Twist Bioscience Corporation (TWST) Earnings Call Transcript & Summary

Okay. Good morning. We're going to go ahead and get started. I'm Tycho Peterson from the Life Science team. It's my pleasure to introduce our next company this morning, Twist Bioscience. We'll do a breakout in the Yorkshire room right after. And with that, let me turn it over to Emily.

Thank you very much, Tycho for the introduction and the invitation. Thank you, everybody. It's my pleasure to give you an update on Twist Bioscience. I will start by saying that I will be making some forward-looking statement. So as you may know, at Twist, we write DNA. And we strongly believe that DNA is the molecule of the 21st century. DNA is changing the world now, and our customers are using DNA to change the way chemicals are produced, to change the way food are produced in a way that's more sustainable, change the way drug discovery -- are discovered, the way diagnostics is done and even our DNA is used to store data, and we are working with some of the best companies in the field. At Twist, we've built a silicon platform to write DNA. And the benefit of silicon is that you can miniaturize the chemistry and miniaturization gives you two great things. First, it gives you high throughput. We can make minimal DNAs, oligos piece of DNA than anybody else, and it gives you cost because the volume of the regions is much smaller. And so based on this platform, we've built a business where we have first tools that are revenue-generating today. And then -- and I'll go through those tools later. And then we also have 2 options that are embedded, that are still early, still a little bit risky, but -- and a small investment, but important investment for big potential upside. So the markets that we cover in the core business have a TAM of $2.3 billion, those are growing fast, and the broader drug discovery and data storage business have massive TAM. So we've been in business for a few years commercially, and we started in 2016. We've grown -- the number of customers is 3x in 2017, 2x in 2018 and almost 2x in 2019. And you can see that our bookings have grown very quickly, both in the area of synbio and in the area of NGS. Our biggest customer is Ginkgo. If you don't know them, they are a great forward-looking company and they are still our biggest customer, but -- and their -- part of their revenue is growing. However, the percentage of the Twist bookings is shrinking because the rest of the business is growing much faster. Looking at revenue quarter-over-quarter. We have seen exceptional growth, again, both in synbio and NGS, and we reported our fourth quarter fiscal '19 ending in September with $15.7 million of revenues. Looking at the entire year, we've gone from $2 million to $11 million to $25 million, and we doubled last year to more than $54 million, thanks to significant growth from NGS and synbio. We have guided to $80 million, $84 million in the -- in 2020. And last year was a key year for us because the first time that we broke gross margin breakeven. There was the semiconductor model. And when we are -- when we passed the $40 million of revenue, we breakeven on gross margin. And then every incremental dollars of revenue, about 70% drop as gross profit. So that's the high-level financial view. I'll go back and now drill down a little bit in each of the parts of the business. First, with synthetic biology. What others do when they make genes for synthetic biology, they use a 96-well plate. And you can make 96 oligos, and you can turn that into 1 gene. The chemistry works, but it doesn't scale well. At Twist, we have a silicon chip where we can make 1 million oligos at the same time. So 10,000x more oligos than the competition. And those oligos are in clusters where we can do molecular biology in each of those clusters, we can make up to 10,000 genes at the same time. That's really good. But in addition to it, we've built a commercial infrastructure that is a heavily software base that enable us to have reach to many customers. So we have more than 1,000 customers and do transaction without any human intervention. And so the strengths of Twist is both the technology and that software infrastructure. Our synbio products are diverse already. We have products in genes, in oligo pools, in libraries, and we continue to develop and we plan to launch more products that expand our reach, and the products that we will launch this year will be focused on pharma and the long term. We have an e-commerce solution, which is very advanced, and you can upload your sequences on our website. We will analyze the sequence on the fly. In a few second, we'll tell you if there is any issues. We -- there is an editor, so you can look, you can put on optimize, you can change whatever you want. And then once you're happy with your sequence, you can choose your vector. And then right away, you get a quote, and you can place the order. It takes a few minutes. It's so easy that even I can do it. And the fact that it's frictionless, intuitively beautiful is really helping us reach that long-term of customer without Twist employee interaction. So why do we win in the market? Well, when we launched, our main selling point in 2017 was price. We were very disruptive. We are 2 to 3x lower priced than the competition. And we're not shrinking the market because our customers have more ideas than they have money. So they will spend their budget, but because we have a lower price, they can take more shots on the goal. Since then, we've had it scale. You can buy 1 gene, you can buy 1,000 gene, and that's very unique value proposition. And we've added user experience with the e-commerce. And we are continuing to add more differentiated feature around speed and more flavors of DNA. And so we have a truly differentiated product thanks to the technology. It's not a marketing gimmick. We have a number of proof point. I just have shared a few with you. We now have more than 1,000 customers, which is impressive, but there's 100,000 customers. So we are still only scratching the surface, and e-commerce will help us get to those. We have shipped more than 8 billion bases last year. So it's more than 2 human genome equivalent, and we shipped almost 300,000 genes. So there's 300,000, the right sequence in the right vector, and the right tube with the right barcode to the right customer with right invoice and collecting the money. And so we have demonstrated that we have the scale. And we have customers on the right, one example of a customer saying that they couldn't do what they do without Twist. We have the scale, the speed, the cost that enables them to do new science. So how do we move forward? We have -- right now in the biomarket, we have -- there is a $300 million market. We have 10% of the market share. It's good, but it's only the beginning. And so we'll continue to drive into the largest account with account managers. We'll go into the long tail with digital marketing, and then we'll launch new products that will help convert makers into buyers. So now moving to the second business, NGS, next-generation sequencing. Our customer use our DNA to read a patient's DNA. And the applications are in liquid biopsy, in rare disease diagnostics, in cancer diagnostics, in population genomics (sic) [ genetics ]. And what we do is exemplified on the right. It's real data. The blue boxes are exomes, are regions that people want to sequence. And they want to sequence anything outside of it. They just wanted to know what in those blue boxes. And so what you want is in green, the Twist data, where the coverage goes up very quickly, passes the red bar, that's the minimum, then come down very quickly and get to a high that is uniform. So uniformity is the name of the game. And below, in gray, is some of our competitors' data. Their data, where the coverage doesn't go up high enough, is too low, it's not uniform, and it's too fat. It's sequencing outside the regions of interest. And that is really -- the key of our performance is uniformity enables our customers to sequence less. We have a full portfolio. We have content. We have exome content. We have custom content. We have pan-viral content. We have mouse exome. But we have all the regions around library prep adapters blocker, you can use all of Twist products from A to Z, but you can mix and match. If you want pure library prep, it's fine. And we are continuing to launch new products to sell applications and broaden our reach. A big push for us would be around methylation, and I'll talk about SNP microarray conversion. Why we win? I alluded to it. On the left, we win because our customer can analyze the sample with a lower cost per sample. The cost of the kit, on the left in gray, is the same as our competitor. But because of our high uniformity, they don't have to sequence as much. And so that means that on a run of Illumina, they can put more samples and they can lower the cost, keep margins for them per sample. In the middle, we also manufacture very fast. If you want a new panel, it takes 6 to 8 weeks with the competition to get it, and then you have to test it. With Twist, it's 2 to 3 weeks. So if you have to do 2, 3 rounds of optimization to get your assay developed, you can do your R&D twice as fast with Twist. And then finally, on the right, we have optimized the workflow, and we have removed the 16-hour hybridization out of the workflow. And now you can do it in 15 minutes, which means that, with Twist, you can go from DNA to sequencer in 1 day. You don't have an overnight step. So all in all, it's a terrific product. Pilots are very successful for us, and with trying it is adopting. It used to be me saying that our product is amazing, and now we have our own customer putting YouTube videos and preaching the truth gospel on our behalf. So some proof point. We have almost 300 customers, and we're trying the top 88 customers that have high volume and 36 of them have moved from pilot to scale-up to validation and now in production every day using Twist. And we already have 2 OEM partner, one of them is PerkinElmer. And those partner help us reach in regions that are maybe a little bit harder for us to reach, like in Asia Pac. And I mentioned SNP microarray earlier. We have demonstrated now that if you do Twist plus NovaSeq, it's cheaper than running a microarray. And so that means that cost -- from a cost point of view, we get the same cost structure, but you are not limited to SNPs. Now you can add exome content. And so if you, for instance, are interested in BRCA, on a SNP microarray, you get 3 SNPs for BRCA. It gives you some information, but you can't get wellness, you cannot get health. If you add the exome now, all the basis of BRCA are covered. And so we're going to push -- continue pushing that conversion of microarray into sequencing. So how do we move from where we are? We grew from $3 million in 2016 -- I'm sorry, 2018 to $21 million in 2019. And we've guided $37 million to $40 million this year. So we're keeping that growth. And so our approach is to keep doing pilots. We have done pilots with 300 customers, and there are more out there. So our sales team is pushing for more pilots. Once we're in the pilot, it's helping the consumer move into production. And then finally, we are adding more application, and SNP microarray is one of them, and we'll look at additional application in the future. So that's what I'll say about our revenue-generating part of Twist, the synthetic biology and NGS. And revenue is very important. We are addicted to revenues, which we were addicted to revenue growth. And we're only as good as our numbers say we are, and we are very proud of what we've done. But in addition to it, we think we have opportunity to add -- to create even more value inflection points. And there are 2 areas there: One is in the drug discovery area, and the second is in data storage area. So first, drug discovery. We have a unique platform because we can make as much DNA as we want on the silicon chip. And also the drug companies when they are discovering antibodies, they have one library that they keep panning again and again. And that library, the content is random DNA. And with Twist, we can make many libraries. Actually we make 5 libraries a month, which means that when you come to us and we'll do a drug discovery for you, we have a library of libraries that we can pan against. And the sequence in those libraries is not random. Because we can print any DNA we want, we have accumulated the human repertoire, all the sequences from antibodies that have been sequenced, we know what those are, and we can introduce that genetic content into libraries. On top of it, we have automated everything. So when we did our IPO in 2018, this was still an idea. And we spent the year of 2019 to generate some data, and we had to pick two areas. The first area that -- where we demonstrated the value of the platform is in bio-better. So we can take an antibody and optimize it, improve affinity, improve half-life, developability, whatever you want to do. So we've shown that it works. And then the second thing we aim to do is we wanted to show that we could do things that other could not do. In pharma, that's really what you have to do to make a name. And so we picked GPCR as a target family, and GPCR very important in pharma. 1/3 of small molecule drug go after GPCR, but there's only 2 antibody approved by the FDA. And the reason why people have a hard time finding GPCR antibodies is because if you put GPCR in a mouse to get an immune response, there's homology, and so you don't get anything. And so we thought that with our libraries of library where we could make a human immune synthetic -- immune systems synthetically, we'll have a better shot at finding antibodies. And so I'm very happy to show you some of the latest data that we have. So on the left -- so we've looked for antibodies against GLP1R. It's a target involved in metabolic disease. The agonist is important in diabetes and the antagonist is important in hypoglycemia. And so on the left, we show that we are able to find nanomolar binder functional antagonist, which is great. In the middle, we are showing that as we push the concentration, we can completely inhibit the performance, the function of that protein. And then on the right is the first mouse data that we are showing is at time 0. When we add the glucose, in orange, the control mouse, the glucose level goes up as you expect, and it goes down as the glucose gets absorbed by the body. That's what should happen. And then, in purple, with a Twist antibody, the glucose level goes up, and it stays up. And so we have changed the function. And there is a rare disease indication for this target with hypoglycemia. It's about 30,000 peoples a year. If you get to gastric bypass and it's a little bit too much, people -- some patient are not able to keep sugar level. And so that could be an interesting asset. Now this is a clinical candidate that's really for -- is a partnering out or spinning out as an independent company. So we've done it with 1 GPCR, which is great, just in 1 year. Again, we got the lab in September 2018. And since then, we've done it with actually a total of 7 GPCRs. So it's not the one-trick pony. We've done it in cancer, in inflammation, in infertility and metabolic diseases. And so now we have a lot of data that we can take on the road to convince partner and to payers to do drug discovery for them. And again, what we've shown is we can do the hard stuff. So right now we are carving a niche for the drug discovery of last resort. If you can't do it, come to us, we'll do it. And of course, if you have the easy stuff, we'll be happy to do that as well. And so the data is trying to convince people. And so last year, we have -- we announced that we had a partnership with LakePharma. And in April, we announced a partnership with Pandion where they paid us to optimize one of their antibody. Actually, in November, we announced that we completed the work with Pandion and they signed up for more. And so now we have repeat customers. And this morning, we announced a partnership with Schrödinger, and they have a great platform based on physics to predict the sequences of antibodies that should work. And so in the computer, they can generate a list of antibodies, and with our silicon platform, we can make those from scratch. So it's a match made in heaven where they can design [ and notably agree ], and we can make it. And then we also have another undisclosed partner where -- with sort of paying us to optimize antibodies for them. And we are -- we've guided that this year, we'll have 5 to 10 pharma partnerships that are paid. And this year, we've guided that we will be able to start getting milestone payment and royalty payments per contract. So we went from an idea to data and then now we have some early commercial validation. And we think that we'll be able to get better deals economics over time. So now moving to the fourth part of Twist. It's the data storage business. And so we all live digital lives. We have a lot of data that are stored. And on the left, I'm showing the example of a picture from NASA. That is the earth rise. So a small crescent in the back, that's the earth, and the big part, that's the moon. And you may have seen that picture before. And the people -- the folks at NASA wanted to go back to the tape. And so they are to find the tape. They had lost the tape. And then they had to find a machine that read the tape. They had lost it, too, and fortunately, an engineer that had retired had kept one in his garage, and then they were able to read the tape. And then they had a bunch of data and they had no software to analyze it. So they had to rewrite the code to analyze the data. And when they finally got it, they got the terrible picture on the right. And that's what happens to tape after 40 years. You just get degradation of the data. And so if that is your important data, it's not great. And so what customers -- what people have to do to make sure that does not happen is that, every few years, they have to move from one media to the next. They have to go from one hard drive to another, to another, to another or they have to go from one tape to another, to another. It's extremely tedious. And the cost, the total cost of ownership, is really high. And we are at a point where now people are sitting on trove of data, they can't afford to pay the next one and have to decide which data they will keep and which data they will drop. And so we have an opportunity with DNA to stop that. We've shown with our partners like Microsoft, like the Montreux Jazz Festival, like EPFL, like Washington University that you can store data in DNA and we get permanent peer review. And we can get -- you can keep your data for 100,000 years or more. It's also very dense. In a very small volume, you can put dozens of data centers. And so it's a great opportunity. Conceptually, the way it works is if you have a file, a bunch of 0s and 1s, you can convert the 0s and 1s into ACGNTs. Then you can synthesize it on the silicon chip that we have. We can store it for as long as you want and then when you want it, you can retrieve it, sequence it and get your data back. It works -- we've done many demonstration, it works. So where we are today is, today, we can do this at a price point of $1,000 per megabyte, and that is based on our current silicon technology of 50 micron. So $1,000 is expensive, and our -- people we've talked to have been telling us that they will switch to DNA if we get to the same price point as a hard drive. And then on the right, the hard drive is about $100 per terabyte. And so we have a technology roadmap, where we're going to go from 50-micron silicon size down to 1 micron and then down to 50 -- sorry, 50 nanometer, and that will enable us to get to a price point that is competitive against hard drive. So it sounds like a lot to go to 50 nanometer, but actually, your phone has 5-nanometer silicon chips. So it's not bleeding edge. It's still technology that 10 years old. And then the second access that we're working on -- so that is the technology access. We're also working on a funding access where we are looking for partners to -- that will invest non-dilutive funding in Twist for this program to help us advance it. So to summarize, we have a very aggressive company. We want a very aggressive revenue growth, and I'm very pleased to report that what we signed up to do last year, we have accomplished. And we are signing up for more this year. We're signing up for continued revenue growth, improvement in margin, improvement operationally, launching new products to reach more markets and signing some additional pharma deals. Those are aggressive goals, but I think we have a great team that will deliver it. To conclude, the last slide, we're going after a large market, and we want to be disruptors in those market. And we can do it thanks to our silicon technology. And that platform is enabling us to build a portfolio of product, not a one-trick pony, but multiple businesses where we come in with a differentiated value proposition, and there is a high and consistent revenue growth. That's what we want to do. And over the last 12 quarters, we've demonstrated that we have a track record of execution and innovation. We've gone into new markets, and being leader, we've done it in synbio, we've done it in NGS, we're doing it in drug discovery, and we'll continue to do that. So with that, I thank you very much for your attention, and I look forward to your questions in Yorkshire. Thank you.