Improving neuropathic pain treatment - by rigorous stratification from bench to bedside

Chronic pain is a constantly recurring and persistent illness, presenting a formidable healthcare challenge for patients and physicians alike. Current first-line analgesics offer only low-modest efficacy when averaged across populations, further contributing to this debilitating disease burden. Moreover, many recent trials for novel analgesics have not met primary efficacy endpoints, which is particularly striking considering the pharmacological advances have provided a range of highly relevant new drug targets. Heterogeneity within chronic pain cohorts is increasingly understood to play a critical role in these failures of treatment and drug discovery, with some patients deriving substantial benefits from a given intervention while it has little-to-no effect on others. As such, current treatment failures may not result from a true lack of efficacy, but rather a failure to target individuals whose pain is driven by mechanisms which it therapeutically modulates. This necessitates a move towards phenotypical stratification of patients to delineate responders and non-responders in a mechanistically driven manner. In this article, we outline a bench-to-bedside roadmap for this transition to mechanistically informed personalised pain medicine. We emphasise how the successful identification of novel analgesics is dependent on rigorous experimental design as well as the validity of models and translatability of outcome measures between the animal model and patients. Subsequently, we discuss general and specific aspects of human trial design to address heterogeneity in patient populations to increase the chance of identifying effective analgesics. Finally, we show how stratification approaches can be brought into clinical routine to the benefit of patients.