Decoding orofacial pain: a translational review of mechanisms and novel therapies
Abstract
Orofacial pain encompasses a diverse group of disorders that pose a significant clinical challenge, often progressing from acute to chronic conditions due to a disconnect between clinical diagnosis and underlying pathophysiology. This review provides a synthesis of the mechanisms driving this transition and evaluates emerging therapeutic strategies that target them. First, we dissect the pathophysiological mechanisms of acute pain, including peripheral sensitization and transient central sensitization; and then detail how these processes can become maladaptive, leading to chronic pain states sustained by a combination of persistent nociceptive input, neuropathic alterations, and nociplastic changes involving dysfunctional descending modulation and central network reorganization. Building on this mechanistic framework, we critically appraise novel therapeutic approaches, including targeted pharmacotherapies such as selective ion channel and endocannabinoid system modulators, alongside non-pharmacotherapeutic interventions that encompass neuromodulation techniques designed to regulate central neural plasticity and psychologically grounded therapies. By integrating molecular evidence with clinical presentations, this review offers a framework for advancing towards a more precise, mechanism-based management of orofacial pain.
Read the paper
Related studies
- Noninvasive and Point-of-Care Surface-Enhanced Raman Scattering (SERS)-Based Breathalyzer for Mass Screening of Coronavirus Disease 2019 (COVID-19) under 5 min
- De Novo Transcriptome Analysis and Detection of Antimicrobial Peptides of the American Cockroach Periplaneta americana (Linnaeus)
- Molecular Characterization and Expression Profiling of Tomato GRF Transcription Factor Family Genes in Response to Abiotic Stresses and Phytohormones
- Assembly of Tight Junction Strands: Claudin-10b and Claudin-3 Form Homo Tetrameric Building Blocks that Polymerise in a Channel-Independent Manner
- Update of the Pompe variant database for the prediction of clinical phenotypes: Novel disease‐associated variants, common sequence variants, and results from newborn screening
- Machine Learning-Based SERS Chemical Space for Two-Way Prediction of Structures and Spectra of Untrained Molecules
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.