Mechanism-based nonopioid analgesic targets
Abstract
Acute pain management has historically been dominated by opioids, whose efficacy is overshadowed by the risks of addiction, tolerance, and dependence, culminating in the global opioid crisis. To transcend this issue, we must innovate beyond opioid-based μ receptor treatments, identifying nonopioid analgesics with high efficacy and minimal adverse effects. This Review navigates the multifaceted landscape of inflammatory, neuropathic, and nociplastic pain, emphasizing mechanism-based analgesic targets tailored to specific pain conditions. We delve into the challenges and breakthroughs in clinical trials targeting ion channels, GPCRs, and other molecular targets. We also highlight the intricate crosstalk between different physiological systems and the need for multimodal interventions with distinct pharmacodynamics to manage acute and chronic pain, respectively. Furthermore, we explore emerging strategies, including gene therapy, stem cell therapy, cell type-specific neuromodulation, and AI-driven techniques for objective, unbiased pain assessment and research. These innovative approaches are poised to revolutionize pain management, paving the way for the discovery of safer and more effective analgesics.
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.