alexa Medication and Illicit Substance Use Analyzed Using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) in a Pain Population | OMICS International
ISSN: 2155-9872
Journal of Analytical & Bioanalytical Techniques
Like us on:
Make the best use of Scientific Research and information from our 700+ peer reviewed, Open Access Journals that operates with the help of 50,000+ Editorial Board Members and esteemed reviewers and 1000+ Scientific associations in Medical, Clinical, Pharmaceutical, Engineering, Technology and Management Fields.
Meet Inspiring Speakers and Experts at our 3000+ Global Conferenceseries Events with over 600+ Conferences, 1200+ Symposiums and 1200+ Workshops on
Medical, Pharma, Engineering, Science, Technology and Business

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

Millennium Research Institute, San Diego, CA, USA

*Corresponding Author:
Elizabeth Gonzales
Millennium Research Institute
San Diego, CA, USA
E-mail: [email protected]

Received date: May 11, 2012; Accepted date: June 16, 2012; Published date: June 21, 2012

Citation: Pesce A, Gonzales E, Almazan P, Mikel C, Latyshev S, et al. (2012) Medication and Illicit Substance Use Analyzed Using Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) in a Pain Population. J Anal Bioanal Tech 3:135. doi: 10.4172/2155-9872.1000135

Copyright: © 2012 Pesce A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Visit for more related articles at Journal of Analytical & Bioanalytical Techniques

Abstract

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.

Keywords

Chronic pain; LC-MS/MS; Mass spectrometry; Urine drug testing; Illicit substances; Opiates; Pain medications

Introduction

The challenge in treating chronic pain with opioid therapy is balancing safety and efficacy; simultaneously responding to the need to relieve chronic pain while detecting and managing use, misuse, abuse and diversion of medications or illicit substances [1]. Urine Drug Testing (UDT) in the pain population is typically utilized to identify prescribed and non-prescribed medication and illicit substance use and analyze the test results for the risk for drug-drug interactions or adverse events and potential diversion or misuse. Monitoring is performed using a combination of clinical tools and assessment methods that often include patient medication histories, risk assessments, and medication monitoring with urine testing [2]. UDT continues to be the most common tool used for medication monitoring in this patient population [2]. UDT is only one component of a comprehensive risk mitigation plan which can include pill counts, state prescription drug monitoring programs, patient self-reports, treatment agreements, informed consent for high risk medications, and an effective patientprovider relationship [2].

Published date: studies have shown that patient self-reports regarding medication or illicit substance use are often unreliable [3-5]. Thus, other tools, such as UDT, are commonly used in conjunction with self-reports, and provide objective data when monitoring patients on chronic opioid therapy [2,6].

Urine drug testing has historically used immunoassay as a screening method before confirming using mass spectrometry [7]. Although inoffice testing immunoassay technology allows for immediate in-office results, it is limited in usefulness, accuracy, and reliability [6,8]. One of the limitations is that an opiate immunoassay will identify whether the patient is positive for an opiate medication but will not specify which opiate is causing the positive result [8]. Furthermore, immunoassays are unable to identify the presence of opiate metabolites, which precludes the ability to determine if a patient is ingesting or properly metabolizing his or her medication [9]. A person who is unable to metabolize opiates or metabolizes them unusually slowly or rapidly or a lack of analgesia [10,11]. Immunoassays are also prone to false positive results from cross-reacting substances and common medications, and false negative results are due to the typically higher cutoffs compared to other technologies, such as liquid chromatography tandem mass spectrometry (LC-MS/MS). Additionally, immunoassays are often unable to identify adjuvant analgesics such as tapentadol or pregabalin.

Recent developments in methods of mass spectrometry analysis have been shown to clearly differentiate between medications within a class, enabling specific opiate identification [6]. The methods are free from interference of any other prescription medications, over-thecounter medications, herbal supplements, or other agents [12,13], thus virtually eliminating false positive results. Additionally, metabolites can be specifically identified, providing information to assist providers in determining the potential for deception (e.g., adding the medication directly to the urine to obtain a positive result) or the patient having slow or rapid metabolism which may be a concern [6]. For these reasons LC-MS/MS is now considered the gold standard for analysis of opioids and their metabolites [14-20].

This retrospective data analysis sought to examine the UDT results from a large cohort of individuals with chronic pain to better define the medications and metabolites observed in the urine of patients on opioid therapy. A secondary goal of this analysis was to assist in defining the optimal test menu required to monitor this patient population.

Materials

This analysis was approved by the Aspire IRB, 11491 Woodside Ave, Santee, CA 92071. Approximately 340,000 de-identified specimens collected between November 2011 and February 2012 were analyzed for commonly prescribed medications as well as illicit substances using validated methods at Millennium Laboratories [15,21-23]. All specimens were analyzed using LC-MS/MS.

An Agilent® 1200 series binary pump SL Liquid Chromatography system, well plate sampler, thermostatted column compartment, paired with an Agilent® 6410 QQQ mass spectrometer and Agilent® Mass Hunter software was used for analysis of all drugs.

The lower limits of quantitation and the upper limits of linearity were obtained using the Agilent® Technologies QQQ LC-MS/MS system. The lower limits of quantitation for synthetic cannabinoids were set at 15 ng/mL. The coefficient of variation for all the analytes at the low and high ends of the quantitation curve were less than 10%. All quantitative data were obtained from calibration curves with R2> 0.95. Most were 0.99.

The number medications and illicit substances tested varied based on physician orders on the requisition. Specimen results were then sorted for each of the seventy medications, substances, and metabolites (Table 1). The percentage of positive tests for each medication or illicit substance and/or their metabolite(s) was then calculated. The mean, median, standard deviation and range of excreted drug concentrations for each medication or illicit substance and metabolite were also calculated.

Analytes Cutoff (ng/mL) N Tested N Positive % Positive Mean (ng/mL) Median (ng/mL) SD ×/÷ Minimum (ng/mL) Maximum (ng/mL)
Opiates                  
Codeine 50 334,248 6,539 2.0% 922.1 807.2 6.2 50 446,192
Morphine 50 334,248 42,051 12.6% 6,359.1 10,068.7 9.4 50 2,161,530
Hydrocodone 50 334,247 101,224 30.3% 923.3 979.9 4.3 50 240,498
Norhydrocodone 50 334,247 106,614 31.9% 1,011.8 1,075.0 4.2 50 261,278
Hydromorphone 50 334,245 110,559 33.1% 286.3 245.8 3.3 50 292,724
Oxycodone                  
Oxycodone 50 329,790 99,354 30.1% 2,376.3 2,480.5 6.1 50 7,344,920
Noroxycodone 50 329,786 105,576 32.0% 2,781.2 3,097.6 5.6 50 1,598,650
Oxymorphone 50 329,790 107,419 32.6% 1,299.3 1,259.3 5.5 50 566,387
Buprenorphine                  
Buprenorphine 10 203,157 24,902 12.3% 130.6 123.5 4.3 10 979,135
Norbuprenorphine 20 203,157 25,558 12.6% 343.2 370.0 3.5 20 25,393
Fentanyl                  
Fentanyl 2 244,612 12,999 5.3% 35.9 35.1 4.6 2 126,511
Norfentanyl 8 244,612 13,575 5.5% 216.7 235.4 4.4 8 35,115
Meperidine                  
Meperidine 50 116,446 245 0.2% 1,008.6 937.2 6.4 51 129,922
Normeperidine 50 116,273 362 0.3% 2,868.9 3,076.4 7.2 52 289,751
Methadone                  
Methadone 100 313,417 21,483 6.9% 2,152.1 2,257.5 4.4 100 328,786
EDDP 100 313,416 21,825 7.0% 3,064.0 3,536.2 4.7 100 255,380
Propoxyphene                  
Propoxyphene 100 60,751 58 0.1% 481.8 339.3 3.9 100 18,497
Norpropoxyphene 100 60,750 150 0.2% 1,425.3 1,081.0 5.4 100 152,388
Tapentadol                  
Tapentadol 50 123,626 1,952 1.6% 7,133.3 11,325.4 5.8 51 720,475
Tramadol                  
Tramadol 100 162,693 12,266 7.5% 7,103.8 9,421.6 6.0 100 1,908,350
Barbiturates                  
Butalbital 200 167,333 3,838 2.3% 1,071.5 993.6 2.6 200 339,238
Phenobarbital 200 167,332 664 0.4% 3,330.6 3,455.9 4.3 200 98,911
Secobarbital 200 167,332 3 < 0.1% 554.6 551.9 2.2 258 1,200

Table 1: Analyte statistics.

Results

The prevalence and range of excretion values varied widely among the medications and substances (Table 1). The most frequently observed medications were hydrocodone and oxycodone, and their metabolites. The next most frequently detected analytes were the benzodiazepine class followed by gabapentin, buprenorphine, and morphine.

The prevalence of detection of the opioid analgesic medications varied. These medications also showed wide variation in concentrations. For example, morphine was found in 12.6% of the specimens (n=42,051; median concentration 10,069 ng/mL), methadone in 6.9% of specimens (n=21,483; median concentration 2,258 ng/mL), and fentanyl in 5.3% of specimens (n= 12,999; median concentration 35 ng/mL).

The benzodiazepines were the medication class that was most commonly observed after the opioids. Percentage positives within the class were as follows: alpha-hydroxyalprazolam 15.7% (n=49,952), 7-aminoclonazepam 10% (n=31,798), diazepam metabolites 8.4- 13.2% (oxazepam 13.2%, n=42,017; temazepam 11.2%, n= 35,501; and nordiazepam 8.4%, n= 26,783), lorazepam 4% (n= 12,579).

The metabolite of the muscle relaxant carisoprodol (meprobamate) was present in 9.3% of the tested specimens (n=20,032). The anticonvulsants gabapentin and pregabalin were shown in 16% (n=5,433) and 5.8% (n=2,059) of the specimens tested, respectively.

Of the antidepressant class, the tricyclic antidepressants represented about 4% of positive test results, the selective-serotonin re-uptake inhibitors (SSRIs) about 7% (fluoxetine 3.9%, norfluoxetine 4.6%, and paroxetine 2%), and the serotonin-norepinephrine reuptake inhibitors (SNRIs) were about 11% (duloxetine 6.4%, venlafaxine 3%, and O-desmethylvenlafaxine 4%).

Illicit substances appeared in about 15% of the specimens with the most commonly observed substance being marijuana metabolite at 10.6% (n=27,635). This was followed by cocaine metabolite at 3.1% (n= 9,942), the Spice compounds (i.e., synthetic cannabinoids) at approximately 0.9%, and methamphetamine and heroin each at 0.7%.

Metabolites were typically identified in the presence of the following parent medications (metabolite appears in parenthesis): codeine (morphine), hydrocodone (norhydrocodone, hydromorphone), oxycodone (noroxycodone, oxymorphone), fentanyl (norfentanyl), buprenorphine (norbuprenorphine), methadone (EDDP), carisoprodol (meprobamate), venlafaxine (O-desmethylvenlafaxine), and fluoxetine (norfluoxetine).

Discussion

Monitoring patient medication and substance use is critical in achieving a balance between optimal analgesia and minimizing adverse effects, misuse, and diversion. Quantitative laboratory testing provides reliable and accurate information for treating clinicians. However, little is published regarding the range of excreted concentrations for individual medications, substances, or their metabolites in the chronic pain population. This retrospective data analysis demonstrated numerous medications and/or substances that are used by the chronic pain population and also demonstrated wide variability in excreted concentrations for medications, substances, and their metabolites.

In the opioid class, oxycodone and hydrocodone represented the greatest percentage of positive test results, each at approximately 30%. This result was expected because these drugs are the most widely prescribed opiate medications.

Morphine positive results were observed in 12.6% of the specimens. The median concentration value of morphine was higher than median concentrations of other opiates.

Codeine was present in only 2% of the specimens because codeine is known to be a poor analgesic compared with other opioids and is therefore not often prescribed for chronic pain [24].

As the detection of analytes varied by medication or illicit substance, the quantitative values also showed significant variability for the parent compounds as well as their metabolites. The morphine, tramadol and tapentadol median excretion concentrations were the highest of the opioid drug class, each with a median value of approximately 10,000 ng/mL. The median value for oxycodone was approximately 2,500 ng/mL. The hydrocodone median value was approximately 1,000 ng/mL. However, all the medication or illicit substances examined showed a wide range of excretion values, with some extreme cases of concentrations greater than 1,000,000 ng/mL of excreted medication or illicit substance. Reasons for the wide variance include differences in medication doses, metabolic variations from person to person, and specimens that had been altered by the patient in an attempt to deceive the physician. In cases with extremely high concentrations, the patient who was not actually ingesting the medication would likely have “shaved” the medication directly into the urine collection cup to produce a positive test result.

The illicit substances most often found were (from most common to least common): marijuana, cocaine, synthetic cannabinoids, methamphetamine, heroin, MDMA, PCP, and synthetic cathinones. Methamphetamine was accompanied by amphetamine more than 95% of the time. Heroin, as determined by the 6-acetylmorphine metabolite, was accompanied more than 92% of the time by the presence of codeine, which has been shown to be an impurity in the manufacturing process [25,26].

For some medications, the presence or absence of the metabolite may be characteristic for that medication. Consistent with previous studies, the metabolite hydromorphone was not always found with morphine [27]. In some cases, the metabolite was observed in absence of the parent drug (e.g. meprobamate was observed without carisoprodol about 41% of the time) [19]. As the results demonstrated, the inclusion of metabolites in the test menu can allow for informed interpretation of results, and to help identify patients attempting deception, as well as those with atypical metabolism.

Conclusions

The guidelines for monitoring patients on chronic opioid therapy are designed to identify prescribed analgesic medications taken by those patients, and to reduce risk for abuse, morbidity, and mortality. LC-MS/MS technology used with validated cutoff levels for each analyte in a specific test menu provides objective data that may contribute to achieving those objectives. Understanding the medications and illicit substances found in UDT specimens from the pain population can help providers optimize medication monitoring for the best possible and safest plan to manage the patient’s pain.

References

Select your language of interest to view the total content in your interested language
Post your comment

Share This Article

Relevant Topics

Recommended Conferences

Article Usage

  • Total views: 11656
  • [From(publication date):
    July-2012 - Dec 18, 2017]
  • Breakdown by view type
  • HTML page views : 7890
  • PDF downloads : 3766
 

Post your comment

captcha   Reload  Can't read the image? click here to refresh

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version