OmniAb, Inc. (NASDAQ:OABI) Q3 2023 Earnings Call Transcript

OmniAb, Inc. (NASDAQ:OABI) Q3 2023 Earnings Call Transcript November 14, 2023

Matthew Foehr: Okay, great, we’re at 8 am Pacific Time, 11 am Eastern time. So we will kick it off. Good morning everyone. I’m Matt Foehr, CEO of OmniAb, and I want to thank you all for joining our first Research and Technology Event. We are presenting today from NASDAQ’s Entrepreneurial Center, in Downtown San Francisco, which is just a short drive across the Bay Bridge from our headquarters and labs over in Emeryville. It’s been just over a year since OmniAb became an independent publicly traded company listed on the NASDAQ Exchange. And I want to thank the NASDAQ team here in San Francisco and back in New York, but especially the team here in San Francisco for hosting us today and furnishing this great space for our event this morning.

But before I begin, I’d like to remind you that we’ll be making forward-looking statements during our presentations. These forward-looking statements, of course, carry risks and uncertainties and actual results may be different from those that are projected. So I’d urge investors to please consult today’s earnings release as well as our SEC filings for more information on these risks. So again, welcome. I’m going to provide some opening remarks this morning, some updates on the business and also give a bit of a roadmap to today’s presentations. But before I do, I wanted to introduce you to our presenters. We’ve got a wider cross-section of management. I’m excited that investors and analysts get the chance to meet more of our management and leadership team here.

So you’ll see presentations from Todd Pettingill, who is our VP of Business Development and Strategy. Todd’s been with the OmniAb business really since the beginning of forming the foundation of what has now become OmniAb. He played a critical role in all six of the acquisitions of the company and technology acquisitions that we did in the less than the six year period to form what is now the foundation of the business of OmniAb. Bill Harriman, heads our antibody discovery. Bill was a Founder of Crystal Bioscience, which was the innovator and inventor of OmniChicken. Bill’s had a really illustrious career in the antibody discovery space, is also the inventor of the GEM Assay, which is a part of our technology. And Bill has the honors of doing the OmnidAb launch today, and you’ll hear more about that as I run through the agenda.

Bob Chen runs our Discovery Systems. Bob joined us when we acquired xCella Biosciences. Bob was the Co-Founder of xCella. He Co-Founded it along with Dr. Jennifer Cochran, who also sits on our Board of Directors. The xCella business was spun out of the Stanford School of Engineering, and Bob is an inventor of our exploration technology, which has become a key part of our business as well. And you’ll also get to meet Doug Krafte. Doug is a well-known individual in the scientific circles around ion channels and transporters. He has a long history of first scientific — first around the ion channel and transporter space and heads our Ion Channel AND Transporter Team based out in Durham. And then you’ll also get to meet Kurt. Obviously, many of the investors on the line and analysts know Kurt very well.

Kurt joined us not long before the split, when we split the business off and became an independent publicly traded company. A long career in biotech and technology, longest of which was at Amgen for many years, heading their European finance operations. So excited that you’ll get a chance to meet all of these colleagues who I’m honored to work with today. Today’s agenda, again, I’ll provide some opening remarks. Todd is going to give an update on creating value for partners. That’s something obviously is very important to us. And Bill will be launching the OmnidAb technology today. We’ll talk a lot about that, get into the science, get into where it fits in the industry and why it’s important. Bob will highlight how we enhance discovery with OmniDeep, which is our suite of in silico tools that are woven throughout our technology stack.

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Q&A Session

And Bob will provide details there and provide some case studies and other details that we think will be interesting to the investment community. And then Doug will talk about the Ion Channel opportunity, some high-value partnerships that we have there and why we see unlocking the antibody potential in ion channels as an important part of the future. And then Kurt will review our financials. Obviously, we reported our Q3 financials earlier this morning. He’ll review those and also provide some other financial insights into our portfolio of partnerships in our business. But I’m going to start with our mission. We are a mission-driven organization. This is what guides us. This is what keeps us focused. And our mission is to enable the rapid development of innovative therapeutics by pushing the frontiers of drug discovery technologies.

We are highly focused on this. We’re highly committed to this. And I think that clarity of mission is one of the things that attracts new partners and also causes a number of our existing partners to expand their use of our platform. Our business, in many ways, is quite simple. It’s a licensing business. So we license our technologies to the industry to allow them to more quickly and efficiently discover antibody-based therapeutics. Our technology offering addresses some of the most critical needs in discovery today. And we’re operating one of the largest Greenfields in the pharma industry. I’ll talk more about that in a couple of slides, but the antibody element, an antibody-related therapy elements of the industry continue to grow. We have a leading and proven technology now with a growing number of partners and a growing number of programs, and we are committed to continued innovation in what we call intelligent expansion of our technology.

Because of where we sit in the industry, we have a bit of a unique position where we can have deep technical dialogue with our partners, not only understanding where they are today, but where they are headed and that informs how we invest in and how we innovate around our platform. So a little bit on OmniAb today. We, as I said at the outset, we just passed the one-year mark of being an independent publicly traded company. And we really do feel like all of the pieces in the business are beginning to become aligned. As we look forward into 2024 and beyond, we do feel like we really have a business that is positioned to sing, if you will, going forward, that the pieces have come into place. And we really do now feel like we have a business that’s positioned to sing.

Our work is centered on driving the business, on accelerating innovation around the platform and consistently staying ahead of our partners’ discovery needs. We are 112 employees across three primary U.S. sites. Our headquarters here in the San Francisco Bay Area, over in Emeryville. We have an ion channel and transporter team in Durham, North Carolina, as I mentioned. And then also an in silico team that’s based in Tucson, Arizona. And that we’ve also built up our business development team more broadly, and recently established ex-U.S. business development presence as well. We see the need for only minimal future headcount growth to support substantial growth in our portfolio and our partnerships. There’s a lot of leverage in this business, a lot of efficiency that’s been built in over time, and we feel like we’re really well positioned to take advantage of that leverage.

Despite a macro landscape in the pharma tools and technology space that has obviously been widely reported and has had a number of headwinds associated with it, our business metrics in our business continue to demonstrate the value that our platform brings to the industry. We’ve had nice growth in new partners, nice growth in new programs over the last 24 months, while the industry has been facing substantial headwinds. We’ve also seen nice partner program advancement and announced this morning that we now have five new clinical entrants in this calendar year 2023. Our work and our technologies are really having an important impact, not only on our partners’ R&D pipelines but also on patients’ lives. This is something that really motivates us.

It motivates the rigor that we put into the science, how we challenge ourselves to stay ahead of partners’ needs. But we’re quite proud of the fact of where we sit in the industry, and it’s quite rewarding to see the impacts we’re starting to have on patients’ lives. There are over 170 active or completed clinical trials that are testing OmniAb-derived therapeutics. That’s a testament not only to our partners’ commitment and conviction around the antibodies that have been discovered out of our technology, but it also speaks a lot about the impact that we can potentially have on patients’ lives. There are over 30,000 subjects either enrolled or to be enrolled in those trials. We have three approved OmniAb-derived drugs. All of those are in cancer currently and a growing portfolio of clinical programs that I’ll touch on a little bit later in this section.

Our business is really designed and funded for continued innovation and intelligent technology expansion. We’re obviously launching an exciting new technology today, our OmnidAb heavy-chain OmniChicken. And importantly, our innovation engine for either producing new transgenic animals or new elements of our technology is becoming more and more efficient. We’re able to do it faster. We’re able to do it more cost effectively. And that’s something that I think will feed well — to feed the business well into the future. We will launch other novel technologies next year as well, and we also expect to roll out other new partner experience enhancements as we call them, in 2024 and beyond. So I’m going to step back for a moment and talk a little bit about the antibody market.

This is a growing market for a variety of reasons. The antibodies are projected to have sales of $279 billion by 2025, up from $238 billion last year. There were 51 blockbuster antibodies. So these are antibodies with sales of over $1 billion in 2022. And the five best-selling antibodies had approximately $75 billion of sales last year. The importance of discovery technology focused on antibodies continues to increase for a variety of reasons. There have been decades of foundational and basic biology research and scientific advancement that are helping create what I see as a continuing need for cutting-edge antibody-related discovery technologies. A lot of foundational investment that is driving that present day and future demand, really starting all the way back with Nixon’s war on cancer back in the 1970s.

And then a number of other scientific advancements that contributed to antibodies becoming a more and more important modality for treating disease. Those include hybridoma, the cloning, obviously, the human genome project and the advent of proteomics as well as other advancements in immunology and a deeper understanding of immunology, molecular biology and higher and higher-speed computing, all of which drive that demand for discovery technology. In addition to that, the industry success rates, the success rates that our industry, the pharmaceutical industry has enjoyed over recent years have been far higher for antibodies than traditional small molecule pills that you may put in your mouth. Overall, the historical success rates for antibodies have been roughly double that for small molecules when you compare it over the fullness of time.

There are also other factors that are driving interest in antibodies, the Inflation Reduction Act, which is shifting R&D spending of large pharma partners away from small molecules is also driving interest in antibodies. And then the data on the right-hand side, in the blue of this slide is actually very new data. This was just presented a couple of weeks ago by the Antibody Society, who do a very meticulous tracking of antibody success rates for antibodies that are being pursued in a therapeutic way by commercial sponsors. So we find this data, one, extremely relevant and also very interesting given what it’s showing. And if you look at it very closely, the way the Antibody Society does is, is they’re tracking these on an individual basis.

And you see in the bar charts moving left to right, and there are overlapping periods here because of the way in which they report the data, and the way in which they track the data. But you can see over time that it appears that the industry is also getting better at developing antibody-based therapeutics. So not only have the historical success rates had been better than small molecules, other macro factors driving investment away from small molecules to antibody-based modalities, but the industry is also getting better at developing antibody-based medicines. So I’m going to talk a little bit about some recent metrics in the business. We’ve signed eight new license agreements so far this year. We announced new platform agreements with GigaMune and Polaris Therapeutics that were both signed in Q3 and more recently entered into a new platform license agreement with a global pharma company, a well-established global pharma company that was also signed recently.

So our active partner count has grown to 76 as of the end of the quarter. And as you can see in the bar chart, we’ve continued to grow the number of active partners net of attrition over a number of cycles in the biotech space. We also think there’s strength and diversity in the types of partners that we are attracting and bringing in, and that creates multiple paths to potential value creation downstream. We’ve also seen nice growth and advancement of active programs. We started this year with 291 active programs and we ended Q3 with 314 active programs, net of attrition. So you can see that growth reflected on the left-hand side, in the bar chart. And then on the pie chart on the right, you see that we’ve had nice growth in diversity as well as graduation and advancement, if you will, of programs.

So we’ve had a number of programs move from discovery to preclinical. We’ve had five programs moved from preclinical to Phase I clinical trials and one program moved from a Phase III to its first international filing. I want to note here as well that our definition of preclinical is something of a high hurdle. We don’t put something in that preclinical slice of the pie chart, unless it is in pre-IND studies and the partner is moving towards filing an IND and entering into clinical trials. So that’s quite a high bar that we use in defining preclinical. But as you see, there are 14 programs in that slice. We’ve also had growth in active clinical programs. We started off 2023, indicating our expectation publicly that we expected three to five new clinical starts this year.

And at the end of Q3, we are at five. So we’ve had five new clinical programs from — of OmniAb-derived antibodies entered the clinic this year. Seagen had a bispecific. Immunovant entered the clinic with IMVT-1402, which is a next-generation FcRn antagonist. They’ve also already reported out positive data for that program, and indicated this morning, they expect more data for that program before the end of this month. Gloria entered into its first clinical trial for an anti-LAG-3. And then in the third quarter, Roche entered into the clinic with a bispecific antibody. And Cessation Therapeutics entered into clinical trials with an antibody with a very interesting and important medical use as an anti-fentanyl. Obviously, fentanyl, a lot of reporting of the medical tragedy and growth in medical need around preventing fentanyl overdose.

And this is a really interesting use of an antibody, and that’s an issue not only here in the United States, as has been widely reported, but also now becoming more of an issue in other countries as well. I do want to note here on the Roche program, which entered the clinic in Q3. That is a program that is subject of a fully paid license that was essentially a grandfathered license that we inherited from a company that we acquired. So there are no economics to that program. So I want to be clear about that. But it is a further validation of the importance of our technology in a variety of disease spaces. We continue to monitor the progress of the 14 preclinical stage programs as those approach Phase I clinical trials. As I mentioned on the prior slide, we have a very high hurdle for what we call preclinical.

These are programs that are in pre-IND studies that are approaching the clinic. This next slide is becoming a bit of an eye chart. As more and more things enter the clinic, we are now at 31 programs that are either in clinical trials, in registration or approved. This is becoming harder and harder to read on a slide. We are considering other ways to reflect our pipeline to investors in the outside world, bulls eye charts and the like, which may be more amenable to showing not only the diversity of partners, the growing diversity of therapy areas as we get deeper into the pipeline, but also the progression as well. And importantly now, as I kind of wrap up my section here, we’re announcing today that we are launching OmnidAb. And in fact, we’ve already launched it.

We have partners now that are already leveraging this newest technology of ours in active programs. OmnidAb put simply is the first and only transgenic chicken that is producing single-domain antibodies. And this is a growing important class and one that creates a lot of interesting opportunities, not only medically and scientifically but for the business as well. And Bill is going to talk more about that, and Todd will also talk about partner perspectives around OmnidAb as well. And as I wrap up here, I just want to talk a little bit about our key areas of focus going forward. We believe we are well positioned for future growth. And we believe we are making an enduring and significant impact on human health and on the industry as a whole. We feel like as we enter 2024, as I said, the business, the pieces are aligning for the business and we feel like it’s positioned to sing, and we are leveraging a highly scalable business where investments in technology and innovation really are informed by deep discovery relationships with our partners.

We have quite — I’ll use a non-scientific word and say quite intimate relationships with our partners around not only the challenges that they’re facing today and the types of antibodies and antibody-based modalities that they want to pursue, but also the challenges that they’re facing tomorrow. We’re focused on partner pipeline development and expansion, continued what we call workflow versatility initiatives, expanding the reach of our platform as well as new technology development and launches. At our foundation is a focus on stakeholders. That’s a really important part of our foundation. I’m quite proud of the team that we have, fantastic colleagues to work with. We have a strong culture. We really do focus on developing, hiring and motivating the best employees.

We focus all the time on our partners, making sure we’re focused on customer service, we’re focused on future needs and that deep collaborative scientific dialogue that they’re looking for. And of course, you investors, as well. That’s obviously superior. Business execution is extremely important to us, and we’ll continue to be committed to that going forward. And then we’re also committed to leading with integrity and responsibility in the communities in which we operate. So with that, I’m going to introduce Todd who just a little bit ago got back from BIO-Europe. He’s looking fresh and ready to go. So Todd, welcome. Thanks.

Todd Pettingill: Good morning, everybody. Great to be here with you. Today, I get to speak about a good topic, the value that we’re creating for our partners and what’s driving that. As Matt mentioned, we’ve had a really, a good run over the past several years of increasing our partner base, especially in the last 24 months, which is especially encouraging in light of the recent market headwinds. So as we think what is driving that? There are several factors that jump out of this. But really, we have had at a good run to improve and increase the platform visibility, both through our business development efforts but also through our partners as they’ve pushed antibodies into the clinic and also gotten them approved. We’ve also put, as Matt mentioned, we’ve put a lot of investment into our business development and marketing presence, both increasing the size of the team, but also spending a lot more time at conferences.

And that’s really been helpful as we’ve been able to have cutting-edge science that’s backed this up and made the message clear. Finally, we’re able to be creative in how we license. We don’t take a one-size-fits-all approach with our partners. We have a range of different contract types and different things that we can work with to find a deal that works for all of our partners. So with that, we’re able to attract a diverse set of partners. We’re now starting to define our partner base as different areas, each one has a unique characteristic. First, we have our discovery technology access partners. These are partners who have full access to the platform for both current and potential programs, but also they can develop and commercialize antibody or OmniAb-derived antibody.

And that’s the majority of our partner base. Next, we have the commercial partners. And these are the partners who have geographic or therapy area rights to commercial and development stage OmniAb-derived antibodies. They’re developing and they’re commercializing. Finally, we have our academic partner licenses. These are working with academics and institutes and these licenses are designed for revenue share. We provide the discovery technology platform, and the understanding is that they’re going to use this to do proprietary research and spin out assets for development and commercial entities. So what’s driving the interest from our partners in using the platform? Well, this is our platform. So we — and this is our tech stack. We like to create or to define it into three separate buckets, if you will.

First, create. We help people create a large diverse set of repertoire of high-quality antibodies to select from. We have a wide range of transgenic and other animals that we consider biological intelligence, and that drives how we create antibodies for our partners. Next, we screen. We help our partners screen through millions of potential antibodies to find the right therapeutic. We have a high throughput automated version, and then we also have a manual version that also dig deep into the repertoire. Finally, we deliver. We help people characterize and select and optimize the right antibody. We do that through traditional wet lab work. And we also have recently incorporated in silico tools to expand and build upon the assets that we provide to our partners.

So going back to the create section. It’s driven by biological intelligence. So what is biological intelligence? We define biological intelligence as using Mother Nature to do what it does best, to generate antibodies in vivo. So we believe that in vivo generated antibodies are superior because they’re generated through an iterative process that selects for antibodies and then increases the specificity and helps select for those with superior developability profile. On top of that, our suite of transgenic animals allows us to provide antibodies with fully human variable regions. So not only are these strong antibody candidates, but they don’t need to be humanized. They’re ready. They’ve already passed that key step. So that’s what we call biological intelligence.

And with that, we view that this increases the efficiency and the probability of success for our partners to bring them a lot of different options to potentially go to the clinic. Now let’s talk about our platform. What makes us different? So we have multiple technologies that make us unique. I’ll go through them at a high level. So first, on the first column, we have our chicken. So our chickens consist of three different technologies, our OmniChicken, which is a chicken that makes fully human antibodies that are standard-sized antibodies. Then we have a chicken called OmniClic that makes human antibodies that have a fixed life chain. So that’s useful in making bispecifics. Then we have OmnidAb, which I won’t speak too much right now because that’s what Bill is going to be speaking about in just a few minutes.

But why is the chicken important? Really, it’s an evolutionary display. Our last common ancestor with a rodent was call it, 90 million years ago. But with the chicken, it was closer to 300 million years ago. So a chicken is that much more different from a human and therefore that much more likely to identify an antigen or a targeted non-cell. So what that allows is our partners can use these chickens to identify antibodies that have a broad range of epitope coverage, a wide range of targets. Next, we have our rats. We have two technologies with our rats. We have our OmniRat, which is full standard antibodies and then we have OmniFlic, which is big slide chain antibody, again, useful for bispecifics. So why would you use a rat? Well, a rat looks similar to a mouse, but it actually is different.

And so it can react to an antigen in a different way than a mouse can. So you can get a different response. But it has a similar ease of use as a mouse. Also a rat is bigger, so you can get higher B-cell quantity, which can potentially lead to broader diversity. Our OmniRat was our first technology that was put into operation, and we have multiple approved antibodies from the OmniRat, both in the U.S. and European Union and in Asia. Next, we have our cow technology. So OmniTaur makes antibodies that have these unique characteristics. They have these ultra-long CDRs that stick out from the variable region of an antibody and look almost like a finger that can reach out and access recessed epitopes, such as interior of ion channels or different parts of GPCRs that are normally not accessible to a standard full-length IgG antibody.

So really, the OmniTaur antibody opens up a potential broad new class of potential targets on the discovery side. Also, these ultra-long CDRs can be cleavable into a very small fully functioning protein that’s about a third of the size of a nanobody. We call it a picobody and they’re used in a lot of different functions. Finally, we have our exploration technology. This is a high-throughput B-cell screening platform, which is a large — has a large amount of throughput, over 1.5 million B-cells at a time it can screen. It also uses integrated artificial intelligence and sequencing to maximize repertoire mining. So these technologies not only are they driving new partners to us, but they’re keeping partners within our ecosystem as they continue to use the innovation that we have to find new antibodies.

This is a quick breakdown of how our partners are using the different technologies. As you can see, OmniRat is leading the pack at the moment. That’s largely based on the fact that it was put into operation prior to all the other technologies. But as you can also see, the other technologies are becoming a bigger presence, and we’re moving to be more and more diverse from our technology usage standpoint. As far as potential uses for our OmniAb antibodies, they’re useful for anything that could be used — that an antibody could be used for. So this is the breakdown of what our partners are doing. So a lot of monospecific antibodies on their own. But also multi-specific, think bispecifics or more, trispecifics. But we also have antibody drug conjugates in our pipeline.

So there’s a wide range. But also, they’re still useful for other technologies, CAR-T, TCR mimetics, things like that. Our antibodies can be useful in a wide range application. Bill is going to talk today about OmnidAb, and that really opens up a — that just in a way, it blows the roof off of other potential formats that can be used with our technology. So the last slide, just before Bill talks about OmnidAb, I’ll just talk about why is it an important tool, why is it important to our partners? Well, our partners are interested because — these are quotes from our partners. They can generate a panel of multiple multi-specific antibodies using tethered single-domain antibodies. They can also rapidly generate high-affinity human sequence from the OmnidAb. These molecules can be used to penetrate deeply into solid tumors.

They can potentially cross the blood brain barrier, and they can also be used as linkers to guide other molecules to various parts of the human body. So after — we’ll present today, but there are several upcoming presentations that if you happen to be at, I would recommend you join. In next week, we’ll be in Lisbon at PEGS Europe. In December, we’ll be in San Diego at the AET Conference. And then in January 2024, we’ll be presenting a webinar series that deals specifically with OmnidAb. That’s it for me. I will now turn it over to Bill.

Bill Harriman: Thanks, Todd. So I want to take you a bit deeper into the technology behind OmnidAb and single-domain antibodies. Let me start first with a conventional antibody. It’s an IgG molecule. This is the predominant antibody formed in most vertebrate species. And it consists of two heavy chains and two light chains held together by disulfide bridge. And at the top of the molecule, there’s two binding domains that are comprised of a VH and a VL. And then at the bottom, the base of the Y, if you will, is the Fc domain. This is important for serum stability and effector function. So that’s a conventional antibody. Now different species will have alternate forms and in particular, relevant here is in camelids. So this includes camels, llamas, alpacas, have an alternate form of antibody called the heavy-chain-only antibody.

So just as it sounds, this form of antibody is devoid of light chains. It just has a VH domain as its binding entity. And this is interesting actually because a typical antibody does need a VH and a VL. And so the llamas or the camelids have had to evolve certain changes in the VH to support stability as a standalone binding element. And that’s actually really important because that allows a very stable but small binding entity to exist. And in fact, researchers have taken this entity by itself as just this 15 kilodalton sized binding unit. And this compact size, this opens up a lot of potential in terms of what kinds of binding molecules you can create with it. And so just to give you a flavor for what can be done here, you can take these individual antigen binding scaffolds and put them on to a variety of different types of molecules.

So on the left-hand side of this slide, I’m showing where it’s been Fc scaffold. So in a llama, there’s obviously a llama, Fc domain, but you can actually put a human Fc domain or put these single-domain antibodies on a human Fc. And there you get the benefits of having a human Fc, again, a longer serum half-life and effector function. So what’s the main benefit of having the single domain antibody there? Well, you can actually control the valency. So how many interactions are you potentially able to have with your target antigen. And so you can stack these single domains together to create a variety of multivalent molecules. You can actually also put the single domains at different ends of the Fc. So there’s a lot of optionality that you have there to create molecules that have certain performance attributes in biological systems.

You can also take two different single-domain antibodies. So raise different specificities and put them together. So you have a path towards bispecifics and multispecifics. These can be scaffolded also on the Fc, but they can also exist sort of on their own as well. So if you look at the middle portion of the slide, here, we’re talking about taking advantage of the smallness, if you will. These are ultra small-sized binding unit. And this allows you to create something with activity that you can deliver drugs. You can conjugate these single-domain antibodies to a variety of different types of molecules included in radionuclides. These are very small therapeutic agents or imaging agents that can penetrate tissues and have a variety of uses. So you can use these well as monospecifics, or single domain units, but you can also tether these together to create a chain of multi-specific molecules.

And you can also tether these to types of antibodies of single-chain antibodies in fact. So the point here is there’s a lot of flexibility just on the therapeutic protein side. And in addition, these have been found — single-domain antibodies have been found to be useful for CAR-T, where you can actually use the single-domain unit as the specifier towards the target, and that’s linked to T-cell signaling. So there’s a lot of potential there, in medicine this can open up a lot of new doors, so to speak. Alternate routes of administration is an important one. So I should mention these single-domain antibodies are very stable and relatively protease resistant. So it can be delivered orally or intra-nasally, so they can actually depending on where the target is, you can deliver these molecules directly to the target.

So that’s an advantage, in particular, this type of compact antibody. And also related to that is tumor penetration, tissue penetration and potential for getting the cross blood-brain barrier to deliver therapeutic molecules across that. For imaging, quite useful, the fast clearance rate. So if you don’t have an Fc or you haven’t done anything to extend the half-life, these molecules are cleared very quickly out of the serum. So for imaging applications, that’s really important because molecules, which are bound to the target can combine very quickly. But then everything else that’s not bound, it’s cleared quickly, creates a very high contrast. So we make some very ideal molecules for that kind of situation. So overall, there’s a lot of broad therapeutic applications for single-domain antibodies in medicine, and it’s growing.

So let’s talk about how, as of today, the single dividend antibodies have been made. What’s the process or at least maybe the ones that are in the clinic now or approved drugs, how were they made? And again, the single-domain antibodies come from llama, so you need to immunize a llama. And when you immunize llama, you get llama antibodies. That’s fine, but more ideally for therapeutic use in humans, you’d like to have a human-based framework. And so a lot of the molecules that are in the clinic now have been originally from llama immunization, but they’ve been humanized. And what humanized really means is, you take the frameworks within the regions. There’s framework regions and there’s CDR regions. CDR regions are the variable sequences, which determine the specificity of the antibody for the target.

You can’t generally change those too much without altering the binding. So those usually stay, but the framework itself is an important thing that you can change the human. However, if you just simply change the llama frameworks to human, that particular molecule is not that stable and not that developable in a way because it’s not really evolved, human genes, the framework have not evolved to be expressed on their own. They’re always paired with VLs. And so that requires you need to sort of introduce some of these llama sequences back into the human frameworks. You got to reengineer those back in, in order to have a good developable molecule. So all of this is just a lot of engineering, a lot of process and after that initial discovery of the antibody.

And this is where OmnidAb can really streamline the entire process. So OmnidAb is a transgenic chicken that starts with a human framework, but not a 100% human framework because we’ve introduced stabilizing mutations, into certain regions that are important that are known to support solubility and stability as a single domain antibody. And in this slide here, it’s rather busy, but what we have on top line is actual fully human sequence. And then just below that, where you see the purple arrow, you can see the positions where we’ve put the stabilizing mutations in. And then below that, are all the clinical molecules, including some that are FDA approved. And what you can see collectively is those positions where we’ve introduced our stabilized mutations are consistent with how these clinical molecules have actually turned out, how they’ve been engineered after all this effort to come at those sequences.

And of course, the CDRs are highly variable because they’re all different targets. They are different antibodies. But the point is that the basic scaffold that you want to have in the end, that’s engineered already into the chicken. So that means that when you immunize OmnidAb chickens, you’ll come out with a panel of antibodies that will already have these engineered attributes to them. So this allows you to get much closer to your final molecule directly from the animal, which basically saves time and decreased risk of things going wrong during the engineering process. So that’s one key benefit. It’s pre-engineered aspect of the transgene, it’s a key benefit of OmnidAb. But also, as Todd mentioned, this is in a chicken, right? So that’s a different animal, too.

And one way to understand the advantage here is basically, all mammals genetically are more related or closer to each other than they are to birds, let’s say. So if you’re talking about raising the antibodies, you’re you really — any animal who is responding to a foreign antigen, the degree of forms, the degree of difference is important. And so within the realm of mammals, there are differences. You can make antibodies in a mouse to a human to various species. You can get antibodies out. But they don’t tend to be recognized as foreign within that group of mammals as if you were to immunize any of those proteins into a bird. Another way to think about this if you indulge me in a metaphor here, let’s say your target of interest is a green apple.

And that’s different than what the mouse version of this protein is. Let’s say, the mouse protein a red apple. So you inject the human protein into the mouse, the mouse will say, yes, that’s different, and I’ll make some antibodies. And we expect this. This happens. Lots and lots of literature and progress based on this, okay? So I’m not saying that doesn’t happen. However, you take that same human protein and you put it in the chicken, man, right? The chicken sees this is a very different protein. I mean there’s places all over this target that the chicken recognizes as different. And those places on the protein, those are called epitopes. So you can talk about the epitopes diversion, and I’ll talk about that some more, too. But that’s one of the key things that chicken bring is a recognition of all these different epitopes and they’re diverse.

They’re all over the target of interest. And this is sort of fundamentally an advantage that the chicken brings. And in fact, all of our chicken-based platforms bring this. They’re all in a chicken host. So it doesn’t matter if it’s OmniChicken with a VH, VL antibody. It’s a matter if it’s OmniFlic, with a common light chain antibody and here in OmnidAb. So these are really kind of summarized the two benefits here. The chicken host recognition plus this pre-engineered nature of the transgene, particularly for single-domain antibodies. And just to give you a little data on this, this is highlighting both of those steps. So on the left side, we’re looking at a process called epitope binning. This is looking at a panel of antibodies from one campaign versus panel of antibodies from another.

In this case, the first campaign was performed in OmnidAb. In other case, the campaign was performed in OmniFlic, which is a rat, a common light chain rat. And this process of epitope binning allow you to see where are these antibodies binding, and why are they different from each other? And you can see here, there’s a clear delineation for this target model antigen called NKp46 that there are some common epitopes that both species hit, but there’s a lot of different ones. And the OmnidAb chicken actually brings a lot of new stuff in terms of epitope recognition. Now this can be driven both by, in this case, by the chicken evolutionary distance, but then also just the nature of the binding element itself, which is a single domain. It’s a smaller paratope.

That’s the part that binds the epitope. It’s smaller than a conventional VH by VL antibody. So anyway, a lot of — and all the results we’ve seen so far with OmnidAb, where you can get the sort of unique epitopes. Finally, on the developability side, these pre-engineered single-domain antibodies appear to be very, very stable and robust in systems that we use to measure this. This is an important feature for actually making drugs out of the molecules. And the types of things you look at are thermal stability and there’s a whole host of things. But one very important metric that’s looked at is self-interactions. An assay called AC-SINS. It’s looking at when you take the protein, you put it a very high concentration. What’s a tendency for it to aggregate?

Because that’s a problem for manufacturing down the road. And the panel on the right here shows you, these are just randomly selected positive binders from OmnidAb, showing you the range. In this case, a low score is better. So you’re seeing the AC-SINS score being quite low for those. And if you can compare it to some other molecules on the right, the very high scores, those are actually clinical failures, which had a problem in this area. But then further to the right, you’ll see a couple of other approved — currently approved antibody drugs, including one, which is an engineered llama-based antibody. And in this metric, those don’t score as well as just a collection of antibodies from OmnidAb. So to summarize, OmnidAb transgenic chickens express an optimized single-domain human framework.

So again, pre-engineered, OmnidAb being a chicken that targets distinct epitopes. These are robust animals, you get bit tighter upon the immunization and high-quality antigen-specific antibodies with good developability profiles and a good expression levels. So we think this is a very strong platform for therapeutic antibody discovery going forward. A lot of options for partners, especially when you think of this in the context of all the other platforms that we have. And this is a good segue speaking on the kind of the more in silico and screening side of things. I’ll turn it over to Bob Chen. He will tell you about how we’ve really developed and push the technology on that and to get the very most we can out of the repertoires we generate from our transgenic chickens.

Bob Chen: Hey, everyone. I’m excited to be here to talk about how we’re enhancing discovery with OmniDeep. In May, we launched OmniDeep to highlight for our partners, the suite of in silico tools that we have for therapeutic discovery and optimization. These tools are woven through our technology stack to help us create diverse repertoires, to help us screen millions of cells. And they help us deliver the right antibody. Our goal is to use these in silico tools to streamline and assist drug discovery. We want to make our capabilities ever more efficient and effective for our partners. OmniDeep is built on four fundamental pillars. It’s based on deep repertoires from our animals. It’s based on deep screening with proprietary hardware and software.

It is based on deep sequencing, which collects in-depth information and is based on deep learning tools that help us understand this information and make insightful decisions. I want to emphasize that the input data of OmniDeep are the deep repertoires that we get from our animals. These repertoires are generated by biological intelligence. This is the interplay between rational genetic design and powerful in vivo process. For example, on this image here, is a illustration of what repertoire space may look like in three different animals. They are immunized with different variants of the same targets, two protein variants and one genetic immunization with either mRNA or DNA. And you can see the repertoires look different between these animal.

And so biological intelligence allows us to create vast and diverse antibody repertoires within an animal and across animals. This is a large space, and we need to bring unique tools to be able to understand and tap into what we create. And here we can bring the xPloration platform. This is our AI-driven deep functional screening platform, and this platform is built around this chip shown here. So this chip, which is about the size of my palm, has 1.5 million 40 micron features in it. This is a unique through-hole format. And in each one of these microcapillaries, we can form an assay of some sort. Very often, we perform binding interactions with florescent antibodies. And then we bring in a suite of various machine learning hit detection models that allow us to automatically look at every single one of these cells and label the target of the hits of interest.

Once we have a list of cells we want to recover, we use a precise and proprietary laser recovery method to recover the cells very quickly, which are then fed to a single cell bar coding workflow or a full sequencing workplace. So what we’re doing is integrating biological intelligence with AI. We feed biological intelligence into these deep screening and sequencing tools, which enable us to have large-scale data collection. All this information is dumped into proprietary databases, which then feeds into deep learning models, which we stack on top structure-based design tools to really have high-quality sequences. These tools and this ecosystem allow us to better mine the diverse repertoires that we have. So biological intelligence and exploration creates a synergy, which generates a large amount of data.

And OmniDeep is the key to navigate this data and to empower what we call large-scale antibody discovery. To illustrate, what I mean is we’re going to talk to our case study. In this case study, we immunized three OmniFlic animals and we screened over 27 million cells that led to over 3,000 positive binding events and over 1,300 unique sequences that span over 124 lineages. We like to visualize repertoire space with the spot here. In this spot, this is the top 30 lineages across the screen I just mentioned. And you can see that every one of these spots represent unique antibody sequence. So this illustrates the scale at which we deliver to our partners. Now to go from a large pool of sequences to therapeutic candidates, we need to have a strategy, and we need to make some decisions along the way.

Traditionally, you may look at perhaps the lineages that are boxed in the squares here. They’re boxed because they are likely to be both protein and cell binding but now with OmniDeep, we can apply some in silico tools to intelligently prioritize and select candidates from this large pool. So how does that work? Well first, we can take the high quality input data, the xPloration hits and perhaps assay data, affinity value from select clones and we can put in all of the NGS data from the immunized animals. So we can put in real sequences that exist in animals and also very high confidence data on a few select clones. And we can feed that into deep learning. Very often, we use a type of model called a variational autopilot. And the goal here is to extend the insight on those confirmed hits to be able to infer the function of untested clones.

In essence, the deep learning gives new suggested clones to go after. For example, in this block here, in the middle, you can see a green box and highlighted in light blue are clones that were suggested to further test the characters. Before we do so, we established one last quality filter. We use a structure based in silico developability filter. This filter and set of tools allow us to create homology models on any given sequence. And that allows us to use our 3D and homology models to calculate some developability properties. And therefore, we can use these to, very efficiently in cost and in time, filter out and focus on the most promising candidates. And so AI gives us additional high affinity and highly developable antibody sequences.

So looking back on this spot, we can go from the lineages that are boxed here to feed that into AI, and AI suggests to focus on maybe some additional columns [ph], which are highlighted in the green in the box lineages. So now we have a systematic way to continue to select antibodies. And one thing I’ll point it tells us to perhaps focus on some rare variants down at the bottom. These are variants perhaps, we would not have known to look or would have prioritized against because of resources. But now we have a way to explore this space in a guided manner. And so OmniDeep provides these new insights specific to these non-obvious insights on our immune repertoires for our projects. So OmniDeep is where the power of Biological Intelligence meets AI.

We study and embed Biological Intelligence into machine learning, helping us assist in discovery and optimization. We’re offering access to our partners’ new workflows, for example, this large-scale discover workflow and also the various optimization tools in OmniDeep for their existing campaigns. We are trying to offer in OmniDeep the best of our in vivo and in silico capabilities. Thank you, and next will be Doug.

Doug Krafte: Thanks, Bob. So Bob and Bill just did a great job introducing some of the innovations that we have in our technology platform. What I’d like to do now is move on to the Ion channel opportunity to that’s coming out of the development of those innovations and some really interesting things that we have ongoing. So for those of you not familiar with this space, I’m just going to walk you through what Ion channel is. Everyone, I think is familiar with ions. These are atoms or molecules that are charged, sodium potassium when we go to our physicians, and we get our blood chemistries done, we measure all these things. Every cell needs to manage the composition and the concentrations of ions across cell membranes to maintain health and a normal status and avoid disease.

And it does that by expressing ion channel proteins primarily. And what these proteins are. They’re shown on the cartoon on the right-hand side of the slide that you see. The green is the protein and the little orange circle spheres are the ions going through, depicted to go through. And you can see on the right-hand side, the ions going through that one channel. And it’s a big hole, and that’s a channel through the membrane. Hence the name ion channel proteins for this class. And when those ions flow through, they can translate signals, they can change a lot of properties and cells. It results in very small currents and that’s what’s shown in the animation on the bottom there that you can see. Those downward blips of deflection are one protein, one ion channel opening at a time and also very, very small electrical current coming from that.

And with sophisticated technologies, we can report these currents and do things in the lab with them. So when you look at single proteins and ion channel function, it’s really cool. If you’re into science that’s a fun thing to do, but why should anybody else care, right? Well, they should care because channels are important in both health and disease. And they’re involved in everything we do, everything, almost every function in the body. We’ve picked some examples here to show you on the left. Ion channels regulate the heartbeat. They regulate movement through muscle and their function. All of our senses, vision, smell, touch hearing. Clay Armstrong, who is an eminent biophysicist and researcher in the field made the statement when he was receiving the Albert Lasker Award.

He said, I think that ion channels are the most important single class of proteins that exist in the human body or any body for that matter. Now Clay Armstrong obviously is biased because he worked on ion channels, but it really is true. They’re everywhere, and you can only get a lot of physiology, biology and disease by studying these proteins. And they’re one of the largest drug classes as a result of that because when ion channels don’t work, a lot of things go awry. We’ve spent at OmniAb many, many years now developing what we believe is one of the industry’s most experienced ion channel teams of drug discovery experts. We have decades of experience that have primarily been applied in small molecules, but now we’re leveraging that in the antibody space.

And there’s a rich heritage from our staff and our scientific leadership in first in the field. As an example, we were the first people to discover selective blockers of a protein called Nav1.8 sodium channel protein. And for those of you who are interested in pain therapeutics and looking at that space, there’s a lot of buzz about potential opportunities for new drugs coming out of that space targeting this protein. There have been other examples where we’ve actually looked to develop technology platforms that our pharma partners are using to enable targeting unique variants of ion channels and driving SAR programs. We’re continuously expanding the platform as well. This isn’t static. We’re developing custom technologies for cell line, high-throughput electrophysiology, applications of x-ray fluorescence.

We use a lot of structural biology now cryo-EM. I’ll show you a little bit of data from that and molecular dynamics. So there’s continuous evolution on the ion channel platform to improve and drive that technology forward. It’s validated in many ways by our pharma partners. I list to here, GSK and Roche, where we have programs in neurological disease and neurodevelopmental and neurodegenerative diseases. So high-value targets with these global partners. So where is the opportunity here? And is there an opportunity? Well, there really is. I wouldn’t be standing here in front of you if there wasn’t. So they are validated the ion channel targets across the genome, whether they’re sodium channels, potassium channels, have been the rich history in neurological disease drugs, cardiovascular drugs and metabolic disease drugs that have come out of this space.

But to be fair, it’s been challenging. It’s been difficult often to obtain small molecules that actually will have all the right properties for making drugs against this target class. And we’ve been successful serving in that space of small molecules, but it’s been challenging. And I think for the industry as a whole. We believe antibodies can solve this problem, and I’ll tell you why in a moment. And we believe we have some solutions in this space that can really help advance this particular area. And specifically, OmnidAb and OmniTaur, which both Bill and Todd mentioned, we feel are very unique from our company, our position and coupling to our platform can potentially provide solutions for targeting ion channels in this space. Now this figure, I want to just go back and reference the first slide I had is, this relates to that.

Remember when I showed you the cartoon of the ion channel. What you see on the left is basically a real structure for an ion channel that we’re interested in. We generated cryo-EM structures, run molecular dynamic simulation in the computer and got structural data to help drive our programs. That’s what the red is in that slide. That’s the ion channel protein. The green is the membrane. So it’s buried in there if you look in. You can see a hole through the middle. That’s the core. And so that’s what if you want to modulate up or down, you want to get things into that. There’s almost four overleaf (phon) kind of thing around the outside where you can see domains that control the opening and closing of that core. And so that’s where we want to get.

We want to drive down into those regions to develop antibody therapeutics for targeting ion channels. Historically, though conventional formats for antibodies don’t do that. They tend to bind to flat surfaces. So if you need to get down on the membrane and access these key domains, you need something else. And that’s where the smaller sizes, Bill had mentioned and Todd had mentioned about different domains and OmnidAb and OmniTaur, give us a leg up, we feel and allow us to push forward and innovate in this space. So we’ve been working on this platform approach in the Ion channels area at OmniAb now since the company got spun out. And we’ve integrated all of these species. We have the animals support species platform that Todd has talked about.

The proprietary screening technologies that was alluded to in a number of different platforms and Bob showed you that chip, which allows us to screen at very high capacity. And then we have very sophisticated high-throughput technology and expert personnel for drug discovery in the space to look at functional readouts. So we feel we can apply this platform now in a very meaningful way for ourselves and our partners to drive ion channel drug discovery biologics, and that’s exactly what we’re doing. My final slide here is just to tease this up for the R&D community really. This is geared towards the scientific folks, but we have a webinar at the end of the month that’s describing some of our strategies and approaches in this area that’s to be presented.

So please join that if you’re interested in going deeper into the science and learning a bit more. So thank you much, and I’ll turn it over to Kurt now to finish the formal presentation.

Kurt Gustafson: Thanks, Doug. So today, I’m going to start by talking a little bit about our third quarter financial results. And then I’ll kind of switch gears and talk about some metrics for the broader portfolio. So let’s start with the third quarter results. So total revenue for the third quarter was $5.5 million compared to $6.9 million in the prior year quarter. We saw an increase in the license and milestone revenue this quarter that’s related to a milestone that we received from Seagen as well as the Genovac program that was partnered with Pfizer. That increase in the milestone revenue was offset by the decrease on service revenue as a result of our completion of work on certain programs. The royalty revenue was $500,000 this quarter, which was comparable to the prior year period, but actually showed an uptick relative to where we’ve been in the first couple of quarters of the year.

On the operating expense side, our R&D expense for the third quarter was $13.9 million compared to $13.2 million in the prior year quarter. The increase was primarily due to higher personnel costs. On the G&A side, we also saw an increase in costs up to $8.5 million. That increase was due to headcount that we’ve hired as we’ve become a public company as well as those public company costs, you go back to Q3 last year, we were not a public company at that point. The net loss for the quarter was $15.7 million or $0.16 per share. Moving to our year-to-date results, on a year-to-date basis, our license and milestone revenue was significantly up from last year. You’ll recall, this is primarily due to the recognition of a $10 million milestone that we got from Janssen for the launch of TECVAYLI in Europe.

The increases in operating expense for the nine month period ended September 30, are basically the same reasons that I stated for the quarter. We’ve had headcount hiring as we become a public company as well as those public company costs drove that increase. The net loss for this nine months period was $36.6 million or $0.37 per share. I wanted to drill a little bit deeper into the cost structure. And if I move to this next slide and just take a look at our year-to-date results, I wanted to highlight something here, and that is that a significant portion of our operating expenses are noncash. If you look at the nine months ended September 30, 2023, you’ll see total operating expenses of $77.6 million. If you look at the callout box here on the right, you’ll see that between stock-based compensation, depreciation, amortization we had over $33 million of noncash charges.

So if you exclude those numbers, actually our cash operating expense is closer to $44 million. So when you’re doing your evaluation of our metrics, it’s important to understand that net income is not a good proxy for our cash burn based on all these noncash items that exist in our P&L. Turning to the balance sheet, we ended the quarter with a total of $96.6 million. This is down $6.5 million relative to where we were at the end of the second quarter, which was a cash balance of $103.1 million, but still above our prior year-end balance. There really aren’t any other changes to speak of for the quarter on the balance sheet. But as I turn over to the cash guidance, Matt talked about the fundamental drivers of the business, right? So signing new partners, that metric looks good.

We continue to see it grow in our active programs. But obviously, the industry is facing some headwinds, and those headwinds are also affecting various elements of our business. Earlier in the year, we saw some attrition in clinical programs for the very first time. And later in the year, we’re starting to see some delays in some of our partner programs. And as a result, we now expect to end the year with slightly less cash than the balance that we had as of 12/31/2022. That being said, we still continue to expect that our cash will provide sufficient runway to fund our operations for the foreseeable future. So that’s sort of it for the third quarter. I want to kind of transition and talk a little bit about some longer-term metrics and maybe drill in a little bit on revenue.

So historically, our total revenue for lack of a better word, has been lumpy. And most of that lumpiness, if you will, has come from milestone and license revenue, which is unpredictable based on the timing of our partners achieving various milestones. Royalty revenue, on the other hand, has been a little more consistent, but it represents a very small portion of our revenue. If you take a look at this chart, it’s the part of the bar chart that’s in purple, and you can almost barely see it. Now I expect that number to grow but I wouldn’t expect it to be meaningful until we see some additional product launches from some of our partners. Service revenue on this line is shown in blue, and that comes from both the ion channel side of the business as well as the work that we do with chickens on the antibody side of the business.

And that work is somewhat more predictable in the short term, based on work orders that we’ve signed, but it will fluctuate based on the number of programs that we’re working on and the type of programs that our partners have asked us to work on. In recent history, that number has been averaging about $3 million per quarter. But what I really want to sort of get into is the milestone revenue here. And so we kind of fast forward or take a deeper dive on just the milestone and license revenue that is where we’re sort of seeing the lumpiness, if you will. And over the last 11 quarters, you’ve seen the milestone revenue range anywhere from $1 million a quarter all the way up to $31 million per quarter. So as you look ahead and think about what this revenue could look like going forward, I think it’s helpful to take a step back and look at where these numbers have been historically.

And the first thing to understand is that these TECVAYLI milestones that are shown here in this orange hashed line, are unusual in both their size and frequency. It’s not to say that we wouldn’t get milestones like this in the future. We have a number of Janssen programs that are in our clinical pipeline that have the exact same structure, right? So those could happen again. But this economic structure is a bit rare in our portfolio. And so I think you need to view these payments as more onetime in nature. So if I exclude TECVAYLI from the calculation and draw a regression line on the remaining milestone revenue, you get this blue line. It starts at $3.2 million and has an upward slope that grows up to about $3.6 million here in this current period.

If you were to include the TECVAYLI technology milestones in this regression line, you’re going to get a much, much steeper line and it wouldn’t be consistent with the base level of our business. So as you think about our license and milestone revenue going forward, I think it would be more appropriate to base your estimates on something that excludes these TECVAYLI milestones. So I also wanted to show you some metrics for our total active programs. But before I do that, I think it would be helpful to understand a breakdown of our active programs by license type. The first thing I want to say the most important thing is that 98% of our programs have downstream economics. There are only five programs out of the 314 programs that are active programs that have what I’m calling either a grandfathered or a prepaid license, right?

So only five, and all five of those are in clinical development. So they’re highly visible programs. But just to be clear, those are the only five. The other one that I think is worth mentioning just kind of calling out is the slice of the pie that’s shown here in green, which is the revenue share licenses. These are mostly from our academic institutions where there’s usually not an intent for that academic institution to take that molecule all the way to commercialization. And so the way these agreements work is that we will receive a portion or a revenue share of whatever that institution that discovers that molecule gets when they do a sublicense and license it out to someone. So while our percentage of that revenue share is defined upfront in our agreement with them, in many cases, we don’t know what the final economics will be until they do a license with someone else and then we’ll be able to understand what our share will be.

So what I’m going to show you in the next couple of slides are the metrics for the vast majority of our portfolio, which is the standard license shown here in purple, and I also have another slide for the ion channels. So if I take a look at that purple section of that pie chart, and we’re talking about 294 of our programs. And we exclude — so this does not include those grandfathered license. It doesn’t include the ion channels. And I’ve excluded the revenue share agreements where the economics are not yet known. So that’s the 294 programs that we’re talking about. And we add up all of the remaining milestones for those programs. That number is greater than $3 billion. Now obviously, we don’t expect every single one of those programs to be successful.

But I think you’ll agree there’s a significant opportunity available to us here. In addition, if you take a look in those 294 programs and take a look at what is the average royalty rate for all of those programs, it ends up being 3.2%. So if we look at the ion channel programs, the economics are even higher on a per program basis. So as Doug mentioned, we have programs ongoing with GSK and Roche. And the remaining milestones on those programs are approximately $1 billion. Now due to the confidentiality of those agreements, I can’t give you sort of the same number that I did for the other programs on royalties. But I can tell you that there are — they are tiered royalties that are higher than the standard antibody platform license that we have.

So I hope this provides you a little bit of additional insight into the economics of our programs. I hear from you and understand that it can sometimes be difficult to model our business given the lack of information to the early stage programs. But hopefully, this information today provides a bit more insight into the value of this pipeline. That actually will conclude our prepared remarks. And so we’re going to have some time to answer your questions. And I’m going to ask my colleagues to come up here on stage. And the way this is going to work is that if you take a look at your Zoom screen you should have a place down at the bottom, where you can raise your hand. And so if you want to ask a question, go ahead and click on that little button to raise your hand.

And that will let us know that you have a question. We will try to do our best to try to take your questions in the order that they come in. And we will let you know when — I’ll sort of announce when your line is up there is — we are going to open your line. One of the things just to remember is we are going to unmute your line, but if you muted your line on your end, you will have to unmute your line there as well. So let’s go ahead and we’ll try to get our first question lined up here.

A – Kurt Gustafson: All right. So the first question is going to come in from Joe Pantginis. Joe go ahead, unmute your line and hopefully we will be able to hear your question.

Joe Pantginis: Great, guys. Can you hear me?

Kurt Gustafson: We can hear you, Joe.

Joe Pantginis: Awesome. Great to see all of you. Todd, I hope you had a great trip. So a couple of topics I’d like to discuss. First, on the underlying business. First, Matt, you mentioned about looking at minimal headcount growth. I was just curious if you could provide any more color with that since you have continued to grow your technology basis, so how those sort of two reconcile?

Matthew Foehr: Yes. Yes, Joe, thanks for the question. Really, so we’ve come off a year, now this last year or so with pretty substantial headcount growth. It’s been one year since we split off to become an independent publicly traded company. And as a result of that, we added our administrative functions. Kurt built out his finance team. We added an internal HR people team and we’ve leaned into other areas of new science. We’ve added new team members in our screening platform as well as on our engineering platforms, also have continued to, as we have for many years, lean into the AI and ML elements of big data management and other things that that Bob highlighted. So we’ve come off a year where we’ve added a significant number of fantastic new colleagues.

But as we look forward, I do see some headcount growth, but it’s fairly minimal in many ways, largely because the business is highly leverageable, right? There’s an efficiency built into the business around how we work with our partners, how they leverage work that we do for them, but we are also the beneficiaries of the fact that our partners have some of their own work streams as well. So really, what is meant by that statement is that we see substantial growth coming in terms of new partnerships and new programs being added to the portfolio, but expect those can grow at a higher clip than we need to grow our infrastructure. And I think that really says something about the levers that we have in the business. I don’t know, Kurt, maybe you want to add anything?

Kurt Gustafson: No, that’s absolutely right.

Joe Pantginis: Great. Thanks. And the second part of the business question is, obviously, Kurt, you talked about headwinds in the markets and what have you and you see attrition of clients or partners, so obviously, the main reasons, which no one should be surprised, failed clinical trials or strategic moves by your partners. I’m curious, has any attrition come from the lack — or companies that face financing headwinds that are not able to commit the capital that they want to at the time to be able to partner with you?

Matthew Foehr: Yes, you could — I mean I can add as well. But I’ll make a comment and then Kurt can fill in anything I missed. But the first point I’d make, Joe, is as you look at our metrics over the recent years, everything we report is net of attrition, right? So we report our partners net of attrition, our programs net of attrition. And those have all continued to grow even in this I’ll say, last 24-month period of headwinds in the pharmaceutical industry. So we’ve continued to grow both our partner count and our new programs net of attrition. But we have had some partners who have, I’ll say, gone away. I mean one that was disclosed was Seeker Biologics. They were one really interesting science and were VC-funded a few years ago.

But early in the year this year, announced that they were closing down. Actually, some of the programs, those assets actually returned to us, and those are ones that could be potential for future partnering events in the future. But I think that’s one example. When you talk about bigger partners, it’s a little harder to define some time. So Kurt referenced clinical attrition that we saw early in the year this year. That clinical attrition was driven by a global big pharma partner who was realigning therapy areas. He decided to get out of inflammation. It was the specific example. I can’t talk about the specific partner, but that is one of those situations very hard to define if that is that macro or is that driven internally? I think even with perfect information and dialogue with the partners or best information we can get, it is a bit hard to know exactly if something like that is driven by macro factors or simply a business refocusing.

So I mean that’s kind of answer. Kurt, if I left anything out?

Kurt Gustafson: No, I mean, I think that’s right. When somebody says they’re going to do a reprioritization, is that truly reprioritization or is that a funding issue? I’m not sure we’ll know. But in some ways, I’m not sure it really matters to us. It’s a program that maybe is being delayed or not going to get the economics for us. So in some ways, it doesn’t necessarily matter.

Joe Pantginis: No, absolutely fair. And then the other topic is — and thank you for indulging me, obviously, I’m very happy to see the launch of DAB today. I’ll keep all the pictures of people dabbing out of my notes. So two parts there. So the first part is seeing the multi-block or building block potential of this makes me think of other companies, say like in the Anticalin space or what have you, where you could come up with a lot of unique structures. Do you have any early information about the ease or difficulties of future manufacturing of these sort of multifaceted structures? Are you looking to present any experimental data from the DAB program publicly? And then lastly, can you talk a little more with regard to DABs and their differentiation or more specific epitopes specificity compared to a traditional antibody. Does it change the calculus on clinical applications?

Matthew Foehr: Yes. Great question, Joe. I’ll let Bill comment on those. I will say that the early feedback from partners has been fantastic. Todd highlighted some of that in his section. But in terms of them understanding the part of the industry, the need that this technology can fill that initial feedback has been great. As I mentioned, we already have a couple of partners who have programs that are in progress that are already in progress. So those started here in the fourth quarter. But those are already moving in part. But Bill can talk about our disclosure plans. We have a couple of big presentations coming up from scientific conferences and then can address your other questions. So Bill.

Bill Harriman: Yes. We have a few presentations where we’re going to go into more depth about the qualities of the antibodies coming out from OmnidAb. So that will be next week in PEGS and also in AET in San Diego in December. And we’ll be continuing to talk about this as more data comes out. We have plans to publish a couple of papers on the topic and to answer your question a bit more specifically on developability, that’s something that we’ve looked at pretty hard because obviously, if you invest as much as we have in engineering Transgene, you want to make sure that you’ve got a good one. We’ve got a lot of good data and it’s growing, which will, I think, really, I would say, specify how developable these single-domain antibodies that come out of our platform are.

Now when it comes to linking multiple single-domain antibodies together, is it multispecific, hooking them to different types of moieties and some of that slide that I presented with all the different variations, obviously, we haven’t checked all of those. That’s too much to do just for us. I mean we will be working with our partners on that aspect. But I think at the core of it, if you can have very high-quality human single-domain units, you’re in a pretty good position to develop all of these kinds of molecules.

Joe Pantginis: Thank you, gentlemen.

Matthew Foehr: Thanks Joe.

Kurt Gustafson: All right. So it looks like the next question is coming in from Puneet. Go ahead, Puneet and looks like you’ve unmuted your line. Go ahead and ask your question.

Puneet Souda: Yeah, thanks guys. Thanks for hosting this. Hopefully, you can hear me well.

Kurt Gustafson: Yes.

Puneet Souda: Okay. Great. So first one, maybe a high level on the partner side, and then I want to get into technology question with Bill and Bob and the team on that. So maybe just how do you expect the partner mix to sort of change given the funding situation that is sort of finally caught up with antibody discovery part of the sector, too? I mean you might recall I was asking pretty much every quarter about that. But finally, we’re here. So now going forward, do you expect more collaboration, I mean more partnerships with larger pharma? And maybe just talk to us about that, how do you see the evolution in 2024? And I hear Kurt’s comments on the revenue side, but I just want to make sure I’m conceptualizing it correctly.

Matthew Foehr: Yes. Puneet. Thanks for the question and I’ll comment on the partnering landscape and Todd can fill in as well. What’s interesting the last two quarters, we’ve disclosed new partnerships with global big pharma companies. And that I think, is really a testament to the innovation work that this team and the team back in our labs are doing and also the visibility of the platform, the level of validation around the platform it’s definitely attracting the big pharma players who understand not only the technologies we have now and the level of validation work we put into them, but also our intellectual and business and scientific medical commitment to continued innovation. I think that’s what’s attracting some of those bigger partners.

But we are still signing up smaller partners as well. We announced a deal with GigaMune and Polaris Therapeutics this last quarter. So we are still seeing some of that, which is good. That’s counterbalanced by some of the examples I gave earlier of Seeker. And I’ll say our business development team has been as busy as ever. We have substantially expanded the team. Todd’s done a great job of bringing in a top-notch, I’ll say, science — scientifically trained and a BD team that really leans into the science because that’s something that our partners find of value. But Todd, maybe you can offer more color around migration and other things that have affected.

Todd Pettingill: Yes. That sounds good. Yes, it’s definitely — there’s been a lot of growth and as things have evolved, it might be helpful to talk to the sourcing of where these partner leads come in. Like Matt said, we’ve expanded the business development team significantly, and we spent a lot of time at conferences. Upcoming, there are some large ones antibodies, AET in San Diego. That’s a great source of leads. Also, we have our marketing team that really is going out in major publications. We’ve set up a series of webinars that drive interest. And then as Matt already mentioned, one of our biggest sources of new deals is what we call scientific migration. People that start in one company, they get familiar with the technology there, and then they move on to another company and they say, hey, I want to keep working with OmniAb. That drives a lot of our current deals.

Additionally, it really drives why we’re working on — one of the reasons why we really want to work in the academic space. Not only are the academics coming up with novel ideas and new things that are interesting, the cutting-edge ideas. But also they’re training the next era of scientists. And these scientists go to other companies, and they are familiar with OmniAb and then that brings additional deal. So really, it’s deals, deals beget deals and we’re doing everything we can to keep pushing that forward. But it’s — in a lot of ways, it starts to feed on itself.

Puneet Souda: Got it. I appreciate that context. And then just on the type of projects that you’re getting, obviously, some challenging projects were the early ones that you were receiving in the early days. How does that look now? Are you getting more sort of routine projects or a slew of projects? Just trying to understand sort of how does that — how is it looking in terms of the — for lack of better work, how challenging the target is versus situations where a company might be in funding — is facing tougher challenges in the market and in the funding situation and they’re offloading the work to you in order to be more efficient capital-wise. Maybe just give us a sense of the type of projects you’re getting now versus over the last two years?

Matthew Foehr: Yes. Puneet, great question, I’m glad you brought it up. In some ways, as we look across our portfolio, it’s, at times, it’s a bit of a misconception that our technology, our group of technologies, our suite of technologies are really suited only for difficult targets. Now there are a number of targets that are hard to track and that partners come to us for, but it’s really not the case that it’s really only highly conserved or only difficult targets that come our way. In fact, because of the diversity of species and diversity of platforms and other things, we often see partners who are approaching us for known targets, maybe ones that are — we’ll call it a household name type target in the community with which we’re talking here, but they’re looking for a new way to approach it, right, that has never been shown before.

And our technology is quite amenable to that as well. But we’ve got a broad array of diverse targets across our growing portfolio of programs and that is driven not only by the innovation with the platform, but also the longer-term benefits partners can get not only scientifically but IP and other things as well. I don’t know, Bill or Bob, you may want to add any color?

Bill Harriman: No, I think you got most of it. I mean there are those partners who have targets that are nonconventional. They’re very novel. Sometimes they’re [indiscernible] so we’ve invested a lot in our protein science capabilities so we can address those targets. So we’re ready for those. We engage on those. We’re happy to do those. But at the same time, we do have, as Matt mentioned, a lot of partners coming in with very well-validated targets, let’s call it that. And in some cases, they’re looking for a next-generation antibody. It might be something where they think they can really benefit from an OmnidAb format or even OmniTaur type of format. So they’re looking for a different modality in a sense or in some cases, just want to leverage the chicken and get a new epitope that hasn’t been seen before in most of the standard therapies or standard discoveries.

Puneet Souda: Got it. And last one, if I could ask, and thanks for hosting this, guys. Do you — how do you see the growth of OmnidAb in these early stages? Can you compare and contrast to what you saw with OmniChicken when you launched it, and OmniRat? And then one for — just for Bob, a quick question, Bruker has invested into a screening platform. Wondering if that changes anything in the market in your view? Thank you.

Matthew Foehr: Yes. You can — well — yes, I’ll let Bob take the Bruker one.

Bob Chen: Yes. I think you’re referring to the Bruker acquisition of PhenomeX and kind of the formerly Beacon — Berkeley Lights Beacon platform. I mean I think that is platform we’ve known well. Actually, we even have partners who use that platform very well with OmniRat and have good successes with it. Our technology is one that — I actually have been working out for a long time. It’s one that I’ve kind of shepherded through from Stanford to Exela to now OmniAb. We have a strong IP portfolio, and we really do things in a very unique way because of that chip. I think we are very proud of kind of our throughput, both on specifically screening and the recovery. I think that’s illustrated with the case study we talked about just the scale data we get out and that exploration here is really a foundational tool to empower not just standard Discover, which it’s been in the workflows now for our partners now for the last few years.

We’re really looking forward as a large-scale data collection tool, and we hope to talk about more in the future.

Matthew Foehr: Yes. Thanks, Bob, and I’ll add to that as well, our — the exploration instruments, the only exploration instruments that exist anywhere in the world are within our walls, if you will. And that I think, is something that our partners value and attracts them to us as well. To your first question, Puneet on kind of comparing and contrasting OmnidAb versus the — I’ll say the early days or initial launch of OmniChicken or the first launch of OmniRat, I think the difference now, one is just a level of validation around our platform. We are leveraging the heritage of OmniChicken, which now has a program in the clinic and others that are quickly approaching the clinic. There’s a lot more visibility for not only our organization, but our technology is really driven by the hard work of these folks on the stage and a number of other folks back in the office and around the country.

From a business development perspective, from a marketing perspective, from a scientific and exploratory research perspective, which is something we’ve leaned a lot more into with some key hires recently. I think there’s a lot more visibility. And I’ll say, getting the kind of feedback that was highlighted in Todd’s section, really leading up to a launch and at the time of the launch, this is inbound feedback from partners. So that visibility is something that we really haven’t had in the past when we’ve launched a new technology. So for me personally, that excites me about the impact it could have and the ramp-up. We’ve invested, as I said, substantially in our infrastructure over the last 1.5 years expanding our capacity for chicken-related programs, really in anticipation of this and other downstream innovations.

And so we feel like we’re well positioned, and we’re excited about the initial feedback we’re getting from partners.

Puneet Souda: Super, thank you guys.

Kurt Gustafson: Thanks, Puneet. All right. Looks like the next question is coming from Steve Willey. Go ahead and unmute your line, Steve and we will take your questions.

Stephen Willey: Yes. Can you guys hear me okay?

Kurt Gustafson: Yes, we can.

Stephen Willey: Okay. Great. Thanks for doing this. I know Kurt, and I guess, Matt, you guys have spoke to some of the attrition that was seen, I guess, earlier in the year. And I think Kurt you mentioned that you’re now starting to see some delays to some of your partner programs. So just wondering if you could comment a little bit on the delay side and I guess, where in the trajectory of clinical progress you’re seeing these delays occurring?

Matthew Foehr: Yes. I’ll comment, and Kurt can fill in, Steve. A couple of things going on. One, as programs approach what we call that preclinical phase, which our hurdle for preclinical is quite high from an industry perspective. We only put something in that preclinical bucket if it is in pre-IND studies and the partner has confirmed an intent to file an IND and go into clinical trials. And I think that’s where — despite the fact that we started the year with the expectation of three to five new clinical starts this year, and by the end of Q3, we’d already reached five. So we’ve seen that progression. That said, the mix of programs of those five was different than we anticipated, one of which was a Roche program, exciting from a medical and scientific perspective, but because it was a grandfathered license doesn’t carry economics with it.

So the mix has been a little bit different. And as we get into the details of those 14 preclinical programs that are really pre-IND, and are approaching first clinical trials, there are, I’ll say, subtle differences from — and what we mean by delays in different programs, right, where maybe there’s — it’s taking more time or they’ve decided to re adjust their manufacturing plans for the first clinical trial, that kind of thing. So we’ve seen some of that with some of the smaller players. It’s always difficult to say what’s exactly driven by macro factors or not, but we feel it’s best to just be transparent about how we’re seeing the pipeline develop. We’re very excited about the growth in our programs that are approaching the clinic, but each one has a slightly different, I’ll say, story behind it.

We did see early in the year, as Kurt referenced, some therapy area realignment by a big pharma partner, and that can either be driven by macro factors or could potentially just be an internal factor as well. It’s somewhat difficult to know exactly. So hopefully, that answers your question.

Stephen Willey: Yes, it does. And maybe just — I guess, as you’ve kind of rolled out OmnidAb, I’m just kind of curious as to what are the applications from partners that you’re seeing the most interest in right now? I guess, is it on the radiopharma side, I guess, as an alternative scaffold to peptides? I think the imaging part that Bill talked about is really interesting. Is it against highly conserved targets where obviously, OmniChicken helps? Is it against these multivalent targets like TNF receptor, family members that require this ligand multimerization or whatever to the agonized? Just curious as to kind of where the industry interest is right now on the single domain applications.

Matthew Foehr: Yes. Difficult for us to say, specifically, obviously, for the programs that are in progress just for partner confidentiality reason. But obviously, this is opening new markets for us in many ways. As you say, radiopharma, blood-brain barrier, application for antibodies. But Bill and Todd, maybe you guys want to add color?

Bill Harriman: Yes, radiopharma definitely is one in certain situations where an ADC might not be as appropriate. We get a lot of interest in the single-domain antibodies for creating novel multi-specifics because that’s really a unique thing you could do with them. Since they’re so small you can tether two or three or four of them together in various ways. You can really sort of titrate the amount of focus you have on a particular target, so you could have three — two or three single-domain antibodies for one target and then one against the other. So this modularity really is driving a lot of interest. So these new modalities. And I think that’s — and that can be applied to known targets and in some cases, well established targets that are then being combined with a novel target, for example. So all sorts of different combinations. But I think that most of what we’ve seen so far is it’s driving on this modularity and multispecific applications.

Todd Pettingill: Yes, there’s not a lot I would add on that front. There’s CAR-T usage and then delivering different particles or different proteins to different areas of the body. I would just add, though, there really has been a lot of partner interest over the last ever since we started making it clear that Omnidab was available. If you go to these scientific conferences, single-domain antibodies are all the rigs. A lot of people are talking about them. There’s a lot of different areas of focus that they can be used in. And people really see the value in not only the combination of being able to generate an antibody, but in the ecosystem or I guess, in the host of a chicken. A joke I make with people at these conferences, is I say, I’ll look at any law man in the face and say, we can make a way better antibody than you because really, it’s just — it’s just a game changer that we can do the same format but in a different species.

So it’s really getting a lot of attention.

Matthew Foehr: And we’ll add that to my list of other BD analogies. I hadn’t heard of, yes, but that’s good.

Kurt Gustafson: Not sure your forward-looking statements…

Stephen Willey: And then maybe just one more, quick one. So I think you talked before about how partners have access to, I guess, a la carte menu on the app technology and — so I guess as you roll out some of these new offerings, whether it’s OmnidAb or OmniDeep, do these just show up on the menu of offerings that a partner can choose from? Or do you think that there’s an opportunity to maybe kind of carve these out as separate entities, increase the economic ask and drive the average royalty rate across the portfolio higher over time?

Matthew Foehr: Yes, Steve, great question, an important one. And as it was kind of generally referenced in my slides in my section of the presentation today, as we’ve continued to innovate around the platform, as we continue to launch new technologies, in general, that is, I’ll say, increasing the value to our stakeholders, rightfully so. And that is something that we see as having the potential to continue as we launch new technologies. Each of our agreements are slightly different in some ways in terms of access to types of technologies or structure, if you will, of economics related to various technologies. But the spirit of your question is very much aligned to how we think about our innovations from a business perspective. So hopefully, that gives you some color. The reality is each agreement is slightly different, but that is part of our strategy when we think about launching new tech.

Stephen Willey: Very good. Thanks for doing this and thanks for taking the questions.

Matthew Foehr: Yeah, thanks Steve.

Kurt Gustafson: Okay. It looks like our next question is coming in from Nishant Gandhi, and unmute your line. You should be able to ask your question, Nishan. Nishant, I’m still showing — it looks like your line is still muted on your side. Let’s see. Nishant, can you hear me? Going once. All right, looks like we’re having some problems with that one. And that is our last question.

Nishant Gandhi: Hello? Can you hear me?

Kurt Gustafson: Oh, there we go. Nishant, yes, here we are.

Matthew Foehr: We got you.

Nishant Gandhi: Sorry, some technical issue. This is Nishant. I’m on for Robin. So Matt, you showed interesting statistics that regarding antibodies that they’re around 30% success rate, right, from Phase I to clinic. Now with your technology, you said you have more capabilities that you can optimize and develop better antibodies. Do you expect this tags to go up with your antibodies? Like do you think you can push this number higher? Like what are your thoughts on that?

Matthew Foehr: Yes, great question, Nishant and one we talk about a lot. And those data that I presented, very fresh data presented two weeks ago by the Antibody Society, a real credit to Dr. Janice Reichert and her team at the Antibody Society, who do a meticulous level of monitoring of novel antibodies that enter the clinic. And specifically, that study was centered around antibodies that enter the clinic that are sponsored by commercial sponsors, right? So these highly relevant to our business. And it was interesting to see, as you look at those kind of overlapping time period bars, just the improvement that would imply based on those data that the industry is getting better at developing antibody-based medicines. It’s been known for a long time that antibodies can be more targeted than small molecules and have a number of other scientific benefits, but it was really striking to see those numbers come out a couple of weeks ago.

Now as you relate that to us, as we look at our portfolio, we have not seen, I’ll say, clinical failures in our clinical portfolio. I say that noting that we are in a business in the pharmaceutical industry where things fail, of course. But it is interesting to say. But at this point, with 31 programs either in the clinic, commercial or in registration, our data set is probably still too early, but our success rates thus far have been great. And I think that is something that validates our technology with partners, attracts new partners. But it’s probably too early or our data set might be a little too small right now to say how it relates to those new numbers that Janice or team from the Antibody Society reported, but we are excited to see that.

We feel great about the work that our partners are doing, the substantial investment into clinical development to have now over 170 clinical trials that our partners are sponsoring that are based on antibody-based medicines. I think it says a lot about their conviction around the molecules that have come out of our tech.

Nishant Gandhi: Great. Along those similar lines, what percent of your programs do you see like advancing from preclinical to clinical? I mean do you have like an internal statistics like how many programs are like advanced kind of like percentage wise?

Matthew Foehr: I don’t believe we’ve disclosed that in the recent past, but it’s in many ways, as you look at that preclinical slice, which again, is really more pre-IND, as I said earlier. We’ve found as we look at the programs that have advanced to the clinic and that are in preclinical that so far in our pipeline, it has not been a matter of if, but when in many ways, right? We’ve had very little attrition in that preclinical pre-IND slice of the portfolio pie. But there can be a lot of variability and time in that space, right, whether it’s manufacturing or designing the first clinical trial or other elements that lead up to an IND. It’s really just been more a matter of when and not if. And I think that says a lot about the technology and also the high bar that we place on putting things in that preclinical bucket.

Nishant Gandhi: Great. And then the last one. So from what I see from what I have heard, a lot of this molecule that you have in clinic, some of these molecules target like same target like LAG-3 you have a couple of them, you have like HLA-G x CD3 which are both developed using OmniRat. So what are the differentiation of this molecule? Like I mean, I believe they developed from same technology like what are the differentiations in this molecule, if you can provide some thoughts on that?

Matthew Foehr: Yes. And I’ll just speak generally about the business and the way we structure our licenses, and then Bill can add color there. Without talking about specific partners’ antibodies to specific targets, I’ll say we do have a number of partners who are pursuing the same targets, maybe for the same indications or maybe for different indications, both with OmniAb-derived antibodies, but those will be different OmniAb-derived antibodies for reasons that Bill can describe. And the way generally we structure our license agreements is that they are open to any target. Partners can pursue any target they want to pursue. The one exception to that can be in the ion channel and transporter space where the economics are far greater, as Kurt described on a per program basis.

And that’s because we’re also, I’ll say, committing to a target space linked to our assays and other things for those programs. But Bill, maybe you can offer some color on kind of how that plays out with different antibodies?

Bill Harriman: Yes. I mean one of the things that we were able to offer our partners in our animal systems is large diverse repertoires to any given target. And those repertoires are particular for that target for that partner, meaning that when you immunize an OmniChicken, you get a certain repertoire. You immunize six different OmniChickens, you get six different repertoires, right? And we’re very good in our screening to go through those and invite the best of those. But even if another partner comes along with that same target, we’ll set up, I mean, not that same cohort of chicken, it’s a different cohort of OmniChickens, and we’ll get six new repertoires and we’ll mine those. And we found, especially as we incorporate more and more of these bio-informatic tools that allows us to look at a lot of sequences, that really, we don’t get overlaps.

So we’re quite confident that the molecules that come out of a campaign, even if it’s the same target, will not be the same sequences for two different partners. And we continue to look at this and monitor it because obviously, as we do more and more programs, the chances go up, but we still are not really worried about that. I don’t know, Bob, maybe you want to add on that?

Bob Chen: Yes. I think actually, we can say that we can — we ensure with our bio-informatic that we do not give different sequences to two drug partners. So all the IP is always going to be separate. And it’s just — I think it’s maybe the appreciation of the repertoire space. It’s like 10 to the — like it’s tremendously large. So I think just imagining that it’s just statistically unlikely, if not possible, for them to be overlapped. And I think also the differentiation is really driven by our partners, right? They sometimes give different target product profiles to us that we kind of mash in-tune. So it’s going to be different as our partners provide their needs.

Nishant Gandhi: Very good. Thanks for the color and thanks for the call. It was very informative.

Matthew Foehr: Okay. Thank you, Nishant. Yes, thanks. So Kurt tells me, we have no further questions — of Kurt’s many talents, I’ve learned that he also can be a conference call operator. So great job, Kurt. But anyway, I want to thank all of you for joining. Thanks for the engagement and questions. Really appreciate investor support and input. Kurt and I will be out on the road. We’ll be back in New York City next week at the Stifel Conference as well as the Craig-Hallum Capital Conference. So we’ll be out on the road. And I just want to finish the program by thanking my colleagues for their presentations today and their input, thanking our team back in the office and also thanking our hosts here at the NASDAQ Entrepreneurial Center in San Francisco for furnishing this great space for the event. So thank you all again, and have a great day.