Re-engineering Precision Therapeutics Through N-of-1 Trials

该提案的目标是开发、测试和实施一个创新的技术平台， N-of-1试验改变了精确治疗。现在，临床医生正在从事临床接触， 他们正试图为个别患者确定最佳治疗方法。这些遭遇很可能是 不成功。临床医生依赖于现有的最佳证据（例如，III期随机临床试验结果; RCT）用于向患者推荐治疗。然而，传统的患者间随机对照试验仅提供估计值， 在这些试验中，治疗对普通患者的影响。然而，个体患者通常会对 与III期随机对照试验中的平均患者不同，因此，治疗反应的异质性 这些临床决定 确定患者最佳治疗方法的最科学严格的--也是潜在的变革性的--方法是单患者（1例中的N例）试验。N-of-1试验是多项交叉试验，通常是随机的， 设盲，在单个患者中进行，客观、连续和在真实世界中收集数据， 足够的时间来确定与安慰剂或其他活性治疗相比， 最适合特定患者。它们还产生关于脱靶动作的信息，例如副作用， 一种疗法对个体患者的整体益处和危害可能会出现更复杂的情况。2临床医生和患者通常不会参与这种类型的科学努力，因为他们缺乏工具。 在本提案中，我们将创建一个电子平台，允许临床医生和/或患者订购， 进行一项单一患者试验。然后，我们将收集RCT数据，以便能够估计使用 这种方法。为此，我们将使用我们的平台针对3种不同的健康状况进行3次实验， 其具有高公共卫生负担、高治疗反应异质性和高精度优先级。 治疗方法由先前采访的临床医生和患者确定。对于每一个，我们将 将60名患者随机分为两组，一组接受N-of-1试验，另一组接受常规治疗。然后，我们将测试一个实用的设计， 模拟平台在发布日期后将如何在临床实践中实施。本申请将被 嵌入在临床工作流程中，临床医生有能力参考和跟踪他们的病人， 直接通过电子健康记录进行N-of-1试验。在这个实验中，我们将随机选择200名患者， 接受务实的N-of-1试验，或接受常规护理。因此，我们将能够比较我们的新精度 治疗方法是通常为患者确定治疗的方式。 这个N-of-1试验平台还将促进发现治疗反应的范式转变方法 表型，因为我们将建立一个面向公众的N-of-1协议和工具注册表，以及一个去识别的 来自N-of-1试验的数据。然后可以挖掘N-of-1试验结果的国际数据库以识别表型 按治疗反应性分类。

The objective of this proposal is to develop, test, and implement an innovative technology platform for conducting N-of-1 trials that transforms precision therapeutics. Right now, clinicians are engaging in clinical encounters at which they are trying to determine the best therapy for individual patients. These encounters are likely to be unsuccessful. Clinicians rely on the best available evidence (e.g., results from phase III randomized clinical trials; RCTs) for recommending therapies to a patient. Yet, conventional, between-patient RCTs only provide estimates of the effect of therapies on the average patient in those trials. Individual patients, however, often respond differently than the average patient in the phase III RCTs, and thus, heterogeneity of therapy response plagues these clinical decisions every day. The most scientifically rigorous-- and potentially transformative--method for determining optimal therapy for apatient is a single-patient (N-of-1) trial. N-of-1 trials are multiple crossover trials, usually randomized, and often masked, conducted within a single patient, with data collected objectively, continuously, and in the real-world, for a sufficient time period to determine whether the therapy, compared to a placebo or other active therapy, is optimal for a particular patient. They also yield information on off-target actions, such as side-effects, so that a more complex picture can emerge about the overall benefits and harms of a therapy for an individual patient.Clinicians and patients do not routinely engage in this type of scientific endeavor because they lack the tools. In this proposal, we will create an electronic platform that will allow clinicians and/or patients to order and conduct a single-patient trial. We will then collect RCT data to be able to estimate the benefit (if any) from using this approach. To do so, we will conduct 3 experiments using our platform for 3 different health conditions, each of which has high public health burden, high heterogeneity of therapy response, and high priority for a precision therapeutics approach as determined by previously interviewed clinicians and patients. For each, we will randomize 60 patients to receive an N-of-1 trial, or to receive usual care. We will then test a pragmatic design that simulates how the platform will be implemented in clinical practice after its release date. The application will be embedded in the clinical workflow, with clinicians having the capability of referring and tracking their patients in N-of-1 trials directly through the electronic health record. In this experiment, we will randomize 200 patients to receive the pragmatic N-of-1 trial, or to receive usual care. We will thus be able to compare our new precision therapy approach to the way therapies are typically determined for a patient. This N-of-1 trials platform will also facilitate a paradigm-shifting approach to discovery of therapeutic response phenotypes, as we will build a public facing registry of N-of-1 protocols and tools and a repository of de-identified data from N-of-1 trials. This international database of N-of-1 trial results can then be mined to identify phenotypes categorized by treatment responsiveness.