Lantern Pharma Inc. ($LTRN)

Hi. This is Craig Brelsford with RedChip Companies. Thank you for joining us for what promises to be an exciting session with Lantern Pharma. Today's session is centered around a live real-time demonstration of withZeta.ai, Lantern Pharma's next-generation AI platform designed to transform how oncology drugs are discovered, particularly in rare cancers. Rather than just talking about the technology, you'll see it in action, executing research workflows, synthesizing complex scientific data and generating insights in real-time. This is a rare opportunity to observe how AI is being applied at the front lines of drug development. Lantern Pharma, which trades on the NASDAQ under the ticker LTRN, is positioning this platform not only as a scientific engine, but also as a scalable subscription-based business with meaningful commercial potential. Joining us today is Panna Sharma, Chief Executive Officer, President and Director of Lantern Pharma, who will guide us through the demonstration and discuss the broader implications of this technology. We will begin with the presentation and demo momentarily followed by a Q&A session. [Operator Instructions] Before we begin, please allow me to read the safe harbor statement. This call may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements pertaining to future financial and/or operating results, along with other statements about the future expectations, beliefs, goals, plans or prospects expressed by management constitute forward-looking statements. Any statements that are not historical facts should also be considered forward-looking statements. And of course, forward-looking statements involve risks and uncertainties. Panna, go right ahead.

Thank you. Thank you, everyone, for joining me this afternoon. I really appreciate you guys taking time to learn about our AI platform withZeta. I'm going to give you a little bit of background before we dive into the actual platform. But the most important thing we're going to do today is everyone on this call is going to witness live the research on a very specific rare cancer, understanding what drugs may or may not work well in that rare cancer, understanding the gaps and issues using our AI architecture and then actually developing a drug for that rare cancer and perhaps even time allowing developing a budget to make that molecule or to run the trial. That's a lot in half an hour or 45 minutes, but that is the power of this multi-agentic tool. We were focused, I'll take a little backstory on how we got to where we are today withZeta. WithZeta started -- I'm going to share my screen also. Let's see how we can do this. I share the screen. Okay. Hopefully, everyone can see the screen. So we started withZeta. It started really as an internal project initially. As all of you may or may not know, Lantern Pharma was founded on the premise that big data and AI could help us better understand how molecules work or don't work in cancer and be able to help us also decide which cancers to go after and help us decide what combinations of drugs. To me, these are all essentially data problems. And if you get a lot of the data, you have a better shot at success. It doesn't mean it's going to be binary like it's going to succeed or it's going to fail, but you have a better shot. You can constantly make better incremental changes and you can better refine what you're going after through data. And so the premise of Lantern was to use lots and lots of data. We use hundreds and hundreds of billions of data points from wherever we can find them, and we had algorithms on top of that and disease models and insights and of course, tons of curated knowledge bases, knowledge bases that help us be better at developing drugs as an AI drug developer first. And then I was at a conference a little over a year ago, and the question came from the audience about what if -- what we were doing internally, what if they could use it. And I thought for a minute, well, we've got a great team. We can do a collaboration. But collaborations are lengthy. They're expensive. They take time. And at the time, AI was really beginning to blossom, cloud-based computing, real-time SQL queries, agentic architectures to translate natural language to machine executable code. And the thought came to me what if we can start doing that at a level and scale where we can actually create our portions of our AI and put it out into the public. Let the public use it. And I thought this would be a wonderful, wonderful AI for good. If we could discover therapies and develop insights not only just for our small emerging biotech, but we could do it for the broad oncology cancer community, especially in cancers where they're so challenging, rare cancers. The data is sparse. It's hard to collect. It's hard to put together. Experts are hard to get on the phone and models break. What if we could do this in rare cancers because we are sitting now at Lantern on 12 FDA designations, 6 orphan, 4 rare pediatric, 2 Fast Track. And the thought was if a small company like ours can have all these targeted FDA designations and have a real conviction that these are going to work and these -- and they all did. They work from in silico to then in vitro to then in vivo and now like all of our drugs or first generation of molecules are all in trials where the mechanisms have been validated. And very importantly, we've seen some very good impact on patients and the drugs are tolerable and they're working the way we imagined that they would work according to our early models. So we had a really good, I would say, library of success doing drug development, the AI win, the Lantern win. We said, why don't we do this now and bring this architecture and bring our ways of thinking to the entire rare cancer community, where it's accessible to any investigator anywhere, 24 hours a day. You can bring the power of all these experts. And in rare cancers, it's very critical because the market has largely failed. There's over 438 distinct and rare cancers in our curated knowledge base. And very importantly, there's about 5 million rare cancer patients globally that have very few options. About 30% of deaths every year occur in rare cancers. So although every rare cancer can be very small, there could be some that only affects 50, 60 people a year in the U.S. and maybe less than 400 or 500 globally and some even smaller than that. But collectively, rare cancers account for 30% of deaths. And it's still, regardless of the patient population size, it costs nearly $1 billion to $2 billion to go and solve for these cancers. We want to make a change in that. It shouldn't cost that much. It shouldn't take that long, and these cancer patients deserve better outcomes. So what if we can compress that time. And we were already doing it at Lantern. We compress the time line significantly. On average, from an idea to an IND filing to dosing of first patient can take upwards of 3 to 5 years for pharma companies, sometimes even longer. For us, we can do it at a fraction of the time and cost. And now we can even do it faster because of Zeta. We can do real-time literature searches like literally in seconds and actually query complex data. We can do pathway analysis, both molecular and permeability questions around the blood-brain barrier, cheminformatic questions, [ acne ] predictions, tox predictions. And we can do -- again, do all that within seconds and minutes. We have the world's fastest performing molecular analysis and molecular intelligence tool, where we can analyze over 99 features of a molecule literally within a second or 2. Nowhere else is that available. And we said, why don't we make this available not just in rare cancers, but for all molecules, and we did that as well. We also created and implemented a de novo molecular structures engine called ether0, where we implemented it to actually create novel molecules, generate new first-in-kind molecules or even optimize existing molecules. And then all of this can be embedded in a real living knowledge graph so that you can retain the information and it's built as you search. So it's like a real living brain that's growing literally in front of you as you explore and go deeper and you can share this knowledge graph with others in your organization or even other collaborators. The other great thing this tool has -- we got this from feedback -- early feedback from the community is they wanted to have various research modes. They wanted to have in-depth investigator mode where you can recurse up to 10 investigation rounds with lots of parallel tools. You could go slightly lighter with an explorer mode, where you can have an interactive dialogue, a quick dialogue and maybe only go 3 tools deep and only a few rounds of recursive thinking. And so it's optimized for speed rather than comprehensive depth. And then we created a synthesized mode, a reporter mode where it's structured into a comprehensive report that you can share with others or share with other organizations and use for transforming your dialogue and your insight with your co-scientists withZeta into more formal documentation. And that's the basis of also of a lot of other future features where we're going to be able to actually generate final regulatory and potentially even FDA-compliant and publishable documents directly from the withZeta platform. As we're creating Zeta, we realized that not all scientists really are the same, right? They're all very different. Just like any industry, there are subtleties between what a medicinal chemist does and the tools they think about, the tools they use, the way they approach a problem versus, say, a clinical oncologist versus perhaps a biomarker and translational scientists versus a clinical trial design person. They're all trying to solve for the same problem, that rare cancer or that cancer or that disease, but they're bringing their collective multidisciplinary skills in different ways. Some people focus on molecular design, some on endpoint selection, some on, is this biomarker available clinically or am I going to have to develop it? Some focus on the treatment landscape, some focus on lines of therapy. And so we thought, well, depending on the user, depending on the problem they're trying to solve, depending on where they are in the journey of developing and understanding the rare cancer, they may want to bring one of those personas and heighten it. And so what we did is we trained them based on each of these personas. And each of these personas had very specific things that they were great at and very importantly, tools that they used. Remember, underlying all of Zeta is a highly curated architecture of knowledge bases, tools and prioritization of tools for recursive thinking. And so that's what these personas are, these personalities. It's like you get the same information, but if you're an accountant, you're going to think slightly different than a marketer, than say, a strategy person. You guys are all solving the same business problem, but you're going to approach it using different lens. And that's what these co-scientist personas do. In future versions, we're actually thinking about having an environment in which all these co-scientists actually all try to solve your problems and then get together to create a Meta answer to your problem. So you get the perspective of all deep perspective from all the personalities. So imagine it's almost like a virtual biotech scientific team collapsing onto your problem and solving it literally within minutes or hours and not weeks and months. So what you're about to see today real-time, and we'll dive into this next is a little bit about the platform, just a general orientation. We're going to query a rare cancer. We're going to see how the knowledge graph evolves. We're going to actually do some molecular analysis on one of the potential molecules for this rare cancer. And if any of you want to put into the chat window or suggest a rare cancer or a question for a rare cancer, I'd be happy to take something live because, again, this is a live demo. I want to show you this is not canned. This is real-time. And we're doing this real-time. Nothing here is going to be canned and hopefully have time to actually generate a novel molecule and actually even generate perhaps even a budget or a route to create the molecule. So I'll come back after the demo and talk a little bit about what's next on the platform. Like any platform, like any tool, we're already excited and thinking about how can we do multiuser investigation? How can we do role-based access for larger organizations? How can we do white label deployment for some of these pharmas that are very excited to be using this and use Zeta to augment what they're doing. These tools in the future are not going to be, are we going to use them, it's going to be how much are we using them. And these tools are going to get better and smarter. And the evolution of science will be with co-scientists. So with that, I'm going to go ahead and move over to the platform. There's a great question. I know it's a little early, but there was a great question. So it's why only cancer? And it's a very valid question, and I don't mean to kind of change or be little any of those. But the reason we focus on cancer is that that's what we're good at. So it would be very difficult for us to have the credibility and more importantly, the know-how. I mean we were already building these tools. We didn't create Zeta because we were trying to be creating an AI platform. We created Zeta because we're a cancer company. Everyone in our team is maniacally focused on creating cancer drugs and insights faster and cheaper. We know that there's a new way to use data to solve these complex biological problems. And all the cancers that we're going after are challenging. They're rare or orphan and there's some of the more difficult cancers, and we enjoy that. And that's the need. We want to go after this white space like one of our trials is for people who don't smoke and they get lung cancer, non-small cell lung cancer. It's a very different disease. The pathways are different. The mutational profile is different. And our drug seems to have some great results. That's in a Phase II trial in Japan, Taiwan, United States, multiple countries. And we've got some great data on that, and we've been able to really make an impact in a lot of these patients in the trial. Another one of our drugs is focused on very aggressive cancers that have what's called DNA damage repair mutation, and they overexpress a certain enzyme called PTGR1. In that drug, LP-184 is so unique that it actually activates inside the cancer cell because PTGR1 is only expressed -- not only expressed, it's highly overexpressed in aggressive cancer cells, and that's what activates LP-184 into being these 2 really potent molecules, one that attacks the nuclear -- the DNA inside the nucleus of the cancer cell and breaks it and one that attacks in the cytoplasmic region, we believe. And then LP-284, which is another synthetically lethal molecule, works very differently. It tends to really prefer blood cancer cells and cells that overexpress CD19 and CD20 and also breaks apart the DNA. Another molecule that we're developing that has a very unique payload. It's an ADC that has a very unique payload and a very, very potent, cryptophycin payload attached to an antibody. And again, going after some pretty challenging cancers. And so we're cancer drug developers. That's kind of almost everyone's background in the company. And we're developing and building these tools not because we just wanted to develop tools. We're doing them because we wanted -- we needed to do it. It came from the essential problem is that we're a small company with limited budget, but we're big believers in data and AI and using big data to solve big complex problems in oncology. And so as we're developing these tools, we realized that in this agentic world, and our team was uniquely positioned and knowledgeable that we can actually share these tools with the world. And that's what we've done, and we're very excited about it. We think the underlying platform is such a massive force multiplier. And it makes sense for us in cancer to do it first because that's where we built the models. That's where we built the algorithms. That's where we built the knowledge base. And so it was very straightforward, if you can call it straightforward to go from an internal set of tools and systems to now a publicly facing one. And whoever asked that question, you're absolutely right. We can do it for many other diseases. And that's our long-term ambition with the platform is maybe to partner with other pharma companies and other disease companies and stamp it out for cardiac, for immunologic disease, other rare diseases. But rare cancers to us is such a prime need, very expensive, very costly, very time consuming and 30% of deaths and no one solved for it. So this is the only platform. So we want to do something that's big, meaningful and unique. And it's -- we have the credibility to do it in this category. So that's why here. So let's go through the platform. Hopefully, you guys can -- I don't know if you can see my screen. Can everyone see it? Yes. Okay. So let's look at the screen. So when we get to Zeta, we go to the sidebar, you can have all your prior chats in the sidebar. We can go to a new chat. And so we'll go to a new chat. You can also have an entire ontology. So if you're ever interested in how these cancers link, you can dig into the ontology of liver cancer and go to liver angiosarcoma and understand kind of what the subtypes are, get the classification, get all kinds of great information. We're launching a trial in bladder cancer, for example, in the next few months. It will be in a certain subtype of bladder cancer. So you can see all the various subtypes. Inverted urothelial papilloma, which, again, I don't know anything about, but it is a subtype. It's one of those ultra-rare cancers. So this is -- gives us an ontological framework for all the 439 cancers. You can see the toolkit. So we have a number of proprietary databases, our rare cancer knowledge base, standard of care database, a rare cancer taxonomy, all the clinical trials over 570,000 clinical trials, including their outcomes, a cancer drug database with all the FDA-approved drugs and all the standard of care protocols as well as our own drug database and then a massive research document library. And so that way, the key for the proprietary database is that gives a structure and guide for how Zeta think. So this is your space. This is your domain in which your lab to function and think about. And then we also give it external resources, medical literature, rare disease ontology, cell line references so that can generate new ideas from biomarkers and of course, FDA drug labels, you're going to see an open FDA to look at all the approved indications, safety warnings, dosing information, pharmacology, et cetera, as it gets updated. So both live and proprietary databases, and that governs the space in which Zeta thinks and that keeps it very, very different than any of the kind of the GPTs that are out there today because it's only thinking in this space. So let's go back to -- this is my account. Let's go back to the tool. Now you can pick a co-scientist or you can pick general or just start with general from now, and you can go into various levels of depth, like I said, explorer mode, which is default, investigator mode, et cetera. And you can also pick tools. So imagine you're a Merck or BMS and you want to attach your internal tools, you can easily go to the toolkit, go to your external resources and you can attach your own internal tools. And that's something that will be quite exciting as well. So does anyone have a rare cancer or a cancer they'd like to discuss? Or let's go ahead and ask Zeta, we can actually also voice activated. So let's go ahead and do it with voice since I'm standing here. Can you tell me which rare pediatric blood cancers have the highest unmet need and which drugs are promising in Phase III for those cancers? So a lot of times when you're researching, you're typing or sometimes you don't want to type all that, so give it a nice cool voice interface, so you can actually just chat with Zeta. The answers are somewhat complicated. So it's not going to chat back to you, not quite yet, but it's now going to go and do a systematic investigation. And so it's going to use multiple tools. You can see which tools it's going through and running through. You can then see how much it's reading. So it's already read through about 36 papers, 20 clinical trials that's gone through, 7 outside references and 9 PubMed references. So you can see a massive amount of data. So this is like real-time reading all this and beginning to connect dots and systematically evaluate the knowledge and to try to get us to the answer. So as it looks and executes on the tools, you can see which tools it's using. And then you can also -- one of the most important things from a lot of these AIs is we want the AI to be transparent. So if you go to a lot of tools today, they may be -- they tell you perhaps about the reference, but not always. But the challenge that they oftentimes have is that they don't tell you how they arrived at the conclusion or what they're making up because they're generative AI. So their focus on coming up with like the next word, the next chunk, the next vector embedding may or may not actually have to do with what you're talking about and may be poisoned or informed by knowledge that's not as relevant. So as you can see, Zeta has seen several rare pediatric blood cancers with significant unmet need, and it's an -- identified specifically now the Phase III one. So it's identified the blood cancers. It's letting you know as a colleague or co-scientist, hey, I'm almost there. I'm now cutting through all the Phase III data, and it's in a search for the specific Phase III therapies that are the most challenging. So again, something like this is very common. You would do this in a CRO, you might do in a pharma company, maybe as an investment bank, maybe as a biotech analyst. And it may take you hours. This has now taken us probably less than 2 minutes. And you can see this color coding on the screen, which is very important. We'll come back to color coding. But you can see that Zeta has given us things right here. And this is very important because it's never, it's trained as a drug developer, a summation of all these scientists, scientists. You're not training it to be a scientist as you might have to if you go to like a ChatGPT or a Claude. Its personality is that of the ultimate kind of Lantern scientist that it's going to talk to you in bullet points, specific direct. And it's going to put things into structures because that's very important as a scientist. So Tier 1, most critical unmet need, no Phase III therapies available. So it automatically thinks about what's critical and what the promising early therapies are. Again, drugs are in orange, diseases are in red, biomarkers are in green or purple. Papers that are referenced are in the inverse white and gray. And it says unmet need severity critical for non-Down syndrome AML -- AMKL actually. And then, of course, it gives us the subtypes terrible survival, high relapse rate and then it gives us Phase Tier 2, where there's a high unmet need, but there's Phase III therapies emerging. And then also moderate need where there's multiple Phase III therapies. So obviously, there's some promise and then it gives us the treatment gap paradox. So the most striking finding is that the pediatric blood cancer, the worst outcomes have no Phase III therapies. So 5-year survival in some of these pediatric blood cancers, Phase III therapies, none, none, none and some very low. So therapeutic landscape is inverted because people are going after the disease with a better baseline outcome, and they have multiple Phase III options, and it gives us the conditions and it has a recommendation. So just like a scientist, it's not going to just sit there and regurgitate. It's driven to action. That's very, very important. It wants to get to an answer. It wants to help you actually solve for a problem. And so for the highest unmet need, JMML, non-DS-AMKL, accelerated development pathways are critical. So expand trametinib evaluation, investigate the Hedgehog pathway, develop the cooperative trial and explore novel targets identified through comprehensive genomic profiling. And so this is one of the things we could do. So probably not -- we don't have time on this call, but users of Zeta would be actually able to do comprehensive genomic profiling by pulling up all the data sets that we have and actually look at what's going on. But let's go ahead and look at are there -- so on Zeta, we can also just do orally the adjuvant typing. Are there any Hedgehog/GLI pathway inhibitors that are currently in development for rare cancers or other cancers that we can learn from? So very broad question. I think it translated GLI to July. So -- yes, but it corrected it, which is great. So it knew what we were talking about. So now it's going to investigate those inhibitors that are currently in development to identify strategies applicable to pediatric AMKL, which is very, very high need. And again, it will give you total transparency in how it's getting there. And as you can also see, as it does it, the number of publications is jumped up to 87. So a lot of publications now it's worked through. And this is very important because as a scientist, you're going to read -- you always have a stack of publications and you're going to be able to read and maybe remember the last 2, 3, 4. But imagine having all of these 87 all embedded into your brain and live and available and you're connecting the dots and all of them real-time, like that is pretty phenomenal. And more importantly, you're connecting things that you may not even know about. So the clinical experience, now it's giving us why this drug failed in Phase III, why it matters, why this drug succeeded. So now it's going to do exactly what a scientist is going to do. It's going to look at failures, successes and look at why these different programs failed or succeeded and perhaps come up with a potentially superior strategy. And so it's going to look at the GLI inhibitors in development, what the lessons are for what we should be doing, even giving us a combination therapy, which is likely, which is great because that's how we think as drug developers as you want to attack these cancers from multiple pathways, multiple combinations, and it's already coming to that conclusion as well. This kind of gives us the most actionable strategy, which combines XYZ, would you like to explore specific mechanism of this or investigate potential resistance mechanisms? And as I mentioned, we're going to look at the knowledge graph. So as it develops this, this knowledge graph, it's creating a knowledge graph of these concepts of the drug, of the protein, of the disease, of the trial that it's involved with. And as we -- and you can drill into any area of the knowledge graph. And as the knowledge graph evolves, it's going to start connecting more and more dots. Remember, we have about -- we have about 60 nodes in it now. You can click on any of the nodes. You can share these with others, so you can export it as an interactive HTML file or you can export it as a JSON object, whichever you prefer, which is really exciting. And going back here, these are the next steps. But what we mentioned is that we want to -- this is an interesting drug. This ruxolitinib plus glasdegib, investigator-initiated trial feasible. Priority is medium, doesn't give it a high priority. It gave arsenic trioxide and chemotherapy because it's already FDA approved for one of those indications, a higher priority. But let's go with this combination. It comes with a question, can we improve this combination on this combo regimen? This is a question that would take companies months and months and maybe even years. And they look at a combination like this and oftentimes in a combination today, a cancer company thinks it's an excellent question, we investigate the target alternatives to the glasdegib-ruxolitinib combo focusing on precision inhibitors and ADCs. Oftentimes, they'll take those and say, can we actually combine the drug to a new type of drug or to a conjugate like an antibody drug conjugate, which is exactly what I asked Zeta, my co-scientist to think about and come back to me. Now historically, and again, just from our own ADC program, when we ask questions like that, it takes weeks of research. You may have ideas, but of course, everyone is always biased and so you want to explore out your first idea, but then you want to validate it. You want to have data. You want to have mechanistic rationale. You want to look at the literature. You want to do some takedown of actual data sets and explore what makes sense. And so this is what Zeta is doing in the background. So all that work that a scientist would do to review research, do primary data, look at structures, analyze molecules, look at making predictions on how those molecules may or may not work in various cancers, look at what's being overexpressed or targeted, all that work is being done in the background now. And also it's reviewing the literature as well. So it's going to continue updating the literature. And then you can actually go through the reasoning process. And it's a deep and meaningful question. And so it's -- and if it doesn't have an answer, unlike a lot of GPTs, it's not going to force an answer just to make you happy. It's going to say, I can answer it or I don't have sufficient information or here's what I need. Now the other thing with this particular question because it's so targeted, maybe I'm a biotech that actually has an ADC that's targeting this Hedgehog pathway. And so it may not be in the public domain. So what I could easily do is I could attach a file. I can go here to the attachment piece right here, and I can attach a file and say, take a look at this file and tell me what you think. And so that's also very, very possible. When I go into light mode, it might be easier to see. Wow, it's a great, great answer, detailed. So this is wonderful. So precision target folate receptor alpha discovered a fusion-specific surface target. It's referencing a discovery made in 2022, the folate receptor specifically and highly expressed on the surface of this positive AMKL cell, making an ideal precision target is great. So it's going to target this for the ADC, limited normal tissue expression, very important. That's a rule that we always look at. So even as it goes and starts targeting this, it already knows fundamental disease models and rules for how you actually think about targets. And you want targets that are highly overexpressed in your disease and highly underexpressed or not expressed or a normal expression level in healthy tissue. And then also you want to make sure you have selective targeting. So you want the right therapeutic window. You want to make sure that the target is accessible on the surface. All these fundamental things that the typical GPTs don't fully understand or may or may not arrive at. That knowledge is already built into Zeta. So if any of us have some targets, Tier 1, floor-directed therapies, [indiscernible], which is great, gives us the mechanism, preclinical efficiency, what its advantages are over the combination. Why trial -- terminated trial, raised questions about off-target, pediatric tolerability, efficacy threshold not met. And then it looks at the [ 4-1 ] CAR-T therapy, what the advantages are, what the challenges are, what the development pathway and then gave us some other ideas around Tier 2, BC-XL selective inhibitors mechanistically superior. This is great. So now we're getting into options that we didn't even ask about. It reached its own conclusion after looking at the ADCs to say it's going to look at another selective inhibitor, something that we hadn't even prompted it to, but it made and revealed a critical therapeutic target by looking at a very, very recent finding that it's dependent on BC-XL, not BCL, BCL-2, which is great because, again, there's so many rare cancers. There's no way to be up on every single possible mechanism and biomarker, could easily overlook it. And then Tier 3, which is CD123-directed immunotherapy, which has been tried as well. So you can see it now tiers all these various approaches and actually gives us an alternative just to the ruxolitinib drug as well. And so what is it proposing? An improved combination strategy, the [ 4-1 ] ADC plus BC-XL most targeted, that's great. A dual fusion-driven vulnerability, potential synergy challenges, thrombocytopenia, overlapping chemotherapy. So again, this is very important because remember, it has access to all the standard of care data as well. So it's going to know what is going on with the side effects are, et cetera. And it gives us 3 specific strategies and also tells us you can actually do a GLI inhibitor, long-term investment, strongest biological rationale. So let's say the strategy, this is long-term investment, inhibitor development program and then it ranks it. So again, puts it into a nice table, how clinically ready is it, what are the advantages and then gives us some really exciting immediate recommendations. So this is for newly diagnosed superiority to venetoclax. So let's ask it about this, although it didn't say that we should do a strategy for GLI inhibitor, I'm kind of intrigued by that. So let's ask it about this GLI inhibitor and ask a very specific question. Given some of the challenges with GLI inhibitors and safety in the past, can you help us develop a novel GLI inhibitor that overcomes some of the issues in prior drug candidates? Let's see what it says. Of course, it said July, it has said GLI in the second line. So let's see how it thinks about this. So I like the challenge. So we're actually now -- just so we know, I should have put this into medicinal chemist mode. I didn't do that, but that's okay. But we're here explicitly asking it to go come up with a potential new design for a long-term GLI inhibitor program that overcome historical pharmacological liabilities. And then someone asked a question about safety and tolerability, we can definitely touch on that as well. It's beginning to touch on some of those because the first generation of GLI and the second generation had some of those issues. So we're asking it explicitly to evaluate and overcome some of those liabilities. We can also -- if we wanted to go into -- I urge you guys all to sign up for withZeta, and you could go into withZeta and actually ask it in these historical trials, what safety and tolerability issues have you seen the most in pediatric blood cancer trials? And you can zero in and say, what about for target inhibitors or what about for immunotherapy. So you can even give it by class, which would be a great problem. So it has a molecular structure of one of the drugs. It has critical structure liabilities revealed. So it analyzes what's out there today, tells you what the pharmacological problems are, looks at case studies, poorly stable. It's a diamine derivative, which is interesting. So it's a prodrug, and it causes dosing challenges and pharmacokinetic is unpredictable, doesn't have great aqueous solubility. So you can see it's calculating all these values. These values do not come from a database. It's actually calculating it. So Zeta is handing this off to our molecular intelligence LQM and it's looking at the SMILE structure and the overall chemical formula and saying what is the logP value? What is the TPSA? What is the [indiscernible]? And those are things that we've taught and trained it to think about when it tries to create a good medicine. And it also tells it has excessive molecular flexibility, which makes sense, too, because look at the structure, probably very bendy. And then it says the drug-likeness, it failed 3 out of the 6 filters, which you can still have a drug and fail 3 out of 6. But again, the more -- the fewer you fail, the more likely is you can have a good drug. So now these are the things that we look at when we want to look at drugs as we look at weight, bond donors, [indiscernible] bonds, it's potential to cross the blood-brain barrier. So BBB penetrability to us is very important because if you're making these molecules, it's great because cancer oftentimes travels to the brain. So if you have a molecule can then both challenge tumors, but also then travel to the brain in case there are brain mets, that's like a really holy grail. It tells us it needs improvement on BBB penetration. So that's something maybe we can live with, very few molecules cross. So maybe we look at that, and it's actually improved the drug-likeness from 3 filters to 4. And the molecule is now firmly in CNS drug space with optimal logP, et cetera. So these are the first-generation improvements. And now it's going to refine it further. And the way it thinks about it is that it's going to constantly do 2 or 3 levels of refinement, just like the scientists would. You're never going to be happy with the first iteration. And they're going to say, okay, well, what do I like? What do I not like? And you're not going to get that like with a general Groq or OpenAI. I mean it doesn't know these things yet. It could definitely learn them, but it's going to learn them in a very awkward way. This is -- we've trained it explicitly to think as like a medicinal chemist or a clinical trial person or a clinical oncologist. And so it has a view of the world that is very different than these general purpose LLMs and isn't just randomly guessing at what the next thing it needs to say or do is. And we have a question from -- can you save an investigation and get alerted? Yes, we are going to talk about some of the features like alerting and saving, sharing knowledge graphs, et cetera. But yes, those are all things that are on the road map, and we'll talk about that after we finish this investigation. And as you can see, as we mentioned, the knowledge graph continues to grow. So this is a live living knowledge graph. And so if I wanted to talk to my colleague, [ Barrett ] or Rick or Reed and say, hey, this is something really interesting. Let's explore this further. Let's do some more sessions. I can actually just export it as a JSON file, which is machine readable, which is really cool, or I can export it as an HTML file that's interactive. And they cannot have to read through the text, they can actually see and click on it and see what is the thing that I'm trying to research. And like any knowledge space that are graphing your head, you're going to have some explicit areas of high density, but then you're also going to have these concepts that flowed out there that you know somehow are involved, but you're not quite sure exactly how. And so you can see it's created this little node around STAT3, which is involved with many cancers. Not quite connected yet and even has this thing floating out here, we'll see what that is. The SMO protein, okay? That's also involved in the Hedgehog pathway and it's involved in some of these GLI drugs, but again, kind of not fully connected into the core knowledge graph. So let's see what the final. So here's what the analysis is, the optimized candidate. So it refined it. The V2 iteration shows improvement. Here's a new drug after 3 iterative design cycles, a comprehensive comparison of a novel GLI inhibitor candidate. Now I want to put this in perspective. This is now -- we're about maybe 44 minutes into a dialogue with this AI. This is a novel drug for an ultra-rare cancer that we've never talked about before. This is not something that I trained Zeta on. This is something that in a traditional biotech environment, including at Lantern, would very likely take upwards of 4 to months to a year. Let that sink in, 44 minutes with one instance of Zeta. The future of Zeta is I can have 5 or 6 of these Zetas. I can have all of them collaborating on this kind of problem and actually compete with one another for different types of questions. So it created this molecule. It gave me the optimization results, told me what it likes and what doesn't like. again, each iteration so I have complete transparency in what it changed and tweaked. Iteration 3, what it's called the final, tells them what test it pass to be a good drug or not a good drug, what it solved for chemical stability, aqueous solubility, molecular flexibility. The BBB penetration is only partial success, but that's okay. We may want to go ahead and say, we don't need that with this molecule because we need to target the systemic tumor and not necessarily the brain mets if they arise. And then it gives us a candidate, what the advantages are for this candidate, what the development challenges are that we need to still look for and then what the key advantages are over the current combination regimen that we first started with. Remember, this was the combination regimen that was initially proposed for some of these cancers that we were intrigued about because we're targeting both the Hedgehog pathway as well as an indirect pathway inhibition. And we said, can we design something that does both. Gives us a summary of what we've achieved, what this represents. I look at 21 tools. That's a lot of tools. 21 tools. It's referenced over 140 different publications and trials and PubMed results and drug studies. And it gives you an answer about how it actually arrived at it. So you can sit there and go through and read it or you can even ask Zeta and say Zeta, what are the flaws in this reasoning process and it can explore alternative delivery strategies. So you can see Zeta is a very, very comprehensive tool for drug development. And again, we focus initially in cancers and rare cancers because that's what we're really great at. That's what we know a lot about. But the same architecture technically with different disease models and some updated data sets, we believe we can do this for multiple other categories. So there, I'm going to pause and go back to some of the future features. Thank you guys for going through this. We know this is incredibly complicated stuff. And -- but this is the only AI that does it. And again, it's a direct result of the work that we've been doing, but now with a wonderful natural language interface. And I can save all this as a PDF file even. So like I can -- there you go, there's a PDF file. I can pop up in the PDF file and also talk about what's next. Let's -- so here's the PDF file. I don't know if you guys can see that. But here's the PDF file, of course, the disclaimer, not medical advice, consult health care professional, et cetera. And then I can share this internally. Some people like knowledge graph, some people like the PDF, some people want both. But again, we made it so that it thinks like a scientist because you may want these tables. You may want to look at what the flaws are. You may want to look at it and come back and say, hey, I don't like this ethyl plus ethanol stability property. Let's tweak that. Or let's look at how this can be slightly different. And so these are all the things that you would want to do. And let's go ahead now, let's go back to the future piece. What else we're doing with Zeta in the future, which is very important, of course. Okay, there we go. So what's next? Let's talk about what -- where this is headed. We've got a lot of exciting biotech companies using it, top researchers and institutions in Europe at Fox Chase at UT Southwest, a lot of different great research groups. We have had over 100-plus people interested at the American Association of Cancer Research. We think this could be a massive multi-hundred million dollar opportunity to sell subscriptions. Obviously, we're coming at a very low price initially to get -- to drive usage, to drive adoption. And we think many people said this has been a great tool. Some of the biggest users so far of the tool are actually consultants, analysts, some CROs and academic researchers. So what's next? We're going to have enterprise features such as team workspaces, shared knowledge graphs, API access, white label deployment potentially and then also social features to make it more sticky, personalized feeds, multiuser investigation sessions so that myself and maybe Barrett or someone else on the call can -- we can all work together toward a problem and have shared research environments. We also plan on deeper biology and smarter tools. We're going to have a mobile optimized interface, so people can look at it in other devices. We're going to expand the rare cancer disease ontology with more molecular and pathway annotation. We're going to incorporate more deeply our antibody drug conjugate module, our antibody assessment development characterization also within Zeta and more pathway mechanism knowledge. So we -- our goal is to make this smarter and smarter and bring the best possible information, knowledge and disease modeling to the power of everyone's desktop. And we think this can crush and collapse the time lines involved in drug development, at least initially in cancer, but eventually lots of diseases. And yes, we can definitely save an investigation and get alerted that those are all part of the features that we're going to have. But we urge you guys to go and sign up, sign up, start using it, give us feedback. And I think this multi-agentic AI is really a fundamental reset. It's a massive force multiplier. And it's our conviction that some of the biggest breakthroughs in the future of medicine are going to come from machine and human expertise coming together. These autonomous co-scientist systems, these multi-agentic, multi-tool, kind of autonomous scientists that are iterating and thinking on their own because we've taught them how to do it. These are -- and we say a co-scientist, and we call it withZeta because it essentially -- it's making us smarter and better. Like every scientist wants to wake up and be the best. But can they read 39 papers in 2 minutes? Probably not. Can they store in their mind space 500 different biomarkers and 200,000 trials and last 12 years of failures and 3 years of successes, maybe there are definitely geniuses that can do that, but they're far and few between. And so this becomes really a tool and especially as it becomes harder and harder to become a great scientist because the knowledge is just piling up, we need these systems to really make scientific intelligence and put it at a different level. It becomes a force multiplier and becomes a fast accelerant to actually using AI for what it's meant to do. It's meant to be doing great things to actually lift us to new levels, not make cat videos and spoofs and have people's bank accounts. It's really here to make humanity better. And scientists, especially, we want to do great things, and now we have the tools to do it. So very excited about this intersection of autonomous scientific intelligence with great minds, and that's why we call Zeta co-scientist. So I'll pause here and see if there are any more questions from the audience.

So question, what does the feedback look like from other biotechs? I have to say the feedback so far has been really very exciting to us. It's been wonderful feedback. They always first ask, well, how is this different from the other GPTs. And then after they start using it for about 15 minutes, 10 minutes, say now, it's very different. And that question never comes up again. We've got people sit down and say, oh, I've been studying this rare cancer for 12 years, and they pull out a paper research and they say, what about these things? And they try to -- and then they walk away like, wow, I need this tool. So the feedback so far has been quite, quite exciting. I mean a lot of the features that we put out like the multiple roles and the recursive thinking and the tool sets all came from very early users and feedback from our early data that we had. The subscriber traction is just beginning. Again, we just launched the public debut at AACR. I think the next 6 months is going to be basically building up a subscriber base and getting this out there into as many hands and into many people as we possibly can. And then we're really going to see the flywheel, the revenue flywheel, I think, more after the first 12 months. But yes, there's a lot of commercial interest. Near-term milestones are obviously continue to get users, to land a couple of biopharma deals, especially larger biopharma deals and very importantly, start building out the enterprise toolkit so that pharma does not have any reason not to subscribe. We want to make it as plain and simple and easy as possible for people to get on to the tool and just be the best at developing great new science. So thank you, everyone, for participating. I always get a joy out of these sessions. It's wonderful to see what these tools can do. And so thank you all for your time this afternoon.

That concludes today's webinar.