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Papers: 19 Mar 2022 - 25 Mar 2022

2022 Mar 21

Pain Med

Predicting treatment response with sensory phenotyping in post-traumatic neuropathic pain.


Gewandter JS, Sohn MB, De Guzman R, Frazer ME, Chiodo V, Sharma S, Geha P, Markman JD
Pain Med. 2022 Mar 21.
PMID: 35312012.


Currently available treatments for neuropathic pain are only modestly efficacious when assessed in randomized clinical trials and only work for some patients in the clinic. Induced-pain or gain-of-function phenotypes, have been shown to predict response to analgesics (vs. placebos) in patients with neuropathic pain. However, the predictive value of these phenotypes has never been studied in post-traumatic neuropathic pain. Mixed-effects model for repeated measures (MMRM) were used to evaluate the efficacy of pregabalin vs. placebo in subgroups with induced-pain phenotypes (i.e., hyperalgesia or allodynia) using data from a recent, multi-national RCT (N = 539) that identified phenotypic subgroups using a structured clinical exam. The difference in mean pain score between active and placebo groups (i.e., delta) after 15 weeks of treatment for the subgroup with hyperalgesia was -0.76 (p = 0.001), compared to 0.19 (p = 0.47) for the subgroup that did not have hyperalgesia. The treatment-by-phenotype interaction, which tests whether subgroups have statistically different treatment responses, was significant (p = 0.0067). The delta for the subgroup with allodynia was -0.31 (p = 0.22), compared to -0.30 (p = 0.22) for the subgroup that did not have allodynia (treatment-by-phenotype interaction p = 0.98). These data suggest that hyperalgesia, but not allodynia predicts response to pregabalin in patients with chronic post-traumatic neuropathic pain. This study extends the growing data supporting the utility of induced-pain phenotypes to predict response to analgesics to post-traumatic neuropathic pain. Sensory phenotyping in large, multi-site trials using a structured clinical exam has the potential to accelerate the development of new analgesics and improve the generalizability of clinical trial results.