Lantern Pharma Inc. (NASDAQ:LTRN) Q3 2025 Earnings Call Transcript

Lantern Pharma Inc. (NASDAQ:LTRN) Q3 2025 Earnings Call Transcript November 13, 2025

Operator: Good morning, and welcome to our third quarter 2025 earnings call. As a reminder, this call is being recorded. 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 third quarter ended September 30, 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, November 13, 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, everyone, and thank thank you for joining us to hear about our third 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 for patients as our industry matures and adopts an AI-centric data-first approach to drug development. This past quarter has been transformative in many respects for Lantern Pharma, a quarter where we have met many clinical, regulatory and validation milestones.

Q&A Session

And we have also significantly advanced the commercial availability and launch of our AI modules. The third 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 AI platform, RADR. And we’ve also set up the future of our CNS-focused subsidiary, Starlight Therapeutics. These achievements position us well for multiple value-creating catalysts in the coming quarters and years. Let me share with you some of the more notable achievements this past quarter. Let me start with what I believe is our most significant milestone to date clinically. Our LP-184 Phase Ia clinical trial successfully achieved all primary endpoints, demonstrating a 48% clinical benefit rate in evaluable cancer patients who received doses at or above the therapeutic threshold.

What’s particularly exciting is that we observed marked tumor reductions in patients harboring DNA damage repair mutations, specifically in CHK2, ATM, and STK11/KEAP1 genes. This validates our AI-driven precision medicine approach and the hypothesis of synthetic lethality and DNA damage repair that guided this program from the start. On the regulatory front, we completed a productive FDA Type C meeting for our subsidiary, Starlight Therapeutics, a company is focused entirely on CNS cancers. The agency provided clear guidance and pathway clarity for our planned pediatric CNS cancer trial targeting an ultra-rare brain cancer, ATRT. Importantly, the FDA confirmed our strategy to combine LP-184, which we will call STAR-001 in this indication with spironolactone based on our preclinical synergy data.

We also made important progress across our broader pipeline. Preliminary Phase II data from our LP-300 HARMONIC trial were presented at the 66th Annual Meeting of the Japan Lung Cancer Society. We’re planning a more comprehensive data update via webinar this December. For LP-284, our non-Hodgkin’s lymphoma program, we showcased clinical data at the 25th Annual Lymphoma, Leukemia and Myeloma Congress. The presentation generated interest from both biopharma companies and clinical investigators, and we’ve initiated several discussions around combination therapy opportunities. Building on the Phase Ia results from LP-184, we’re now positioned to advance LP-184 into multiple targeted Phase Ib, Phase II trials. Our precision biomarker-driven strategy will focus on 4 high-value indications, triple-negative breast cancer, non-small cell lung cancer with KEAP1 or STK mutations, bladder cancer with DNA repair deficiencies and first recurrent GBM.

Collectively, these indications represent a combined annual market potential exceeding $7 billion. To provide additional insight into the LP-184 data and our development plans, we’re hosting a KOL-led scientific webinar on November 20 at 4:30 Eastern. Dr. Igor Astsaturov from Fox Chase Cancer Center will join us to discuss the clinical results and what they mean for the future of this program. Beyond our clinical programs, we demonstrated the commercial readiness of our RADR AI platform at the inaugural AI for Biology and Medicine Symposium. We showcased several platform modules as deployable, highly scalable web accessible AI tools that can be licensed to biopharma partners and research centers. It’s an important step in our strategy to monetize the technology that powers our drug discovery efforts.

Finally, I want to emphasize our continued commitment to disciplined capital management. As of September 30, we had approximately $12.4 million in cash, cash equivalents and marketable securities. Based on our current operating plans, we expect this provides runway into approximately the third quarter of 2026. Before we turn to the financials, let me provide some color and details around our programs, both our drug programs and our growing program of AI modules, which we believe have the market potential of several hundred million on their own as AI tools and services. First, some context on the Phase Ia trial. This is a first-in-human study that enrolled 63 patients, a fairly large number given that we started at a very low dose and escalated upwards.

This was in advanced solid tumors who had exhausted all standard treatment options, which is fairly normal for Phase I studies. These are heavily pretreated cancers, oftentimes in very difficult to treat tumors. The trial, which you can find on clinicaltrials.gov as NCT05933265, successfully met all of its primary endpoints. The headline number that I want you to focus on is this. We observed clinical benefit with 48% of evaluable patients who were treated at or above the therapeutic dose threshold. In a Phase Ia trial in heavily pretreated patients with advanced disease, that’s a unique and promising signal of activity. But what’s even more compelling is where we saw that activity. The data validated our core hypothesis about synthetic lethality.

Patients whose tumors harbor specific DNA damage repair mutations, particularly in CHEK2, ATM and also STK/KEAP1 and actually also BRCA showed marked tumor reductions. This is what exactly what our RADR platform predicted well before starting this trial. And seeing it play out in actual patients is tremendously validating, but also very uplifting for our team where we can see how AI is being used for good and having a real-world impact on improving and changing outcomes. For us, this also gives us a very clear safety standpoint. LP-184 demonstrated a favorable profile with minimal dose-limiting toxicities. This is critical because it gives us flexibility. We can now pursue both monotherapy approaches and combinations with agents that we have identified as synergistic such as PARP inhibitors and immunotherapy, also spironolactone.

Both — these all have been predicted through our AI platform, again, as I note, before the trials even began. Let me give you a few clinical examples that really illustrate the potential here. In recurrent GBM, one of the most aggressive in treatment-resistant cancers, 2 out of 16 patients showed disease stabilization despite prior exposure to multiple therapies such as TMZ, lomustine and radiation. In GBM, as you will learn during our webinar on the 20th, we have the flexibility to modulate and enhance the efficacy of LP-184 by a factor of 3 to 6x, a potentially game-changing improvement. Even more encouraging, 2 patients at a dose level 10 have now maintained disease control for over 8 months and remain on treatment today. This is much more durable than has been expected for most Phase I studies.

We also saw durable clinical benefit in other notoriously difficult tumor types, gastrointestinal stromal tumors and thymic carcinoma. These aren’t common cancers, but they’re devastating when they occur and options are extremely limited. Our work in these rare cancers has also encouraged us to double down on our desire to transform the world of rare cancers and develop an open access tool for rare cancer drug development, codenamed withZeta, which I’ll talk about a little later this morning. Transitioning to clinical expansion. So the obvious question is this, what do we do with these results? And this is where our AI-driven development strategy really shines and demonstrates its value. Rather than pursuing a traditional broad Phase II basket type trial, we’re taking a precision medicine approach.

We’re positioning to launch in 4 targeted Phase Ib, Phase II trials. Each one focused on a specific biomarker-defined patient population, where LP-184 has the highest probability of success and the best synergy agent for that particular tumor indication. One of these trials in Denmark in recurrent advanced bladder cancer is an investigator-led study. We have made this molecule into a portfolio of opportunities using data and precision oncology approaches. So let me walk through these quickly. The first one is in triple-negative breast cancer. It’s our largest market opportunity, almost $4 billion. We’re pursuing 2 parallel approaches, one in monotherapy with DNA repair gene mutations and a combination study with a PARP inhibitor, olaparib, specifically in BRCA-mutated patients.

We’ve already received FDA Fast Track designation, which will expedite our development time line. We expect to enroll approximately 60 patients across both arms upon full enrollment. Second, non-small cell lung cancer with KEAP1 or STK11 mutations. This is a genetically defined subset of lung cancer who typically have very poor responses to immunotherapy. We’re combining LP-184 with nivolumab and ipilimumab, 2 checkpoint inhibitors in patients with low PD-L1 expression. This represents, we believe, just in the U.S., close to $2 billion and probably closer to $3-plus billion globally. Again, we have an FDA Fast Track designation submission in process, and this trial will enroll approximately 34 patients. Third, an investigator-led trial in bladder cancer, recurrent advanced bladder cancer.

This is being led by Dr. Pappot at Rigshospitalet in Denmark. It’s focused on patients with advanced urothelial carcinoma who have specific markers indicating DNA repair deficiency. This represents, we believe, about a $500 million-plus global market opportunity, and we expect to enroll about 39 patients. Finally, first recurrent GBM, which we’re pursuing through Starlight Therapeutics. Here, we’re combining LP-184, which we will call STAR-001 and CNS indications with spironolactone. This combination showed synergistic activity in our preclinical models. We have both FDA Fast Track and Orphan Drug Designation for this indication. This trial will use a Simon 2-stage design with 2 separate arms based on IDH mutation status. We expect to enroll about 38 to 40 patients and represents what we believe is about $1 billion in U.S. market and probably closer to $2 billion globally.

When you add up these indications, they represent a combined market opportunity exceeding $7 billion. And critically, each trial is designed with biomarker-driven enrollment criteria that increase our probability of success. In fact, as you’ve probably heard me say in the past, biomarker-driven cancer trials increased the success by 4 to 12x. Now rather than pursuing broad basket-like development, we’re taking a very directed approach investing our resources exclusively in patient populations where the Phase I data and our AI-driven RADR insights predict meaningful clinical benefit and where there is real commercial opportunity and patient need. This is precision oncology at its best, using AI to identify the right patients in the right indications with the right combination drugs.

And it all flows directly from what we learned in the Phase Ia trial, which was also heavily supported and predicted by the in silico AI work of our team and with multiple publications prior to that. Now let me turn to our LP-300 program and the HARMONIC trial, which addresses a significant growing need in lung cancer, lung cancer and never smokers that have progressed after treatment with TKIs. This is an important distinction. In Asia, never smokers represent 33% to 40% of all cases compared to only about 15% to 16% in the U.S. and Europe. This demographic reason is one of the reasons why we expanded this trial into Japan and Taiwan. It gives us access to the patient population, and it gives access to pharmas who want to develop therapies for this population.

The market opportunity here is substantial globally, approaching $4 billion annually, and there are no current therapies approved for this patient population. But it is a space that more companies are interested in and are developing interested — and are developing interest and are trying to approach it with various targeted combination opportunities. There’s a real white space here that we’re going after and the potential even to get to an earlier line of treatment. We completed enrollment in Japan this past quarter at 5 clinical sites, and we presented data at the 66th Annual Meeting in the Japan Lung Cancer Society, which was presented by Dr. Jonathan Dowell from UT Southwest. Now the preliminary data from this trial, which we’ve already shared publicly, showed 86% clinical benefit rate, which is very encouraging.

And we have one patient who has demonstrated a durable complete response with survival continuing for nearly 2 years, a remarkable outcome. I think we have another patient, which is now approaching a year. Now we’re planning a more comprehensive webinar in December before the year closes where we’ll present additional patient follow-up data and clinical readouts from both the Asian and U.S. cohort. This will give us an opportunity to discuss the data in much greater depth and provide regulatory strategy insight and positioning moving forward. I should also mention that during the third quarter, we made a strategic change in our clinical operations in Asia. We transitioned our CRO services in Taiwan with a specific focus on cost reduction and operational efficiency.

In Japan, we supplemented our team by bringing more activity in-house. This is part of a broader commitment to disciplined capital management and efficiency while maintaining the quality and integrity of the trial. The strategic positioning of Harmonic also opens doors for potential regional partnerships in Asia and co-development opportunities where the never smoker population is most prevalent. Now let me turn to LP-284, a program targeting recurrent non-Hodgkin’s lymphoma, which has generated interest from clinical communities and also from biopharma to approach combination approaches. This is our first in-human trial for LP-284, which we expect to enroll about 30 to 35 patients with aggressive recurrent non-Hodgkin’s lymphoma, including mantle cell and high-grade B-cell, where we have orphan indications for both.

This represents a global market opportunity of about $3 billion and with patients who have failed multiple prior lines of therapy and have very limited options. In fact, in October, we presented clinical data from this ongoing trial at the 25th Annual Lymphoma Leukemia Myeloma Congress in New York City. The cornerstone of that presentation was a heavily pretreated patient with aggressive grade 3 B-cell lymphoma, specifically DLBCL, who had exhausted standard therapies, and we saw a complete metabolic response with LP-284 as monotherapy after 2 doses, 2 cycles. This is exactly the kind of signal we’re hoping to see and validates many of our preclinical hypothesis for this drug. It also validates the mechanistic insight, and we saw complete metabolic response and the lesions around the hips and spine completely went away.

This patient has now remained cancer-free since we initially reported this result in July Q2 of this year. LP-284 has a novel mechanism of action. It demonstrates particular lethality in cells with DDR, a targetable vulnerability that’s common in non-Hodgkin’s lymphoma. This mechanistic differentiation is what’s driving interest from partners. Now following this presentation, we’ve started discussions with investigators and companies around opportunities for combination therapy development with existing FDA-approved agents, post-immunotherapy treatment strategies and leveraging the 284 mechanism where current therapies are failing, especially in what’s exciting indications beyond lymphoma. Based on preclinical data, we’re evaluating 284 and rituximab as a potential alternative to cyclophosphamide and methotrexate in lupus, systemic lupus SLE.

Our preclinical models showed that 284 reduced urinary microalbumin and kidney damage — which is a key marker of kidney damage in lupus by approximately tenfold and depleted B cells by fourfold when combined with rituximab. We saw even greater B-cell depletion when both agents were used together. This suggests LP-284 could become a next-generation B-cell depleting therapy in a number of autoimmune diseases, which would dramatically expand the commercial opportunity for this asset. LP-284 also benefits from strong intellectual property protection. We have composition of matter patents granted in U.S., Europe, Japan, India and Mexico, providing exclusivity through at least 2039. The molecule, as I mentioned, also has Orphan Drug Designation in mantle cell and high-grade B-cell lymphomas.

We’re now focused on recruiting additional sites with a focus on non-Hodgkin’s lymphoma and high-grade B-cell lymphomas. The momentum we’re seeing with LP-284, both clinically and in terms of partner interest reinforces our view that this asset has significant opportunity, both stand-alone as a wholly owned program or as part of a strategic collaboration. And we’re very open to those discussions, both again in combination in non-Hodgkin’s or in other autoimmune categories. Now transitioning to our AI platform discussion. As I mentioned earlier, I want to shift gears and talk about what I believe is an increasingly important value driver for Lantern, our RADR AI platform and the commercial opportunities it represents independent of our drug development programs.

For those less familiar with RADR, it’s our proprietary AI and machine learning platform. And it’s not just a tool we use internally. It’s now a commercial asset with its own revenue potential, which is growing. The platform has demonstrated over 80% prediction success across multiple use cases, and now it’s been validated in natural clinical trials through programs like LP-184, LP-284 and also with Actuate Therapeutics. In all cases, it’s correctly predicted biomarker responses and in many cases, combination synergies before we’ve even actually enrolled a patient. We’ve developed 8 distinct AI-powered modules that address critical pain points in oncology drug development. And we’ve developed cases for these pain points, which we’re now developing into modules for the broader drug development community.

In October, we showcased the commercial readiness of 2 RADR modules at the inaugural AI Biology and Medicine Symposium. We demonstrated that our AI platform, PredictBBB achieves a 94% accuracy for BBB permeability prediction and can screen 200,000 molecular candidates in under a week. To put that in context, our algorithms currently hold 5 of the top 11 positions on the therapeutic data comments, and that’s a best-in-class performance. We also presented our LBx-AI liquid biopsy platform, which has achieved 86% to 90% accuracy in predicting treatment response initially in non-small cell lung cancer, which will be very useful for us. And now we’re extending it through collaborations with research centers into other indications as well. Both of these opportunities, we believe, are significant.

Blood-brain barrier technology market alone is predicted to be close to $1 billion. And when you consider a very few percentage, 2% to 6% of the molecules actually cross the BBB, there is a need for better predictive tools, one that don’t take weeks or months and end up destroying animals. So the need there is obvious and urgent. The interesting thing, PredictBBB is it also gives us access to a lot of other molecular characteristics of that compound, and we can predict a lot of other drug-like features that are important, both for drug manufacturing and also predicting potential drug activity once delivered internally. Now let me introduce Zeta. It’s our multi-agentic AI platform for rare cancers. Very excited about this and what connects directly to our experience with both LP-184 and 284 in rare tumors like gastrointestinal and thymic carcinoma.

It’s our newest initiative. We’re calling it withZeta. It’s a multi-agentic co-scientist. Now here’s the fundamental challenge. In rare cancer research and drug development, which often comes after molecule developed that often comes much later, the critical insights in rare cancers are scattered across disconnected data sources. A researcher or a clinician trying to understand treatment options for a patient with a rare sarcoma or a rare pediatric brain tumor has to manually search through clinical trial databases, PubMed genomic databases, drug interaction databases, molecular feature databases. It’s fragmented, time-consuming and inevitably incomplete. For drug developers, this fragmentation slows discovery, increases cost and often means that promising connections between existing molecules or indications and rare cancer vulnerabilities are [Audio Gap].

What Zeta does is a multi-agentic AI system. Think of it as a co-scientist that addresses this problem head on or actually a series of co-scientists. We’ve integrated curated rare cancer databases and ontologies across over 500,000 clinical trials, 250,000 publications with over 1.2 million knowledge objects into an agentic large language model architecture that uses recursive reasoning loops to transform fragmented biomedical knowledge and insights into an interconnected investigational platform. And it interacts with you in plain English. So — and it’s an AI system that thinks like a scientist, connects dots across disparate data sources and can answer complex questions in minutes about rare cancers. These are things that would otherwise take researchers weeks or months to investigate manually.

We’ll dig into more of the details about Zeta in the coming days, and we’ll have more information as — but the key is that it will help you design and improve and optimize molecules that can target vulnerabilities or mechanisms across these hundreds of rare cancers. So you can ask questions to Zeta like what existing molecules with blood-brain barrier penetration have shown activity against mutations commonly found in a specific pediatric brain tumor and will search, reason and provide evidence-based answers with citations, and you’ll be able to have it quickly pick potential combination regimens as well for that rare cancer benchmarked against successful and not successful trials across drug classes that you can help Zeta understand. And it can actually also predict potential efficacy in subtypes of that rare cancer and give you considerations that can then be taken to the lab.

From an industry and business value perspective, withZeta delivers several things: Speed, smarter decision-making, novel discovery and potential for improved patient outcomes faster and most importantly, massive cost and time savings across the rare cancer drug development cycle. I’d like to think about withZeta strategically is that we’re positioning Lantern as a unified team of AI co-scientists, always available, always updated for rare cancer research and drug development, a unified AI interface for complex scattered data and models that accelerates and improves novel therapy discovery and trial design. This is a tool that can shorten development timelines by months and years, particularly in rare cancers where that data is sparse and every delay means challenges and time and lives lost.

By making Zeta available to researchers and clinicians over the next month, we’ll establish Lantern as a central hub for rare cancer drug development and insights. This creates network effects, brings more users and data into our ecosystem and positions us as a trusted partner when those researchers need to take the next step, whether it’s preclinical development, biomarker validation, clinical trial design or co-development. Now we believe our AI tools and services in the future can represent several hundred million dollars in stand-alone market potential and will attract a lot of interest in the broader big tech community, and but most importantly, lower the risks and costs associated with creating cancer drugs. And that’s a very powerful complement to our drug development strategy.

Now I’ll turn over the call to our CFO, David Margrave, who will provide details on our financial results for the quarter.

David Margrave: Thank you, Panna, and good morning, everyone. I’ll now share some financial highlights from our third quarter ended September 30, 2025. Our R&D expenses were approximately $2.4 million for the third quarter of ’25, down from approximately $3.7 million for the third quarter of 2024. The decrease was primarily due to decreases in research study and materials expenses relating to the conduct and support of clinical trials as well as decreases in consulting expenses and in payroll and compensation expenses. Our general and administrative expenses were approximately $1.9 million for the third quarter of 2025 compared to approximately $1.5 million in the prior year period. The increase was primarily attributable to increases in business development and investor relations expenditures as well as increases in other professional fees and increases in patent costs.

We recorded a net loss of approximately $4.2 million for the third quarter of 2025 or $0.39 per share compared to a net loss of approximately $4.5 million or $0.42 per share for the third quarter of 2024. Our cash position, which includes cash equivalents and marketable securities was approximately $12.4 million as of September 30, 2025. We believe our cash, cash equivalents and marketable securities on hand as of the date of this earnings call will enable us to fund our anticipated operating expenses and capital expenditure requirements into approximately Q3 2026. We will need substantial additional funding in the near future, and one of our key objectives is to pursue additional funding opportunities. In July of this year, we entered into an ATM sales agreement with ThinkEquity as sales agent, pursuant to which Lantern may offer and sell up to $15.53 million of its common stock from time to time in at-the-market offerings to or through our sales agent.

During the quarter ended September 30, 2025, we sold 212,444 shares of common stock under the ATM for gross proceeds of approximately $989,000. Between October 1, 2025, and the date of this earnings call, we’ve sold an additional 144,204 shares of common stock under the ATM for gross proceeds of approximately $634,000. As of September 30, 2025, we had 11,040,219 shares of common stock outstanding with outstanding options to purchase 1,218,828 shares and no warrants outstanding. These outstanding options, combined with our outstanding shares of common stock, give us total fully diluted shares outstanding of approximately 12.26 million shares as of September 30. And I’ll now cover some near-term milestones that we think will accelerate value for investors.

And these are several value-creating catalysts that we see in the near future. In the immediate near term, in this November, and Panna talked about this earlier, and we’re very excited about this discussion. Next week, November 20, at 4:30 p.m. Eastern, we’re going to have a KOL-hosted scientific webinar on LP-184 Phase Ia details from the clinical study and clinical development strategy. And in December of this year, we’ll be giving for LP-300, an interim patient follow-up and additional clinical data. And then also in this upcoming quarter, we’ll be discussing continued commercial developments for the AI platform modules, including the multi-agentic system that Panna discussed about withZeta for rare cancer development. And I’ll now turn things back to Panna for some closing remarks.

Panna Sharma: Thanks, David. As you know, we’ve had a number of catalysts and objectives that continue to ’26, which you can see on the slide, but we’ll be talking about those in follow-up meetings with investors as well. But as you can see, by integrating our capabilities in AI and bringing them to the public, we’re not just building better tools. We’re actually fundamentally reimagining what’s possible in precision oncology, an era that I call the golden age of AI in medicine. As we advance into 2026, we’re laser-focused on executing our dual engine strategy. We got really 2 powerful engines of the company. One is the ability to generate new molecules that are very precise and focused on very unique cancers. And the second engine is the engine of our AI platform that we’re now ready to commercialize and make available.

So we’re advancing our clinical assets while simultaneously scaling our platform for commercial deployment. So I want to thank our exceptional team, our partners, our shareholders for their continued support. Together, we’re lighting the way toward precision oncology solutions, solutions that can improve outcomes for cancer patients while very importantly, transforming the economics of drug development. With that, I’d like to open the call to questions and also thank our team for helping to prepare us for these calls and preparing the content.

Panna Sharma: So we’ve a question in about tracking toward an interim event analysis for LP-300 trial. At the December webinar, we do not believe we’ll be at the 31 events, which is good news because that means that patients are coming off of the trial. So the positive news is that patients are on the trial longer, but we will report out data, clinical data and insights that have resulted. We expect 31 events right now, we’re tracking to be sometime in early ’26, which we think is actually a very positive news. We do expect to see the Denmark trial. There’s a question for the Denmark trial. That has now been approved. IRBs are set. project manager has been assigned. We expect that to start sometime either in late December or early January at one site, which is investigator-led in Denmark.

Another question is that we’ve guided for an IND submission for the pediatric CNS program. Yes, now that the FDA is kind of back in business and looking and renewing new INDs, we’re already prepared to submit that, and I expect that submission to happen here in the next few weeks. In terms of when we anticipate initial patient dosing, hard to say. We’re already beginning discussions with sites, but I expect that to be sometime in early ’26. There’s a question about the withZeta portion of our AI platform. We will have additional news next week on withZeta, which is very exciting. Like most software, we expect the early rollout to be interesting and bumpy. We’ll learn a lot from it. We’ve already begun using it internally. And in fact, we’ll talk about this next week, but we’ve got a number of really exciting programs that have already been designed and are now being tested as a result of withZeta.

But it will be available as select demo to collaborators and select partners. And so December will be a lot of demo and learning and broader rollout throughout January and February and Q1. Next question is for 184. Yes, for the indications, we do plan on figuring out what is the best of those indications where we’re getting the biggest impact and move that into larger scale trials ideally with partners. As I mentioned, [ Boris ], all those indications are very exciting indications, and we’ve had interest from pharma companies. Of course, they want to see some of the early Phase Ib, Phase II data, but all of those are potentially partnerable. Next question is Zeta. Yes, Zeta was initially developed as a culmination of our internal efforts to develop drugs initially 184 and 284 for rare cancers.

We wanted to go after categories where there was no therapy approved, categories there was high need, categories where we thought the mechanism would work and could be exploited. As we did that and we gathered information about some of these cancers, we said, well, we can do it for all rare cancers. There’s no tool out there. In fact, when we talk to other rare cancer experts, many of the cancers we’re pursuing, it was scattered. Papers were hard to get, hard to get in front of experts, hard to get data. Trials were oftentimes took way too long and standards of care often changed or the best drug often changed. And we said, this is part of the frustration in these cancers, and that’s why they take time or too much money. What if you could actually have one source and then train that source to think in the way that a drug developer thinks.

So yes, it was an internal effort, and now it’s going to be a front-facing natural language interface tool. And I’m happy to give you [ Boris ], if you’d like peek at it and even early demo, happy to provide that to you. Another great question on STAR-001 trial design for pediatric brain tumors. Yes, I do believe that the trial design allows for inclusion of other pediatric high-grade gliomas. Yes, we designed it to allow for that, including specifically diffuse midline gliomas. Okay. If there are no further questions. I want to thank everyone for joining and very importantly, for listening in this morning. We know it’s a little past the market open. So I appreciate all of you staying online. Thank you very much for your time, and I appreciate everyone’s effort and also more importantly, your support as Lantern Pharma continues to transform drug development in oncology.

David Margrave: Thanks a lot.