Urine Drug Screening in the Era of Designer Benzodiazepines: Comparison of Three Immunoassay Platforms, LC-QTOF-MS, and LC-MS/MS

ABSTRACT This study investigated the presence of designer benzodiazepines in 35 urine specimens obtained from emergency department patients undergoing urine drug screening. All specimens showed apparent false-positive benzodiazepine screening results (i.e., confirmatory testing using a 19-component LC-MS/MS panel showed no prescribed benzodiazepines at detectable levels). The primary aims were to identify the possible presence of designer benzodiazepines, characterize the reactivity of commercially available screening immunoassays with designer benzodiazepines, and evaluate the risk of inappropriately ruling out designer benzodiazepine use when utilizing common urine drug screening and confirmatory tests. Specimens were obtained from emergency departments of a single US Health system. Following clinically ordered drug screening using Abbott ARCHITECT c assays and lab-developed LC-MS/MS confirmatory testing, additional characterization was performed for investigative purposes. Specifically, urine specimens were screened using two additional assays (Roche cobas c502, Siemens Dimension Vista) and LC-QTOF-MS to identify presumptively positive species, including benzodiazepines and non-benzodiazepines. Finally, targeted, qualitative LC-MS/MS was performed to confirm the presence of 12 designer benzodiazepines. Following benzodiazepine detection using the Abbott ARCHITECT, benzodiazepines were subsequently detected in 28/35 and 35/35 urine specimens, respectively, using Siemens and Roche assays. LC-QTOF-MS showed the presumptive presence of at least one non-FDA approved benzodiazepine in 30/35 specimens: flubromazolam (12/35), flualprazolam (11/35), flubromazepam (2/35), clonazolam (4/35), etizolam (9/35), metizolam (5/35), nitrazepam (1/35), and pyrazolam (1/35). Two or three designer benzodiazepines were detected concurrently in 13/35 specimens. Qualitative LC-MS/MS confirmed the presence of at least one designer benzodiazepine or metabolite in 23/35 specimens, with 3 specimens unavailable for confirmatory testing. Urine benzodiazepine screening assays from three manufacturers were cross-reactive with multiple non-US FDA-approved benzodiazepines. Clinical and forensic toxicology laboratories using traditionally designed LC-MS/MS panels may fail to confirm the presence of non-US FDA-approved benzodiazepines detected by screening assays, risking inappropriate interpretation of screening results as false-positives.

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A computational approach to identify interfering medications on urine drug screening assays without data from confirmatory testing

10.1101/2020.04.03.20052621 ◽

2020 ◽

Author(s):

Nadia Ayala-Lopez ◽

Layla Aref ◽

Jennifer M. Colby ◽

Jacob J. Hughey

Keyword(s):

Drug Screening ◽

Odds Ratio ◽

Drugs Of Abuse ◽

Computational Approach ◽

Target Drug ◽

Urine Drug ◽

Screening Assays ◽

Spurious Results ◽

Related Compounds ◽

Urine Drug Screening

AbstractBackgroundUrine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse, but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data.MethodsFrom our institution’s EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol, and salicylates. Both datasets included individuals’ prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering ingredient independently of exposure to the assay’s target drug(s). For a given assay-ingredient pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments.ResultsCompared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering ingredients. Applying our approach to the new data, three ingredients had a higher odds ratio on the acetaminophen assay than acetaminophen itself did: levodopa, carbidopa, and entacapone. The first two, as well as related compounds methyldopa and alpha-methyldopamine, produced presumptive positives at < 40 μg/mL.ConclusionsOur approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.

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A Computational Approach to Identify Interfering Medications on Urine Drug Screening Assays without Data from Confirmatory Testing

Journal of Analytical Toxicology ◽

10.1093/jat/bkaa140 ◽

2020 ◽

Author(s):

Nadia Ayala-Lopez ◽

Layla Aref ◽

Jennifer M Colby ◽

Jacob J Hughey

Keyword(s):

Drug Screening ◽

Odds Ratio ◽

Drugs Of Abuse ◽

Computational Approach ◽

Health Record ◽

Target Drug ◽

Urine Drug ◽

Screening Assays ◽

Spurious Results ◽

Urine Drug Screening

Abstract Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. From our institution’s EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol and salicylates. Both datasets included individuals’ prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering medication independently of exposure to the assay’s target drug(s). For a given assay–medication pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering medications. Applying our approach to the new data, we discovered and validated multiple compounds that can cause presumptive positives on the UDS assay for acetaminophen. Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.

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Analysis of electronic health records reveals medication-related interference on point-of-care urine drug screening assays

10.1101/2020.04.03.20052647 ◽

2020 ◽

Author(s):

Nadia Ayala-Lopez ◽

Jennifer M. Colby ◽

Jacob J. Hughey

Keyword(s):

Drug Screening ◽

False Positive ◽

Point Of Care ◽

Positive Screen ◽

Urine Drug ◽

Screening Assays ◽

Electronic Health ◽

Drug Free ◽

Positive Results ◽

Urine Drug Screening

AbstractBackgroundPoint-of-care (POC) urine drug screening (UDS) assays provide immediate information for patient management. However, POC UDS assays can produce false positive results, which may not be recognized until confirmatory testing is completed several days later. To minimize the potential for patient harm, it is critical to identify sources of interference. Here we applied an approach based on statistical analysis of electronic health record (EHR) data to identify medications that may cause false positives on POC UDS assays.MethodsFrom our institution’s EHR data, we extracted 120,670 POC UDS and confirmation results, covering 12 classes of target drugs, along with each individual’s prior medication exposures. For a given assay and medication ingredient, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference experimentally by spiking compounds into drug-free urine and testing the spiked samples on the POC device (Integrated E-Z Split Key Cup II, Alere).ResultsOur dataset included 446 false positive UDS results (presumptive positive screen followed by negative confirmation). We quantified potential interference for 528 assay-ingredient pairs. Of the six assay-ingredient pairs we evaluated experimentally, two showed interference capable of producing a presumptive positive: labetalol on the MDMA assay (at 200 μg/mL) and ranitidine on the methamphetamine assay (at 50 μg/mL). Ranitidine also produced a presumptive positive for opiates at 1600 μg/mL and for propoxyphene at 800 μg/mL.ConclusionsThese findings support the generalizability of our approach to use EHR data to identify medications that interfere with clinical immunoassays.

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