分析与生物分析技术杂志

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Medication and Illicit Substance Use Analyzed Using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) in a Pain Population

Amadeo Pesce, Elizabeth Gonzales, Perla Almazan, Charles Mikel, Sergey Latyshev, Cameron West and Jennifer Strickland

Background: Patients on chronic opioid therapy are often closely monitored to identify prescribed or nonprescribed medications and/or illicit substances and to identify medication use that may lead to adverse events. Monitoring is typically performed using a combination of clinical tools and assessment methods that often include patient medication histories, risk assessments, and medication monitoring with urine testing. The chronic pain population may be prescribed an average of three to five medications for pain and associated symptoms. In addition to prescribed therapies, this patient population often takes non-prescribed medications and/or illicit substances. Medication monitoring with Urine Drug Testing (UDT), particularly when performed using mass spectrometry, provides accurate information about medications and illicit substances present in the urine.

Purpose of the study: To use LC-MS/MS analyses to describe the variety of medications and metabolites observed in urine specimens from individuals on opioid therapy.

Methods: Analytical procedures were developed using LC-MS/MS that could detect and differentiate between various opioids and their metabolites, other medications commonly prescribed for pain, and certain illicit substances. This retrospective analysis used approximately 340,000 de-identified specimens tested between November 2011 and February 2012 at Millennium Laboratories. Data was sorted to determine frequency of detection and concentrations of the excreted drugs and metabolites.

Results: The most frequently observed medications were hydrocodone and oxycodone, and their metabolites. The next most frequently observed medication was the benzodiazepine class followed by gabapentin, buprenorphine, and morphine. Additionally, illicit substances were detected in 15% of specimens; the most common illicit substances were cannabinoids and cocaine.

Conclusions: Urine drug testing, using LC-MS/MS technology with validated cutoff values for each analyte, provides objective data for providers to use when assessing medication use, potential drug-drug interactions, potential adverse events, and possible diversion. Specific identification of both the medication or substance and the associated metabolites allows for informed interpretation of UDT results. Understanding the medications and illicit substances found in UDT specimens from the pain population helps providers optimize medication monitoring for the best possible plan for pain management.