AbCellera Biologics Inc. (ABCL) Earnings Call Transcript & Summary

Perfect. Well, thank you, everyone, for joining us this afternoon to speak with AbCellera, CEO, Carl Hansen. Thank you so much for joining us.

Thanks, Andrea. Great to be here.

So maybe to start, AbCellera, a company that's positioned at the intersection of biotech and tech, which is hot right now. But maybe for some of -- some of the people in the crowd who may be less familiar with your company, maybe describe your business thesis here and how that really helps fit you into the drug discovery and development process.

Great. Sure, happy to. So AbCellera was founded a little over 10 years ago. And the business thesis was essentially that when you looked at drug development across the industry, something strange was happening, and that productivity has largely been going down. So if you -- if you really do the accounting, drug development is and has been and is getting worse in terms of total economic return. And that stands in opposition to a lot of other sectors where we're seeing great productivity gains. So our thesis was that if you started to look at the structure of drug development and particularly focused on antibody therapies that there was a lack of investment and the lack of access to technology because technology and innovation tend to be the thing that helps to accelerate and improve productivity. So we set up AbCellera, now a decade ago with a really big, ambitious vision, which was to rethink and rebuild the part of drug discovery that happens after you have a thesis. So after the target biology has been done and you've got a specification for a target, and it takes all the way up to the beginning of clinical testing because that's the area of drug development, the creation of drugs where it looks as though technology could have the biggest lever or the biggest return and where there are a lot of problems we're solving them once, helps you learn and get better in solving the next one. So AbCellera really was built on the idea of solving that technology problem and then using that technology problem with partners to build a large portfolio of physicians in antibody therapies that would be developed in the coming years.

The core to your technology is this research engine, this discovery engine. You've talked about the investments that you've put in here to build this up from the front, the middle of the back end of extension. Maybe speak through some of those components and those technologies that you have that position you to do the antibody drug discovery.

Sure. So building off of what I just said, we define technology really as -- we take a functional definition to technology, okay? And the objective is not to create a protein and not do a screen, not to come up with candidates. But to be able to put together all of the pieces they let you go from that idea right through to a drug that goes into patients for testing to do that with greater speed, to succeed where others have failed. And to do that at a greater scale than has previously been possible. To achieve that requires that you bring together technologies as we think about the various disciplines of complication, engineering, molecular biology, but it's every bit as much about building the teams and the know-how and physical infrastructure and putting that all together. So when we say AbCellera's antibody discovery and development engine, that is shorthand for roughly 600,000 square feet of lab space that's either exist or is under construction for proprietary technologies, for technologies that are at the state of the art, for some technologies that are standard or best practice in the industry. All bound together with people and software and data science to make this thing much more than some of its parts. So integration is the theme. And we've been at that 10 years, and we're I'd say, roughly 2.5 years, 2 years from the first full implementation of that. And then, of course, it will get better and stronger from there.

Perfect. And then as you think about -- there's many components that make up this engine. One of the questions that we get asked frequently is how are you differentiated from others in the field who may be trying to do similar things? Maybe help us understand that differentiation and the moat that you've created around your technologies?

Yes, I get that question a lot. So there are multiple modes. So we have within our engine technologies that we have invented and they're at state-of-the-art and they're patented technologies. We have built up increasing know-how in data. And one of the core ideas or strategies is that by specializing and doing things you do it more, you work with people that are sophisticated in the industry and you build up expertise and knowledge. So that is a moat in itself. The teams, of course, are difficult to assemble the infrastructure. But if I had to pull it to one thing, I would say it's about complex assembly of technology, people, infrastructure, workflows that allow you to achieve a functional goal, which is bringing an idea to a drug to do that faster, to do that at greater scale, and do that with a greater chance of success. And as soon as you start to talk about this, people naturally want to -- they dig down the details. But what about your single cell screening technology? What about your repertoire sequencing or what about your transgenic animal. And what I find is that always flips the conversation into a comparison of 2 things that are not actually in the same category. It's a category error. So it's like if you said, what -- how does your iPhone compared to someone's touchscreen. Touchscreen is a great technology. It's part of an iPhone, but an iPhone is much more than that. And the functional output and what makes it difficult to reproduce is that it's been engineered and brought together beautifully. So complex coordination is when we talk about the company. And for me, that is the moat. And we've said before, it's literally the case that if you handed someone, here's all the IP, here's the workflows. Here's the money, go build this. I think you're waiting many years for someone to pull it off, even they can because bringing things together, it's what's difficult in making this actually work.

Interesting. I'm going to ask you about AI. There's a lot of investor interest, general interest. Yes. I guess maybe what is your perspective on AI and drug discovery? Is it all hype? Is there something real behind that? And how is your own engine using AI?

So I'll bite on that. So first that the AI is not hype. And we're seeing ChatGPT, Large Language Models previously AlphaFold. We're seeing results come out of artificial intelligence-based methods that are astounding and in many cases, have even surprised the people that were developing them, okay? So there's a lesson there, which is there is the potential with artificial intelligence machine learning to get step changes in your ability to predict and to compute and to automate. So all that is true. But what I find in drug development is that the lay person sees these things out and you see ChatGPT. And the lesson that they pull out of it is that AI has arrived. AI is now all powerful. What are you doing about it? How is this going to disrupt your industry, your field, your business. Carl, what does ChatGPT mean for your business? I'm like nothing. ChatGPT doesn't mean anything. Because the real lesson from that is that what it took to make that, a powerful technology, was the ability to read the entire corpus of written language on the Internet, an unbelievable amount of data. That's the first piece. So access to data. The second piece is the ability to quickly generate models and spit out the models and test them and iterate. So if you're building a large language model, you have hundreds or thousands of people that are taking the output of the model and say, no, that doesn't work. We're going to tweak it and you iterate. Right, you have to have experiment and iteration. And in the drug development world, that doesn't exist. Where is the data, like the high-quality data that we'll organize on which to do this learning. That's something that our business is focused on enabling the creation of and we invest a lot of time in software engineers and data science to make sure we're capturing that and keeping it. But it doesn't exist out in the real world. And if it does exist, and you build a model, now you need the experimental might to test that and iterate. And so if I was to use artificial intelligence to say, here is an antibody drug. How would you know if I'm right? Well, you would have to make it, you'd have to test it, you'd have to bring them into animals, you'd have to do the development work. And that is what's going to take to get the same kind of step change improvements in drug development that we've seen in other areas. So our business is -- we've been building our bids for 10 years in order to be able to generate data, high-quality data, faster at greater scale than just about anyone else that I'm aware of. That data connects drug discovery programs from the beginning to the end. So there are hundreds of different types of data, hundreds of thousands or millions of data points per experiment. And we've done a huge lift in building modern software systems to pull all that together and organize it. So we're doing that because it's necessary to execute the business. Without modern software, you won't be able to do this kind of drug development at scale. But the consequence of it is that you will also, through running your business and helping people solve difficult problems, build up a data asset that is exactly what you need to start building these advanced models. So we are very bullish on AI. But we're very cautious about promising what it can do today because I think a lot of that is the hype and will lead to some disappointment. So it's kind of a schizophrenic view on it.

Sure. Maybe in your best estimate, then how close are we to generative AI, being able to replicate your entire process?

I'd say today, we're not close. I put that in the realm of science fiction.

Okay. Maybe turning to your business model. There are -- or maybe walk through your business model. and the different structures that you have and how the economics are recognized for each of these 3 different steps.

Okay. So maybe I'll set it up by making a few comments about the structure of the industry and then how our strategy and technology investment implies or fits with the business model. So one of the observations we had is that we think that there's a technical debt in drug development, okay? So if we had better technical capabilities, there are opportunities that could be executed on that are not being executed on today. An example would be, you've got a new company, they've got insight in biology. It's a great founder maybe they've got backing. There are no labs. There's no know-how, no expertise. In the industry today, that group has to go out and try to reassemble all of the machinery just to execute on the thing that is novel and unique to them. And they will not have enough time. And so that leads to either it doesn't get funded or it's -- or you're wasting capital and time doing that. On the other side, you've got companies -- big companies that are the best or have been traditionally the best in the industry at doing this. But their attention has been where the money gets spent and made, which is the clinical development and the commercial sale. And they haven't made it a priority to reinvest in the technologies internally. So what's happened now is that there are targets that are exceedingly difficult or that cannot be prosecuted with existing technologies. So that's the gap. And we think there are good reasons why people do this, but it creates an opening. So our strategy has been, we're going to step into that and have a long view, do the hard work, build up the engine. Once you've decided to do that, you realize I need to use this a lot to make it make sense. That's the reason small companies don't do it. So that investment in technology means that you need to work with others because no company would either have all the ideas to work on or would have the bandwidth or the capital to actually move those through the clinic. So our business model has been very much focused on partnerships from the beginning. So roughly, we are investing in technology, and we're looking for ways to develop antibody therapies that can be put into the hands of partners for clinical development and ultimately commercial sale and making sure that we are always having a piece of that product. So deal structures always emphasize a share in the downstream success of a product, typically through a royalty, sometimes through a co-ownership structure. Now apart from that, we're not dogmatic in how we do deals, and there's roughly 3 categories that we've talked about. So the first one is what we call discovery partnerships. This is the first type of deal we've done. It's the largest volume, and it is sort of the bread and butter of the business. So if someone will contact us. They have identified problem in antibody discovery. We have the means to make their molecule and take it all the way through to a value inflection point, which may be the beginning of IND-enabling studies. We'll sign a contract, they send us the work. We take it up to that point and then hand it off. And after that, we're hands off. So we don't control the program. We don't invest in the program. But as that molecule moves forward, we are eligible to receive milestone payments and most importantly, a royalty, which is a share of the ultimate success of that drug. So those are discovery partnerships. If you think about it, we normally get paid for the work. So that's an infinite return on investment. So we get for the marginal cost that's covered by the client or by the partner, and then we're hands off and we wait. And over time, we'll get royalties and then -- we'll get milestones in royalties and those come in at 100% margin. So if you do a lot of those and you run the experiment over time, you end up with this P&L that looks like a royalty farmer, but hasn't required the huge balance sheet because it's not a cost of capital play. It's an exchange of access to innovation and capability for a piece of success, I would say, a win-win exchange. So that's a discovery partnership. We have a version of that, we call co-development, which is largely the same and typically done with small companies, except that we will sometimes take less payment upfront for the research, so we'll support some of that payment. And we will come out of the initial work with a 50% ownership position. And then we have an option to continue to financially support that program through various stages of development. So let's say, from a final lead candidate to IND filing, from Phase I to Phase II and various steps along the way. So that's -- if we don't, we still get the royalty, so it's like a discovery partnership but it has the extra value of an option, and it's an option that we are very well positioned to decide if we want to participate on because we know the partner. We've been intimately involved in the experiments in the molecule. We get a chance to see how the commercial landscape is shaping up. So those are the first 2. Those are both working on ideas that have come from the outside into AbCellera. There's another camp or another section of the business, which we call pre-partner programs. And pre-partner programs arise from long-range research efforts that we have underway that seek to unlock big areas of therapeutic antibodies that are presently difficult or impossible to develop drugs for. So it could be -- and we have 2 areas right now. One is in T-cell engagers, where it's really about being able to better control T-cell responses, increase specificity and help to make that class more successful across more indications. Another area that we're very excited about is in complex membrane proteins, which is working on the technologies to let you find antibodies against ion channels and GPCRs. In both cases, there are common technology problems that are solved allow you to work on dozens of high-value targets. So in that work, we naturally work on targets. And as we start to succeed, we end up having wholly owned assets that we will take up to final lead candidate or perhaps even to the end of Phase I. But for all of these, the objective is to hand these off to partners who are best suited to do the clinical development. So that's what we call them pre-partner. Because it's work that has started, we've anticipated needs. We see an opportunity and where ultimately we'll hand them back to partners. And all 3 of those are part of 1 strategy, which is build a diversified portfolio. They have different risk reward, different cash flow profiles, and we work to make sure we're balancing that in light of the bandwidth and the resources we have in the business at the time.

Perfect. Maybe some follow-ups there. On the co-development sector. Maybe help us understand what would be the considerations to step into a more, I guess, maybe more fruitful or constructive partnership with the company. I guess what would make you decide to take on that additional role.

Right. So what would make us decide to negotiate a co-development deal or to participate along financially?

The first.

The first one. Okay. So the first thing is that type of deal structure is almost exclusively for smaller companies. And that's -- as you might expect, a large -- let's say a large well-established pharmaceutical company. They have a low cost of capital. They want to completely control and own the programs. So it's very difficult to negotiate at the discovery stage, what would be a 50-50 ownership position. Smaller companies are in a different position. So very often, their cost of capital is high. They don't have any of the capabilities that the pharma partner has. So we bring a much -- a very large value proposition to them, saving them time and money and making sure that we've derisked and accelerated their path to getting to that point. And so we have better -- I wouldn't say better leverage, but a better negotiation position with smaller companies. And what we found is those engagements have been some of the most productive. So we're doing them when we find groups, we really think they're on to something, groups that have deep insight into targets or unique technology or business model that we think is compelling. And that type of engagement, the nature of it makes it more collaborative. So we've become an extension of that team. And in the best case, this has led not to just 1 program, but 2 or 3 in a pipeline behind it that we're quite excited about. So it's part of the portfolio. We won't do this on all of them. It's a smaller piece. I think we've done 6 or 7 to date out of what would be 75 to 80 programs with downstreams. But each of these on its own could be material for the business because you own 50% of it, which is also true for the pre-partnered programs.

All right. And then you've had some interesting partnerships or deals with [ TCEs ] Maybe speak a little bit to that and how that feeds into these, I guess, these models.

Sure. When we think about the partner landscape, I'd say there's sort of a 2-dimensional grid. And there's companies that are large and well capitalized and established companies that are small out of the gate. There's companies that are highly enabled in antibody therapeutics, take the Lillys and the Regenerons of the world that are partners. And there are companies that are just starting to have no capability. Now in that bottom left quadrant, let's say the small companies with no capability. There are a huge number of opportunities. So there are probably somewhere like 700, 750 antibody discoveries that are coming from that class of company at any given time. It is a challenge partnering to see all those opportunities and to sift through them to find the ones that are best from our perspective. So we do not swing at every pitch. We say no to far many -- far more partnerships than we engage in. And we want to make sure that if we are working with someone because we believe that team, that idea, that investor has the potential to bring something that will be successful in the market and ultimately create a drug for patients. So what we've worked on is how can we best do this. And it turns out there's a set of institutions in our industry that have made this -- their business and are very good at it. And these are the top-tier venture capital investors that are either sourcing and funding those companies or that are starting them up. So what we believe we can offer to these investors and the entrepreneurs is the ability to work on ideas that otherwise would be too difficult to get off the ground to do that with less capital and to ultimately provide a better return on investment to the investor. And one of my favorite examples is one recently that we talked about a company called Abdera, which is local in Vancouver. There's a back story. But we had -- we'd essentially formed the alliance and committed to work with them before the financing came in to start the company. And our presence, I believe, was one of the main drivers for the financing coming in because what otherwise looks like a very long slog to get started could start on the day that the company was formed with teams that are in place and technology that's already been proven to be best-in-class. So we started -- I think the company was founded in early 2021. We started in March of 2021. By the end of last year, the first lead candidate was in hand and early this year, that lead candidate led to a Series B financing, a substantial financing of $140 million with a pipeline behind it. And the labs were only set up a short time ago, right? So it shows that -- it allowed the Abdera team, which has created a terrific technology and great executives to focus on what was unique and special in their business proposition and not have to reinvent everything and accelerate the entire path of making companies. And it's our thesis that there are many opportunities like that. Particularly -- well, obviously, we work in antibodies. Where ideas have not been funded because it's too difficult, and it need not be difficult if you've got a partner that has invested in the capabilities and has a business model that allows you to access that for a fair share of the success of the enterprise.

One of the key metrics that I think investors look at when we look at AbCellera, we talk about this each quarter, the new program starts. The number of new program starts as you bring them into the discovery phase as you then watch these programs progress towards the clinic. What is the right way to think about the cadence of these new program starts.

It's a great question. It's one we get a lot. And I think what I'd want people to look at is not the volume of program starts. I think it's very tempting to say, okay, yes, this many starts this quarter. These many starts this quarter. I can estimate some growth, and I can build a model on that, and that's sort of satisfying. But it misses entirely what is most important about programs? And what's most important is what are you actually working on? Is it a good idea? Is it a reputable team? Have you negotiated good terms? Is the commercial opportunity big or not? What is the chance, the technical chance of success? So when we tell our partnering team to go out and source deals, we don't tell them to close a certain number of deals per quarter. We say, let's make sure that as a company, we are allocating our resources and time and technology on what we think is going to be the most important work possible. And for that reason, I'd say 80% of the things that come across the team's desk, we don't engage on because we're trying to be selective. And we're selecting on the basis of the partner, the biology, commercial opportunity, chance of success, competitive landscape, deal terms that we can negotiate. And all that have to come together. The other dynamic that's happening is we are expanding our capabilities more and more. And so the engagements that we did 2 years ago, we handed off at a much earlier stage than we do today. Today, we will take molecules all the way through to the final development candidate that's ready to go into cell line development. And now we are in a position to start doing cell line development. And in a couple of years, we'll be having the first molecules that are coming off GMP manufacturing, and we're adding clinical and regulatory capabilities. And so the depth of work that is happening per partnership or per program is also going up. And the final piece is that I'll just highlight is, I get asked a lot about capacity. So in terms of capacity, if you look at the amount of work being done and in my view, the quality of work, it's gone up remarkably over the last few years. So we're doing much more for program. We also have done a lot of work on the pre-partner program side, and we're simultaneously building out the capabilities to go downstream. So the way that we think about it is the -- we want diversification, but we don't want diversification at the expense of quality.

Perfect. Maybe with that in mind and in that context, how do you help investors understand the long duration cash flows that are associated with your business as they think about the value that AbCellera provides?

Yes. So the first thing I'll point out is that long cash flows or long time lines to cash flows is not a feature of AbCellera business model, right? It's a feature of biotech. So like any biotech company that's developing drugs needs to take drugs through the early preclinical and into clinical development. And on average, historically, that time line has been 8 to 12 years. It's about right. So we are setting up our business to participate not in the market of doing a fee-for-service work in some research, but to participate in the actual end market. And because we participate in the end market, our time lines are going to be similar. But like biotech, you get evidence that value is being created and there's traction long before you finally get an approval and hit commercial sales. And that's the reason why there are biotech companies that are not commercial that have large market caps and people recognize the value because the science starts to get derisked as things move through. Now in our case, this is not going to be one thing. It's going to be a portfolio of things that mature over time. So we're keeping our eye on the long game. And we believe and we have seen in negotiations that by doing that, we believe we can maximize value. If we try to bring put up numbers in volume of partners and try to maximize revenue today, it will be at the expense of long-term value. And so that's how we're running the business. Yes.

Perfect. I guess maybe in this environment, we're hearing a number of companies talk about cutting back on spend. In this lower spend environment. How is that impacting your business in terms of the partnerships that might be coming through? Have you seen any impact from that?

I've had that question a few times. It's always hard you get an incomplete sampling of the space. And so it's hard to get a gauge on exactly what's happening. My sense is just logically, this is a tough time for biotech. I mean these are down markets. There are fewer companies that are getting funded. Companies are having to prioritize programs. And so from that perspective, we're fishing in a smaller pond. I think it doesn't impact us that much for a couple of reasons. And one is that we are looking for the highest quality opportunities. And I believe that even today, the highest quality opportunities are finding capital and they're moving forward. And that's a good thing for the industry and for patients. So if you're fishing for the very top of the sector, I think you're more resilient, although perhaps not immune, but more resilient. The other piece of it is that, as I said with the Abdera example, but generally with small companies, our model allows you to continue to advance your business and your science and to do that in a capital-efficient way because once we had vetted a program and we have conviction, we are tying our economic stake to the downstream success of that, not requiring a large cash outlay today. And so I think that business model, which should -- honestly, should be attractive in all environments becomes particularly attractive in an environment like this.

When you talked about your technology being able to speed up the process from ideation to drug discovery, maybe help provide context for what it is in maybe in the field right now? How can your technologies improve that speed? And where can it ultimately go with your technology?

Sure. So when we talk about speed, we're talking about the path from the specification of the drug through to the beginning of clinical development. Right now, if you're really fast, you're doing that in about 3 years for the best programs where the biology and the animal models are amenable to that. I think that's probably gold standard in the industry. The average time of the industry is probably closer to 5 or more. It might even be 6. It's hard to actually get a number on that, but it is considerably long. We look to speed that up in a few ways. So one way is through technology and technology in particular, that can start with a much, much broader search of potential antibody space. So the paradigm in antibody discovery for a long time has been, I'm going to do some kind of early discovery and I get a small number of candidates. And one of them sort of looks like it might be a drug. Then I'm going to spend a lot of time working on that and trying to fix the affinity or the developability and then it -- and that process is what takes time. So we have a workflow that's just running it through I believe is faster than anything that's out there, but the real speed comes from going hundred times deeper at the start, thousand times and taking more candidates through because you've invested in automation, you invested in data science and then you can run all of those candidates through the filters and just let go the ones that don't make it. And at the end, you end up with more than enough candidates to bring forward. It's not to say you don't need to do any engineering, but you minimize that. And that can greatly shrink the time. So the first piece of getting a speed is depth and being able to take the attrition. The second part which I think is underrecognized is integration. So one of the reasons we're so excited about forward integration to translational science to CMC, to GMP to regulatory, particularly for small companies, is that the way the sector works right now, you lose 6 months, a year, 1.5 years, trying to negotiate with contractors, trying to get in the queue, trying to access technologies. And in the meantime, you're losing your competitive advantage against the rest of the world. If we integrate everything and we know what the process is, we can select molecules upfront that fit well into that process, and we can begin work at risk long before you could ever do that with a partner. And so just through execution and integration and understanding from the beginning of what you need at the end, we believe you can take 12 months maybe more of a program. And that's not so much a technology innovation as just getting your business tight and really understanding the end-to-end workflow.

Perfect. Maybe some quick questions and the last couple of minutes. You recently announced a co-investment by the Canadian government. Maybe just help us understand how significant was this? And what does this mean for your liquidity?

Great question. So you asked about time lines. An important question around time lines is are you properly financed to have the business turn the corner and survive? And the answer to that is yes. So we currently have about $800 million in cash. We have considerably more liquidity and a big part of that comes from this contribution agreement or a co-investment agreement with the Canadian government, which is a $700 million project and brings about $225 million from the Canadian government and the [ principal ] government that is directed towards infrastructure building and building up the capabilities and working on programs to take them right through initiation through to the end of Phase I. So that investment alongside of a previous investment that we had in the GMP manufacturing adds roughly another, let's say, $250 million, $300 million liquidity. So AbCellera has about $1 billion in liquidity today in hand. And as our business runs forward, what we're going to see is the research fees will grow, milestones start to add on, eventually royalties start to hit. Pre-partner programs have the potential to bring cash flows forward because they're handing things off when they're derisked and they can bring large upfronts. And through all of that, when you make it together, we believe we have a really solid path to profitability and to not have to raise money again. Now that doesn't mean we never will. But we -- particularly in this environment, you want to make sure you've got a plan that puts you in charge or in control of your faith, and we're feeling great about that.

Perfect. Last 10 seconds, what should we be focused on over the next 12 months? We really didn't touch very much on the pre-partnered data that could be coming but very quickly.

Yes. So what I'm most -- or not most things, you should be excited about in the pre-partner side. We're doing work on T-cell engagers and GPCRs. On the GPCR ion channel front, these are some of the hardest problems, and there are multiple targets that would be first-in-class blockbuster or have potential to be first-in-class blockbuster drugs for large indications. Everyone knows what the targets are, you haven't been able to do them. Internally, we're very excited about the progress that's being made towards that. So we had Sigma before that we thought the first of those move into IND-enabling studies. I think we're on track for that. The same is true on the T-cell engager front, where we have moved programs forward. We've shown that we have built a CD3 panel that can help control the level of T-cell activation. And we're getting, I think, very strong reception from the field as this class of drug becomes increasingly important, it's attracting a lot of attention. So those 2 areas, I think there hasn't been much seen because they're difficult problems and they take time to service, but they're parts of the business we're very excited about.

Perfect. With that, thank you so much, Carl, for joining us. Thank you, everyone, for joining us.

Thanks so much.