Lantern Pharma (LTRN) 2026年第二季財報電話會議：營業虧損減少，LP-300 與 Open Medicine AI

重點摘要

• 2026 財年第二季營業損失年減 25% 至約 350 萬美元，主因研發費用下降 42% 至 180 萬美元。
• 淨損失擴大至約 710 萬美元（或每股 0.57 美元），主要反映約 360 萬美元的認股權證相關費用，包括大幅度的非現金公允價值調整。
• 截至 2026 年 6 月 30 日，現金、現金等價物及可交易證券總計約 740 萬美元。Lantern Pharma 在 5 月透過登記直接發行（registered direct offering）募集了約 440 萬美元的總收益。
• 在 LP-300 HARMONIC 試驗中，完成 6 個療程的 9 名 L858R 患者中位無惡化存活期（PFS）達到 8.9 個月。管理層強調該佇列規模仍小且屬於探索性質。
• 在與美國 FDA 舉行 C 型會議後，Lantern 針對 L858R 患者優化了 LP-300 試驗，將治療期從 6 個療程延長至多達 8 個療程，並改採單臂設計。
• Lantern 於 2026 年 8 月將 Open Medicine AI 成立為一家獨立的全資子公司。該公司意圖獨立進行融資，同時 Lantern 保有對其藥物專案平台的完整使用權。

主要財務數據

截至 2026 年 6 月 30 日，Lantern 流通在外普通股為 12,759,146 股。該公司報告本季度未在其按市價發行 (ATM) 銷售管道下進行任何交易。

業務與營運表現

LP-300 與 HARMONIC 試驗

Lantern 正集中招募 HARMONIC 試驗患者，對象為在接受 TKI 標靶治療後出現疾病進展且帶有 L858R 基因突變的無吸菸史非小細胞肺癌患者。

在完成 6 個療程的 9 名 L858R 患者中，中位無惡化存活期 (PFS) 為 8.9 個月，截至 5 月 11 日數據截點時有 3 名患者尚未出現疾病進展。整個 L858R 佇列的中位 PFS 為 8.4 個月，風險比 (HR) 為 0.37，信賴區間為 0.15 至 0.89。

管理層亦報告超過 70% 的 L858R 患者目標病灶縮小，臨床效益率達 77%。部分緩解維持超過兩年。公司表示，4 個療程與 6 個療程之間的安全性相當。

在與 FDA 召開 C 型會議後，Lantern 將治療期延長至最多 8 個療程，並採用旨在提高效率和降低成本的單臂設計。招募工作將繼續在美國和台灣進行。

LP-184 臨床拓展

歐洲藥品管理局 (EMA) 批准了一項由研究者發起的 LP-184 治療晚期膀胱癌 Phase Ib/II 試驗。這項在哥本哈根進行的 39 例患者試驗將採用基於 PTGR1 過度表達和 DNA 損傷修復缺陷的雙生物標記策略。

FDA 還批准了 LP-184 單藥治療復發性或難治性三陰性乳癌的 Phase Ib/II 試驗。Lantern 預計該研究將在兩個劑量佇列中招募最多 40 名患者，隨後進行 Simon 二階段療效評估。

Lantern 收到了一份專利核准通知，涵蓋在卵巢癌、肝癌、腎癌和甲狀腺癌中使用 PTGR1、PTPN14 和 ASPH 的三基因患者篩選方法。

LP-284 與 Starlight Therapeutics

LP-284 繼續進行用於血液惡性腫瘤和成人軟組織肉瘤的研究。該計畫已於 2026 年較早前獲得孤兒藥資格認定。

Starlight Therapeutics 正推進 STAR-001（LP-184 在腦癌領域的應用）。Lantern 正與兒童腫瘤學聯盟合作規劃臨床途徑，並在尋求針對罕見兒童腦腫瘤的同情使用選項。Starlight 仍為 Lantern 全資擁有，預計將尋求獨立融資。

Open Medicine AI

Lantern 於 8 月正式成立 Open Medicine AI (OMAI) 作為獨立公司，並簽署了商業授權協議。OMAI 可商業化營運公司的多代理 (multi-agent) AI 系統，該系統整合了文獻綜合、醫藥化學、通路分析、資料整理、投資組合優先順序確定和臨床試驗開發等領域的專業代理。

Open Medicine AI 目前由 Lantern 100% 控股。管理層表示，計劃透過出讓 OMAI 股權來募集資金，長遠目標是成為一家獨立上市公司。Lantern 預計將繼續作為其最大股東之一，並保留對該平台的完整使用權以用於自家藥物開發。

管理層展望

管理層預計在修改後的計畫下，LP-300 試驗在未來四到六個月內於美國和台灣將新增約 15 至 16 名患者招募。現有佇列的進一步更新可能於 2026 年底前發布。

Lantern 計劃於 9 月中旬舉辦 Open Medicine AI 專題說明會，涵蓋該平台、市場機會、發展藍圖及商業模式。

補充資金仍是當務之急。該公司表示，計劃尋求融資、合作及其他機會，以延長其營運資金流轉時間 (operating runway)。

風險與關注焦點

• 管理層強調，LP-300 L858R 的結果來自未具備統計顯著性檢定力的小型探索性佇列。隨著數據漸趨成熟，來自 9 名患者的中位 PFS 可能會有所變動。
• Lantern 在本季度結束時擁有約 740 萬美元的現金、現金等價物及可交易證券，並將獲取補充資金列為首要任務。
• Open Medicine AI 需要額外資金來擴展並維持競爭力。其擬議的融資及長期獨立上市計畫仍屬於管理層的目標，而非已完成的交易。
• 認股權證負債會計處理可能會對申報的淨利潤或淨損失產生重大影響。第二季業績包含了約 360 萬美元的認股權證相關費用，主要由非現金公允價值調整所致。
• 如公司前瞻性聲明披露中所強調，臨床開發仍受試驗結果、監管流程及競爭情況的影響。

分析師問答環節亮點

管理層表示，修訂後的 LP-300 試驗計畫已獲得各臨床試驗機構的人體試驗委員會 (IRB) 批准。招募工作預計將在 8 個療程的計畫下恢復，接下來的 15 到 16 名患者預計將提供更有意義的療效反應數據。

關於 Open Medicine AI，管理層強調系統透明度、稽核軌跡以及企業使用者調整工作流程的能力，是其與早期 AI 藥物開發工具相比的差異化優勢。公司還透露大型製藥公司對此表示興趣，但尚未提供商業收入或客戶指標。

管理層將使用者黏著度描述為，一旦使用者體驗了平台的專業工具後便「非常高 (very sticky)」。公司正在增加電子郵件推廣，並使用專屬存取碼來提升知名度，包括大型製藥公司在內。

關於股東價值，管理層表示 Lantern 正評估向 Lantern 股東分發 Open Medicine AI 股票的潛在途徑。目前尚未敲定具體架構或時間表。

電話會議完整逐字稿

完整財報電話會議逐字稿

管理層陳述

Operator

Earnings call. As a reminder, this call is being recorded, [Operator Instructions] 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 June 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 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, 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 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'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 you for joining us to discuss our second quarter 2026 results. As I've said before, AI and computationally driven approaches are now becoming central to have both large and emerging biopharma companies discover and develop drugs but also how they allocate the resources and think about staffing their scientific teams.

Today, we're at an inflection point that's 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 and medicine isn't beginning, it's 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'll spend some real time on that today because I think it's the most consequential structural decision we've 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 for one of our most valuable assets, LP-184, and an FDA-cleared trial in triple-negative breast cancer that's 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 launched in '27. We've achieved clinical validation across mobile programs while establishing the foundation for our next phase of growth in both of our engines, our drug development engine and also now our AI engine.

In addition, our midyear 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 into one 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 pediatric oncology with a combined annual market potential estimated at over $15 billion.

Let's start with our Phase II program, LP-300 and the HARMONIC trial in never-smokers non-small cell lung cancer who progressed after TKI therapy. We believe there's about 400,000 to 500,000 patients diagnosed globally each year that have no specific therapy aimed at never smokers that progressed after TKI. In Asia, it's about 35% to 40-plus percent of non-small cell lung cancer cases in U.S. and Europe, it's between 15% and 20%.

In June, we reported emerging data as the May 11 cutoff, and it shows something we didn't 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's 9 patients, 3 of them hadn't 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 means more than -- also more than 70% of the L858R patients saw a target lesion reduction and some of the response is sustained beyond 2 years. We've had a 77% clinical benefit rate, which is phenomenal for that line of therapy. I'll 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 COGS regression controlling for race, gender TP53 status, which is very important, confirmed L858R as an independent predictor. This is not a demographic or a 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's no other great therapy for these patients. And that's actually the science and the data behind what we did next. We have a successful Type C meeting, where no objections were raised to our key proposed amendments. We've concentrated the enrollment now on the L858R patients. These patients actually tend to do worse on current therapy regimens. That's why we also think there's a great need. We've extended the treatment from now 6 to up to 8 cycles, and we've 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've used this data set and other observations, of course, about the future of the program in active partnering discussions.

Let's talk a little bit about LP-184 this quarter. We've made several advances, 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 Ib/II trial in advanced bladder cancer. This is in Copenhagen, Denmark's national referral center for urologic cancers Rigshospitalet tout. And this is with Professor Rohrberg and Pappot. They're the coordinating investigators. This will be a 39 patient trial and very uniquely on 2 biomarker or a dual biomarker strategy, one on PTGR1 overexpression and then combining that with DNA damage repair deficiency. And we're hoping to enroll patients, very importantly, that our platform has predicted should respond and more importantly, have a mechanistic basis to be held by that drug.

Second major milestone is the 184 monotherapy in relapsed or refractory triple-negative breast cancer. That will be a Phase Ib/II trial that protocol has been FDA cleared and is now moving toward launch with a number of sites. We've also applied for grants for that trial for that study as well, which we're pretty excited about. This drug targets tumors of DNA damage repair alterations, homologous recombination deficiency or a genomic loss of heterozygosity. We expect to enroll up to 40 patients across 2 dose cohorts and will follow by a Simon 2-stage efficacy [indiscernible].

Third very important is that we received a notice of allowance in July, covering our 3 gene selection where we use 3 genes, PTGR1, PTP N-14 and AS PH for selection of patients most likely to respond to LP-184. We issued a notice of allowance in 4 tumors ovarian, liver, kidney and thyroid cancer. So that's a patent on the selection logic itself, which is one of the hardest parts of this to replicate and then map that directly to an incredible therapeutic intervention, where safety is known and mechanism is beginning to be more and more observable. This all built on our 63 patient trial that we did for 184. And now that we have a dose of 0.39 mg 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, CHEK2, ATM, BRCA 1, STK11, KEAP1. Those alterations conferred exceptional sensitivity to the drug.

Unlike conventional chemotherapies and other DNA damaging agents that indiscriminately target defining cells both LP-184 and 284 exploit specific genomic vulnerabilities in cancer cells. And that precision is a thread that runs parallel to 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 cercocomas, where we've got orphan designation earlier this year. And Starlight briefly on the science, STAR-001, which is LP-184 in brain cancers. Our RADR platform identified that those particular brain tumors would be very sensitive if ERCC 3 was removed as a protein because that's involved in the repair mechanism.

Well, what we did is we characterize that with our group at Johns Hopkins that we collaborate with. And we're using spironolacton, which is already well characterized, safe and pediatric and adults. And it actually does exactly that. It degrades the ERCC3 protein and shuts down the repair route. And we've had great preclinical data, and now we're taking that into the clinic. We're taking it into disease designations where we have orphan designation and also rare pediatric such as ATRT, hepatoblastema, rhabdomyosarcoma and malignant rapidoid tumors. Bear in mind that each of these is independently eligible for a priority review voucher upon approval, and they recently transferred for $150 million to $200 million or more, and Lantern holds 4 of those.

On the pediatric program specifically, I'm very excited, and I want to give you an update. We're actively working with several pediatric oncology consortia to determine the best and most expeded path to bring these into a trial. As soon as possible. We've got 2 consortia that we're working with, and we'll have more data in this coming quarter.

We're also working closely to enable compassionate use for the drug, especially in some of these rare pediatric brain tumors where there's an exceptional need. Again, Starlight is 100% owned by Lantern. We expect to raise additional funding for it as a separate funding, holds its own INDs down, its own regulatory designations. And it's not just a program status. It's actually a way to monetize it independently of the rest of Lantern. And more importantly, it's a template. We're 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, OMAI as a separate company, executed our Board-approved commercial licensing agreements. And more importantly, OMAI now can operate the multi-genic AICOscientists that we launched as with Zeta and use it in the commercial setting.

Here's the logic. Most people using AI 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 for query. So we've built and 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 that -- 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 this multigenic non-monolithic model is really the standard infrastructure for specialized domains that are multi disciplinary, 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 a 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 deserved its own capital structure.

Clinical drug development and enterprise software are priced by different investors and different metrics held inside a clinical stage company, a software business may or may not get the credit for what it's worth because investors who price AI and software generally don't own clinical stage biotech and vice versa. That's 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 one of its largest shareholders.

So Lantern continues to retain the rights to the full access to the platform for our own drugs. And this changes nothing about this program's priority or timing. And we believe that the market there is much, much larger than just on early oncology companies like ourselves. Analysts project the market to reach about $10 billion by 2030, 2031. It was oncology is one of its largest segments.

Even doing my own bottoms-up analysis on companies in drug discovery, drug discovery technology, AI-enabled, I expect it to easily reach $9 billion to $10-plus billion by 2031. We'll host a dedicated informational call in mid-September on Open Medicine AI's market opportunity, platform, road map, commercial model. But putting all this together, a clinically validated platform with drugs and trials, a commercially accessible AI platform and software company with models and state-of-the-art tools and a drug pipeline that these all feed each other. You get a business model that extends well beyond just the clinical assets. We think it's 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 stand-alone 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 the real chance at driving a significant piece of that. So these are 2 great growth engines in the company.

And I'll let David talk a little bit -- David Margrave, to discuss our financials or 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's actually underneath the hood.

So David, I'll turn it over to you.

David Margrave

Thank you, Panna, and good morning, everyone. I'll now share some financial highlights from our second quarter ended June 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'll discuss this topic in detail later in my discussion.

Cash, cash equivalents and marketable securities were approximately $7.4 million at June 30, '26, consisting of approximately $6.7 million in cash and cash equivalents and approximately $0.7 million in marketable securities compared to approximately $10.1 million in cash, cash equivalents and marketable securities as of December 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 May 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 June 30, 2026 compared to approximately $3.1 million for the 3 months ended June 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 $0.3 million in salaries and benefit expenses.

G&A expenses were approximately $1.7 million for the 3 months ended June 30, 2026 compared to approximately $1.6 million for the 3 months ended June 30, 2025. This was an increase of approximately $0.13 million or 8%. The increase was primarily attributable to increases in business development and investor relations expenses of approximately $0.36 million and salaries and benefit expense increases of approximately $0.14 million, offset in part by decreases in other professional fees of approximately $0.35 million.

Loss from operations was approximately $3.5 million for the 3 months ended June 30, 2026 compared to a loss from operations of approximately $4.7 million for the 3 months ended June 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 2,135,923 shares of common stock at an exercise price of $2.27 per share and placement agent warrants to purchase up to 106,796 shares of common stock at an exercise price of $2.75 million 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 June 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 May 14, 2026 warrant issuance date and June 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 June 30, 2026 compared to a net loss of approximately $4.3 million or $0.40 per share for the 3 months ended June 30, 2025.

For the 6 months ended June 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 6 months ended June 30, 2025.

From a capitalization standpoint, as of June 30, 2026, the company had 12,759,146 shares of common stock outstanding. And as we described, in May 26, we closed a registered direct offering and concurrent private placement, comprising 1,454,175 shares of common stock, prefunded warrants to purchase up to 681,748 shares of common stock, investor warrants to purchase up to 2,135,923 shares of common stock at an exercise price of $2.27 per share and placement agent warrants to purchase up to 106,796 shares of common stock at an exercise price of $2.55 per share. There was no activity under our ATM sales facility during the 3 months ended June 30, 2026. I'll now turn the call back over to Panna for an additional update on our programs and operations. Panna

Panna Sharma

Thank you, David. So 2 closing points. First, the number I want all of you to remember is that we advanced programs from AI-derived in science to first-in-human clinical trials in a time line under 3 years and roughly 2 to 3 years at approximately $2 million to $3 million each. The industry norm to reach that same point and is 5 to 10 years is at 25 to 100. We have 3 molecules in clinical trials of dosed over 100 patients and at the same time, have been able to advance an AI platform that's launching commercially. Those numbers are not a marketing claim. It's actually our operating model, and it's a key part of our core advantage. Secondly, what we now have structurally that we didn't 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 and 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 losses 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's a really important and smart way to build, and that's the argument for continuing to operate this way. We're not just building better tools. We're reimagining what's 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's 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, and we expect to have a lot of great additional results over the coming quarters. And I want to especially thank our own team here at Lantern PAUSE especially a longtime member of our team, who's moving on to a new leadership opportunity in media and technology after 5 years with us. PAUSE Five years of building this company's brand, voice communications and also being an amazing colleague. So thank you very much. With that, I'd like to now open the call to questions. You can type your question using the QA tool, or raise your hand, and we'll try to unmute your line and repeat your question. So any questions with the remaining time that we have.

Operator

Michael should be unmuted. PAUSE SP578731125 Can you hear me? PAUSE. Yes. PAUSE SP-2 Two questions, Panna. -- 1 on LP-300 and then the other on OMI. Just on 300 PAUSE -- can you talk about what -- where are you in the data analysis? It's obviously nice to see the PFS stretching a little bit more. But how PAUSE matures this data set? Will it mature further? When do you plan to update us again? And any other PAUSE well, and then the next question related to that is now that you've got the protocol amendment in place, have any patients been PAUSE enrolled under the new protocol. PAUSE. All right. Let's go a lot of questions. But we -- once we got the product -- once we had sufficient confidence that the protocol would be I mean it in the data was trending that way.

We wanted to get the new IRBs approved at all the sites, and that's 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 deep in response. So we expect to be enrolling patients in Taiwan to the U.S., specifically under the new amended protocol. We hope to expect another 15, 16 patients that will give us meaningful data, and we expect to enroll those over the next 4 to 6 months, both in the U.S. and Taiwan. That's the initial focus.

分析師問答

Unknown Analyst

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

Panna Sharma

We may have an update toward the end of the year. I mean, I think other than just extending PFS PAUSE we're really relying on the next batch of patients coming in to see what we would kind of responses that we continue getting.

Unknown Analyst

Okay. Very good. And then just on open medicine. Can you talk about -- I think most of us that come from sort of therapeutics background or not, experts. Most of the technology is a black box because the companies like in silico medicine and others don't open their mono to see what's actually operating internally. Maybe you can help us understand 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.

Panna Sharma

Yes. So there's actually been working on something for our mid-September webinar, but the AI cycle in drug development, we're kind of on our fourth cycle. I mean if you go back to early days of supercomputers and molecular modeling and large installed bases was kind of like the first wave limited limited compute resource, but infrastructure heavy. We're almost at the opposite end of that now, where we have almost limitless compute resource and infrastructure install super light. And there are 2 ways caught in between that. And we really didn't have the capability to kind of get the transparency that you would want real time until after an 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 what is the process that happened. We also didn't have the software and tools to do large-scale algorithm mapping and analysis because it was just extra overhead. But now we have the ability to do that. So we get transparency that we didn't have that was a luxury in the past. Now it's commonplace and people expect it. And so a lot of the large-scale AI providers, including the anthropics and open AIs of the world, and even to some extent, KEMET and Deep Seek have made some levels of transparency to how the system operates more expected. PAUSE And that is something that we rest on the shoulders of. We can do it very differently. And so that's a platform that we've built.

And more importantly, what you see it's the transparency you as an enterprise user or end user, can actually tweak it and alter it, and that just didn't exist before. So yes, we're in a different way of how AI I expect -- and I'll 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 one, people who provide professional knowledge labor basically. And then second, it's going to be the existing installed base of software providers into pharma. Those days of going in being able to charge $100,000, $500,000, $300,000 for some very, very specific functionality of an installed base. Those days are going to be gone. They're all going to go to providers like open medicine. And also, you're not going to hire teams of buying from aticians and teams of data analytics people. It's just we can do all that now in the cloud with 1 smart engineer, data science person. And you can launch swarms of people, swarms of agents doing this work for you, and that's especially what we've proven with open medicine.

So I think that's the future. And I think that's where leading-edge providers like Cloud Science and others are going toward. People are going to expect greater transparency. And if you really want to democratize the development of drugs, you're 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's exactly what we see open medicine playing is a new category that just hasn't been valued in price. I'm writing a piece you'll see by mid-September, and it's called the deflation of discovery and the birth of a new category. And that specifically talks to Agentic AI and drug development and drug discovery.

Another question. I'll take -- sorry -- so someone's asking an interest with data from large pharma. The quick answer is, yes, we've got a lot of pharma companies, both biologic groups as whether it's small molecule groups. We've had some -- had several calls with us, some visit. So the answer is yes. Large pharma is definitely interested. This is something that they're all evaluating cutting deals on looking at and large pharma will have to partner with Agentic AI to make it commonplace. I mean it's transforming the economics of early development and also late-stage development.

So yes, very much increasing interest. The more marketing, more dollars we can put behind driving awareness of open medicine and with Zeta, the more I expect. The one thing that we've seen that has been solid is that once we put the tool in front of people, it gets very sticky. So yes, thank you. Take another important question.

Let's see if we can do this one live. We're trying to live I don't know go ahead and get to live. I can also we don't want to do it live.

I can read it also if you don't want to do it live.

So this is another question. is our models, we expect will be standards in computational biology and drug development. What are you doing to ensure that and that other competitors don't copy your methods? Well, I -- first of all, everyone will copy one or another. And that's 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 doesn't necessarily mean you're going to be the surviving entity. You can look at any industry and capital efficiency is important long term, which we've proven to be very capital efficient at. We're at a point where it needs to be a separate entity and raise its own capital to stay ahead of the curve.

The things that we're 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're doing. We're also constantly benchmarking like we did with our BBB algorithm like we're doing with our bio computational tools. We're trying to pick some of the toughest challenges and go deep as opposed to go abroad. And that's one of the things that were big components of is going deep in certain categories versus broad across all of science. I don't think we ever would have claimed, hey, we're going to 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 biocomputational tools, specific problems like blood brain barrier or penetration into any tissue type and do it and resolve it really, really well. So we're going to go after certain diseases that we think require that kind of depth and then march forward in that fashion. But yes, capital, no doubt, more capital is needed to drive that.

Let's go ahead and get to the next question. Yes, let's go to Baird and Red ship team. Maybe we can answer that 1 live Bard and Red ship team, if you guys want to ask your question live. They can ask their question live. You have to read the question.

Okay. All right. Dave, asking a question on what does adoption and feedback look like? The 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've introduced a new code called Wood Zeta 14 that people can sign up for and get the full professional addition, people who play the professional edition, specially generative chemistry, biocomputational tools, the investigator mode, it tends to be very, very sticky. So that's exciting news. Key is getting them to that point. So we're also beginning to implement some more aggressive e-mail campaigns to drive the awareness and specialized codes for certain larger pharma companies. But yes, great question. Okay. Another question is

Anonymous. What would you contemplate the biggest benefit of the open medicine spin-out will be for shareholders?

Well, Lantern owns 100% of open medicine today. We think it's poised to be very disruptive, disruptive companies are usually valued -- can be valued higher, and we're going to raise capital. Matter will continue being the largest shareholder, we think, for a while. PAUSE -- and we may explore ways to distribute those -- the underlying shares to all shareholders and lanterns. So those are things that we're talking about and potentially distribution of the shares of open medicine to all Lantern shareholders. Again, we're having discussions. We're 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're coming up to almost 45 minutes into the call. And we look forward to answering questions and one-on-ones as it continues. I know we have a couple of requests for some one-on-one follow-up meetings. We'll take those as well. And thank you, guys, for participating. I want to thank all the Lantern investors, people who are interested, and I look forward to giving you guys more updates as the year continues. Thank you, and thank you again to our team as well.

David Margrave

Thanks a lot.