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Updated: Feb 26, 2026

Author Spotlight: An Efficient Methodology to Confidently Differentiate and Characterize Fentanyl Analogs
Published on: November 8, 2024
Research Progress of Fentanyl
1Department of Anesthesiology, Henan Medical University, Xinxiang, Henan Province, China.
Fentanyl offers potent pain relief but requires careful dosing, especially for special populations, to manage risks like respiratory depression. Integrating anti-abuse technologies and regulations is crucial for safe and effective fentanyl use.
Area of Science:
- Pharmacology
- Pain Management
- Drug Delivery Systems
Background:
- Fentanyl, a potent μ receptor agonist, is vital for perioperative and cancer pain management.
- Risks of respiratory depression and abuse pose global public health challenges.
- Novel drug delivery systems enhance fentanyl utility but necessitate balancing benefits with safety.
Purpose of the Study:
- To review current evidence on fentanyl pharmacology and clinical applications.
- To focus on multimodal administration, dose adjustment for special populations, and anti-abuse strategies.
- To synthesize information on regulatory policies impacting fentanyl use.
Main Methods:
- Systematic literature search (PubMed, Embase, CNKI) from database inception to September 2025.
- Inclusion of randomized controlled trials, meta-analyses, reviews, and clinical guidelines.
- Search terms: 'fentanyl', 'analgesia', 'pharmacokinetics', 'respiratory depression', 'abuse prevention'.
Main Results:
- Fentanyl's μ-receptor binding and pharmacokinetics drive rapid, potent analgesia.
- Intravenous administration offers rapid relief; mucosal routes provide sustained exposure.
- Dose reduction is critical for elderly and hepatic impairment patients; anti-abuse measures are vital.
Conclusions:
- Effective fentanyl use demands integrated strategies: pharmacology, patient factors, and regulation.
- A proposed clinical pathway optimizes analgesia and minimizes adverse outcomes.
- Future research should focus on Chinese population guidelines and genomic predictors for personalized pain management.
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