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 bottom of 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 fact should also be considered forward-looking. 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. 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 two 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. 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. We're focused on using AI for one thing, AI for good. 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 two now that are being dosed in patients every week. We also repurpose or actually rescue drugs, so drugs that have failed and never gotten approved but actually have some promise. Of those, we have one, LP-300, that's currently in a phase II. Our other two drugs are 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. We've got a pretty robust portfolio. On top of that, we actually have gained 11 FDA designations. Five of those are orphan designations, four of these are rare pediatric designations, and two fast track. This is a small company. Our burn rate is fairly small for a biotech, about $4.5 million a quarter. We're managing three trials and developing an AI engine. 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. 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 worked best with, 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 a 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, not months or years. That's the power of how to use AI in the development of cancer medicine. In order to do that, you have to have the right data. 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. 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. At the end of our second quarter, we announced second quarter results. We also talked about the completion of our phase I trial with LP-184, which is a great, great milestone for us. We also talked about some very exciting complete responses that we had seen in two of our trials. One in an 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. That's a trial for never smokers. The two 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 de-risked, that we now 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 that we can predict outcomes. Also, to have an AI platform that is able to do this and grow this on its own over and over and over. 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 in their very unique drug as well. 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 Pharma 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. More importantly, understand how drugs can be combined. 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. We've seen that over and over in trials. One of our big things is to drive combination therapies. A lot of our AI work has been focused on combination therapies and finding how combinations will work together. 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- $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. 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. We've developed a program that focuses on exploiting these kinase mutations. We have the only pan kinase modulator that's in a phase II clinical trial that also is very synergistic with chemotherapy. This drug targets and kind of denatures the kinase receptor. 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 was 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 two years. That's very exciting. I mean, it's to change that kind of outcome for those patients. We will have a readout with this patient group that now has expanded to Asia, specifically Japan and Taiwan, where about 33%- 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. Eventually, they do fail some of the targeted kinase therapies. That's where our drug comes in. 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. We expect to then have more data readouts later this fall, but also perhaps toward the end of the year as well. That is an asset that can be partnered out. We think there's no other drug targeting this never smoker population. Again, this is a $4 billion - $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. It's targeting a large range of solid tumors. We just completed enrollment in the phase I trial. Large trial, about 65 patients across a wide range of cancers, including GBM and brain cancer, including some lung cancers, et cetera. A big range of cancers. Like most phase Is, these will be fairly late-stage patients, meaning these patients have exhausted existing therapies. The focus of the phase I really is to find an optimal dose that we can proceed with. The reason for this is really clear because the drug is very potent. We feel we have a very good maximum tolerated dose and a recommended go-forward or phase II dose. This is a very small dose. The drug is nanomolar potent, meaning very tiny amounts, nanograms per mL of this drug, seem to kill off these tumors. It does it under certain very unique conditions. Like most precision oncology therapies, when the conditions are right in the tumor, and this happens to be in about 20% - 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. We find that a wide range of tumors respond to this drug, but it's very potent. Finding the right dose, finding the right tolerability level, understanding the pharmacokinetics of this drug is very important. We also have now clearance on two phase I-B/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. All those cancers I mentioned, TNBC, bladder, STK11/ KEAP1 -mutated lung cancer, these are all very large indications, multibillion- dollar indications. Most importantly, they're all categories in which therapies are needed. 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. 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. It targets B- cells, which we have two orphan indications in mantle cell lymphoma and in high-grade B- cell lymphomas. We think about $3 billion- $4 billion are spent every year. Again, clinical positioning is really important. 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. Our drug seems to work really well and continues to drive a response in those tumors. We actually saw this. This was all theorized. We actually saw this in a recent patient. It's really a wonderful moment for the company, kind of transformative. 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. There was a patient that had failed three prior lines of therapy. In fact, internally, there was some debate whether this was going to be an ideal patient because they'd failed some really state-of-the-art therapies, a bispecific antibody made by Janssen, great bispecific. They'd failed a stem cell transplant. They failed a CAR-T. Or they had some partial response and were not very durable. I think it was only a month or so. There is the question, you know, are we going to be able to make an impact? After two doses on our LP-284 drug, this is in a high-grade B-cell lymphoma, recurrent, and the patient had complete metabolic response. This patient had lesions, cancer lesions up and down their spine and into their pelvis. We saw complete resolution of those lesions. That is 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. 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. When patients in these high-grade B-cell lymphomas and mantle cell lymphomas fail, the outcome is pretty poor. To be able to make an impact in that patient group is very exciting. That is 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, 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. Each time, you'll learn something unique that then you can go do experiments. You can gather more data and recurse. 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. That is what allows us to have the model that we have. We're maniacally focused on the next generation of cancer therapies. Today, we have three 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. Our business model is to 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 deep sea playbook. We publicly released our very first module called P redictBBB.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%- 6% of molecules actually cross the blood-brain barrier. It's an area that can take tens or hundreds of thousands or millions of dollars to figure out properly. We can do it in seconds. That's the revolutionary potential of these kinds of approaches to 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. We're going to take something that we do really well. We have 11 FDA designations, five of them in orphan designations, four of them in rare pediatric designations, two of them that are fast track. If you think about that, for a company of 22, 24 people, we have 11 designations. It's something we do very well, something we think about, something we enjoy thinking about. 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. That would be very exciting, that will come out sometime in September. The platform, major engine of growth and value in each of the molecules also. We're very focused on maintaining a very disciplined fiscal profile. We burn about $4.5 million a quarter, even with our three trials going on. We'll have data from all the trials. We'll have data from our AI platform. We have cash into the middle of 2026. 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. Lots of news coming out over the next several months. I'll take a quick pause. Again, our ticker is LTRN, publicly traded as Lantern. I'd love to take questions from everyone today. Thanks, Panna. Great presentation. We are now going to open up the call for questions. As I mentioned earlier, you can type your questions in by simply clicking the Q&A button at the bottom of the screen, and we'll get to those as they come in. 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? Yeah, I think part of the challenge with any kind of AI is that it's so black -box. 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. I think we're going to take a freemium 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. We can talk till you're blue in the face that our AI does X, Y, or Z, but when you actually have pharma companies using it or developers using it, it becomes a whole different game. We want to be disruptive. 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. 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? Yeah, yeah. I think we have about 140 issued and pending applications now. Composition of matter is always important, but also, a lot of it is very important in terms of the methods. How do you plan on using that composition? You know, where, how, under what circumstances, meaning what's biomarker signature, with what other drugs? We've patented all those findings that we've had. 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 longest patents are the ones that we've filed in the last few years. Those would be around LP-284 and also some of the findings for LP-300 and LP-184. I don't think there's a strongest. I think they're all important and strong. LP-284, since it's the newest, probably is very strong, but it's also the smallest of the indications. You've 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. One of the things that we're very excited about is that we're thinking about how we see the wonderful world of how the current chip technologies have changed computing and computing costs. Imagine now what certain aspects of quantum computing, and I know that a lot of people think it's hype. Yeah, yes and no. I think that the sort of chip aspects are definitely much further along than we think, but the software embedded quantum computing is actually here now. 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. 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 two or three or four of these things and do it in a multiplex way. It's really unreal what quantum computing is going to open us up into for really deep biology modeling. I think that's not 20 years out. I think that's much more near term. I think two to five years. 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. 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. It's generated a lot of interest. The challenge always, and a part of why it's public, is that historically, these public tools have never been much more than about 70% accurate, maybe in the 60%s, and they have limitations. Our algorithm has limitations too. I don't expect $100 million to flow in as a result overnight. Just to gauge that, I expect partnerships to occur. I expect that PredictBBB will be a game changer in the way people start thinking about opening up AI systems for large-scale development. We're going to follow it up with lots of other topics as well. I don't want to get into details. 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 with algorithm cards that they can look at if people register, you can see that we're taking into account thousands and thousands of parameters. These parameters are molecular features. We have all those features of millions of molecules. We're just using those features to give one piece of data, which is our prediction on BBB. We can use all those features to predict lots of other features on a molecule. Imagine the next iteration of the PredictBBB 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 its 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. BBB is just a gateway to now being able to predict dozens of things about any molecule. That's really the game plan. BBB is just a gateway. If people who actually know the space and look at, OK, 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. 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. Thank you, Panna. Another question we had, why expand the LP-300 study to Japan and Taiwan? What should we watch for from those new sites? Yeah, great question. Never- smokers are a 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 criteria, inclusion exclusion criteria, not label, it says, exclude people who are never -smokers because never -smokers have a very poor response to immunotherapy. Now in the U.S., about 15% - 20% of new cases of non-small cell lung cancer are never-s mokers, and that's 30,000- 40,000 patients a year. It's a pretty sizable population. Globally, it's closer to 170,000 - 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% - 40%+. It's double that than in the United States. It's a known issue there. A lot of KOLs have been trying to study it. Many of the never-s moker population have 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 thioredoxin-glutathione and makes the cancer cell sensitive to chemotherapy drugs. Why Japan and 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. 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. Doing that in their backyard is very much letting them know that we're open to partner the asset out. We've actually just completed enrollment in Japan, so that's also, enrollment happened pretty quickly ahead of schedule. Now we're focused on in Taiwan and also continuing enrollment in the U.S. Yeah, very good question. Thank you for that. Thank you, Panna. Could you share your latest thinking around combination therapy opportunities, particularly in Triple N egative Breast Cancer and non-small cell lung cancer? Yeah, very, very good question. Yeah, so both of those, we have new INDs that were approved. These are for phase I-B/ phase II trials. I think 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 LP-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. Like most cells, it can try to repair it. What we thought about early on is what if we could find a mechanism that we could stop that repair from happening? That drug exists. Those are PARP inhibitors. These are drugs that inhibit the PARP enzyme. PARP inhibitors are about, I don't know, about $1.8 billion- $3+ billion plus drug class out there. There are a number of PARP inhibitors already in the market. Our thought was we cause the DNA to break. On the other side, totally synergistic, is that we're giving them the drug that blocks its repair. It's kind of a one-two punch. This was theoretical, just very much theory. We put it in the lab. It was really, really brilliantly done work by our team. In the early work that we saw is that we reduced the amount of our drug. We reduced the amount of PARP. The response was even better, nearly 100% tumor growth inhibition, meaning we destroyed tumors at 100%. Our drug alone saved 75% or 60% or dose dependent. Just the PARP alone was a certain percentage. We can reduce the amount of both drugs, therefore actually having a better safety profile, which is really important, especially with PARP inhibitors, because PARP started getting tolerance issues more than safety issues. We can reduce the amount. We actually have a better overall impact, again, theory. Now we model that using lots of different models, both models for binding, using the data we got. 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? So far, what we've seen in all the preclinical studies and all the models that we've done, multiple sites, multiple labs, it's very, very promising. They're very synergistic. Like I said, one is completely destroying the DNA. The other is stopping it from repairing. It's kind of like two bookends where we're attacking the problem. That's what combination therapy is supposed to be. Can we attack this problem from multiple places so that you're getting the maximum impact? As we see in a lot of cancers, you want to get as much of that impact up front because that'll 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. In lung cancer particularly, they tend to come back. They tend to be multi-drug resistant. What we saw is that even PD-1 drugs, which have an impact, don't really have a durable impact. The only current method that's used is to give a combination of checkpoint inhibitors, nivolumab and ipilimumab, two of them. They actually tend to make the patient survive about a year, sometimes less. That's not great. Our thought was, how can we synergize with PD-1 drugs, these checkpoint inhibitors? The mechanisms of our drug and PD-1 are so wildly different. What we found is that when our drug is given with PD-1 drug, it actually makes the PD-1 much more durable. We drive cancer death because of our drug, because the cancer cell tends to have more upregulated PTGR1. We try to find those signals. 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. 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. Some of our collaborators said, yes, this STK11/ KEAP1 is really important. We think it makes sense because you're going to re-energize the tumor to be a hot tumor. We think we'll get a better, more meaningful response. We think you're introducing a new mechanism, which also will be favorable. That's how that STK11 /KEAP1- mutant lung cancers trial is organized. It's the power of data and the power of modeling. In all these combinations, we try to create unique models. We 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. That's the power of the future of AI and data-driven drug development. It's not just take it off a shelf, have a prediction, go run after it. It's constantly enriching and learning from it. Those two trials, that's how those combinations were created. Excellent, excellent question. Very, very meaningful categories. 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? When will results be made public? We don't have a timeline public, probably sometime in the next few months as we dissect. We still have patients that are on the trial, so that's great. That's good news, actually. There's not 20 or so patients in the last cohort. Each cohort is between three and six patients. Our maximum, as we said, our maximum dose level achieved was in cohort 12, dose level 12. Our recommended phase I dose will probably be dose level 10, which is 0.39 mg / 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 with dose level 10. That's going to be our recommendation. We'll see how the Drug Safety Review Committee hopefully agrees with that recommendation. Ten, we're very comfortable because if we get good responses at 10, we can go to 11. We know at 12, we're getting into pushing the boundaries of what's going to be tolerated. Ten is great because you can go up to 11, but you can also back down to nine. That kind of nine to 11 range, we still have enough drug substance we think to be biologically active. Thanks, Panna. We actually have another LP-184 question. Which tumor types are you most focused on first? How does your PTGR1 biomarker help pick the patients most likely to benefit? That's exactly how it helps. I'm sure I was asked that question. Great question. If you have high levels of PTGR1, what we've mapped as a threshold, we've 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 hyper-responsive. We may use that eventually as a companion diagnostic. The two signatures that we're going to look for are, one, DNA damage response mutations. There are about 13 -1 5 genes that are in that DNA response signature. 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. 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'll be investigator-sponsored, and it'll be paid for by a different research institution. That'll 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%. Every cancer has its own percentages. We're going to go after those in which there is really, we believe it 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. 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. With that, I'll just pass it back to you real quick. Do you have any final comments you would like to leave for the audience? Great questions from your audience. This is great. Again, you know 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. We also have the AI platform that we'll be making more and more publicly available. 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. AI and these frameworks can actually be used to totally transform the development of cancer therapies. Thank you for taking time out on your afternoon to listen to our webinar. Very exciting. Thank you, Panna. 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 email us at LTRN. That's the ticker symbol LTRN at redchip.com. 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.