SAN FRANCISCO--(BUSINESS WIRE)--Unlearn®, a company at the forefront of AI in medicine, today announced the newest version of its TwinRCT™ solution, TwinRCT 3.0. TwinRCTs are randomized clinical trials that use digital twins of trial participants—comprehensive forecasts of individual clinical outcomes—to optimize clinical trials.

旧金山——（商业新闻短讯）——Unlearn®，一家处于医学人工智能前沿的公司，今天宣布了其TwinRCT™解决方案TwinRCT 3.0的最新版本。TwinRCT是一项随机临床试验，使用试验参与者的数字双胞胎对个体临床结果进行综合预测，以优化临床试验。

Participants’ digital twins are generated by AI models trained on patient-level data, and they enable trials with higher power or smaller control arms..

参与者的数字双胞胎是由根据患者水平数据训练的AI模型生成的，它们可以使用更高的功率或更小的控制臂进行试验。。

Unlearn’s updated TwinRCT can now incorporate additional previous clinical trial data to provide an even more robust framework for Phase 2 trial evaluations, which represent a crucial stage in the drug development process. Phase 2 trials serve as a litmus test for a new drug's effectiveness. The more highly powered a trial is at this stage, the more likely a treatment effect can be detected—determining whether or not the trial will advance to the more resource-intensive Phase 3 stage.

Unlearn更新的TwinRCT现在可以纳入以前的其他临床试验数据，为第二阶段试验评估提供更强大的框架，这是药物开发过程中的关键阶段。第二阶段试验是检验新药有效性的试金石。在这个阶段，试验的效率越高，就越有可能检测到治疗效果，从而确定试验是否会进入资源密集型的第三阶段。

Seeking more power in Phase 2 trials is a common approach in order to understand a new drug's potential benefits quickly and accurately. Traditionally, increasing the number of participants could help, but it's a slow and expensive approach..

为了快速准确地了解新药的潜在益处，在第二阶段试验中寻求更多权力是一种常见的方法。传统上，增加参与者人数可能会有所帮助，但这是一种缓慢且昂贵的方法。。

TwinRCTs can overcome this issue by increasing power without adding additional participants. The results of a case study reanalyzing a completed Alzheimer’s Disease study demonstrate that a traditional trial would require 23% more trial participants to achieve the same power as Unlearn’s updated TwinRCT.

TwinRCT可以通过增加功率而不增加额外的参与者来克服这个问题。重新分析已完成的阿尔茨海默氏病研究的案例研究结果表明，传统试验需要额外23%的试验参与者才能获得与Unlearn更新的TwinRCT相同的功效。

Adding these additional participants could increase the time to enrollment by five months. This power gain boosts the efficiency and precision the updated TwinRCTs bring to the table, marking a substantial step forward in clinical trial design and analysis..

增加这些额外的参与者可以将注册时间延长五个月。这种功率增益提高了更新的TwinRCT的效率和精度，标志着临床试验设计和分析迈出了实质性的一步。。

Additionally, the updated TwinRCT version has been added to TrialPioneer, Unlearn’s free web-based application, which enables pharmaceutical and biotech companies to collaborate and understand how trial design choices affect sample size requirements, statistical power, and time to enrollment. TrialPioneer is currently available for seven major disease areas (Alzheimer’s Disease, ALS, Crohn’s Disease, Frontotemporal Dementia, Huntington’s Disease, Parkinson's Disease, and Ischemic Stroke), with more diseases added regularly.

此外，更新的TwinRCT版本已添加到TrialPioneer（Unlearn的免费网络应用程序）中，该应用程序使制药和生物技术公司能够合作并了解试验设计选择如何影响样本量要求，统计能力和注册时间。Trialpioner目前可用于七个主要疾病领域（阿尔茨海默氏病，ALS，克罗恩病，额颞叶痴呆，亨廷顿氏病，帕金森氏病和缺血性中风），并定期增加更多疾病。

TrialPioneer also highlights the major advantages that Unlearn’s novel AI-powered trial designs offer over traditional approaches..

TrialPioneer还强调了Unlearn新颖的人工智能驱动的试验设计相对于传统方法提供的主要优势。。

About Unlearn

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Unlearn is a San Francisco-based technology company advancing AI to eliminate trial and error in medicine. Unlearn's technology powers the clinical trials of leading global pharmaceutical companies, helping them to reach full enrollment faster and bring new treatments to patients sooner. Their methods using participants’ digital twins are qualified by the European Medicines Agency and align with current FDA guidance.

Unlearn是一家总部位于旧金山的技术公司，致力于推进人工智能，以消除医学中的反复试验。Unlearn的技术为领先的全球制药公司的临床试验提供了动力，帮助它们更快地达到全部注册，并更快地为患者带来新的治疗方法。他们使用参与者数字双胞胎的方法得到了欧洲药品管理局的认证，并符合当前FDA的指导。

For more information, please visit https://www.unlearn.ai or follow @UnlearnAI on X and @unlearn-ai on LinkedIn..

有关更多信息，请访问https://www.unlearn.ai或者在X上关注@UnlearnAI，在LinkedIn上关注@UnlearnAI。。