QuantHealth斩获4500万美元,用于在患者入组前模拟临床试验
QuantHealth Snags $45M to Simulate Clinical Trials Before Patients Ever Enroll
大约90%的进入临床试验阶段的药物最终都无法上市——这一失败率每年给制药行业造成数十亿美元的损失。
周二,一家寻求降低这一比例的以色列初创公司获得了4500万美元的B轮融资。
QuantHealth
销售一种在实际开展临床试验前对其进行虚拟测试的模拟平台。自2020年成立以来,该公司筹集的资金总额已达7000万美元。本轮融资由Qumra Capital领投,其他参与方还包括Sanofi Ventures Pitango HealthTech Artofin Venture Capital Fund和Esplanade Ventures QuantHealth首席执行官Orr Inbar表示,药物开发的临床阶段基本上仍未受到AI创新的影响。他指出,大多数针对药物发现的AI投资都流向了流程的早期阶段,使临床试验设计基本上仍停留在过时的方法上。
呈现方赞助内容2026年面向全球员工队伍的七大现代AI驱动EAP提供商了解2026年顶尖的AI驱动EAP提供商。比较Kyan Health和Spring Health等平台在分诊速度、全球覆盖范围和临床质量方面的表现,以变革员工福祉。
作者:Tiffany Cabasso,Kyan Health运营总监|FSP心理学家Inbar解释说,通过在临床试验开始前使用AI进行模拟,这家初创公司的平台能够准确判断不同患者将如何对治疗产生反应,最终应能减少试验失败,并让患者更快获得更有效的疗法。
“医疗行业产生了全球近30%的数据。在生命科学领域,这些数据不仅规模极其庞大,而且复杂程度也极高,”他表示。
Inbar称,QuantHealth的平台将AI与大型生物医学知识图谱相结合,使其能够以高分辨率捕捉临床模式,并识别药物在人体内实际发挥作用的相关洞见。他说,这种方法还使平台能够将预测范围从大型患者群体扩展到罕见疾病和小型亚群体,并利用迁移学习对洞见进行泛化;否则,对于这些群体而言,所需数据量将远远超出实际存在的数据量。
Inbar指出,QuantHealth的AI模型能够以最高达90%的准确率预测患者对现有疗法和新型疗法可能产生的结果。他表示,在试验开始前掌握这些预测结果,可以让制药公司减少患者接触那些不太可能有效的治疗方法。
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Inbar补充说,截至目前,QuantHealth已在30个适应症领域模拟了600多项临床试验。
不过,QuantHealth并不是唯一一家将AI应用于患者匹配的公司。谈到竞争对手时,Inbar指出,Noetik——一家专注于肿瘤学的AI公司,利用生物学数据帮助药物开发商为患者匹配治疗方案——偶尔会在与QuantHealth的相同讨论中被提及。
“我们的范围和所处阶段不同。Noetik在单一疾病领域——肿瘤学——内开展工作,重点是对患者进行生物学分层;而我们则跨多个临床试验阶段、终点和治疗领域进行模拟,回答诸如:这项试验会成功吗?我们是否应该改变终点、优化患者人群,或以其他方式调整设计?”Inbar解释说。
QuantHealth并不是要识别适合某种治疗的患者,而是帮助确定该治疗,以及为测试该治疗而设计的试验,从一开始是否具备成功的条件。
这家初创公司计划将新筹集的资金投入三个领域:改进其AI模型并扩大疾病覆盖范围,壮大团队,以及让其平台进一步深入药物开发流程——包括治疗方案如何定位以及如何推向市场。
图片:BlackJack3D,Getty Images
Roughly 90% of drugs that enter clinical trials
never make it to market — a failure rate that costs the pharmaceutical industry billions of dollars every year.
On Tuesday, an Israeli startup seeking to shrink that rate picked up $45 million in Series B financing.
QuantHealth
, which sells a simulation platform that tests clinical trials virtually before running them, has now raised $70 million since its founding in 2020. The Series B round was led by Qumra Capital, with participation from other funds including Sanofi Ventures Pitango HealthTech Artofin Venture Capital Fund and Esplanade Ventures QuantHealth CEO Orr Inbar argued that the clinical stage of drug development has remained largely untouched by AI innovation. He noted that most AI investment in drug discovery has flowed to early stages of the process, leaving clinical trial design largely stuck with outdated methods.
By using AI to simulate clinical trials before they begin, the startup’s platform can pinpoint how different patients will respond to treatment, which should ultimately lead to fewer trial failures and quicker access to more effective therapies for patients, Inbar explained.
“The healthcare industry generates nearly 30% of the world’s data. In life sciences, this data is not only incredibly large, but also incredibly complex,” he stated.
QuantHealth’s platform combines AI with large biomedical knowledge graphs, allowing it to capture clinical patterns at high resolution and identify insights into how drugs actually work in the body, Inbar said. That approach also lets the platform extend its predictions beyond large patient populations to rare diseases and small subpopulations, using transfer learning to generalize insights that would otherwise require far more data than exists for those groups.
Inbar pointed out that QuantHealth’s AI models can predict potential patient outcomes to existing and novel therapies with up to 90% accuracy. Having those predictions in hand before a trial even begins allows pharma companies to reduce patient exposure to treatments that are unlikely to work, he remarked.
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To date, QuantHealth has simulated more than 600 clinical trials across 30 indications, Inbar added.
QuantHealth isn’t the only company applying AI to patient matching, though. As for competitors, Inbar noted that Noetik — an oncology-focused AI company that uses biological data to help drugmakers match patients to treatments — is occasionally brought up in the same conversation as QuantHealth.
“We’re different in scope and stage. Noetik works within a single disease area, oncology, and centers on biological stratification of patients, while we simulate across multiple clinical trial stages, endpoints and therapeutic areas — answering questions like: Will this trial succeed? Should we change the endpoints, optimize the patient population, or adjust the design in some other way?” Inbar explained.
Instead of identifying the right patients for a treatment, QuantHealth helps determine whether the treatment — and the trial designed to test it — is set up to succeed in the first place.
The startup plans to pour its fresh capital into three areas: sharpening its AI models and expanding disease coverage, growing its team, and pushing its platform further into the drug development journey — including how treatments get positioned and brought to market.
Photo: BlackJack3D, Getty Images