Neuropathic pain therapy: a puzzle of different approaches to stratify patients.

In recent years, more and more effort has been invested in the identification of biomarkers for the treatment of neuropathic pain. Given the fact that many clinical trials on development of new analgesic drugs have failed in the past, one important goal is the prediction of treatment response by identification of patient subgroups that would benefit the most from a certain therapy. The sensory phenotype of a patient, ie, the sensory symptoms and signs, has been proposed to provide indirect insights into the underlying pathophysiological mechanisms and can thus be used to realize the concept of a personalized pain therapy. In support of this concept, earlier studies have shown that quantitative sensory testing (QST) allows to distinguish 3 subgroups of neuropathic pain patients with different sensory profiles and that these subgroups, prospectively stratified, have a differential response to the sodium-blocker oxcarbazepine. As a consequence of this, the European Medicines Agency has classified sensory phenotyping with QST as suitable for identifying subgroups of neuropathic pain patients with certain sensory profiles in a “CHMP qualification advice. ” However, several points of critique have been raised recently questioning the QST profiling as an adequate stratification approach. First, QST relates on evoked sensory perceptions. Spontaneously occurring sensations such as burning, prickling, and pain attacks are not assessed but may have important implications for pain mechanisms. Second, QST uses experimental sensory stimuli rather than naturally occurring stimulation, which may have different mechanisms. Third, QST equipment is expensive and the testing procedure time-consuming, making the protocol difficult to be used in large clinical trials. In this issue of PAIN, a new method of sensory profiling is presented that can circumvent some of the caveats of QST. Bouhassira et al. performed a deliberate study investigating the use of the Neuropathic Pain Symptom Inventory (NPSI), a patientreportedquestionnaire, as a simple, fast, andcheap stratification tool for neuropathic pain. In contrast to QST, the NPSI captures spontaneous pain sensations as well as evoked perceptions as reported by the patients. Furthermore, similar to QST, the authors showed 3 patient clusters and developed an algorithm to classify individual patients with neuropathic pain, accordingly. Last, they demonstrated the relevance of this classification approach by comparing the therapeutic response to a selected therapy between these clusters. A post hoc analysis revealed a significant pain reduction by treatment with botulinum toxin A (BTX-A) only in the 2 clusters of patients who showed moderate to high scores for the evoked pain NPSI items, but not in patients with reduced scores. The work presented clearly adds another piece of evidence supporting the idea of prediction of treatment response based on sensory phenotypes. It further underpins the notion that response prediction can also be achieved by patient-reported sensory profiling. Can we further improve sensory profiling? To assess the entire picture of sensory perceptions in neuropathic pain, the optimal approach could be a combination of tools including the measurement of sensory symptoms and signs: spontaneous sensory symptoms and the percept of naturally occurring stimuli can be examined by patient-reported questionnaires such as the NPSI and painPREDICT, whereas QST can assess signs evoked by a standardized test battery. To implement QST in large clinical trials and clinical practice, a bedside QST stratification tool has been developed recently, which allows the allocation to the 3 previously published QST clusters. The future: deep profiling and novel outcome parameters Although promising, subgrouping of patients solely based on their sensory profile cannot be the ultimate solution. In the presented study, BTX-A led to a similar pain reduction in the evoked pain cluster (cluster 2) and the deep pain cluster (cluster 3). This implies that the presence of evoked pain predicts treatment response to BTX-A. However, patients assigned to cluster 3 only showed average and not increased scores for evoked pain. If the therapeutic response depends on the phenotype only, one would have expected greater pain reduction in cluster 2 with higher scores for evoked pain compared to cluster 3. Because this was not the case, it is reasonable to assume that other parameters influenced the treatment as well. In general, a patient whose phenotype actually indicates a response to a certain therapy may still experience insufficient pain relief. It is very likely that a cornucopia of complex clinical characteristics (psychosocial factors, comorbidities, sensory abnormalities, and pathomechanisms) as well as genetic variants influences the overall treatment response. This specific clinical response pattern depends on the drug or intervention used. Deep profiling using all available data from both existing and new trials as well as consequent statistical modelling of treatment response will reveal certain clinical baseline profiles that will be used for stratification and will increase the likelihood of response. Sensory profiling of patients with, eg, painful polyneuropathy may indicate a potential response to a certain treatment. However, he or she may still not benefit because the pain may not be the main concern for this patient. The quality of life of this particular patient might mainly be affected by a limited walking ability due to sensory ataxia and hence, pain reductionwould not be themost appropriate Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.