Next up, Lantern Pharma, ticker LTRN on the Nasdaq. Presenting today will be Panna Sharma, the CEO, President, and Director. Panna, are you there? All right, just a moment. Let's wait for Panna Sharma to join. Good afternoon, Craig. Hi, Panna. Great to see you again. How are you doing? Wonderful. How are you? Really good. Thank you. If you're going to present your presentation today, please go ahead and show it while I get through these preliminaries. This segment may con- Great. I'll pull it up. Thank you, Craig. Yeah. This segment 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. Of course, forward-looking statements involve risks and uncertainties. Again, this is Lantern Pharma, ticker LTRN on the Nasdaq. If you have a question, please click the Q&A button at the bottom of your Zoom window and type your question into the text box. Panna, whenever you're ready. Great. Thank you. Thank you, everyone, for joining me this afternoon to hear about the Lantern story. Lantern is an AI company developing cancer medicines. We have a number of therapies that are already in clinical trials that have been developed and built on the back of our AI platform. One of the things that's really important, and I want to touch on it today. Hopefully, you guys can see the screen. Can you guys see my screen? Hopefully. Okay. Not yet, Panna. Not yet. My screen should be live, the presentation. That's strange. Okay. Yes, now we can see it. some reason it's not large. Maybe it's not the PDF file. Let me try this again. I'm going to stop and try re-sharing it. Panna, we are now downloading your investor presentation. We would be happy to show it for you. Great. Okay. We're going to share our screen. Let me just try it one more time. Okay. You're going to give it one more try. Is that working or not? I don't know. For some reason, it doesn't show the window. Why don't you go ahead and share it, Craig? I'm sorry. Okay. I don't know what's going on here. Give us just a half second here. There should be one on our website. We're downloading right now. Thank you, everyone, for waiting. Is it live? We're almost there. All right. Panna, do you see it now on your screen? I do not. Oh Let's just go ahead. Okay, I see it now. I see it. Yeah. Thank you. Okay, good. Great. Let's go on to the next slides. Let's talk a little bit about what we're doing as an AI company. We're an AI company focused on developing therapies, and we have three drugs in clinical trials, and all of our programs have been powered by AI. One of the most exciting things that's happening at Lantern now, if you go on to the next slide, please, is that we're continuing to compress the timeline to develop these therapies. We have a number of trials that are going on, three of them which I'll talk about today. Also very importantly, we've founded an entirely new company, a subsidiary called Starlight Therapeutics. A couple of years ago, we found a molecule that we're developing that worked really well in brain cancers, and we now have Fast Track designation from the FDA in GBM as a result of that. Our proprietary AI program increases the odds of the clinical trial success. We compress timelines, and we've launched over 10 new programs in a record-setting timeline, 2 years. The most important thing is we can understand how a molecule can work or not work, and we can also predict how patients will respond. We've done it not only for ourselves, but we've actually done it for collaborators and other companies. Let's go on to the next slide. Our business model, and this is fairly unique, is not only to help rescue and reposition drugs. We have a drug in phase II trial. We'll have some data coming out next week at AACR, which we're very excited about, focused on a unique problem, which is people who do not smoke or don't have a history of tobacco use, but they still get lung cancer. Molecularly, it's a totally different type of lung cancer. Our drug seems to work really well. Again, we'll have some data next week on it that we're very excited about. So far in the first cohort of patients, we had a very good clinical benefit rate. 86% of the patients had a positive clinical benefit as a result of taking our drug, and we continue to see some very good benefit, including patients who actually have gotten a complete response in this trial. We're very excited about that, and that, again, was repositioned and rescued from our AI platform. We're also using our AI platform to develop totally new drugs that have never been seen or developed before. In fact, today, we actually showed the platform to 25 top biotech investors in New York, where we actually, over lunch, used the platform to develop a totally new drug. This is the platform now that we have a unique mechanism, and we developed this drug in record amount of time, and we actually manufactured it, and we also got another complete metabolic response in LP-284. Third, we've also used our platform to help other companies. We actually own equity and patents with other companies, a number of collaborators. In fact, one of them is public, called Actuate. They had a really great response in pancreatic cancer and also in melanoma with their drug. That was, again, validated through our platform. Next slide. In addition to our platform, one of the biggest growth drivers is that we have these very targeted precision molecules, LP-300, which I mentioned, focused on never smokers, LP-184, which is focused on a wide range of solid tumors, including in triple-negative breast cancer, non-small cell lung cancer, and now also in PTGR1-positive bladder cancer. That trial is actually being done in Denmark, paid for by the Danish Cancer Society, and it's an investigator-led trial. We also have another drug, LP-284, that is in phase I for a number of really exciting indications, all of which we have orphan designation for. It's in mantle cell lymphoma, double-hit lymphomas, and adult soft tissue sarcoma. We just got an orphan designation earlier this year in Q1 for that indication. Unfortunately, adult soft tissue sarcoma is actually a growing disease globally. This is one of the very few drugs that works in that cancer. Again, these aren't just ideas. These are actually in trials where we're dosing patients. Again, all of these drugs have been built in the back of our platform. We also, as I mentioned, have a whole new company inside of Lantern called Starlight. I'm very excited about that because that company has both Fast Track designation and GBM. We just recently cleared our FDA IND, which are very, very exciting in pediatric brain tumors. Again, very exciting new company, all focused 100% on precision neuro-oncology, which is going to be a great area to be in for cancer companies going forward. We have one of the few drugs that is novel in that category. Next slide. These are companies that are using our platform. Again, we have access to not only their drugs, but also the data, and that has helped validate our platform. Next slide. Let's talk a little bit about the platform. Our platform is an integrated experimental biology platform. Some of the key features is that this platform has been used not only by us, but it actually has saved tons of time, and time equals money in biotech. More importantly, it helps us pinpoint what a mechanism of action is, and we've published on it, and we've got patents on this platform, and we've used it to design ADCs, understand potential binding sites, generate molecular signatures for patient stratification, and also understand specialized attributes of the molecule. This is RADR. Now, one of the most exciting things about RADR, if we go on to the next slide, please, is we're uniquely positioned in the upper right-hand quadrant. Next slide. Parts of RADR now we're making available to the public. Anyone can go to predictbbb.ai and actually get all these specialized attributes of their molecule. You just enter the SMILES string, and we can predict whether a molecule can cross the blood-brain barrier. We can convert the molecular structure into thousands of predictive features and really understand how the molecule can be best positioned as a potential medicine. It does it not only for cancer drugs, but really any molecule. All this is real-time. It's not pulling from a database. It's actually calculating as a large quantitative model, all the attributes of this molecule, and this is what we've been able to use. Next slide. We are now moving it well beyond just BBB permeability into structural analysis, drug likeness analysis, surface area profiling, topology analysis. It's a multidimensional molecular intelligence tool. Again, it's not just internal. We actually have put this tool now out for anyone to use. If your users want to go to predictBBB.ai, they can, and they can sign up and use this tool. We're really trying to use AI for good, not only for our clinical programs, but really for the broader community. Next slide, please. Today, we launched withZeta. Again, we're taking all the power of our AI and making it available. People can go to withZeta, and withZeta addresses a fundamental challenge in rare cancer research, where you can actually develop your own drug. You can look at rare cancers, and it's a multi-agentic tool. It's the first of its kind globally, and you can get insight in minutes. We featured this today at Nasdaq with a number of biotech investors who were quite blown away by this tool. We real-time, over an hour or two of lunch, actually went deep into one of a BRAF cancer and actually designed a novel structure to go after this BRAF. Imagine this would have taken literally some of these guys in this room months to really create, and we did it in minutes. It's a very powerful tool. We'll be launching it also at American Association of Cancer Research next week in San Diego, and we expect hundreds of people to try out this tool. Again, people can go and subscribe to it as well. Now, these are tools that aren't just theoretical. These are actually tools that we use to actually develop our own molecules, and these are molecules where we've dosed over 100 patients. These are real big challenges in cancer because data is complex, it's scattered, it's tough to get really good predictions. What we've done is we've simplified it by using the power of natural language and an agentic interface to really revolutionize rare cancer drug development. Our goal then is to do this not only for cancers, but broadly with the power of other pharma companies across multiple diseases. Again, this is what we think is going to position us as kind of like the OpenAI or Perplexity for cancer drug development. Next slide, please. All of our data comes not only from us, but also world-class institutions. We have academic collaborations with Hopkins, Fox Chase, University of Texas, UMass Boston, MD Anderson, Cornell, and again, this powers all the models and all of the algorithms that we use to do various facets of drug development, from molecular design, to rare cancer research, to biomedical, pinpointing of biomarkers, et cetera. All of these modules are all inside of this multi-agentic platform. Next slide, please. Again, what gave us this idea is that we had a number of very exciting designations. We have 12 FDA designations, two Fast Track, a number of orphan designations, six of them, and then also rare pediatric. In fact, we probably have more rare pediatric disease designations for one drug than any other drug, LP-184. We're quite excited because those orphan and rare pediatric disease designations can be monetized. We can get through a trial faster. We can sell the voucher at the end of a successful outcome. We can sell the voucher for $150 million-$200 million in the case of these rare pediatric disease designations. We not only have one or two, we have four of them. That alone is, we think, potentially pretty groundbreaking. Next slide. Let's talk about our first drug. This is in a phase II trial. It's going on today for people who do not smoke. It's a very unique molecule, which is a disulfide-based molecule. Next slide. This is aimed at this very unique population of never smokers. Never smokers are a very different disease, and this momentum that we're getting in never smokers is really important because there's no drug approved for this class of patients. In fact, it's a pretty large disease, unfortunately, globally. About one in six lung cancer deaths are occurring in patients that have non-small cell lung cancer, and about 20% of those cases are people who don't smoke. It's a real challenge, and the molecular profile of this disease is very different. Next slide. Let's talk a little bit about how this drug works. It has a one-two kind of punch that we've seen, and again, we'll have data on this drug from the trial next week that we're going to be sharing with the public and with the biotech community on the back of our AACR work. The mechanism is that it directly modifies TKI receptors through direct engagement, and it modifies the cysteine at the surface of the cancer cell. Then once it's inside the cell, it modulates the redox cycle, and it resets the redox cycle once it's inside the cell. It's a kind of a one-two punch because it denatures the receptor, and then once it's inside the cell, it kind of resets the oxidative stress modulation. That's key because that then allows the cancer cell to actually react to the chemotherapy doublet that's given. It's a very unique pan-kinase modulator. Next slide, please. This is in a trial. It's about a 90-patient trial, multinational. It's in Taiwan, Japan, and the U.S. We're looking at PFS, which is very important, progression-free survival, and also overall survival. We've had some very good results so far in the trial. Next slide. Again, this isn't just theoretical. We've had a lot of good patient outcome, next slide, across a wide range of patients. You can see we clearly have seen two groups of patients, people who have some really good partial response, and then patients who have good stable disease. In fact, one of those partial responders actually went on to become a complete responder, which is very, very rare in these. These are typically patients that are second, third, or fourth line. Again, these never smokers don't have a lot of potential options when TKIs fail, and that's exactly where we're pinpointing our drug is post-TKI failure. Next slide, please. If you look at the range of diseases, again, initial cohort, we had a wide range of partial responses and stable disease across a wide range of prior treatment histories. We know that this drug has a mechanistic validity to it, and that's one of the most important things pharma companies look for is that is there clarity on the signal, which we believe we have. Next slide. Next slide, we're going to go on to the next drug, which will be LP-184. Keep forward. LP-184 is a blockbuster potential drug. We think over $10 billion in potential value, and it works across a wide range of cancers. It's a novel drug, first in human. Number of patents in this drug that extend our claims well into the future. This is a very unique molecule. It also crosses the blood-brain barrier. Next slide, please. About one in four, if you imagine, one in four to one in five cancers have a DNA damage repair deficiency. This is very important because these cancer cells, as they become more aggressive, they basically give up the validity of their DNA damage response. We're able to attack that vulnerability, which is why this drug works across a wide range of cancers. The key marker, if you go on the next slide, is a very specific enzyme called PTGR1. That enzyme is overexpressed in these aggressive cancers. When we were first looking at how this drug works and why it works so well in certain cancers and not others, our AI platform led us to say PTGR1 is the smoking gun. Of course, the first thing you want to do is that really the smoking gun? We took our insights and ideas, and we validated it in a set of gene editing CRISPR experiments. There was a wonderful experiment design at Fox Chase where they gene-edited out PTGR1 so that it was no longer available to the drug. If you look at the slide, you can see the cells that no longer had PTGR1 stayed stable. Nothing happened to them, really. You look at the stark, drastic change versus the cells that had PTGR1, they all died off. For us, this was wonderful confirmation that PTGR1 is in fact the driver of response. Now we look for that. In fact, in the bladder cancer trial, PTGR1 is the biomarker. If a person has bladder cancer and they have a high expression of PTGR1, they'll be enrolled in the trial. Next slide, please. We saw that not only in bladder, but we saw in triple-negative breast cancer. In fact, one of the most exciting cancers that's responsive is triple-negative and also pancreatic. We saw an excellent response in both these tumors. It didn't just work in one or two types of triple-negative. It worked across a wide range of all the 10 different types of triple-negative breast cancers. If you can see the red and pink lines, that's where our drug worked. Regardless of BRCA and regardless of PARP, our drug seemed to work. We've got orphan designation and fast track in TNBC, which is very important. Again, this is a trial that's about to start in a phase I-b/II, and there's a very important need for second- and third-line treatments in this terrible disease. Next slide. We just finished a very large trial last year where we had very good results. We had 63 patients in this trial. Let me show you some of the outcomes from that trial. Next slide, please. We had very good treatment. We had very good, robust safety profile. We achieved all our primary endpoints. We got a great biomarker insight. In fact, what the machine predicted, our AI platform, was that people who had certain DNA damage repair mutated cancers, mutations in CHEK2 or ATM or STK11, would be very responsive. In fact, that's what we saw in the trial. The most durable benefits came to those patients that actually had those DNA damage repair mutations. We had mechanistic insight, we have a clear path forward, and we have wonderful Fast Track and Orphan Drug designations in the cancers that we think can get this to being a multi-billion-dollar drug. Of course, our goals are then obviously license this out and sell it to larger pharma companies. Next slide, please. We'll be going into multiple trials, all influenced by the AI. Every one of these indications, we have either Fast Track, Orphan, or both, and very importantly, we have mechanistic insight as to why it'll work in that cancer, in that biomarker, and that's very unusual for this. This is why the momentum for us is very real. We have targeted trials, all which have been approved, cleared by the FDA. We've dosed patients. We have a very clear path on how to get this to Fast Track approvals or, more importantly, actually into the hands of larger pharma. Next slide, please. We're going to go on to LP-284, and then I'll take some time for questions. Let's go on to the LP-284 slides. It's a lot of wonderful data slides. Yeah, LP-284. This is a very exciting drug. LP-184 didn't work in blood cancers, and that got us thinking, "Well, could we use the same mechanism, the same scaffold, and we create a new drug?" We went from an idea on a whiteboard to less than two years, a GMP manufactured trial in multiple B-cell cancers and also in adult soft tissue sarcoma. Next slide. This drug works really well, and obviously, we have orphan designation both in mantle cell now and in high-grade B-cell lymphoma, and these are cancers that are not curable with the current standard of care, and the prognosis and outcome is very bad. In fact, the very first lymphoma patient that we had in this trial, let's move on to the next slide, had a wonderful response. Now, this is theoretical. This is what we saw in the preclinical models, that we'd get this kind of massive translation from tumor growth to almost nothing. Of course, that's theoretical, right? When we saw this in the clinic, we can go on to the next slide, this is theoretical. What we saw in the clinic, that's exactly what happened. We saw the very first patient, heavily pretreated patient, and we put the patient on it, and we got a complete metabolic response. We presented this last year at the Lymphoma, Leukemia & Myeloma Congress. On the back of that, as we understood the mechanism, we actually got an orphan in soft tissue sarcoma because we're using a similar mechanism around CD19, CD20 positive B-cells to attack and destroy those cells. This patient went from having all these lymph nodes swollen up and down their spine and their pelvis, and we got a complete metabolic response in that patient, which is just absolutely great to see that, to see the outcome in this patient, how we were able to change the person, but also that it validated what we saw in our AI, and it validated what we saw in the lab, and now it's happening in patients. Again, these are mechanistically validated drugs, which is one of the most exciting things and one of the most challenging things for biotechs to get to, which we have. Next slide. We also think that this drug will be very synergistically with rituximab, which is a known approved drug. We're also looking at trials to do this in combination with rituximab. Very clear strategy of how to develop and progress this drug, and we're in early phase of discussions of this drug with other pharma and biotech companies. That's very exciting for us. Next slide. We don't have time today, but one of the most important things is also to look at our CNS portfolio company called Starlight Therapeutics. Starlight Therapeutics, very similar precision developed molecule focused on brain cancer, both adult and pediatric. We've received Fast Track and Orphan Designation. We've completed the enrollment in the phase Ia trial, and we're going to now launch into phase Ib and phase II, and we have great collaborators. Every shareholder in Lantern will own a piece of Starlight, so that's very exciting. This is an insight that we developed from billions of data points using our AI platform. I'm going to turn over to some questions for the remaining time that we have. Craig, why don't we go ahead and go into Q&A, please. Thank you very much, Panna. A lot of companies talk about. You can stop the sharing if you want. Yes. Absolutely. Yeah. Great. Yep. There we go. Panna, we've got a lot of questions here for you already. A lot of companies talk about AI in drug development, but Lantern seems to have both a platform story and multiple clinical programs. Yeah. Yeah. How should investors think about where the real value is being created today? Well, I think the value is definitely going to be in both sides. I think we're uniquely positioned because we've got a dual engine strategy. Part of our company is making breakthrough molecules that can be licensed out, and the other part is creating AI platforms that we can monetize. I think it's a unique position to have that. I think both sides are very real. Obviously, AI in medicine is becoming one of the hottest categories right now. We're well-positioned both with novel molecules and with a platform that works and has been used to develop medicines, a validated platform. What do you think will be the clearest proof to investors over time that RADR is more than just a compelling platform narrative? Well, we have drugs and trials, and we have companies using it. More importantly, you can go now to withzeta.ai or predictbbb.ai and actually use it. It's more than a compelling narrative. It's actually road tested and out there in use. Very few AI companies can go and say, "Hey, go to a URL and use the platform." We can say that today. I think that's very unique. The recent withZeta launch Mm-hmm It was interesting, yeah, because it looks like it expands Lantern's AI capabilities beyond just internal development work. Yes. Good. He wants to know, how should investors think about that opportunity strategically? AI in medicine is becoming a very hot category. You saw Anthropic recently buy Coefficient Bio, and you saw the deal with OpenAI, Novo Nordisk in metabolic disorders and diseases. You saw the Eli Lilly Insilico partnership. We opened up the platform in rare cancers because no one else is doing it. Rare cancers are hard, they're complex. That's why they're rare, but it still kills 30% of the cancer patients. It's terrible diseases, hundreds of diseases. What we wanted to showcase is we wanted to say, "Look, we're good at developing medicines quickly, efficiently, and let's now open this platform up for anyone to use to think like a Lantern scientist." withZeta opens us up to potentially, I think, about a $15 billion-$20 billion market opportunity where pharma companies, biotech companies, cancer centers, individual researchers, they all have these wonderful ideas, but it's so hard to do in cancer. Now you have inside of a box, 24/7 available, the world's thousands and thousands of brains all at your doorstep. This isn't just an encyclopedia. This is actually a real engine. It's a co-scientist, and it's never been anything like that before. I think this opens us up to monetize the platform in a massive space that's critically needed. We're at the forefront of it, and we're launching it. It's going to generate revenue tomorrow for us. We've got a lot of subscribers, a lot of eyeballs on it. We're launching it next week at American Association for Cancer Research, and it's proven. The great thing about this platform is that we've used it, our collaborators have used it, and it's very difficult to replicate this kind of engine with nearly half a trillion data points. It's a great spot. Tons and tons of models. Yeah, it's a massive opportunity, and we're hoping to exploit it. Perfect, Panna. We are right at the top of the hour. Thank you, Craig. Thank you very much, Panna. This has been Lantern Pharma, LTRN on the Nasdaq. For more information, write us at ltrn@redchip.com. You can always call us, 1-800-REDCHIP. Panna, thank you very much. Thank you. We'll see you next time. See you soon. Thank you. Bye-bye.