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DATE

Friday, Aug. 14, 2026 at 9:00 a.m. ET

CALL PARTICIPANTS

• President and Chief Executive Officer - Panna Sharma
• Chief Financial Officer - David R. Margrave

TAKEAWAYS

• Cash, Cash Equivalents, and Marketable Securities -- $7.4 million at June 30, 2026, reflecting the receipt of $4.4 million in gross proceeds from a registered direct offering closed on May 14, 2026.
• Research and Development Expenses -- $1.8 million, representing a 42% year-over-year decrease primarily due to a $1 million reduction in research studies and materials for clinical trials.
• General and Administrative Expenses -- $1.7 million, an 8% increase driven by higher business development, investor relations, and salary expenses.
• Loss From Operations -- $3.5 million, a 25% year-over-year reduction reflecting disciplined execution across clinical programs and the company's AI-enabled operating model.
• Net Loss -- $7.1 million, or $0.57 per share, compared to $4.3 million in the second quarter of the prior year, influenced by noncash warrant liabilities.
• Noncash Warrant Liability Expense -- $3.6 million, resulting from an increase in the fair value of investor warrants following an increase in the company stock price during the quarter.
• Median Progression-Free Survival -- 8.9 months for LP-300 in EGFR exon 21 L858R patients who completed six cycles, compared to 8.4 months for the full L858R cohort.
• LP-300 Hazard Ratio -- 0.37 for the L858R patient subgroup, indicating a progression-free survival benefit favoring this specific genetic mutation.
• LP-300 Clinical Benefit Rate -- 77%, with target lesion reduction observed in over 70% of evaluable L858R patients.
• Regional Lung Cancer Prevalence -- 35% to 40% of non-small cell lung cancer cases in Asia involve never-smokers, compared to 15% to 20% in the U.S. and Europe.
• LP-184 Bladder Cancer Trial Enrollment -- 39 patients for the investigator-initiated Phase 1b/2 trial at Denmark's national referral center for urologic cancers, using a dual biomarker selection strategy.
• LP-184 TNBC Trial Enrollment -- 40 patients across two dose cohorts for the monotherapy Phase 1b/2 trial targeting triple-negative breast cancer with DNA damage repair alterations.
• Development Timeline Efficiency -- two to three years from initial AI-derived insights to first-in-human clinical trials, compared to an industry norm of five to 10 years.
• Development Cost Efficiency -- $2 million to $3 million per drug program, compared to industry averages of $25 million to $100 million to reach the same stage.
• Priority Review Vouchers -- four vouchers potentially worth $150 million to $200 million each, held through rare pediatric disease designations for ATRT, hepatoblastoma, rhabdomyosarcoma, and malignant rhabdoid tumors.
• Annual Market Potential -- over $15 billion estimated for the company's AI-driven clinical pipeline across solid tumors, blood cancers, and pediatric oncology.
• Projected AI Drug Discovery Market -- $10 billion by 2030 to 2031, with oncology representing a primary therapeutic segment.
• Common Stock Outstanding -- 12.8 million shares as of June 30, 2026, following the May 2026 registered direct offering.
• ZetaOmics Module Launch -- July 2026 launch of the autonomous computational biologist agent that executes biological data analysis independently with an exportable audit trail.
• LP-184 Patent Protection -- Methods for a three-gene expression signature allowed by the USPTO for selecting patients across ovarian, liver, kidney, and thyroid cancer indications.

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RISKS

• CFO Margrave stated, "Additional funding is a top priority and we intend to pursue additional capital raises, collaborations, and other opportunities to extend our operating runway," regarding the company's need for future capital.
• CEO Sharma noted that current results are from "small exploratory cohorts not powered for statistical significance yet and a median from 9 patients can move up or down," indicating the preliminary nature of the LP-300 dataset.

SUMMARY

Management reported the establishment of Open Medicine AI as a separate company to independently commercialize its multiagentic AI software platform. The company stated that the HARMONIC clinical trial for LP-300 is concentrating enrollment on the L858R mutation subgroup following data indicating that progression-free survival benefit increases with treatment duration. Lantern Pharma Inc. (LTRN +0.44%) reported advancing multiple clinical programs, including LP-184 in bladder cancer and triple-negative breast cancer, while reducing loss from operations by 25% year over year. Management indicated that the separation of AI and drug development entities is intended to optimize valuation and funding opportunities across two distinct business models.

• CEO Sharma stated, "We have achieved clinical validation across multiple programs while establishing the foundation our next phase of growth in both of our engines, our drug development engine and also now our AI engine."
• The company amended the HARMONIC trial protocol to increase LP-300 treatment from six to eight cycles, which CEO Sharma stated "will extend durability and maybe even deepen response" for L858R patients.
• Lantern Pharma is working with pediatric oncology consortia to establish a clinical trial path for its pediatric brain cancer program as soon as possible.
• CEO Sharma stated that large pharma companies have had "several calls with us, some visited" regarding interest in the withZeta platform and its impact on development economics.
• Management intends to raise capital at the Open Medicine AI level with the longer-term objective of it becoming a separately listed company while Lantern Pharma remains a major shareholder.

INDUSTRY GLOSSARY

• RADR: Response Algorithm for Drug Positioning & Rescue, Lantern Pharma's AI and machine learning platform.
• OMAI: Open Medicine AI, a newly established subsidiary focused on commercializing AI drug discovery software.
• L858R: A specific mutation in the EGFR gene common in never-smoker lung cancer patients that is targeted by LP-300.
• TKI: Tyrosine kinase inhibitor, a type of targeted therapy that blocks specific enzymes to help stop cancer cells from growing.
• PFS: Progression-free survival, the length of time during and after treatment that a patient lives with a disease without it getting worse.
• ZetaOmics: A computational biology module within the OMAI platform for autonomous biological data analysis.
• ADCs: Antibody-drug conjugates, a class of biopharmaceutical drugs designed as a targeted therapy for treating cancer.

Full Conference Call Transcript

Operator: 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 2 thousand.

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.

Panna Sharma: Good morning, everyone, and thank you for joining us. To discuss our second quarter 26 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, but also how they allocate their resources and think about staffing their scientific teams. Today, we are at an inflection point that is actually accelerating not just for Lantern, but for how science itself will be conducted. And 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.

And this quarter, that idea has 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 real 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 actually validated in using big data, a European regulatory clearance in a challenging recurrent cancer and allowed patent on a patient selection method 1 of our most valuable assets, LP-184, and a 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. What those observations were is that the mechanistic insights gained during our preclinical work actually have real world parallels. And they could be the basis for meaningful activity in actual cancer patients.

The remainder of 2026 is a defining year for Lantern Pharma, and especially as we launch into 2027. We have achieved clinical validation across multiple programs while establishing the foundation our next phase of growth in both of our engines, our drug development engine and also now 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 launched an entirely new company 1 of the most promising and disruptive areas of AI. Medicine.

Our AI driven clinical pipeline now encompasses multiple drug candidates across solid tumors, blood cancers, and now 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 in non small cell lung cancer who progress after TKI therapy. We believe there is about 400 thousand to 500 thousand patients diagnosed globally each year that have no specific therapy aimed at never smokers that progress after TKI. In Asia, it is about 35 to 40 plus percent of non small cell lung cancer cases. In The US and Europe, it is between 15 and 20 percent.

In June, we reported emerging data as of the May 11 cutoff, shows something we did not expect to see this clearly but the benefit of LP-300 deepens the longer patients stay on it. Among L858R patients who completed 6 cycles, median progression free survival reached 8.9 months, that is 9 patients, 3 of whom had not progressed at 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. So that is more than 70%. Also, more than 70 percent of the L858R patients saw target lesion reduction and some of the responses sustained beyond 2 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 9 patients can move up or down, but what makes us take it very seriously is that a Cox regression controlling for race, gender, TP53 status, which is very important, confirmed L858R, as an independent predictor. This is not a demographic or statistical artifact. And safety was comparable between 4 and 6 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. And that is actually the science and the data behind what we did next. We had 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 actually tended to do worse on current therapy regimens. We are that is why we also think there is a great need. We have extended the treatment from now 6 to up to 8 cycles, and we have moved into a single arm design, which should be more efficient and less costly.

The trial continues enrolling in The US and Taiwan, and we have used this dataset and other observations, of course, about the future of the program in active partnering discussions. Talk a little bit about LP-184 this quarter. We made several advancements, all of which were driven by data and AI leverage methodologies. First, the EMA clearance. In July, we got clearance for an investigator initiated Phase 1b/2 trial in advanced bladder cancer. This is in Copenhagen, Denmark's national referral center for urologic cancers, Rigshospitalet, And this is with Professor Roerberg and Pappot. They are the coordinating investigators.

This will be a 39-patient trial, and very uniquely on 2 biomarker, a dual biomarker strategy, 1 on PTGR 1 overexpression, and then combining that with DNA damage repair deficiency. And we are hoping to enroll patients very importantly, that our platform has predicted should respond and, more importantly, have a mechanistic basis to be helped by that drug. Second major milestone is the LP-184 monotherapy relapsed or refractory triple negative breast cancer. 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 trial for that study as well, which we are pretty excited about.

This drug targets tumors with DNA damage repair alterations, homologous recombination deficiency. We will enroll 40 patients across 2 dose cohorts and we will followed by a Simon 2-stage efficacy read. Third, very important, is that we received a notice of allowance. In July covering our 3-gene selection, we used 3 genes, PTGR 1, PTPN 14, and ASPH for selection of patients most likely to respond to LP-184.

We were issued a notice of allowance in 4 tumors ovarian, liver, kidney, and thyroid cancer. that is a patent on the selection logic itself, which is 1 of the hardest parts of this to replicate, and then map that directly to a credible therapeutic intervention safety is known and mechanism is beginning to be more and 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 mgs per kg, And very importantly, what we saw in that trial is that we saw tumor reduction in patients that were carrying these DNA repair deficiency genes: CHEC2, 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. And that precision is the thread that runs parallel through both programs and which we expect to give our programs a meaningful advantage in their development.

LP-284 continues in hematologic malignancies and in adult soft tissue sarcomas where we got orphan designation earlier this year. And STARLIGHT, briefly on the science, STAR-001 is LP-184 in brain cancers, Our radar platform identified that those particular brain tumors would be very sensitive if ERCC 3 was removed as a protein. Because that is involved in the repair mechanism. Well and it is we what we did is we characterized that with our group at Johns Hopkins that we collaborate with, and we are using spironolactone, which is already well characterized, safe in pediatric and adults, and it actually does exactly that. It degrades the ERCC 3 protein, and shuts down the repair route.

And we have had great preclinical data, and now we are taking that now into the clinic. We are taking it into disease designations where we have orphan designations and also 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, and they have recently transferred $150 to $200 million or more, and Lantern holds 4 of those. On the pediatric program specifically, I am very excited and I want to give you an update.

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 got 2 consortia that we are working with and we will have more data in this 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 an exceptional need. Again, STARLIGHT is 100% owned by Lantern. We expect to raise additional funding for it. as a separate entity, it holds its own INDs now.

Its own regulatory designations, And it is not just a program status. it is actually 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 August, we formally established Open Medicine AI, OMAI, as a separate company. Executed our board approved commercial licensing agreements, and more importantly, OMAI now can operate the multi agentic AI coscientist we launched as RADR withZeta. And use it in the commercial setting. Here's the logic.

Most people using AI drug development today ask 1 model a question and get an answer. We now see that things are moving well beyond a single line of questioning or query. So we built an orchestrated system and this orchestra brings together specialized agents for literature synthesis medicinal chemistry, pathway analysis, data curation, literature analysis, portfolio prioritization, clinical trial development, and then they challenge each other and they pass information and ideas. And they cross validate before delivering hardened results or ask the scientists or drug developer to get more engaged and ask them questions.

And this, we believe, multi agentic nonmonolithic model is really the standard infrastructure for specialized domains that are multidisciplinary, and we think it will be the standard infrastructure for drug discovery. And we think this is something that will be critical. In addition to that, we believe that the computational biology model and the computational chemistry model that run deep and in their own large quantitative models is critical And more importantly, it 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 for a user will feed the next.

And this is exactly the kind of dynamic that deserves its own capital structure. Clinical drug development and enterprise software are priced by different investors and different metrics held inside a clinical stage oncology company, a software business, may or may not get the credit for what 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 racing forward with 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 1 of its largest shareholders. So Lantern continues to retain the rights, the full access the platform for our own drugs, and this changes nothing about those programs' priority or timing. And we believe that the market there is much, much larger than just early oncology companies like ourselves. Analysts project the market to reach about 10 billion by 2030-2031. It with oncology as 1 of its largest segments. Even doing my own bottoms up analysis on companies and drug discovery, drug discovery technology, AI enabled. I expect it to easily 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 commercial model But putting all this together, a clinically validated platform with 3 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 these 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 that are going after meaningful challenging, rare, and aggressive diseases. And we think these are very complementary.

The AI tools and services we think, can grow to being several hundred million dollars in standalone value as part of this larger $10 billion market. We think a nice chunk of that 10 billion market will be agentic in nature, and open medicine will have a real chance at grabbing a significant piece of that. So these are 2 great growth engines in the company, and I will let David talk a little bit, David R. Margrave, to discuss our financials, our key metrics, and also dig into the details behind the noncash expenses that are 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.

David R. Margrave: 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.

Compared to approximately $10.1 million in cash, cash equivalents and marketable securities as of 12/31/2025. Funding received during the second quarter of 26 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 3 months ended 06/30/2026, compared to approximately $3.1 million for the 3 months ended 06/30/2025. This was 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 benefit expenses. G&A expenses were approximately $1.7 million for the 3 months ended 06/30/2026 compared to approximately $1.6 million for the 3 months ended 06/30/2025. This was 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 3 months ended 06/30/2026, compared to a loss from operations of approximately $4.7 million for the 3 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 3 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 non cash 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 3 months ended 06/30/2026. Compared to a net loss of approximately $4.3 million or 40¢ per share for the 3 months ended 06/30/2025. For the 6 months ended 06/30/2026, our net loss was approximately $10.4 million or 88 cents per share compared to a net loss of approximately $8.9 million or $0.82 per share for the 6 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.

And as we described, in May 2026, we closed a registered direct offering and concurrent private placement comprising 1.45 million shares of common stock prefunded 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 3 months ended 06/30/2026. I will now turn the call back over to Panna for additional update on our programs and operations. Panna?

Panna Sharma: Thank you, David. 2 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 3 years. Roughly 2 to 3 years at approximately $2 million to $3 million each. The industry norm to reach that same point is 5 to 10 years at 25 to a hundred. 3 molecules in clinical trials, dosed to over 100 patients, and at the same time, have been able to advance an AI platform that is launching commercially. Those numbers are not a marketing claim. it is actually our operating model. And it is 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, which will be led by investigators in Denmark in a challenging recurrent bladder cancer setting. And FDA cleared a 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 just walked you through, we actually did all that while our actual operating loss or loss from operations were down approximately 25% year over year.

And 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 more importantly, it is the platform that we think will be 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. And I wanna especially thank our own team here at Lantern especially a long time member of our team who is moving on to a new leadership opportunity in media and technology after 5 years with us, 5 years of building this company's brand, voice communications, and also being an amazing colleague. So thank you very much. With that, I would like to now open the call to questions.

Operator: You can type your question using the QA tool. Or raise your hand, and we will try to unmute your line and repeat your question. So any questions with the remaining time that we have? I am gonna go to the Q&A. Hey, Michael. You should be unmuted.

Michael: Can you hear me? Yep. Good morning. 2 questions, Panna. 1 on LP-300, and then the other on OMAI. Just on LP-300, can you talk about where are you in the data analysis? You know, it is obviously 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 any other well 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?

Panna Sharma: Alright. Let's go. A lot of questions, but I--you know, we once we got the--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 is all been done now. So we expect enrollment to resume under the new 8 cycles, which is important. We think that will extend durability and maybe even deepen response. So we expect to be enrolling patients in Taiwan and the US under the new amended protocol.

We hope to--you know, another 15 to 16 patients that will give us meaningful data, and we expect to enroll those over the next, you know, 4 to 6 months. both in the US and Taiwan. that is the initial focus.

Michael: Will there be any other updates coming on the current cohort?

Panna Sharma: We might--we may have an update toward the end of the year. I mean, I think other than just extending PFS, you are really relying on the next batch of patients coming in to see what kind of responses that we continue getting.

Michael: Okay. Very good. Thanks for that update. And then just on open medicine, can you talk about I think, you know, most of us that come from sort of a therapeutics background are not AI experts, you know, most of the technology is a black box. Because, you know, the companies, you know, like in silico medicine and others do not open their, you know, kimonos to see what is actually operating internally. Maybe you can help us understand, you know, what your--you know, what your system looks like or how it compares, how should we think about it in the context of the other tools that are out there that the pharma industry seems to be taking advantage of.

Yeah.

Panna Sharma: So there is actually I am working on something for our mid September webinar, but the AI cycle in drug development, you know, we are kind of on our 4th cycle. I mean, if you go back to early days of supercomputer and molecular modeling, and large installed bases. It was kind of like the 1st wave limited resource limited compute resource, but infrastructure heavy. We are almost at the opposite end of that now. Where we have almost limitless compute resource and infrastructure install super light And there are 2 waves caught in between that.

And we really did not have the capability to kind of get the transparency that you would want real time until after a algorithm was run. And oftentimes, those algorithms would take days or weekends or long term But now those can be done in seconds, and so you can get real-time, you know, what is the process that happened. We also did not have the software and tools to do large scale algorithm mapping and analysis, you know, because it was just extra overhead. But now we have the ability to do that, so we get transparency that we did not have that was a luxury in the past. Now it is commonplace, and people expect it.

So a lot of the large scale AI providers, including the Anthropics and OpenAI's of the world, even, you know, to some extent, KIMI 3 and DeepSeqs have made some levels of transparency into how the system operates more expected. And that is something that we are we rest on the shoulders of. You know, we can--we can do it very differently. And so that is a platform that we have built. And more importantly, once you see this transparency, you as an enterprise user or end user, can actually tweak it and alter it. And that just did not exist before.

So, yeah, we are in a different wave of how AI, and I--and I expect and I will mention this in the webinar in September is that the people who are going to be hit the hardest are going to be 2. Number 1, people who provide professional knowledge labor, basically. And then second, it is gonna be the existing installed base of software providers into pharma. Those days of going in and being able to charge $100, $500, $300, for some very, very specific functionality of an installed base Those days are gonna be gone.

They are all gonna go to like Open Medicine And, also, you are not gonna hire teams of bioinformaticians and teams of data analytics people. it is just you can do all that now in the cloud with smart engineer or data science person. And you can launch swarms of people, swarms of agents doing this work for you. And that is especially what we have proven open medicine. So I think that is--I think that is the future, and I think that is where, you know, leading-edge providers like Claude Science and others are going toward. People are going to expect greater transparency.

And if you really wanna democratize, the development of drugs, you are going to have to be able to allow people to go to a URL, to go to an app, and start their inquiry. And that is exactly where I see open medicine playing. Is a new category that just has not been valued and priced. I am writing a piece you will see by mid September. it is called the deflation of discovery and the birth of a new category. And that specifically talks to agentic AI in drug development and drug discovery. Okay. Thank you.

Operator: Another question. I will take sorry.

Panna Sharma: Another--yeah. Someone's asking any interest in withZeta from large pharma, and the quick answer is, yes. We have got a lot of pharma companies both biologic groups as well as small molecule groups We have had some have had several calls with us, some visited. So the answer is yes. Large pharma is definitely interested. This is something that they are all evaluating, cutting deals on, looking at. And, you know, large pharma will have to partner with agentic AI to make it commonplace. I mean, it is transforming the economics of early development and also late stage development. So yes, very much increasing interest.

The more marketing, the more dollars we can put behind driving awareness of open medicine and with Zeta, the more I expect. The 1 thing that we have seen that has been solid is that once we put the tool in front of people, it gets very sticky. So yes. Thank you.

Operator: Take another important question. Let's see if we can do this 1 live. We are trying to do some live. Can we--so, I do not know. Go ahead. Get the live person. I think Beau Parsons. You should be on live.

Panna Sharma: I can read it also if you do not want to do it live. But okay. So this is another question. As our models, we expect, will be standards in biology and drug development. What are you doing to ensure that? And that other competitors do not copy your methods. Well, first of all, of course, everyone will copy 1 another. And that is part of putting open medicine separately is to allow it to move faster, further, and have its own independent balance sheet to ensure that you always stay 1 or 2 steps ahead.

Companies--there are definitely companies that have more capital More capital does not necessarily mean you are gonna be the surviving entity You know, you can look at any industry. And category by category, but capital efficiency is important long term, which we have proven to be very capital efficient at. And but, you know--you know, we are at a point where it needs to be a separate entity and raise its own capital to stay ahead of the curve. Things that we are doing in addition to continuing to train our models and try to grow intelligently using our center in Bangalore, India. Those are things that we are doing.

Also constantly benchmarking like we did with our BBB algorithm, like we are doing with our bio computational tools, we are trying to pick some of the toughest challenges and go deep as opposed to go broad. And that is 1 of the things that big components of that is going deep in certain categories versus broad across all of science. I do not think we ever would have claimed, hey. We are gonna be cloud science and do all of science. I think that just makes no sense to me.

You can pick specific categories like rare cancers, specific areas like bio computational tools, specific problems like blood brain barrier or penetration into any tissue type, and do it and resolve it really, really well. So we are gonna go after certain diseases that we think require that kind of depth and then march forward in that fashion. But, yeah, capital, no doubt, more capital is needed to drive that.

Operator: Let's go ahead and get to the next question. Let's go to this. Yeah. Sorry. Let's go to Baird and Redshift team. Maybe we can answer that 1 live. Baird and Redchip team, if you guys wanna ask your question live, They cannot--they cannot ask their question live.

Panna Sharma: You have to read the question. Oh, okay. Alright. David, I am asking a question on what adoption and feedback look like. Adoption is very sticky. Like I said before, once we get it in front of users, we were taking certain measures to make sure that 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 with the professional edition, especially generative chemistry, bio computational tools, the investigator mode, tends to be very sticky. So that is exciting news. Key is getting them to that point.

So we are also beginning to implement some more aggressive email campaigns to drive the awareness and specialized codes for certain larger pharma companies. But, yeah, great question.

Operator: Okay.

Analyst: Another question is anonymous. What would be the biggest benefit of the open medicine spinout will be for shareholders.

Panna Sharma: Well, Lantern owns a 100% of open medicine today. We think it is poised to be very disruptive. Disruptive companies are usually valued--can be valued higher, and we are gonna raise capital. Lantern will continue being the largest shareholder, we think, for a while. And we may explore ways to distribute those--the underlying shares to all shareholders in Lantern. So those are things that we are talking about. You know, potentially distribution of the shares of Open Medicine to all Lantern shareholders. Again, we are having discussions.

We are looking at the most efficient ways to do that, but I expect Lantern shareholders to continue being beneficiaries of that asset as we monetize it, both in private financings and very importantly, as it potentially goes into an exchange. Public exchange. Okay. I think we are coming up almost 45 minutes into the call. And we look forward to answering questions in 1-on-ones As it continues. I know we have a couple of requests for some 1-on-1 follow-up meetings. We will take those as well. And thank you guys for participating. I wanna thank all of the Lantern investors, people who are interested, and I look forward to giving you guys more updates as the year continues.

Thank you, and I thank you again to our team as well. Thanks a lot.