ranges between 15 and $20 million MPV. And so you can see that if let’s say we did 10 programs this next year, we would be generating a hundred to 200 million of really lifetime value. And that’s really where we want investors to be focused in on is growing the number of programs and what is the MPV and the long-term value of each of those programs that we are working on.

Sean McClain:
So we do value-based pricing here at Absci. So the more value we bring the higher, the higher the MPV. Selling development is going to be a lower MP because we don’t provide as much value versus target discovery or drug discovery, you’re going to be in that 15 to 20 million range again. We do value-based pricing, more value, higher MPV. Now, I’m really excited to announce we have a partnership with Merck that was announced on Friday. This really illustrates when we have a successful program with a partner, it can evolve to be something so much bigger. And so the history of Merck was we had an active program with them in cell line development. We ended up being able to be very successful on that program. We were developing our AI drug discovery capabilities. We showed them that technology and they championed two different types of programs with us. The first one is on the drug discovery. The other is on the bionic enzymes.

Sean McClain:
And the exciting part about this is that we’re starting to integrate across the organization from drug discovery to biomanufacturing. And so what is this deal from an economic standpoint look? So drug discovery, Merck has the option to elect up to three targets. Those three targets and upfront and milestones are worth up to 610 million and tiered royalties on approved drug products. And then on the other side, we have the bionic enzymes where they’re going to be using our non-standard amino acid technology to integrate into API manufacturing and so this is a really exciting deal for Absci, really being able to leverage our AI drug discovery capabilities and the non-standard amino acids in the bioproduction enzymes.

Sean McClain:
This last year was hugely transformational for Absci. We made two acquisitions. The first was Denovium. So we had our AI engine that, or sorry, our synbio engine, our data engine, and we needed to put on top of that really cutting-edge AI. And that’s what Denovium brought in. It’s fully integrated now, allowing us to be on the bleeding edge of AI drug discovery and Tuscan was the second acquisition that brought in the target discovery. And since the inception, we’ve raised over 435 million dollars to see this vision through and be able to work on cutting-edge research. We’ve opened up a brand new campus, 77,000 square feet, and we’ve expanded the team to 225 unlimiters. And we’ve signed flagship partnerships with Merck EQRx. Those are drug discovery deals. We recently, at the beginning of last year, rolled out our AI drug discovery. And now we already have two partnerships with that. Again, EQRx and Merck, and those have higher MPV value. So it’s more on the 15 to 20 million range versus the 10 to 15 million and we currently have 15 active programs.

Sean McClain:
All of this work would not be possible without our team of unlimiters. Here we have on the best world experts in AI, in synbio and this team that has come together is allowing us to see that vision through of going fully in silco. And there’re some days here where it’s really hard when you’re working on bleeding edge stuff, it can be extremely difficult, but that’s where Penelope our chief morale officer comes in. She ensures that everyone’s morale is at that absolute best. And today I’m excited to announce a new unlimiter that has joined our team. Dr. Joseph Sirosh. Dr. Joseph Sirosh came from Microsoft and Amazon. He was the head of AI at both of those companies, a world-renowned AI expert. And this just really demonstrates how cutting-edge we are in the science that we are doing. And he came on to our board because he sees that our vision is possible. We have the data engine that’s going to allow us to ultimately go fully in silico for protein-based drug discovery.

Sean McClain:
Now in conclusion, and I know that you’re tired of hearing about this, but it’s all about the data. The data is what is going to allow us to go fully in silico. And we have the data engine that’s going to make this possible. That is our synbio platform. That’s our SoluPro^® E. coli strain capable of producing next-generation biologics, along with our cutting-edge breakthrough screening assays that allow us to screen billions of drug candidates in a single experiment, looking at the functionality, the manufacture ability, and that’s compared to screening tens of thousands within a few weeks. And so that is our engine that’s getting us our data day in and day out and pairing that with our team of unlimiters, along with our AI models is what’s going to allow us to go fully in silico. Just because something hasn’t been done, doesn’t mean it can’t be done. Join Absci, but only if you believe in the impossible. Thanks. Back to you, Casey.

Casey Woodring:
Thanks. That was a great overview. I guess, why don’t we start on the pipeline here. If you could just walk us through what your pipeline looks like of multi-project platform deals such as the one that you just signed with Merck.

Sean McClain:
Yeah, definitely. So we are seeing a huge trend in multi-program deals. You saw it with EQRx, which was three deals, same with Merck, another three, and the pipeline going into 2022, we’re really excited about. We have a ton of traction and Merck is just the first of many other really exciting multi-program deals that’s on the horizon.

Casey Woodring:
Gotcha. How should we think about upside and downside to the number of projects you can book and start within the next couple of years, maybe both in the near term next year in 2022, and then looking kind of farther out?

Greg Schiffman:
You bet. So unlike a CDMO, our business model really is built on a pretty low CapEx and very nimble. So it forges us. If we’re seeing a huge demand, we can build out a new facility with the total facility and CapEx costs of about 35, 40 million. And we actually moved into the facility we’re in right now in a six-month period of time. With the current facility we have, we think we could do around 24 programs in a year with only a small number of additional hires, we are not capacity-constrained. We’re pretty thoughtful in who we partner with. We look to find the best management teams with backing from topnotch BCs, or big farmers that have a high pedigree.

Greg Schiffman:
And what we want to ensure is that every time we do a project, our partners not only bring more projects to us, but also become our biggest supporters. And I think as Sean indicated with Merck, we’ve already seen that in an example there where a cell line development project that we entered into a while ago became a multi-program collaboration utilizing our AI-enabled drug discovery and non-standard amino acid capabilities. And so I think we have a lot of ability to be able to ramp up with very little increase in additional expense structure.

Sean McClain:
Yeah. And I will say that we are very particular about who, who we partner up with. Because one, we have to believe in that the target we’re going after, but then also two having partnerships like Merck, EQRx, of this high profile can continue to develop date our platform and our technology along with the research that’s coming out that’s going to be published. And so I think all of these, we really make sure that we’re selective because there’s only limited capacity, and we want to have that capacity to be used for the most important partnerships that’s going to drive business development for us.

Casey Woodring:
Gotcha. You mentioned you’re not capacity constrained at the moment. I’d like to dive into how you’re choosing customers. Are you choosing customers based on probability of success here? And as a follow-up to that, are you intentionally controlling the early phase of rollout to ensure high-quality delivery of these early projects so you can generate validation that drives an inflection point later on?

Sean McClain:
Did you want to take that one, Matthew?

Matthew Weinstock:
Why don’t you take the first part, Greg, and I’ll pick up the second piece?

Greg Schiffman:
Well, actually it might be Sean’s the one closest, I think, on the deals and actually how we’re choosing and we’re partnering with them in terms of the assets and the focus. Everything we’re doing, we are wanting to make sure that they’re well backed that these are drugs that are going to move forward that have a high probability of being able to be funded and going to the clinical programs because that’s where our revenue and our real value comes is getting these drugs through the process and hopefully approved it final state. And so we are selective in making sure that these are companies that we believe are positioned to be successful and will have the funding to be able to move their products forward with partners. Maybe that turn it over to Matthew.

Matthew Weinstock:
I was just going to say just to compliment what Greg said. So in addition to looking at the management team and the funding, we also do technically assess the target and the technical risk associated with the program because we definitely want to have high conviction around molecule that we’re working on that it has the best chance possible of actually making an impact in the lives of patients.

Sean McClain:
Yeah. And we also want to partner with large pharma and biotech companies that really see AI as the future, because it is. And so we want to just partner with thought leaders in the space. I think that also helps just drive more successful and better collaborations when you’re aligned with the partner on where the future is headed.

Casey Woodring:
Gotcha. Makes sense. Sticking with this theme, what do potential customers need to see in order to adopt app size solution? Is it about platform de-risking or is it more about having options other than the royalty pricing model? Yeah. Any sort of color on that aspect.

Matthew Weinstock:
Yeah, absolutely. I’ll take this one. So as Sean said, we’re really sitting on the bleeding edge of AI-guided drug discovery, and really being on that bleeding edge, the most important thing that we can show to potential partners to really earn their confidence is data demonstrating that our technology can deliver on their need. So if we just look to deals that we’ve closed recently, such as the Merck deal that Sean has already talked about, a crucial component of that was internally generated technical data, really demonstrating the strength of the platform and the breadth of the applications. And that’s been critical across the board at getting buy-in from partners. Another key piece that’s really important is external validation from existing high-quality partners. Having that reference really helps to drive conviction within the marketplace.

Casey Woodring:
Gotcha. For biopharma companies specifically that are currently using mammalian cell lines or alternative microbial expression systems for next-gen biologics, are there any barriers to switching to Absci for their upcoming new drug projects?

Sean McClain:
No. The reason why I partners are coming to us is because we’re able to be able to create a cell line or a drug candidate that they couldn’t create without our technology. And we’re able to get it to the market faster and at lower cost. And so when partners come to us and they have a dire need we have not run into a barrier on switching at all.

Casey Woodring:
Gotcha. Makes sense. What are your guys’ latest expectations around discovery versus development project mix in 2022?

Sean McClain:
Yeah. So we’re the guidance that we are giving for the street is eight active programs this year. Merck is three. So we have five left and Merck was those three were discovery, and we definitely see a majority of the programs this year being drug discovery versus cell line development.

Casey Woodring:
Gotcha. How important of a role do CDMOs play in next-generation biologics and besides your existing partnership with KBI, what’s your strategy of penetrating other CDMOs, and maybe how attractive or feasible is the CDMO opportunity relative to biopharma? Maybe you can just compare and contrast the two.

Matthew Weinstock::
Yeah. So CDMOs don’t really play a critical role in helping us close deals with partners because CDMOs are typically only involved in the late stage manufacturing activities. If at all, if we’re working with a big partner, they have their own in-house manufacturing capabilities. But relationships with CDMOs do help us streamline the transfer of technologies from our facility. Two CDMOs when partners elect to use those for manufacturing when it comes time for the GMP manufacturing portion. As you alluded to, we’ve been fortunate to build high-quality relationships with many CDMOs, including KBI to really help streamline bringing these processes to the market. And in the instance of KBI we’ve already successfully demonstrated that we can transfer and scale up our SoluPro^® E. coli strain from our facility to theirs and this existing relationship is helpful when you want to move fast.

Casey Woodring:
Gotcha. You recently acquired human antibodies’ discovery capabilities. What do you expect to generate meaningful revenue from that acquisition? And do you plan to offer that as a standalone product, or only offer it as an integral part of a discovery project?

Matthew Weinstock::
Yeah. Great question. So we’re really excited about the Tuscan acquisition. So in a world where, as Sean pointed out, roughly a 50% of approved therapies, they target essentially the same dozen targets. Lots of people are just inventing better mouse traps against the same targets. It is time that we see novel targets emerge in therapies to actually target those to really impact on me medical needs and the Tuscan platform we’ve acquired enables us to leverage data taken directly from patient tissue samples to identify these new targets and to develop therapies against them. So the platform is useful in many ways. It also generates large amounts of data as Sean alluded to that we can use directly internally for our AI training efforts, for example, large amounts of fully human antibody sequences, which is a primary output of the Tuscan platform enables us to train our models to design antibodies that look human, which allows us to minimize the immunogenicity risk associated with many engineered antibodies.

Matthew Weinstock:
And additionally, the data on antibody-antigen interactions, which is also a product of this platform is very valuable from an antibody design perspective. As we look to design antibodies fully in silico from scratch. So our vision is to stand up an integrated AI platform that allows us to go directly from those patient samples to new drugs fully in silico at the click of a button. Regarding monetization, which is a point you brought up. In addition to integrating this technology into our existing tech stack and making it available to partners, which is something we’re already doing, we’re also looking to develop some of our own assets internally and partner them out pre-IND.

Sean McClain:
Yeah. And I will say to that monetization, the data that’s being fed in is already training our models that are being used for both EQRx and Merck. So we are already seen monetization out of the data that Tuscan has already generated for us.

Casey Woodring:
Gotcha. Maybe a follow-up on Tuscan. And I know it was brought up on the three Q call, but maybe you guys can give some more color on the diagnostic opportunity. I was just wondering your updated thoughts around that.

Sean McClain:
Yeah. I would say that we are currently not pursuing diagnostics. Our sole focus is being able to partner with large pharma and biotech to develop therapeutics and the associated cell line. And so as we penetrate that market and we become the market leader there, we do see ourselves going into other industries or other areas within healthcare.

Casey Woodring:
Gotcha. Maybe going back to the technology, what advantages does the SoluPro^® E. coli offer compared to alternative methods such as chemical conjugation or other microbial expression systems?

Matthew Weinstock::
Yeah, so SoluPro^® E. coli is a size proprietary microbial expression system. And it’s just one component of our broader technology stack. One, key differentiator of SoluPro^® E. coli, if you compare it to say mammalian or yeast-based systems is the size of libraries that we can build and the speed at which we can build them. And when we couple that increase in size and the speed with our proprietary screening assays, we’re able to generate massive data sets that we can feed into our AI models to enable the AI-guided drug discovery that Sean presented on.

Matthew Weinstock:
So, for example, the case study that Sean presented, using this approach, we’ve successfully constructed AI models that can be used to design antibodies with a desired binding affinity for a target of interest. One additional advantage is that SoluPro^® E. coli can be used in itself as a scalable manufacturing solution for the drugs and molecules that we design. And the ability to generate a manufacturing solution during the discovery process, which is a byproduct of the way that we do our discovery process. It really allows us to further accelerate our partners drug development timelines because we essentially obviate the need for any subsequent cell line development activities.

Sean McClain:
Yeah. But just to really hit home again, the SoluPro^® E. coli is along with the screening ad of the proprietary screening assays, that’s really the engine that generates that data. We wouldn’t be able to do that without our SoluPro^® E. coli or the screening assays that we’ve developed. And so that’s just a really core piece to the data generation to be able to predict and design better protein biologics, as well as the associated cell lines.

Casey Woodring:
Gotcha. How would you describe the industry’s current level of awareness for SoluPro^® E. coli? What are your strategies for further increasing market awareness, be it sales and marketing efforts or otherwise?

Sean McClain:
Yeah, it’s really two-fold. It’s continuing to publish on data that shows us creating the bleeding edge, just like I showed today, being able to predict the protein affinity off of the sequence and subsequent advancements that come out that really helps drive and convince our partners that we are on the bleeding edge and that we have a lot of value we can provide be because of that. And then the second is continuing to announce and close high profile deals like Merck, and EQRx, that just provides validation within the marketplace as well. And so focusing on again, publication of the exciting work that we’re doing on our vision of going fully in silico and then continuing to develop very high profile partnerships that’s going to be the basis for our growth in the future.

Casey Woodring:
Sorry, I was on mute. AI is a key piece to your long-term story. Where does your capability stand now in terms of AI and how much of your current discovery workflow is handled by AI versus humans?

Sean McClain:
Yeah, so it just goes back to the presentation. Again, we right now are creating the bleeding edge. We showed for the first time ever, we can take an antibody sequence and predict the affinity to a target. And that is demonstrating again that we are taking major steps to really seeing this whole process be fully in silico and we are on that bleeding edge. And we couldn’t be more excited about just the full stack that we’ve got it. So it’s a close loop system, it’s that synbio platform generating all that data along with some of the most cutting-edge models and scaling those as well with large tech companies. I think all of it together is really showing that we are the leader in AI drug discovery for protein-based biologic. Matthew, please chime in if I missed anything there.

Matthew Weinstock::
No, I think that’s a really good summary.

Casey Woodring:
Gotcha. Maybe a follow-up to that. How much ongoing cash do you think you need to fully develop your AI capabilities. And then I guess that leads into the all-important question, what are your current expectations around cash burn, and do you have a break-even timeline?

Greg Schiffman:
Sure. So first we’re going to be sharing a lot more detailed guidance on operating specific cash flow report on a Q4 earnings. However, we are currently making fairly substantial important investments in the platform capability. I think as we talked about earlier, we will be able to increase the number of partnered programs we’re doing over the next couple of years without really having to hire very many more people within the operations. AI itself, to talk about the investment specifically, everything we’re doing is feeding the AI engine. There’s some very specific aspects of AI. There’re individuals that are only engaged in the modeling or so forth, but really everything we’re doing is feeding that engine. It is generating data, it’s positioning it so that we’ll become capable of doing the in silico work as Sean’s discussed. And so from that standpoint, I don’t know that we could specifically say, well, what is the AI investment? Because it really is broadly based, it is integrated in the core of what we do.

Greg Schiffman:
We do expect, like I say, to increase the number of programs with very selective, additional OPEX, and we’ll be sharing the cash burn. And when we think about break-even. I think the break-even, the important way to think of this and Sean talked about it earlier is really on an MPV basis because we upfront payments in cash. We get milestones, and then we get royalties, the milestones and royalties really comprise the very interesting components for the investors. We’re sharing in the success our partners have. And we do discount all of that with the probabilities that you’re going to see those milestones or success happen. So it truly is today where we range somewhere between 10 to 20 million dollars in a program that we execute today, where there is no additional work that we will do once we’ve completed that program and tech transferred it, we don’t do anything.

Greg Schiffman:
We just are able to continue to see downstream royalties based upon the success of the programs. And on that basis, I don’t think we’re a long way out from hitting a break-even. When you talk about break-even on an absolute cash flow basis, the upfront is the smallest amount of cash we receive. And so I don’t think we are targeting and expect that we’ll be break-even in the next year or two, certainly, as we look at the upfront, but on an MPV basis, it’s not that far out and we actually start becoming profitable.

Casey Woodring:
Gotcha. We’re bumping up to the end of our time here. I guess I’ll toss it back to Sean for any closing remarks and I would be curious to hear you guys thoughts on maybe what investors are missing with the story, if anything. So, yep. I’ll throw it back to you guys.

Sean McClain:
Yeah. I’m just going to go back to it’s all about the data and the reason why Absci is going to be the first to go fully in silico for designing and discovering protein-based therapies is because of the synbio platform that allows us to get the data at the throughput and the quantity that’s needed as well as the quality. And then we’re feeding that into our cutting-edge AI models. We have some of the most world expert AI folks that are on our team. And that’s also demonstrated too today when we announced Dr. Joseph Sirosh, the former head of AI of Amazon and Microsoft that joined our board. And we’re really excited about this year. And Merck is just the first of many exciting partnerships that are going to be coming. But today or 2022 is ramping up to be a very exciting year for Absci.

Casey Woodring:
Definitely. All right. Well, this was a great overview. Looks like we’re out of time here. So thank you to the outside team and yeah. Have a great rest of the conference.

Sean McClain:
Awesome. Thank you so much, Casey.

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