Twist Bioscience Corporation (TWST) Earnings Call Transcript & Summary

Good morning, and welcome to the Twist Biopharma virtual biopharma analyst and investor event. [Operator Instructions] Please note, this event is being recorded. I would now like to turn the conference over to Emily Leproust, Chief Executive Officer of Twist Bioscience. Please go ahead.

Good morning, everybody, and good afternoon, if you are in Europe or Asia. It's my great pleasure to host you today for our virtual biopharma analyst and investor event. As you know, biopharma is becoming an important part of Twist, and we thought it would be useful to you to hear from some of our customers about what they do with our platform and the benefit that it brings to their work and their companies. So with that, we go to the next slide, and I will mention, first, that we will be making some forward-looking statements today. Next, I'd like to very quickly go over the agenda. So I'll give you a brief welcome and Twist overview. Then we'll hear from the Chief Scientific Officer of the Twist Biopharma division, Dr. Aaron Sato on the latest from Twist. And then we'll have a presentation from 3 companies. First, from Takeda is Dr. Robert Mabry; then from Invetx, we'll hear both from Dr. Jurgen Horn and Dr. Bill Brondyk. And then to close, we'll hear from Dr. Carsten Linnemann in -- from Neogene. And then we'll have a Q&A session. You will be able to ask a questions on the phone. I believe there's a -- you can also type in your question in the platform, and we'll be able to answer both. We'll start with the live questions, and then we'll move on to the written questions. And so before going forward, I want to really thank our 4 speakers. I know that it's an investment of their precious time. So we very much appreciate their involvement into this event. So at Twist, we write DNA from scratch, as you know. On the next slide, you'll see some of the ways our DNA is used. Some customers use our DNA to engineer yeast, algae and E. coli to produce chemical by fermentation instead of using oil. It's more sustainable, which -- but it's also cheaper, and you can make materials that you could never make from oil. Our customers are using our DNA to improve food security. For instance, by using our DNA, you can engineer bacteria to deliver nitrogen at the root of plants instead of using fertilizer, which eliminates potentially a lot of fossil fuel usage. We'll hear today how our DNA can be used to develop therapies. As you know, our DNA is also used in diagnostic application for liquid biopsy, cancer diagnostics or rare disease diagnostics. And last but not least, our DNA can be used to store data in DNA for the long term, which is potentially a great archival solution. The basis for that on the next slide is our silicon platform in the middle. And to contrast with -- on the left, what our competition does, which is using the 96-well plate format to make 96 oligos, 96 piece of DNA at the same time. What we do at Twist in the middle is in the same format of a 96-well plate in a silicon chip, we can make 1 million oligos, so 10,000x more oligos than the competition. And we do that using the power of silicon to miniaturize the chemistry, which gives us advantage in throughput and cost. And then in addition, almost as important as the silicon, we've built a very sophisticated software infrastructure to be able to produce all the products for all the customers at the same time on the same chip and then, at the end, be able to split and send the right DNA to the right customers at the right time. So that combination of silicon and software gives us massive scale that we are turning into commercial advantage. Next slide, please. And since we went commercial, in 2016, we've been able to show some very fast revenue growth. Last year, we've achieved $54 million of revenue. And in the first 3 quarters of this year, in the June quarter, we've achieved even more than that, which is remarkable in the context of COVID. And our business model is such that as we raise our revenues, we're able to get better gross margins. And we've also, as a company, been quite focused on delivering improvement in gross margin at the same time as the revenue growth, which is enabled by our business model. Next, I want to very briefly describe the 4 businesses that we are building of our silicon platform. First, to our tools business in synthetic biology and next-generation sequencing. Those are somewhat analog business. As we go into more customers and sell more products, we see the great revenue growth that I've mentioned in the previous slide. And there, we have no reimbursement risk. We have no FDA approval. It's a transactional -- those are transactional businesses. In addition, we have 2 embedded option for potentially big upside. One is in drug discovery. This is the focus of today. And then the second is in data storage. Next slide. In the last 9 months, so again, talking all the way to June, the last quarter that we reported, we have made progress in each of those 4 areas. In synthetic biology, we have expanded our product line with DNA preps that enable us to serve more of our pharmacustomer needs in next-generation sequencing with developed products that are useful for the COVID-19 crisis that we are dealing with and more broadly with infection diseases with synthetic controls and NGS panels. In drug discovery, you'll hear today some of the work that we've done on developing our own antibodies against COVID-19, and you'll hear from 3 of our customers on the external validation that our platform has had. And then finally, in data storage, as you probably know, we've received some nondilutive funding from IARPA. So next, I'll talk very briefly 3 slides to introduce our biopharma platform. What we have is through the platform, we offered, first, on the top left, a library of library. And Aaron will describe it, but it's basically, we have content that is available to our customers. Next, we can discover and optimize antibodies against target. And now we've shown that we're actually quite good at hard-to-drug target. Of course, we can do the easy to drug as well. But we are able to do hard-to-drug targets. Then -- sorry, if you can go back. Thank you. Then because in our biopharma division, we've built high throughput production of IgG. And now we're able to move that IgG production away from the biopharma division into the synbio production such that all our customers can benefit from the high throughput IgG platform that we've built. And then last but not least, and now that we have had some external success, we have a few targets that we are pursuing as a Twist -- sponsored program through internal lead discovery programs. And the intent would be to -- after we've done the discovery, some of the initial work, license out those antibodies with a higher share of the economic value. Next slide. We are very thankful that a number of customers and partners have now signed Twist. And as you see, we're able to achieve partnerships where we can participate in the upside through milestones and royalties. So we are not the CROs. CROs typically do not get milestones or royalties. But we believe we can offer something that is of higher value through hard-to-drug targets, through speed, through universal platform that enable us to participate in some of the upside. And then last, people sometimes ask, what is the unique differential capabilities that Twist brings? Why is Twist also good in discovering antibodies? And there are 3 factors to that. The first one is that we have an unlimited DNA buffet that Aaron can access. And so we have more molecules than others. And so we can take more shots on goal. Second, each of those mutants are never randomly made, but they are explicitly made, meaning that we can use the human repertoire, the knowledge of the human repertoire to choose variant that follows that human repertoire. So all of our molecules are fully human and human-derived, which means that they will behave better in vivo. And then last, because we not only have the great science of Aaron's biopharma team, but we also have engineering knowledge and experience. At Twist, we have automated and miniaturized most of the processes from library production, screening, reformatting of the IgG as well as affinity and functional testing. So those 3 advantages, we believe, are unique. And basically, at the end of the day, we are more productive because we can take more shots on goals in a way that's automated. And that higher productivity will enable our partners to do things that hopefully they could not get from other teams. So that is the unique difference of Twist. With that, I'll pass the microphone to Dr. Aaron Sato, who's our Chief Scientific Officer, and he will tell you the latest on Twist Biopharma. Aaron?

Thanks, Emily. So today, I'll give you an overview of the Twist Biopharma vertical at Twist Bioscience and then also use our work that we've done on SARS-CoV-2 as a case study to really show you the power of our platform. Next slide. Again, I always say the best companies and teams and groups out there really understand the one thing that they really bring to the table. And so as Emily went through, for Twist, that's really our silicon platform. That's kind of the flagship of our company and really enables us to do all kinds of fantastic things. For Twist Biopharma, it's really taking advantage of our library capabilities. So basically, using the silicon platform, we can make explicit sequences of oligos in pools, and we can then use those pools of oligos, again, upwards of even 1 million oligos, to build fantastic DNA libraries. And we use those libraries in Twist Biopharma to build our own proprietary antibody phage display libraries that I'll talk about later is our library of libraries. Utilizing all the pools in this way gives us a lot of tight control over what goes into the library. I always say scientists are some of the most creative people that I know. And this technology that we have really enables them to use that creativity to make the next-generation of antibody libraries for discovery of novel biologics. And shown here, just all these great advantages of using oligo pools to build libraries. Next slide. And again, how do we do that? So we basically, again, synthesize explicit sequences of -- in this case, CDR sequences and pools. And for a particular antibody domain, say, heavy chain or light chain domain, we can basically make exact sequences of CDR loops that are actually seen in nature. So rather than making a random oligo, as Emily mentioned before, we can actually make exact sequences of human CDRs or even, as you'll hear later, even CDRs derived from animals. And we can then put those into a library by shuffling them together in the context of a single germline domain. This allows us to even though we're making a synthetic library to make it in a very natural way because we're using fully germline scaffolds, fully germline antibodies and then also using fully human or maybe, as we'll see later, pet-derived sequences in our libraries as well. As we do that, I also say we also can take away things that might lead to deleterious effects. So we can remove sequences that might lead to developability risk. And then as -- if we choose to, we also can even insert motifs into our CDR sequences, and that's actually key and central to a lot of our difficult-to-drug target libraries I'll talk about later. Next slide. So at Twist Biopharma, we're really -- we really sit on the front end of the discovery and optimization workflow for the biotech and pharma industry. We really want to help others, really help them to discover antibodies using our library of libraries, I'll talk about in a second. And then also once biotech and pharma companies have their initial leads, either derived from our libraries or maybe even from other platforms, we have our TAO platform, which stands for Twist Antibody Optimization, where we can basically use this library platform I just talked about, coupled with some fantastic software that we've developed, to optimize antibodies and do it in a very natural way. Next slide. In terms of that discovery effort, we created now a whole suite of libraries in our library of libraries offering. They ranged from a fully human fab library that we call the Hyperimmune library. We've also created a whole suite of single domain VHH libraries. Again, these are derived from camelids. And so they're very small. It can be used for bispecifics. And then -- or actually, as you'll see later, really helpful in combating infectious diseases. We have a library that's derived from all the antibodies that ever been crystallized, which is our structural antibody library. And then finally, we have a whole suite of libraries of difficult-to-drug targets from GPCRs, ion channels and carbohydrates. And as I mentioned before, this is really enabled by the use of the oligo pool platform we have at Twist to make these fantastic libraries. Next slide. So how do we do that? So again, all of our libraries are in phage display. We can pan and screen using those libraries against any target we wish, whether it's a recombinant protein to a cell to peptides. We rapidly do those through those pannings and screenings. We then sequence all of our clonal outputs from our pannings. And then as Emily mentioned, we have this all-you-can-eat DNA buffet, which is fantastic. That allows us to make lots and lots of antibody sequences and then we can express and purify them and then do all the downstream testing. Next slide. So now I'll jump into the SARS-CoV-2 case study, which is, again, will really help exemplify the power of this platform. So again, we took several of our libraries from library of libraries, and we panned and screen them against both SARS-CoV-2 S1 antigen, which, again, is really -- is very important for the infection of the virus as well as the human ACE2 receptor, which is the co-receptor for the virus. Each of our libraries, as I showed, has really over 10 million different antibodies. And we -- and we did multiple rounds of panning using these libraries and then did a whole host of screening. And at the end of the day, we made a whole panel of antibodies to do all the downstream binding and functional assessment, which included affinity, receptor competition as well as some developability assessment. So next slide. So how do we -- again, how are we combatting the virus? Really, we took a two-pronged approach. So of course, like a lot of others, we try to find antibodies to the spike protein on the virus to prevent infection, but we also thought that protecting the natural receptor for the virus, in this case, ACE2, would also be a potential therapeutic strategy. And as you'll see later, we have a lot of data validating our S1 antibodies and the ACE2 antibodies are also proving to be very promising as potential therapeutics. Next slide. So again, we did that whole panning and screening that I talked about before. It came up with a whole suite of antibodies both against S1 as well as ACE2 that were single-digit nanomolar to even picomolar antibody binders to both those different targets. And then we moved them ahead for further functional screening as I'll show in the next couple of slides. On the IgG antibody front, we did a project in collaboration with Vanderbilt and [ Dr. Croslab ], where were we generated a library derived from -- sequences derived from a COVID-19 survivor. We panned in, panned and screened those libraries against the SARS-CoV-2 S1 antigen, as I said. And we actually came up with a whole series of antibodies derived from that library that bound with double-digit nanomolar affinity to both the S1 protein as well as the S protein trimer. Shown at the bottom, you can actually see that these antibodies also block and neutralize very well in pseudo virus assays. And then finally, we did a study with Dr. James Brien at Saint Louis University, and he did a live virus FRNT assay and show that they actually made these antibodies also to neutralize the virus very potently against live virus. Next slide. But the real -- really very potent antibodies that we discovered against SARS-CoV-2 actually were derived from our VHH libraries. So here we show that, again, using the workflow I talked about before, we discovered several single domains against the SARS-CoV-2 S1 antigen. And then did the same analysis I just showed for the IgGs that we did similar to the work we did with the Vanderbilt group. And what we see is that in live virus testing for these antibodies, you can see all the ones in yellow are the VHH antibodies. You can see very potent neutralization with these antibodies compared to the IgGs, which are shown in green on the left on the table. You can see that our VHH single domains are actually giving [ NC 50 ] values in live virus FRNT on the order of less than 0.1 micrograms per mil, which again, I'm told, is a very good range for neutralization of the virus. Next slide. So just to kind of summarize the work that we've done on SARS-CoV-2. We've developed and discovered antibodies to both IgG antibodies as well as VHH antibodies that bind very tightly to the virus as well as neutralizes. As I mentioned, we're also pursuing the ACE2 target as well and pursuing leads there that we'll have very soon have some data around as well. But we're characterizing these leads, and they're all, again, derived from our synthetic antibody phage display libraries. And finally, I've shown that we have a very potent both pseudovirus as well as live virus data. So just to summarize, again, the future plans that we have for the antibodies. We're planning to push them into in vivo studies to show not only the in vitro activity, but also the in vivo activity of these antibodies. We're also trying to see whether we can create homotrimeric and homo -- heterotrimeric version, so really take advantage of the affinity effects by combining these VHH into combination -- different combinations. And then finally, we're trying to scale up these antibodies further to do additional assessments in vivo, not only for efficacy, but potentially for PK and toxicity down the road. Next slide. So just to summarize on a high level for Twist Biopharma, as I mentioned, we're creating this whole suite of our library of libraries that we want to use for helping pharma and biotech companies discover antibodies for their biologics discovery efforts. We -- our flagship library is our hyperimmune library, which is a fully human-naive library, again, takes advantage of our oligo pool platform to create a very diverse fully human library. And we have, again, a whole suite of libraries, different scaffolds from fabs, single chain and VHH that, again, enable us to find high affinity antibodies to a whole host of different -- difficult-to-drug targets. As I mentioned, we have -- we really want to focus on difficult-to-drug targets as well. And we've actually created libraries focused on several different difficult-to-drug classes, including G-protein-coupled receptors. We really pride ourselves on our ability to optimize antibodies using our TAO platform, which really takes advantage of our library platform, coupled with the software I talked about before, to enable us to make optimization libraries that are inspired from the natural human repertoire. And then finally, as Emily mentioned, we're pushing for, in a lot of products, drive from biopharma into the synbio component of Twist, and we're about to kick off a new alpha product within Twist, not only offer up the ability to synthesize DNAs and coding for antibodies, but also to make the physical antibody itself. Next slide. So just on -- again, on a high level, for our drug discovery capabilities, the library of libraries that's derived from our silicon platform is -- again, really allowed us to create a whole panel of library of libraries that allows us to go after any target we wish. And it's been really game-changing in this space. And then once we have those leads, we can, of course, use the TAO platform to optimize them, to make them better. And again, it's really not only the library capability, but also this amazing software that we have that allows us to create optimization libraries around specific leads. Next slide. And the way TAO works just schematically is, again, we input the antibody sequence into our software. It gives us a whole series of oligo pools and sequences derived from natural human repertoire. We synthesize those oligos, put them into a library. And then we pan and screen them against the target. And shown here at the bottom is just one example where we've taken a parental PD-1 antibody and actually, affinity matured at seventytwofold to give us an optimized lead that now rivals many of the PD-1 antibodies that are on the market today. Next slide. And then we're also, as Emily mentioned, really focused on developing out and improving our library technology and doing some proof of concepts, discovery efforts against a number of targets. We've really focused on G-protein-coupled receptors. As I mentioned, they are very difficult-to-drug class targets. And we've been able to find leads against a number of them shown on this slide, where we've not only found antibodies that bind very tightly to these receptors, but also have amazing functional activity. Shown here is just one example where we've done panning and screening using our GPCR library against GLP1 receptor. GLP1 receptor is a receptor involved in sugar maintenance. You might be familiar with it in the field of diabetes. We actually found an antagonist of this receptor using our library that could be used for diseases resulting in -- where you have low levels of sugar or higher levels of insulin. And so what we see on this slide is just showing that our antibody very potently inhibits the signaling of GLP1 receptor. And also in, in vivo study, we're able to show that this antibody also maintains a high level of glucose and actually inhibits receptor very strongly, so that we can actually have high levels of sugar in this glucose tolerance study. And then finally, one last exciting piece of information is derived from our SARS-CoV-2 case study. We actually -- we've been trying to see if we could use these antibodies for other applications besides just therapeutics. And since rapid tests for SARS-CoV-2 are actually -- are going to be really key to our -- opening up our economy, we're actually able to show that we can use our SARS-CoV-2 S1 antibodies in a lateral flow test. And so shown on the left is just a dose titration of the spike protein in saliva. And you can actually see -- as you go from left to right, you can see that second lower band. It gets more intense, just showing that we can actually detect the spike protein. And on the right, it's just a collection of negative saliva samples where we don't see that line. So again, really nice applications of our SARS-CoV-2 S1 antibodies where we can actually not only use them for therapeutics, but also potentially for a lateral flow diagnostic test for the virus. And so just to conclude, ways you can work with us, where we really want to get our libraries out there into multiple partners to license them out. And we've been, again -- that's been really key and central to our success. We also want to do projects associated with utilizing those libraries as well as projects to optimize antibodies using our TAO platform. We continue to innovate and discover more antibodies to help validate our libraries. And from that, we've actually come up with several leads that we want to potentially out-license at some point. And then finally, we are working very closely with our synbio team to offer up screening services as well, associated with libraries that people might make on their own and design on their own with the custom library team. And then finally, as I mentioned, we're also trying to push out a new product we've called high-throughput IgG, where we can, again, offer up the ability to not only synthesize hundreds of thousands of antibody genes, but also physical antibody as well.

Thank you very much, Aaron, for this overview. Next, we'll move on to our customer presentation. And before I introduce our next speaker, I want to remind you that you can either ask a verbal question through the phone or through the application, you can submit written questions. So next, we'll hear from Dr. Robert Mabry from Takeda, who will tell us what he's doing with -- in collaboration with Twist Biopharma.

Great. Thank you, Emily, and thank you to the Twist team for the invitation to speak with you today. I'm going to start with a brief overview of Takeda in global biologics and then transition over to our partnership with Twist. Next slide, please. This is our forward-looking statements. Next slide, please. Takeda is a global pharmaceutical organization with approximately -- a presence in approximately 80 countries. Post-acquisition of Shire, we're now around 50,000 members with a significant presence in the U.S. We have 3 main research and 36 manufacturing sites across the globe. And I'll briefly touch on our vision and values on the next slide, but it is inherently reflected by the recognition we receive as a top employer across many countries. Next slide, please. So throughout the 240 years of Takeda's existence, we have held our values dear to our work with a focus on patients and the development of innovative therapeutics to improve lives. The long history of Takeda is an impressive success story. We continue to evolve our organization with a commitment to agility and innovation. We are steady in our vision and hold integrity at the forefront of our values and how we approach both science as well as our business practice. Next slide, please. So for R&D, these are the 6 areas that we focus on. We are committed to cutting-edge technologies and identifying automation solutions. There have been significant investments in R&D for data, and I'll speak to those in the next slide, please. So looking at the investment in our R&D, this is an investment in both internal and external collaborations. We have gone through an evolution of a small molecule focus to biologic focus. And these are modalities similar to recombinant protein cell and gene therapies, oncolytic viruses, viral delivery, microbiome. And that diversification is a balance between internal and external partnerships. We have over 200 partnerships; and looking at what those partnerships yield, has approximately 40 different new molecular entities in clinical stage assets. So our partnerships are a significant component to our success. Next slide, please. So the drug discovery process is rather complicated and by no means is my intent to trivialize the process with this slide. I think what we're -- what is implied is that there is synergy that's needed across several different categories to really drive a successful pipeline. Choosing the right target, having the translational aspects allow you to identify novel targets, but also how you engage with those targets and whether you're wanting to move on beyond 1 single target or looking at multiple targets as part of your multi-faceted approach. We are modality-agnostic, which creates a lot of complexities, especially when you're seeking to diversify your portfolio across the different therapeutic areas that we mentioned. And by no means,is execution a small feat. Many ideas in pharma, they're ubiquitous. You will find similar ideas, but it's really the execution and the synergy with these other components that can drive success. Next slide, please. So looking at our partnerships, there is a greater focus for Takeda on earlier platform deals. Again, the diversification of the modalities, the ability to have external translational tools as well in patient samples and biobanking. What I've been very impressed by Takeda is the flexibility in the different range of deal structures that have been finalized and have been successful with our external partners. And this is kind of the flexibility that we also expect out of our partnerships. Looking at the external orientation, we do benchmark ourselves with the industry. And I think we've been very proud of the partnerships that we've been able to establish as part of our strategy. Moving to Slide 9. Next slide. So looking at the evolution of our modality deployment. As I mentioned before, we are evolving our pipeline from a small molecule focus to a biologic focus. And what you see here is indicative of a stage of portfolio entry, where there's more formal investments made for each one of these programs. And that's really yielded a transformative pipeline in such a short amount of time, and that's certainly attributed to our leadership at Takeda. The topology of these different types of biologics are extremely complex. And really developing subject-matter expertise in all of these areas is a very tall order, but we try to leverage and build internally and balance that with the external access to platform technologies and know-how as part of our partnerships. And for each one of these projects and modalities, we take multifaceted approaches. And that creates a lot of complexity because the drug discovery process is not the same for these different modalities. There is disparity, but what we need to balance is the external access to the technologies, our internal expertise and really drive a multifaceted approach to really pressure test hypotheses and have thorough evaluation on our strategy. Next slide, please. So many of you have seen a different version of this across industry. And agility is one of the main components that we focus on. And we operate very similar to what you would see for a small biotech -- multiple small biotechs within our research group. Looking at each one of these categories. There are -- they're comprised of many different processes that are very complex. So if we think of this as an iteration, how can we compress this iteration, if we're going to fail, let's fail quickly. But if we can compress this iteration and reduce the number of iterations by making these processes more streamlined, that ultimately can create a competitive advantage for us. So our ability to pivot quickly is essential. But also our internal and external stakeholders, we need to share ownership across the board. The days of having a CRO throw things over the fence, that's very much beyond us. And we focus and commit a lot of time and investment in not only the due diligence and evaluation of the landscape, but also the fostering very healthy relationships with our external stakeholders. Next slide, please. So getting to our partnership with Twist, leveraging the agility and expert know-how, that's a balance between Takeda and Twist. We want to leverage both in the premier platforms of Twist, the library of libraries that you were seeing before and, as Emily spoke to, the DNA buffet. This allows us to get access to custom library, generate constructs, reagents very quickly and also the custom libraries that we want to use for our internal purposes. This agility is very important. And I think through our due diligence and the landscape analysis, we identified Twist as an ideal partner. So that flexibility has not only been reflected in the science, but also in the agreement getting in place. So I've been very happy to see how fast we've been able to do this and very excited about the future. And maybe at the next investor conference, we'll be able to share some of the case studies for how -- what we anticipated was realized through this partnership. So I'm going to stop there. Again, thank you to the Twist team, and I can answer any questions you have.

Thank you very much. Next, we will move to the presentation from Invetx, and we'll hear from both from Jürgen Horn and -- Dr. Jürgen Horn and Dr. Bill Brondyk. And again, please continue to put in your questions, and we'll answer them at the end. So with that, Jürgen, you want to go ahead?

Thank you. Thank you, Emily, and thank you for the opportunity to present to this event and to introduce Invetx, the animal health market and our partnership with Twist. Invetx is an animal health biotech start-up company, and our mission is to close the innovation gap between human and animal health. Our model is unique in that it is a very capital-efficient virtual model that is leveraging strong partnerships with best-in-class human biotech companies. And our key partners are AbCellera, Twist and WuXi Biologics. We do have an experienced leadership team that's composed out of experts from the human drug development expertise as well as the animal health industry. Our platform sets a new standard for veterinary monoclonal antibodies in that we can deliver fully canine and feline optimized and half-life extended monoclonal antibodies. And what is also very important, especially in the animal health industry, is access to a best-in-class product development and manufacturing for these product candidates. We already have a pipeline of 6 active programs in high-value markets in animal health. Two programs have been completed in the discovery phase, and the lead candidates have entered pilot studies in the target species this year. We also have built a proprietary half-life extension platform, which is ready to be applied to all programs. The value proposition in animal health is slightly different than it is in the human health side as the size of the prize is typically a bit smaller. But there is a strong return on investment, and that's based on the fast time to market, the low product development cost in animal health and the reduced risk of our programs as we are mainly using validated targets and validated technologies. We were able to close a Series A with $25.5 million just recently, and we're very happy that we're able to build a very solid shareholder base around that. So if you go to the next slide, I would like to talk a little bit about the animal health market because many of you may not know it. It is significantly smaller than the human pharma market, of course. But it is nevertheless a sizable market with strong market fundamentals and continuous growth of roughly 6% over decades now, even during challenging times like the financial crisis or even during the current situation, the veterinary businesses and industries are holding up quite well. There is a sizable number of pets, and my slide here is focusing on the U.S. market. However, there are equally impressive numbers in other markets and impressive growth in developing markets. The total pet expenditure in the U.S. was estimated at $95 billion in 2019 and is expected to grow this year to $100 billion. And this is mainly driven by increasing pet spending as well as increasing pet ownership. If you move to the next slide, I want to point out that the differences in animal health to human health is that for the field of biotherapeutics, in veterinary medicine -- there is a huge innovation gap between human health and the veterinary markets. Monoclonal antibodies entered the human medicine field about 35 years ago; whereas on the veterinary side, the first monoclonal antibody and to date still the only monoclonal antibody on the market was launched in 2017. And this is especially surprising as human biotech offers a rich set of validated targets that are applicable and where there is an unmet need on the veterinary side. So the field for biotherapeutics is wide open, and we see that as an opportunity. Can monoclonal antibodies address these unmet needs? And we are absolutely convinced they can as they deliver targeted, safe therapies and a convenient dosing intervals. And this sounds maybe not so important on the human side. But on the veterinary side, it is very important that dosing intervals are longer as it is quite difficult to dose a dog or a cat on a daily basis with a pill. So more convenient dosing intervals not only make it more easier for the owners and veterinarians to apply the medication, it will increase compliance rate dramatically. The large animal health Zoetis, former Pfizer Animal, Health has already demonstrated that monoclonal antibodies can address these unmet needs and address the consumer and veterinary needs. With the launch of Cytopoint, the currently only available monoclonal antibody in the market. They do not itemize their sales figures any longer, but it is said that they will achieve $250 million in sales in 2020. And not only do these new drugs capture a big market share, they, in fact, build and expand the market segment as has been demonstrated quite impressively in atopic dermatitis, a market that has grown from roughly like $300 million about 15 years ago to more than $1.5 billion. And the same is expected, which is shown here on the graph to the right, for the canine pain market when the antibodies that are -- have been announced already by Zoetis -- will hit the market. If you go to the next slide. How do we approach this? The Invetx approach is unique in that we are a virtual company that is set up with strong external partnerships. That allows us to be quite lean and capital efficient. In-house, we have the expertise for all areas that we are -- that we need to discover and develop monoclonal antibodies and bring them to the market. But all our researchers conducted by external partners and that it was very -- therefore, it was very important to select the right partners, the right technologies that are advanced and can be tailored to the specific needs in veterinary medicine. We believe this is a strong competitive advantage. And that -- it gives us the capacity and the capabilities, which we would never be able to transfer at a small-scale, to a veterinary laboratory. So with that I want to hand over to Bill Brondyk, our CSO, to talk more about the collaboration with Twist and the value that is added to our company and our programs.

So next slide, please. So I'd like to thank Twist biopharma for inviting me to present. It's really my pleasure as you'll see from just a few of my slides, they're really play an integral part of our drug discovery process. So at Invetx, we've developed for our drug discovery process, really an integrated network that enables us to go seamlessly from a target sequence all the way to the clinical antibody lead. We've established IP in the half-life extension for both canine and feline IgGs. And through all of our drug discovery efforts, we're really acquiring what I think is some deep knowledge about canine and feline IgGs, including the antibody repertoires and the CMC properties. As Jürgen mentioned, we're a very small but experienced organization that's really laser-focused on delivering the best antibody leads in a timely fashion for the animal health market. Next slide, please. So this slide provides an overview of our antibody discovery platform. And we've been using what we think are some of the best technologies to deliver the next-generation veterinary monoclonal antibodies. For the initial part, the antibody discovery, we are using 2 platforms, both in vitro and an in vivo platform. For the in vitro platform, we partnered with Twist Biopharma, and they are currently in the midst of generating fully canine and fully feline antibody phage display libraries. For our in vivo strategy, we've partnered with AbCellera and what they do is they take B cells from immunized animals and use their advanced microfluidic technology to screen these B cells. Further downstream, our screening strategy is robust. So we're screening for affinity, [ cross reactivity ], specificity, and of course, functional activity, along with developability properties because we don't want to see any unwelcome surprises downstream in either CMC development or manufacturing. Sometimes, the hits that we identify can be carried right out to a lead but other times, there's optimization required. For example, an antibody, if it came from a rodent may need to be caninenized or felinized or the needs to alter the affinity, so affinity mature. So for all these efforts, we've partnered once again with Twist Biopharma. Once we've identified the optimal lead of the variable domain, then we fuse that to our FC variant, which extends the half-life of the IgG. In the next 2 slides, I'll describe in greater detail how Twist Biopharma is helping out for our whole drug discovery platform. So the Twist Biopharma, they're once again, contributing 2 ways with the in vitro antibody discovery and the antibody optimization, which you heard from Aaron is called TAO or Twist Antibody Optimization. And all -- these 2 technologies all have as their core is their -- the Twist Bioscience DNA-based platform, which you heard about from Emily. This silicon technology for me is just incredible and really allows for speed and precision in the DNA synthesis, which is really part of the Twist advantage. We have an exclusive relationship with Twist Biopharma for the field of animal health. So as I mentioned previously, Twist is currently making both fully canine and feline antibody phage display libraries. What we've done is Twist has generated a database of natural canine and feline CDR repertoires by doing next-gen sequencing on PBMCs from both canine and feline. So cats and dogs. Twist has an incredible amount of expertise in phage display, selections and screening, including cell-based technologies. And then as I mentioned, our characterization analysis will be very thorough. And the other area that will be contributing and are contributing currently is their Twist antibody optimization platform. So they've created a veterinary equivalent to their human platform, which you heard about from Aaron. And so once again, on the cartoon on the right, you can see kind of a schematic how this process occurs. So the input antibodies can be basically anything as long as the antibody binds to the target of interest and the species of interest. So it can be a chimeric antibody made in rodents. It could be a canine or feline antibody, and it could even be a human therapeutic antibody. And as long as the affinity is somewhat good, these can be used. So then they use these sequences in their proprietary software, along with their database with a natural canine and feline repertoires to develop a library based on that sequence. And the library once again is consisted only natural CDR and frameworks found in canine and/or feline. And then they do the selections and screens. And then the lead antibody will be affinity optimized and fully canine or feline. And also, they, as Aaron mentioned, they can remove all potential sequence liabilities. Next slide. So these are -- these 2 technologies are what we're really working with Twist on, right, currently. We did have an initial project, and I wanted just to briefly go over that. This was a protein engineering project, and it was to extend feline IgG half-life. And so our objective was to generate feline Fc variants with an increased affinity to feline FcRn. A similar strategy has been done on the human side and very successfully. So Twist customed design a precise phage display library of the Fc -- feline Fc, and they generated defined residue substitutions in more than 50 feline Fc positions. And on average, each molecule had 2 positions substituted and a very high diversity of the library of over 10 to the 10th. So they completed the selection screens and characterization, selections with feline FcRn at both low and high pH by high, I mean, physiological pH and completed ELISA screens. And for the output, they sequence by next-gen sequencing and then reformatted those Fcs into full IgGs and completed the complete analytics, which includes binding kinetics to feline FcRn at both low and high pH aggregation by SEC and thermal stability by nanoDSF. The results, frankly, were outstanding. So they identified Fc variance with a desired binding characteristics to feline FcRn at more than 20 positions. Just to give you an example how successful this was. Because the extension of feline IgG half-life is so important for our platform, we did another approach in parallel with a different company. They used an [ MMK ] immunogenesis approach, and they were only able to identify Fc variants at 3 positions. So these Fc variants have been tested in a feline PK study, and they've been shown to increase half-life by at least 2.5 fold. We filed IP, and now this is part of our platform. So just to close with my discussion about how we're working with Twist, I wanted to just give a few comments on how -- what I think of the collaboration so far, and it's really been enjoyable. And as you can see from this one project, incredibly productive. They're really experts in antibody discovery, protein engineering and computational biology. And their expertise in computational biology, I feel, really gives them a differentiation from the competition. And as a partner for all of our projects, I find them to be very collaborative. There's open communication. They really come up with innovative solutions for a very challenging technical issues. And output has just been outstanding. Next slide. So just to summarize for our platform. For antibody discovery, we have both in vitro and in vivo approach. With the in vitro, we're working with Twist. For antibody optimization, we have the ability to caninize and felinize and also affinity mature. And all this, again, is with Twist. And we've developed a half-life extension technology. So this will be used for all of our monoclonals going forward. And we've really worked hard to optimize our time line, and our goal is to generate lead candidates within 6 to 12 months. And now next slide, and I think I'll turn it over now back to Jürgen.

Thank you, Bill. This is great. This is just a quick snapshot of our portfolio and where we stand today with the programs. Bill described in more detail about our platform technology, the half-life extension and the proof of concept for that has been established, and Twist played an important role in that. We currently have 6 active programs. And at the moment, Twist is involved in 3 of those programs, and we foresee more involvement in future programs as well as we go forward and start new programs. If you go to the next slide, this is really just a summary, who we are. We believe we're really well positioned to take innovation leadership in this sector of animal health. The monoclonal antibody platform, we built with our partners is a very strong platform and sets a new standard for veterinary monoclonal antibodies in the animal health industry. And we couldn't have done it without Twist. So thank you very much.

Thank you very much, Jürgen. And Bill, you were one of the early adopters of the Twist platform, and we very much appreciate the vote of confidence in the business. So last but not least, we're going to hear from Dr. Carsten Linnemann. And we've been working with him and his team for a while, and they're doing absolutely fantastic science. And I will let Carsten go ahead.

Thank you, Emily -- thank you, Aaron and Emily for inviting Neogene to join today. What I would like to do in the next few minutes is to introduce Neogene, our unique team, our science, which we believe really pushes the frontier of what is possible with engineered T cell therapies and, most importantly, how we partner with Twist and the biopharma division in developing novel engineered T cell therapies for cancer patients in need. I will make some forward-looking statements. Next slide, please. The team of Neogene is really a very unique blend of, on the one hand, individuals that have dedicated their scientific careers and understanding T cell receptor biology and exploring it to develop cancer therapies and people who have really shown that cell therapy can be not only an effective treatment for cancer, but also become an industrial reality in a commercial drug. I will not walk you through the whole slide and all the names. Just to point out a few key individuals. Ton Schumacher is the Co-Founder of Neogene, the Chairman of our SAB and really a key opinion leader for T cell receptor gene therapy and neoantigen biology. We've been fortunate to work with Arie Belldegrun, former CEO of Kite Pharma and our Executive Chairman of Allogene Therapeutics who has been supporting the company since its inception. And we most recently announced that Franz Humer, former CEO and Chairman of Roche Holding has joined the company as Executive Chairman of the Board. Next slide, please. In September this year, we announced our Series A financing. We have been fortunate to find support for our mission of developing novel engineered T cell therapies from a strong consortium of cell-therapy-experienced investors. But we also have been very excited to actually announce our first strategic partnership this month with Twist, and this is what I would like to spend the next few minutes on explaining how we work with Twist and how the library of libraries approach not only is applicable to antibody discovery, as you have seen in the previous 2 presentations, but also really can expand into the T cell receptor therapeutics space. Next slide, please. The problem we, at Neogene, try to address is the observation of T cell therapy while being widely successful in B cell malignancies is still largely unavailable for most solid tumors. And the approach we have developed at Neogene really combines aspects of the 2 dominant adoptive T cell therapy approaches that are available. What we have developed is a very unique way how we access the therapeutically active component of tumor-infiltrating lymphocytes, which, by themselves, have been used for 3 decades successfully to treat certain patients with solid cancers. And we use these receptors in an engineered T cell therapeutic approach by introducing them into patient-derived blood T cells, which are after this genetic modification infused back into the patient. And this is really the therapeutic approach that has revolutionized how we treat B cell malignancies today. We believe that combination of these 2 approaches provides us not only with a safe, but also potentially very effective novel engineered T-cell therapy that is broadly applicable to a spectrum of solid tumors. Next slide, please. This slide gives an overview of the therapeutic approach from start to end. I'll walk you through. Everything really starts off with a routine tumor biopsy. And we use this tumor biopsy for some in depth, but nevertheless, fairly routine genetic analysis. The information we derived from this biopsies is twofold. First, we determine which mutations are found in the patient's tumor. In a second set of analysis, we also determined which T cell receptor sequences are found in this tumor biopsy. So essentially, which receptors are expressed by the tumor-infiltrating lymphocytes. We then use this genetic information in combination with high-throughput DNA synthesis to create libraries of genes that allow us to express both the mutated proteins as well as the T cell receptor sequences in reporter cells, which are then brought together in a next step in a functional genetic screening platform, which, in essence, ultimately delivers with very high specificity and high sensitivity, those T cell receptor sequences that are specific for certain mutations identified in the tumor. Once these T cell receptor sequences are identified, we use them to engineer cell product, which ideally is multi-specific and that can be subsequently used to treat the patient. Next slide, please. The reason why we are trying to target mutated protein, so-called neoantigens is our belief that these kind of antigens really represent the prototypic example of an ideal target for these kind of therapies. They're absolutely tumor-specific because their origin lies in a mutation, which inherently is connected to the evolution of the tumor. And hence, these mutated proteins are really restricted in their occurrence to the tumor cells, but at the same time, very strong antigens for tumor rejection because they, in essence, represent a foreign sequence to the human immune system, just like a viral protein would be. So there's really no inherent immune tolerance mechanism that may dampen a T cell response against these antigens. The flip side and the challenge with these antigens is that targeting neoantigens in cancer means a fully personalized treatment. Any given set of mutations found in a patient's tumor is typically confined to that particular individual. That means a mutation you successfully target for that particular patient will not be reoccurring in another patient in the vast majority of all cases. The reason why we believe it's worth targeting these antigens is depicted on the next slide because we believe these kind of treatments can address some of the fundamental challenges with current neoantigen-specific T cell therapies. This kind of approach working from a routine tumor biopsy and using synthetic TCR components overcome scalability hurdles that would be associated with needing large amounts of biospecimens that, of course, have to be removed by surgery as well as handling viable cells from these biospecimens. The genetic screening approach we use also allows us to cast a very wide net in identifying new engine, specific T cell receptor leads, and I'll come back to that a little bit more on the next slide. And then thirdly, the engineering approach we're using, we believe, can successfully address some of the challenges that are associated with the dysfunctional immune system that you will find in patients with advanced metastatic disease because engineering T cells means you can control the cell number and you have an opportunity to also engineer the potency of these cells, for example, address immunosuppressive microenvironments that may be encountered in solid tumors. Next slide, please. Synthetic biology is really key to the T cell receptor discovery process, which is depicted here again. Everything starts off with targeted genomic sequencing and then synthetic library generation to replicate the genetic information that is relevant for the subsequence screening that will deliver the T cell receptor sequences. The screening platform we use provides the broadest possible repertoire of T cell receptor sequences because it can access both fundamental classes of T cell receptors that occur in humans [ MNG ] Class 1 and [ MNG ] Class 2 restricted T cell receptors. The platform provides tremendous sensitivity because not only can we tap into the tumor infiltrating lymphocyte repertoire, a repertoire of T cells that is enriched for neoantigen-specific T cell receptors, but it's also extremely sensitive because it's a functional genetic screening where DNA sequencing provides for sensitivities that are otherwise unfeasible. And most importantly, the use of synthetic components to replace patient-derived components allows us to standardize this process to a high degree. Next slide, please. So really synthetic DNA tools are fundamental to what we want to do. They allow us to achieve scale because we can sequence genomic information and afterwards rapidly synthesize those DNA sequences that are relevant for us, having synthetic DNA libraries for genes allows us to use repertoire cell lines, which provides for a highly standardized process. And then largely, very important for a fully personalized engineered T cell therapy, you need to have access to high-throughput DNA vector production because these kind of approaches realistically will require nonviral gene deliveries to perform the genetic modifications. These are all areas we are -- have been working on very successfully with Twist, and we recently expanded this into a whole new area. Next slide, please. We entered into a collaboration with the Twist Biopharma division to develop what we call a synthetic TCR library. And really, the vision behind this collaboration is that we believe that the tools Twist has developed and the tools Neogene has developed will allow ultimately to develop a way that makes T cell receptor genes accessible with similar ease as modern antibody screening. The way we're going to approach this will be that we're going to use Twist capability to synthesize TCR libraries with maximal possible diversity, utilizing the best-in-class DNA synthesis capabilities as well as the expertise in building precise libraries and screening them. We'll combine that with Neogene's expertise in identifying therapeutic T cell receptor sequences of interest and merge this together in a rational screening process. In first instance, what we will do is we're going to develop TCR candidates against 2 undisclosed tumor targets. But really, our vision goes beyond that. And I'd like to show you that on the next slide. Because ultimately, we believe that these type of synthetic TCR libraries may be able to accelerate fully personalized TCR discovery as well. As you may remember from the slides I showed you, currently, with the help of Twist, we're creating TCR libraries for every single patient that are tailored for that particular patient. You could envision that once a synthetic TCR library is available and can be screened efficiently, that you can ultimately replace these personalized TCR libraries with a universal synthetic TCR library. So the process would then essentially only require to identify the neoantigen targets for every patient, and then we would identify suitable T cell receptor leads within the synthetic TCR library using a novel screening approach we're developing together with Twist Biopharma and that would ultimately provide us with TCR needs to engineer cell product for the patient. Next slide, please. We at Neogene are tremendously excited about the fully personalized T cell receptor gene therapy we're developing. We believe we have a very unique team, novel paradigm changing science. But we're very dependent on working with aggressive innovators like Twist and the Twist Biopharma division, and we're tremendously excited about the collaboration we recently entered with them.

Thank you very much, Carsten. Next, we will enter the Q&A session. Operator, will you start with the questions from the phone?

[Operator Instructions] Our first question from the phone today will come from Doug Schenkel with Cowen.

Emily, thanks for organizing this with your team and for bringing some of your partners out like you did today. I guess just a basic question. As we think about some of the different opportunities you've outlined for Twist Biopharma, from an investor standpoint, and I have a couple along these lines, what are the different economic models? And I guess what I'm getting at is in which of these examples is Twist simply providing product to customers and partners versus what opportunities are there where Twist could do that, but also participate in downstream economics?

And pardon me, ladies and gentlemen, we may be having some technical difficulties. Please hold for a moment. [Technical Difficulty] And again we do seem to be having some issues from our speakers' line. If you will just hold for a moment while we get them back.

Emily, you could start up again.

Thank you, everybody. Apologies for the technical difficulties. Such is what happens when we are virtual. So Doug, you were asking a question. We will answer the beginning of your question, and we'll try to get back to you for the remaining. So your question, what are the different economic models that we're considering for biopharma? And so there are a few. So the first one that we are currently pursuing is to have partner choose us to do work for them. And that can be licensing our content, that could be having Twist do discovery, that can be Twist do optimization. And that -- the economic model is some upfront payment and some participation in the upside with milestones and our royalties to be very transparent. Initially, we were not always getting milestones and royalties. But now that we have data, we're able to get those. And the economics are, I would say, typical discovery deals. Also, as you know, in pharma, more risk means less economics. So as these models are coming in, we're able to get a -- [Technical Difficulty] Now I hear some echo. So that's -- Doug, that is the first economic model. The second economic model is -- so that's -- I would call them partnership. The second economic models are collaboration. And so for instance, we have a published collaboration with Schrodinger, so in that case, we bring our technology with the collaborator's technology and we do some drug discovery jointly, where there is no exchange of money. They cover their costs. We cover our costs. But any therapeutics, any molecule that is discovered is jointly owned. And then if or when those molecules get licensed out or spinned out or moved forward by either 1 of the 2 partners or a third party, then the economic value is shared. So that's the second model, there's a partnership model. And then the third model, which is we're trying to get into is a Twist-sponsored program. Now that we have the confidence that the platform works, we've done some work internally to pick what we believe are important targets. And so -- and then we use our own Twist resources to develop, discover, optimize assets against those targets with the vision of spinning them out, licensing them out once we discovered them. And by doing the work ourselves, we believe we'll be able to extract a higher economic value when those get spin out. So I'll say those are the 3 buckets of economic value coming to the Twist Biopharma division. In addition to that, I'd say there's also some real synergy with the synthetic biology business. For instance, the high-throughput IgG production that Aaron built for Twist Biopharma will now be available to the broad community through synthetic biology business. And therefore, we are a one-stop shop for biopharma. They can buy products from Twist and do the work themselves. They can buy genes. They can buy IgGs. They can be library or they can have a service agreement with biopharma, where we do more of the work. And we're happy to serve all our customers along that spectrum. So that's why I think we -- your first question.

No, that's super helpful, Emily. And just one thing is I think it's [indiscernible] questions, going back to some of the challenges. I've heard clearly, number 2, the partnership; number three, develop your own product, what you described as synergies within bio. I just want to make sure for one, which at least [ I'm part ] coming back online. I think you described it as a collaborator that's where customers are paying for your products or services and the economic [ tells ] to make sure we got all 3 of the models, which you're talking about. Is that about right?

Sorry. So let me rephrase quickly. The first one is a partnership, where we get paid upfront milestones and our royalties. And the partner decides what work they want us to do, so that's the first one. The second one is a collaboration where there is no exchange of money upfront, where the collaborator pay their own costs, we pay our own costs and the molecule is owned jointly. And then the third business model is the Twist-sponsored research where we are developing our own antibodies against our own target with the intent to license them out later.

Okay. Super helpful. And then just as related follow-up and then I'll get back in the queue and give some of the other great folks on the line. How does Twist prioritize which customers to work with? And I asked this because over the last half or so we heard from 1 multinational pharma, 1 animal health company and an emerging T cell biopharma company. I guess I'm wondering, are you prioritizing certain verticals? And will you consider limiting or even going exclusively with certain customers in a specific vertical? And anything you can share in there would be really helpful.

Thank you, Doug. Aaron, do you want to start answering that question?

Yes, of course. I think one theme that I think you see throughout the 3 collaborators you heard today is just them seeing us as a partner. So we want to work and find people that see us as a part of their projects and a partner to help to move things forward. As Emily mentioned, we're not just a service provider. We're really a key attribute in their discovery. So that is I think one thing we want to probably present is really looking for strategic partnerships. And the 3 companies you heard today are definitely examples of that. So that would probably be one of the main priorities of choosing if we need to. To date, we haven't had to do that yet. But I can see as we become more and more successful, we might need to do that. But again, I think we'll also expand our capacity and our ability at the same time.

Yes. I think to build on what you said, Aaron, totally agree. Doug's question is around capacity. In the first part of the business model with -- which is a partnership, since we get paid upfront payment, as those number of partnership increases, we'll be able to scale our capacity. So there's no real capacity limitation since, again, that work is paid for. And of course, if someone comes in with a big check, we probably would be willing to offer exclusivity in some -- for some regions or for some targets or for some diseases. We're open for business, and our goal is to maximize the economic return to our investors.

Our next question will come from Tycho Peterson with JPMorgan.

A question for Aaron, I guess. I wonder if you can talk a little bit more on some of the initiatives you highlighted, the time lines for the 2 antibodies you're working on as you go into next month. How do you think about that opportunity? Do you open to a lateral test that's a fairly [indiscernible] as you think about the time line and partnering with a lateral [ flow]. And then on [indiscernible] production, how quickly could you scale up there?

So just to be clear, on the in vivo studies, those are the -- really the S1 antibodies initially. On the ACE2 antibodies, we're doing some additional in vitro tests to see if we may have some leads there. So that's where we are currently with the therapeutic side of the COVID project. And then on the -- can you clarify the question again on the lateral flow test? I didn't understand that.

I'm just curious, you alluded to a study, [indiscernible]. How do you think about commercializing on the [indiscernible] side?

Yes. Again, we're still working with our third-party collaborators on that. I really can't comment quite yet. But as you mentioned, it's a pretty -- it's an exciting space, and we're continuing to see if we can bring forward our antibodies for a second application, in this case, diagnostics, which I think as you see, it's a really important part of opening up eventually. And then the differentiator for our assay, I think, which might be helpful in working with others is just that it might potentially be very rapid in terms of being potentially useful in saliva, which we really see as differentiating.

And then you made a comment on the COVID case study [indiscernible] antibody. [indiscernible] just curious, could you maybe talk to a little bit more on the data and the quality of the data you have there?

Yes. So for SARS-CoV-2 S1 antibodies, it's been a pretty challenging project, more on the back end for testing. I think as -- in terms of just validating our antibodies, we were really lucky to find actually 4 different sites where we could do the testing of all our antibodies to confirm the potency that we're seeing. And we -- across all 4 sites, we saw very -- a common set of antibodies that continue to be very potent in the live virus neutralization. So that was really key for us to show that the antibodies are very potent. And they were also in the range based on literature that we've been seeing -- to be in the good range to potentially be used as therapeutics. So I think the combination of all the different sites plus the comparison to other antibodies that are in publications gives us a good sense that we're on the right track.

And then a question for the Invetx folks if they're still on the line. I'm curious, they [indiscernible]. Curious, I know that's more in vivo [indiscernible] in vitro. But curious if you can compare and contrast getting from AbCellera or Twist? And why the decision was to go with AbCellera, to maybe bring [ in-house ] was something you like.

Yes. Thank you for the question. Happy to answer that. Maybe, Bill, you want to jump in if my answer is incomplete. So we reach out to multiple companies to select the best technology and partner to tailor a platform for animal health. We wanted and now have multiple approaches to discover monoclonal antibodies. But discovery monoclonal antibody is not sufficient. We also need to optimize it for the needs. And therefore, we brought the different partners together that we thought would facilitate that best. So for each program that we are intending to start and each target, we select the technology or technologies that we think are the most promising ones. And depending on the output, we will decide whether we need to optimize or not, including whether we need to canalize or not. So we see this not as competitive at all. We see this actually as a nice integrated platform between the 2 companies, so AbCellera and Twist deliver and provide very unique technologies that both add a lot of value to our platform and enable us to really generate the best possible antibodies for the veterinary markets. So I'm not sure, Bill, whether you want to add anything to that, but I think that's kind of the big picture.

Yes. Just a quick comment. I would say having both an in vitro and an in vivo platform is really advantageous because the 2 platforms are complementary. As you kind of alluded to, there are certain times with certain targets where it's advantageous to use one over the other. And for really high-value targets, it's best to do both to really get a good diversity of antibodies.

And then maybe one last one for Emily, a higher-level question, but we've gotten it from investors, which is just on the cost side, and it does tie into some of what we're talking about today. But the cost for you on the fragments is obviously much lower than most of your peers in the market. But on the clonal side, that's not necessarily the case. So the question is, why does the cost advantage for fragments not necessarily translate over to clonal for you guys?

So on the gene synthesis side, our fragments are $0.07 per base compared to the market average of $0.15 per base, so about half the price of -- for non-clonal fragments. On clonal fragments, our price is $0.09 per base for short genes and $0.15 per base for long genes. And my read of the market that when we compete, the fragment side -- sorry, the clonal costs are in the $0.20 to $0.25 per base for short genes and $0.40 to $0.50 per base for long genes. So I'm not -- yes, I'm not really aware of strong competitive pressure that we are getting from customers -- sorry, from competitors on price. I think our prices are very attractive. And in addition to that, we offer the ability to have massive scale. So you get that price, not only if you want 1 gene. But for our competition, if you want to get 1,000 genes at the same time, it's almost -- it's very difficult for the competition to deliver a large number of genes in a short period of time. Whereas for us, it's -- that's what we do every day. So I'm not really seeing the price pressure yet on the clonal gene. I think we're actually the one putting the pressure on the competition.

Okay. That's helpful. And actually, maybe one more before I hop off for the Neogene folks. I'm just curious, as we think about the TCR therapy approach, how you think your platform compares to what Adaptive is doing with Genentech because they seem to be taking some -- a similar approach.

Yes. I mean I hope you will understand, it's difficult for me to comment on other companies' approaches. I think on a high level, we're excited about our platform for a few reasons. First one is we really believe there's value in working from a routine tumor biopsy and utilizing the validated therapeutic potential of T cells. And the second characteristic is we believe it's really important to have full access to both Class 1 and Class 2 TCRs, which our platform provides.

Our next question will come from Vijay Kumar with Evercore.

Appreciate the team putting together the presentation, it's fascinating. It's certainly helpful. But maybe one very simple high-level question, Emily. So you guys make a really high-quality oligos. I think that's Slide #12, where you talk about your differentiation as being human-derived, high-quality and unlimited DNA buffet. Like how -- like is competition not doing these things? Or maybe -- just maybe spend a quick minute on, is competition nonhuman-derived? Is that a differentiation? Or why is it that's Twist such a high-quality standout in this market?

So great questions. Maybe I'll start and I can ask Aaron to add to my answer. At a high level, the drug discovery process is finding the needle in a haystack. So you need to create a library of high diversity, and we're able to achieve diversity of 10 billion variants in a chip. And then there's a downstream screening process to find out, out of those 10 billion, which is the 1 or the 10 or the hundreds that have the affinity and the function needed. So the -- at a high level, the goal is to create that 10 billion -- that library of 10 billion variant. And so others can do it, but how that is typically done is by creating mutations. And so for instance, when we synthesize an oligo, at 1 position, you can put an A or you can put all 4 bases. And you can do that many times. And so you create a lot of variants. You can achieve very cheaply, very, very high diversity. The problem is that, that diversity is completely random. You don't control really what's happening. And so a lot of the mutants that you create will actually never be -- it could never be a drug because they just don't follow the rules of the human repertoire. And so in contrast to that, because at Twist, we have the capacity to make a lot of oligos from scratch in a way that's explicit, each of those mutant is chosen in a computer. And so you can create a file with exact mutations that follow the rules of the human repertoire. And then when you build it, you actually explicitly chose each of the mutant. And so you can have a library that is much higher value because you don't have -- excuse my French, you don't have garbage DNA, you have real mutants that you chose. And so that is why our library is better. Maybe, Aaron, is there anything that you'd like to add?

Yes. I'll just add on top of that. Again, I think I mentioned it before, the ability to use oligo pools to build libraries. It gives you this really tight control, as Emily mentioned, really create libraries that aren't full of garbage, and they're just all these basically high-quality antibody sequences that potentially you can pull out and that will bind and potentially be developable against your target. Another really huge advantage of the Twist platform that a lot of others can't do is just this ability to scale the number of oligos in a pool. And so that, again, gives us huge diversity in the different regions of an antibody that that's enabled by the silicon platform. So again, the advantage of oligo pools in general, but also just the scale at which we can synthesize oligos and pool, allows the antibody side of our business to be really creative and making unprecedented libraries that no one else has ever been able to make.

Understood. That's helpful. And then I had 2 quick follow-ups, one for the panelist and one for you guys. Aaron, Emily, I guess, when you take this process, right, I guess, going down to biopharma, I think you mentioned antibody discovery. Maybe in a very simple step, maybe -- so once you have this library of, right, 10 billion libraries, right, like what do you do with that? Is that like your -- are you synthesizing antibodies? Or maybe just a step from creating the library to creating the -- identifying the specific antibody, right? How does it happen? And where -- why is it that you're able to compress this process in the like 5 or 6 weeks? I mean that seems like a really, really in a short time frame. So maybe explain that part.

Do you want to take that, Aaron?

Sure. I mean I always say one of the major bottlenecks in antibody discovery and optimization is DNA. So it's been a bottleneck for almost my entire career. And now I'm actually working at a company where that's not a bottleneck. So we can quickly make libraries. We can quickly make genes. And so it's really just operating and doing the workflow and automating that process. And so that enables us to really compress the time and make it faster and do things -- do a lot of things in parallel that we want -- I've never been able to do before in my career. So I think the combination of the expertise that we bring to antibody discovery, coupled with this amazing DNA technology that we have, allows us to make it much, much faster than I've been able to do in my past.

Got you. And then maybe one for the panelist here, Dr. Mabry, Horn and Linnemann. I guess what is your -- when you deal with Twist, right, maybe explain to us how these relationships develop? Do you use them for perhaps a one-off project? And then you see these results coming out in a few weeks' time, and then that allows you to broaden this into a strategic partnership. Is that how these things process? And how does the ordering pattern, is your typical order -- I don't know, I'm making up a number, right, let's say, it's $10,000. And then once it evolves into a strategic partnership, does this go into millions of dollars? Is that how we should be thinking about in a Twist partnership with biopharma?

Robert, do you want to start?

I'll start. Thank you for the question. So to not be too vague, it's a range of everything that you just mentioned. We do have small projects. We have DNA synthesis or construct needs to be produced versus the more high-level transfer of libraries so that we can dive into off-the-shelf diversities. Again, these libraries that have already been developed. And the confidence that they have been -- they're using design space in a very cerebral manner. When I say design space, rather than random mutagenesis, looking at specific residues. That's the power of the technology and versus kind of the old school way of creating diversities with [ NNK ]. So the more you can focus on that, the more information you can yield just based on the output of your lead candidate panel. And that can feed back into your design and to utilize and maybe through iterations even improve the design of your libraries. And I think that's where Aaron and the team at Twist are going. They want to create more libraries. They want to get better at it as time goes on. And that's outside of the custom library work that we're doing with them to where we have our own ideas of what libraries we would like to generate. So it could go from a very simple construct order, which is not very expensive, to utilizing all of these libraries, custom and off-the-shelf libraries, with the appropriate milestones and royalties that have been agreed upon. So it's -- we're just at the very forefront of doing this, but this technology is automatable. And that's a standard benchmark, I think, for biopharma is to have selections ongoing, really leverage automation solutions. And that hopefully will yield a large number of projects that will benefit from the Twist technology. So it's difficult for me to answer that question, and we'll just have to wait to see how the logistics stack up over the next year, but we are leveraging the platform in that manner.

Thank you, Robert. Jürgen or Bill, anything you'd like to add based on your experience?

Sure. Yes, absolutely. So first of all, I want to say we don't see Twist as a CRO or a service provider. We see it as a long-term partner. And of course, we started off with one product initially, and we saw the power of the platform and it kind of evolved from there. And we have multiple projects running, and we are planning to do multiple projects more with no end in sight, really. So I think if that answers your question, it's really a long-term strategic partnership that we see with Twist. And as their technology evolves, our technology is evolving with it, and that's a big advantage for us.

Thank you. Carsten, anything else you'd like to add from your point of view?

Yes. I think we have a very similar position. So I think Twist for us really is a thought partner and not a CRO. I think many of the projects we pursue together actually arise out of a shared belief in the power of synthetic biology and discussing the problems we see in our case, in the space of engineered T-cell therapies. And the discussions are usually highly interesting. And most of the projects actually arise out of the scientific discussions we have among the teams and have actually yield a number of, I would argue, a nonstandard project. And one of them I just showed you a few minutes ago with the synthetic TCR library. And beyond that, of course, we're also using the other products with this offering. We have an antibody campaign with Twist. We're buying synthetic DNA, of course, as well.

And our next question comes from Catherine Schulte with Baird.

First one for Dr. Mabry. I know Takeda has also worked with Adimab in the past. What drove you to choose Twist here? And how would you compare and contrast the offerings and capabilities between those 2 companies? And then if either of the other panelists have comments on what other companies they considered and what led them to select Twist?

Sure. Thank you for the question. One thing that I try not to be in the habit of is talking about necessarily other platforms in an investor call. But what I can give you is kind of a high-level understanding of what our thought process is. Similar to, I believe, what Bill was mentioning, having access to multiple technologies is very important. The target classes that you're working on, the indication, the modality that you needed to deploy, it is very important and to have those technologies at your access, whether that's internally or externally. As a Takeda group that is building a premier global biologics group, we began to expand our access to the ability to generate antibodies very quickly from display systems as well as in vivo systems. And I didn't go through our overall strategy on how we deploy all of these technologies. But I think Twist is a significant component when it comes to library and assembly, library design, again, really leveraging the computational power and then the phage display systems that they have for off-the-shelf. That fills a rather big bucket. So I think from a high level, when you're thinking about supporting 6 different therapeutic areas, in global biologics, it's managing 50 to 60 projects at any given moment, we have to have access to those technologies and look at them in an orthogonal way. But the ability to quickly develop libraries with diversities that have been tailored for a specific project is very powerful. Again, time gets lost in these iterations. It's amazing. Just looking back 10 years ago, 15 years ago, how long it took just to get a gene in place. So it is very fast, and that's what we leverage to really accelerate. Hopefully, that answers your question.

Yes, absolutely. And then I guess one for you, Emily. For the high-throughput IgG production launch, how should we think about pricing for that offering and timing of when that will be available?

Yes. So that platform for high throughput IgG production has been used internally for a while now by Aaron's team. And so the commercial team -- Twist team saying, "Oh, we can make that available to more customers." As Aaron mentioned, we are, I would say, in alpha or beta testing with a few partners. So we've been shipping IgG proteins to outside parties. And in -- we're following the standard Twist product launch, which is when we launch fully, it would be at scale, meaning you can come and you want 1,000 antibody IgGs, no problem. And so that scale to get it really right takes a little bit of time because you have to integrate everything. You integrate the e-commerce. You have to integrate the invoicing. You have to integrate the shipping. And so it has to be almost touchless from a human point of view. So there's quite a bit of software to develop. So what that means is that outside customers are right now using IgG from Twist. But it will take a little bit of time to make it available at scale, where anybody can just go on the web and order it. And from our point of view, we have confidence that we can deliver a high scale and speed. In terms of pricing, we have not publicly disclosed the pricing, but our philosophy usually is to leverage automation and to have lower variable cost, which means that we can be very aggressive pricing-wise. And the benefit is that with an aggressive pricing, the customer, can do more work. And so therefore, they are more likely to choose us. The benefit to Twist is that our customers have a fixed budget. And so if we are competitive, we get a chance to get all of that budget. And then ultimately, the patient also wins because since more shots on goal are taken to get to a better outcome. So definitely, the pricing would be aggressive in the spirit of what we do. But still, we want to achieve good margins, thanks to our platform.

[Operator Instructions] Our next question comes from Puneet Souda with SVB Leerink.

So a couple of key questions here that are maybe not addressed or not addressed in-depth that we've been getting from investors. So Emily , just starting on the milestone payments and the economics. Could you walk us through the framework of milestone payments and royalties longer term? Because I think the real question here is that the drug discovery process is a multiyear process. We're looking at 5 to 10 years. And so clearly, milestone payments for antibody optimization for the targets are going to be important here in the near term. And Twist -- as Twist Biopharma is getting off the ground, could you walk us through the size -- maybe the size and the pace of these milestone payments? And maybe what you see in an average model. And I assume these payments would be small to start with and then sort of increase over time because the royalties are not going to be visible until you -- until the drug actually is approved, which is several years away. So maybe just if you could talk through that. And then is there any expectation for the number of annual contracts that you want to have here, given sort of the early stages of the development of a number of these projects.

Thank you, Puneet. Great, great questions. So the first question around details of the economic model. So what's important for us is that the upfront payment pays for the work because we cannot sponsor other people, our partners' work. And so we -- so we make sure that the upfront payment at least covers our costs with some margin. And then as we are successful, we are basically building a stack of milestones and royalties. And you are correct that royalties will come in later. Also there is some advantages to, for instance, the pet market because it may be a bit faster to get to launch product. And between now and the royalties, there will be some milestones. But in terms of reporting the economic value, we report every quarter the orders, so that's the upfront payment. As Aaron does the work and delivers onto what we do for our partners, we capture that order into revenue. So you can get a sense from the delta between orders and revenue how fast we are in doing the project. And so that's the only thing that we can guide to in the short term, and milestones and royalty will all be upside. And initially, we will not be guiding on those because we're not really in control of the speed at which those milestones and royalty gets delivered. But the one thing I can say is that we expect that the majority of the economic value will come from the milestones and royalty. So the upfront payment is very useful to us because it enables us to participate and build a stack of milestones or royalty that will deliver economic value over time. And then you had the second question?

On annual contracts that you have any expectations here, obviously, these are a number of projects that are ramping up?

No. Great, great question. So initially, in 2018, when we did an IPO with an idea about biopharma. 2019, Aaron spent his effort on the science, building the data package to show that we could do it. And then 2020 was focused on commercialization to show that we could monetize our platform in biopharma. And so we -- so far, we've prioritized getting as many partners as possible. There is quantity -- has a quality of its own. So that has been our goal. We had guided 5 to 6 paid contract. And as of June, we reported 9 paid contract with 6 with milestones and/or royalties. So we are on track with our goals. In the future, I think we will start to shift from prioritizing just a number more towards prioritizing value. So for instance, if someone came in with a big check saying, we want exclusivity for a year or for this disease, I think we'll be interested in that. We will be more interested in actual economic value than just sheer numbers of projects.

Okay. That's helpful. On competition front, if I could ask about there's a competitor antibody optimization platform, and since you talked about AbCellera, I'll ask about that. AbCellera helps get a COVID therapeutic. That's the Lilly's 555 antibody into the clinical trial in a very short period of time, and that too is a functional antibody. I think the time line there from patient to lead to trial was fairly short. So as you compare that to the Vanderbilt data that is looking promising but is not in clinical trials, I mean, wondering, what are your expectations there. When can we potentially see that product into the trial? Because I think, ultimately, the question here is that getting into the trials and eventual success with the approval of drug will be your true validation of your platform. So just trying to get a sense of what's your expectation on that. Or any other programs that could potentially see an approval in the sort of the near term or the next 1 to 2 years?

Thanks for a great question. We definitely -- we understand that -- so when I send the sequence of validation, so as I said, we're focused on science first then signing partnership. And I totally agree that one of the next catalytic event, inflection point in terms of validation of the platform is getting into the clinic. And so that getting into the clinic will happen from one of the partners that we've signed, first. When we did the work on our COVID-19, we started very late. We started end of March when others started early in January. So it was more for marketing purpose to show that we also could make some really good antibodies. But our platform does not necessarily have an intrinsic advantage in a COVID-19 world because COVID is not hard to drug. You can get antibodies as always. So we do have the advantage of being very fast, and so that helps. But the intent in doing our COVID work was more to validate the platform, which we did. And so the data that we got from COVID has been actually very useful to sign partners. And so it has been great for business, but we are not necessarily expecting an upside from that COVID work. However, that upside is possible. It is possible that someone comes to license our antibodies for therapeutics. It could be that someone come and license it for diagnostic. And we are talking to a number of parties, but we have not guided to it. And this should be considered an upside. But as I said, the quality of the data has been very good to make sure that the funnel for standing partnership is strong.

Okay. Maybe if I could ask the panelists, Dr. Mabry and Jürgen, Bill and Carsten. As you look at the targets or antibodies that need optimization and you're looking at the partners and evaluating partners, could you walk us through that evaluation process in terms of antibody discovery and optimization, which is what a number of these companies are offering in the marketplace? And Twist is leading with its scale in that platform with being able to deliver CDR repertoire that is unmatched in this space versus something that is maybe in vivo, versus something that is functional or maybe, let's say, a company that is doing great at this display. So when you look at the different technologies and partners out there, how do you sort of -- how do you evaluate? Do you expect to start with a number of them at first? And then sort of whittle down to select as you're seeing data emerge from these platforms? Just walk us through your sort of broad thinking there. Maybe start with Dr. Mabry first.

Sure. Well, I'll try to capture that in a somewhat condensed response so we can move on. That is a very good question. And I think when it comes to targets, there are different idiosyncrasies, whether this is a multi-spinner that is difficult to express on recombinant cells or if this is a soluble cytokine. Those -- the nature of these targets actually are very different, and it requires different technologies to approach them. Now going back to what I said before, when it comes to optimization, certainly, library generation is something that is very important regardless of what systems that you use. So if you're looking at optimizing across our activity, affinity, biophysical properties, I think that Twist has a very good platform for generating diversities and that can go into any system. So this could go into the phage system. It could go into access that we have to use libraries. it could go into a free display. Again, our model is likely going to be different than the other panelists just due to the number of projects and the scope and the scale of our work to support these therapeutic areas. So we need access to all of these technologies in order to maintain a focus that is tailored for each one of these projects. But when it comes to how we pick and choose technologies, it's really based on the target.

Anything from Bill or Jürgen?

Yes. So I think the same applies to us when it comes to target and what the technology -- to apply. But we have another dimension on that, that we are working in different species. And not only one different species, but several. So we're talking dogs and cats, but possibly more other species as well. So we were -- we have to find the technology or have to find a technology that is adaptable to that and still deliver the same quality as you would expect for human medicine so that we get to that new standard we want to set. And the Twist platform is uniquely positioned, I think, to do that. And what Aaron and his team has demonstrated really impressively how the -- not only the human repertoire, but also a feline or canine repertoire, and I'm sure other repertoires as well, can be rapidly built to deliver really a fantastic output. Certainly, for the optimization, we have seen really fantastic results. So for us, that is really the key point of this technology. And I hand over to Bill for a second, maybe he has something to add to that.

I think you captured it quite well, Jrgen. So in the interest of time, I'll stop there.

Carsten, anything else you want to add?

No, I think Robert summarized it perfectly for us as well. We'll look at the target. And then we believe that the diversity of different libraries, together with the perspective of optimizing any leads coming out of it using, again, different libraries was the most promising approach for us.

And at this time, I'm showing no further questions from our phone line.

Thank you. I know we're out of time, but there's a few questions in the chat. So we will answer a few of them, maybe we'll go a few minutes over. One question maybe for Jim, when does biopharma revenue exceeds the rest of the business?

So that's a very good question. I mean, obviously -- thank you, Emily, for that question. Obviously, our business is growing very rapidly. So through the first 9 months of this year, overall bookings for the company were about $75 million. Biopharma bookings were about $2.5 million. From a revenue point of view, the revenue for the first 9 months was about $58 million, biopharma was $1.1 million. I think from what you've heard from the analysts here -- or the panelists here, we're designing and working with some great partners. My view is that biopharma is going to be a significant contributor to the economic outcome of the company. However, the other elements are going to grow just as fast. We're doubling our business, growing from $25 million to $54 million overall company. My view is that biopharma is going to be a significant contributor as we scale this company to $200 million, then to $500 million through the partnerships and the exciting technologies and diversity that we're working on.

Thank you. A question for Aaron. If an antibody is successful, will Twist make commercial quantities too or does this get made on a different platform?

Great question. So again, if an antibody is successful that we're working on internally, yes, we would have to work with a partner to help us with scaling it. And the traditional way of [ mid ] scaling antibodies typically in CHO cells to make a stable cell line. But again, we're, of course, also exploring other expression technologies to scale up and make antibodies as well.

Thank you very much. So there are many more questions, but in the interest of time, we will draw through to a conclusion. Thank you very much for all the questions, all the engagements, and hopefully, we'll be able to answer them in the future. So to conclude, I think I have 2 more slides. Next slide, please. As you heard today, we can -- we have DNA libraries, which is content. We can do antibody discovery, antibody optimization and high throughput production of antibodies. That -- this is now being validated with partner. And at Twist Biopharma, we are also advancing some of our own Twist-sponsored development, again 7 targets that we have analyzed and deemed valuable. And the last slide, as a reminder, we have built a platform for writing DNA on silicon. We're going after large and growing markets. We always have a differentiated value proposition. And you've heard from 3 customers today, how -- this is also true in biopharma, we have differentiation. And we are building a portfolio of high-growth business with now validated business models. And we have been able to deliver so far high revenue growth. And so as a company, we have a track record of execution and innovation, thanks to the grit and innovation and the dedication of the Twisters. And we are very thankful for the support of our investors to help us get there. So with that, we'll conclude this session. Thank you again to our 4 speakers. We very much appreciate the partnership, the business, but also you taking the time. I know it's been a lot of prep, and so we very much appreciate that. And thank you as well for those of you that joined on the phone and participated in the Q&A. With that, looking forward to talk to you soon. Thank you.

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