Editorial: optimization strategies for pain management with neuromodulation

• Autor(a) principal: Pacheco-Barrios, Kevin
• Data de Publicação: 2022
• Outros Autores: Carvalho, Sandra, Leite, Jorge, Caumo, Wolnei, Fregni, Felipe
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/34886
• topic: Neuromodulation; Chronic pain; Biomarkers; Pain management; tDCS – transcranial direct current stimulation

Chronic pain is a high-priority global health issue due to its high prevalence, impact on quality of life, and cost (1). In most cases, chronic pain is challenging to manage, and the existing treatment modalities have reported frequent and severe adverse events, including gastritis (2), cardiovascular complications (2), or even addiction (1) in the case of opioids. Although, during the past two decades, neuroscientific studies have increased our understanding of the pain experience as a complex individual multidimensional phenomenon, the current widespread management methods are still ignoring this nature. Moreover, due to geographical and socioeconomic barriers, there is high inequity in pain treatment access (3). Therefore, chronic pain management urgently requires innovative approaches to shift the target of interventions and modify the “delivery model” from an in-person only provider-centered system to a hybrid patient-centered model (4, 5). Neuromodulatory interventions are promising management options that target maladaptive neuroplasticity, which has been associated with chronic pain conditions (6). Techniques such as transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) have shown adequate efficacy and safety profiles (7). However, recent meta-analyses have reported a high within-and between-study variability and mixed effect sizes, hindering their implementation in clinical practice (7). Additionally, small sample sizes, parameters variability (6), and also lack of device portability and limited easy-to-use profile reduce its competence and applicability compared to “standard” pain treatments. Indeed, one of the main issues in this field is that the alternative, pharmacological treatments, is very easy to use (taking a pill is something very quick) and has a large immediate effect size (compared to neuromodulation which may take several sessions to have an effect). Under this scenario, one potential solution is to systematically optimize these interventions, including treatment protocols, biomarkers, and delivery models, inducing a shift from a pathway of “sustaining innovation” to “disruptive innovation” (8). According to Christensen's theory (8), disruptive innovation defines a process by which an enterprise, product, or service initially takes root in simple applications in an overlooked sector of the market—usually by being effective, safer, affordable, and accessible—and then persistently moves upmarket, eventually displacing established products. This approach does not necessarily require a breakthrough technology but a strategic (and creative) use of technology and user-centered design (8). The principles of disruption theory can be easily applied to pain neuromodulation to finally harness its potential therapeutic applications. This research topic aimed to gather original research and reviews illustrating the recent advances in this optimization pathway. To provide a broader context for the studies, we propose a framework for “disruptive pain neuromodulation” (Figure 1), where we underscore four optimization domains that require development: (1) Digital health framework (9)—the inclusion of these methodological tools will allow home-based interventions and remote trials targeting populations that are usually excluded or not receiving pain management (the overlooked sector of the “market”). (2) Bioengineering development (10)—which will provide portable, safe, and low-cost devices, including user-centered designs with the potential of closed-loop and easy-to-use systems (the “convenient” and “affordable” product in the “market”). (3) Applied data science methods (11)—using machine learning and big data to detect phenotypes of responders and non-responders to treatments and develop personalized treatment protocols. Also, the use of modeling and simulation to optimize stimulation parameters and reduce adverse events or interactions. (4) Network-based approaches (12, 13)—this strategy will be aligned with the multidimensional nature of the pain experience, guiding a new method for developing chronic pain biomarkers that require multimodal and composite assessments (clinical, neuroimaging, and omics). The last two domains can provide the technological advantage and “paradigm change” that would allow to “compete” against dominant treatment strategies. All the papers included in this research topic (two original articles, two reviews, and one case report) can fit in one or more of our proposed domains.