Determination of oxycodone and its major metabolites in haematic and urinary matrices: Comparison of traditional and miniaturised sampling approaches.

ABSTRACT

Oxycodone is a widely prescribed, full agonist opioid analgesic. As such, it is used clinically to treat different kinds of painful conditions, with a relatively high potential for doping practices in athletes. In this paper, different classic and innovative miniaturised matrices from blood and urine have been studied and compared, to evaluate their relative merits and drawbacks within therapeutic drug monitoring (TDM) and to implement new protocols for anti-doping analysis. Plasma, dried blood spots (DBS) and dried plasma spots (DPS) have been studied for TDM purposes, while urine, dried urine spots (DUS) and volumetric absorptive microsamples (VAMS) from urine for anti-doping. These sampling techniques were coupled to an original bioanalytical method based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the evaluation and monitoring of the levels of oxycodone and its major metabolites (noroxycodone and oxymorphone) in patients under pain management and in athletes. The method was validated according to international guidelines, with good results in terms of precision, extraction yield and accuracy for all considered micromatrices. Thus, the proposed sampling, pre-treatment and analysis are attractive strategies for oxycodone determination in human blood and urine, with advanced options for application to derived micromatrices. Microsampling procedures have significant advantages over classic biological matrices like simplified sampling, storage and processing, but also in terms of precision (<9.0% for DBS, <7.7% for DPS, <7.1% for DUS, <5.3% for VAMS) and accuracy (>73% for DBS, >78% for DPS, >74% for DUS, >78% for VAMS). As regards extraction yield, traditional and miniaturised sampling approaches are comparable (>67% for DBS, >74% for DPS, >75% for DUS, >75% for VAMS). All dried matrices have very low volumes, leading to a significant advantage in terms of analysis feasibility. On the other hand, this also leads to a corresponding decrease in the overall sensitivity.