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

通过 N-of-1 试验重新设计精准治疗

• 批准号: 10674029
• 负责人: Karina W. Davidson
• 金额: $ 129.57万
• 依托单位: FEINSTEIN INSTITUTE FOR MEDICAL RESEARCH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10674029
• 关键词: Address; Alternative Therapies; Amlodipine; Blinded; Blood Pressure; Categories; Chronic; Chronic Insomnia; Clinical; Clinical Engineering; Clinical Management; Collection; Complex; Cost Effectiveness Analysis; Cross-Over Trials; Data; Data Collection; Data Pooling; Databases; Disease; Disease remission; Effectiveness; Effectiveness of Interventions; Electronic Health Record; Electronics; Engineering; Focus Groups; Friends; Health; Healthcare Systems; Heterogeneity; High Prevalence; Hydrochlorothiazide; Hypertension; International; Interview; Learning; Masks; Medical; Medicine; Methods; Modeling; Outcome; Patient Care; Patients; Pharmaceutical Preparations; Phase; Phenotype; Phototherapy; Placebos; Population; Precision therapeutics; Process; Protocols documentation; Public Health; Randomized; Randomized, Controlled Trials; Recommendation; Regimen; Registries; Relapse; Research; Science; Scientist; Selection for Treatments; Sleeplessness; Statistical Data Interpretation; Symptoms; System; Testing; Therapeutic; Time; Time and Motion Studies; Work; active comparator; arm; clinical encounter; clinical practice; compare effectiveness; data de-identification; data warehouse; depressive symptoms; design; effective therapy; experimental study; heuristics; implementation trial; individual patient; innovation; innovative technologies; markov model; novel strategies; optimal treatments; point of care; psychologic; randomized trial; randomized, clinical trials; repository; side effect; symptom management; technology platform; tool; treatment as usual; treatment optimization; treatment response

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.

该提案的目的是开发，测试和实施进行创新的技术平台 N-OF-1试验会改变精度治疗。目前，临床医生正在参加 他们试图确定对个别患者的最佳疗法。这些遭遇可能是 不成功。临床医生依靠最佳证据（例如，III期随机临床试验的结果； RCT）建议对患者推荐治疗。但是，常规的，患者之间的RCT仅提供估计 在这些试验中疗法对普通患者的影响。但是，个别患者经常做出反应 在III期RCT中的平均患者不同，因此治疗反应的异质性 每天这些临床决策。 用于确定Apatient的最佳治疗的最科学严格的方法是单一患者（N-OF-1）试验。 N-1-1试验是多个跨界试验，通常是随机的，通常是 被掩盖，在单个患者中进行，并在客观，连续和现实世界中收集数据，因为 与安慰剂或其他活跃疗法相比，确定该治疗的足够时间段是 对特定患者的最佳选择。他们还产生有关脱靶动作（例如副作用）的信息，以便 更复杂的情况可能会出现对个体患者的疗法的总体益处和危害。诊所和患者不常规从事这种科学努力，因为他们缺乏工具。 在此建议中，我们将创建一个电子平台，该平台将允许临床医生和/或患者订购和 进行单人试验。然后，我们将收集RCT数据，以便能够估算使用的好处（如果有） 这种方法。为此，我们将使用平台进行3种不同的健康状况进行3个实验 具有很高的公共卫生负担，高度异质性反应以及高度优先级 治疗方法是由先前访谈的临床医生和患者确定的。对于每个，我们都会 随机将60名患者随机接受N-1-1试验或接受常规护理。然后，我们将测试一种务实的设计 模拟该平台在发布日期后将如何在临床实践中实施。该申请将是 嵌入在临床工作流程中，临床医生具有参考和跟踪患者的能力 直接通过电子健康记录进行N-1-1试验。在此实验中，我们将将200名患者随机 接受务实的N-1-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 通常为患者确定治疗方式的治疗方法。 这个N-1-1试验平台还将促进一种范式转移方法，以发现治疗反应 表型，因为我们将建立N-OF-1协议和工具的公共面向注册表以及一个被识别的存储库 来自N-1-1试验的数据。然后可以开采这个国际N-1-1试验结果数据库以识别表型 按治疗反应能力分类。

项目成果

Estimating the predictive value of negative severe acute respiratory coronavirus virus 2 (SARS-CoV-2) results: A prospective study.

• DOI: 10.1017/ice.2020.1362
• 发表时间: 2021-10
• 期刊: Infection control and hospital epidemiology
• 影响因子: 4.5
• 作者: Hirschwerk D;Foley M;Lesser M;Farber B;Crawford JM;Davidson KW;Berry GJ;Smith E;Kast C;Volel V;McGinn T
• 通讯作者: McGinn T

Effectiveness of SARS-CoV-2 Decontamination and Containment in a COVID-19 ICU.

• DOI: 10.3390/ijerph18052479
• 发表时间: 2021-03-03
• 期刊: International journal of environmental research and public health
• 作者: Brune Z;Kuschner CE;Mootz J;Davidson KW;Pena RCF;Ghanem MH;Fischer A;Gitman M;Teperman L;Mason C;Becker LB
• 通讯作者: Becker LB

A Series of Personalized Virtual Light Therapy Interventions for Fatigue: Feasibility Randomized Crossover Trial for N-of-1 Treatment.

• DOI: 10.2196/45510
• 发表时间: 2023-09-18
• 期刊: JMIR FORMATIVE RESEARCH
• 影响因子: 2.2
• 作者: Butler, Mark;DAngelo, Stefani;Ahn, Heejoon;Chandereng, Thevaa;Miller, Danielle;Perrin, Alexandra;Romain, Anne-Marie N.;Scatoni, Ava;Friel, Ciaran P.;Cheung, Ying-Kuen;Davidson, Karina W.
• 通讯作者: Davidson, Karina W.

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

• DOI: 10.1186/s42234-020-00050-8
• 发表时间: 2020-01-01
• 期刊: Bioelectronic medicine
• 作者: Debnath, Shubham;Barnaby, Douglas P;Zanos, Theodoros P
• 通讯作者: Zanos, Theodoros P

Risk factors and outcomes for acute-on-chronic liver failure in COVID-19: a large multi-center observational cohort study.

• DOI: 10.1007/s12072-021-10181-y
• 发表时间: 2021-06
• 期刊: Hepatology international
• 影响因子: 6.6
• 作者: Satapathy SK;Roth NC;Kvasnovsky C;Hirsch JS;Trindade AJ;Molmenti E;Barish M;Hirschwerk D;Da BL;Bernstein D;Northwell Health COVID-19 Research Consortium
• 通讯作者: Northwell Health COVID-19 Research Consortium