ランタン・ファーマ (LTRN) 2026年第2四半期決算説明会：営業赤字の縮小、LP-300とOpen Medicine AI

主要ハイライト

• 2026年第2四半期の営業損失は、研究開発費が42％減の約180万ドルに減少したことにより、前年同期比25％縮小の約350万ドルとなりました。
• 純損失は主に、巨額の非現金公正価値調整を含む約360万ドルのワラント関連費用を反映し、約710万ドル（1株当たり0.57ドル）に拡大しました。
• 2026年6月30日時点の現金、現金同等物および有価証券の合計は約740万ドルでした。ランタン・ファーマは、5月の登録直接募集を通じて約440万ドルの総募集額を調達しました。
• LP-300のHARMONIC試験において、6サイクルを完了したL858R変異患者9人における無悪化生存期間（PFS）の中央値は8.9ヶ月に達しました。経営陣は、このコホートが依然として小規模かつ探索的な段階にとどまっていることを強調しました。
• ランタン社は、FDAとのタイプCミーティングを経て、LP-300の試験デザインをL858R変異患者中心に洗練させ、治療期間を6サイクルから最大8サイクルに延長するとともに、単群デザインへと移行しました。
• ランタン社は2026年8月、Open Medicine AIを独立した完全子会社として設立しました。同事業は独立して資金調達を行う予定ですが、ランタン社は自社の創薬プログラム向けにプラットフォームへの完全なアクセス権を維持します。

主要財務データ

2026年6月30日時点で、ランタン社の発行済普通株式数は12,759,146株でした。同社は当四半期中、ATM（アット・ザ・マーケット）株式売り出しプログラムによる実績はなかったと報告しました。

事業および業績の動向

LP-300およびHARMONIC試験

ランタン社は、HARMONIC試験の被験者登録を、TKI（チロシンキナーゼ阻害薬）治療後に進行した、L858R変異を有する非喫煙者の非小細胞肺がん患者に集中的に行っています。

6サイクルを完了したL858R変異患者9人のうち、無悪化生存期間（PFS）の中央値は8.9ヶ月であり、5月11日のデータカットオフ時点で3人の患者は増悪していませんでした。L858Rコホート全体におけるPFS中央値は8.4ヶ月、ハザード比は0.37、信頼区間は0.15〜0.89でした。

また、経営陣はL858R変異患者の70％以上で標的病変の縮小がみられ、臨床有用率は77％であったと報告しました。奏効が2年以上持続した例もありました。同社によると、安全性は4サイクルと6サイクルの間で同等でした。

FDAとのタイプCミーティングを受けて、ランタン社は治療期間を最大8サイクルに延長し、効率向上とコスト削減を目的とした単群デザインを採用しました。被験者登録は米国および台湾で継続されます。

LP-184の臨床拡大

欧州医薬品庁（EMA）は、進行性膀胱がんに対するLP-184の医師主導第Ib/II相試験を承認しました。コペンハーゲンで実施される39人を対象とした本試験では、PTGR1過剰発現とDNA損傷修復欠損に基づくデュアルバイオマーカー戦略が用いられます。

FDAはまた、再発または難治性のトリプルネガティブ乳がんに対するLP-184単剤療法の第Ib/II相試験を承認しました。ランタン社は、本試験で2つの用量コホートにわたり最大40人の患者を登録し、その後サイモン2段階デザインによる有効性評価を行う予定です。

ランタン社は、卵巣がん、肝臓がん、腎臓がん、甲状腺がんにおいて、PTGR1、PTPN14、ASPHの3遺伝子を用いた患者選定方法に関する特許査定通知を受領しました。

LP-284およびスターライト・セラピューティクス

LP-284は血液悪性腫瘍および成人の軟部組織肉腫を対象とした開発が継続されています。本プログラムは2026年はじめに希少用医薬品の指定を受けました。

スターライト・セラピューティクスは、LP-184の脳腫瘍適用版であるSTAR-001の開発を進めています。ランタン社は小児腫瘍学コンソーシアムと共同で臨床開発方針の策定に取り組むとともに、希少な小児脳腫瘍に対する人道的使用の選択肢も模索しています。スターライト社は引き続きランタン社の完全子会社であり、別途資金調達を行う見込みです。

Open Medicine AI

ランタン社は8月にOpen Medicine AI（OMAI）を別会社として正式に設立し、商用ライセンス契約を締結しました。OMAIは、文献合成、創薬化学、パスウェイ解析、データキュレーション、ポートフォリオ優先順位付け、臨床試験開発などの分野に特化したエージェントを組み合わせた同社のマルチエージェントAIシステムを商用運用することができます。

Open Medicine AIは現在、ランタン社が100％保有しています。経営陣は、OMAIの株式と引き換えに資金調達を行う意向であり、長期的には独立した上場企業を目指すとしています。ランタン社は大株主の一社にとどまり、自社の創薬のために同プラットフォームへの完全なアクセス権を維持する見込みです。

経営陣の見通し

経営陣は、改訂プロトコルに基づくLP-300の被験者登録により、今後4〜6ヶ月間で米国および台湾で約15〜16人の患者が追加されると予想しています。既存コホートに関するさらなる進捗状況は、2026年末頃に提供される可能性があります。

ランタン社は9月中旬に、プラットフォーム、市場機会、ロードマップ、事業モデルを網羅するOpen Medicine AI専用の説明会を開催する予定です。

追加の資金調達は引き続き最優先事項です。同社は、事業継続期間を延長するため、資金調達、提携、その他の機会を模索する計画であると述べました。

リスクと注目点

• 経営陣は、LP-300のL858Rに関する結果が、統計的有意性を検証するよう設計されていない小規模な探索的コホートから得られたものである点を強調しました。9人の患者から得られたPFS中央値は、データが蓄積するにつれて変化する可能性があります。
• ランタン社は当四半期を約740万ドルの現金、現金同等物および有価証券で終え、追加資金の確保を最優先事項として挙げました。
• Open Medicine AIが事業を拡大し競争力を維持するには、追加の資本が必要です。計画されている資金調達および長期的な単独上場は、完了した取引ではなく、依然として経営上の目標にとどまっています。
• ワラント負債の会計処理は、報告される純利益または純損失に重大な影響を与える可能性があります。第2四半期の業績には、主に非現金公正価値調整による約360万ドルのワラント関連費用が含まれていました。
• 同社の将来予想に関する記述の開示で強調されている通り、臨床開発は引き続き試験結果、規制プロセス、競合環境に左右されます。

アナリストQ&Aの要点

経営陣は、改訂されたLP-300のプロトコルに対する治験審査委員会（IRB）の承認が全治験施設で完了したと述べました。8サイクルプロトコルのもとで被験者登録が再開される見込みであり、次の15〜16人の患者により、より有意義な奏効データが得られることが期待されています。

Open Medicine AIについて、経営陣はシステムの透明性、監査証跡、企業ユーザーがワークフローを調整できる柔軟性を、従来のAI創薬ツールとの差別化要因として挙げました。また、大手製薬企業からの関心が寄せられていると報告したものの、商業的売上や顧客指標については示しませんでした。

経営陣は、ユーザーが同プラットフォームの専門ツールにアクセスした後のエンゲージメントを「非常に維持されやすい（リテンションが高い）」と表現しました。同社は認知度を高めるため、大手製薬企業などに向けて電子メールによるアウトリーチを強化し、専用のアクセスコードを活用しています。

株主価値に関して、経営陣はランタン社の株主に対してOpen Medicine AIの株式を分配する可能性のある方法を検討していると述べました。構造や時期についてはまだ決定していません。

決算説明会文字起こし全文

決算説明会の完全なトランスクリプト

経営陣による説明

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.

質疑応答

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.