Our next company on today's RedChip AI Investor Conference is Lantern Pharma, ticker LTRN on the Nasdaq. With us today is CEO and President, Panna Sharma. Panna, are you there? Okay, we're waiting for Panna to join us. Panna? Good morning. Good morning, Panna. We see you have your presentation already up on the screen. We are ready to go if you are. Absolutely. Thank you. Okay. Well, let me just read the safe harbor statement very briefly. 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. Panna, go right ahead. Thank you very much. Appreciate everyone's time. I know today's major topic is AI. I want to highlight how Lantern Pharma is using AI and artificial intelligence for good to really develop potentially breakthrough new medicines in cancer. Last year, we dosed over 100 patients in our trials, actual clinical trials, with drugs that were developed, advanced, and targeted using our AI platform, RADR®. We've brought down the average amount of time to bring any drug to a actual human clinical trial by 70%-80%, and we continue to smash that timeline even further. Let me walk you through some of the ways in which we do that. First and foremost, in cancer and actually many diseases, it's best to understand what are the biomarkers that are going to indicate that you'll get a good response. AI is used in our platform from RADR to do that, and it increases the likelihood of clinical trial success. Not only have we done that, but we've actually also now published on it. A lot of the findings from our AI about which patients and which types of cancers and which mutations specifically would respond to our drugs are now true. We had 63 patients in a trial that ended last year, and the patients that were predicted to do best were, in fact, the ones that did best, the patients that had DNA damage repair mutations like CHEK2 and ATM and BRCA. We can also now predict what patients should be included in future trials. That's very important because that increases the likelihood of later stage success and also focuses your group that can cut down dramatically by 30%-50% the cost of doing a trial. We've seen a lot of success. We have over 10 programs that we've launched in the last two years that are actually in trials doing phase I and phase II. Our business model, which is important to take a step back, is why are we using this AI-driven business model? Number one, we can find drugs and put them into trials quickly. We've done that now in a phase II trial that's focused on never smokers. We'll have some data coming out later this month on that. We can also develop totally new drugs with new interesting mechanisms, which we've also done with LP-284. In fact, in that drug, we got a complete metabolic response with one of our patients. Again, that was predicted a year and a half earlier by our platform, and it was great to see it proven in the lab. Even that is not enough. When we saw it with the patient, that was pretty phenomenal. We also do it not only for ourselves, but we've seen a lot of interest from other pharmas to use our platform. You know, it's difficult to put together an operational AI platform at scale, and so that's a very different skill set. It's one that's very unique at Lantern, where we have both the discipline of drug development in addition to the discipline of creating large-scale AI systems. Now, the focus of these areas is to really get us to targeted clinical trials, whether alone or with pharma partners, and then to license those drugs or assets out. That's what we've been able to do successfully. This is our pipeline today. As you said, we've got a phase II trial. We've got three trials that are now entering phase II. This just completed, and these are all in very specific precision indications. We also got fast track designation in TNBC. In addition, we also created a totally new company using our AI platform. As you can see, a number of orphan designations, a number of fast track designations. This entire subsidiary would not have been generated if it were not for our AI platform. This is all in brain cancers. We plan on privately or publicly financing this company in the future, and we expect this to be very exciting because it'll be very, very focused only on brain cancer. It's called Starlight Therapeutics. We'll hear more about this as this year progresses. We recently got an approval for our phase I-B, phase II study. Again, this is not theoretical. This is actually a drug that's been manufactured, that's already been put into patients, and is now progressing both in adult and also in pediatric brain cancers. We also have collaborators, other companies that are beginning to use, other institutions that are beginning to use our platform. That's also very exciting because we have equity in Actuate Therapeutics, we have IP rights, and we have development rights. Our platform is really being used as currency, not only to help us compress the timeline and accelerate and discover new things, but also do it for others where we then take equity or payment, which is very great. Now let's talk a little bit about our platform. Our platform, we've really developed it as an integrated experimental biology platform focused in oncology. I know the slide says 200 billion. It's probably approaching something now closer to 500 billion data points from our own studies, from studies around the world, from publications, from public sources, with hundreds of algorithms, many of these running all the time to kind of look at different topics that are of interest to us as an oncology drug developer. We've used these data and the algorithms, and more importantly, these capsules, as we call them, to create modules that are absolutely essential in drug development. These modules help us discover mechanism of action, prioritize disease indications, determine optimal drug combinations, generate signatures, characterize specialized attributes, understand binding site interactions, discover combinations. All these are essential for drug development, and these are all things that our team has done over the last few years. Not only just for ourselves, but also for other companies. We're typically recognized as one of the top end-to-end AI drug discovery companies. It's a great time to hopefully get into the stock at, you know, with all these highlights in this current depressed macro environment. Very importantly, we're not just a drug discovery company using AI. We're actually in trials. We've published two platforms. I would urge you guys to take a look at it. We'll do a quick demo here later in the presentation. We have an integrated, molecular characteristics platform called PredictBBB.ai. Urge you guys to go look at it. It's just the beginning of first in a series of transformative platforms. The next one we released is called with Zeta. Again, we'll demo that here today. With Zeta is a co-scientist. Again, you can go to withzeta.ai and learn more about it. It's a multi-agentic platform that leverages large-scale recursive reasoning through all of our data and all of our modules. It's trained on really being a top co-scientist in rare cancers and cancer broadly. It is the largest curated database and ontology in rare cancers in the world. It's built on over 500,000 trials, 250,000 plus publications, 1.2 million knowledge objects. You can do anything from assess a molecule to design a molecule, and it takes minutes. We'll get into a demo for this. The strategic impact of this cannot be underscored enough because this is now available to the public. It's not just our own work. We've now taken the transformative action of making this data available for anyone. We plan on monetizing this as a subscription-based platform for anyone who's interested in understanding how molecules work, anyone who's interested in developing a drug, anyone who wants to optimize their drug. The market for this is pretty large, and very importantly, people do not develop in rare cancers, but we've got a lot of credibility because we've got three drugs, we've got six orphan indications, four rare pediatric, two fast track. The urgency of this cannot be underscored enough. We'll do a demo of this. Again, all this knowledge isn't just from us, it comes from top researchers. Places like Johns Hopkins, Fox Chase, University of Texas, UMass Boston, Dana-Farber, Danish Cancer Society, MD Anderson. We've worked with all these institutions, and what we do is take the data, the knowledge, the models, and then put them into digital form so that our AI can use them. Now we're actually making a lot of this now available broadly. As I said, we've had a number of collaborations with pharma companies, and that's only growing. This slide says we have 11 FDA designations. We actually now have 12. We just got another one last month. It's pretty phenomenal for a small company of 20 people to have 12 now FDA designations, two fast track. We now have six orphan. The most recent one being in a very challenging cancer for adult sarcomas. It's one of the few cancers that's actually growing globally as a percentage and also as an incidence rate on every continent, adult sarcomas. It can't be underscored enough. LP -384, which is a drug that seems to work well in adult sarcomas, is also in a trial today. More importantly, that drug has actually shown a complete response in one of the patients in the trial, which we published last year. We know the mechanism works. We know that it's safe, and the trial continues to go. Again, all these drugs, our goal is to out-license them to larger pharma companies and then continue to generate the next generation of molecule. I'll talk a little bit about how we've used our AI platform, not get too heavy into the science and go into the demo, but we've used it in a drug called LP-300. It's for people who get lung cancer but are not smokers. We used it to really validate the molecular features of the drug. Why does it work in these kinase mutations? What we did early on is that we modeled LP-300 to really look at why does it work in these never smokers, and we uncovered the mechanism of tyrosine kinase receptor binding. Again, theoretical, but now we've actually seen it in the clinic. We've dosed almost 40 patients now. We've seen that regardless of kinase receptor, the drug seems to slow down the cancer, and then once it's inside the cell, it seems to turn off the cancer cell from being aggressive and allows the cancer cell to then die. Again, it goes from being theoretical and modeled to actually in real life. This is the kind of work that really takes so much cloud infrastructure and compute data, but now with the advent of all these new inference engines, we've been able to compress this. What took us about a year ago when we first started the company to do this modeling and analysis now can be done in the course of weeks. Let that sink in. This is work that would have taken years in the past and years in the clinic. We boiled it down to less than a year using our first generation of AI, and now that same process can be boiled down to weeks. It is really going to be a phenomenal time in medicine to use AI for good. We also modeled using digital twin capabilities, how people would respond, and we're seeing that, now coming through in the clinic. We had an 86% clinical benefit rate, which is pretty great for this trial. Again, as I mentioned, we've had good response regardless of the underlying tyrosine kinase inhibitor mutation. Let's talk about our next drug, LP-184. Again, we found the activator and mechanism. We optimized this drug using AI. The AI also pointed us to some very interesting indications. Because the drug is so potent, it's really important to figure out what is the indication that's going to be best. You've seen a lot of drugs that fail, not because they're a bad drug or a bad molecule, because the indication that was selected wasn't perfect. Indication selection really needs to be a data-driven form, and that's really the way to do a very streamlined trial. We have fast track and triple-negative and also in GBM. We're pursuing it now in a form of lung cancer called metastatic multidrug-resistant lung cancers. All this was predicted from the AI and now is being proven true in the clinic. We dosed over 60 patients on this drug. When we used our RADR AI platform, PTGR1 was identified as the biomarker. You can see here, very, very high scale SHAP value, almost straight line correlation. More PTGR1, more potency of the drug. We then took it to lab. We did this with Fox Chase. They say, "Well, let's make PTGR1 not available. Let's gene edit it out." As you can see here, when we took it out, the cells lived. When we had expression of PTGR1, the drug used it and killed off the cells. This was very great confirmation, and this again was all done in months as opposed to years. We then took it to mouse models. Again, some of the predictors were TNBC and pancreatic, and we saw wonderful activity, and now it's in a trial. We just completed a phase I-A basket trial with over 63 patients. The market opportunity for this drug is massive, over $10 billion, we believe in market opportunity. We achieved all our primary endpoints, got a very good safety profile, very promising anti-tumor activity. Again, the AI pointed to activity in certain cancers, which we're now pursuing into phase II trials. One of our phase II trials is paid for by the Danish Cancer Society in Denmark. This will be about a 40-patient trial, metastatic recurrent bladder cancer. Again, you know, we validated a lot of the insights in a number of models, a number of studies, and we've published all of them. The next drug was a really exciting story because this drug was thought of after we went public. On a whiteboard, we were thinking about what could we do with 184 slightly different, and that's how we created 284. Actually, initially on the back of a whiteboard, we then were able to manufacture it, get it into a trial, get a complete response in a patient, all in the course of about two and half to three years at under $3 million. We're now focused on non-B-cell non-Hodgkin lymphomas, which are about 4% of all cancers in the U.S. and about similar number globally. Very aggressive, and unfortunately, they always come back. We had some great data in the lab to show why our drug works well, again, validating it in silico, moving into lab, and then moving into patients. This active trial is going on today, and we expect this trial to be done later this year. Our goal, again, is to license out the drug to a larger pharma for them to pursue into approvals. We also created a new company called Starlight. This was an insight developed from billions of data points using AI with a very large market. Again, we've received Fast Track and orphan designation. We've already completed the phase I-A trial, and we're now moving into phase I-B and phase II. Again, this is something that is really needed. There are over 120 different types of brain cancers, and the vast majority of them have no curative treatment, both in adult and pediatric. Finding this indication, finding how the drug works has been a phenomenal effort that has typically can take five, 10 years. Because of the power of AI, we did this in a matter of going from an idea to a whole company to Fast Track to a trial all in the course of the last three, four years. The wonderful thing is now we can even do it faster. Got a lot of great data, a lot of publications, multiple patents around this, and then the landscape of biotech obviously is challenging but the research indicates a lot of interest in CNS oncology, and that's why we're spinning it out. We've got a board member on who came from the Chimerix acquisition. It was acquired last year for $953 million. As you can see, when a drug has shown some activity and works, the takeouts from pharma are pretty significant. Again, a lot of this is all driven through patents. We've got a lot over 100 patents issued and pending across our AI platform and across our drugs. As of the last reported quarter, we had about $12 million in capital. Our burn rate's about $4 million a quarter, so we've got capital through the majority of this year into Q3. No warrants, no debt, 11 million shares outstanding. Let's now talk a little bit about what some of the highlights are for this year. We will be reporting clinical data later this month. We're reporting updates on Starlight and also the commercialization, as I mentioned, and monetization of our AI platform. Let's take a quick look at that today. With Zeta, you can go to it now, and this is taking our platform and solving one of the biggest challenges, rare cancers. Hard to get data, hard to get experts, takes a long time, economically not sustainable to have large teams. Why not use a co-scientist? It's the perfect area for doing the kind of work that AI for good can do. We developed a rare cancer knowledge base, a very specific Lantern proprietary cancer drug database, a library powered by over 500,000 clinical trial results, connected real-time, though, to scientific knowledge around the world, to the NCI Thesaurus, to an ontology lookup service, to openFDA, to PubMed, and also to our own LLMs. This is a multi-agentic system. Again, I urge you guys to go to it, but let's take a look at it. If we go to withZeta today, we can answer questions about clinical trials, about molecules, drug characteristics, review literature, do biomarker analysis. The great thing is you can actually develop your own drug. This is gonna be a very powerful tool in the hands of pharma companies. Again, our goal is to give this up through a subscription. Let's look at one of the conversations that I had. In this conversation, as you can see, it highlights things in green and orange. This is thinking the way a drug developer would think. It wants to know what is potentially important, and also very importantly, it creates a knowledge graph. Real-time, it creates a knowledge graph as you interact with the system. I'm not just sharing my screen in terms of, you know, cut and paste what the results are to my colleagues. I can share with them the entire knowledge graph 'cause this is how people think, or at least in knowledge industries. We create concepts in our head and connect dots and ideas. Sometimes there are things that aren't connected, just like here. Then I can share this entire knowledge graph. I can export it as a JSON file, as an HTML file, and work on it. Now again, the question I'd asked it, "What cancers can we target by impairing KRAS signaling?" Very common question. It real-time will tell me what are the cancers most amenable to KRAS inhibition. Tells me even additional targets, early-phase trials, tells me what the landscape is like for some of these drugs, and tells me how many number of trials and what the considerations I should have. Actually, with this, I can actually go even further. As you can see, this is some of my historical conversations. All my conversations are here. We can actually look at and design new drugs. Let's look at this one. This is a METTL3 inhibitor for bladder cancer immunotherapy, and this was quite exciting. This was not coming out of a database. This is actually real-time recursive thinking. With the help of Zeta, I actually designed a drug, and it gave me its molecular weight, its all the medicinal properties, and it told me how it modified an existing molecular scaffold. Again, this is work that would have taken months, if not a year. I did this literally in 20 minutes, designed a novel METTL3 inhibitor, which now we're creating in the lab and has unique characteristics. Not only that, I can actually get a game plan to actually how are we gonna test this. This is quite exciting. This is a co-scientist. The whole point of these tools is to do in minutes or days what used to take months or years. Just even at Lantern, our own team now has been able to use this to develop seven new drugs. Now, will they get into trials? I don't know. We did this in 90 days. The next generation of our portfolio is already being developed with a lot of precision. We're pretty excited about the future of how AI will totally transform drug development, accelerate it, and Lantern is well-positioned to monetize on that. With that, I'll turn it over to questions people may have and answer any questions about our platform, about our drugs, about our strategy. Thank you very much, Panna. Excellent presentation. To reach Panna with your question, if you're on Zoom, go to the bottom there of your Zoom window, look around, there's the Q&A button, press it, and then you can type in your question. We already have several questions for you, Panna. Why don't we start with this one. How are you balancing internal development with partnerships or licensing opportunities? Yeah. Well, you know, that's the challenge of any biotech. I mean, we're very focused on out-licensing our drugs. I think our goal is to develop and show that the drugs, compounds, molecules work in patients the way we want them to work, they're safe, they're tolerable, they're effective, and that they have good IP around them, and then license them out to pharma partners. Our model is not to go all the way to commercializing the drugs on our own. We know that the best people to actually take it to all the cancer centers and deal with the regulatory frameworks, pricing, and insurance is gonna be big pharma. You know, as a small company, we're maniacally focused on being innovators, and people will pay up for innovation. If innovation works, it's a great place to be, and that's where we're at. Panna, with multiple clinical programs in development. Now, I know you went over this on your 2026 slide, but just to reiterate, this person wants to know, multiple clinical programs in development, which assets or catalysts do you believe are going to drive the most value, not only this year, but going into 2027 as well? Yeah. This year, LP-300 and LP-184. Those are gonna be the primary catalysts. Then also the launch of Starlight as its own independent company. We've put up in about $10.7 million so far into the development of Starlight, the creation of the molecule, development of the trials, dosing of patients, pursuit of the FDA indications, the IND reviews, all that. We're gonna monetize that. I mean, that Starlight alone has the potential to be a billion-dollar, multi-billion dollar, but at least a billion-dollar company after the phase II. The phase II trial for adult recurrent has already been cleared by the FDA, so that also will be a pivotal milestone. To reiterate, 300 data, that's coming out probably in the next 30 days or less. LP-184 launch of that trial in phase II, and then Starlight Therapeutics. Those are the three things that I expect over the next six, nine months. Going into 2027, I think it's all gonna be about the AI platform. I think the AI platform has a lot of potential to be spun out and be its own company or to be licensed by big pharma for massive dollars. There's no multi-agentic AI platform focused on rare cancers and cancers that works as well as our platform does, and more importantly, is easily accessible. I think that's gonna be very important going into the future. Panna, final question for you. What do you say to those skeptics who claim that RADR® is simply improving workflow and not creating a real competitive advantage? Yeah, I mean, I think, you know, competition changes. I think in, you know, for us, the proof is in the pudding. I don't know any company that's spending $4 million a quarter that has multiple trials and multiple drugs that are in phase II. So I think that's a big competitive mode for us. I also don't know any company our size that has 12 FDA indications. So always good to be skeptical about AI. There's a lot of hype and fluff around it. But I think ultimately, the proof is in what can the company actually do. So, yeah, I think it's always good to be a healthy skeptic, but at the end of the day, we have drugs developed by RADR®, by our platform. RADR® now is available publicly under the withZeta.ai. I urge all the skeptics to go there and try it out. It's pretty earth-shattering. The feedback we've gotten from our first 50 power users has been quite phenomenal. These include big cancer institutions, pharma companies, independent researchers, investment banks, CROs. It's, you know, always good to be a skeptic in the AI space. I think the proof is in our own portfolio and the cost and efficiency which we're conducting development. Also very importantly, we're opening up the kimono and showing the world that, you know, here's how you can do development in the new world of GPT-driven AI solutions. That does it, Panna. Thank you very much for your excellent presentation and for answering the questions. For more information on Lantern Pharma, reach us at 1-800-RedChip or write us at ltrn@redchip.com. Panna, thanks again.