Lantern Pharma Inc. (LTRN) Earnings Call Transcript & Summary

Hello, everyone. This is Paul Kuntz with RedChip Companies. I want to thank you for joining today's event with Lantern Pharma, which trades on the NASDAQ under the ticker LTRN. With us today, we have Panna Sharma, Chief Executive Officer, President and Director of Lantern Pharma. We will begin with a brief presentation in a moment, and then we'll open up the event to your questions. You may submit your question at any time by simply clicking the Q&A button at the Zoom window. Before we begin, I will 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. And of course, forward-looking statements involve risks and uncertainties. With that, I will now turn the webinar over to Panna. Please go ahead.

Paul, thank you very much, and I appreciate everyone taking time this afternoon to learn a little bit about our company. I'm going to talk a little bit today about how we've developed Lantern Pharma and also give you appreciation of the 2 major engines that drive our growth and uniqueness. A lot of you have heard about AI. A lot of you have heard about how drug development is becoming increasingly data-driven. And I'm going to give you real-life examples of how we're doing it. Lantern Pharma, as Paul pointed out, we're publicly traded, LTRN. We're about 24 people headquartered in Dallas, Texas. And we're focused on using AI for one thing, AI for good. And we're focused on developing cancer medicines. Our methods, the way we do it is to develop new first-in-human drugs, of which we have 2 now that are being dosed in patients every week and also to repurpose or actually rescue drugs. So drugs that have failed have never gotten approved, but actually have some promise. And of those, we have one, LP-300, that's currently in the Phase II. Our other 2 drugs is LP-184. That currently just finished enrollment in a Phase I, a pretty large-scale Phase I, about 65 patients. And also LP-284, which is a sister drug to LP-184 that's in a trial in blood cancers. So we've got a pretty robust portfolio. And on top of that, we actually have gained 11 FDA designations. 5 of those are orphan designations, 4 of these are rare pediatric designations and 2 Fast Track. And so again, this is a small company. Our burn rate is fairly small for a biotech, about $4.5 million a quarter. We're managing 3 trials and developing an AI engine. And so we've always taken the approach that we want to be able to develop drugs not only faster, but also more cost effectively and with greater precision. Without data and without AI, that's something that just is not possible. And so when I joined the company, our big focus was to better understand the molecules that were in front of us that we could be developing, but also to understand how they worked, where they worked, where they didn't work, what molecules they work best in, what cancer indications do they seem most likely to actually make an impact? Where are they going to work better than other drugs that are approved or in late-stage development. These are all the kind of questions that traditional drug developer has. Oftentimes, they'll spend years in a lab or years in different groups trying to work these problems out. Imagine being able to do those thousands of times over in silico. Imagine being able to have thousands of algorithms compete to give you an answer that you can get within days or weeks and not months or years. That's the power of how to use AI in the development of cancer medicine. But in order to do that, you have to have the right data. And not only the data, you actually have to have the right models. Not only do you have to have models, but you have to have models that can be learning constantly that you can iterate and have a recursive process so that you're not just using the AI once off the shelf and putting it back on the shelf. The AI has to be a living, breathing system. Our AI platform has grown tremendously, and it continues to grow, and we're going to be rolling out some aspects of it publicly. In fact, one of the most recent things we did was we announced the public release of one of the modules in our platform that predicts blood brain barrier penetrability. We also, at the end of our second quarter, when we announced second quarter results, we also talked about the completion of our Phase I trial with LP-184, which is a great milestone for us. We also talked about some very exciting complete responses that we had seen in 2 of our trials, one in aggressive B-cell lymphoma that was recurring with LP-284, another complete response in the primary lesions of a lung cancer patient with LP-300, and that's a trial for never smokers. So again, the 2 engines, both of which are very important, but the vast majority of our spend really is on the trials and on the biotech side is to develop precision oncology medicines that are derisked, that we know have a certainty and have a clear mechanism that is driving action in the tumors that we want -- in the way that we want so we can predict outcomes and then also then to have an AI platform that is able to do this and grow this on its own over and over and over. And we've seen that not only in our own drugs, but we've seen that in our collaborators. We have some great collaborators, Oregon Therapeutics, Actuate Therapeutics, companies developing very unique drugs on their own. Actuate is public. We own some stock in Actuate as a result of allowing them to use our platform. We have some potential IP with Oregon Therapeutics and their very unique drug as well. So part of our business model is to continue to mature the AI platform, RADR, but also to then develop these medicines and then license them out to biotech and larger pharma companies. Lantern wants to focus on our mission, which is to develop innovative new insights develop those insights into molecules, bring those molecules into trials and do it faster and cheaper than ever before. We think this is the model of the future of drug development to do it using data, to use it using insights that can be replicated from theoretical cancer biology to real-world patients and then more importantly, understand how drugs can be combined because oftentimes in cancer, it's not just one mechanism that will destroy cancer. You've got to use multiple mechanisms to actually drive a more durable, more deepened response. And we've seen that over and over in trials. And so one of our big things is to drive combination therapies. And so a lot of our AI work has been focused on combination therapies and finding how combinations will work together. So that's a little bit of an overview. I'm going to talk a little bit about each of the drugs because the cancer indications that we're going after are really exciting, but more importantly, very much needed. The first indication is in Phase II. It's a $4 billion to $5 billion opportunity globally in annual sales. These are patients who are not smokers. They're never smokers, but they still get non-small cell lung cancer. And ironically, there's no wonderful outcome for these patients. After they fail kinase therapy, if they're eligible for kinase therapy -- kinase inhibitors, there's really not a lot of good options. For us, it's white space. So we've developed a program that focuses on exploiting these kinase mutations. And so we have the only pan-kinase modulator that's in a Phase II clinical trial that also is very synergistic with chemotherapy. So this drug targets and kind of denatures the kinase receptor. So it slows down the growth of the cancer. And then once it's inside the cancer cell, it resets the redox cycle and allows the chemotherapy to kill off the cell. So far, we've seen some great results in the early readout of the first cohort, we saw an 86% clinical benefit rate. We also saw a patient move from a partial response to a complete response, which is also very exciting. This is not just a temporary kind of complete response. This is fairly durable. We've seen this patient now have a complete response in their primary lesions now going on 2 years. So that's very exciting. I mean it changed that kind of outcome for those patients. And we will have a readout with this patient group that now has expanded to Asia specifically Japan and Taiwan, where about 33% to 40% of new cases in non-small cell lung cancer are people who don't smoke. People who don't smoke have a totally different biological profile of their cancer. The mutations they have are different. The responses to chemotherapy are different. They don't respond to immunotherapy. And eventually, they do fail some of the targeted kinase therapies, and that's where our drug comes in. So we have a very clear clinical path. We've got excellent initial data from the cohort. We've finished enrollment now in Japan and are looking for partners in Asia, and we expect to then have more data readouts later this fall, but also perhaps toward the end of the year as well. And so that is an asset that can be partnered out. We think there's no other drug targeting this never smoker population. And again, this is a $4 billion to $5 billion a year spent on this patient group. The next drug that I'm going to talk about, LP-184 is a very unique molecule, first in human, and it's targeting a large range of solid tumors. We just completed enrollment in the Phase I trial, large trial, but 65 patients across a wide range of cancers, including GBM and brain cancer, including some lung cancers, et cetera. So a big range of cancers. Like most Phase Is, these will be fairly late-stage patients, meaning these patients have exhausted existing therapies because the focus of the Phase I really is to find an optimal dose that we can proceed with. And the reason for this is really clear because the drug is very potent. And we feel we have a very good maximum tolerated dose and a recommended go-forward or Phase II dose. And this is a very small dose, but the drug is nanomolar potent, meaning very tiny amounts, nanograms per ml of this drug seem to kill off these tumors. But it does it under certain very unique conditions. So like most precision oncology therapies, when the conditions are right in the tumor, and this happens to be in about 20% to 25% of cancers have either high levels of PTGR1, that's an enzyme that activates this drug inside of the cancer cell or they have what's called a deficiency in their DNA repair pathway. Either one of those conditions is around for LP-184, this drug really lights up. And so we find that a wide range of tumors respond to this drug and -- but it's very potent. And so finding the right dose, finding the right tolerability level, understanding the pharmacokinetics of this drug is very important. And we also have now clearance on 2 Phase Ib, Phase II trials for this drug, including in triple-negative breast cancer and also in STK11, KEAP1 mutated lung cancers. We also have an IST, an investigator-led study in bladder cancer. And all those cancers I mentioned, TNBC, bladder, STK11, KEAP1 mutated lung cancer, these are all very large indications multibillion-dollar indications. But most importantly, there are all categories in which their therapies are needed. And so for each of those, we have very clear signals that this drug works in that cancer, but actually works even better in synergy with other drugs. And so many of these will actually be combination trials. Our last drug, LP-284 that's now in clinical trials is also very exciting. It's a sister drug to LP-184. And it targets B-cells, which we have 2 orphan indications in mantle cell lymphoma and in high-grade B-cell lymphomas. We think about $3 billion to $4 billion are spent every year. And again, clinical positioning is really important. So what we've discovered is that when people fail first-line or second-line mantle cell or high-grade B-cell therapy, there's not a lot of great options, and our drug seems to work really well and continues to drive a response in those tumors. We actually saw this. This was all theorized, and we actually saw this in a recent patient. And again, it's really a wonderful moment for the company, kind of transformative because a lot of the theories and ideas that we had about how does the drug work? Where is it best positioned? Will it work in later-stage tumors that have become resistant to other therapies. All these publications and all these theories that we have, now we're seeing in patients. And there was a patient that had failed 3 prior lines of therapy. And in fact, internally, there was some debate whether this was going to be an ideal patient because they had failed some really state-of-the-art therapy. A bispecific antibody made by Janssen, great bispecific. They had failed stem cell transplant, they failed a CAR-T or they had some partial response and but not very durable. I think it was only a month or so. And so there's the question, is this -- are we going to be able to make an impact? Well, after 2 doses on our LP-284 drug, this is in a high-grade B-cell lymphoma, recurrent and the patient had complete metabolic response. And this patient had lesions, cancer lesions up and down their spine and into their pelvis, and we saw complete resolution of those lesions. And that's very exciting. We know it's only one patient, but it gives us a very clear signal that this drug is working. It's doing something. Now we have to get stats on our side and get a lot of these types of patients and drive similar kinds of response. And hopefully, the responses are durable enough and meaningful enough that we'll be able to get to potential accelerated or even breakthrough in that indication because when patients in these high-grade B-cell lymphomas and mantle cell lymphomas fail, the outcome is pretty poor. And so again, to be able to make an impact in that patient group is very exciting. So that's why I think about our company as being a company that's doing AI for good. We're trying to take all this great data, all this great algorithmic capability, all the great infrastructure of cloud computing and leverage it to focus on the development of precision cancer therapies. We focus on all sorts of problems every day at the company. It could be a problem around manufacturing, improving manufacturing. It could be a problem around combinations, what combinations. It could be a problem around what other drugs -- sorry, cancer indications can this drug be pointed at that we're excited about. All those are wonderful problems and can be solved not just once or twice, but hundreds of times or thousands of times using data and algorithms. And each time you learn something unique that then you can go do experiments, you can gather more data and recurs. Again, our model is not use AI and get an answer. Our model is use AI hundreds of times, thousands of times, get a library of answers, have those answers compete, enrich those answers with real-world data and iterate. And that's what allows us to have the model that we have. We're maniacally focused on the next generation of cancer therapies. Today, we have 3 small molecules in the clinic. We have already a new generation of molecules that we're working on preclinically. Most of these are antibody drug conjugates or other forms of drug conjugates, which we think will be revolutionary in the market. That's a completely exciting new modality. But our business model is a focus on that innovation, do early execution and then sell the assets off to larger biotech and larger pharma partners. Again, our AI platform, we plan on making that public. I'll talk a little bit more about that, what I call taking a chapter out of kind of the DeepSeek playbook. We publicly released our very first module called predictBBB.ai. You can go there today, sign up for an account. And you can predict any molecule and predict whether it's going to actually cross the blood brain barrier. We have probably the most reliable and most scalable algorithm to predict any small molecule's ability to penetrate the blood brain barrier. Only about 2% to 6% of molecules actually cross the blood brain barrier. And it's an area that can take tens or hundreds of thousands or millions of dollars to figure out properly, and we can do it in seconds. And that's the revolutionary potential of these kinds of approaches of doing drug development in a totally different level, scale and time frame. We'll have many more modules. This is a very tiny, tiny taste. We have a very important module coming out later this fall that's going to be focused on a much larger range of what's called a multi-agentic system. And we're going to take something that we do really well. And we have 11 FDA designations, 5 of them in orphan designations, 4 of them in rare pediatric designations, 2 of them that are fast track. So if you think about that, for a company of 22, 24 people, we have 11 designations. So it's something we do very well, something we think about, something we enjoy thinking about, and we're going to bring that out to the public and allow that to see the light of day and allow people to start developing molecules and understanding rare cancers. And so that would be very exciting that will come out sometime in September. So the platform, major engine of growth and value in each of the molecules also. So -- and again, we're very focused on maintaining a very disciplined fiscal profile. We burned about $4.5 million a quarter, even with our 3 trials going on. And we'll have data from all the trials. We'll have data from our AI platform, and we have cash into middle of 2026. So we're pretty well managed. No warrants, no toxic overhang, no debt. We've got a total of about 10.8 million, 10.9 million shares outstanding and about 12 million shares in total on a fully diluted basis. And lots of news coming out over the next several months. So with that, I'll take a quick pause. Again, A ticker LTRN publicly traded as Lantern, and I'll love to take questions from everyone today.

Thanks, Panna. Great presentation. We are now going to open up the call for questions. [Operator Instructions] One of the first questions we had, Panna, was, is there a business reason for making the modules available free to the public? And how will that increase revenues and shareholder value?

Yes. So I think part of the challenge with any kind of AI is that it's so black box and until tools like ChatGPT and DeepSeek and other tools became publicly available and open, it was hard for people to imagine and spend on this kind of AI black box. And so I think we're going to take a premium kind of approach where we open up these modules. We allow people to take and test these modules, use it 5x, use it 10x for free and then start purchasing tokens or enter into collaborations. And so we can talk until you're blue in the face that our AI does X or Y or Z. But when you actually have pharma companies using it or developers using it, it becomes a whole different game. And so we want to be disruptive, and we think there's plenty of opportunity to do collaborations and charge for tokens, charge for use and allow people to transform their own work.

Great. Thank you. And we had another question. You have over 100 issued and pending patents. Where do you see the strongest moat, composition of matter, methods or RADR?

Yes. I think we have about 140 issued and pending applications now. Composition of matter is always important, but also a lot of -- is very important in terms of the methods, how do you plan on using that composition, where, how, under what circumstances, meaning what's biomarker signature with what other drugs. And so we've patented all that -- all those findings that we've had. And we also have patented several aspects of RADR as well. I think each of the molecules has a wonderful patent estate around it. It's very important. Obviously, the most -- the longest patents are the ones that we've filed in the last few years. So those would be around 284 and also some of the findings for 300 and 184. So I don't think there's the strongest. I think they're all important and strong, but 284 since it's the newest probably is very strong, but it's also the smallest of the indication. So you got to juggle size of the market and duration of the patent, all those matter. Software patents and algorithm patents are a little bit more challenging. They take longer to eventually issue. And by the time some of these will issue, there's going to be constant new things. I mean I think one of the things that we're very excited about is that we're thinking about is how is -- we see the wonderful world of how the current chip technologies has changed computing and computing cost. But imagine now what certain aspects of quantum computing, and I know that a lot of people think it's hype. Yes and no. I mean I think the certain chip aspects are definitely much further along than we think. But the software embedded quantum computing is actually here now. And I think there's a lot that can be done in terms of being able to simultaneously design different states of molecules all at once. And that really can be a game changer because you can take that same concept and do it to simultaneously modeling different outcomes in cancer patients all at once instead of serial or even in parallel. Imagine a system being able to think about 2 or 3 or 4 of these things and do it in a multiplex way. I mean you can -- I mean, it's really unreal what quantum computing is going to open us up into for really deep biology modeling. And so I think that's not 20 years out. I think that's much more near term. I think 2 to 5 years. And so I think that's going to be game-changing in terms of where AI can go next to be able to predict biology.

Thank you, Panna. And with your PredictBBB at around 94% accuracy, is that opening any doors in CNS, either for Starlight or for external partnerships?

Yes. We're in discussion with a couple of companies to use the algorithm to screen libraries. We're actually in discussion with groups to talk about using it as part of a trial as well to select in a basket trial that's going on for CNS cancers. So yes, it's generated a lot of interest. The challenge always and part of why it's public is that historically, these public tools have never been much more than about 70% accurate, maybe in the 60s, and they have limitations. And our algorithm has limitations, too. But I don't expect $100 million of flown as a result overnight. So just to gauge -- I mean, I expect partnerships to occur. And I expect that PredictBBB will be a game changer in the way people start thinking about opening up AI systems for large-scale development. And again, followed up with lot of as well. So I don't want to get into details, but when you predict a molecule's blood brain barrier penetrability and if you look at the details that we put on the BBB white paper and the details of the actual algorithms, we have algorithm cards that they can look at if people register, is that you can see that the -- we're taking into account thousands and thousands of parameters. These parameters are molecular features. And so we have all those features of millions of molecules. So we're just using those features to give one piece of data, which is our prediction on BBB, but we can use all those features to predict lots of other features on a molecule. And so imagine the next iteration of the predict BBB will not just predict its BBB, but it's going to predict all kinds of others. It's going to predict its kinetics. It's going to predict whether it's lipophilic or lipophobic. It's going to predict its bonding strength. It's going to predict how it donates electrons. It's going to predict how many open rings it has. It's going to predict its drug ability. It's going to predict potential safety issues. So BBB is just a gateway to now being able to predict dozens of things about any molecule. And so that's really the game plan is BBB is just a gateway. And again, if you -- people who actually know the space when we look at, okay, well, how are they predicting it, they'll realize that in order to predict it, we've looked at, and it's in the white paper, over 8,742 molecular features of any compound in real time. And being able to do algorithms and machine learning across those features, again, in real time, across the web and then be able to give those is potentially revolutionary in the development of medicines.

And another question we had, why expand the LP-300 study to Japan and Taiwan? And what should we watch for from those new sites?

Yes, great question. So never smokers are fairly understudied historically group of patients. They historically have not responded very well to chemotherapy. They don't respond very well at all to immunotherapy. In fact, there's an immunotherapy trial that just launched and it's on the label, it says -- sorry, on the criteria, inclusion/exclusion criteria, not label. It says exclude people who are never smokers because never smokers have a very poor response to immunotherapy. But now in the United States, about 15% to 20% of new cases of non-small cell lung cancer are never smokers. That's 30,000, 40,000 patients a year. It's not a -- it's a pretty sizable population. Globally, it's closer to 170,000 to 200,000. In Japan, Taiwan, South Korea, parts of China, the never smoker population, people who have less than 100 cigarette events or tobacco events in their lifetime is a much larger percentage approaching 35% to 40-plus percent. So it's double that in the United States. It's a known issue there. A lot of KOLs have been trying to study it. Many of the never smoker population has EGFR mutations or other kinase mutations, and that's how this drug works. This drug works by denaturing the kinase receptor. It's one of the unique drugs that actually has a pan-kinase capability. It modulates multiple kinase receptors and denatures them to slow down the growth. And then once it's inside the cancer cell, it resets the thyodoxone glutaphyone cycles and makes the drug -- cancer cell sensitive to chemotherapy drugs. Why Japan, Taiwan? The incidence rate is there. We know that in Asia, the pharma companies are looking for this kind because the population has it as a higher percentage and Japan, in particular, and also Taiwan have studied this. And so they have the patient population. They studied it. They have a need for a drug. So doing that in their backyard is a very much letting them know that we're open to partner the asset out. And we've actually just completed enrollment in Japan. So that's also -- enrollment happened pretty quickly ahead of schedule. And now we're focused on in Taiwan and also continuing enrollment in the U.S. But yes, very good question. Thank you for that.

And our next question, could you share your latest thinking around combination therapy opportunities, particularly in triple-negative breast cancer and non-small cell lung cancer?

Yes. Very, very good question. Yes. So both of those, we have new INDs that were approved. These are for Phase Ib, Phase II trials. I think they can -- we can hopefully even get to accelerated or even fast track because we have fast track now for TNBC already. The way our drug works for 184 is that it causes really high amounts of double-stranded DNA breaks inside the cancer cell. That's important because once this drug gets inside the cancer cell and PTGR1 is there to activate it, it breaks apart the cancer cell by causing breaks in its DNA. But like most cells, it can try to repair it. And so what we thought about early on is what if we could find a mechanism that we could stop that repair from happening? Well, that drug exists. Those are PARP inhibitors. These are drugs that inhibit the PARP enzyme and PARP inhibitors are about, I don't know, about $1.8 billion to $3-plus billion drug class out there. There are a number of PARP inhibitors already in the market. And so our thought was we cause the DNA to break. On the other side, totally synergistic is that we're giving it with a drug that blocks its repair. So it's kind of a one-two punch. And again, this is theoretical, just very much theory, but then we put it in the lab, and it was really, really brilliant done work by our team. And the early work that we saw is that we reduced the amount of our drug, we reduced the amount of PARP and the response is even better, nearly 100% tumor growth inhibition, meaning we destroyed tumors at 100%. So like our drug alone, say, it was 75% or 60% or it's dose dependent. And just the PARP alone was a certain percentage, but we can reduce the amount of both drugs, therefore, actually actually having a better safety profile, which is really important, especially with PARP inhibitors because PARP start getting tolerance issues more than safety issues. And so we can reduce the amount, and we actually have a better overall impact, again, theory. And so now we modeled that using lots of different models, both models for binding, using the data we got. And we said, can we do a trial where in triple-negative breast cancer, we use the PARP with our drug and see this kind of exquisite response. And so far, what we've seen in all the preclinical studies and all the models that we've done in multiple sites, multiple labs, it's very, very promising. And they're very synergistic. Like I said, one is completely destroying the DNA and the other is stopping it from repairing. So it's kind of like 2 bookends. We're like attacking the problem. And that's what combination therapy is supposed to be. Can we attack this problem from multiple places so that it's -- you're getting the maximum impact. And as we see in a lot of cancers, you want to get as much of that impact upfront because that will give us a much longer, more durable response. Very similar in non-small cell lung cancer that has what's called KEAP1 or STK11. These are mutations that are really, really bad. Having an STK11 mutation or a KEAP1, these are very aggressive cancers. And in lung cancer, particularly, they tend to come back. They tend to be multidrug resistant. And we saw is that even PD-1 drugs, which have an impact, they don't really have a durable impact. And the only current method that's used is to give a combination of checkpoint inhibitors, nivolumab and ipilimumab, 2 of them. And they actually tend to make the patients survive about a year, sometimes less. And so -- but that's not great. And so our thought was how can we synergize with PD-1 drugs, these checkpoint inhibitors because the mechanisms of our drug and PD-1 are so wildly different. What we found is that we actually -- when our drug is given with PD-1 drug, it actually makes the PD-1 much more durable, and we drive cancer death to -- because of our drug because the cancer cell tends to have more upregulated PTGR1. So again, we try to find those signals. Again, this is all data. It's all data that's available. We dissect that data. We model it. We then put it in the lab. We bring it back on the exquisite combination we found due to some of the KOLs that we work with, not just our own because we're a very collaborative company. But some of our collaborators said, yes, this STK11 and KEAP1 is really important, and we think it makes sense because you're going to reenergize the tumor to be a hot tumor. We think we'll get a better, more meaningful response. And we think you're introducing a new mechanism, which also will be favorable. And that's how that STK11 KEAP1 mutant lung cancers trial is organized. So again, it's the power of data and the power of modeling. And in all these combinations, we try to create unique models did it for DNA damage repair agents. We did it with checkpoint inhibitors. These are models that we've built. These are models that we've enriched with our own data. These are then models that we continue to enrich by taking preclinical lab results and even patient results that we can get and putting it back into the model to learn from. And so that's the power of the future of AI and data-driven drug development. It's not just take it off the shelf, have a prediction, go run after it. It's constantly enriching and learning from it. And so those 2 trials, that's how those combinations were created. Excellent, excellent question. And again, very meaningful categories. Again, we think, hopefully, we get some good early responses and can move toward some accelerated pathways to get these drugs and combinations to patients that need them.

Excellent. We had another question. I assume that the LP-184 study of 65 patients would have 20 patients at the highest last dose escalation. What is the length of follow-up planned? And when will results be made public?

We don't have a time line public, probably sometime in the next few months as we dissect, we still have patients that are on the trial. And so that's great. That's good news actually. So -- but no, it's not 20 or so patients in the last cohort. Each cohort is between 3 and 6 patients. Our maximum -- as we said, our maximum dose level achieved was in cohort 12, dose level 12. And our recommended Phase II dose will probably be dose level 10, which is 0.39 mg per kg -- milligrams per kilogram. We're not going to have 20 patients in dose level 12. Dose level 12, we backed off on and we're going to go dose level 10. That's going to be our recommendation, and we'll see how the Drug Safety Review committee hopefully agrees to that recommendation. But 10, we're very comfortable because if we get good responses at 10, we can go to 11. And we know 12 that we're getting into pushing the boundaries of what's going to be tolerated. But 10 is great because you can go up to 11, but you can also back down to 9 and that kind of 9 to 11 range, we still have enough drug substance, we think, to be biologically active.

We actually have another LP-184 question. Which tumor types are you most focused on first? And how does your PTGR1 biomarker help pick the patients most likely to benefit?

That's exactly how it helps. So ask that question. Great question. So if you have high levels of PTGR1, what we've mapped is a threshold we published that if you have a 4.2x or 4.5x, I believe, fold log higher of that enzyme in the tumor, there's a very high chance you're going to be hyperresponsive. We may use that eventually as a companion diagnostic. But the 2 signatures that we're going to look for is, one, DNA damage response mutations. And there are about 13 to 15 genes that remember that are in that DNA response signature. And if you have a mutation or aberration in either the nucleotide excision repair pathway or the homologous repair pathways, you're going to be a great candidate for this drug. And TNBC, GBM, bladder and STK11, KEAP1, which are part of those global repair pathway genes are excellent candidates for LP-184. We do have a bladder cancer trial that we'll talk about that we believe will be an IST, which means it will be investigator-sponsored and will be paid for by a different research institution. So that will be very exciting. In those bladder cancers that we intend on studying with LP-184, about almost 40% of those bladder cancers have what's called DNA damage repair mutations. It's a very high percentage. In lung cancer, it's about 7%. So every cancer has its own percentages. We're going to go after those in which there's really, we believe is to be a clear path towards having a meaningful impact and getting to a white space, meaning there's either no drug approved in that line of therapy or there is a need for one or both. And that's how we think about indications. Like where can we -- where is the earliest possible place where we can get this drug to an approval? Excellent question.

Great. Thank you, Panna. And with that, I mean, I'll just pass it back to you real quick for do you have any final comments you would like to leave for the audience?

No, great questions from your audience. So this is great. Again, we've got a lot of milestones, a lot of data that we're going to be releasing over the next several months. We've got a good runway in front of us, and we also have the AI platform that we'll be making more and more publicly available. So I hope you guys have learned a little bit from today's webinar. I hope you guys participate in the upside and see how AI can be used for good, not just making pretty pictures and writing research papers for people, but AI and these frameworks can actually be used to totally transform the development of cancer therapies. So thank you for taking time out on your afternoon to listen to our webinar.

Very exciting. Thank you, Panna. And for our audience, for more information on Lantern Pharma, you can always call us here at RedChip. That's 1-800-REDCHIP or you can e-mail us at LTRN that's the ticker symbol [email protected]. You can also visit ltrninfo.com where you can download the investor presentation, fact sheet and even sign up for news alerts on Lantern. With that, I want to thank everyone for joining us today. Thank you again, Panna.

Thank you, everybody.