Wednesday, January 15, 2025
1/15/2025
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.
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