Prepared remarks
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 market opened today, summarizing our financial results and progress across the company for the second quarter ended 06/30/2026. 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 2000. 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-Ks for the year ended 12/31/2025, which is on file with the SEC and available on our website. Forward-looking statements made on this conference call are as of today, August 14, 2026, and Lantern Pharma does not intend to update any of these forward-looking statements to reflect events or 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 R. Margrave.
Panna will start things off with 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 will open the call for Q&A. I would now like to turn the call over to Panna Sharma, President and CEO of Lantern Pharma. Panna, please go ahead.
Good morning, everyone, and thank you for joining us to discuss our second quarter 2026 results. As I have said before, AI and computationally driven approaches are now becoming central to how both large and emerging biopharma companies discover and develop drugs, and also how they allocate their resources and think about staffing their scientific teams. Today, we are at an inflection point that is accelerating not just for Lantern, but for how science itself will be conducted. We are watching it happen in real trials with real patients at Lantern. The golden age of artificial intelligence in medicine is not beginning; it is actually accelerating. This quarter, that idea resulted in the development of a new company, Open Medicine AI. In August, we established Open Medicine AI as a separate company with commercial licenses and agreements with Lantern in place to take the AI data models to the next level.
I will spend some time on that today because I think it is the most consequential structural decision we have made since starting Lantern. But let me first walk you through what got us here. A clinical signal that sharpened into a defined patient population, a signal that was validated using big data, a European regulatory clearance in a challenging recurrent cancer, and an allowed patent on a patient selection method—one of our most valuable assets, LP-184—and an FDA-cleared trial in triple negative breast cancer that is moving toward launch. All of these were backed by numerous observations in our trials, the LP-300 trial, the LP-184 trial, and even the LP-284 trial. Those observations were that mechanistic insights gained during our preclinical work actually have real-world parallels and could be the basis for meaningful activity in actual cancer patients. The remainder of 2026 is a defining year for Lantern Pharma, especially as we launch into 2027.
We have achieved clinical validation across multiple programs while establishing the foundation for our next phase of growth in both of our engines: our drug development engine and our AI engine. In addition, our mid-year financial results reflect highly disciplined execution with a 25% reduction in total operating expenses year over year, even as we advanced multiple clinical programs through key inflection points and launched an entirely new company in one of the most promising and disruptive areas of AI in medicine. Our AI-driven clinical pipeline now encompasses multiple drug candidates across solid tumors, blood cancers, and pediatric oncology, with a combined annual market potential estimated at over $15 billion. Let's start with our Phase 2 program, LP-300, and the HARMONIC trial in never-smokers with non-small cell lung cancer who progress after TKI therapy. We believe there are about 400,000 to 500,000 patients diagnosed globally each year who have no specific therapy aimed at never-smokers who progress after TKI.
In Asia, it is about 35% to 40% plus of non-small cell lung cancer cases. In the U.S. and Europe, it is between 15% and 20%. In June, we reported emerging data as of the May 11 cutoff that shows something we did not expect to see this clearly: the benefit of LP-300 deepens the longer patients stay on it. Among L858R patients who completed six cycles, median progression-free survival reached 8.9 months—nine patients, three of whom had not progressed at the analysis. Across the full cohort of L858R patients, median PFS was 8.4 months. The hazard ratio for that group was 0.37 with a confidence interval of 0.15 to 0.89, which is more than a 60% reduction in risk. Also, more than 70% of the L858R patients saw target lesion reduction and some of the responses sustained beyond two years. We have had a 77% clinical benefit rate, which is phenomenal for that line of therapy. I will be direct: these are small exploratory cohorts not powered for statistical significance yet, and a median from nine patients can move up or down.
What makes us take it very seriously is that a Cox regression controlling for race, gender, and TP53 status confirmed L858R as an independent predictor. This is not a demographic or statistical artifact. Safety was comparable between four and six cycles with no added toxicity from longer exposure. So, a drug that helps more the longer you stay on it without costing you more in side effects is a drug worth extending, especially where there is no other great therapy for these patients. That is the science and the data behind what we did next. We had a successful Type C meeting where no objections were raised to our key proposed amendments. We have concentrated the enrollment now on the L858R patients. These patients tended to do worse on current therapy regimens, which is why we also think there is a great need. We have extended the treatment from six to up to eight cycles, and we have moved into a single-arm design, which should be more efficient and less costly.
The trial continues enrolling in the U.S. and Taiwan, and we have used this dataset and other observations about the future of the program in active partnering discussions. A little about LP-184 this quarter: we made several advancements, all of which were driven by data and AI-leveraged methodologies. First, the EMA clearance: in July, we got clearance for an investigator-initiated Phase 1b/2 trial in advanced bladder cancer at Rigshospitalet in Copenhagen, Denmark's national referral center for urologic cancers. This will be led by Professor Roerberg and Dr. Pappot as coordinating investigators. This will be a 39-patient trial, uniquely using a dual biomarker strategy—one on PTGR1 overexpression and combining that with DNA damage repair deficiency—and we are hoping to enroll patients our platform has predicted should respond and, more importantly, have a mechanistic basis to be helped by the drug.
The second major milestone is the LP-184 monotherapy relapsed or refractory triple negative breast cancer trial. That will be a Phase 1b/2 trial. That protocol has been FDA-cleared and is now moving toward launch with a number of sites. We have also applied for grants for that study, which we are excited about. This drug targets tumors with DNA damage repair alterations, homologous recombination deficiency. We will enroll 40 patients across two dose cohorts, followed by a Simon two-stage efficacy read. Third, and very important, we received a notice of allowance in July covering our three-gene selection. We used three genes—PTGR1, PTPN14, and ASPH—for selection of patients most likely to respond to LP-184. We were issued a notice of allowance in four tumors: ovarian, liver, kidney, and thyroid cancer. That is a patent on the selection logic itself, which is one of the hardest parts of this to replicate, and then map that directly to a credible therapeutic intervention where safety is known and mechanism is beginning to be more observable.
This all built on our 63-patient trial that we did with LP-184, and now that we have a dose of 0.39 mg/kg, what we saw in that trial is tumor reduction in patients carrying DNA repair deficiency genes: CHEK2, ATM, BRCA1, STK11, and KEAP1. Those alterations conferred exceptional sensitivity to the drug. Unlike conventional chemotherapies and other DNA damaging agents that indiscriminately target dividing cells, both LP-184 and LP-284 exploit specific genomic vulnerabilities in cancer cells. That precision is the thread that runs parallel through both programs and which we expect to give our programs a meaningful advantage in development. LP-284 continues in hematologic malignancies and in adult soft tissue sarcomas, where we received orphan designation earlier this year. On STARLIGHT and the science: STAR-001 is LP-184 in brain cancers. Our RADAR platform identified that those particular brain tumors would be sensitive if ERCC3 was removed as a protein because it is involved in the repair mechanism.
We characterized this with our collaborators at Johns Hopkins, and we are using spironolactone, which is already well characterized and safe in pediatric and adult patients; it degrades ERCC3 and shuts down the repair route. We have had great preclinical data, and now we are taking that into the clinic and into disease designations where we have orphan and rare pediatric designations such as ATRT, hepatoblastoma, rhabdomyosarcoma, and malignant rhabdoid tumors. Bear in mind that each of these is independently eligible for a priority review voucher upon approval; those have recently transferred for $150 million to $200 million or more, and Lantern holds four of those. On the pediatric program specifically, we are actively working with several pediatric oncology consortia to determine the best and most expedient path to bring these into a trial as soon as possible. We have two consortia we are working with and will have more data in the coming quarter.
We are also working closely to enable compassionate use for the drug, especially in some of these rare pediatric brain tumors where there is exceptional need. STARLIGHT is 100% owned by Lantern. We expect to raise additional funding for it as a separate entity; it holds its own INDs now and its own regulatory designations. It is not just a program status; it is a way to monetize it independently of the rest of Lantern, and more importantly, it is a template. We are about to use that same template again, this time with the underlying platform itself. Now, going back to Open Medicine, and this is, we believe, the structural news of the quarter: in August, we formally established Open Medicine AI, OMAI, as a separate company, executed our board-approved commercial licensing agreements, and, more importantly, OMAI can now operate the multi-agentic AI co-scientist we launched as RADR with Zeta and use it in the commercial setting.
Here's the logic: most people using AI for drug development today ask one model a question and get an answer. We now see that things are moving well beyond a single line of questioning. So we built an orchestrated system that brings together specialized agents for literature synthesis, medicinal chemistry, pathway analysis, data curation, portfolio prioritization, clinical trial development, and they challenge each other, pass information, and cross-validate before delivering hardened results or asking scientists or drug developers to get more engaged. This multi-agentic, non-monolithic model is the standard infrastructure for specialized, multidisciplinary domains, and we believe it will be the standard for drug discovery. The computational biology and computational chemistry models that run deep, and their own large quantitative models, are critical. More importantly, they can generate publication-quality results with a full audit trail.
As the platform gets smarter and more users use it and data flows through it, each engagement feeds the next. This dynamic deserves its own capital structure. Clinical drug development and enterprise software are priced by different investors and metrics; a software business held inside a clinical-stage oncology company may or may not get the credit it is worth because investors who price AI and software generally do not own clinical-stage biotech, and vice versa. That is the entire rationale for separating and advancing Open Medicine AI. Open Medicine AI is 100% owned by Lantern today. It intends to raise capital at its own level in exchange for Open Medicine equity, with the longer-term objective of becoming a separately listed company. Lantern expects to remain one of its largest shareholders. Lantern continues to retain rights and full access to the platform for our own drugs, and this changes nothing about those programs' priority or timing.
We believe the market is much larger than just early oncology companies like ourselves. Analysts project the market to reach about $10 billion by 2030–2031, with oncology as one of its largest segments. In our bottoms-up analysis on companies and drug discovery technology that is AI-enabled, we expect it to reach $9 to $10+ billion by 2031. We will host a dedicated informational call in mid-September on Open Medicine AI's market opportunity, platform roadmap, and commercial model. Putting all this together—a clinically validated platform with three drugs in trials, a commercially accessible AI platform and software company with models and state-of-the-art tools, and a drug pipeline that all feed each other—you get a business model that extends well beyond just the clinical assets. We think it is a very powerful complement to have both of these engines: an AI engine that can be separated and power dozens of companies and drug assets targeting meaningful, challenging, rare, and aggressive diseases.
The AI tools and services can grow to several hundred million dollars in standalone value as part of this larger $10 billion market. We think a nice chunk of that market will be agentic in nature, and Open Medicine has a real chance to capture a significant piece. These are two great growth engines in the company, and I will let David talk a little bit about our financials, our key metrics, and also dig into the details behind the noncash expenses related to warrants that drive a higher net operating loss than what is actually underneath the hood. So, David, I will turn it over to you.
Thank you, Panna, and good morning, everyone. I will now share some financial highlights from our second quarter ended 06/30/2026. Before getting into the details of the quarter, I want to note that this quarter was different from prior quarters because we had a substantial noncash expense related to the issuance of warrants in connection with our May financing transaction and the way those warrants are treated for accounting purposes. I will discuss this topic in detail later in my discussion. Cash, cash equivalents, and marketable securities were approximately $7.4 million at June 30, 2026, consisting of approximately $6.7 million in cash and cash equivalents and approximately $700 thousand in marketable securities. This compares to approximately $10.1 million in cash, cash equivalents, and marketable securities as of 12/31/2025. Funding received during the second quarter of 2026 consisted of approximately $4.4 million in gross proceeds from our registered direct offering that closed on 05/14/2026.
Additional funding is a top priority and we intend to pursue additional capital raises, collaborations, and other opportunities to extend our operating runway. R&D expenses were approximately $1.8 million for the three months ended 06/30/2026, compared to approximately $3.1 million for the three months ended 06/30/2025, a decrease of approximately $1.3 million or 42%. The decrease was primarily attributable to reductions of approximately $1 million in research studies and materials expenses relating to the conduct of our clinical trials, and decreases of approximately $300 thousand in salaries and benefits expenses. G&A expenses were approximately $1.7 million for the three months ended 06/30/2026 compared to approximately $1.6 million for the three months ended 06/30/2025, an increase of approximately $130 thousand or 8%. The increase was primarily attributable to increases in business development and investor relations expenses of approximately $360 thousand and salaries and benefits expense increases of approximately $140 thousand, offset in part by decreases in other professional fees of approximately $350 thousand.
Loss from operations was approximately $3.5 million for the three months ended 06/30/2026, compared to a loss from operations of approximately $4.7 million for the three months ended 06/30/2025, representing a decrease of approximately 25%. In connection with our May 2026 registered direct offering, in which we raised approximately $4.4 million in gross proceeds, the company issued investor warrants to purchase up to 2.14 million shares of common stock at an exercise price of $2.27 per share and placement agent warrants to purchase up to 107 thousand shares of common stock at an exercise price of $2.575 per share. These warrants are accounted for as liabilities due to a settlement feature that may be triggered in the event of a fundamental transaction. During the three months ended 06/30/2026, the company recorded an aggregate of approximately $3.6 million of expense related to these warrants.
The main component of this was noncash expense arising from an increase in the fair value of the warrants that was driven primarily by a substantial increase in the company's stock price between the 05/14/2026 warrant issuance date and 06/30/2026. Other components related to warrant expense were loss on issuance of the warrants and warrant issuance costs. After including the noncash and other items related to warrants, our net loss was approximately $7.1 million, or $0.57 per share, for the three months ended 06/30/2026, compared to a net loss of approximately $4.3 million, or $0.40 per share, for the three months ended 06/30/2025. For the six months ended 06/30/2026, our net loss was approximately $10.4 million, or $0.88 per share, compared to a net loss of approximately $8.9 million, or $0.82 per share, for the six months ended 06/30/2025. From a capitalization standpoint, as of 06/30/2026, the company had 12.8 million shares of common stock outstanding.
As we described, in May 2026, we closed a registered direct offering and concurrent private placement comprising 1.45 million shares of common stock, pre-funded warrants to purchase up to 682 thousand shares of common stock, investor warrants to purchase up to 2.14 million shares of common stock at an exercise price of $2.27 per share, and placement agent warrants to purchase up to 107 thousand shares of common stock at an exercise price of $2.575 per share. There was no activity under our ATM sales facility during the three months ended 06/30/2026. I will now turn the call back over to Panna for additional updates on our programs and operations. Panna?
Thank you, David. Two closing points. First, the number I want all of you to remember is that we advanced programs from AI-derived insights to first-in-human clinical trials in a timeline under three years—roughly two to three years—at approximately $2 million to $3 million each. The industry norm to reach that same point is five to ten years at $25 million to $100 million. Three molecules in clinical trials, dosed to over 100 patients, and at the same time, we have been able to advance an AI platform that is launching commercially. Those numbers are not a marketing claim; it is our operating model and a key part of our core advantage. Secondly, what we now have structurally that we did not have just in April is a lung cancer trial refined around a specific patient population—L858R mutations. We have European clearance for a dual biomarker trial to be led by investigators in Denmark in a challenging recurrent bladder cancer setting.
We have an FDA-cleared second trial in triple negative breast cancer, post-PARP refractory patients, moving toward launch, and an AI and software company with executed licenses, multiple engineering centers, and a growing user base. As David walked you through, we actually did all that while our loss from operations was down approximately 25% year over year. We did all of this while continuing to advance both engines of growth. We believe that is a really important and smart way to build, and that is the argument for continuing to operate this way. We are not just building better tools; we are reimagining what is possible in precision oncology and building the tools to support it. We believe this will be the standard for the rest of the industry and the platform is positioned to scale. I want to thank our team, our investigators, and our shareholders as we light our way through Precision Oncology Solutions.
We expect to have a lot of great additional results over the coming quarters. I want to especially thank a long-time member of our team who is moving on to a new leadership opportunity in media and technology after five years with us—five years of building this company's brand, voice, communications, and being an amazing colleague. Thank you very much. With that, I would like to now open the call to questions.
Questions and answers
You can type your question using the Q&A tool or raise your hand and we will try to unmute your line and repeat your question. Any questions with the remaining time that we have? I am going to go to the Q&A. Hey, Michael. You should be unmuted.
Can you hear me? Yep. Good morning. Two questions, Panna. One on LP-300 and then the other on OMAI. Just on LP-300, can you talk about where you are in the data analysis? It is nice to see the PFS stretching out a little bit more, but how mature is this dataset? Will it mature further? When do you plan to update us again? And then the next question related to that is, now that you got the protocol amendment in place, have any patients been enrolled under the new protocol?
All right. Let's go. Once we had sufficient confidence that the protocol would be amended and the data was trending that way, we wanted to get the new IRBs approved at all the sites; that has been done now. We expect enrollment to resume under the new eight-cycle regimen, which we think will extend durability and potentially deepen response. So we expect to be enrolling patients in Taiwan and the U.S. under the new amended protocol. We hope to enroll another 15 to 16 patients that will give us meaningful data, and we expect to enroll those over the next four to six months in both the U.S. and Taiwan. That is the initial focus.
Will there be any other updates coming on the current cohort?
We might have an update toward the end of the year. Other than extending PFS, you are really relying on the next batch of patients coming in to see what kind of responses we continue getting.
Okay. Very good. Thanks for that update. And then just on Open Medicine, can you talk about—most of us from a therapeutics background are not AI experts; much of the technology looks like a black box. Maybe you can help us understand what your system looks like or how it compares to other tools out there that pharma is taking advantage of.
I am working on something for our mid-September webinar, but the AI cycle in drug development is evolving and we are on roughly the fourth cycle. If you go back to the early days of supercomputing and molecular modeling, it was infrastructure heavy and compute limited. We are almost at the opposite end now, with almost limitless compute resources and light infrastructure installs. There are two waves in between. We did not have the capability to get the real-time transparency you would want until after an algorithm ran, and often those algorithms would take days or weekends. Now those can be done in seconds, so you can see the process in real time. We also did not have the software and tools to do large-scale algorithm mapping and analysis because it was extra overhead. Now we have the ability to do that, so transparency is more commonplace and expected. Large-scale AI providers have made some levels of transparency into how systems operate more expected, and we stand on those shoulders.
We can do things differently: once you see this transparency, you as an enterprise user can tweak and alter it in ways that were not possible before. That did not exist earlier. I expect people who provide professional knowledge labor and the existing installed base of software providers into pharma to be most affected. The days of charging high fees for very specific functionality of an installed base are changing. You will not need to hire large teams of bioinformaticians and data analytics people; you can do much of the work in the cloud with a smaller smart engineering or data science team and launch swarms of agents doing this work for you. That is what we have proven with Open Medicine. I think that is the future and where leading-edge providers are moving. People will expect greater transparency and democratization: being able to go to a URL or an app and start your inquiry. That is exactly where I see Open Medicine playing: a new category that has not been fully valued or priced yet. I am writing a piece you will see by mid-September called 'The Deflation of Discovery and the Birth of a New Category,' which specifically talks to agentic AI in drug development and discovery.
Another question. I will take—sorry.
Someone's asking about interest from large pharma in RADR with Zeta. The quick answer is yes. We have had interest from many pharma companies—both biologics groups and small-molecule groups. Several have had calls with us and some have visited. Large pharma is evaluating these tools and cutting deals on them. Large pharma will have to partner with agentic AI to make it commonplace. It is transforming the economics of early development and also late-stage development. One thing we've seen is that once we put the tool in front of people, it gets very sticky.
Take another important question. Let's see if we can do this one live. We are trying to do some live. I think Beau Parsons, you should be on live.
I can read it also if you do not want to do it live. Another question: as our models become standards in biology and drug development, what are we doing to ensure that competitors do not simply copy our methods? First of all, everyone will copy one another to some degree. Part of spinning Open Medicine out separately is to allow it to move faster, further, and have its own independent balance sheet to ensure we stay one or two steps ahead. More capital helps, but more capital does not guarantee survival. Capital efficiency is important long term, which we have proven to be capital efficient at. We are continuing to train our models and grow intelligently using our engineering center in Bangalore, India. We constantly benchmark our tools and pick some of the toughest challenges to go deep rather than broad. That deep focus in specific categories—rare cancers, bio-computational tools, problems like blood-brain barrier penetration—allows us to resolve specific issues really well. We will go after certain diseases that require depth and then march forward. But yes, more capital is needed to drive that.
Let's go ahead and get to the next question. Let's go to Baird and Redchip team. Maybe we can answer that one live. Baird and Redchip team, if you guys want to ask your question live, they cannot ask their question live.
You have to read the question. Oh, okay. All right. Adoption and feedback: adoption is very sticky. As I said before, once we get it in front of users, we are taking measures to make sure users get the benefit of the full platform. We have introduced a new code called with Zeta 14 that people can sign up for and get the full professional edition. People who apply for the professional edition—especially generative chemistry, bio-computational tools, and investigator mode—tend to be very sticky. That is exciting news. Key is getting them to that point. We are beginning to implement more aggressive email campaigns to drive awareness and specialized codes for certain larger pharma companies.
Okay.
What would be the biggest benefit of the Open Medicine spinout for shareholders?
Lantern owns 100% of Open Medicine today. We think it is poised to be very disruptive, and disruptive companies can be valued higher. Open Medicine will raise capital and Lantern will likely continue being one of its largest shareholders. We may explore ways to distribute underlying shares to all Lantern shareholders; those are things we are discussing and looking at the most efficient ways to do that. I expect Lantern shareholders to continue being beneficiaries of that asset as we monetize it in private financings and potentially if it goes public on an exchange. We are coming up on about 45 minutes into the call. We look forward to answering questions in one-on-ones as this continues. I know we have requests for follow-up meetings and we will take those. Thank you to Lantern investors and everyone interested. I look forward to giving you more updates as the year continues. Thank you, and thank you again to our team.