Objectives: Fentanyl, a potent μ receptor agonist, is a cornerstone of perioperative and cancer pain management due to its high efficiency and multi-dosage characteristics. Despite its efficacy, the risks of respiratory depression and abuse present significant global public health challenges. The development of novel drug delivery systems expands its utility but intensifies the need to balance clinical benefits with safety. This review synthesizes resent evidence on fentanyl pharmacology and clinical use,with a focus on multimodal administration strategies,does adjustment for special populations,and the impact of anti-abuse technologies and regulatory policies. Methods: From the establishment of the database to September 2025, researchers conducted a systematic literature search in PubMed, Embase and CNKI databases. The search terms included ‘ fentanyl ‘, ‘ analgesia ‘, ‘ pharmacokinetics ‘, ‘ respiratory depression ‘ and ‘ abuse prevention ‘. High-quality randomized controlled trials, meta-analysis, literature review and the latest clinical guidelines were included. Results: Analysis confirms that the fentanyl’s unique pharmacokinetic and its newly discovered multivalent binding mode to the μ-receptor underpin its rapid onset and potent analgesia. Quantitative comparisons show intravenous administration excels in rapid-onset pain relief, while mucosal routes provide superior sustained drug exposure. Critically, does reduction is essential for elderly patients and those with hepatic impairment. The intergration of anti-abuse formulations and stringent regulatory measures is vital for mitigating misuse risks while ensuring medical accessibility. Discussion: The effective use of fentanyl resquires an integrated strategy combining pharmacological knowledge,individual patient factors,and a robust regulatory framework. The clinical decision-making pathway proposed herein aids in optimizing analgesia minimizing adverse outcomes.Future research should prioritize developing evidence-based guidelines for the Chinese population and exploring genomic predictors for personalized pain management.
Rui Feng (Wed,) studied this question.