数字农科院2.0

Dual SPE–HPLC–MS/MS Platform for Cross-Class Antiparasitic Surveillance: Simultaneous Quantification of Oxyclozanide and Levamisole Hydrochloride in Ovine Tissues with Applications to Withdrawal Period Optimization

文献类型： 外文期刊

作者： Guonian Dai;Xuzheng Zhou;Weiwei Wang;Bintao Zhai;Jiang Li;Yangling Liu;Yong Zhang;Jiyu Zhang

作者机构：

关键词： HPLC–MS/MS;MRLs;oxyclozanide and levamisole hydrochloride suspension;sheep;withdrawal period

期刊名称： Molecules

ISSN： 1420-3049

年卷期： 2025 年 30 卷 7 期

页码：

收录情况： SCIE（2025版）

摘要： This study presents a novel high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) method for simultaneous determination of oxyclozanide (OXY) and levamisole hydrochloride (LEV) residues in ovine tissues, addressing the critical gap in cross-class antiparasitic drug monitoring. Leveraging dual solid-phase extraction strategies—MAX anion-exchange for lipophilic OXY and MCX cation-exchange for hydrophilic LEV—we achieved efficient purification of these pharmacokinetically divergent compounds from complex matrices (muscle, liver, kidney, and perirenal adipose). The method demonstrated superior sensitivity with limits of detection (1.5 μg/kg) and quantification (2.5 μg/kg) below international maximum residue limits (MRLs), validated through Codex Alimentarius guidelines (CAC/GL 71-2009). Linear responses (2.5–1000 μg/kg, R2 > 0.9900) and robust precision (intra-day RSD: 1.44–12.51%; inter-day RSD: 0.29–17.70%) were maintained across spiked concentrations (LOQ, 0.5×, 1×, and 2 × MRLs), with recoveries of 80.94–115.36% confirming matrix-agnostic accuracy. Stability assessments under diverse storage conditions further validated method reliability. Applied to pharmacokinetic profiling in medicated sheep, this protocol established a 28-day withdrawal period for edible tissues, reconciling regulatory compliance with food safety requirements. As the first reported simultaneous quantification platform for OXY and LEV antiparasitics, our methodology advances veterinary residue analytics by enabling efficient multi-class surveillance and evidence-based withdrawal period optimization.

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