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.