Lantern Pharma Inc.

Good afternoon and welcome to our fourth quarter and year end 2024 earnings call. As a reminder, this call is being recorded and all attendees are in a listen only mode. We will open the call for questions and answers after our management's presentation. A webcast replay of today's conference call will be available on our website at lanternpharma.com shortly after the call. order and year, ended December 31st, 2024. A copy of this release is available through our website at lanternpharma.com, where you will also find a link to the slides management we'll be referencing on today's call. We would like to remind everyone that remarks about future expectations, performance, estimates and prospects constitute forward-looking statements for purposes of safe harbor provisions under the Private Securities Litigation Reform Act of 1995. Lantern Pharma cautions that these forward looking statements are subject to risks and uncertainties that may cause actual results to differ materially from those anticipated. A number of factors could cause actual results to differ materially from those indicated by forward looking statements, including results of clinical trials and the impact of competition. Additional information concerning factors that could cause actual results to differ materially from those in the forward looking statements can be found in our annual report on Form 10-K for the year ended December 31, 2024, which is on file with the SEC and available on our website. Forward-looking statements made on this conference call are as of today, March 27, 2025, and Lantern Pharma does not intend to update any of these forward-looking statements to reflect events from circumstances that occur after today unless required by law. The webcast replay of the conference call and webinar will be available on Lantern's website. On today's webcast, we have Lantern Pharma CEO, Pauna Sharma, and CFO, David Margrave. Pauna will start things off with introductions and an overview of Lantern's strategy and business model and highlight recent achievements in our operations, after which David will discuss our financial results. This will be followed by some concluding comments from Pauna, and then we'll open the call for Q&A. I'd now like to turn the call over to Ponna Sharma, President and CEO of Lantern Pharma. Ponna, please go ahead.

Matti, thank you. Hello, everyone, and thank you for joining us this afternoon to hear about our fourth quarter and fiscal year 2024 results and corporate progress. As many of you have heard me say in the past, computational and AI-driven approaches are increasing their presence and usage at both large and emerging pharma companies for all facets of drug discovery and development. Our leadership and the innovative use of AI and machine learning to transform the process of developing precision oncology therapies should yield significant returns for investors and patients as our industry matures and adopts an AI-centric, data-first approach to drug development. 2024 was a transformational year for Lantern Pharma across many measures, portfolio, the platform, and patient impact. I would like to highlight Lantern Pharma and our team's extraordinary progress across our clinical pipeline and our AI platform. These developments aren't merely incremental advancements. They actually represent transformative approaches that are reshaping how we develop precision oncology therapies for patients that usually have very limited treatment options and hopefully enable a much more efficient future for drug developers. Our team today is about 23 people focused and comprised of leaders and high value contributors. And they have made significant strides over the past quarter and actually throughout all of 2024 across all of our clinical programs. and with our AI platform, Radar, and also in our ongoing efforts in developing an entirely new company, Starlight Therapeutics, which was largely possible due to our AI and data-driven model, which is used to understand how and where a molecule can work best against a particular cancer and actually identify the cancers that are going to be most sensitive to a molecule. In fact, this is one of the core features of our AI. Radar, our precision oncology AI platform, has guided the rapid and efficient development of three AI-driven drugs into clinical trials at a pace and cost that has traditionally been unheard of in our industry. Some of our peers, many of whom already reported, their burn rate in one quarter is more than our burn rate has been over the past three years. So just, and their pipeline is not all that as advanced. So we're talking 12 to 14 quarters of our burn versus one quarter of theirs. Well, our team has been very focused on executing of our mission, which is first and foremost, which is transform oncology drug discovery and development. All of our clinical stage drug candidates are now in phase two and phase one trials. They've all dosed multiple cohorts of patients. And we actually have some very exciting preclinical assets like our antibody drug conjugates that are in early development for the next generation of our portfolio. All of our clinical trials I'll be talking a little bit about today, and we will have multiple clinical readouts over the next several quarters as we get insightful data on how patients and cancers are responding to our precision drug candidates. Last year, we shared with you information from our first lead-in cohort of patients in the Phase II harmonic trial, and I'll provide an update on that as well today. Good news is that we're continuing to see some of the remarkable results from patients as we've expanded the trial. Now, our team and many clinicians are particularly excited about and interested also in the programs for our first in human drug candidates, LP184 and LP284. And also, of course, LP300, which is aimed at a very unique population of never smokers that have been impacted by non-small cell lung cancer adenocarcinoma, but have failed other treatment options. This is a growing problem, not only in the US, but globally. And we are actively screening and dosing patients, not only in the US, but in Japan and Taiwan, where the incidence of non-small cell lung cancer among never smokers is nearly two and a half to three times than here in the US. Now, our phase two asset, LP300, which is aimed at a $4 to $5 billion opportunity annually, but also one that's growing, has seen acceleration in enrollment. Our harmonic trial for LP300 has delivered remarkable preliminary results that demand attention. The lead-in cohort achieved an 86% clinical benefit rate and a 43% objective response rate in never-smoker, non-small-cell lung cancer patients. These aren't just numbers. They represent a potential change in survival and hope for patients who have historically been underserved by conventional treatments. What makes these findings particularly significant from a clinical perspective is that never-smoker, non-small-cell lung cancer patients typically have showed very limited response to existing therapies. Their genomic profiles are fundamentally different from smokers, with higher frequencies of actionable driver mutations, but poor response to immunotherapies. They also generally have a very different genome, quieter genome, with much lower tumor mutation burden than smokers. Now, LP300's mechanism, which enhances the efficacy of chemotherapy while potentially protecting normal cells, addresses this major need for never smokers directly. Our strategic expansion to Japan and Taiwan, regions where 33% to 40% of non-small cell lung cancer cases occur, in never smokers compared to just 15% in the US positions us to accelerate enrollment this year and generate robust data sets with great power, statistical power. The geographic strategy strengthens our potential for compelling 2025 readouts. That could transform treatment protocols for this distinct and growing patient population. Additional patient data from the expansion cohort, which has randomized two patients to one in favor of our LP300 arm, continues to support, at the current time, a similar patient response and clinical benefit trend. Lantern plans on sharing additional results, which will include data from patients enrolled in Taiwan and Japan from the expansion cohort later this year, most likely during middle to late of Q2 of 2025. Now, the two FDA fast-track designations that we received for LP184 in glioblastoma and triple negative breast cancer, coupled with three additional rare pediatric disease designations, represent extraordinary regulatory validation of our approach. We're going after very precise cancers, precise data, and these designations aren't really administrative milestones. They're actually indications that we can expedite our clinical development timeline through enhanced FDA interactions and potential for priority review. From a technical standpoint, these designations were underpinned by the mechanistic elegance of LP184's synthetic lethality approach. its ability to exploit specific genomic vulnerabilities in cancer cells while sparing normal cells, particularly through its PTGR1-mediated bioactivation. This offers a precision that conventional therapies cannot match. The market potential across these indications exceeds $10 billion annually, addressing over 150,000-plus patients with limited therapeutic options across solid tumors. Our Phase Ia clinical trial for both LP184 and 284 has successfully progressed through multiple patient cohorts, systematically establishing safety profiles while advancing toward pharmacologically active dose levels. The methodological dose escalation strategy has been executed with efficiency, with no serious adverse events related to the drug candidates observed across multiple cohorts that have been executed. What distinguishes our synthetically full approach is its mechanistic precision. Unlike conventional chemotherapies that can indiscriminately target dividing cells or other alkylating agents, LP184 and 284 exploit specific genomic vulnerabilities in cancer cells, particularly those with deficiencies in DNA damage repair, or DNA damage repair pathways. The pharmacokinetic data from these trials suggest we're approaching concentration levels of the drug that correlate with the nanomolar potency observed in preclinical models. This is a critical inflection point that could demonstrate definitive proof of mechanism in patients and pave the way for future trials and partnerships. LP184 continued advancement through a phase 1A trial in multiple solid tumors, which is targeted to finish enrollment during this coming quarter. And we believe we're very close to the final set of cohorts. But now let us talk about the current phase one status. The phase one results so far for safety, tolerability, and pharmacokinetics, including the MTD determination. We're now on cohort 11 and have early indications of clinical activity that have been observed at these higher dose levels, consistent with the preliminary PK data that we have. Now, during Q4 of 2024, dose levels 7, 8, and 9 were cleared without safety concerns, and preliminary PK data suggests dose proportionality with exposure. Enrollment in dose level 9 and above, we made a concentrated effort to focus that on including patients with advanced solid tumor patients that actually have identified DNA damage repair mutations. A broader clinical data update is slated for Q2 2025 when recruitment for this phase is expected to be completed and we will have a safety and dose response data available to be shared. In terms of future planned phase 1b2 trials, we're already getting to planning the future because we see that we've already submitted a clinical trial protocol to the FDA for a phase 1b2 study in triple negative breast cancer, where we evaluate a combination regimen with the PARP inhibitor olaparib. In our preclinical work, we saw tremendous synergy between olaparib and our drug LP-184. What's again unique about the combination of PARP inhibitors and LP-184 is actually also very elegant. Now PARP inhibitors work by stopping the repair mechanism. So when there is DNA damage caused as a result of killing off cancer cells, PARP inhibitors stop the ability of the cancer cell to repair that. Our drug starts at a different point. It actually breaks apart the DNAs of the cancer cell. So they actually work in a wonderful mechanistic synergy, LP184 breaking apart the DNA, and then PARP inhibitors stopping it from any attempts at repair. So we believe the synergy that we saw in preclinical models that was driven by our AI platform and also validated in a number of studies done with our partners across a number of institutions has a very solid biological basis. So far, the FDA has raised no objections to the protocol and Lantern expects to initiate this trial in both the US and a leading academic center in Nigeria, subject to further funding and clinical priorities. Now in Nigeria, a lot of you may ask, why Nigeria? Nigeria actually has been a hotbed of triple negative breast cancer research and studies. In fact, Roche actually did a trial in TNBC there. But in Nigeria, TNBC occurs at a much higher rate than in many other parts of the world. And there is an active community that has done some wonderful research and epidemiological reviews of TNBC in Nigeria and Sub-Saharan Africa. We'll be working with one of the leading academic centers there. And also, bear in mind, the Nigerian Breast Cancer Study, which is published with the University of Chicago and with Harvard, has published that nearly 46% of breast cancer cases are triple negative breast cancer in Nigeria. And they present with mutations that many of the DNA repair genes, LP184 seems to be particularly attuned to. So this is very exciting. We believe that we'll be able to do our trial with a group of clinicians and experts who are really zeroed in on this disease. And we'll be able to get more rapid enrollment, which is critical. And again, it's critical for our highly efficient model. So we'll have sites in the U.S., but also sites in Nigeria. And we'll talk more about that in the coming weeks. Additionally, an investigator-led study of LP184, which I know many of you are excited to hear about, for recurrent bladder cancer. It's planned to start in Denmark. The clinical trial will test LP184 as a monotherapy, specifically in advanced bladder cancer with patients that have DNA damage repair mutations. Dr. Hele Papat at the Copenhagen University Hospitals, who focuses on prostate, bladder, and some renal cancers, will be the PI on this study. And she's very focused on DNA damage drugs and actually molecular profiling of bladder and prostate cancers. Now, based on work that we have done, but also on published research, about 25% to 30% of bladder cancers at presentation have DNA damage repair mutations, and about 40% at recurrence So again, we think this is a great population to study, great population to validate further the mechanism of this drug, and most importantly, patients that we think we can actually prolong and help their long-term survival. Another great opportunity for MD Anderson, came about with MD Anderson, is our collaboration with them revealed that LP184's remarkable ability to transform immunologically cold tumors into hot tumors. It's a breakthrough with profound implications for expanding immunotherapy benefits to previously unresponsive patients. This isn't merely additive efficacy. It represents a mechanistic synergy that addresses one of immunotherapy's most significant limitations. The technical details here have been fascinating. In fact, LP184 induces replication stress in tumor cells. This then triggers cytosolic DNA accumulation. This accumulation stimulates the immunogenic pathway. This leads to favorable remodeling of the tumor microenvironment, which we've shown in our publications. It reduces the immunosuppressive M2 macrophages and enhances the right kind of T cell functionality. In fact, it's a combination of those two factors, because we have seen T cell functionality change with other immunogenic or immunostimulant type environments, but also the M2 macrophage is fairly unique. So really in preclinical TNBC models, this combination enhanced the tumor growth inhibition from about 51% for our drug alone to over 72% when combined with anti-PD-1 therapy. And this is a therapeutic enhancement that was, again, in cold tumors. And we believe this could translate into meaningful survival benefit for patients with currently very limited options or patients that basically stop responding to PD-1 and PD-L1 therapies. This was publicly shared at a recent poster at the AACR Immuno-Oncology Conference in February and also at the IO Conference last year. IO Summit. This opens up significant opportunities for code development and new indication expansion where PD-1 and PD-L1 have stopped working. And again, this is a massive multi-billion dollar opportunity in a combination setting. Moving on to some of our very exciting new space is Starlight. Starlight is a company that wouldn't exist if it weren't for data and AI. And we unveiled a very unique, innovative trial design for STAR-001 at the Society for Neuro-Oncology 2024 meeting. It featured a unique combination of spironolactone. Now, this exemplifies the power of computational approaches and biomarker-driven approaches in identifying non-obvious therapeutic synergies. Spironolactone is not used in cancer. In fact, this approach exploits synthetic lethality in GBM through a mechanistically elegant interaction. Spironolactone degrades the protein ERCC3, a critical DNA repair protein. It creates a transient vulnerability that the drug STAR-001 then exploits. And so this transient ERCC3 degradation stops the cancer cell from being able to repair itself. And the way that these double-stranded breaks work is that ERCC and some other proteins are needed. And so it actually demonstrates that what we've seen preclinically, a three to six X increase in GBM cell sensitivity. That's pretty massive, three X to six X when you combine these agents. And actually, many of the tumor preclinical models in GBM and other brain cancers actually have shown complete tumor eradication with minimal recurrence. Now, this can be especially critical in very sensitive patients, such as children, the elderly, or those who have undergone multiple lines of prior therapy. Even more interesting is that STAR-001 has shown anti-tumor activity in GBM regardless of MGMT status. And some of you have seen some of that data that we've published in the past. So not only does Star001 have what we believe can be a great anchor molecule, but now through the use of data and biomarker-driven medicine, we've actually been able to now exploit the ability to look at and modulate ERCC3. So we actually increase the potential of this therapy. We make the therapeutic window much more attractive. Now, during Q4, we also started the inaugural scientific advisory board, which I'm very excited about. Dr. Mark Chamberlain and Dr. Kishore Bhatia both worked closely with myself to provide strategic guidance And we were very excited to establish a scientific advisory board that is joined by experts such as Drs. Mitch Berger at UCSF, Dr. Lisa DeAngelis at Memorial Sloan Kettering, and Dr. Stuart Grossman and John Letera at Johns Hopkins, all four of which have deep subject matter expertise, accomplished scientific experts, and leaders in neuro-oncology. In fact, two of them are actually lifetime achievement award winners at Society of Neuro-Oncology. And they're able to now help us shape the development and path for Star001. Remind you, Starlight is 100% owned by Lantern. We'll have the potential to this be a very positive impact on our investors as we monetize this unique asset, the patents, the insights, and its ability to work in certain brain cancers. The dosage and safety data in Phase 1 trial will be used to advance the indications for the 1B Phase 2 trial, which Lantern's wholly-owned subsidiary will sponsor. And we think the market potential for both this drug as STAR-001 and as LP184 will exceed $14 billion, consisting of about 4 plus billion in CNS cancers, both pediatric and adult, and about 9 to 10 billion for other solid tumors. So we believe this has the potential to be a blockbuster drug across a number of indications. And to support a lot of this, we actually were working quite a bit on trying to understand how do we predict the blood-brain barrier permeability And our team did a fantastic job at our patent-pending blood-brain barrier permeability predictive algorithm. It represents what we believe is a computational breakthrough of exceptional significance. With five of the top 11 rankings in the Therapeutic Data Commons leaderboard and the ability now to be a very high-performing algorithm, we can do maybe 100,000 molecules an hour. That translated can mean a million molecules or more in a workday. So we've developed an AI system that outperforms industry standards in terms of accuracy and throughput for a CNS drug therapeutic development. This will also be, in fact, one of the first agentic AIs that we make publicly available for drug developers. So we're going to open this up and partner this with precision medicine groups to help guide their development and also potentially for therapy selection in patients. So we're in fairly advanced discussions now with a number of institutions and organizations a brain tumor group to actually use this algorithm as part of their work. Now, this technological advantage has profound implications for accelerating CNS drug discovery, a notoriously challenging domain where over 98% of small molecules fail to effectively penetrate the blood-brain barrier. And where some of the traditional algorithms have been kind of in the two-thirds to mid-70s, maybe, percent accuracy, now we're seeing a whole new generation of algorithms, including ours, which have taken that up into the low to high 90s. And so this unprecedented accuracy allows us to identify promising CNS penetrant compounds with extraordinary efficiency. Now, again, I also mentioned ours is also high performing. So we've taken some very unique engineering steps to actually decrease the amount of time required. And the computational capability doesn't merely enhance our existing programs. It actually opens up entirely new therapeutic possibilities across multiple neurological indications for not only us, but also for other drug development teams. Now, our AI-powered antibody drug conjugate development module also represents a fundamental reinvention of traditionally resource-intensive high-risk development process. Our AI module for ADC development identified 82 very promising targets and over 290 target indication combinations. And many of these are actually validated because some of them are already in preclinical and clinical trials. So this is one of oncology's most rapidly growing therapeutic modalities. And the technical implications for this ADC module for using AI is pretty substantial. Traditional ADC development requires a lot of iterative testing of antibodies, nanobodies, any kind of, maybe by specific, and then the linkers and various payloads, and a process that can take years and millions or maybe even tens of millions of dollars, just in early stage work. Our computational approach reduces these timelines, we believe, by a third to half and preclinical costs by even more than half, while simultaneously enhancing the target selection process. So this efficiency advantage positions us to rapidly advance multiple ADC candidates with exceptional selectivity profiles and potential for superior therapeutic windows and enables us to allow others to take advantage of this AI. Now, this will be one of the many AI modules we place into what we call an agentic framework, which is really the the kind of the vanguard of AI work today. And once we put into an agentic framework, we can allow it to be used by collaborators and partners. And I'll talk more about this later in today's call. The RADAR platform expansion beyond 100 billion oncology specific data points represents a computational resource of unprecedented scale and specificity in precision oncology. The vast repository of molecular, clinical, pharmacological data enables increasingly sophisticated analysis that traditional approaches simply cannot match, but very importantly, don't have the underlying data and curation already that we've done. Now, the technical sophistication of radar enables multidimensional analysis that identify non-obvious relationships between genomic features, drug responses, and potential combination strategies. This capability has directly enabled our biomarker discovery initiatives, including PTGR1 signature, mechanisms underlying synergistic combinations, such as checkpoint inhibitors or spironolactone with 184, or even rituximab with 284, And as we continue to refine the methodologies and feed data from studies back into the platform, radar evolves from just an analytical platform to a predictive engine capable of identifying promising therapeutic approaches with unprecedented efficiency and precision, and ultimately in the next generation with its own level of automation. So through the integration of advanced AI, computational biology, and precision medicine approaches, we're systematically addressing some of oncology's most challenging domains with an unprecedented level of efficiency and scientific rigor. Our burn rate is a fraction of that of other companies, yet our advancements across multiple molecules, putting them into patients and advancing the platform is something I'm quite excited about. Financially, we closed the year with 24 million in cash, cash equivalents and marketable securities, which I believe will give us runway to execute in our business this year and take our programs to inflection points with data and outcomes. David Margrave, our CFO, will discuss this in more detail in a moment. Our continued execution across these clinical trials and with our precision oncology programs positions us for multiple value trading milestones throughout this year and with the potential to deliver transformative therapies for patients with limited treatment options. Now, I'll turn the call over to David Margrave. We'll talk about our financials and other key metrics. David?

Thank you, Pana. And good afternoon, everyone. I'll now share some financial highlights from our fourth quarter and full year ended December 31, 2024. I'll start with a review of the fourth quarter. Our general and administrative expenses were approximately $1.6 million for the fourth quarter of 2024, up from approximately $1.3 million in the prior year period. R&D expenses were approximately $4.3 million for the fourth quarter of 2024, up from approximately $3.6 million in the fourth quarter of 2023. We recorded a net loss of approximately $5.9 million for the fourth quarter of 2024, or 54 cents per share, compared to a net loss of approximately $4.2 million, or 39 cents per share, for the fourth quarter of 2023. For the full year 2024, our R&D expenses were approximately $16.1 million, up from approximately $11.9 million for 2023. This increase was primarily attributable to increases in research studies of approximately $2.95 million relating to the conduct and support of our clinical trials. as well as increases in research and development payroll expenses of approximately $897,000 and increases in consulting expenses of approximately $376,000. Our general and administrative expenses for 2024 were approximately $6.1 million, up slightly from approximately $6 million for 2023. The increase was primarily attributable to increases in other professional fees. Our R&D expenses continue to exceed our G&A expenses by a strong margin, reflecting our focus on advancing our product candidates and pipeline. Net loss for the full year 2024 was approximately $20.8 million, or $1.93 per share, compared to approximately $16 million, or $1.47 per share, for 2023. Our loss from operations in the 2024 calendar year was partially offset by interest income and other income net, totaling approximately $1.4 million. Our cash position, which includes cash equivalents and marketable securities, was approximately $24 million as of December 31, 2024. Based on our currently anticipated expenditures and capital commitments, We believe that our existing cash, cash equivalents and marketable securities as of December 31, 2024 will enable us to fund our operating expenses and capital expenditure requirements for at least 12 months from today's date. We expect that we will need substantial additional funding in the near future. And one of our key objectives for the remainder of 2025 will be to pursue additional funding opportunities. As of December 31, 2024, we had 10,784,725 shares of common stock outstanding, outstanding warrants to purchase 70,000 shares, and outstanding options to purchase 1,245,694 shares. These warrants and options combined with our outstanding shares of common stock Give us a total fully diluted shares outstanding of approximately 12.1 million shares as of December 31, 2024. Our team continues to be very productive under a hybrid operating model. We currently have 24 employees focused primarily on leading and advancing our research and drug development efforts. And I'll now turn the call back over to Pana for an update on some of our development programs. Pana?

Thank you, David. So our leadership and the use, innovative use of AI and machine learning, in many ways AI for good, to transform costs and timelines in the development of precision oncology therapies has allowed us to have a pretty exciting pipeline. It's allowed us to bring three molecules to market with teams, cost and efficiency that continues to make massive year over year improvements. And we have LP300 in phase two. Again, we plan on having another readout during the second quarter. We've accelerated enrollment because of our expansion into Japan and Taiwan. Specifically there, the disease occurs in never smokers at about a two to X to three X higher rate. So this is particularly important because we'll also use that to leverage the phase two data to look at partnerships, perhaps geographic partnerships as well, which we've already begun having conversations with. Our phase one trials for LP184 and LP284, both really potent synthetic lethal agents, one for solid tumors, has advanced to over 50 patients. We expect to enroll about 60 patients. So we're getting very close to what we believe will be the completion of the trial. LP284, slightly different dosing schedule, but similar cohort structure, is a few months behind. And we think we'll be able to have that 30 patient enrolled later this year. So all three trials will have data. But very importantly, we also expect to have great ideas on how to pinpoint the use of these molecules in specific therapeutic areas. This is why we have over 11 orphan, rare pediatric and fast track designations. It's very important to note. So for a small company, we have 12 designations across 11 different programs, ATRT having both orphan and rare pediatric. So that's really almost, we have for every head count, we almost have a half a designation. And in fact, it's the only molecule that I know of that actually has four designations for a rare pediatric. So pinpointing how a molecule will work is really one of the most challenging things. And so this is really about not only understanding your molecule, but also actually knowing where and how to use it. So we've achieved this in a very, very short period of time. Remember, LP284 did not even exist when we raised money to go public. LP184 wasn't in the clinic. LP300 wasn't. was just beginning to peel the onion in terms of its mechanistic potential. So during 2024, we achieved our goal of reaching 100 billion data points, growing that cancer-focused data more in one year than we had in the prior three years. And more of this data growth and data ingestion campaigns will be automated, freeing up our team to focus on intelligent curation and analysis of data, and also on creating upstream engineered data sets to solve more specific problems. And these problems, we think, can start making use of certain types of generative AI, AI that'll transform our analytic capabilities to actually autonomous agents. So today, I'd like to share with you our vision for the next evolution of our radar platform, a future where agentic AI and autonomous intelligence dramatically accelerates our ability to transform oncology drug development, not only at Lantern, but also for other drug companies. At Lantern, we've consistently demonstrated how our proprietary platform has revolutionized our approach, but we believe also traditional oncology drug development paradigms. And as we've shared in our previous quarters, our AI-guided approach has enabled us to advance all these candidates into the clinic at a fraction of the traditional cost and actually have more pinpointed and more precise trials. It takes us an average of about two to two and a half million per program from scratch to get it into a trial, whereas the industry standard is somewhere in the range of 10 to 15 million. Now we're entering a transformative phase where we're going to start leveraging agentic AI capabilities. So autonomous systems capable of making complex decisions, analyzing intricate biological data sets, and executing sophisticated workflows without constant human supervision. So our enhanced radar platform will feature autonomous intelligence and will modularize these into agents. And these agents will continuously monitor and integrate real-time data from relevant biomarker and cancer studies and publications, enabling dynamic protocol insight that can be used in real trials and to make precision medicine decisions. They'll autonomously identify potential combination regimens by analyzing billions of unique molecular interactions across multiple therapeutic modalities, similar to our recent significant insights on 184 and checkpoint inhibitors that demonstrate a transformation of an immunologically cold tumor into a hot tumor, but with a totally different level of scale. Imagine being able to do thousands of these molecules in a week. And it'll also deploy advanced reinforcement learning algorithms that will optimize lead compound selection or elucidate target characterization across for antibody drug conjugate development or peptide or drug-drug conjugate development. And again, we've already identified 82 promising targets and over 290 target indications, many of which are already validated in the clinic from other companies. Now, this next generation of our platform represents a fundamental shift in drug development methodology, moving from human limited analytics and reactive to proactive, continuously self-learning systems capable of identifying non-obvious patterns and opportunities and benchmarking those across multiple therapeutic dimensions. So for us, though, it is we have certain dimensions, specifically in oncology or specific therapies in neuro-oncology. But while our current platform has already proven exceptional with over 100 billion data points oncology focused, by deploying agentic architecture and interfaces on top of very specific modules, we will have the potential to create systems that reduce key development decision timelines and compress complex data gathering analytics, creating unprecedented efficiency advantages. rapid biomarker identification and validation, in our case, PTGR1 and others, autonomous design and optimization of combination regimens, instantaneous evaluation in molecular libraries, The financial implications for this are pretty substantial, potentially reducing preclinical development costs by 60, 70, and 80%, while simultaneously increasing successful transition rates in early development and perhaps later development phases. So we're strategically positioning AriGentic architecture with radar platform to not only drive our internal pipeline, but also as a valuable collaborative asset for biopharma partners seeking to overcome drug development bottlenecks. We've had very successful collaborations with Oregon Therapeutics and Actuate Therapeutics, both collaborations where we offer targeted radar modules for these partners. And we believe we'll generate some near-term commercial traction as a result of that. We anticipate launching our first agentic AI around the blood-brain barrier permeability prediction algorithm. That's now being commercialized as a module that will be publicly upcoming, and it'll leverage our unprecedented performance metrics and also have the algorithm hopefully guide actual treatment decisions being made in a number of trials. Additionally, our ADC development module, which has already demonstrated capabilities as compared to traditional approaches, will also become more broadly available later this year, and along with another project that we'll be publicly facing probably in early summer called Project Zeta. Now, all three of these will be leveraging agentic architectures, wildly very different, but they'll put into the public face the ability to actually start thinking about drug development at a level of scale and data access that's usually unheard of. So the golden age of AI in medicine isn't just beginnings. It's accelerating exponentially. By integrating agentic capabilities, we believe our AI radar will transform from an analytical platform to a true technology development partner, one that is awake 24 hours a day, one that's capable of operating continuously at a scale that's unprecedented across multiple research dimensions and constantly grows, connecting insights across previously siloed areas of cancer biology and ultimately helping us deliver life-changing therapies to patients faster. We aren't just building better tools, we're actually fundamentally re-imagining what's possible in precision oncology. And as we continue this journey, our agentic radar platform positions us at the forefront of an entirely new paradigm in drug development, one in which AI doesn't merely assist human researchers, but actively participates alongside through autonomous continuous learning and insights that can be tested and recursed back into the system and hopefully deployed into the clinic faster. So this golden age is actually accelerating and it's being driven by large scale, highly available computing power, incredibly massive data storage, and also great people. At the end of the day, you have to have great imaginations and wonderfully dedicated people to be able to deliver this ultimately for patients and to improve human life. And so we're at levels of quality and data that have never been imagined before. Companies that harness these capabilities are really the future of the tech bio industry, and I believe will become long-term leaders that create massive value for patients and investors. And we think, of course, industries go through their cycles and ups and downs, but I've never been more bullish on the potential for AI to really transform and change outcomes for patients, but also it'll make our medicines faster, cheaper, and with increased precision. I think it'll help us change the direction of R&D productivity and output in the pharma industry. So we believe our approach is the future of developing cancer therapies where data can be used to accelerate programs, de-risk identification, identify combinations and patient populations faster, and get life-changing medicines into actual trials. So I want to express my deep gratitude to our team, our partners, our stakeholders for their unwavering support, and especially to our clinical trial sites and to the patients participating in our trials. I think together we're lighting a way towards a brighter future in oncology and solving real-world problems. that enable rapid development of precision therapies that can alter the cost and timelines in drug discovery, and very importantly, place Lantern at the forefront of a new era of unprecedented insights. Now with that, I'd like to now open the call to any questions or clarifications, but also as we do so, I'd like to take a quick moment to thank our team for helping us to prepare for these calls and to prepare for our quarterly filings. So again, let's go ahead and take questions from our audience. I ask you to do so in one of two ways. You can type your question directly into the QA tool, or you can click on raise the hand and speak directly, and we'll try to unmute your line. Thank you. So you've got the first question. I'll repeat the question before I answer it. Thank you, John, for your question. The question is from John, is how is the pace and quality of enrollment in Asia compared to the U.S.? It is about 2 to 4x faster. They got ramped up faster. Some sites are slower than others, but in terms of output just in this past year, few months, we saw an equal amount of output from Asia as we saw in the US. But of course, their timeline from onboarding to first patient was phenomenally faster. And it's just accelerating. So I think it'll be 3 to 4x faster ultimately this year because of Asia. Great question. Next question is from John, also. In the ADC realm, and with help from radar, what are the opportunities for ADCs that substitute the toxic payload with another immunotherapy? So with an amino, well, it depends on what kind of immunotherapy, whether it's a modulating agent or a binding. I think doing an antibody contact conjugate is potentially challenging. Um, but if you do it with a small molecule, that's an immunomodulating agent, like an IL agent or others, I think, yes, that's possible. You're going to start seeing many of those. You're going to see the, one of the things that we're actually looking at. It's a great question, John is actually, um, things that have multiple payloads, so more than one payload. So that's actually very exciting. It's a space that probably, you know, it's not on our plate right now, but I do think that design of multi-payload and bispecifics with multi-payloads is definitely going to be in the future. You may have payloads that are both immunomodulating and also immunomodulating. So I think you're going to see a lot of innovation. Now, the challenge is, of course, always then testing those, because right now one of the most expensive points in testing ADCs is testing them in the non-human primates. So how you test in non-human primates for some of these more complex architectures that are being imagined will be something that we got to sort out. But yeah, theoretically, that's definitely doable. It requires a level of precision biology and data collection that is just beginning to happen. So that's perfect area for AI. It's a wonderful question. I'm going to turn it over to Chad. Chad?

Yeah. Hi, Pana. Thank you. Just wondering for the harmonic update in the later this year that we expect to get, if you could just set the stage for, you know, where you guys think you're going to be, how much data you think you're going to have, what we should look for in that update?

Your question was on harmonic data, correct? Yeah. So we've enrolled a nice chunk of patients in Asia and also in the U.S. in the last few months. So we are continuing to seeing the same kind of trend in terms of the clinical benefit. I think we hope to have a nice chunk of patients that will have multiple. scans in terms of resist criteria. So I think sometime in mid to late Q2, we'll have the next readout. But the key one will come at 30 events. So if we have 30 events, that will probably be closer to the end of the year. And that'll be an important time because then we'll be able to decide, do we take this into a larger trial? And also we'll give confidence that we'll have enough data to partner out the asset. But we'll probably do something more near-term to kind of showcase that the trends that we saw in the early cohort are continuing in the existing cohort, which has included a lot of patients from Japan and Taiwan.

Okay. And then if I may, just a follow-up in a different direction on your ADC programs. What should we be looking for next? Obviously... Yeah, there'll be two things.

We've talked about that. We made a conscious effort on this call not to focus on it because we wanted to focus more on the clinical assets and... some of the other AI features, but we've got some exciting preclinical data that we're validating. We put out some data last year in terms of HER2 low and HER2 medium, but definitely HER2 low expressing cancers where we saw tremendous potency, several fold higher than existing FDA approved agents, um, with our cryptophycin linked, um, ADC that we've designed. We also have another one that's in the, um, Allude in NASA full theme family that we're working on some very exciting new payloads that are super, super potent, you know, 100 to 500 times more potent than LP184. And we have some targets in mind. So we'll have more preclinical data as the year progresses. And we also will announce a couple of partnerships with groups that are using our ADC AI platform as an analytical tool. So those are the two things to expect.

Thank you.

Got a great question from Clay Heighton. So Clay asked a question about providing results in LP184 in Q4, and then it was pushed. When will you provide results? That's a great question on the 184 data, Clay. So the 184 data originally was expected in Q4 because we expected to see MTD around dose level 9 or 10. What's mostly changed is that the enrollment has gone to higher dose levels. And so that's basically added to the time. So the calculations for PK and availability of the drug seem to end up more like rats than dogs. So our thought was, you know, we'll probably end up somewhere in between, but we're definitely much more like rats in terms of the amount of drug that humans can take. It's actually a good thing because we're seeing a higher therapeutic rate Sorry, a higher likelihood of having therapeutic doses at these higher cohorts, these double digit cohorts. We're now in cohort 11, 12. And so each cohort takes about a month. And so that's exactly why we see that. So nothing other than the. The dose levels have gone higher and we haven't seen any significant serious adverse events. And we're now just beginning to see therapeutic levels of efficacy. So that's added to the time. Hopefully that answers your question. Next question. Yeah. is on the dose in cohorts 11 and 12. I believe the dose is 0.61, right, MG? I believe it's 0.61. I will have to, I'll get back to that. Let me write that down. I'm gonna have to look that up on my board, but I believe it's 0.61 MGs per kg. But let's find that out right now. While we look that up, I'm going to take another question from anonymous attendee. When will likely we see STAR for pediatric? Wonderful question. We're working very closely with the Poetic Consortium. We're very close to getting a protocol that everyone can agree to for pediatric brain cancers. Dr. Mark Chamberlain and Sandra are leading up the efforts to interact with the Poetic Consortium. I think We will probably see that mid to late this year. So we do have a protocol that seems to have enough people around the table and we'll be able to then exploit the rare pediatric disease designations and hopefully march towards getting our drug to patients. And part of that also is to have a protocol clear signal in adult gliomas. So we think those two factors will be easily checkmarked. And so we'll then launch into pediatric. Of course, all subject to the right approvals. Next question is, from Luca. Luca, thank you very much for your question. I'll answer it. It says, what is missing to sign deals with other firms to discover new drugs? Yeah, great question, Luca. We constantly look for deals. I think if there are deals out there, I think we'd love to do it. I think partly is it does take a lot of financial, but really actual people resources. If you want to do this for others, they're going to pay you on an hourly or as a target amount. And so as a small company, bear in mind, our scientists and data engineers are somewhat limited. And so we have focused on our own pipeline. But yeah, we'd love to focus more on other people's pipeline, long as they're willing to pay us for it. I don't think our shareholders want us to do a lot of work unless we get either equity in the drug or and get reimbursed significantly. So I think, you know, we're happy to have discussions. So, yeah, thank you. Great question. I mean, I think if there are there are definitely conversations we have, they usually tend to break down quickly. really around, you know, are they willing to give us enough equity in the molecule or enough upside to make it worth our while for us to stop working on our programs? But again, you know, one of the things that we're doing now is using agentic AI architecture to take some of these more simple initial analytic modules and put them out to the public. So that's something that we plan on doing with three or four of these modules, the blood-brain barrier module, the ADC module or aspects of the ADC module, some of the modules around differential gene expression and transcriptomic analysis, and a very exciting project codenamed Zeta that we'll be talking more about in the next 45 to 60 days. Thank you. Great question from Michael Mantagas. Michael asked the question, have we reached out to Amazon? Yeah, we've had a lot of discussions with Amazon. Unfortunately, probably not at the right levels, but we've done a lot of education of Amazon about how big pharma needs are very different from drug developer kind of needs. And they're very good at kind of thinking about data storage and making data available. But the problems that we solve tend to be more computation rather than compute intensive, rather than necessarily just data intensive and data storage intensive. But yeah, I think... Groups like Amazon, like NVIDIA are beginning to understand the potential this has. But again, we're looking for people who would love to help us have those conversations with big tech. And part of our goal in making the agentic AI architectures publicly available is to drive those conversations. Thank you for that question. So again, please raise your hand if you have a question. We can put you live like we did with Chad, or please enter into the chat window. I think we have a question on the dose levels.

So that was from just responding to Clay.

Yeah. So do you want me to do it?

Yeah. Clay, I think you'd ask a question about the dose levels for 184. And the current dose level, 12, is 0.61 milligrams per kilogram. So that's where we are now.

Hopefully that answers your question. You can raise your hand, right? And we're at what percentage dose level we're increasing from dose level? Is it 25%? Yeah, I think we're at a 25% level. So I think it was 150 and 33 or 20. I think we're at 25% increase. Okay. Well, I would love to answer any other questions as they come in. Again, we think we're well positioned for the year. We've got multiple readouts. We believe we're getting very close to some of the final cohorts for both 184 and approaching 284 later this year. We'll have data at least once, maybe twice for 300. We think, you know, once as we get the next big chunk of data from the current subjects that have been enrolled. And we'll also have And in that update, we'll also have updates from the initial lead-in cohort. So we'll have some exciting data report on those initial patients where we saw the 86% clinical benefit rate as well. So that'll be coming more near term. And then the larger report on 300 probably later in the year as we get 30 events. So thank you, everyone. And I look forward to talking with many of you in upcoming meetings or one-on-ones. And thank you for your time today. And thank you to the Lantern team as well. Thanks very much.