Forensic Science International 243 (2014) 79–83
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Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Prevalence of heroin markers in urine for pain management patients$ Julie Knight a , Brandi L. Puet a , Anne DePriest a, * , Rebecca Heltsley a , Cheryl Hild a , David L. Black a,b , Timothy Robert a , Yale H. Caplan c, Edward J. Cone d a
Aegis Sciences Corporation, 515 Great Circle Road, Nashville, TN 37228, USA Vanderbilt University, Department of Pathology, Immunology and Microbiology, Nashville, TN 37232, USA c University of Maryland, School of Pharmacy, 20 North Pine St., Baltimore, MD 21201, USA d Johns Hopkins School of Medicine, Department of Psychiatry and Behavioral Sciences, Baltimore, MD 21224, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 9 May 2014
Surveys of current trends indicate heroin abuse is associated with nonmedical use of pain relievers. Consequently, there is an interest in evaluating the presence of heroin-specific markers in chronic pain patients who are prescribed controlled substances. A total of 926,084 urine specimens from chronic pain patients were tested for heroin/diacetylmorphine (DAM), 6-acetylmorphine (6AM), 6-acetylcodeine (6AC), codeine (COD), and morphine (MOR). Heroin and markers were analyzed using liquid chromatography tandem mass spectrometry (LC–MS–MS). Opiates were analyzed following hydrolysis using LC–MS–MS. The prevalence of heroin use was 0.31%, as 2871 were positive for one or more heroin-specific markers including DAM, 6AM, or 6AC (a known contaminant of illicit heroin). Of these, 1884 were additionally tested for the following markers of illicit drug use: 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxyamphetamine (MDA), methamphetamine (MAMP), 11-nor-9-carboxy-D9-tetracannabinol (THCCOOH), and benzoylecgonine (BZE); 654 (34.7%) had positive findings for one or more of these analytes. The overall prevalence of heroin markers were as follows: DAM 1203 (41.9%), 6AM 2570 (89.5%), 6AC 1082 (37.7%). MOR was present in 2194 (76.4%) and absent ( 168, 290 > 105; BZE-d3, 293 > 171; DAM, 370 > 165, 370 > 152; DAM-d9, 379 > 61; 6AM, 328 > 165, 328 > 152; 6AM-d3, 331 > 165; and 6AC, 342 > 225, 342 > 165 (6AC was quantified with DAM-d9). Data acquisition, peak integration, and calculation were performed by a computer workstation running Analyst 1.4.2 or Analyst 1.5.2 software. Inter-run precision (%CV) and accuracy (% deviation from the weighed-in target concentration) of control samples prepared in urine containing 4 ng/mL and 250 ng/mL of each analyte in the heroin assay were as follows: DAM (n = 6), precision, 1.9%, 7.5% and accuracy, 6.8%, 11%; 6AM (n = 6), 2.5%, 3.7% and accuracy, 0.8%, 9.3%; 6AC (n = 6), 2.9%, 3.9% and accuracy, 3.6%, 7.5%, respectively. General criteria for identification and measurement of the analytes were as follows: relative retention time (RRT) of each analyte in the specimen had to be within 0.01 of the RRT in the calibrator or the retention time of each analyte in the sample had to be within 3% of its respective RT in the calibrator; ion ratios for the product ions derived from analytes and internal standards in
J. Knight et al. / Forensic Science International 243 (2014) 79–83
controls and donor specimens had to be within the 20% mean range of those obtained from the corresponding substances in the calibrator; control samples had to measure within 20% of the inhouse determined mean value; and negative controls must not have analytes above the LOQ. All quantitative data for drugs and metabolites were included in this report that met identification and quantitation (LOQ) criteria. The LOQ for DAM, 6AM, and 6AC was 4 ng/mL. The LOQ for MOR and other opiates was 50 ng/mL. The LOQ for MDMA, MDA, and MAMP was 80 ng/mL. The LOQ for THCCOOH and BZE was 2 ng/mL and 20 ng/mL, respectively. 3. Data analyses The frequency distributions of pH measurements were compared between those specimens positive for DAM (DAM LOQ) and specimens negative for DAM (DAM < LOQ). The Kolmogorov–Smirnov–Lilliefors (KSL) test was applied to the two distributions to assess for normality, and the assumption of normality of the two distributions of pH values is not justified. Hence, the non-parametric Wilcoxon rank sum test was applied to evaluate similarity in distributions of pH values for those specimens positive for DAM and specimens negative for DAM. Due to the large sample sizes in our analysis, the Wilcoxon test statistic, T, is approximately normal; hence, the standard normal z-score was computed to determine the p-value associated with the test for equal distributions. A comparison of methadone and buprenorphine prescriptions in the subsets of specimens positive or negative for MOR was made using Fisher’s exact test. Analysis was performed with SAS JMP1 10.0.0 software. 4. Results Out of 926,084 urine specimens tested, 2871 (0.31%) were positive for at least one heroin-specific marker. Prevalence of the heroin-specific markers were as follows: DAM 1203 (41.9%), 6AM 2570 (89.5%), 6AC 1082 (37.7%). MOR and codeine (COD) were present in 2194 (76.4%) and 1218 (42.4%) specimens, respectively. For the combination of heroin markers observed relative to the presence/absence of MOR refer to Table 1. Our findings revealed that 23.6% (677 out of 2871) of specimens positive for heroinspecific markers were negative for MOR. Of interest, specimens without MOR were often associated with DAM or 6AC alone. Median concentrations for all heroin positives were 18.9 ng/mL for DAM, 118.6 ng/mL for 6AM, and 13.5 ng/mL for 6AC. Median concentrations for MOR-negative specimens were lower, especially for 6AM: 12.9 ng/mL for DAM, 14.2 ng/mL for 6AM, and 5.9 ng/mL for 6AC. The potential metabolism of MOR to hydromorphone (HM) was considered for those specimens positive for a heroin-specific marker. HM concentrations in urine after MOR metabolism are typically low relative to MOR, with early studies quoting urinary Table 1 Combinations of heroin markers in urine of chronic pain patients. Analytes
N specimens (%), MOR presenta
N specimens (%), MOR negativeb
DAM only 6AM only 6AC only DAM/6AM/ 6AC 6AM/6AC DAM/6AM DAM/6AC
13 (0.59%) 1140 (52.0%) 24 (1.1%) 710 (32.4%)
161 (23.8%) 217 (32.1%) 92 (13.6%) 50 (7.4%)
188 (8.6%) 113 (5.2%) 6 (0.27%)
7 (1.0%) 145 (21.4%) 5 (0.74%)
a b
Above LOQ. Below LOQ.
81
Table 2 Illicit drugs present in addition to heroin markers. Illicit drug combinations
N specimens (%), overall (n = 1884)
N specimens (%), MOR negative (n = 415)
THCCOOH BZE BZE/THCCOOH MAMP BZE/MAMP THCCOOH/MAMP THCCOOH/MDMA BZE/THCCOOH/ MAMP
270 (14.3%) 250 (13.3%) 115 (6.1%) 9 (0.5%) 4 (0.2%) 4 (0.2%) 1 (0.05%) 1 (0.05%)
18 (4.3%) 19 (4.6%) 2 (0.5%) 0 0 0 0 0
HM to MOR ratios up to 0.024 [15]. Later investigators have cited ratios up to 0.06, with most patients exhibiting lower relative concentrations (ratios below 0.024) [16–19]. In this study, 492 of 2796 (17.6%) specimens tested for hydromorphone showed evidence of potential metabolism of MOR to HM based on the following criteria: no evidence of a prescription for HM or hydrocodone, no evidence of hydrocodone ingestion (dihydrocodeine, norhydrocodone, and hydrocodone below LOQ), and HM/ MOR ratio 0.06. Based on prescription history provided by the clinics, a total of 272 (9.5%) patients were prescribed methadone, and 237 (8.3%) were prescribed buprenorphine. In addition to their use for chronic pain, both agents are frequently prescribed for the treatment of opioid dependence. A comparison of patient subgroups with MOR-negative or MOR-positive specimens demonstrated higher prescription rates associated with MOR-negative specimens: 74 (10.9%) of MORnegative patients were prescribed methadone compared to 198 (9.0%) in the MOR-positive subgroup (p-value = 0.15), and 77 (11.4%) MOR-negative patients were prescribed buprenorphine compared to 160 (7.3%, p-value = 0.001) in the MOR-positive subgroup. Of the 2871 specimens which were positive for a heroin-specific marker, 1884 were tested for the presence of additional illicit drugs by provider request. Illicit drugs tested include BZE (metabolite of cocaine); MDMA/MDA; MAMP; and THCCOOH (metabolite of marijuana). A total of 654 (34.7%) had positive findings for additional illicit drugs, with combinations listed in Table 2. The median pH value in the DAM-positive group was 5.7 0.64 compared to 6.0 0.89 in the DAM-negative group. With review of a graphical frequency distribution of pH values between the two groups, the DAM-negative group appeared to have more variation and higher frequency of specimens at higher pH values. The difference between the distributions of pH measurements between the two groups was statistically significant at a 0.05 significance level with a p-value < 0.00001. 5. Discussion The prevalence of heroin abuse was found to be 0.31% in the pain patients studied. Our finding of 23.6% of specimens with heroin-specific markers lacking the presence of MOR is similar to another study of the pain management population conducted by Crews et al., in which 23% of urine specimens were positive for 6AM in the absence of MOR [11]. In the present study, concentrations of DAM, 6AM, and 6AC tended to be lower for MOR-negative specimens. Of MOR-negative specimens, only 61.9% were positive for 6AM; therefore, 6AC and DAM were particularly useful as indicators of heroin abuse in this subset. Furthermore, 9.0% of all heroin positives had DAM and/or 6AC in the absence of 6AM and MOR; 5.6% were positive for DAM in the absence of all other markers. This is a significant unexpected finding given the rapid hydrolysis of DAM by esterases and its susceptibility to spontaneous hydrolysis. To our knowledge, DAM presence in urine
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J. Knight et al. / Forensic Science International 243 (2014) 79–83
in the absence of MOR and 6AM has not been previously reported in literature. The explanation for this finding is unknown; one would expect that impaired metabolism of DAM into 6AM and very recent heroin ingestion may play a role. There are several circumstances which may lead to impaired metabolism by pseudocholinesterases (also known as butyrylcholinesterase, plasma cholinesterase). For example, pseudocholinesterase deficiency has been well documented due to prolonged recovery from the administration of neuromuscular blockers metabolized by pseudocholinesterase (e.g., succinylcholine). At least 65 variants of the gene associated with pseudocholinesterase deficiency (BChE gene) exist, with degrees of reduced enzyme activity varying from completely absent to slight reduction in activity. Frequencies as high as 4% have been reported for some variants; however, the silent variant known for absent/minimal enzyme activity has a significantly low prevalence of 0.008–0.01% [20]. In addition, acquired pseudocholinesterase deficiency has been documented in relation to disease states or conditions such as liver disease (including hepatitis), renal disease, malnutrition, pregnancy, malignancy, burns, inflammation, critical illness, acquired immunodeficiency syndrome (AIDS), and myocardial infarction [20,21]. There are numerous substances that are documented to inhibit or decrease pseudocholinesterase activity. For example, commonly prescribed drugs such as oral contraceptives, metoclopramide, antidepressants (e.g., citalopram, sertraline, etc.) and galantamine have been reported to decrease or inhibit pseudocholinesterase [20,22,23]. In vitro inhibition of HCE-1, HCE-2, and pseudocholinesterase hydrolysis of DAM to 6AM by cocaine interaction has also been reported [8]. Surprisingly, even ingestion of certain vegetables such as eggplants, tomatoes and potatoes may inhibit pseudocholinesterase. An in vitro study showed that concentrations of solanaceous glycoalkaloids (SGAs) equivalent to what is found in serum after ingesting a serving of potatoes inhibited pseudocholinesterase [24]. A randomized, controlled study showed a statistically significant increase in neuromuscular block from succinylcholine in patients ingesting potatoes prior to surgery versus those not ingesting potatoes [25]. Although exposures are presumably rare, organophosphate insecticides are also characterized as interfering with pseudocholinesterase and carboxylesterase [26]. Even if an individual experienced one of the above explanations for decreased pseudocholinesterase activity, this does not clarify why other esterases such as hCE1 and hCE2 would not compensate for this loss of activity. There is existing evidence of genetic variation in these enzymes, and they are involved in the metabolism of various commonly prescribed drugs/drug classes including angiotensinconverting enzyme inhibitors (ACE inhibitors), statins, methylphenidate, clopidogrel, and aspirin, thus, introducing the potential for drug interactions and competitive inhibition. Alcohol is also thought to inhibit hCE1 and potentially hCE2 [27]. Heroin is known to be able to hydrolyze in aqueous solutions, especially at higher temperatures and elevated pH. A stability study by Barrett et al. of DAM in aqueous solution demonstrated a degradation half-life of 32.9 h at pH of 7.4 (similar to pH of blood) and 37 C (body temperature), and an extended degradation halflife of 592 h at pH of 5.6 at room temperature [28]. The median pH in specimens with DAM present in our study was 5.7. Specimens directly shipped by the physician’s office typically arrive at the laboratory within a day. Therefore, although spontaneous hydrolysis of DAM may occur, the degradation half-life may be prolonged for specimens in this pH range, providing the opportunity to measure DAM alone if metabolizing enzymes were not present or fully functional. Given that a larger number of specimens were observed with higher pH in the DAM negative group, improved stability may have played a role in the ability to capture DAM in DAM positive specimens.
A recent study indicated that in vitro incubation of aspirin and MOR in gastric contents may yield detectable 6AM (but not DAM) [29]. Such findings have not been replicated in vivo. Atypical excretion patterns of heroin markers and MOR may introduce interpretive uncertainty, especially when a clinical explanation has not been fully elucidated in literature. Though definitive guidance may not be provided by this study, methadone and buprenorphine, which are used for the treatment of opioid dependence, were noted as prescribed for a significant number of patients (17.7%). Prescription information is not always accurately completed by the ordering clinic, and the actual prescription prevalence could not be verified; therefore, prevalence may be underestimated. Furthermore, prescriptions for methadone or buprenorphine were noted at a higher prevalence in MOR-negative specimens (22.3%) than MOR-positive specimens (16.3%); the difference for buprenorphine is statistically significant at a 0.05 significance level. The reasons for this finding are unclear, but the authors hypothesize that many of these patients may have been enrolled in treatment for opioid dependence, lending further support to interpretation of heroin use in patients with atypical excretion patterns. A limitation to this finding is the inability to determine the indication for prescribed medications. Finally, a significant number of patients positive for heroin markers were also positive for additional illicit drugs. Although the incidence of BZE positives was greater for MOR-positive than MORnegative specimens (23.8% vs. 5.1%), the findings for BZE in MORnegative specimens is surprising given shared metabolic pathways for cocaine and heroin. With increasing focus on pharmacogenetics in recent years, it has become clear that great variation and complexity exist between individuals in regard to drug pharmacokinetics and pharmacodynamics. The excretion of DAM in the absence of 6AM and MOR is indeed a surprising observation. A conclusive explanation for this finding is unknown and requires further study. The explanation is unlikely to be one answer but a combination of exposures, genetic considerations, and patient factors. The high risk lifestyle of individuals abusing heroin and subsequent exposures and disease states may play a role as well. One would expect very recent ingestion to be an additional causative factor, as well as storage conditions/stability contributing to the ability to detect DAM. At this time, DAM, 6AM, or 6AC, when present, must be interpreted as clear evidence that recent heroin use occurred. Conclusion The ability to identify recent heroin use from urine drug tests is an important component of effective monitoring of pain patients as part of a comprehensive assessment. Urine specimens from chronic pain patients were positive for heroin-specific biomarkers DAM, 6AM and 6AC with an overall prevalence of 0.31%. DAM and 6AC were frequently observed in absence of 6AM or MOR, with the latter two analytes undetected in 10.5% and 23.6% specimens, respectively. These data illustrate the need for comprehensive analytical detection systems capable of monitoring multiple heroin analytes in populations that are vulnerable to the addictive effects of heroin and other opioids. References [1] IOM (Institute of Medicine), Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research, The National Academies Press, Washington, DC, 2011. [2] L. Manchikanti, B. Fellows, H. Ailinani, V. Pampati, Therapeutic use, abuse, and nonmedical use of opioids: a ten-year perspective, Pain Physician 13 (2010) 401–435.
J. Knight et al. / Forensic Science International 243 (2014) 79–83 [3] Substance Abuse and Mental Health Services Administration. Results from the 2012 National Survey on Drug Use and Health: Detailed Tables, 2013. Available at: http://www.samhsa.gov/data/NSDUH/2012SummNatFindDetTables/Index.aspx. [4] P. Muhuri, J. Gfroerer, M. Davies, Center for behavioral health statistics and quality, Associations of Nonmedical Pain Reliever Use and Initiation of Heroin Use in the United States, Substance Abuse and Mental Health Services Administration, Rockville, MD, 2013. [5] M. von Euler, T. Villen, J.O. Svensson, L. Stahle, Interpretation of the presence of 6-monoacetylmorphine in the absence of morphine-3-glucuronide in urine samples: evidence of heroin abuse, Ther. Drug Monit. 25 (2003) 645– 648. [6] E.J. Rook, A.D.R. Huitema, W. van den Brink, J.M. van Ree, J.H. Beijnen, Pharmacokinetics and pharmacokinetic variability of heroin and its metabolites: review of the literature, Curr. Clin. Pharmacol. 1 (2006) 109–118. [7] R.C. Baselt, Disposition of Toxic Drugs and Chemicals in Man, ninth ed., Biomedical Publications, Seal Beach, CA, 2011 1900 pp. ISBN 978-0-96265238-7. [8] L.M. Kamendulis, M.R. Brzezinski, E.V. Pindel, W.F. Bosron, R.A. Dean, Metabolism of cocaine and heroin is catalyzed by the same human liver carboxylesterases, J. Pharmacol. Exp. Ther. 279 (1996) 713–717. [9] R. Brenneisen, F. Hasler, D. Wursch, Acetylcodeine as a urinary marker to differentiate the use of street heroin and pharmaceutical heroin, J. Anal. Toxicol. 26 (2002) 561–566. [10] O. Beck, M. Bottcher, Paradoxical results in urine drug testing for 6acetylmorphine and total opiates: implications for best analytical strategy, J. Anal. Toxicol. 30 (2006) 73–79. [11] B. Crews, C. Mikel, S. Latyshev, R. West, C. West, A. Pesce, P. Friel, 6acetylmorphine detected in the absence of morphine in pain management patients, Ther. Drug Monit. 31 (2009) 749–752. [12] L.R. Glass, S.T. Ingalls, C.L. Schilling, C.L. Hoppel, Atypical urinary opiate excretion pattern, J. Anal. Toxicol. 21 (1997) 509–514. [13] E.J. Cone, Y.H. Caplan, D.L. Black, T. Robert, F. Moser, Urine drug testing of chronic pain patients: licit and illicit drug patterns, J. Anal. Toxicol. 32 (2008) 530–543. [14] R. Heltsley, A. Zichterman, D.L. Black, B. Cawthon, T. Robert, F. Moser, Y.H. Caplan, E.J. Cone, Urine drug testing of chronic pain patients. II. Prevalence patterns of prescription opiates and metabolites, J. Anal. Toxicol. 34 (2010) 32– 38. [15] E.J. Cone, H.A. Heit, Y.H. Caplan, D. Gourlay, Evidence of morphine metabolism to hydromorphone in pain patients chronically treated with morphine, J. Anal. Toxicol. 30 (2006) 1–5.
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[16] A.D. Wasan, E. Michna, D. Janfaza, S. Greenfield, C.J. Teter, R.N. Jamison, Interpreting urine drug tests: prevalence of morphine metabolism to hydromorphone in chronic pain patients treated with morphine, Pain Med. 9 (2008) 918–923. [17] E.J. Cone, Y.H. Caplan, F. Moser, T. Robert, D. Black, Evidence that morphine is metabolized to hydromorphone but not to oxymorphone, J. Anal. Toxicol. 32 (2008) 319–323. [18] P.C. McDonough, B. Levine, S. Vorce, R.A. Jufer, D. Fowler, The detection of hydromorphone in urine specimens with high morphine concentrations, J. Forensic Sci. 53 (2008) 752–754. [19] G.M. Reisfield, C.W. Chronister, B.A. Goldberger, R.L. Bertholf, Unexpected urine drug testing results in a hospice patient on high-dose morphine therapy, Clin. Chem. 55 (2009) 1765–1768. [20] F.K. Soliday, Y.P. Conley, R. Henker, Pseudocholinesterase deficiency: a comprehensive review of genetic, acquired, and drug influences, AANA J. 78 (2010) 313–320. [21] L. Santarpia, I. Grandone, F. Contaldo, F. Pansanisi, Butyrylcholinesterase as a prognostic marker: a review of the literature, J. Cachexia Sarcopenia Muscle 4 (2013) 31–39. [22] B. Zencirci, Pseudocholinesterase enzyme deficiency: a case series and review of the literature, Cases J. 2 (2009) 9148. [23] R. Walsh, K. Rockwood, E. Martin, S. Darvesh, Synergistic inhibition of butyrylcholinesterase by galantamine and citalopram, Biochim. Biophys. Acta 1810 (2011) 1230–1235. [24] D.S. McGehee, M.D. Krasowski, D.L. Fung, B. Wilson, G.A. Gronert, J. Moss, Cholinesterase inhibition by potato glycoalkaloids slows mivacurium metabolism, Anesthesiology 93 (2000) 510–519. [25] A. Bestas, H. Goksu, O.L. Erhan, The effect of preoperative consumption of potatoes on succinylcholine-induced block and recovery from anesthesia, J. Clin. Monit. Comput. 27 (2013) 609–612. [26] S.D. Cohen, Mechanisms of toxicological interactions involving organophosphate insecticides, Fundam. Appl. Toxicol. 4 (1984) 315–324. [27] S.C. Laizure, V. Herring, Z. Hu, K. Witbrodt, R.B. Parker, The role of human carboxylesterases in drug metabolism: have we overlooked their importance? Pharmacotherapy 33 (2013) 210–222. [28] D.A. Barrett, A.L.P. Dyssegaard, P.N. Shaw, The effect of temperature and pH on the deacetylation of diamorphine in aqueous solution and in human plasma, J. Pharm. Pharmacol. 44 (1992) 606–608. [29] C.K. Naso-Kaspar, J.F. Wyman, C.R. Clark, L.R. Glass, E.S. Lavins, C.E. Cushman, K. L. Pekarski, T.P. Gilson, In vitro formation of acetylmorphine from morphine and aspirin in postmortem gastric contents and deionized water, J. Anal. Toxicol. 37 (2013) 500–506.
Contents lists available at ScienceDirect
Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Prevalence of heroin markers in urine for pain management patients$ Julie Knight a , Brandi L. Puet a , Anne DePriest a, * , Rebecca Heltsley a , Cheryl Hild a , David L. Black a,b , Timothy Robert a , Yale H. Caplan c, Edward J. Cone d a
Aegis Sciences Corporation, 515 Great Circle Road, Nashville, TN 37228, USA Vanderbilt University, Department of Pathology, Immunology and Microbiology, Nashville, TN 37232, USA c University of Maryland, School of Pharmacy, 20 North Pine St., Baltimore, MD 21201, USA d Johns Hopkins School of Medicine, Department of Psychiatry and Behavioral Sciences, Baltimore, MD 21224, USA b
A R T I C L E I N F O
A B S T R A C T
Article history: Available online 9 May 2014
Surveys of current trends indicate heroin abuse is associated with nonmedical use of pain relievers. Consequently, there is an interest in evaluating the presence of heroin-specific markers in chronic pain patients who are prescribed controlled substances. A total of 926,084 urine specimens from chronic pain patients were tested for heroin/diacetylmorphine (DAM), 6-acetylmorphine (6AM), 6-acetylcodeine (6AC), codeine (COD), and morphine (MOR). Heroin and markers were analyzed using liquid chromatography tandem mass spectrometry (LC–MS–MS). Opiates were analyzed following hydrolysis using LC–MS–MS. The prevalence of heroin use was 0.31%, as 2871 were positive for one or more heroin-specific markers including DAM, 6AM, or 6AC (a known contaminant of illicit heroin). Of these, 1884 were additionally tested for the following markers of illicit drug use: 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxyamphetamine (MDA), methamphetamine (MAMP), 11-nor-9-carboxy-D9-tetracannabinol (THCCOOH), and benzoylecgonine (BZE); 654 (34.7%) had positive findings for one or more of these analytes. The overall prevalence of heroin markers were as follows: DAM 1203 (41.9%), 6AM 2570 (89.5%), 6AC 1082 (37.7%). MOR was present in 2194 (76.4%) and absent ( 168, 290 > 105; BZE-d3, 293 > 171; DAM, 370 > 165, 370 > 152; DAM-d9, 379 > 61; 6AM, 328 > 165, 328 > 152; 6AM-d3, 331 > 165; and 6AC, 342 > 225, 342 > 165 (6AC was quantified with DAM-d9). Data acquisition, peak integration, and calculation were performed by a computer workstation running Analyst 1.4.2 or Analyst 1.5.2 software. Inter-run precision (%CV) and accuracy (% deviation from the weighed-in target concentration) of control samples prepared in urine containing 4 ng/mL and 250 ng/mL of each analyte in the heroin assay were as follows: DAM (n = 6), precision, 1.9%, 7.5% and accuracy, 6.8%, 11%; 6AM (n = 6), 2.5%, 3.7% and accuracy, 0.8%, 9.3%; 6AC (n = 6), 2.9%, 3.9% and accuracy, 3.6%, 7.5%, respectively. General criteria for identification and measurement of the analytes were as follows: relative retention time (RRT) of each analyte in the specimen had to be within 0.01 of the RRT in the calibrator or the retention time of each analyte in the sample had to be within 3% of its respective RT in the calibrator; ion ratios for the product ions derived from analytes and internal standards in
J. Knight et al. / Forensic Science International 243 (2014) 79–83
controls and donor specimens had to be within the 20% mean range of those obtained from the corresponding substances in the calibrator; control samples had to measure within 20% of the inhouse determined mean value; and negative controls must not have analytes above the LOQ. All quantitative data for drugs and metabolites were included in this report that met identification and quantitation (LOQ) criteria. The LOQ for DAM, 6AM, and 6AC was 4 ng/mL. The LOQ for MOR and other opiates was 50 ng/mL. The LOQ for MDMA, MDA, and MAMP was 80 ng/mL. The LOQ for THCCOOH and BZE was 2 ng/mL and 20 ng/mL, respectively. 3. Data analyses The frequency distributions of pH measurements were compared between those specimens positive for DAM (DAM LOQ) and specimens negative for DAM (DAM < LOQ). The Kolmogorov–Smirnov–Lilliefors (KSL) test was applied to the two distributions to assess for normality, and the assumption of normality of the two distributions of pH values is not justified. Hence, the non-parametric Wilcoxon rank sum test was applied to evaluate similarity in distributions of pH values for those specimens positive for DAM and specimens negative for DAM. Due to the large sample sizes in our analysis, the Wilcoxon test statistic, T, is approximately normal; hence, the standard normal z-score was computed to determine the p-value associated with the test for equal distributions. A comparison of methadone and buprenorphine prescriptions in the subsets of specimens positive or negative for MOR was made using Fisher’s exact test. Analysis was performed with SAS JMP1 10.0.0 software. 4. Results Out of 926,084 urine specimens tested, 2871 (0.31%) were positive for at least one heroin-specific marker. Prevalence of the heroin-specific markers were as follows: DAM 1203 (41.9%), 6AM 2570 (89.5%), 6AC 1082 (37.7%). MOR and codeine (COD) were present in 2194 (76.4%) and 1218 (42.4%) specimens, respectively. For the combination of heroin markers observed relative to the presence/absence of MOR refer to Table 1. Our findings revealed that 23.6% (677 out of 2871) of specimens positive for heroinspecific markers were negative for MOR. Of interest, specimens without MOR were often associated with DAM or 6AC alone. Median concentrations for all heroin positives were 18.9 ng/mL for DAM, 118.6 ng/mL for 6AM, and 13.5 ng/mL for 6AC. Median concentrations for MOR-negative specimens were lower, especially for 6AM: 12.9 ng/mL for DAM, 14.2 ng/mL for 6AM, and 5.9 ng/mL for 6AC. The potential metabolism of MOR to hydromorphone (HM) was considered for those specimens positive for a heroin-specific marker. HM concentrations in urine after MOR metabolism are typically low relative to MOR, with early studies quoting urinary Table 1 Combinations of heroin markers in urine of chronic pain patients. Analytes
N specimens (%), MOR presenta
N specimens (%), MOR negativeb
DAM only 6AM only 6AC only DAM/6AM/ 6AC 6AM/6AC DAM/6AM DAM/6AC
13 (0.59%) 1140 (52.0%) 24 (1.1%) 710 (32.4%)
161 (23.8%) 217 (32.1%) 92 (13.6%) 50 (7.4%)
188 (8.6%) 113 (5.2%) 6 (0.27%)
7 (1.0%) 145 (21.4%) 5 (0.74%)
a b
Above LOQ. Below LOQ.
81
Table 2 Illicit drugs present in addition to heroin markers. Illicit drug combinations
N specimens (%), overall (n = 1884)
N specimens (%), MOR negative (n = 415)
THCCOOH BZE BZE/THCCOOH MAMP BZE/MAMP THCCOOH/MAMP THCCOOH/MDMA BZE/THCCOOH/ MAMP
270 (14.3%) 250 (13.3%) 115 (6.1%) 9 (0.5%) 4 (0.2%) 4 (0.2%) 1 (0.05%) 1 (0.05%)
18 (4.3%) 19 (4.6%) 2 (0.5%) 0 0 0 0 0
HM to MOR ratios up to 0.024 [15]. Later investigators have cited ratios up to 0.06, with most patients exhibiting lower relative concentrations (ratios below 0.024) [16–19]. In this study, 492 of 2796 (17.6%) specimens tested for hydromorphone showed evidence of potential metabolism of MOR to HM based on the following criteria: no evidence of a prescription for HM or hydrocodone, no evidence of hydrocodone ingestion (dihydrocodeine, norhydrocodone, and hydrocodone below LOQ), and HM/ MOR ratio 0.06. Based on prescription history provided by the clinics, a total of 272 (9.5%) patients were prescribed methadone, and 237 (8.3%) were prescribed buprenorphine. In addition to their use for chronic pain, both agents are frequently prescribed for the treatment of opioid dependence. A comparison of patient subgroups with MOR-negative or MOR-positive specimens demonstrated higher prescription rates associated with MOR-negative specimens: 74 (10.9%) of MORnegative patients were prescribed methadone compared to 198 (9.0%) in the MOR-positive subgroup (p-value = 0.15), and 77 (11.4%) MOR-negative patients were prescribed buprenorphine compared to 160 (7.3%, p-value = 0.001) in the MOR-positive subgroup. Of the 2871 specimens which were positive for a heroin-specific marker, 1884 were tested for the presence of additional illicit drugs by provider request. Illicit drugs tested include BZE (metabolite of cocaine); MDMA/MDA; MAMP; and THCCOOH (metabolite of marijuana). A total of 654 (34.7%) had positive findings for additional illicit drugs, with combinations listed in Table 2. The median pH value in the DAM-positive group was 5.7 0.64 compared to 6.0 0.89 in the DAM-negative group. With review of a graphical frequency distribution of pH values between the two groups, the DAM-negative group appeared to have more variation and higher frequency of specimens at higher pH values. The difference between the distributions of pH measurements between the two groups was statistically significant at a 0.05 significance level with a p-value < 0.00001. 5. Discussion The prevalence of heroin abuse was found to be 0.31% in the pain patients studied. Our finding of 23.6% of specimens with heroin-specific markers lacking the presence of MOR is similar to another study of the pain management population conducted by Crews et al., in which 23% of urine specimens were positive for 6AM in the absence of MOR [11]. In the present study, concentrations of DAM, 6AM, and 6AC tended to be lower for MOR-negative specimens. Of MOR-negative specimens, only 61.9% were positive for 6AM; therefore, 6AC and DAM were particularly useful as indicators of heroin abuse in this subset. Furthermore, 9.0% of all heroin positives had DAM and/or 6AC in the absence of 6AM and MOR; 5.6% were positive for DAM in the absence of all other markers. This is a significant unexpected finding given the rapid hydrolysis of DAM by esterases and its susceptibility to spontaneous hydrolysis. To our knowledge, DAM presence in urine
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in the absence of MOR and 6AM has not been previously reported in literature. The explanation for this finding is unknown; one would expect that impaired metabolism of DAM into 6AM and very recent heroin ingestion may play a role. There are several circumstances which may lead to impaired metabolism by pseudocholinesterases (also known as butyrylcholinesterase, plasma cholinesterase). For example, pseudocholinesterase deficiency has been well documented due to prolonged recovery from the administration of neuromuscular blockers metabolized by pseudocholinesterase (e.g., succinylcholine). At least 65 variants of the gene associated with pseudocholinesterase deficiency (BChE gene) exist, with degrees of reduced enzyme activity varying from completely absent to slight reduction in activity. Frequencies as high as 4% have been reported for some variants; however, the silent variant known for absent/minimal enzyme activity has a significantly low prevalence of 0.008–0.01% [20]. In addition, acquired pseudocholinesterase deficiency has been documented in relation to disease states or conditions such as liver disease (including hepatitis), renal disease, malnutrition, pregnancy, malignancy, burns, inflammation, critical illness, acquired immunodeficiency syndrome (AIDS), and myocardial infarction [20,21]. There are numerous substances that are documented to inhibit or decrease pseudocholinesterase activity. For example, commonly prescribed drugs such as oral contraceptives, metoclopramide, antidepressants (e.g., citalopram, sertraline, etc.) and galantamine have been reported to decrease or inhibit pseudocholinesterase [20,22,23]. In vitro inhibition of HCE-1, HCE-2, and pseudocholinesterase hydrolysis of DAM to 6AM by cocaine interaction has also been reported [8]. Surprisingly, even ingestion of certain vegetables such as eggplants, tomatoes and potatoes may inhibit pseudocholinesterase. An in vitro study showed that concentrations of solanaceous glycoalkaloids (SGAs) equivalent to what is found in serum after ingesting a serving of potatoes inhibited pseudocholinesterase [24]. A randomized, controlled study showed a statistically significant increase in neuromuscular block from succinylcholine in patients ingesting potatoes prior to surgery versus those not ingesting potatoes [25]. Although exposures are presumably rare, organophosphate insecticides are also characterized as interfering with pseudocholinesterase and carboxylesterase [26]. Even if an individual experienced one of the above explanations for decreased pseudocholinesterase activity, this does not clarify why other esterases such as hCE1 and hCE2 would not compensate for this loss of activity. There is existing evidence of genetic variation in these enzymes, and they are involved in the metabolism of various commonly prescribed drugs/drug classes including angiotensinconverting enzyme inhibitors (ACE inhibitors), statins, methylphenidate, clopidogrel, and aspirin, thus, introducing the potential for drug interactions and competitive inhibition. Alcohol is also thought to inhibit hCE1 and potentially hCE2 [27]. Heroin is known to be able to hydrolyze in aqueous solutions, especially at higher temperatures and elevated pH. A stability study by Barrett et al. of DAM in aqueous solution demonstrated a degradation half-life of 32.9 h at pH of 7.4 (similar to pH of blood) and 37 C (body temperature), and an extended degradation halflife of 592 h at pH of 5.6 at room temperature [28]. The median pH in specimens with DAM present in our study was 5.7. Specimens directly shipped by the physician’s office typically arrive at the laboratory within a day. Therefore, although spontaneous hydrolysis of DAM may occur, the degradation half-life may be prolonged for specimens in this pH range, providing the opportunity to measure DAM alone if metabolizing enzymes were not present or fully functional. Given that a larger number of specimens were observed with higher pH in the DAM negative group, improved stability may have played a role in the ability to capture DAM in DAM positive specimens.
A recent study indicated that in vitro incubation of aspirin and MOR in gastric contents may yield detectable 6AM (but not DAM) [29]. Such findings have not been replicated in vivo. Atypical excretion patterns of heroin markers and MOR may introduce interpretive uncertainty, especially when a clinical explanation has not been fully elucidated in literature. Though definitive guidance may not be provided by this study, methadone and buprenorphine, which are used for the treatment of opioid dependence, were noted as prescribed for a significant number of patients (17.7%). Prescription information is not always accurately completed by the ordering clinic, and the actual prescription prevalence could not be verified; therefore, prevalence may be underestimated. Furthermore, prescriptions for methadone or buprenorphine were noted at a higher prevalence in MOR-negative specimens (22.3%) than MOR-positive specimens (16.3%); the difference for buprenorphine is statistically significant at a 0.05 significance level. The reasons for this finding are unclear, but the authors hypothesize that many of these patients may have been enrolled in treatment for opioid dependence, lending further support to interpretation of heroin use in patients with atypical excretion patterns. A limitation to this finding is the inability to determine the indication for prescribed medications. Finally, a significant number of patients positive for heroin markers were also positive for additional illicit drugs. Although the incidence of BZE positives was greater for MOR-positive than MORnegative specimens (23.8% vs. 5.1%), the findings for BZE in MORnegative specimens is surprising given shared metabolic pathways for cocaine and heroin. With increasing focus on pharmacogenetics in recent years, it has become clear that great variation and complexity exist between individuals in regard to drug pharmacokinetics and pharmacodynamics. The excretion of DAM in the absence of 6AM and MOR is indeed a surprising observation. A conclusive explanation for this finding is unknown and requires further study. The explanation is unlikely to be one answer but a combination of exposures, genetic considerations, and patient factors. The high risk lifestyle of individuals abusing heroin and subsequent exposures and disease states may play a role as well. One would expect very recent ingestion to be an additional causative factor, as well as storage conditions/stability contributing to the ability to detect DAM. At this time, DAM, 6AM, or 6AC, when present, must be interpreted as clear evidence that recent heroin use occurred. Conclusion The ability to identify recent heroin use from urine drug tests is an important component of effective monitoring of pain patients as part of a comprehensive assessment. Urine specimens from chronic pain patients were positive for heroin-specific biomarkers DAM, 6AM and 6AC with an overall prevalence of 0.31%. DAM and 6AC were frequently observed in absence of 6AM or MOR, with the latter two analytes undetected in 10.5% and 23.6% specimens, respectively. These data illustrate the need for comprehensive analytical detection systems capable of monitoring multiple heroin analytes in populations that are vulnerable to the addictive effects of heroin and other opioids. References [1] IOM (Institute of Medicine), Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research, The National Academies Press, Washington, DC, 2011. [2] L. Manchikanti, B. Fellows, H. Ailinani, V. Pampati, Therapeutic use, abuse, and nonmedical use of opioids: a ten-year perspective, Pain Physician 13 (2010) 401–435.
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