TTC Oncology LLC

十余年求索，AI已经逐渐长成了那时IBM Waston期望的样子。它甚至跨出了诊断和治疗的范畴，生根于医疗场景中的每一个角落。不过，医疗AI领域中的落地与商业化是两个截然不同的概念。通过合作、共研形成的AI落地并不意味着这一算法能够规模应用于医院，规模应用于医院也不意味着这一算法能够成功回收过往投入的海量资金。因为这些模糊概念的存在，医疗AI的商业化形势始终透着一种朦胧感，亦难区分其中企业的实际进展。从这一现状出发，蛋壳研究院对医疗AI的整体情况进行了调研分析，希望借助数据这一工具，还原2023年最真实的医疗AI。殊途同归，各式AI一并涌入治疗环节近6年来，第三类医疗器械注册证年通过量一直保持高速增长，尚未出现增速放缓的迹象。未至年终，审评审批数据已告诉市场：这是影像学AI市场准入表现最好的一年。截至2023年10月31日，国内已有118款智能软件获得市场准入。仅2023年前10个月，国家药监局公布的审评审批数量已至44个，超越了2022年的32个。至此，影像学AI审评审批体系趋于成熟，相关AI的注册准入步入常态化，智慧化的应用或将在数年后覆盖整个临床体系。历年人工智能产品三类证获批数量具体分析历年AI注册证分布情况。2020—2021年获批的影像学AI以辅助诊断类AI为主，内含基于深度学习的分析模块，作用于特定影像设备下的特定病种。2022年辅助诊断类AI仍是主流，但多了一定数量的放疗规划、心电分析、病理分析应用，AI应用场景进一步拓宽。2023年延续了过往的趋势，不同之处在于各类智能手术机器人规划导航类应用相继获批（总计10款），极大提升了AI获批产品的总量，医疗AI的主要场景也由“辅助诊断”变为“辅助诊断辅助治疗并行”。从公司角度看，辅助诊断类AI企业持有的三类证数量较多。拥有11张三类证的联影集团目前位列数量榜的第一（联影智能7张、联影医疗3张、联影智融1张），深睿医疗、数坤科技均有9张并列第二，推想科技、博动医学、医准智能紧随其后。相较之下，辅助治疗类AI企业持有的三类证数量普遍在1—3张左右，市场准入相对滞后。从病种方面看，肺结节、肺炎、眼底等拥有公开数据集支撑的病种对应的AI最易获批，已然是各企业实现AI商业化的重要抓手。新增的三类证则大多来自于手术治疗领域，腰椎、髋关节、膝关节、动脉瘤、口腔、神经外科领域的突破为这一赛道带来了新的增量，但鉴于2023年下半年手术机器人市场暂时降温，2024年的智能规划导航类应用的注册数量可能会有所下移。医疗AI产品不同病种三类证获批情况放射治疗领域同样存在突破。过去研发相关智能技术的企业多为国内头部影像企业及世界放疗巨头，但在近两年时间内，初创公司陆续获得市场准入,亦成为这一赛道中的重要力量。生命科学AI的发展虽在商业化方面有所收紧，但研发方面一点没有落下。据蛋壳研究院不完全统计，截至2023年11月，进入临床的生命科学AI管线已有16款停止研发或从官网撤下，1款药物被降低了临床试验优先级。不过，管线总量仍在以较快速度持续增加，全球处于临床阶段且保持活跃状态的AI参与研发的管线总计97项，超过一半的管线处于临床Ⅰ期，超过1/3的管线处于临床Ⅱ期。全球AI参与研发的进入临床阶段且处于活跃状态的管线临床阶段分布（不完全统计，截止时间2023年10月）这些管线有67项来自国外，占比69.07%，30项来自国内，占比30.93%。英矽智能、冰洲石科技、未知君、埃格林医药、药物牧场、锐格医药等企业均有多条管线同时进行临床试验，推动中国进入AI制药全球领先队列。自研新药是目前生命科学类AI企业研制新药的主要模式。上述管线中，85.57%的管线为企业自研管线，14.43%的管线为合作研发管线，多为生命科学类AI企业协助大型药企进行研发。合作研发的管线数量虽少，却多为老牌生命科学类AI企业所持有。Exscientia现有的两条进入临床的管线分别来自于百时美施贵宝、湃隆生物；英矽智能负责推进Aulos Bioscience的单克隆抗体AU-007；Lantern Pharma手持TTC Oncology、Harmonic、Actuate Therapeutics三家公司的管线；Schrödinger则握手Structure Therapeutics的ANPA-0073、GSBR-1290与Morphic therapeutic的MORF-057。上述管线中，Schrödinger、BioXcel Therapeutic等AI企业经手的已经上市的药物均来自收购，进入临床Ⅲ期的管线也多为老药新用。换句话说，生命科学类AI公司自研且进入临床阶段的管线均处于临床Ⅰ期或临床Ⅱ期，尚无药物完整跑通临床试验。对于生命科学类AI而言，若想获得可观的营收、创造有力的价值，唯一的方式就是自研或协助MNC取得获批上市药物。2023年间尚无满足上述条件的药物突破临床Ⅱ期，一定程度降低了市场对于生命科学类AI的估值，进而导致年内合作及投资收紧。相较于影像学和生命科学开拓的创新市场，信息学AI面临的是一个相对传统的市场，缺乏创新的弹性，因而形成了差异化的研发思路。影像学和生命科学将AI作为核心技术底座，形成独立产品或解决方案。而在信息学中，除专科CDSS以独立产品形态进行销售，绝大多数AI以支撑技术存在，置于成熟产品或解决方案中，通过优化性能、提供额外服务提高竞争力。不过，这一属性并未干涉医疗IT企业对于信息学AI的开发热情。一方面，卫宁健康、东软集团、东华医为等头部医疗IT厂商已对医院信息管理系统的架构进行调整，使其能够更好适应智能化应用的运行，更便捷地实施智慧化的医院管理，不断降低医院日常运营中出错的可能；另一方面，数字疗法的崛起强化了AI在人机交互、量表分析、智能预警、质量控制等场景下的能力，帮助信息学AI跃入治疗领域，进一步发挥数智价值。虽然路径各不相同，AI赋能的影像学、生命科学、信息学都以各自的方式朝着治疗领域的赋能不断努力。需要注意的是，这种趋势很大程度影响了2023年医疗AI的融资格局。资金流速回暖，医疗人工智能进入稳步发育期根据蛋壳研究院统计，2022年8月31日—2023年10月31日期间（后称为“2023统计年”），医疗AI板块一共完成了170笔融资，融资总额161.24亿元人民币。2023统计年的融资事件数较2022年同期的127件和2021年同期的112件发生了较大规模的增长，分析其原因，辅助诊断AI向辅助治疗AI的转型及基于影像的手术导航、手术规划类AI的崛起，共同拉高了AI影像与AI信息化企业融资规模。图表14 医疗AI不同赛道融资情况数据来源：公开信息整理AI影像融资当前更偏向商业落地。即便是在早期A轮融资阶段，产品申报注册和商业化情况也已经是资本投资的重要考量因素。譬如，医智影完成了A轮融资，产品“放射治疗轮廓勾画软件RT-Mind”已获得NMPA三类医疗器械注册认证及FDA入市许可；慧维智能处于A轮融资阶段，公司的二类证产品已经在多家医院应用。AI影像各个轮次融资事件数和金额（单位：亿人民币）AI制药板块融资首次降温。2023统计年的融资事件数仅有32笔，与2021年同期的43笔和2022年同期的47笔，有了一定下降。且2023统计年AI制药板块的融资以A轮以下的项目为主。核心原因在于AI制药距离商业化还有一段距离，行业缺乏典型的成功案例，再加上2022年明星产品DSP-1181的失败让行业遇冷。根据统计，融资用途中，仅有完成C轮融资的药物牧场和完成B+轮融资的红云生物将资金用于推进药物临床试验，其余融资均是处于较早期阶段，用于搭建技术平台和推进药物临床前研究。图表16 2023统计年AI制药融资用途信息化板块的融资事件在2023统计年出现了小幅度的增加，共有42笔融资事件。大多数传统的信息化企业已经发展到后期阶段，行业整体变化不大，较大的亮点在于数字疗法企业，2023统计年中，数字疗法占据了AI信息化融资事件25%的比例。生成式AI在信息化板块也有了初步的探索性应用，Glowe阁楼完成A+轮融资，用AIGC赋能心理咨询，万木健康完成战略融资，将加速构建单病种及医生个人知识数据库，通过AIGC重构医患交互场景下的内容创作流并实践探索垂直领域大语言模型应用。相较一级市场，二级市场的变化则相对较小。考察A公司、鹰瞳科技、Lunit、Nanox AI四家海内外具有代表性的医疗AI企业。单看营收，A公司在上一个财年的营收有所下滑，鹰瞳科技基本持平，Lunit和Nano-X Imaging的营收则实现了大幅增长。医疗AI企业营收数据对比（单位：万人民币）再看净利润数据。医疗AI的规模化盈利能力还不理想，4家企业均处于净利润为负的状态，2家企业的亏损正在持续扩大。A公司归母净亏损6.28亿元，同比减少17.62%，原因在于销售成本和行政成本有所降低；鹰瞳科技归母净亏损1.80亿元，亏损同比扩大26.20%；Lunit净亏损391亿韩元（约2.13亿人民币），同比增长46.9%；Nano-X Imaging归母净亏损1.13亿美元。医疗AI企业净利润对比（单位：万人民币）不过，各家AI企业的资产负债率均保持较低的水平，且已有不少企业找到了控制亏损的有效路径。对于仍然处于商业开拓期的众AI企业而言，较好的财务状况意味着他们还有多年的试错空间，最终找到一条可靠有效的盈利路径。医疗AI企业资产负债率情况在逆势之下存活，医疗AI仍需高频创新那么，财务数据之中反应的机遇可能出现在哪里？我们不妨先对AI当下的问题进行分析。尽管医疗人工智能的价值已在临床、制药之中得到初步确认，很多医疗AI产品已成为医生日常工作流程中必不可少的一部分，也确确实实为药物靶点、先导化合物等筛选提高了不错的效率，但各家医疗AI的经营情况，却仍面临一些问题。造成这一结果的原因是多元的。其一，对于影像学AI而言，该技术核心需要解决的问题是AI能够提升多少效率，为医院创造多少价值；对于生命学科AI而言，其核心需要解决的问题是AI能否制造一款成功上市的新药。如今没有医保支付模型的支撑，亦无成功通过临床的新药作为案例支撑，AI的购入多个成本项，价值的验证仍然处于悬而未决的状态，没有被精准计算出来。其二，许多AI产品/解决方案的内容、形式过于单一，医院/企业很难为了单一产品付出大量成本，制药企业也没能直观感受到AI对于制药成本的节省作用。换句话说，产品没有达到改善整个产业的地步，进而不足以让客户买单。其三，医疗AI技术本身存在一些问题。譬如，封装AI，扼杀了AI在实践中不断进化的优势。不封装AI，如果在收到AI的建议之后对患者进行干涉，那么AI捕捉不到给定数据下应该发生的结果。这种情况下，运行过程中的AI会在短期高准确率预测后逐渐偏离，预测能力变得越来越差。要解决上述问题，医疗AI企业们首先应该建立行业标准，在过往深耕的领域中确立简单可行的价值计算路线，其次需要舍去部分实在无法盈利的技术，及时止损或比死磕商业化带来更多收益。以上为报告主要内容节选，扫描二维码，即可免费下载报告全文。第一章 人工智能，已是医疗领域必不可少的一部分1.1 超百个AI获批三类医疗器械，AI跃出辅助诊断1.2 近百项AI赋能的管线进入临床试验，但无一行至上市阶段1.3 信息学重构进行时，基础设施建设成为最大壁垒第二章 资金流速回暖，医疗人工智能进入稳步发育期2.1 二级市场考察：AI盈利尚有距离，疫情影响增速普遍放缓2.2 一级市场考察：宏观遇冷，影像放缓第三章 在逆势之下存活，医疗人工智能仍需高频创新3.1 标准制定，影像AI进入医保的可能前提 3.2 模式创新，再寻AI盈利源动力3.3 以数据为核心，新技术改变AI格局第四章 大模型入局，医疗人工智能再遇新变量4.1 九大应用场景，大模型已潜入医疗4.2 部署与应用局限发展，大模型暂时难以规模落地4.3 机遇与挑战并存，大模型有望成为颠覆医疗的下一项技术第五章 三大赛道，汇聚医疗人工智能核心竞争力*封面图片来源：123rf文|赵泓维微信|qq850860074添加时请注明：姓名-公司-职位网站、公众号等转载请联系授权：Rekkiiie近期推荐声明：动脉网所刊载内容之知识产权为动脉网及相关权利人专属所有或持有。未经许可，禁止进行转载、摘编、复制及建立镜像等任何使用。动脉网，未来医疗服务平台

Collaborative efforts are aimed at using Lantern’s AI platform to accelerate and sharpen the drug development of TTC-352. The collaboration will utilize Lantern’s AI-based capabilities and RADR® platform to uncover efficacy-associated biological signatures and biomarkers to advance the clinical development and strategic positioning of TTC Oncology’s leading drug candidate, TTC-352. Phase 2 ready candidate, TTC-352, is a novel, first- and best-in-class selective human estrogen receptor (ER) partial agonist (ShERPA) for the treatment of patients with metastatic ER+ breast cancer. Lantern is receiving exclusive rights to license TTC-352 during an exclusive option period, which includes rights to intellectual property generated during the collaboration. DALLAS--(BUSINESS WIRE)--Lantern Pharma, Inc. (NASDAQ: LTRN), a clinical stage biopharmaceutical company using its proprietary RADR® artificial intelligence ("AI") and machine learning (“ML”) platform to transform the cost, pace, and timeline of oncology drug discovery and development, today announced that it has entered into a research and development collaboration with TTC Oncology. The collaboration will focus on leveraging RADR® AI insights to advance TTC Oncology’s first- and best-in-class drug candidate TTC-352 for recurrent ER+ breast cancer patients and additional patient populations potentially identified by RADR®. In US women, breast cancer remains the most commonly diagnosed cancer and second leading cause of cancer related deaths. ER+ breast cancers are estimated to account for 75-80% of all breast cancer cases and can have a recurrence rate between 13% and 41%. Globally, the treatment of ER+ breast cancer is estimated to have a $44 billion market potential by 2027. ”It is of the utmost importance for cancer drug development to understand targeted tumor biology and mechanisms of resistance in order to select the patient population that will benefit the most from novel therapy. We expect that by using Lantern’s RADR® AI platform it can save us time and costs in the further successful clinical development of TTC-352 while providing important information for precision patient selection,” said Dr. Arkadiusz Dudek, TTC Oncology Chief Medical Officer. The collaboration will be powered by RADR®’s 25+ billion oncology-focused data points, 200+ advanced ML algorithms, as well as its 4 multi-faceted AI drug discovery and development modules. The initial aims of the collaboration will be to 1) identify biomarker or gene signatures to power potential patient selection for an upcoming TTC-352 Phase 2 clinical trial, 2) further characterize TTC-352’s mechanism of action, and 3) discover additional treatment indications for TTC-352. “Using AI insights generated by RADR®, we are able to both sharpen existing clinical programs and uncover additional unrealized clinical potential of Lantern’s and our collaborators’ drug candidates,” stated Panna Sharma, Lantern Pharma’s CEO and President. “We believe our AI-powered collaboration with TTC Oncology will accelerate the clinical development of TTC-352 for patients with metastatic ER+ breast cancer and will also identify new potential patient populations that can benefit from TTC-352 treatment,” continued Sharma. Under the terms of the collaboration, Lantern Pharma is receiving an exclusive right to license TTC-352, including any collaboration intellectual property (IP), during an exclusive option period. Additionally, Lantern and TTC will each participate in upfront, milestone, and royalty payments in the event a third party licenses IP resulting from the collaboration. No further financial details were disclosed. About RADR® RADR® is one of the world’s largest AI and ML oncology drug discovery and development platforms, consisting of over 25+ billion oncology-focused data points. These data points consist of large-scale multi-omic data, derived from 130,000+ patient records, 150+ drug-tumor interactions, thousands of drug classes, and covering over 135 cancer subtypes. RADR® leverages this data and over 200+ advanced ML algorithms to power its drug discovery and development modules. RADR®’s data, capabilities, and insights have powered the development of new Lantern drug candidates, advancement of new indications for existing drugs, and identification of new combinations at a fraction of the cost and time of traditional development approaches. About TTC-352 TTC-352 is a novel, first-in-class and best-in-class orally available small molecule being developed as a treatment for patients with metastatic estrogen receptor-positive (ER+) breast cancer who have failed 2 or more prior therapies. TTC-352 is a selective human ER partial agonist (ShERPA) that induces unfolded protein response leading to breast cancer cell death, and acts in a similar manner as hormone therapy by modulating estrogen actions. In the US there are estimated to be around 290,000 cases of breast cancer annually, 80% of which are ER+. TTC-352 was recently evaluated in a Phase 1 accelerated dose escalation study (NCT03201913) for hormone receptor positive metastatic breast cancer. A total of fifteen patients (n=15) were enrolled in the study and five escalating doses were evaluated. Despite the small cohort size, TTC-352 showed early efficacy signals in heavily pretreated hormone refractory patients (Median PFS was 58 days (95% CI = 28,112)) and showed a favorable safety profile. Additional published trial results can be found here. About TTC Oncology TTC Oncology is an emerging biotechnology company founded in 2015. TTC Oncology’s mission is to develop and bring to market a novel, small-molecule therapy, TTC-352, to address the unmet needs of breast cancer patients. TTC has a license from the University of Illinois at Chicago covering the therapy. About Lantern Pharma Lantern Pharma (NASDAQ: LTRN) is a clinical-stage oncology-focused biopharmaceutical company leveraging its proprietary RADR® AI and machine learning platform to discover biomarker signatures that identify patients most likely to respond to its pipeline of genomically-targeted therapeutics. By targeting drugs to patients whose genomic pro them as having the highest probability of benefiting from the drug, Lantern's approach represents the potential to deliver best-in-class outcomes. Forward-looking Statements This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. These forward-looking statements include, among other things, statements relating to: future events or our future financial performance; the potential advantages of our RADR® platform in identifying drug candidates and patient populations that are likely to respond to a drug candidate; our strategic plans to advance the development of our drug candidates and antibody drug conjugate (ADC) development program; estimates regarding the development timing for our drug candidates and ADC development program; expectations and estimates regarding clinical trial timing and patient enrollment; our research and development efforts of our internal drug discovery programs and the utilization of our RADR® platform to streamline the drug development process; our intention to leverage artificial intelligence, machine learning and genomic data to streamline and transform the pace, risk and cost of oncology drug discovery and development and to identify patient populations that would likely respond to a drug candidate; estimates regarding patient populations, potential markets and potential market sizes; sales estimates for our drug candidates and our plans to discover and develop drug candidates and to maximize their commercial potential by advancing such drug candidates ourselves or in collaboration with others. Any statements that are not statements of historical fact (including, without limitation, statements that use words such as "anticipate," "believe," "contemplate," "could," "estimate," "expect," "intend," "seek," "may," "might," "plan," "potential," "predict," "project," "target," "model," "objective," "aim," "upcoming," "should," "will," "would," or the negative of these words or other similar expressions) should be considered forward-looking statements. There are a number of important factors that could cause our actual results to differ materially from those indicated by the forward-looking statements, such as (i) the impact of the COVID-19 pandemic, (ii) the risk that our research and the research of our collaborators may not be successful, (iii) the risk that none of our product candidates has received FDA marketing approval, and we may not be able to successfully initiate, conduct, or conclude clinical testing for or obtain marketing approval for our product candidates, (iv) the risk that no drug product based on our proprietary RADR® AI platform has received FDA marketing approval or otherwise been incorporated into a commercial product, and (v) those other factors set forth in the Risk Factors section in our Annual Report on Form 10-K for the year ended December 31, 2021, filed with the Securities and Exchange Commission on March 10, 2022. You may access our Annual Report on Form 10-K for the year ended December 31, 2021 under the investor SEC filings tab of our website at or on the SEC's website at . Given these risks and uncertainties, we can give no assurances that our forward-looking statements will prove to be accurate, or that any other results or events projected or contemplated by our forward-looking statements will in fact occur, and we caution investors not to place undue reliance on these statements. All forward-looking statements in this press release represent our judgment as of the date hereof, and, except as otherwise required by law, we disclaim any obligation to update any forward-looking statements to conform the statement to actual results or changes in our expectations.