Inside Moderna’s Personalized Cancer Vaccine

深入解析Moderna个性化癌症疫苗 在全行业数十年的癌症疫苗研发屡遭挫败之后，Moderna与默克（Merck）通过将标准疗法Keytruda与编码多达34种患者特异性新抗原的个性化mRNA疫苗联合使用，取得了积极的3期临床试验结果。该疗法通过对肿瘤组织与健康组织进行测序，引导免疫系统识别精准的体细胞突变，在2期随访中实现了5年约80%的无病生存率，并在3期试验中达到了无复发生存期和无远处转移生存期终点。Moderna设计了全自动、全合成的无细胞生产流程，大约42天即可产出供单个患者使用的药瓶。除了黑色素瘤之外，该平台正向非小细胞肺癌、膀胱癌、肾癌和胰腺癌领域拓展，并正将mRNA技术应用于罕见的儿科代谢性疾病和自身免疫性疾病。 Moderna与默克宣布，其个性化mRNA癌症疫苗（intismeran）在黑色素瘤的3期临床试验中取得积极结果，在首次中期分析中同时达到了无复发生存期和无远处转移生存期终点。 (3:45) 5年期2期随访数据显示，接受联合方案治疗的黑色素瘤患者中约有80%保持无病状态，相比Keytruda单药治疗改善了约50%。 (3:45) 该个性化疗法对肿瘤和健康组织的DNA均进行测序，将多达34种患者特异性新抗原编码进单一mRNA转录本中，端到端自动化生产周期约为42天。 (15:00) 监管申报工作正在推进中，目标是在2027年前向患者供药；马萨诸塞州马尔伯勒专门建造的生产设施将为此提供支持，旨在每年交付数万剂定制药剂。 (25:00)

Moderna and partner Merck reported positive Phase 3 results for an individualized mRNA cancer therapy in melanoma, providing clinical proof of concept for computationally designing bespoke treatments targeting a patient's specific tumor mutations. Clinical Efficacy and Complementary Immunotherapy Mechanism (3:45) In a randomized Phase 3 melanoma trial, the combination of Moderna's individualized mRNA vaccine and Merck's Keytruda met its primary endpoint of recurrence-free survival and secondary endpoint of distant metastasis-free survival at its first interim analysis. This builds on five-year Phase 2 follow-up data presented at ASCO, where approximately 80% of combination-treated patients remained disease-free compared to the roughly 60% historical benchmark for Keytruda alone. Stéphane Bancel attributes this clinical success to paired, orthogonal mechanisms. Checkpoint inhibitors broadly remove immune suppression—which can trigger severe autoimmune side effects while leaving 40% of patients without durable protection—whereas the mRNA vaccine directs T cells toward tumor-specific targets. Unlike past vaccine efforts that relied on circulating recombinant proteins, intramuscular mRNA migrates to lymph nodes, enters antigen-presenting cells, and stimulates immune training from within the cell. Computational Neoantigen Selection and Versioning (15:00) Because roughly 90% of tumor antigens differ between patients, Moderna sequences three billion base pairs of tumor DNA alongside healthy tissue to identify patient-specific somatic mutations. A proprietary algorithm isolates up to 34 neoantigens, which are concatenated into a single mRNA transcript synthesized for the patient. Bancel emphasizes that the Phase 3 melanoma trial utilized the initial "version 1.0" algorithm established a decade earlier. Moderna plans to apply artificial intelligence to blood and tissue samples from the 20% of patients who failed to respond at five years. The company intends to submit an updated "version 2.0" algorithm to regulatory authorities, targeting improved outcomes in non-responders and immunologically "cold" tumors. Synthetic Manufacturing and Process-Based Regulation (20:00) The needle-to-needle timeline—from tumor biopsy to sterile formulation—averages roughly 42 days. Unlike autologous CAR-T cell therapies that manipulate living cells in large bioreactors, Moderna's platform relies entirely on cell-free synthetic enzymatic chemistry run in water, bypassing bacterial plasmids and cell cultures. This chemistry allows hardware to be miniaturized, optimizing cleanroom space inside Moderna's commercial facility in Marlborough, Massachusetts, which is designed to produce tens of thousands of doses annually. Because every patient receives a distinct genetic sequence, regulatory review cannot evaluate each individual batch. Following the precedent of autologous CAR-T therapies, Moderna is pursuing a process Biologics License Application (BLA), requiring the company to prove to the FDA that the automated system consistently produces reproducible therapeutic batches from variable genomic inputs. Therapeutic Expansion Across Oncology and Rare Diseases (30:00) Having demonstrated de novo T-cell generation against encoded mutations, Moderna is expanding the platform across three oncology vectors: combining with checkpoint inhibitors in responsive tumors (Phase 3 in lung cancer; Phase 2 in bladder and kidney cancers); evaluating mRNA monotherapy in early-stage disease to avoid checkpoint autoimmune toxicities (Phase 3 in stage I lung cancer); and testing refractory indications (pancreatic and gastric cancers), including potential pairings with KRAS inhibitors. Beyond oncology, Moderna expects late-stage pivotal trial readouts by the end of the year for mRNA therapies treating pediatric metabolic liver diseases, alongside early-stage programs aimed at training the immune system to eliminate autoreactive cells in autoimmune diseases. Moderna and Merck target initial regulatory availability in melanoma as early as 2027. Key commercial questions remain unresolved, including pricing models, value-based reimbursement frameworks, and whether algorithmic iterations can successfully overcome treatment resistance in checkpoint-refractory solid tumors.

Moderna 首席执行官 Stéphane Bancel 指出，通过全基因组测序与算法筛选患者专属的新抗原突变，个体化 mRNA 癌症疫苗在黑色素瘤 3 期临床中实现了突破，使癌症疫苗从过去的通用靶点探索转向定制化的精准免疫治疗。 个体化突变筛选与胞内递送突破 (0:00) 在经历过去二十多年、超千次癌症疫苗试验失败后，Moderna 转变策略，将肿瘤视为 DNA 遗传疾病。该方案通过比对患者肿瘤与健康组织的 30 亿个碱基对，利用算法筛选最多 34 种关键新抗原并编码进单链 mRNA。临床数据显示，患者间约 90% 的肿瘤抗原完全不同，这解释了过去通用肽段疫苗难以见效的原因。与传统在血液中循环的重组蛋白疫苗不同，肌肉注射的 mRNA 进入淋巴结的抗原呈递细胞（APC）并在细胞内部翻译和呈递，从而有效诱导针对突变的主动免疫应答。 黑色素瘤 3 期试验与双机制协同 (3:45) Moderna 与默克（Merck）合作推进的 mRNA 疫苗联合 Keytruda 治疗黑色素瘤的 3 期试验，在首次中期分析中同时达到无复发生存期（RFS）和无远处转移生存期（DMFS）终点。在 2 期临床的五年随访中，联合用药组约 80% 的患者保持无病生存，较单独使用 Keytruda 的疗效提升约 50%。Bancel 解释，Keytruda 等免疫检查点抑制剂主要负责解除免疫刹车，但可能引发严重的自身免疫毒性且缺乏靶向性；而 mRNA 疫苗则为 T 细胞提供精确的突变识别特征，二者协同实现针对肿瘤细胞的精准攻击。 42天全合成工艺与流程BLA监管 (20:00) 与需要体外培养活细胞的自体 CAR-T 疗法不同，Moderna 采用纯体外无细胞酶促反应合成 mRNA，规避了质粒与大肠杆菌发酵，并将从肿瘤活检到完成制剂注射的生产周期控制在约 42 天。其位于马萨诸塞州马尔堡的工厂设计年产能可达数万剂，并正通过自动化和提高洁净室装配密度来降低固定成本。在监管审批方面，由于每位患者的核酸序列独一无二，FDA 参照 CAR-T 先例，通过“工艺生物制品许可申请”（Process BLA）对整个从测序输入到制剂产出的全流程重现性与质控体系进行审查，双方计划最快于 2027 年将该疗法推向市场。 拓展单药治疗与难治性实体瘤 (30:00) 基于在患者体内观察到的从头（de novo）生成 T 细胞的数据，Moderna 规划了三大拓展方向：一是在肺癌 3 期及肾癌、膀胱癌 2 期试验中推进与 Keytruda 的联合疗法；二是在早期 1 期肺癌术后作为单药使用，以避免检查点抑制剂的自身免疫毒性，仅承担类似常规疫苗的轻微副作用；三是进军胰腺癌、胃癌等传统对免疫检查点抑制剂耐药的“冷肿瘤”，并探索与新型 KRAS 抑制剂等靶向药的联用方案。 当前临床成果仍基于开发初期的“1.0 版本”算法，Moderna 计划利用人工智能分析 3 期临床中约 20% 未缓解患者的样本以升级算法模型；同时，该疗法涉及的生产成本与价值导向医保定价机制仍有待官方进一步披露。

## Background 本期节目由 a16z 普通合伙人 Jorge Conde 与 Moderna 首席执行官 Stéphane Bancel 联合主讲，于 2026 年 9 月 2 日发布在各大播客平台。节目简介指出，双方围绕 mRNA 技术的重大历史性里程碑展开深度对谈，重点探讨了 Moderna 与默沙东（Merck）联合研发的黑色素瘤个性化癌症治疗药物取得的三期临床阳性结果，以及该技术从制造工艺、算法筛选到多癌种拓展的全貌。 ## TL;DR 节目深入拆解了 Moderna 历时十余年研发的个体化 mRNA 癌症疫苗（Intismeran）如何在三期临床中打破癌症疫苗 20 多年的失败魔咒，并在 5 年随访中实现 80% 的无病生存率。Stéphane 详细解释了该疗法通过比对健康细胞与肿瘤 DNA，利用专有算法筛选出至多 34 个新抗原，以“纯信息输入”与全酶促合成实现 42 天“针头到针头”的定制化工业流程。与 CAR-T 或传统免疫检查点抑制剂相比，该方案以正交机制在分子层面精准“训练 T 细胞”，大幅规避了自身免疫副反应。此外，Moderna 正在推动算法向 2.0 迭代，并将这一平台由黑色素瘤拓展至肺癌、肾癌、胰腺癌等多个适应症，乃至罕见遗传病与个体化自身免疫疾病前沿。 ## Atmosphere & style 对话洋溢着一种严谨务实却又极具变革意味的科技乐观主义氛围。Jorge Conde 作为敏锐的行业投资人，提问直切工程瓶颈与底层监管逻辑，而 Stéphane Bancel 则展现出典型的工程师执念与创业者坚韧，在回顾十余年试错历程时既有战胜千次失败的底气，也有对未应答 20% 患者的深刻反思。整场对谈技术密度极高，没有浮夸的营销宣讲，而是通过“放狗与训狗”、“磨石刀与刀具店”等精妙比喻，让听众切实感受到前沿生命科学与工业自动化在交汇点上迸发出的历史性突破感。 ## Bulletin - `00:00` Moderna 与默沙东联合研发的个体化 mRNA 癌症疫苗（Intismeran）在黑色素瘤三期临床中取得历史性突破，不仅达成主要终点 RFS，并在首次中期分析中意外达到次要终点 DMFS。 - `05:00` 二期临床 5 年随访数据显示，mRNA 疫苗联合 Keytruda 治疗使患者无复发生存率较单药提升约 50%，术后 5 年无病生存率高达约 80%。 - `20:00` 该疗法完全基于纯数字化基因测序信息与无细胞全水相酶促合成，实现从活检到成药 42 天的周转期，规避了传统大肠杆菌发酵及 CAR-T 的体外细胞培养瓶颈。 - `27:32` 监管审批采用针对整套系统的“流程 BLA”（Process BLA）路径，核心标准在于输入相同样本时黑盒制造系统终产物的一致性与可重复性。 - `30:00` Moderna 正将管线拓展至肺癌、肾癌、胰腺癌等适应症，并已在实验室布局根治自身免疫性疾病的个体化 mRNA 疗法。 ## Key takeaways - `00:00` 这是肿瘤学历史上 20 多年来、经历 1000 多次失败后，首个在黑色素瘤中证明切实有效的癌症疫苗。 - `05:00` Keytruda 类似“打开大门放狗”，但存在 40% 的无应答盲区及终身性自身免疫损伤；mRNA 疫苗则通过分子机制直接精准训练 T 细胞识别靶向抗原。 - `10:00` 癌症疫苗并非预防初发，而是重塑免疫监视系统，防止既有癌症的复发。 - `10:00` 传统疫苗主要在血液循环中被动接触，而 Moderna 的 LNP 递送使 mRNA 进入淋巴结的抗原呈递细胞内部，实现高质量的原位抗原呈递。 - `10:00` 测序比对显示不同患者间的新抗原约 90% 完全不同，彻底打破了过去行业追逐“共享抗原”的局限，必须全面走向“一人一药”。 - `15:00` 当前临床使用的 Intismeran 筛选算法仍为 1.0 版本，Moderna 计划利用三期临床样本全面挖掘，推动算法迭代至 2.0 以覆盖 20% 未获益人群。 - `20:00` 与 CAR-T 依赖体外扩增活细胞不同，Moderna 仅需要基因序列信息，采用微型反应器进行无细胞的全水相酶促反应直接合成药物。 - `22:15` 早期制造策略优先杜绝假阴性与保证质量，临床数据验证后则通过压缩设备占地与缩短循环时间来极致降本。 - `25:00` 位于马萨诸塞州马尔伯勒的工厂具备年产数万剂个性化疫苗的能力，足以覆盖全球黑色素瘤的全部需求。 - `27:32` 针对个体化药品的特性，FDA 审评走的是类似 CAR-T 的“流程 BLA”路径，重点审查输入相同组织和血液样本后黑盒输出的复现性与稳健性。 - `30:00` 临床试验证明 mRNA 疫苗能够在患者体内从头诱导生成全新的肿瘤特异性 T 细胞（de novo T cells）。 - `30:00` 新抗原疫苗扩展战略涵盖三条路径：Keytruda 获批癌种的联合用药、早期一期非小细胞肺癌的单药辅助治疗，以及胰腺癌等冷肿瘤的全新联合攻坚。 - `35:00` 除肿瘤外，Moderna 的 mRNA 平台正向儿童罕见肝脏遗传病推进关键试验，并在实验室阶段探索清除自身反应性免疫细胞的个体化自身免疫疗法。 ## Detailed walkthrough ### `00:00` 个性化癌症疫苗的历史性突破与三期临床成功 a16z 合伙人 Jorge Conde 邀请 Moderna 首席执行官 Stéphane Bancel 重返播客。两人曾在 2020 年 12 月（播客题为《制造疫苗的机器》）讨论过 Moderna 如何在极短时间内设计并“打印”出 mRNA 新冠疫苗。Jorge 指出，2026 年 8 月双方再次对谈，是因为 Moderna 在利用 mRNA 技术攻坚癌症领域取得了里程碑式的飞跃。 Stéphane 宣布，Moderna 与默沙东（Merck）经过长达十年的联合研发，其针对黑色素瘤的个体化 mRNA 癌症治疗药物在三期临床试验中获得了积极结果。这是肿瘤学领域极其重大的时刻——在过去 20 多年里，该领域开展了超过 1000 次癌症疫苗临床试验，但几乎全部以失败告终。正如 Stéphane 所言：“It's the first time there is a cancer vaccine working.”（这是有史以来第一次有癌症疫苗真正起效。） 本次三期试验不仅成功达到了主要终点——无复发生存期（Recurrence-Free Survival, RFS，即避免癌症复发或死亡），而且令人惊喜的是，在首次中期分析（interim analysis）中就达到了次要终点——无远处转移生存期（Distant Metastasis-Free Survival, DMFS）。远处转移指标通常需要非常漫长的时间才能成熟，因此在首次中期分析中就达成该终点超出了团队的预期。 - **三期临床试验达成**：主要终点 RFS（无复发生存期）与次要终点 DMFS（无远处转移生存期）双双达标。 - **历史对比**：癌症疫苗领域此前 20 余年间累计有超过 1000 次临床试验全部宣告失败，本次是首个在黑色素瘤中证明疗效超越 Keytruda 单药的疫苗类产品。 --- ### `05:00` 长期生存数据与传统免疫检查点抑制剂的局限 Stéphane 回顾了在 2026 年春季美国临床肿瘤学会（ASCO）年会上公布的二期试验 5 年随访数据。在肿瘤临床中，5 年无病生存通常被临床医生视为临床治愈（cure）。二期数据显示，接受 mRNA 疫苗联合 Keytruda 治疗的患者，其 5 年无复发生存率较仅接受 Keytruda 单药治疗的患者提高了约 50%；手术联合治疗 5 年后，约 80% 的患者依然保持无病状态（disease-free），而在 Keytruda 单药组这一比例仅为约 60%。 针对 Jorge 提出的“Keytruda 是什么以及为何单药不够”这一问题，Stéphane 做出了深入剖析： 1. **未满足的应答率盲区**：Keytruda 作为主流的免疫检查点抑制剂（checkpoint inhibitor），其机制通俗来说就像是“打开大门放狗去咬肿瘤”。虽然对于产生应答的 60% 患者来说效果显著，但仍有 40% 的患者治疗无效，最终面临复发甚至死亡。 2. **严重的自身免疫毒副作用**：免疫疗法与放化疗不同，其毒性主要体现为机体自身免疫性疾病。使用检查点抑制剂的患者常出现 1 型糖尿病、红斑狼疮（Lupus）、克罗恩病（Crohn's disease）等终身性免疫疾病。对于那 40% 无法获益的患者而言，他们不仅未能控制癌症，反而平白承受了严重的自身免疫损伤。 为了解决这一难题，Moderna 在 2015 至 2016 年间利用在传染病 mRNA 疫苗开发中积累的免疫学机制认知，主动寻找免疫治疗领军企业默沙东展开战略合作。团队希望开发出一种与免疫疗法正交（orthogonal）的全新作用机制： - **Keytruda 的角色**：解除免疫抑制开关，释放体内的 T 细胞（“放狗”），但缺乏精准的导航方向，容易漫无目的地攻击或误伤正常组织。 - **mRNA 个性化疫苗的角色**：基于患者肿瘤细胞全外显子测序，在分子层面直接“训练 T 细胞（教狗认准目标）”，精准识别患者肿瘤细胞特有的突变抗原（新抗原），驱动 T 细胞发起定向清除。 ```mermaid flowchart TD accTitle: 联合疗法协同机制示意图 accDescr: 展示 Keytruda 解除免疫抑制与 mRNA 疫苗提供特异性制导在肿瘤杀伤中的协同作用 subgraph KeytrudaAction ["默沙东 Keytruda (免疫检查点抑制剂)"] K1["阻断免疫检查点信号"] --> K2["释放免疫系统 T 细胞 ('放狗')"] K2 -.-> K_limit["局限: 缺乏特异性制导 / 40% 患者无应答 / 自身免疫副反应"] end subgraph mRNAAction ["Moderna 个性化 mRNA 癌症疫苗"] M1["比对患者健康细胞与肿瘤细胞 DNA"] --> M2["算法筛选出患者特异性突变抗原"] M2 --> M3["编码 mRNA 疫苗注射入体内"] M3 --> M4["分子层面精准训练 T 细胞识别靶标 ('训狗')"] end K2 ==> Synergy["协同杀伤黑色素瘤细胞"] M4 ==> Synergy ``` - **获批预期**：Moderna 目前正与监管机构密切合作推进申报工作，力争在 2027 年实现该药物的上市上市，并尽全力加快流程。 - **生产制造基础**：位于马萨诸塞州的工厂已经就绪，Moderna 已跑通为每一位患者按需规模化制造专属个性化药物的全流程。 ### `10:00` 重新定义“疫苗”与 mRNA 癌症疗法的双重突破 主持人 Jorge Conde 首先厘清了“癌症疫苗”这一概念。在常规认知中，疫苗的作用是“预防疾病”，但在这项研究中，患者本身已经患有癌症；因此，它预防的不是癌症本身的初发，而是癌症的复发。Stéphane Bancel 完全赞同这一界定，并指出使用“疫苗”一词并非 Moderna 独创，而是沿用了该领域长达千余次临床试验的历史习惯。其核心逻辑在于对免疫系统的“再教育”：常规的流感或新冠疫苗是在病毒感染前教育免疫系统，而癌症疫苗则是教导免疫系统识别它此前所遗漏的癌细胞信号。人体每天都在发生细胞复制错误并产生突变细胞，正常情况下免疫系统能及时清理；但当肿瘤开始生长，就意味着免疫监视失效，必须通过治疗手段重新唤醒免疫认知。 过去数十年间，癌症疫苗领域经历了上千次临床试验的全面溃败。谈到 Moderna 联手默沙东（Merck）为何能打破这一僵局时，Stéphane 归结为两大关键维度的突破： 1. **mRNA 的胞内抗原呈递机制**：根据 Moderna 早在 2015 年与瑞典卡罗林斯卡学院（Karolinska Institute）联合发表的研究，当其特定的 mRNA-脂质纳米颗粒（LNP）肌内注射后，会定向迁移至淋巴结，并直接被抗原呈递细胞（APC）摄取。细胞在内部将 mRNA 转译为抗原多肽，实现“由内向外”的原位递呈。这与过去基于重组蛋白或多肽的传统疫苗有着本质区别——后者注入体内后主要在血液循环中游弋，虽然免疫系统也能接触到，但激发的呈递深度与免疫应答质量完全不可同日而语。 2. **纯粹的个体化定制理念**：癌症本质上是 DNA 突变积累的疾病。以往的研究往往试图寻找多名患者之间的“共享抗原”（Shared Antigen），但由于近二十年来测序成本的大幅骤降，Moderna 选择对患者进行“一人一药”的全面定制。 整个技术流程高度依赖精准计算：团队获取患者肿瘤活检组织并提取 DNA，读取其全部 30 亿个碱基对（约 3GB 数据量），再与患者本人的健康细胞进行逐个碱基的精准比对。随后，通过算法从成百上千个突变中筛选出免疫相关性最高的突变，锁定至多 34 个新抗原编码拼接进一条完整的 mRNA 分子中，并在约 30 天内制造完成送往医院进行肌内注射。Stéphane 重点强调了他们在 ASCO（美国临床肿瘤学会）大会上公布的惊人发现：“患者与患者之间大约有 90% 的抗原是完全不同的。”过去行业执着于共享抗原，而实际数据证明，没有个体化，所谓的肿瘤特异性免疫就是无源之水。 ```mermaid flowchart TD accTitle: mRNA 个体化癌症疫苗的作用机制对比 accDescr: 对比传统蛋白疫苗的外周循环机制与 Moderna 个体化 mRNA 疫苗的原位免疫呈递流程。 subgraph Traditional["传统外周蛋白/多肽疫苗"] T1["体外反应器制备共享蛋白/多肽"] --> T2["注射入血液循环"] T2 --> T3["抗原在血流中循环被动暴露"] T3 --> T4["免疫识别效率有限，千余次试验失败"] end subgraph Moderna["Moderna 个体化 mRNA 疫苗"] M1["活检测序比对 30 亿碱基 (健康 vs 肿瘤)"] --> M2["算法筛选至多 34 个特异新抗原"] M2 --> M3["拼接合成单条 mRNA 并肌注"] M3 --> M4["转运至淋巴结并进入 APC (抗原呈递细胞)"] M4 --> M5["APC 细胞内部转译并高强度原位呈递"] end ``` --- ### `15:00` 揭秘“34 个抗原”算法黑箱与 1.0 版候选药物（Intismeran） Jorge Conde 追问这套筛选至多 34 个新抗原的算法细节：究竟是一个行业通用的公用方案，还是 Moderna 高度机密的黑箱系统？Stéphane 解释称，算法底层融合了深厚的专有技术与商业机密。在启动之初，Moderna 整合了顶尖免疫学及肿瘤学文献与公共数据库，随后不断加入内部积累的大量私有实验数据，并与外部 AI 诊断及细胞疗法公司合作，通过海量 T 细胞图谱数据训练模型。 Stéphane 透露，当前临床试验中使用的候选分子（Intismeran）其核心筛选算法实际上是“1.0 版本”。因为该项目已推进十年，从早期 Phase 1、Phase 2 到如今的 Phase 3，为了维持方案的一致性，沿用的是同一套算法。尽管已是十年前构建的算法模型，Phase 2 临床数据依然展现出惊人的疗效：80% 的患者在五年后依然处于无病生存状态。 然而，面对另外 20% 无响应的患者，Moderna 计划全面挖掘 Phase 3 阶段积累的丰富患者血液样本与全基因组测序数据，深入剖析应答与不应答人群背后的分子机制。如果发现确凿的科学依据，他们将向监管机构（如 FDA 等）申请升级算法，推动算法从 1.0 演进至 2.0，进而提升治疗覆盖率。Stéphane 用 AI 领域的一个经典警句打了个生动的比方： > “当谈论 AI 时，我们常开玩笑说当前版本的 AI 将是我们有生之年见过的最差版本。这对 Intismeran 来说也完全一样，Moderna 目前版本的 Intismeran 就是人类医学史上最差的一版 Intismeran。” 这一视角令整个团队对未来充满信心。这不仅意味着黑色素瘤中 20% 未获益人群有望迎来转机，更为胰腺癌等传统免疫检查点抑制剂束手无策的“冷肿瘤”破局提供了技术可能。 - **阶段总结与数据概览**： - 核心候选药物代号：Intismeran（当前运行算法版本为 1.0）。 - Phase 2 临床追踪结果：受试患者在长达 5 年随访中的无病生存率（Disease-free survival）高达 80%。 - 患者特异性：不同患者之间的肿瘤新抗原重合度仅约为 10%，90% 的抗原标签具有严格的个体排他性。 - 升级路径：利用 Phase 3 期间获取的大规模多组学与血液配对样本进行回溯挖掘，在保证不损失既有疗效的前提下推动算法由 1.0 迈向 2.0。 随后的对话切入工业工程与运营落地。Jorge 回忆起早年结识 Stéphane 时对他的深刻印象：Stéphane 骨子里是一位“不折不扣的工程师”，从创业第一天起就对工业流程、操作规范与制造极致效率有着近乎偏执的追求。面对这种 90% 抗原全不相同的极限场景，Moderna 必须为每一位患者单独定制一条 mRNA。为了阐明这种运营工程的严苛程度，Stéphane 开始引入同为个体化定制前沿的 CAR-T 细胞疗法作为对比标杆，准备深入拆解其背后的供应链与商业化工业奇迹。 ### `20:00` 个性化 mRNA 癌症疫苗与 CAR-T 的本质区别：纯信息驱动与全化学酶促合成 Jorge Conde 提到，外界常将这种个性化癌症疫苗与 CAR-T 细胞疗法相提并论。CAR-T 的基本逻辑是从患者体内提取免疫细胞，在体外进行基因重编程改造，使其能识别并攻击肿瘤，再回输到患者体内。Moderna 的流程虽然也是以患者肿瘤活检切片为起点，但其制造逻辑完全不同。Jorge 询问，从获取患者肿瘤活组织到完成疫苗回输，所谓“从静脉到静脉”（vein-to-vein）的周转时间究竟是怎样的。 Stéphane Bancel 阐明，Moderna 目前实现的“针头到针头”（needle-to-needle）周期大约是 42 天——即从医院完成肿瘤活检采样，到个性化疫苗制备完毕送回医院供患者注射，总共约需 42 天。随着未来进一步引入自动化与机器人技术，这一周期还将继续缩短。更重要的是，该疗法与 CAR-T 存在根本性区别：“我们不需要提取你的免疫细胞，在工厂反应器里进行体外编程再送回医院；我们唯一需要的只是信息。”（“We don't have to take your immune cells. We program them ex vivo in a reactor in our factory and send them back to your hospital. The only thing we need is the information.”） 实验室只需要把患者健康细胞的基因测序数据和肿瘤细胞的基因测序数据生成电子文件发送给 Moderna。随后，Moderna 依据该数据直接进行纯人工合成。他们完全摒弃了传统生物制药利用大肠杆菌（E. coli）发酵扩增质粒 DNA 的繁重路线，采用纯水相全酶促反应（all enzymatic in liquid, in water）合成 DNA 模板，再转录出 RNA 并包裹脂质纳米颗粒（LNP）。这种无细胞的合成工艺使其制造逻辑更接近小分子化学药，而非传统重组生物大分子。CAR-T 类似于重组蛋白发酵，活细胞一旦被过度挤压就会死亡，因此必须依赖体积庞大的生物反应器；而 Moderna 的全水相酶促催化反应效率极高，所使用的反应器极其微小。 ```mermaid flowchart LR accTitle: 从肿瘤活检到注射的 42 天个性化疫苗流程 accDescr: 展示从医院获取样本测序，经数字化文件传输，到无细胞化学酶促合成并回输患者的完整闭环。 subgraph S1["临床端获取样本与测序"] A["患者活检与血液样本"] --> B["测序健康与肿瘤细胞"] B --> C["数字化序列文件"] end subgraph S2["Moderna 工业化制造"] C --> D["全水相酶促合成 DNA 模板"] D --> E["体外转录合成 mRNA"] E --> F["包裹脂质纳米颗粒 (LNP)"] end subgraph S3["成品回输"] F --> G["42 天内送达医院注射"] end ``` - **42 天针头到针头周期**：涵盖临床采样、双基因测序比对、算法设计、mRNA 酶促合成、LNP 包裹、质检出厂的全流程时间。 - **纯数字化输入**：生产端不接收患者活细胞，仅接收健康细胞与癌细胞的基因组测序数据文件。 - **全合成与微型反应器**：告别大肠杆菌质粒繁殖模式，全水相酶促反应大幅缩减了物理反应体积。 --- ### `22:15` 冰箱大小的初始原型、厂房面积强迫症与极致降本工程 Stéphane Bancel 回忆了制造设备演进的坎坷历程。为了赶在 1 期临床前实现针对单一患者定制生产的技术验证，团队必须在极短时间内造出原型机。第一代机器体积庞大，像一台巨大的美式双开门冰箱，笨重且运行不够流畅。当时管理层给工程团队明确下达了指令：优先确保生化输出质量零失误，绝不在早期过度追求自动化效率。Bancel 解释背后的惨痛逻辑：如果在科学结论尚不明确前浪费五年去打造一台完美无瑕的机器人，而最终临床证明机理不成立，那就是彻底浪费时间；更致命的是，如果制造设备质量不稳定导致临床试验出现假阴性，“如果它本来有效，却因为设备问题得出科学不成立的结论，那对全人类来说显然是一场灾难。”（“If it should have worked, right, that would be a disaster for humanity, obviously.”） 工程团队严格执行了这一策略。当 2 期临床取得积极的中期数据（随访 2 年显现显著疗效，如今数据已成熟积累至 5 年且疗效持久），Bancel 立即意识到制造效率必须全面升级。公司抽调了大批顶尖工程师重新设计硬件，全力压缩设备体积与循环时间。Bancel 强调，在固定洁净室（Clean room）物理空间下，降低制造成本的核心在于降低设备占地面积：如果同一洁净室内能容纳 2 倍乃至 10 倍密度的生产单元，产能就会成倍扩张，固定资产折旧分摊便会大幅下降。Bancel 自嘲道，自己每次去工厂视察时都是个让人头疼的“挑刺者”，对占地平方英寸和周转时间极度痴迷，甚至要求把计算服务器等非核心硬件搬出洁净室，只为给流水线抠出更多空间，从物理底层拉低终产品成本。 - **原型开发策略**：1 期临床前采用“保证质量与杜绝假阴性优先、不苛求生产效率”的研发导向。 - **数据成熟度**：从 2 年期积极中期数据，进一步成熟至 5 年期的长期疗效持久性追踪。 - **降本两大杠杆**：极致缩短单批次周转时间（Cycle Time）以提升年资产周转率；极致压缩洁净室地面占用（Footprint / Square inches）以放大单位面积吞吐量。 --- ### `25:00` 黑色素瘤产能覆盖、商业化定价与工业化防线 在产能与供需方面，Jorge Conde 询问若只针对黑色素瘤这一单一适应症，全面商业化阶段每年需要生产多少剂疫苗。Stéphane Bancel 指出，由于目前有 9 项临床试验正在同步推进，Moderna 已经累计生产了数千剂个性化疫苗。Moderna 位于马萨诸塞州马尔伯勒（Marlborough, Mass.）的专属生产基地，未来将具备年产“数万剂”个性化疫苗的能力。随着装配技术与工艺的持续升级，该厂房的单位产能还会进一步提升；如果未来适应症全面铺开，公司也会在全球复制建设更多同类工厂。但仅就黑色素瘤的发病率而言，每年数万剂的制造能力已完全足以全面覆盖这一适应症的全球市场需求。 当 Jorge 问及目前的生产成本（COGS）及未来商业定价策略时，Bancel 表示目前尚未对外公开。Moderna 计划在正式公布完整的临床终点数据后，再与医疗保险支付方（Payers）展开面对面的准入沟通，基于疫苗为患者带来的无复发生存期等长期临床价值来厘定价格。 Jorge 还提到当前硅谷与科技界出现的新现象——例如 GitLab 创始人兼 CEO Sid Sijbrandij 在确诊骨肉瘤后，利用技术资源和极客思维开启“创始人模式”（Founder Mode）自救。针对未来这类“N=1”超级个性化自救者是否会演化为独立平行的自制路线，Bancel 直言，未来绝大多数类似患者最终依然会选择 Moderna。核心原因在于注射剂药品的安全门槛极高：“向人体注射的产品只要污染了一个细菌，就可能引发患者的败血症。”（“If you inject to somebody a product that has even one copy of bacteria you might give the patient's sepsis...”）复杂的生物合成链条容不得丝毫差错。Bancel 打了一个生动的人类演化比方：当你在远古荒野、食不果腹且没有刀具店时，你必须亲手磨制第一把石刀来喂饱家人；但当街角已经出现工业化量产高品质精密刀具的专卖店时，正常人都会把宝贵的时间留去做更重要的事。 - **马尔伯勒工厂产能**：当前单体工厂具备年产“数万剂”个性化疫苗的设计吞吐量，可完全满足黑色素瘤的整体临床需求。 - **商业化准入路线**：优先公布确证性临床数据，再与医保支付方围绕临床增量价值协商定价。 - **GMP 工业化壁垒**：防范无菌注射剂微生物污染及流程错误的高昂合规门槛，构成了普通自制疗法无法逾越的安全护城河。 --- ### `27:32` 监管机制的底层逻辑：从审批“单一产品 BLA”走向审批“黑盒生产流程 BLA” Jorge Conde 探讨了 personalization 疗法所面临的最深层次制度性挑战：既然每一位患者拿到的 mRNA 序列与最终药剂都是独一无二的，美国 FDA 究竟该如何对这种“每一剂都不同”的药物进行审批？审核的对象究竟是 mRNA 合成工艺、新抗原挑选算法，还是整套集成系统？ Stéphane Bancel 回应称，行业对此并非毫无先例。CAR-T 疗法的获批已经为监管探索铺平了道路——它遵循的是流程生物制品许可申请（Process BLA），而非传统单一化学结构的药物许可申请（Product BLA）。Moderna 过去虽已手握 5 款获批上市的传统 mRNA 疫苗与产品，但在个性化癌症疫苗（Intismeran）上，从最初申报临床试验研究许可（IND）阶段开始，走的就是“流程 IND”（Process IND）。早在多年前启动 1 期临床前，Moderna 就与 FDA 展开了深度的工艺控制安全性论证；在启动后续 3 期临床前，双方也在 2 期临床结束会议（End of Phase 2 Meeting）上就试验方案设计和生产规范标准达成了严密共识。 Bancel 强调，外界常误以为药企是闭门研发十年再突然把新概念推到监管机构面前，但事实是双方多年来一直保持着极其紧密的技术交流。当 Moderna 在制造前沿遭遇技术瓶颈或工艺改进时，会主动向 FDA 申请补充指导会议，既向监管层科普新技术演化，也同步吸取其合规指引。Bancel 一针见血地指出，FDA 监管审查的核心诉求非常朴素且合法：“FDA 真正想知道的，也是如果换成我为自己家人考虑时绝对想要确定的，就是如果你在前端输入相同的样本——也就是同一份肿瘤活检和血液，经过这整个庞大的‘黑盒’处理后，在后端制造出的是否是完全相同的药物产品。”（“Jorge, what the FDA wants to know, which is very legitimate, which I would want for my own family's sake, obviously, which is if you get the same sample at the beginning, the tumor, and the blood, so you get the same product make at the end of a big black box.”）Moderna 目前需要并正在用全流程数据向自己及 FDA 坚实证明的，正是这套全自动算法与生产管线拥有绝对的工业稳健性（Robustness）：即确保在输入条件一致的情况下，黑盒系统的生产输出永远稳定复现。 - **监管审评路径**：走以生产系统为核心的“流程 BLA”（Process BLA）路径，而非针对固定分子结构的传统“产品 BLA”。 - **长达数年的技术沟通**：从最初的 Process IND 到 2 期结束会议，再到 3 期试验生产方案，监管互动始终高度同步。 - **黑盒复现性验证**：整个审评审批的核心考核标准，在于证明同一原始输入样本通过整套算法及制造黑盒后，其终产品的质量与活性具备 100% 的一致性与可重复性。 ### `30:00` 个体化癌症疫苗的扩展路径：从黑色素瘤走向更多癌种 Jorge Conde 询问，在黑色素瘤取得里程碑式成果之后，Moderna 如何思考将这种个体化治疗拓展到其他癌种，以及癌症疫苗未来究竟有多大的应用空间。Stéphane Bancel 表示他们的雄心非常巨大，因为团队已经向自己乃至全世界证明：Moderna 的 mRNA 平台能够实现对 T 细胞的“从头教育（de novo education）”。在当年的 ASCO（美国临床肿瘤学会）年会上，Moderna 展示了患者接受治疗前后的血液对比分析，结果表明体内不仅发生了 T 细胞扩增，而且切实生成了能够识别 mRNA 所编码新抗原的全新 T 细胞——而这些 T 细胞在患者用药前是完全不存在的。Stéphane Bancel 明确指出：“So we already in my book have proven to ourselves and to the clinical community that Moderna's mRNA technology can teach the immune system to develop new T cells to go attack your cancer.” 基于这一已被证实的机制，Moderna 正在沿着三个主要方向推进其适应症扩展战略： ```mermaid flowchart TD accTitle: mRNA癌症疫苗临床扩展三条路径 accDescr: 展示Moderna基于不同作用机制从黑色素瘤向其他癌种推进的三种临床策略。 subgraph V1["路径 1：Keytruda 已获批的癌种（联合用药）"] A1["肺癌 (III期)"] A2["肾癌 / 膀胱癌 (II期)"] end subgraph V2["路径 2：早期肿瘤阶段（单药治疗）"] B1["I期非小细胞肺癌"] B2["手术切除 + mRNA 单药辅助治疗"] end subgraph V3["路径 3：免疫检查点无效的癌种（探索全新联合）"] C1["胰腺癌"] C2["胃癌"] C3["联合新型靶向药 (如 RAS 抑制剂)"] end V1 --> R1["正交机制产生协同增效"] V2 --> R2["规避免疫检查点抑制剂的终身自免毒性"] V3 --> R3["依靠从头激活T细胞突破冷肿瘤限制"] ``` 第一条路径是进军所有帕博利珠单抗（Keytruda，抗 PD-1 单抗）已获批有效的癌种。由于个体化新抗原疫苗（mRNA-4157 / V940，即 intismeran）与 PD-1 抑制剂的作用机制完全正交，二者具有高度协同作用。目前，Moderna 针对非小细胞肺癌的临床 III 期研究以及针对肾细胞癌、膀胱癌的临床 II 期研究均在全面推进中。虽然在正式临床数据读出前无法断定具体疗效能提高 30%、40% 还是像黑色素瘤 II 期那样达到近 50%，但这类研究已有极强的科学依据支持。 第二条路径是切入疾病早期，将 mRNA 疫苗作为单药使用。Moderna 宣布计划于 2026 年春季启动一项针对 I 期肺癌患者的 III 期临床试验，该试验中 intismeran 将作为单药辅助治疗，不联合任何免疫检查点抑制剂。在早期癌症中，临床上普遍不使用检查点抑制剂，因为这类药物存在严重的终身性自身免疫副反应风险；而对于戒烟人群等高危群体的定期 X 光或 CT 筛查，一旦发现 I 期肿瘤并进行手术切除后，给予副作用仅类似于普通疫苗（如仅有单日疲劳感）的 mRNA 疫苗，能够为防止复发提供兼顾极高安全性与有效性的新方案。 第三条路径则是进军检查点抑制剂完全无效的癌种，例如胰腺癌和胃癌。尽管以往 Keytruda 在这些癌种中的试验均告失败，但由于 mRNA 能够从头激发靶向肿瘤的特异性 T 细胞，Moderna 与默沙东（Merck）认为这依然值得尝试。此外，随着近期 Revolution Medicines 针对 RAS 突变胰腺癌新药取得突破，未来将靶向机制完全正交的 mRNA 疫苗与此类小分子药物进行联合治疗，有望为“癌中之王”带来突破性生存获益。 - 在 ASCO 展示的血液免疫学分析证实，治疗后患者体内出现了 mRNA 诱导生成的全新特异性 T 细胞（de novo T cells）。 - 黑色素瘤的关键研究覆盖了 II 期、III 期及 IV 期高危术后患者。 - 肺癌已进入临床 III 期试验阶段，肾癌和膀胱癌处于临床 II 期阶段。 - I 期肺癌单药 III 期试验于 2026 年春季正式宣布推进。 --- ### `35:00` 算法优化与下一代平台：罕见病与自身免疫疾病的新前沿 针对当前 intismeran 1.0 版本取得的成绩，Stéphane Bancel 强调这仅仅是起点。他极力敦促内部研发团队跳出传统框架，利用人工智能（AI）深入挖掘海量临床数据。他尤其关注临床试验中那 20% 在 5 年随访期内仍然没有应答的患者：“Because I think you always learn more from things that don't work than anything that works, so I want to obsess about the 20% of patients that do not respond five years out so we can understand why they did not respond.” 他希望弄清楚其中的生物学原因，从而迭代算法与递送技术，使下一代疫苗能够帮助到这部分未应答患者。 Jorge Conde 感叹道，Moderna 在疫情前并非专门针对全球大流行构建平台，但在疫情期间成功向全球亿万人群提供了疫苗，如今又将积累的工业化与生物学实力重新聚焦回最初立项的方向。Stéphane Bancel 对此表示赞同，并透露在罕见遗传病领域，Moderna 针对儿童罕见肝脏遗传病的注册关键性研究（pivotal studies）将于年底前获得数据，早期 I/II 期研究中接受治疗长达三年的患儿目前状况极佳。 此外，在当年的年度科学日（Science Day）上，Moderna 宣布其 mRNA 技术平台要征服的下一座高峰是自身免疫性疾病。传统制药行业主要侧重于缓解症状，而无法解决自身免疫的根本病因；Moderna 凭借在传染病与肿瘤领域对人体免疫机制积累的深刻认知，正在开发靶向根因的疗法。当 Jorge Conde 询问这会是标准化现货型产品还是个体化流程时，Stéphane 解释称两者兼具：春季展示的是适用于所有人的标准化通用产品，而在实验室阶段，他们正在探索极具突破性的“个体化自身免疫疗法”，通过精确引导免疫系统消灭那些错误将自体组织识别为抗原、失控攻击身体的特定自身反应性免疫细胞，从根本上终结自身免疫反应。 - 研发重点：利用 AI 分析临床未应答数据，持续优化 mRNA 抗原选择算法与底层技术。 - 罕见病管线：儿童罕见肝脏遗传病的 mRNA 关键临床研究预计在年内读出数据。 - 自身免疫管线布局：同步推进标准化现货型产品和实验室阶段的个体化自体免疫细胞清除疗法。 - 节目结语：Jorge Conde 盛赞 Moderna 从传染病跨越到癌症乃至自身免疫疾病的平台扩展历程，并相约未来就自身免疫新进展录制播客访谈第三部曲。 ## Notable quotes > `00:00` It's the first time there is a cancer vaccine working. — Stéphane Bancel > `30:00` So we already in my book have proven to ourselves and to the clinical community that Moderna's mRNA technology can teach the immune system to develop new T cells to go attack your cancer. — Stéphane Bancel > `35:00` Because I think you always learn more from things that don't work than anything that works, so I want to obsess about the 20% of patients that do not respond five years out so we can understand why they did not respond. — Stéphane Bancel ## Mentioned Moderna, 默沙东 (Merck), Stéphane Bancel, Jorge Conde, Keytruda (帕博利珠单抗), Intismeran (mRNA-4157 / V940), FDA (美国食品药品监督管理局), ASCO (美国临床肿瘤学会), Karolinska Institute (卡罗林斯卡学院), CAR-T, Sid Sijbrandij, GitLab, Revolution Medicines ## Video description a16z General Partner Jorge Conde sits down with Moderna CEO Stéphane Bancel to discuss a major milestone for mRNA technology: positive Phase 3 results from Moderna and Merck’s individualized treatment for melanoma, after more than a decade of work on personalized cancer vaccines. Stéphane explains how the treatment works by sequencing an individual patient’s tumor and healthy cells, identifying the mutations most relevant to their cancer, and encoding up to 34 of them into an mRNA designed specifically for that patient. Rather than simply unleashing the immune system, the goal is to teach it exactly what to recognize and attack. They also unpack the engineering challenge of manufacturing a different medicine for every patient, how Moderna has brought the process down to roughly 42 days from biopsy to treatment, and what it would take to manufacture personalized medicines at scale. Finally, Stéphane looks beyond melanoma to lung, kidney, bladder, pancreatic, and gastric cancers, as well as Moderna’s longer-term work applying mRNA to rare genetic and autoimmune diseases. [Source](https://mgln.ai/e/1344/afp-848985-injected.calisto.simplecastaudio.com/3f86df7b-51c6-4101-88a2-550dba782de8/episodes/457cc002-6623-42f4-8cc7-1bc2f129144b/audio/128/default.mp3?aid=rss_feed&awCollectionId=3f86df7b-51c6-4101-88a2-550dba782de8&awEpisodeId=457cc002-6623-42f4-8cc7-1bc2f129144b&feed=JGE3yC0V)

[0:00] Stéphane Bancel: It's the first time there is a cancer vaccine working. [30:00] Stéphane Bancel: So we already in my book have proven to ourselves and to the clinical community that Moderna's mRNA technology can teach the immune system to develop new T cells to go attack your cancer. [35:00] Stéphane Bancel: Because I think you always learn more from things that don't work than anything that works, so I want to obsess about the 20% of patients that do not respond five years out so we can understand why they did not respond.

[3:45] Stéphane Bancel: It's the first time there is a cancer vaccine working.

[0:00] It's the first time there is a cancer vaccine working. The field has been doing that for 20 plus years, more than a thousand clinical trials that have all failed. What was different this time? What is it about mRNA technology that enables the immune system to learn in a way that other approaches were unable? We all have cancer cells all the time in our body. Our immune system is very well trained to basically notice those cancer cells very early and get rid of them. then the question is how can you retake the immune system? We're going to basically take a biopsy of your tumor. We're going to read all the letters of its DNA. And then we're going to do the same things with a healthy cell of your body. And we're going to literally compare letter by letter, nucleotide by nucleotide. And then we're going to use an algorithm to identify which one of those mutations are the most relevant. And so when this is injected in your body, it teaches the immune system the signature of your cancer cell that it missed. What gets regulated here? Because every dose is different. Basically, what the FDA wants to know. the company was already working toward another goal: using mRNA to fight cancer. A decade later, That bet has reached a major milestone. A-16Z general partner Jorge Conde sits down with Moderna CEO Stefan Banzel following positive phase 3 results from Moderna and Merck's individualized mRNA treatment for melanoma. It's the culmination of a project Moderna has been working on for roughly a decade. Stefan explains how Moderna takes a patient's tumor, sequences it against their healthy cells, identifies the mutations unique to that cancer, and uses that information to manufacture a to attack. And because roughly 90% of the selected antigens differ from one patient to another, personalization isn't an edge case. It's fundamental to how the treatment works. They also get into the extraordinary operational challenge behind this. How do you manufacture thousands of different medicines, one patient at a time, quickly and reliably enough to make personalized medicine work at scale? And finally, they look at where Hi, welcome to the A16Z podcast. I'm Jorge Conde, a general partner on the A16Z BioN Health team. I am thrilled today to welcome back Moderna CEO, Stefan Bancel. For folks that have been long-time listeners may recall... Stefan joined us on the A6 and Z podcast back in December of 2020. when we were talking about all of the work Moderna did to bring us the mRNA COVID vaccine. And at the time, if you can go back and listen to that episode, you'll hear how quickly, Moderna was able to, to react to the existence of the virus to analyze it, to essentially print a vaccine that would protect people against the COVID-19 vaccine and hence put us in a much better position with respect to the COVID pandemic. We titled that episode, The Machine That Made the Vaccine. And the reason why we're talking again today in August 2026, is because Moderna... has put out news on a really big advancement on what they can do with mRNA technology. when it comes to cancer. And so I want to hand it over to you, Stefan. Again, a very big welcome. Thank you for coming back. But maybe the place to start is let's lead with the news. Moderna and Merck announced earlier this month in August that you had conducted a phase three clinical trial in melanoma and have gotten very encouraging results. So why don't I hand it over to you and tell us, what have you announced? What have you seen in this phase three trial? And then I do want to dig in into what Moderna is doing in cancer. Wonderful. So, Roy, thank you so much for having us back. We're very happy to be with you. So, indeed, last week now, We shared the news with our colleagues at Merck that after working 10 years, on an individualized treatment against cancer using mRNA technology, that the phase three was positive. It's a first of many. It's the first time that there is an agent, in melanoma that is better than Ketudra alone. So it's a big deal for patients, of course, with melanoma. It's the first time there is a cancer vaccine working. The field, as you know, has been doing that for 20 plus years, I think more than a thousand clinical trials. That's how it all failed. And if you look at the phase two data, because what we announced last week is that we met the primary goal. and part of a study which was Recurrence-Free Survival, I hate people having the cancer back or dying that we met. And then to our own surprise, because this was [5:00] The first interim analysis. This is not the end of a study. It's the first interim analysis of a study. And what was a surprise event to us is we met the secondary endpoint, which was distant metastasis-free survival. which is, of course, takes more time to mature because it means that you have distance metastases from your primary tumor. And we also made that endpoint, which was great. Again, unexpected from our side, but again, means it is really good. We will share the data very soon at a big medical oncology conference, which is what the field does. But just to give you a sense, maybe to orient kind of directionally, We showed at ASCO, the big oncology conference in the spring of 2026, a few months ago, that our phase two study, which was also randomized against Ketudra alone, So the same type of study showed That's the wrong... 50% of people had recurrence-free survival versus people only getting ketudra. And this is five years after treatment. And as you know, in oncology, five years is considered by a doctor like a cure. And so it's a big, big deal. And if you look at the data from, again, that phase two, you had around 80% of people that were disease-free five years after their treatment and their surgery. of melanoma. And so we're very excited about what it means for the field. We are working very hard already with regulators to file So the drug can be available to patients as soon as possible. And we hope in 2027, and then we're going to try to make it as fast as we can. The factory is ready in Massachusetts, but we have figured out how to make and scale a product for every human being at the time. I mean, there's so much to unpack here, so this is an extraordinary advance for the treatment of, in this case, melanoma and hopefully over time, cancerous more broadly. So let's unpack some of this if we could, Stefan. The first one, for folks that are less familiar with with cancer treatment. What is Keytruda and why is Keytruda alone not sufficient? Why was this particular mRNA cancer vaccine necessary in conjunction with Keytruda? Sure. So if you look at Keytruda, which is kind of one of the leading immunotherapy that people might have heard of, is basically, if I oversimplify for non-biologists, it's basically a molecule that basically opens the gates for letting the dogs out that are going to go and attack your cancer from your immune system, if you want. The thing about checkpoints is when they work, they are fantastic because those people, five Bye. cured in the sense of not remaining disease But... Only 60% of people are This is free after five years. If you look at the... phase three published data of Ketudra. So again, for those 60% of people, it's amazing. But it means that there's 40% of people where you go through a treatment, you're fighting for cancer, and the treatment doesn't really work for you. And also what is difficult is those three beds are wonderful, but a lot of time come with very serious side effects. Very different from chemotherapy or radiotherapy. The side effects of immunotherapy are mostly immune diseases. So you see people, if you look at the label of clinical studies, People that get checkpoints, whether it's K2DRA or any other type of checkpoints from other companies, They end up having type 1 diabetes, lupus, Crohn disease, these type of things. Of course, it's better to have those diseases than, of course, being dead from your cancer, which is why those have become standard of care. But think about those 40% of people who don't respond to checkpoint. They get an autoimmune disease most of the time. and they don't get the benefit of a medicine. And so what we try to do with Merck And it's really a technology that emerged from Moderna Labs back in 2015, 2016, when we did a partnership with Merck, as we were looking for. one of the best companies in immunotherapy to partner and to have a complement approach. The idea that we have at the time using our infectious disease learning from our infectious disease vaccine, we learn a lot about the immune system and how mRNA interacts with the immune system, we thought we could develop a mechanism of action that was totally orthogonal, very different. from immunotherapy. because you will be able to start from a sequence of your tumour, So that we could design a product to basically teach, if I go back to the dog analogy, To teach those dogs... what to look for. very specifically. And that's really the beauty of our technology is if you think about K2D, it basically unleashes the dogs. but they go a bit randomly sometimes. It's your immune system. Whereas, More than a product is able to molecular level. inside your immune system, to very specifically teach your T-cell, this is what you need to look for. And that thing is actually on your cancer cell so that those T cells go and basically attack your cancer cells. Okay, so now that's where the personalized cancer vaccine technology will make a big impact on the treatment of melanoma. We'll come back to the personalized piece because I think that's just fascinating, not only [10:00] from a technological standpoint, But just from an operational standpoint, so I do want to come back to that. But let's focus on the word vaccine. So typically when you think of the word vaccine, it is to prevent a disease. Here, in this context, these patients already have cancer. So in a sense, you are preventing something, you're not preventing the cancer, what you're preventing is the return of the cancer. which is in and of itself an extraordinary thing to think about from a therapeutic intervention perspective. So number one, is that a fair characterization? That's a fair characterization. And the reason the field has used the word vaccine, it was not us using it, we just followed the field. As I mentioned, there's been a thousand plus clinical trial is because it's about teaching your immune system. If you think about vaccines, a COVID or a full shot, you teach your immune system before you get the virus infecting your body. Here, you teach the immune system not about the virus, But about basically... the cancer signal, that your immune system missed. Because what we know today in the field is that We all have cancer cells all the time in our body. whether it's outside factors or just as you have cell replication and you have mistakes that happen that create mutations of DNA that are cancer cells. Our immune system is very well trained. to basically notice those cancer cells very early. and get rid of them. Okay. But if your cancer grows, then the question is how can you reteach your immune system? So I think that's why the field use award vaccine for that approach as well. Even as you said, it's a therapeutic treatment approach. post-cancer, it's about the teaching of an immune system. - Okay, and so as you point out, the field has tried this many times before. on the order of a thousand clinical trials have tested this theory. They've all failed. You and Moderna and Merck have succeeded here. What was different this time? What is it about mRNA technology that enables the immune system to learn in a way that other approaches were unable to be successful? Yeah, I think there are two components. One is the mRNA technology, and I think the other one is the individualization by technology. designing a product for one human at a time. So let me go through those two. On the technology of mRNA side, what we have known and published... actually with Karolinska as you know, the institute in Sweden that gave the Nobel Prize for Medicine back in 2015, is that With all technology, I cannot speak about over mRNA companies that have different mRNAs, different lipids and so on. But with all technology, when we inject mRNA in the muscle, whether it's a COVID shot or flu shot or this cancer treatment, basically the mRNA goes down to your lymph node, and enters the APCs, the antigen presenting cell, which as you know, are key component of your immune cells and the mRNA gets inside the APCs. This we've demonstrated and proven at the time of Karolinska, And then he basically translated the message contained in the MRA. inside the APCs, inside your immune cells, and present it from within. So I think it's a very important differentiation from most of the previous vaccine in cancer in the field that were made by protein or peptide. that basically are made in reactors, are injected in the in the patient. But they basically turn into the blood because, as you know, recombinant or protein, when you inject them, they just go into your blood and they turn around. So your immune system sees them, but not in the same manner from within. as it's done with the mRNA. So I think that's one very important component of immune presentation, if that makes sense. The other component is really the individualization. In the past, A lot of times people have tried with non-mRNA, i.e. protein technology or peptide, but also we tried to share the antigen. Whereas here what we said is because DNA is a disease, sorry, cancer is a disease of DNA. What we're going to do here, because the cost of sequencing has dropped so much in the last 20 years, is we're going to basically take a biopsy of your tumor. We're going to read all the letters of its DNA, the three gigabytes of its genes. And then we're going to do the same things with a healthy cell of your body. And we're going to literally compare... letter by letter, nucleotide by nucleotide, and then we're going to use an algorithm, to identify of your hundreds or thousands of mutations, which one, based on the current knowledge of a field of immunology in cancer, which one of those mutations are the most relevant, and, We select the 34 that we believe are the most relevant, and we stitch them together into one big mRNA molecule, which we make. in 30-ish days for you. That is injected then in a hospital intramuscularly. And so when this is injected in your body, basically it teaches the immune system the signature of your cancer cell, that it missed. not the signature of every other patient with a shared antigen, [15:00] with a very specific signature, of your cancer cell. And what we showed at ASCO and we published, from a phase two study, but we believe it's the same thing in the phase three. because it's mechanistic. is at around 90, 90% of the antigen. are different patient to patient. Because when we started, we had no idea because, again, the field came from shared antigen. So when we started, we were like, we have no idea. We're going to get 2%, 5%, 90% of the same antigen across the field. For the patient, actually, 90% of the antigen are different from a human to another one. Wow. So the only way this can work is through personalization. We can individualize, exactly. You know, assuming that carries over. And so in that regard, uh, uh, You know, if you're doing, let's say, this normal to tumor comparison of the genome, you find the differences. I'm curious how you arrive at 34 is up to 34 is the right number. I'm sure there's a very good reason for that. But what is the algorithm that enables you to do that? Is this something proprietary to Moderna? Is this something that is known within the field? Help us understand, help us look into that black box. Sure. So I want to share a little bit about the black box, not too much, because there's a lot of know-how and things are very confidential to us. But basically, we started with, of course, what is known in the field. And so we basically use a lot of database and publication and a lot of scientists and doctors to kind of best in class in immunology and in oncology. And then from that starting point, we use a lot of internal data that we generated over time. We also partner with some companies that because they are in the diagnostic space, of AI, let's say cell therapy and other space in oncology, had access to a lot of data, a lot of T-cell mapping and so on. That's why they're useful. for the learning. The thing that is interesting about the data we shared last week is this is what I consider... in Tizmoran version 1.0. because the algorithm from a phase three, was the same from a phase two, was the same from a phase one. But because we've been doing this for 10 years, it's a 10 year old algorithm. So as you look at the data, it works pretty well, right? As we said about the phase two data, 80% of people are disease-free after five years. It's amazing for those patients. But there are still 20% of patients that don't respond. And so one other thing we're going to be doing now that we have access to a phase three patient data and samples. is to go back and mine that data. to figure out why some patients responded And why some of our patients did not respond because we have access to all their blood samples, the sequence, everything. And when they try to see, can we improve the algorithm? And we will go to CFDA if we find... scientific reason why we should change the algorithm to go from, let's say, 1.0 to a 2.0 algorithm, and then change it. Of course, we have to do that in a very controlled way to ensure we don't lose efficacy. very obviously. But the way I think about it is like when we talk about AI, we always joke that the current version of AI is the worst we're going to see in our lifetime. Well, it's exactly the same for Intismerine, which is the current version of Intismerine that Moderna is the worst version of Intismerine you're going to see for the rest of medical history. And so that gave me a lot of hope, not only in melanoma for those 20% of patients that don't respond, but also for potentially over tumors that have been really hard in the field like, you know, pancreas cancer and others where immunotherapy doesn't work. We want to be able to learn a lot about the technology using also what the field has learned in the last 10 years, because the field has learned a lot, as you know. This is even not in Intisperine 1.0. So that's why I'm so excited about what's coming next. That's fantastic. So, um, Looking back, uh, You and I have had, we've known each other for a very long time. I won't depress you or me by saying how long. But you were very young, you were in kindergarten. So I've had the benefit of seeing Moderna from the earliest days. And one thing that was true then is true today. is you are, well, first of all, you are an engineer at heart. And you have, from the very, very beginning, been obsessed with, with process, with operations, with being efficient. And those things need to be absolutely true. If you're going to attempt to do what you're trying to do here with personalized cancer vaccines and make a medicine for each individual patient, precisely because in 90% of the cases, there's no overlap in terms of the antigens. Can you talk us, walk us through a bit the operational lift that is required here that you've already had to do to even run the trial, but that you would have to do if you eventually commercialize this product? Sure. And let me, maybe one way to frame it is, I think a lot of people think about the other big personalized therapy that exists in cancer is CAR T cell therapy, right? And in that case, the thing [20:00] and CAR T-cell therapy for folks that may not be familiar is this idea of, that you take a patient's tumor, and then you take the patient's immune cells You take them out of the body, And you essentially reprogram the immune cells and re-engineer them to be reactive to the tumor and put them back into the patient. I'm oversimplifying, of course. But that's CAR T cell therapy in a very sort of simple nutshell. In this case, you're doing in some ways things that are very similar, right? You're taking a piece of the tumor that you have in the form of a biopsy, presumably. specific to that patient's tumor. How do you think about essentially the vein to vein time? Like what needs to be true from the moment you sort of see a patient and get access to the tumor to the moment that patient receives their personalized vaccine? Sure. So the time is around 42 days now, needle to needle. So from taking the biopsy to getting the vaccine in the hospital ready for you. I think we're going to be able to improve that as we still have a lot of efficiencies to work on and automations and robotics. the A place where this is very different from Cartier, is that We don't have to take your immune cells. We program them ex vivo in a reactor in our factory and send them back to your hospital. The only thing we need is the information. As you and I talked about, the beautiful thing of mRNA is an information molecule. And so basically what we get from the lab is the sequence of your healthy cells. and the sequence of your cancer cells. So we just get the file. And then we use that information to basically... Make the DNA. But now we don't make it with Plasmid. growing E. coli or whatever. We make it all synthetic. So it's all enzymatic in liquid, in water. Then we make the RNA from the template. Then we put the lipid around it. And because of that, and that it's a synthetic process, it's much more like small molecule. then a large molecule. If you think about CAR-T, for me, the analogy is the recombinant world. where you have cells and big reactors and you have big volumes. Because as you know, the reason you have big volumes in biotech industry is if you compress the cells too much, they die. You have the same issue with CAR T. So everything is big. Whereas here, because it's all in water and it's all enzymatic, meaning it's very catalytic, the reactors are very, very tiny. And so what we've been doing since before the clinic, because as you said, we had to start developing the technology to individualize it, one human at a time, to even do a phase one study, right? Is we shrunk everything down. So the first version of a machine that looks like a big American fridge was a bit big and cranky because we told the team, make it good enough so you have good quality. But we're not serving for efficiencies yet because if it doesn't work in a clinic, what's the point of wasting five years? making a beautiful, amazing, optimized robot, even science doesn't work, right? So we told the team, make it good so that we have no quality issue and we don't have a false experiment, a false negative in the clinic, because it would be terrible for patients if you build a robot kind of working but not working. You run the study and it tells you the science doesn't work and you don't. If it should have worked, right, that would be a disaster for humanity, obviously. And so the team did exactly that. They developed a robot. That was good in terms of quality, but it was not very efficient. And when we got the phase two data, that this was working. The first interim of phase two data at only two years. Now we have five years of data maturing beautifully for duration of efficacy. We told the team, "Okay, now we're behind." I knew this was going to happen in case of success, which is a happy program. And so we dedicated a lot of very smart engineers to think about, okay, now how do you make a very efficient machine that you can compress even the volume of a machine? Because one of the important vectors, of course, is time, as you mentioned, cycle time from needle to needle, but also cost. And so one way to reduce the cost is reduce the footprint on the floor. Because if you have a fixed envelope of a clean room, facility and you can put a 2x or 10x more machines in that soft face area, Of course, you're going to get a bigger throughput and a much lower price on your fixed cost. And so we are obsessed about... cycle time. Because the more you can reduce cycle time, the more you can get a turnaround, let's say, in a year. on your assets. And the second vector I'm obsessed about is square inches. Literally, I'm always a pain in the butt, sorry, when I go to the factory to look at all the space we can save, how we can be creative, how even we can move some compute out of the clean rooms just to shrink things as much as you can. It's got a huge impact on cost of the product at the end of the day. [25:00] and had, um, at scale, um, How many eat? Roughly, let's just focus on melanoma. How many doses would you need to produce in a given year? So we've already got thousands of doses because of nine clinical studies that are ongoing. The facility will be able to make the one in Marlborough, Mass., at tens of thousands of doses, And then as we keep improving the technology, that number is going to go up in the same facility. And then we might need to build several other facilities. But if you look at the incidence of melanoma, if you're in a tenth of a thousand of doses, you're going to cover... You're going to easily leave them in the middle of my market. Yeah, I can believe that. Have you disclosed how you think about cogs and price, or is that something that— We have not disclosed yet. We need first to disclose the data. We want our colleagues. We need to engage with the payers once we can share the data with them in terms of what is the value being driven there and so on. But this has not been discussed yet. So maybe one place to focus is on this concept of personalization. I'm sure you've seen the story of the GitLab founder who went founder mode on his own osteosarcoma. Number one, uh, Do the future of the future? SIDS OF THE WORLD. Um, come to Moderna? Or is this end of one phenomena something that you think will just happen and exist in parallel? So I think they will come most of them to Moderna. Because he will just going to be easier and safer. Because as you know, Making an injectable product always carry risk. of contamination of a product. If you inject to somebody a product that has even one copy of bacteria you might give the patient's sepsis, then you always have a question of quality because when you have a multi-step process, a mistake can happen. And of course, if it's industrialized, and has been validated in terms of good manufacturing practice, kind of FDA standard, you have much less chance of this happening. So it's a bit like every tool in life, which is, you know, do you make your first knife because there's no knife store and you are in a cave and you need to feed your family? Yes, of course, you make your first knife because you have to feed your family, right? But when you have a store making high-quality knives down the street, you're going to use your time to do something else. So I think it's a bit of the same phenomenon, which is like in any technology, which is when you have industrial scale, of high quality product You use your time as a human to do something else with your time, right? And in that world, how does the regulatory apparatus function here? So in other words, You mentioned earlier, you along with Mark will prepare a regulatory filing soon. What gets regulated here? Because every dose is different, so obviously every dose doesn't get approved. Is it Is it obviously the process for synthesizing mRNA? Is it the algorithm? Is it a combination of the entire system? Help us understand that and build intuition around how we think that these kinds of personalized medicines will be regulated in the future? Yes. So the good news is there are precedents. As you mentioned, CAR-T. CAR-T was also approved in the same way as we believe Intismeran will be, which is as a processed BLA, not a product BLA. So as you know, Moderna has five products approved. So... That's really product approval. On this one, the whole process since we started in the clinic, the IND was a process IND. because we had to ask the FDA, can we go to the clinic? Do you think it's safe? And do you think we have a good control of the process so we can do safely a phase one study? So we already had that discussion just to go into the clinic years ago. And then before we started every phase three, we need to have end of phase two meeting and And agree with design. of a study the manufacturing protocol with the FDA. So those discussions have happened for years. And so it's not like we have not talked to FDA for the last 10 years and we're going to show up at their front door in a week or two and tell them this is a new product. And they're going to go, why is this? There's been a lot of discussion, a lot of engagement. Several times we've had technical questions on the manufacturing front where we basically requested additional meetings to ask the guidance to also educate them on the technology, what we've learned and so on. So there's already been a lot of discussions. And... there's a very clear regulatory pathway in terms of approving the entire process. Basically, Jorge, what the FDA wants to know, which is very legitimate, which I would want for my own family's sake, obviously, which is if you get the same sample at the beginning, the tumor, and the blood, so you get the same product make at the end of a big black box. And that's what we have to demonstrate first to ourselves, and then with the data to the FDA, so that we have really robustness of the whole process. So if we have the same input, we're going to get the same output going [30:00] to patients as an individualized medicine. How do we think about moving beyond, or how do you all think about moving beyond melanoma? Is this an approach that's going to be applicable to a broad range of cancers? Are there cancers that are much more likely where this is going to be a viable option versus others? And sort of what's your, I'll use the word, what's the ambition here for where cancer vaccines can have an impact? So the ambition is pretty big because we believe we have demonstrated, at least to ourselves and we hope to the world, as well as we'll be skeptics, but that's always true, that we are able to create a de novo education of T-cell. And this, we showed it even at ASCO this year, where we took the blood before treatment, after treatment of cancer patients, melanoma, with our technology, and we showed that I don't think we have an expansion of T-cells, But we have de novo, so new T cells being created that recognize what we code in the mRNA that was not in the patient's body before the treatment. So we already in my book have proven to ourselves and to the clinical community that modernized mRNA technology can teach the immune immune system to develop new T to go attack your cancer So based on that, there are basically, I would say, three different vectors we're going after in terms of expansion from melanoma. So this study, to remind people, was a cancer patient in stage two, stage three, and stage four that were enrolled in that phase three study. So what we're doing is we're going first everywhere where K2DRA works. Because as I told you, we believe the mechanism of action of a PD-1 and Moderna and Tismaran are totally orthogonal. So we think these allow to have synergistic element and performance of efficacy for the patients. And so we are in phase three for lung. We're in phase two for kidney cancer, bladder cancer. So we have a whole slew of studies ongoing where the world knows that KTU-DRA works because KTU-DRA has been approved there. And we believe you're going to see a material improvement versus KTU-DRA alone. Before you run the clinical experiment, it's impossible to know are you going to get 50% like we saw in the phase two of melanoma, or are you going to get 30% or 40% or another number? We have to run the study. So these are a lot ongoing. The second vector is to go early in disease where checkpoints work And the best example is we announced in the spring of 2026 starting a phase three study for patients with stage one lung cancer. But as Intismarine, so Moderna's product, as a monotherapy without checkpoint. And we are doing that because we believe when you go early in disease, checkpoints are not used because of a side effect that they bring. Because if you have stage one cancer, the medical field thinks it's worth monitoring your cancer versus giving you a checkpoint. Because not everybody is going to respond, but everybody is going to get pretty serious lifelong side effects like autoimmune disease. But what if you could have an mRNA made for cancer patient that has lung disease stage one, which you can find easily with x-ray, let's say, in former smokers is the easiest target population. just screen regularly with x-ray your pharma or smokehouse and if you do it regularly you're going to go from not seeing the cancer to seeing the cancer and then the idea is do you do a surgery which is to the side of care and you give intismarine monotherapy which the side effect is similar to a vaccine you know you might feel tired for a day but that it So in cancer it a pretty cool type of side effect right And that another approach we have in terms of clinical studies where checkpoints are not available today. The third vector is where checkpoints don't work. So of course, it's where you have the highest risk. But because the mechanism of action is different from a checkpoint, we and Merck believe that there is a very good scientific rationale to go try. So two places we're trying right now is pancreas cancer and also gastric cancer. Those two cancer type checkpoints and K2R do not work. The clinical studies have been run in the past and they were negative. But we think because again the mechanism of action is different from checkpoint and we know now that we have a proof that we can create de novo T cell. We think it's an experiment worth running. If we have good signal we'll think about combination as you know Literally yesterday, Revolution Medicine had a wonderful new medicine approved for pancreas cancer using the CARA mutation. What if you could combine that medicine and intismaran? Those are very orthogonal mechanism of action. For me, it makes no scientific sense that if intismaran works, and we should know soon, [35:00] in pancreas cancer, by itself. And of course, the revolution of medicine those great improvement of survival in pancreas cancer. If you combine those two things, we believe it should have benefit. Again, you need to run the clinical experiment to see how much, but that's the type of things we're going to want to do. So if you think about In this modern medicine, it's going to be used with keturon. It's going to be used early in disease work at the dry, and it might be used In places where K2J doesn't work, But with other agents. Well, that gives a lot of reason for, I think, cancer patients and their families to have a lot of hope for the future of novel therapies in this field. And on top of what we just said, remember, this is Intismeran 1.0. So what I saw is very powerful and I'm really pushing our team. to think really outside the box and to do a lot of analysis and to use AI to look at that gigantic set of data that we have, which is, What are the things we can learn from the clinical studies? to understand about the people that did not respond. Because I think you always learn more from things that don't work. Anything that work? So I want to obsess about the 20% of patients that do not respond five years out so we can understand why. They did not respond. And can we tweak anything in the algorithm or in the technology? to be able to help them. Well, that's remarkable. And I, Just to wrap, I think It's remarkable to see how you were able to take a technology platform that originally wasn't built, for a pandemic. Um, pointed at a pandemic, create a vaccine for millions and millions and millions of people, and essentially point it back towards treating some of the diseases that you had originally intended 10 years later, as you described. Yes, it's really remarkable. And the piece that's going to be exciting, maybe to close, is before the end of the year, we should have our pivotal studies or late-stage study, for rare genetic disease for kids that have rare genetic disease of the liver. So it's another vertical at which we're pointing the technology. The phase one, two, I've shown kids three years on drugs doing fantastic. So we'll see when we get that data. And in June, we had our annual science day. And we announced that the next mountain where we are pointing on mRNA platform is autoimmune disease. Because if you think about it, We've learned a lot from infectious diseases which are mediated by the immune system. cancer, we've just been talking a lot about immune system. So we learned so much about the immune system that we think we have some very novel approach. on how to treat the root cause of autoimmune disease, not the symptoms, which is what the pharma industry has been doing. It's, of course, very helpful to patients to treat the symptoms so they can have higher quality of life. But it doesn't treat the root cause. And we think we might not find ways. to use the immune system to treat the root cause of autoimmune disease. So there's still a lot of ways to point the platform. So we're quite excited about what's to come. Would the theory there be that you'd have of personalized autoimmune modulators. Or would this be more product or more process? So we're doing both. So what we presented in the spring was a product that would be the same for everybody. But what I'm most excited about, which is in the lab still, is the ability to do individualized autoimmune treatment where you target directly to the immune cells that are attacking your body. as self when you have an autoimmune disease and who are basically part of your immune system going attacking those immune cells that are out of order so that you are able to take the symptom of the immune disease out. Again, it's still early days, but that's what I'm excited about today. Well, going from infectious disease to cancer to eventually autoimmune disease, we would love to have you back on the podcast to film episode three and complete the trilogy when you're ready. Thank you so much for joining us on the A16Z podcast. It's always, as always, it's great to see you. And congratulations. Thanks again for listening, and I'll see you in the next episode. and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z.com forward slash disclosures. [40:00] . you Thank you.