Sensory Phenotyping to Enhance Neuropathic Pain Drug Development

项目摘要/摘要 尽管神经病理性疼痛(NP)的发病率和影响很高，但患者仅有30%的概率发生 对任何一种药物都有意义的反应。此外，还不知道哪些患者会对哪些 药物治疗。精确疼痛医学(PPM)考虑了患者表型和基因的个体差异，以 优化疼痛治疗结果。推进PPM的关键第一步是识别能够 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，我们的首要假设是疼痛表型是一种临床症状。 代表潜在的疼痛机制，将允许基于机制，而不是基于疾病 治疗(即，它可以被用作一个预测性的生物标志物，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，可以用来创建可使用的感觉生物特征 作为NP的预测性生物标志物。基于实验室的QST可以量化阳性和阴性的严重程度 感官现象，并已被广泛用于建立感官表型，有力地分类之间的 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规模、单中心、回溯性的，并使用 以实验室为基础的资源密集型QST。为了增强QST的实用性，我们开发了一种简单、方便、 廉价的床边QST电池，具有与实验室QST相同的可靠性和有效性，可以使用 将患者或研究参与者快速分类为感官表型(例如，“易怒”和“不易怒” 痛觉感受器)。在此，我们建议开发一种基于床边QST的表型生物签名并进行严格的测试 它能够预测对两种不同机制的已知止痛药的治疗反应。我们还探索了 基于血液的蛋白质组生物标志物是否可以单独或与QST表型联合预测疗效 为治疗干杯。在目标1中，我们将建立一个训练有素的5站点网络，能够可靠地执行床边操作 QST电池，收集NP患者的数据，并使用这些数据来开发基于聚类分析的 NP感觉表型分类算法(S)。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点交叉随机对照试验启动阶段的要求(例如，获得 IRB审批、培训工作人员、创建数据管理系统)。在目标3中，我们测试床边QST的能力- 在普瑞巴林，度洛西汀，普瑞巴林，度洛西汀， NP患者服用安慰剂。AIM 4将确定蛋白质组生物标记物和QST之间的关系 表型和这些生物标记物单独或组合的预测能力。这项研究将确定 基于QST的廉价且可扩展的生物特征能否预测普瑞巴林和度洛西汀的疗效 并有可能确定新的基于蛋白质组的生物标记物，可以增强基于QST的预测。

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.