Biomarkers for Chronic Pain: Significance and Summary of Recent Advances

Electrical neuromodulation for the treatment of chronic pain: derivation of the intrinsic barriers, outcomes and considerations of the sustainability of implantable spinal cord stimulation therapies

INTRODUCTION

For over 60 years, spinal cord stimulation has endured as a therapy through innovation and novel developments. Current practice of neuromodulation requires proper patient selection, risk mitigation and use of innovation. However, there are tangible and intangible challenges in physiology, clinical science and within society.

AREAS COVERED

We provide a narrative discussion regarding novel topics in the field especially over the last decade. We highlight the challenges in the patient care setting including selection, as well as economic and socioeconomic challenges. Physician training challenges in neuromodulation is explored as well as other factors related to the use of neuromodulation such as novel indications and economics. We also discuss the concepts of technology and healthcare data.

EXPERT OPINION

Patient safety and durable outcomes are the mainstay goal for neuromodulation. Substantial work is needed to assimilate data for larger and more relevant studies reflecting a population. Big data and global interconnectivity efforts provide substantial opportunity to reinvent our scientific approach, data analysis and its management to maximize outcomes and minimize risk. As improvements in data analysis become the standard of innovation and physician training meets demand, we expect to see an expansion of novel indications and its use in broader cohorts.

Influence of Transcutaneous Electrical Nerve Stimulation (TENS) on Pressure Pain Thresholds and Conditioned Pain Modulation in a Randomized Controlled Trial in Women With Fibromyalgia

Transcutaneous electrical nerve stimulation (TENS) effectively reduces pain in fibromyalgia (FM). The purpose of this study was to examine the influence of TENS use on pressure pain thresholds (PPT) and conditioned pain modulation (CPM) in individuals with FM using data from the Fibromyalgia Activity Study with TENS trial (NCT01888640). Individuals with FM were randomly assigned to receive active TENS, placebo TENS, or no TENS for 4 weeks. A total of 238 females satisfied the per-protocol analysis among the active TENS (n = 76), placebo TENS (n = 68), and no TENS (n = 94) groups. Following 4 weeks of group allocation, the active TENS group continued for an additional 4 weeks of active TENS totaling 8 weeks (n = 66), the placebo and no TENS groups transitioned to receive 4 weeks of active TENS (delayed TENS, n = 161). Assessment of resting pain, movement-evoked pain (MEP), PPT, and CPM occurred prior to and following active, placebo, or no TENS. There were no significant changes in PPT or CPM among the active TENS, placebo TENS, or no TENS groups after 4 weeks. Individuals who reported clinically relevant improvements in MEP (≥30% decrease) demonstrated increases in PPT (P < .001), but not CPM, when compared to MEP non-responders. There were no significant correlations among the change in PPT or CPM compared to MEP and resting pain following active TENS use (active TENS + delayed TENS). PPT and CPM may provide insight to underlying mechanisms contributing to pain; however, these measures may not relate to self-reported pain symptoms. PERSPECTIVE: Pressure pain threshold increased in individuals with clinically relevant improvement (≥30%) in MEP, indicating the clinical relevance of PPT for understanding mechanisms contributing to pain. CPM was not a reliable indicator of treatment response in MEP responders.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

Transforming medicine: artificial intelligence integration in the peripheral nervous system

In recent years, artificial intelligence (AI) has undergone remarkable advancements, exerting a significant influence across a multitude of fields. One area that has particularly garnered attention and witnessed substantial progress is its integration into the realm of the nervous system. This article provides a comprehensive examination of AI's applications within the peripheral nervous system, with a specific focus on AI-enhanced diagnostics for peripheral nervous system disorders, AI-driven pain management, advancements in neuroprosthetics, and the development of neural network models. By illuminating these facets, we unveil the burgeoning opportunities for revolutionary medical interventions and the enhancement of human capabilities, thus paving the way for a future in which AI becomes an integral component of our nervous system's interface.

Advancements in Understanding and Classifying Chronic Orofacial Pain: Key Insights from Biopsychosocial Models and International Classifications (ICHD-3, ICD-11, ICOP)

In exploring chronic orofacial pain (COFP), this review highlights its global impact on life quality and critiques current diagnostic systems, including the ICD-11, ICOP, and ICHD-3, for their limitations in addressing COFP's complexity. Firstly, this study outlines the global burden of chronic pain and the importance of distinguishing between different pain types for effective treatment. It then delves into the specific challenges of diagnosing COFP, emphasizing the need for a more nuanced approach that incorporates the biopsychosocial model. This review critically examines existing classification systems, highlighting their limitations in fully capturing COFP's multifaceted nature. It advocates for the integration of these systems with the DSM-5's Somatic Symptom Disorder code, proposing a unified, multidisciplinary diagnostic approach. This recommendation aims to improve chronic pain coding standardization and acknowledge the complex interplay of biological, psychological, and social factors in COFP. In conclusion, here, we highlight the need for a comprehensive, universally applicable classification system for COFP. Such a system would enable accurate diagnosis, streamline treatment strategies, and enhance communication among healthcare professionals. This advancement holds potential for significant contributions to research and patient care in this challenging field, offering a broader perspective for scientists across disciplines.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.