Comprehensive Insight into Analytical Strategies for the Determination of Zolpidem and Its Metabolites in Different Matrices

Zolpidem (ZLP), a non-benzodiazepine hypnotic of the imidazopyridine class, is widely prescribed for the short-term management of insomnia owing to its rapid onset of action and minimal residual effects. However, its extensive metabolism, low plasma concentration, and instability in pharmaceutical formulations, biological matrices, and environmental samples create significant analytical challenges. This review provides a comprehensive overview of analytical strategies for the accurate, sensitive, and selective quantification of ZLP and its metabolites across various matrices. Articles were retrieved from major scientific databases, including Scopus, Web of Science, PubMed, ScienceDirect, and Google Scholar. Chromatographic methods such as HPLC, liquid chromatographic-tandem mass spectrometry (LC-MS/MS), and ultra-high-performance liquid chromatography (UHPLC)-MS/MS demonstrate high sensitivity, precision, and selectivity, while spectrophotometric and electrochemical approaches provide faster, simpler, and more economical alternatives suitable for quality control and routine screening. While LC-MS/MS and UHPLC-MS/MS offer superior sensitivity and selectivity for trace-level analysis, their high cost and operational complexity limit routine use, whereas HPLC-UV, spectroscopic, and electrochemical methods remain valuable for quality control and screening despite lower sensitivity. The incorporation of nanomaterials, molecularly imprinted polymers (MIPs), and biosensor-based systems has markedly enhanced analytical sensitivity and selectivity and enabled miniaturization. Additionally, adopting green analytical chemistry (GAC) principles, eco-friendly solvents, and microextraction techniques has improved method sustainability and environmental compatibility. Looking ahead, the integration of artificial intelligence (AI), machine learning (ML), and lab-on-a-chip (LOC) technologies is expected to enhance ZLP determination by enabling automation, real-time monitoring, and predictive analysis. These innovative, eco-conscious approaches will yield robust, intelligent, and sustainable platforms for ZLP detection in pharmaceutical, biological, and environmental samples.