Beam Therapeutics Inc. (BEAM) Earnings Call Transcript & Summary

Okay. Good afternoon, and welcome again to the 40th Annual JPMorgan Healthcare Conference. I'm Eric Joseph, senior biotech analyst at the firm. Our next presenting company is Beam Therapeutics, and it's my pleasure to welcome CEO, John Evans, to talk to us about the company. There is a Q&A session after the presentation. [Operator Instructions] And I'll work them in where appropriate. So with that, John, thanks again for sharing some of your time with us this afternoon.

Great. Thank you, Eric. And it's really great to be here, albeit virtually, but really excited to tell you all a little bit about Beam and some of the things we've been up to. So on, I guess, Slide 2, probably just remind you that I may make forward-looking statements today. Okay. So on Slide 3. So Beam, for those of you who are getting to know us, our vision is to provide lifelong cures for patients suffering from serious diseases. So this is a big mission but one that can be profound for patients and their families. We see ourselves as part of a coming revolution in medicine around onetime curative therapies. These are medicines that we would give once that potentially would give a lifetime benefit to the patients for the rest of their lives. This is also about both rare and common disorders. We will, of course, start with rare, severe genetic diseases, but over time, leveraging our knowledge of clinical genetics, we have the opportunity to treat to either correct or even prevent much more common diseases. And finally, this is a platform, this is a platform technology that we can rapidly program to go from one site in the genome to another site in the genome and then therefore create a quickly growing and sustainable pipeline once the initial engineering is clear. So there's a huge opportunity that can get unlocked by the technologies that we are accumulating. On Slide 4, I give a sort of summary of the gene editing field to date. So the first generation of gene editing technologies are called nucleases. They begin with Zinc Fingers and TALENs, and then most famously, you get CRISPR. And so these tools are quite amazing. They solve one very important problem, which is you have 3 billion letters in your genome, A, G, C and T, in various combinations, and you want to find one single address and leave alone the rest of the genome. And so that targeting problem is quite challenging, but it was solved, and these different tools do it. Maybe the drawback is once they get that there's only one kind of edit they can make, and that's a cut. So they create a double-stranded break in the genome, and then the cell will put the pieces back together again. And generally, you're trying to get the cell to do so in a way that would be therapeutic. But often, that's done with the outcome being a bunch of random insertions and deletions at the target site. So you don't really have full control over what you're doing to the gene in each cell. So Beam is developing a next-generation version of this technology called base editing. And so what base editing does is that it uses that same targeting ability you get with CRISPR, guide RNA-driven. But we -- once we get to the target site, we aren't making the double-stranded break. Instead, we are landing there, opening up the DNA, and then we make a very predictable single base change at the target site, an A to a G or a C to a T. And again, that's a permanent change but now one that is much more precise and it didn't need a double-stranded break for it to happen. So on the next slide, why do we care about this? Well, it turns out that single base DNA variants are everywhere in health. So certainly, that's true in rare diseases. So it turns out that over half of all genetic mutations are what we call point mutations or single-letter misspellings in genes. And a famous one, for instance, is sickle cell disease. So every patient who has sickle cell disease has the same single-letter misspelling in their hemoglobin gene, and we have a technology that can potentially turn that back to normal. At the same time, as I mentioned, we think a lot about common diseases as well. So we know so much now about why certain people or populations may be predisposed to get a disease or may be protected from that disease. And so when we think about preventing heart attack or Alzheimer's or liver disease, we can think about giving those same single base changes to everyone that could potentially protect them or shield them from that disease. So clearly, we want to have this ability to control the genome down to the level of the single letter, and that is exactly what we now have the ability to do. So on Slide 6, I show you exactly how this works. So this is a base editor. So we do use CRISPR, and we have that same guide RNA-driven targeting that I mentioned before. And you can literally see it here that this purple element, the guide RNA has about a 20 base address in it that encodes the place in the genome we're going to search and then bind. And the beauty of that is that if you then exchange that guide RNA for another, you get an entirely new medicine, which will go to a different part of the genome, bind there and make a different edit. That said, we have changed the CRISPR, so it no longer makes the cut. And instead, we've added a deaminase, which is a naturally occurring chemical enzyme. It's in all of your cells, and giving deaminase will recognize one letter of a certain kind and turn it to another. So for instance, an adenosine deaminase does an A to G edit. A cytidine deaminase does a C to T edit. So this is an incredible system. It is -- obviously would allow us to directly edit genes that are problems and turn them into functional copies. We can correct genes. We can activate and silence genes, turning them on and off. We can change gene function. We can do single edits but also do multiple edits at the same time, taking advantage of the fact that we don't make cuts. We're very specific and predictable in our editing outcome and have very high levels of editing. And we generally avoid the chromosomal changes and genotoxicities that can result from the double-stranded breaks used in previous editing systems. So we're convinced that this is a potentially best-in-class editing technology. On the next slide, we're surrounding this with a lot of other capabilities. We really do want to build a platform that is a leading platform for precision genetic medicine in general. And that means payload. It means great editing. And so it begins with base editing. Most of what we do is still base editing just because of how differentiated and strong it is. That said, we do own a nuclease called Cas12b. We have RNA editing, prime editing and many other systems actually in the works. Nothing matters, though, unless you can get it to the right cells of the body to make a difference. And so that goes to delivery. So we are also very much active in innovating on delivery technologies. This includes autologous cell therapies like in hematology, allogeneic cell therapies in oncology. Every one of our programs is delivered as mRNA, where the mRNA encodes for the base editor. We then in vivo deliver that with lipid nanoparticles or viral vectors, and again, innovating now on all of these areas, in particular, I would highlight for you the LNP platform where not only do we think about going to the liver, but we have a great technology that allows us to barcode LNPs and search many different tissues for whether LNPs can get to those tissues. And so that's going to accelerate the innovation potential in in vivo base editing in the future. Finally, this is new medical technology, so making it with high quality is critical. And so we are investing in manufacturing as well. We have a large facility, 100,000 square feet GMP facility in North Carolina that's coming online next year. This, I believe, will be the largest dedicated facility in the gene editing field, and this will allow us now under one roof to have everything we need to put the editing, the delivery and the manufacturing together to make great products both for our own pipeline as well as for partners. So to that end, on Slide 8, you can see one of those partnerships. So just today, we've announced a very significant deal. This is Beam with Pfizer. So Beam bringing our next-generation base editing technology plus those mRNA LNP delivery technologies for in vivo base editing of several different tissues. Pfizer, of course, comes to this through the COVID vaccine where they made a lot of progress in becoming leaders in the design and development and commercialization of medicines that have this mRNA and lipid nanoparticle feature. And they were looking to apply that beyond just vaccines and chose base editing as the place where they wanted to do it. So we were thrilled to receive that call. We've been working with them over the last 4 to 5 months to think about the scope of the deal, and we landed on a really good outcome. So it's $300 million upfront to Beam in cash, over $1 billion in potential milestones. This is a 4-year research term. Pfizer opts in at development candidate stage and then takes over from there, which means that it doesn't take a lot of our development capacity to move these forward. It's a 3-target deal. These are not included in our current pipeline programs, that's important. But it does leverage our delivery abilities to liver, muscle and CNS, one for each of those targets. Finally, Beam does have an option at the end of Phase I/II for a 35% worldwide cost profit split on any one program. So it's at our election, any of the 3, as they reach that point, we can make that decision. So a really exciting deal and really happy to be working with Pfizer on this. This does put our cash balance obviously up even higher. So we're now at about $1.2 billion estimated in unaudited cash balance. So this gives us a lot of financial strength to invest obviously in base editing, obviously in these new programs as well as in the delivery systems we're developing for these various organ systems. On the next slide, just to note, we do have other partnerships as well. This has been a feature for us. Again, having accumulated so much technology under one roof, we do want to find homes for it when it can do more for patients than we can do on our own, things like our Verve collaboration in cardiovascular editing and prevention of heart attack, the Apellis collaboration for complement mediated diseases, the Sana collaboration for highly complex cell engineering, Magenta looking at next-generation conditioning for our hematology programs and Prime Medicine giving us access to prime editing technology for a wide variety of applications. So this has been a feature of how Beam is building the company, and I think you should expect us to continue to do creative deals like this as we continue to grow. So on Slide 10, you can see our pipeline. So we have a wide variety of programs, about 10 disclosed here. They're sort of roughly split into 3 different buckets. You have the ex vivo program for hematology in sickle cell disease, which I'll tell you about. Those are our leads. You have the T-cell programs, specifically CAR-T products moving quickly now. And then of course, in vivo editing starting to really come into its own and just announced today the first nomination of our in vivo liver candidate, BEAM-301 for glycogen storage disease Ia, which I'll tell you a little bit more about. So very excited to see this all starting to move very quickly. So on Slide 11, we had a great 2021. We hit all of our milestones for the year significantly, including FDA clearance of the BEAM-101 IND, giving us a safe to proceed to move into the clinic. That's the first-ever base editor move through the FDA's IND review and happened on the first cycle. I would also note the BEAM-301 DC declaration as I mentioned. And obviously, all the data we generated in nonhuman primates, editing hepatocytes in nonhuman primate livers, which gives us pretty good line of sight to a potential efficacy in humans. For 2022, looking ahead, it's going to be a very busy year. So for BEAM-101, we'll be opening the clinical trial there. I'll tell you a little bit more about that trial. And the goal will be to enroll our first subject in that trial in the second half of the year. We hope to file 2 more INDs by the end of the year as well, BEAM-102 and BEAM-201 for sickle cell and T-cell leukemias, respectively. We also anticipate nominating a second development candidate out of our CAR-T portfolio. The IND-enabling studies for BEAM-301 for glycogen storage disease should initiate this year. And we anticipate filing a second -- or nominating a second liver development candidate this year as well. Finally, we do intend to continue the business development track record we had, obviously today getting a lot of momentum on that front with our Pfizer relationship. So on Slide 12, now let me run you through a couple of our franchise areas and just show you some data of what this really looks like. So autologous ex vivo cell therapies in hematology, this is where you're taking a sickle cell patient, you take their blood out. This is obviously disease blood. We're going to fix it by putting our editor and our guide RNA in. We then condition the patient to get rid of the rest of their old blood and then transplant the newly edited cell product, which replaces their blood system with newly corrected blood. So on Slide 13, BEAM-101 is our lead program for sickle disease and taking advantage of the now clinically validated approach to upregulate fetal hemoglobin, which compensates for the presence of sickle globin. And sickle globin is the adult form that's mutated that's causing these crises, pain, organ damage, ultimately death. If you have a high level of hemoglobin from your fetal form, which is turned off in adults, it can compensate and protect you. And so we're making single base changes with the base editor in the on/off switch of the fetal hemoglobin gene to turn it back on. And these are single base changes that are known in people to have this effect. And so we're just replicating that clinical genetics insight here for everybody. So really nice editing profile here. We get over 90% editing at this location and over 65% F upregulation. This is the highest level of both editing and of F that we've seen in the industry comparing these preclinical models. And it turns S down. You get less sickle as your F turns on down to about 40% or below. And that is -- looks like a best-in-class profile. So on the next slide, our trial here will be called BEACON-101. So we're really excited about this program. This will be, of course, for severe sickle cell patients. We will, for each patient, mobilize them to gather their stem cells, of course, manufacture, then conditioning and transplant. It's obviously an intensive process. The endpoints here have been designed to hopefully not just establish proof of concept and early efficacy but potentially be enough to measure clinical benefit over the course of the trial. And ideally, we'd love to see from the FDA the potential to register a trial like this. It will begin with 3 different patients in a sentinel cohort, one at a time, where you make sure that the engraftment is successful in between each. And then the FDA will let us go faster. We start to expand more quickly, adding up to 45 patients in total, very similar to the kinds of trials that others in our field are doing. So we're very excited about this trial and look forward to advancing it. Slide 15. The second program in our sickle franchise is BEAM-102. So here, we're achieving the direct correction of the sickle mutation itself. So it's a natural fit for Beam, and we're really the first company to do this efficiently. So you can see here the sickle globin gene has a single-letter misspelling. We can turn it into a normal copy. With the A to G change, what we get is we actually get a form of hemoglobin called Makassar or HbG. This has been known in the human population for about 50 years. It's a normal hemoglobin. It does not sickle. It carries oxygen normally. We've actually published a lot of information on that at ASH of last year. So our editor, again, very efficient. We had over 80% editing at this target site. And you can see the effect of that editing where we're literally now converting 1 to 1 a mutant form of the hemoglobin into a normal form. And the residual effect is you're down to about 10% sickle out of the total amount of globin, which is a huge change. And you can see the effects on the right, which we expect. So sure enough, under low oxygen conditions, we eliminate sickling in these cells that we've edited. So very excited about 102. Both 101 and 102 will move forward into the clinic and test these hypotheses, and we'll use science to tell us which one is going to move forward. On Slide 16, we talked a little bit again at ASH about our long-term vision. So we're not satisfied with our first ex vivo programs even though they look like best-in-class products because we need to go and make the overall delivery of these products better. So that's going to have 2 different phases. So initially, we'll, of course, start with base editing and a transplant that uses busulfan, which is the chemo we use for conditioning, which is the standard of care in transplant. But in wave 2, we want to improve that conditioning. We'd like to get rid of busulfan. And so we are planning to do that. We're partnering with Magenta, which has been a pioneer in this field. We're very excited about the prospects that we will be able to eventually get rid of busulfan. Then in wave 3, we're also thinking about even in vivo delivery. So this would be just an infusion, let's say, of a lipid nanoparticle that we have developed that doesn't go into the liver and more goes to the blood cell. And now you can in vivo edit those cells, and you don't even need the transplant at all. And so that's obviously a super exciting and groundbreaking idea. So over time, we intend to do this all together. We're working on all 3 of these waves all at once. And this is really one integrated, potentially best-in-class portfolio of technologies as part of a life cycle plan to bring the best editors and the best delivery regimens to patients with sickle cell disease both in the U.S. and around the world. Just a preview of that last piece on wave 3. On the next slide, Slide 17, this is our lipid nanoparticle screening technology in action. So you see on the left, what we do is we create as many recipes as we want of different LNPs. You can use different lipids, different other components. You can formulate them in different ways and all creating diversity. Then we add barcodes into each LNP, and they're unique. And then we screen the animal in parallel, tens or even hundreds of LNPs all at once. And then you simply look in the tissue of interest, and you see what got there and use the barcode to tell you which LNP recipes were most effective. And then you use that as you're starting point for the next round of lead optimization. So this is a very exciting technology, and moving very quickly now, we show here at 1 mg per kg expression of an mRNA payload in the blood cells of mice after just an injection into the body of over 40%. We showed at ASH a similar experiment where we showed blood cell expression of mRNA up to almost 20% in primates. So clearly making traction here, a lot of work here to do but very excited about the prospects of this technology. All right. So now let me switch gears a little bit to CAR-T. So here, we're doing allogeneic cell therapy. So we're going to take cells out of the body, edit them and make many edits all at once and then put those back in as an allogeneic product and treat many patients with that product. So here, all we do is we add multiple guide RNAs into the mix, and we will simultaneously edit at multiple target sites. So why is that important for base editing? So it turns out, you can do the same thing with a nuclease. So you could add multiple guide RNAs in a nuclease, but you start to get a bunch of problems especially as you add more and more edits. So first, you get chromosomal rearrangements where the cell is putting the pieces back together again in the wrong order. You get cell viability loss and a lack of expansion because the cells are starting to arrest because of all the double-stranded breaks that are accumulating and huge changes in gene expression, things like apoptosis pathways, p53 pathways because the cell is sensing all this genotoxicity from the double-stranded breaks. With base editors, we see none of that. So you get no detectable chromosomal changes, no detectable changes on cell expansion and no detectable upregulation of genes in these pathways. So we're convinced that base editing is a best-in-class way to do cell engineering and advanced cell therapy. And as the field advances, I think we're only going to want to do more and more engineering, and that's going to play to our strengths as well. On Slide 20, this is what it looks like in action. This is our lead program BEAM-201. So here, we are targeting T-cell acute leukemia. This is the subset of ALL that's been left behind by the B cell CAR-T field because you frankly just need more editing to make it work. And so that's what we've done here with -- this is a 4-edit product. Every one of those edits is a 96% knockout. And you can see on the right-hand side the strong efficacy of these cells. So we're very excited about this profile and think it could be a game changer for patients who are really needing more options in this disease. And then of course, the same principle and even some of these same reagents can be used much more broadly as we move forward with more advanced cell therapy products. So then on Slide 21, let me round out with a discussion of our in vivo editing systems and plans. So here, we're pursuing nonviral delivery for in vivo base editing of the liver. So we are going to do an mRNA with the editor, a guide RNA for the target, put it in a lipid nanoparticle, put it in the bloodstream and it'll -- and then it'll go somewhere useful. Of course, initially, that will be the liver, and so the programs I'll talk to you here about are liver-targeted. But again, that screening platform may open up new tissues like muscle, CNS and others. So this is obviously clinically validated technology. This is transient delivery, which we like. It's scalable and synthetic. That means it's easier to manufacture with lower cost of goods. You can redose it. There's no preexisting immunity as you get with viral vectors. So a lot to like in this format. So we worked over the last year to get to a proprietary Beam formulation, which showed up to 60% editing in nonhuman primates at a clinically relevant dose of 1 mg per kg. And so that, we think is the sweet spot where we want to be. That's effectively editing the vast majority of all hepatocytes and at a dose that we are convinced will be tolerated by humans and puts us in a great position to execute. As a reminder, now that we have that formulation, creating new liver programs is fairly easy. It may be as simple as swapping out the guide RNA and you have a new program. And so that's that leverage and platform scalability that we talked about in the beginning. So a couple of examples. Our first program here is going to be BEAM-301, which we just nominated as a development candidate. This is an A base editor to precisely correct the single causative point mutation in glycogen storage disease 1a for patients who have the R83C mutation. And so an A to G change will cure this disease. So there's about 900 patients in the U.S. This is a horrible disease where you can't fast. You literally could die of hypoglycemia if you don't feed every 2 to 3 hours. And that includes overnight. And if you sleep through an alarm, it can be life-threatening. It's really quite terrifying. So here, you see the killer experiment that we did in a mouse model of the disease. And sure enough, after onetime treatment with the base editor, not only do we normalize metabolism and activity of the enzyme quickly, but we also, of course, have rescued these mice who live normally under normal glucose control, whereas the untreated mice are generally dead within a couple of days. So really dramatic benefit. We think about 10% editing in the humans will be enough to potentially cure the disease. So that's moving forward very quickly now. Another program we've talked a lot about and we're very excited about is the alpha-1 antitrypsin deficiency program for, again, in vivo correction. So here, every patient with alpha-1 has 2 copies of the Z allele, which is the E342K mutation. And again, an A to G change is enough to turn that back to normal. And so patients who have this mutation, you end up creating a mutant copy of the protein in your liver. That builds up and becomes toxic, so you can get liver failure, but also is not secreted successfully into the bloodstream where the normal copy is supposed to be circulating to protect your lungs from degradation. So you end up with an emphysema-like condition often leading to double lung transplant. So you see here in the in vivo disease model here with our editor, we're getting significant correction of liver aggregates because we are producing less mutant protein when we correct, and we're, of course, then for every correction, secreting more normal protein into the bloodstream for the first time. And you can see here an almost fivefold increase in successfully secreted protein, which in this case if we received -- achieved something like this in humans, we believe that would be potentially curative for patients. So a third liver program that we've just talked a little bit about, which we're excited about is in hepatitis B. It's an earlier idea, but it gives you a good flavor of what we're thinking about here. So hep B, it remains a huge chronic problem for those who are infected, 850,000 patients in the U.S., hundreds of millions worldwide. And you can suppress the activity of the HBV genome using antivirals, but you can't eliminate it because that is DNA elements that are still resident in 2 forms. One is a circular form of cccDNA, but then also, it is often integrated throughout the human genome. And so one of the things that creates is a problem for using nucleases. So if you use a nuclease, you would be, again, by definition creating cuts everywhere the integration happens, and that simultaneous double-stranded breaks across the genome would start to cause many of the problems we talked about around multiplex double-stranded breaks in the CAR-T section. So with base editing, we don't have that work. So we can silence HBV DNA without worrying about cutting. And so it seems like it might be an attractive approach to seek a cure for some of these patients. So you see on the right-hand side the mouse data, obviously exciting results. This is still just in cells. But you can see the difference with -- in yellow, you got an antiviral response. But when you stop treating with the antiviral, there's a rebound, and it quickly comes back up, whereas with the base editor, you can see a response initially, but then even though we are no longer treating with the base editor, it stays down. And so that would be that long-term silencing of the HBV genomic elements that we would like to see happen. So on Slide 25, just to note the team here, this is the executive team at Beam. It's a really incredible group of people. Our mission is to create an entirely new class of medicines with base editors. This is a group of people who've done it before, creating new medicines often based in novel modalities and with very creative development pathways. And so ultimately, we want to deliver a wave of new novel medicines to patients, and we intend to do so. So with that, let me say thank you very much for the time. And Eric, I would love to continue the conversation.

Great. Well, thanks for that overview. A couple of questions in the portal here related to the Pfizer deal, so I think I'll start there. And really, the center on the scope of the targets, I guess, or the mechanics behind the target selection really, right? So the deal focuses on 3 different tissue types identified. Are the targets within those tissues specified as well? Or does Pfizer kind of have an option to kind of pick among development candidates down the road for a given tissue?

Yes. Great question, and I'm glad to clarify that. So the targets are named. So these are 3 targets, 3 diseases. We know what they are. We think Pfizer would be a great partner for them. And there's no residual uncertainty as to their access to anything else that we're doing. And so in its way, it's actually a fairly targeted deal where it really is a 3-target deal, one each in liver, CNS and muscle, and then today or tomorrow, we could choose some new targets in those tissues either to do ourselves or to do another partnership, and we will be free and clear to do that. So that's an important point in terms of how we think about this. Ultimately, the disclosure of those targets will happen over time. I would defer to Pfizer on the timing for that.

Got it. All right. Actually, so it's interesting, right, so given the breadth of the platform, certainly there's the potential for additional business development activity such as -- like the deal that we -- you're announcing today. Are there, I guess, certain areas, certain diseases where you are sort of off-limits, right, from a partnering standpoint that you'd totally want to retain within Beam? And then going the other way around, are any of the internal candidates today presenting opportunities for -- partnering opportunities down the road?

Yes. It's a great question. And I wouldn't be dogmatic about anything. I mean we are, if anything, very scientific and strategic in our process. We like to get a lot of things moving and then make decisions as we see data accumulate. But I would say that we've been pretty clear from the beginning on our BD strategy. And that is we prefer to keep internally driven programs as wholly owned as long as we can. And that lets us have more control, we can go faster, and we can make better decisions on our own for what we want to build, and ultimately, because we can get to a Phase I, Phase II result relatively early and relatively fast in these programs, and then you have a lot more data and information to say, do I want to commercialize this, would this be better in someone else's hands or should I partner it? And so all things equal, I think we prefer to keep the whole -- the internal pipeline wholly owned. And that's exactly what we've done. And so for instance, even this Pfizer deal, we've touched none of the targets that are on our internal pipeline or where there's internal activity going. That said, nothing is off-limits. I think could we do more deals like this one? Yes. There's certainly a lot more target space out there, and we won't be able to do everything ourselves. I mean that's clear. And so one of the strategies here is to accumulate so much technology under one roof that we can be a one-stop shop for people looking to do great precision genetic medicines. We will take some of that for ourselves, but we can't do everything, so we need to find a home for it. Even on the internal pipeline, at some point, we may do a deal on that. I think we -- I think it's probably likely that we can't commercialize every one of these programs on our own on a global basis. So somewhere in there, we will eventually find a program where there's a partner who could help us go faster or do more or where there's some really significant strategic or scientific synergy, that will be a great time to partner. But what we won't do is give away a whole large amount of our portfolio too early where there's this overhang of someone else controlling the agenda and we've lost control of creating value and delivering to patients.

That makes a lot of sense. Okay. The questions keep pouring in here. So grouping the next couple, which really ask to kind of make contrast between base editing and prime editing. How do the different approaches differ in terms of their editing efficiency and capabilities? Are there certain applications for which base editing might be better suited than prime editing? Yes, I think that sort of covers the confluence of questions, yes.

Sure, yes. So I think as I said in the talk, we're quite convinced that base editing is a potential best-in-class technology. That is still true. I think that both base and prime are going to be in this sort of next generation of editing tools where we're trying to move away from the double-stranded break. And I think if possible, you'd like to do that because you have sort of lost control of the genome at the point where you have a break. But once you've done that, you may want to do different things. And I think base editing is really good at precise, high-efficiency, targeted single base modification of the genome. And that may be true for base correction, things like sickle cell disease, Alpha-1, turning genes on and off, modifying their function, really anywhere that single base has function, we can change that function with an efficient edit. Prime, what's nice about prime is you get a little more flexibility, right? So you can sort of program changes with a little more options. And so that is going to open up new kinds of edits. So for us, it gives us another tool and toolkit to do the kinds of edits we're doing already. That's the field in which we control prime editing. I wouldn't say it's going to cannibalize anything we're doing today just yet, but it's certainly something we're active on and we're eager to see where it goes over the long term. And then Prime Medicine, the company, is working on other applications that are different from what we're doing, and that's the sort of divide-and-conquer approach that we set up originally. So I think the future has a lot of room for all of these technologies. And again, I think you've seen in our investment that I think we are incredibly bullish in base editing specifically for the long term. I think with the Pfizer deal, you can see that that continues.

I guess one of the takeaways from the Pfizer deal just given the indication -- or the target organs encompass there is the potential for -- the optimism for using the Guide Therapeutics LNP to reach muscle and CNS tissue types. What -- can you just speak to sort of where maybe kind of relative to targeting to bone marrow, the ability to deliver LNPs specifically or the enough tropism to muscle and CNS is relative to that? And do you anticipate sort of needing to build on or build in additional targeting capabilities such as conjugation akin to, let's say, GalNAc for liver that -- as part of the development of those product candidates?

Yes. It's a great question. So I would say -- I think it's safe to say CNS and muscle are earlier. Those are growth areas for us. We obviously do have some data, and we're excited about the prospects, but I think that will be a campaign that we do together with Pfizer. Obviously, we've been sharing the HSC data, which we're very excited about. And I think in terms of how this happens, it is currently un-targeted. I think it really does come down to just knowing how to change the lipid, the other components, the ratios, the process. There's a lot of different techniques that go into this that we can use. And that does change the biodistribution of the LNP. Now from that foundation, we can also think about adding targeting elements. And so that is something that is possible over time but not necessarily required. And so that's something that I would just highlight, and we'll obviously learn more as the science proceeds.

Another question here focused on BEAM-101 and BEACON-101, sort of just walking through the time lines to starting a first patient in the BEACON-101 study and generally how to think about time lines to initial data, right? To some extent, I think maybe looking at competitive programs might be -- or yes, competitive programs might be useful, but I'll let you comment on that.

Yes. I mean I think competitive programs are useful to look at. As far as I can tell, we're on a very similar operational path to what they've had to do as well, which is actually in its way good because it gives us some predictability in what we're going through. And so basically from open IND, we're now going through IRB approval processes and Safety Committee processes with some of these sites. So that is an important first step. That takes about 6 months actually to get through that. Then we open up for screening. And now we're looking for that right patient to be the first patient to get this trial and be sick enough that we can make a difference but not so sick that they would fall out of the trial, right? And so this is an important balance to get right. And then the preparation for the transplant is extensive in this ex vivo setting. So we've got to do some transfusions to calm down the marrow, several rounds of mobilization to get the stem cells out of the patient. Once you have enough of those, then you manufacture, you edit, and then we condition the patient to get rid of the rest of the old cells and transplant. And so those are all the steps. It's obviously a lot that's going to happen in the hospital. And then we follow the patient to confirm good engraftment has happened, and then the FDA will let us go and start mobilizing the next patient, and that's that one patient at a time. So it is a slow and steady beginning to the trial for sure. Again, pretty similar to all the time lines and processes that others went through. In terms of timing of data, I think we will do this responsibly. So we want to be a company that really shares data in a responsible way at medical meetings. So at some point, we will identify a medical meeting where we have a meaningful story to tell about this data. I think that's important. It won't be forever into the future, but we're also not going to do the very first biomarker we get off of the machine. So we'll find a balance there. Long term, I think the analogy of the competitive programs will continue to hold. So the overall trial design, as I said, 3 patients followed by the expansion of 45 patients, endpoints that have been carefully selected, and so I'm quite hopeful that the long-term path from the IND filing to a filing of a BLA can be similar to what we're hoping to see out of CRISPR and Vertex on the order of 4.5 to 5 years. And so I think in precision medicine, when you know who to treat, you should get an early signal of response, I think you really should be trying to make those sorts of fast path time lines happen in the clinic. That's certainly the approach we take with all of our programs. The precision medicine development paradigm is one that we take very seriously. I can't hear you. Eric, can you hear me?

I'm sorry, I was on mute there.

Okay. As long as it's you, not me.

No, totally on my end. I was just saying the questions keep coming in, but we're going to have to leave it here for time, unfortunately. So I want to thank you for your time this afternoon. Really appreciate it. And thanks, everybody, for tuning into the session.

Great. Thanks very much.