Background: Monitoring of medication compliance and drug abuse is used by clinicians to increase patient prescription drug compliance and reduce illicit drug abuse and diversion. Despite available immunoassays, chromatography-mass spectrometry-based methods are considered the gold standard for urine drug monitoring owing to higher sensitivities and specificities. Herein, we report a fast, convenient ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) assay to detect or quantify 37 clinically relevant prescription drugs, drugs of abuse, and related glucuronides and other metabolites in human urine by single diluted sample injection.
Methods: Analytes consisted of prescription and illicit opioids, benzodiazepines, and drugs of abuse, including parent compounds and glucuronidated and nonglucuronidated metabolites. Urine samples were diluted with water and supplemented with deuterated internal standards without enzymatic hydrolysis, analyte extraction, or sample purification. Analytes were separated by reversed-phase UPLC and quantified by positive-mode electrospray ionization and collision-induced dissociation MS. Assay validation followed Food and Drug Administration bioanalytical guidelines.
Results: Total analytical run time was 5.5 min. All analytes demonstrated acceptable inter- and intraassay accuracy, imprecision, and linearity throughout clinically relevant analytical ranges (1-2000 ng/mL, depending on analyte). All analytes demonstrated acceptable selectivity, stability, matrix effects, carryover, and performance compared to national reference laboratory or previously validated in-house methods. A total of 23 and 14 analytes were validated for quantitative and qualitative testing, respectively.
Conclusions: A convenient UPLC-MS/MS assay for simultaneously monitoring 37 analytes in human urine was validated for use in pain management testing. Advantages of this multiplex assay include facile sample preparation and higher-throughput definitive detection including glucuronide metabolite quantification.
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http://dx.doi.org/10.1373/jalm.2018.027342 | DOI Listing |
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