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.
Xiangsunze Zeng, Rasheen Powell, Clifford J. Woolf
Usage data is cumulative from June 2025 through August 2025.
Usage | JCI | PMC |
---|---|---|
Text version | 2,849 | 278 |
676 | 97 | |
Figure | 751 | 0 |
Table | 580 | 0 |
Citation downloads | 134 | 0 |
Totals | 4,990 | 375 |
Total Views | 5,365 |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.