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Presentation

Lantern Pharma Inc. beats earnings expectations. Reported EPS is $-0.42, expectations were $-0.49.

Operator: Good morning, and welcome to our First Quarter 2025 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. We issued a press release before the market opened today, summarizing our financial results and progress across the company for the first quarter ended March 31, 2025. A copy of this release is available through our website at lanternpharma.com, where you will also find a link to the slides management will 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, May 15, 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’s CEO, Panna Sharma, and CFO, David Margrave. Panna 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 Panna, and then we’ll open the call for Q&A. I’d now like to turn the call over to Panna Sharma, President and CEO of Lantern Pharma. Panna, please go ahead.

Panna Sharma: Good morning. Hello, everyone. Thank you for joining us to hear about our first quarter 2025 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. Lantern’s leadership in the innovative, efficient, and pragmatic 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. The first quarter of 2025 represents a pivotal inflection point for Lantern Pharma.

We’ve made significant advancements across our clinical stage portfolio, while simultaneously expanding the capabilities of our proprietary radar AI platform to over 200 billion oncology focused data points. These achievements position us well for multiple value creating catalysts in the coming quarters. Let me organize today’s remarks around three strategic pillars. First, our clinical pipeline progress. Second, our AI platform advancements. And third, our initiatives to maximize shareholder value. Starting with our clinical pipeline, we continue to advance multiple programs that have the potential to address significant unmet patient needs for cancer patients globally. Our Phase 1a trial for LP-184 has progressed well with enrollment now through cohort 12.

We expect to complete enrollment with 62 to 65 patients across a wide range of solid tumors by the June 2025. Importantly, we’re beginning to see early indications of clinical activity at higher dose levels, which aligns with our preliminary pharmacokinetic data. This quarter, our Safety Review Committee made the decision to backfill doses level 10 and 11 to ensure clarity on determining the maximum tolerated dose while maintaining patient safety. What distinguishes our synthetic lethal approach is its mechanistic precision. Unlike conventional chemotherapies and targeted agents that indiscriminately target dividing cells, LP-184 and LP-284 exploit specific genomic vulnerabilities in cancer cells, particularly those deficiencies in DNA damage repair pathways.

The pharmacokinetic data from these trials suggest we’re approaching concentration levels 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. With LP-184 now holding dual fast track designations for both glioblastoma and triple-negative breast cancer, plus four rare pediatric disease designations. We’ve positioned this molecule for accelerated development across multiple high value meaningful indications. The FDA has also recently cleared two clinical trial protocols that can provide paths toward regulatory approvals, especially, in triple0negative breast cancer where we also have a fast track designation.

The first of these two protocols that has been cleared recently is a Phase 1b/2 study in TNBC evaluating LP-184, both as monotherapy and in combination with the PARP inhibitor, Olaparib. With an estimated annual market potential exceeding 4 billion in metastatic TNBC, this represents a major significant opportunity. The second, a Phase 1b/2 study in a biomarker defined subset of drug resistant, non-small cell lung cancer with STK11 and/or KEAP1 mutations, a patient population with particularly poor prognosis, and a market opportunity exceeding 2, billion annually. Additionally, an investigator led exploratory clinical trial for LP-184 and recurrent bladder cancer is planned to begin in Denmark during Q3 2025, which could create a pathway toward commercial clinical usage in the third-line setting.

Based on work we have done with Dana-Farber and The Danish Cancer Research Group and in other published research, about 25% to 30% of bladder cancers have DNA damage repair mutations at presentation and over 40% at recurrence. Now turning to our HARMONIC Phase 2 trial for LP-300, we continue to make strong progress with enrollment in Japan and Taiwan, where never smokers represent about 33% to 40% of new non-small cell lung cancer cases compared to about 15% to 17% in the U.S. Following our compelling preclinical — following our compelling preliminary data showing an 86% clinical benefit rate and 43% objective response rate in the safety lead in cohort, additional patient data from the expansion cohort continues to support a similar positive trend.

We look forward to sharing updated results, including data from patients in our Asian expansion cohort during Q3 and data from the ongoing benefits from our initial lead in cohort. Through our wholly-owned subsidiary, Starlight Therapeutics, we’re advancing STAR-001 for indications in CNS and brain cancers. Recently, our collaborators at Johns Hopkins have provided independent confirmation of hypersensitivity in rare pediatric brain tumors to LP-184 supporting our planned clinical trial with the pediatric consortium focused on CNS tumors. A Phase 1b/2 trial in recurrent GBM is anticipated to begin in late 2025, subject to successful additional protocol clearance and funding. Also, bear in mind that LP-184 has multiple pediatric disease designations that, upon approval in that indication, can yield a priority review voucher, which can then be marketed and sold for $100 million to $150 million each.

And Lantern and Starlight have the potential and pathway for four of those opportunities. Starlight, which is a 100% owned by Lantern, will have the potential to be another very positive impact on our investors as we monetize this unique asset, the patents, and the clinical indications and insight. The dosage and safety data obtained in the Phase 1 trial for LP-184 will be used to advance the central nervous system indications as STAR-001 for future Phase 1b and Phase 2 trial sponsored by Lantern’s wholly-owned subsidiary, Starlight Therapeutics. Globally, the annual market potential for LP-184’s target indications is estimated to be about 14 billion consisting of 4 billion to 5 billion for CNS cancers, both primary and secondary, and about 10 billion for other solid tumors.

Turning now to our second pillar, which is our AI platform. Let’s talk about the expansion and commercialization now of our radar AI platform. This quarter, our proprietary radar platform grew to approximately 200 billion oncology focused data points. The platform continues to deliver value across multiple dimensions from drug candidate optimization and developing combination strategies, to biomarker signature development and mechanism of action clarification. We’ve made an important and exciting decision to open up the radar AI platform on a module by module basis to the broader scientific and research community. We expect to initially do this as a freemium type approach, which will be expected to drive collaborations and economics to Lantern.

The large scale and highly inexpensive evolution of RAG (ph) and agentic technologies has completely changed the ability for small emerging companies like Lantern to use cloud infrastructure to open up algorithms and unique processes to a broader community at a scale, cost, and level of complexity unimaginable in the past. A milestone this past first quarter was a strengthening of our AI intellectual property portfolio with the PCT publication of our proprietary blood brain barrier penetration prediction patent application. This technology received a favorable PCT search report indicating no significant prior art, and our algorithms currently hold five of the top 10 positions on the Therapeutic Commons leaderboard, a remarkable achievement demonstrating our leadership in AI drug development.

This will be one of the first modules that we make publicly available in the coming quarters. Our BBB permeability prediction tool can process up to a hundred thousand molecules per day with industry leading accuracy, and the algorithm continues to evolve and improve. This technological advantage has profound implications for accelerating CNS drug discovery and the ability to predict in a domain that’s been notoriously challenging. But 98% of small molecules historically have failed to effectively penetrate the blood brain barrier, and our algorithm’s unprecedented accuracy enables us to identify promising CNS penetrant compounds and also optimize existing compounds with extraordinary efficiency, potentially reducing traditional discovery timelines by months while dramatically increasing success probabilities.

This computational capability doesn’t merely enhance our existing programs. It opens up entirely new therapeutic development possibilities across not only cancer but other neurological indications for many other drug development teams. We’re particularly excited about our plans to make this and other radar AI modules commercially available to the scientific and research community this year. This represents a new potential revenue stream and opportunity to foster collaborative open source innovation in cancer drug development. We’ve also expanded radar with an innovative AI powered module to improve the precision, constant timeline of antibody drug conjugate development. This multiomic approach leverages proprietary algorithms to design and optimize target selection, payload efficiency, and tumor selectivity, addressing a rapidly growing segment of the oncology market that has been notoriously difficult and very time consuming.

Our AI powered antibody drug conjugate development module represents a fundamental reinvention of a traditionally resource intensive high risk development process. By identifying promising targets and target indication combinations, we’ve established a robust pipeline of opportunities in one of oncology’s most rapidly growing therapeutic modalities. The technical implications for this are substantial. Iterative testing of antibodies, linkers, and payloads, which can take years and consume tens of millions of dollars can be narrowed down, streamlined, and derisked. Our computational approach, we believe can reduce these timelines by 30% to 50% and preclinical cost by up to two-thirds while simultaneously enhancing target selection and understanding of real world target availability in an involved cancer environment.

This efficiency advantage positions us to rapidly advance our own candidates with exceptional selectivity profiles, but also to enable other companies to take advantage of this. This module will also be one of the many modules we place into an agentic interface and framework for use by our collaborators and partners. We’ll talk about this more later this quarter and probably host a specific call talking about the evolution of our AI platform to a more public facing commercial opportunity. AI and platform driven insights continue to guide our clinical development strategy. For LP-184, we’ve also developed a qPCR assay for PTGR1, which as we know is the bioactivation agent for LP-184. And by measuring PTGR1 levels, we can help guide patient stratification and also at the same time identify indications that may be very promising.

For LP-284, we’ve also used our platform to identify promising combination strategies. For example, the rituximab, which have shown compelling preclinical synergy. Moving on to our third strategic pillar. To maximize shareholder value through — we’ve done this now through disciplined capital management and a number of strategic initiatives. We’ve maintained our disciplined approach to capital deployment, ending the quarter with approximately $19.7 million in cash, cash equivalents and marketable securities, providing an expected operating runway through at least middle of May next year. Our quarterly net loss decreased to approximately $4.5 million compared to $5.4 million in the same period last year, reflecting our continued focus on operational efficiency.

Want to bear in mind that the company’s last capital raise was in January of 2021. So we’ve maintained tremendous fiscal discipline in getting our molecules into clinical trials, into meaningful inflection points, and executing on our dual strategy of advancing clinical programs while expanding vastly our AI platform capabilities. And now we’re going to enter into — we believe productive discussions with potential biopharma partners, whether through licensing agreements, technology partnerships, or co-development. Now I’ll turn the call over to our CFO, David Margrave, who will provide more details on the financial results for the quarter.

David Margrave: Thank you, Panna, and good morning, everyone. I’ll now share some financial highlights from our first quarter 2025 ended March 31, 2025. Our general and administrative expenses were approximately $1.51 million for the first quarter of 2025 compared to approximately $1.48 million in the prior year period. R&D expenses were approximately $3.3 million for the first quarter of 2025, down from approximately $4.3 million in the first quarter of 2024. The decrease was primarily due to reductions in CRO and clinical site costs for LP-184, which also reflected our objective to accomplish more with our internal clinical operations team. We recorded a net loss of approximately $4.5 million for the first quarter of 2025 or $0.42 per share compared to a net loss of approximately $5.4 million or $0.51 per share for the first quarter of 2024.

Our cash position, which includes cash equivalents and marketable securities was approximately $19.7 million as of March 31, 2025. Based on our currently anticipated expenditures and capital commitments, we believe that our existing cash, cash equivalents, and marketable securities as of March 31, 2025 will enable us to fund our operating expenses and capital expenditure requirements for at least 12 months from today’s date, May 15, so until at least mid-May 2026. We will need additional funding in the near future, and one of our key objectives is to pursue additional funding opportunities. As of March 31, 2025, we had 10,784,725 shares of common stock outstanding, outstanding warrants to purchase 70,000 shares, and outstanding options to purchase 1,242,378 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 March 31, 2025. Our team continues to be very productive under our hybrid operating model. We currently have 23 employees focused primarily on leading and advancing our research and drug development efforts. I’ll now turn the call back over to Panna for additional updates and closing remarks. Panna?

Panna Sharma: Thank you. Thank you, David. Our leadership in the innovative use of AI and machine learning to transform costs and timelines in the development of precision oncology therapies has allowed us to bring three important molecules to market with teams, costs, and efficiency that is only beginning to make massive year-over-year improvements. During the first part of 2025, we achieved our goal of reaching nearly 200 billion data points, growing that cancer focused data more in six months 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, analysis of the data, and creating upstream engineered solutions, and frameworks to solve specific problems that can then be transformed into autonomous agents.

Now we’re entering a transformative phase where radar will be — will leverage agentic AI capabilities, autonomous systems, capable of making complex decisions, automating intricate biological datasets, and executing sophisticated workflows without constant super human — super human supervision. This next generation platform represents a fundamental shift in drug development methodology, moving from reactive, human limited analytics to proactive, continuously learning systems, capable of identifying non-obvious patterns and opportunities across multiple therapeutic dimensions simultaneously. We’re strategically positioning our agentic radar platform not only to drive internal pipeline growth, but also as a valuable collaborative asset for biopharma partners, seeking to overcome drug development bottlenecks.

The golden age of AI in medicine, as many of you have heard me say in the past, isn’t just beginning. It’s accelerating exponentially. By integrating agentic capabilities, radar will transform from an analytical platform to a true development partner, one capable of operating continuously across multiple dimensions, connecting insights across previously siloed areas, and ultimately delivering — helping to deliver life changing therapies to patients faster than ever thought possible. The speed will also drive reduced costs. We aren’t just building better tools. We’re fundamentally reimagining what’s possible in precision oncology. As we continue this journey, our agentic radar platform positions us at the forefront of an entirely new paradigm in drug development, one where AI doesn’t really assist human researchers but actively drives discovery forward through autonomous continuous learning and insights that can be tested in laboratories and then deployed safely into the clinic for patients.

As we advance through 2025, we at Lantern are laser focused on the following key value creating milestones. First, completing our LP-184 Phase 1a trial enrollment in June with comprehensive data rate readouts after that, including biomarker correlations, potentially establishing proof of mechanism for our synthetic lethal approach, and setting up pivotal future trials. This is an opportunity that we believe represents over $10 billion in annual spend that LP-184 is well poised to take a great share of. Second, delivering expanded Harmonic Trial results that include our Asian expansion cohort, further validating our never smoker non-small cell lung cancer thesis for LP-300. We expect this to occur also in Q3 in July. Third, initiating our FDA cleared Phase 1b/2 trials for LP-184 in both TNBC and a biomarker defined subset of non-small cell lung cancer, which is drug resistant, and we believe we can leverage our fast track status to accelerate development and potentially partner in those trials and those indications with large pharma companies.

Fourth, commercialize our initial modules from radar to the scientific community, beginning with our industry leading BBB permeability prediction tool and then moving on to other modules on a select basis. Fifth, strategically advancing partnership discussions that could accelerate our pipeline, whether they’d be through geographic rights for certain assets or co-development rights in certain indications or spinning out assets such as our CNS and Starlight focus capability or monetizing our AI platform capabilities. This quarter’s progress, while maintaining fiscal discipline and a focus on bringing our assets closer to patients and approval reinforces what makes Lantern unique in the oncology landscape. We’re not just developing drugs. We’re pioneering a fundamental transformation in how cancer therapies are discovered, developed, and delivered to patients using AI for an approach that is both efficient and focused.

Our dual engine approach, clinical assets plus an AI platform, provides shareholders with multiple value creation paths. Each clinical advance demonstrates our AI platform’s power, while every platform enhancement accelerates our pipeline and creates new partnership opportunities. As agentic AI capabilities emerge in our radar platform, we’re not merely participating in this AI revolution in drug discovery. We’re helping to build it. I want to express my sincere gratitude to our exceptional team, partners, and shareholders. Together, we’re lighting a path in a way toward precision oncology solutions that we believe can fundamentally improve outcomes for patients while transforming the economics and timeline of cancer drug development. With that, I’d like to now open the call to any questions or clarifications.

A – Panna Sharma: If you’d like to ask a question, you can do so in one of two ways. You can type your question using the QA tool, or you can click on the raise hand tool to speak directly, and we will unmute your line. Okay. I think Chad has his hand raised. Okay. I think we’ve got two hands raised.

Unidentified Participant: Can you hear me now?

Panna Sharma: Yeah. No. Sorry for the delay there.

Unidentified Participant: All right. Good. I’ll start up. I had a couple of questions. The first on making AI modules, commercially available. It sounds like the blood brain barrier penetration module might be one of the lead candidates there. It’s a very interesting, sounding module. What are the sort of broader plans to roll this out? Are we going to charge a fee for access to these? Are we going to make some free and hope that, people kind of get hooked and really like these modules and it leads to broader collaborations? And then I guess also besides just money when other people start using these modules, of course, they will have data they want to put in there, and that, of course, could benefit the platform overall. So just, do you intend to aggregate additional data and strengthen the platform that way? What are the plans there?

Panna Sharma: Yes. Great questions. I do think we’re going to start with a freemium type approach to get people used to getting questions answered this — using this method. The challenge that we’ve seen with a lot of the existing AI tools out there answering some of these questions is just, they’re slow. They’re not scalable. You can’t count on the quality of the data. So I think that we’re going to take an approach initially where the tool is kind of a freemium model with a drive towards collaboration so that we can continue to monitor closely the type of data and use that the research community has. We have a road map that we’ll be discussing, probably toward the end of this quarter or early next quarter on what that road map is and also some of the business models underlying, bringing radar into kind of an agentic life form module by module.

Of course, we’ll pick the easier modules that we think can be readily scaled and then go into the more complex, workflow enabling modules over time. But bear in mind, we are primarily focused on advancing our pipeline at this time, and our goal is to introduce these modules to drive a larger tech partnership.

Unidentified Participant: Yeah. That makes a lot of sense. That’s just sort of folds into your business model and approach well. And then just on the HARMONIC trial, very excited to be getting another data update there. You referred to the Asian patients as a cohort at least once. I just I want to make sure I understand the design here. Is that cohort, like, are we still enrolling more patients in the U.S.? I guess, is one question.

Panna Sharma: So, yeah, maybe not technically a cohort. So what we did is when we started the LP-300 trial, we obviously knew the numbers in East Asia. But as you know, for a small U.S. biopharma company, it’s expensive and costly and introduces management risk to do trials, in Japan and which is also very expensive. So our goal was to make sure we got a quality signal in a population that we could have ready access to. So the lead in cohort was the seven patients in the U.S., and six out of seven of them responded, which to us was fantastic. And the initial objective response rate was very positive. And we had one patient over a year on the drug with, 57% tumor volume reduction. So by almost all measures, it was positive and also, the underlying population was also pretty mixed.

We had Hispanic. We had white. We had some Asian. It was more male than female. So — and we also had multiple TKIs, right, not just EGFR. So we looked at that. We said we had a good heterogeneous population. We had a 86% clinical benefit rate, 43% objective response rate, a nice set of tumors that had about 50% reduction. It gave us confidence that now we can go ahead and spend time and money and energy on expanding to where there was a higher — a bigger amount of patients. Now that — after that seven, it goes into what is called the expansion cohort. The expansion cohort will be both U.S. and Asian, but the expansion cohort is randomized. So it’s two to one randomization. So it’s not the Asian cohort. It’s just the Asian patients as part of the expansion cohort, if that makes sense.

Unidentified Participant: Yes. Okay. Appreciate that. Appreciate that clarification. Thank you.

Panna Sharma: And for us, it’s important because I don’t think it would have made a lot of sense to spend all that money getting set up and operating and getting all the things done in Asia unless we were certain that, hey, this is going to head in the right direction.

Unidentified Participant: All right. Thank you.

Panna Sharma: Thank you, Chad. I think John has a question. John, do you want to? John? I think, John, for some reason, we cannot hear you. Okay. We have one more question coming in on LP-184. Yeah. So LP-184, we expect the trial to be fully enrolled next month. This is now a 60 to 65 patient trial. I believe we’ll be — we’re almost concluded with now — we’re over mid-50s, high-50s, and we believe that enrollment will be completed next month. And, we’ll have preliminary data after we start getting the clinical data, the biomarker correlations, etc., shortly after that. Next question is on FDA and using AI. I — that’s a great question. I want to believe that the FDA, will definitely have to use AI in its evaluation of scientific literature, data, and perhaps better come through mechanistic inputs from companies on the evaluation of the safety and direction of their new molecules.

So I think, yes, I think they will do it. I think they’ll do it in pretty quick scale over the next 12 months. I think it’ll help to bring down some of the costs of the FDA and hopefully speed things up. But as the — as John pointed out, it does introduce some risks. I can’t say, I’m an expert on all those risks, but I think it’ll ultimately, I think the trade-off is going to be improvements in cost and improvements in speed. In terms of the risk, I do think that, there’ll probably be a period in which they evaluate these methods in parallel to the existing methods, and I don’t think they’re going to roll out anything across the board until they’re have concluded, like, six months to one year of these efforts. So I think I would give this at least two years.

By that time, the risks will be well known and pointed out, obviously, by the industry and probably easily addressed. Next question is about new funds in AI. That’s exactly one of the reasons why we also have decided to go directly to market on opening up these modules. I’ve seen a lot of the AI work that’s being offered by many AI first companies in drug development, and they lack some of the precision or focus or they have a lot of noise. But AI funds are very aggressively looking at AI, which I think will help our long term profile and also attract new investors into the company or into our efforts. Thank you for that question.

Panna Sharma: Well, no further questions at this time. We’re always open to having discussions with investors and shareholders. I’d like to thank members of our team for helping us prepare for this call, and I look forward to talking with all of you in the near future. Thank you.

David Margrave: Thanks a lot.