Sensory Phenotyping to Enhance Neuropathic Pain Drug Development

感觉表型增强神经病理性疼痛药物的开发

基本信息

  • 批准号:
    10724809
  • 负责人:
  • 金额:
    $ 160.05万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-21 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

PROJECT SUMMARY / ABSTRACT Despite the high prevalence and impact of neuropathic pain (NP), patients have only a 30% probability of meaningful response to any single medication. Furthermore, it is not known which patients will respond to which medication. Precision Pain Medicine (PPM) considers individual variation in patient phenotype and genotype to optimize pain treatment outcomes. A critical first step to advance PPM is the identification of biomarkers that represent underlying pain mechanisms, which can then be matched to drug mechanisms. Based on the consistent finding that different pain conditions have common phenotypes, and preliminary evidence that pain phenotype predicts treatment outcome, our overarching hypothesis is that the pain phenotype is a clinical representation of the underlying pain mechanism that will permit mechanism-based, rather than disease-based treatment (i.e., it can be used as a predictive biomarker to enhance the likelihood of therapeutic success). Quantitative sensory testing (QST) is a promising technique to create a sensory biosignature that can be used as a predictive biomarker in NP. Laboratory-based QST can quantify the severity of positive and negative sensory phenomena, and has been broadly used to establish sensory phenotypes that robustly categorize inter- patient variability in the sensory features of NP. Preliminary data suggest that specific sensory phenotypes may predict response to specific drugs, but these studies are mostly small, single-center, retrospective, and use the resource-intensive laboratory-based QST. To enhance the utility of QST we have developed a brief, convenient, inexpensive, “bedside” QST battery with reliability and validity equal to laboratory-based QST that can be used to rapidly classify patients or study participants into sensory phenotypes (e.g., “irritable” and “non-irritable” nociceptor). Herein, we propose to develop a bedside QST-based phenotyping biosignature and rigorously test its ability to predict treatment response to two known analgesics with different mechanisms. We also explore whether proteomic blood-based biomarkers alone or in conjunction with QST phenotypes can predict response to treatments. In Aim 1, we will establish a highly-trained, 5-site network that can reliably perform the bedside QST battery, collect data from patients with NP, and use those data to develop cluster analysis-based algorithm(s) for classifying NP sensory phenotypes. In Aim 2, we meet the scientific milestones and feasibility requirements to design and complete the start-up phase of a 5-site crossover RCT in NP patients (e.g., obtain IRB approvals, train staff, create a data management system). In Aim 3, we test the ability of the bedside QST- derived phenotypes to predict response to NP medications in a 3-period cross-over trial of pregabalin, duloxetine, and placebo in patients with NP. Aim 4 will determine relationships between proteomic biomarkers and QST phenotypes and the predictive ability of those biomarkers alone or in combination. This study will determine whether an inexpensive and scalable QST-based biosignature can predict response to pregabalin and duloxetine and potentially identify novel proteomic-based biomarkers that can augment QST-based predictions.
项目摘要/摘要 尽管神经性疼痛(NP)的患病率和影响很高,但患者只有30%的概率发生神经性疼痛。 对任何一种药物都有意义的反应。此外,尚不清楚哪些患者会对哪些反应。 药精准疼痛医学(PPM)考虑了患者表型和基因型的个体差异, 优化疼痛治疗效果。推进PPM的关键第一步是鉴定生物标志物, 代表潜在的疼痛机制,然后可以与药物机制相匹配。基于 一致的发现,不同的疼痛条件有共同的表型,初步证据表明,疼痛 表型预测治疗结果,我们的总体假设是,疼痛表型是一个临床 潜在疼痛机制的表示,允许基于机制,而不是基于疾病 处理(即,它可以用作预测性生物标志物以提高治疗成功的可能性)。 定量感觉测试(QST)是一种很有前途的技术,可以创建一个感官生物签名, 作为NP的预测性生物标志物。基于量化的QST可以量化积极和消极的严重程度, 感官现象,并已被广泛用于建立感官表型,有力地分类间- NP感觉特征的患者变异性。初步数据表明,特定的感觉表型可能 预测对特定药物的反应,但这些研究大多是小型的,单中心的,回顾性的,并使用 资源密集型实验室QST。为了提高QST的实用性,我们开发了一种简单、方便、 廉价的“床边”QST电池,其可靠性和有效性等同于可以使用的基于实验室的QST 为了将患者或研究参与者快速分类为感觉表型(例如,“易怒”和“不易怒” 伤害感受器)。在此,我们建议开发基于床边QST的表型生物签名,并严格测试 其预测对两种具有不同机制的已知镇痛剂的治疗反应的能力。我们还探索 基于血液的蛋白质组学生物标志物单独或与QST表型结合是否可以预测反应 到治疗。在目标1中,我们将建立一个经过高度训练的5站点网络,可以可靠地执行床边 QST组合,收集NP患者的数据,并使用这些数据开发基于聚类分析的 用于分类NP感觉表型的算法。在目标2中,我们达到了科学里程碑和可行性 在NP患者中设计和完成5中心交叉RCT启动阶段的要求(例如,获得 IRB批准、培训员工、创建数据管理系统)。在目标3中,我们测试床旁QST的能力- 在普瑞巴林,度洛沙汀, 和安慰剂治疗NP患者。目的4将确定蛋白质组学生物标志物与QST之间的关系 表型和这些生物标志物单独或组合的预测能力。本研究将确定 一种廉价且可扩展的基于QST的生物特征是否可以预测对普瑞巴林和度洛沙坦的反应 并有可能确定新的基于蛋白质组学的生物标志物,可以增强基于QST的预测。

项目成果

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ROBERT R EDWARDS其他文献

ROBERT R EDWARDS的其他文献

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{{ truncateString('ROBERT R EDWARDS', 18)}}的其他基金

Impact of Theory of Mind Training on Brain-to-Brain Patient-Clinician Concordance
心理理论训练对脑-脑患者-临床医生一致性的影响
  • 批准号:
    10544363
  • 财政年份:
    2023
  • 资助金额:
    $ 160.05万
  • 项目类别:
Mentorship in precision pain medicine via EPPIC-NET
通过 EPPIC-NET 进行精准疼痛医学指导
  • 批准号:
    10426787
  • 财政年份:
    2021
  • 资助金额:
    $ 160.05万
  • 项目类别:
Clinical Coordinating Center for the Health Initiative in Early Phase Pain Investigation Clinical Network
早期疼痛调查临床网络健康倡议临床协调中心
  • 批准号:
    10246465
  • 财政年份:
    2019
  • 资助金额:
    $ 160.05万
  • 项目类别:
Clinical Coordinating Center for the Health Initiative in Early Phase Pain Investigation Clinical Network
早期疼痛调查临床网络健康倡议临床协调中心
  • 批准号:
    10703234
  • 财政年份:
    2019
  • 资助金额:
    $ 160.05万
  • 项目类别:
Clinical Coordinating Center for the Health Initiative in Early Phase Pain Investigation Clinical Network
早期疼痛调查临床网络健康倡议临床协调中心
  • 批准号:
    10480912
  • 财政年份:
    2019
  • 资助金额:
    $ 160.05万
  • 项目类别:
Opioid-induced change in pain sensitivity and modulation: Links to opioid misuse
阿片类药物引起的疼痛敏感性和调节变化:与阿片类药物滥用的联系
  • 批准号:
    9035522
  • 财政年份:
    2016
  • 资助金额:
    $ 160.05万
  • 项目类别:
Brain Mechanisms Underlying CBT-Related Reductions in Fibromyalgia
CBT 相关减少纤维肌痛的大脑机制
  • 批准号:
    9071290
  • 财政年份:
    2014
  • 资助金额:
    $ 160.05万
  • 项目类别:
Brain Mechanisms Underlying CBT-Related Reductions in Fibromyalgia
CBT 相关减少纤维肌痛的大脑机制
  • 批准号:
    8631674
  • 财政年份:
    2014
  • 资助金额:
    $ 160.05万
  • 项目类别:
Young Investigator Travel Support for 2013 APS Annual Scientific Meeting
2013 年 APS 年度科学会议年轻研究者旅行支持
  • 批准号:
    8529153
  • 财政年份:
    2013
  • 资助金额:
    $ 160.05万
  • 项目类别:
Biobehavioral Risk Factors for Persistent Pain following Total Knee Arthroplasty
全膝关节置换术后持续疼痛的生物行为危险因素
  • 批准号:
    8101280
  • 财政年份:
    2010
  • 资助金额:
    $ 160.05万
  • 项目类别:

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