Environ Sci Pollut Res DOI 10.1007/s11356-013-2403-5
RESEARCH ARTICLE
Stability of cocaine and its metabolites in municipal wastewater – the case for using metabolite consolidation to monitor cocaine utilization Kevin J. Bisceglia & Katrice A. Lippa
Received: 5 September 2013 / Accepted: 25 November 2013 # Springer-Verlag Berlin Heidelberg (outside the USA) 2013
Abstract Transformations of cocaine and eleven of its metabolites were investigated in untreated municipal sewage at pH≈7 and 9, 23, and 31 °C. Results indicated that hydrolysis—possibly bacterially mediated—was the principal transformation pathway. Residues possessing alkyl esters were particularly susceptible to hydrolysis, with pseudo-first-order rate constants varying from 0.54 to 1.7 day−1 at 23 °C. Metabolites lacking esters or possessing only a benzoyl ester appeared stable. Residues lacking alkyl esters did accumulate through hydrolysis of precursors, however. As noted previously, this may positively bias cocaine utilization estimates based on benzoylecgonine alone. Reported variability in metabolic excretion was used in conjunction with transformation data to evaluate different approaches for estimating cocaine loading. Results indicate that estimates derived from measurands that capture all major cocaine metabolites, such as COCtot (the sum of all measurable metabolites) and EChyd
(the sum of all metabolites that can be hydrolyzed to ecgonine), may reduce uncertainty arising from variability in metabolite transformation and excretion, possibly to≈10 % RSD. This is more than a two-fold reduction relative to estimates derived from benzoylecgonine (>26 % RSD), and roughly equivalent to reported uncertainties from sources that are not metabolite-specific (e.g., sampling frequency, flow variability). They and other composite measurands merit consideration from the sewage epidemiology community, beginning with efforts to evaluate the stability of the total cocaine load under realistic sewer conditions. Keywords Cocaine . Ecgonine . Benzoylecgonine illicit drugs . Wastewater . Stability . Sewage epidemiology . Sewer information mining
Introduction
Responsible editor: Philippe Garrigues Electronic supplementary material The online version of this article (doi:10.1007/s11356-013-2403-5) contains supplementary material, which is available to authorized users. K. J. Bisceglia Department of Geography and Environmental Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA K. J. Bisceglia : K. A. Lippa Chemical Sciences Division, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8392, Gaithersburg, MD 20899-8392, USA K. J. Bisceglia (*) Department of Chemistry, Hofstra University, Hempstead, NY 11549, USA e-mail: [email protected]
Monitoring illicit drugs and their metabolites in municipal wastewater (often called sewage epidemiology) is becoming increasingly attractive as a means for conducting preliminary assessments of drug use within municipalities (Castiglioni et al. 2013). The procedure for converting measurements of occurrence into estimates of per capita drug utilization was developed by Zuccato et al. (2005). Conversions are simple to perform, but they entail several important sources of uncertainty. Uncertainties arising from variations in wastewater flow, spatial and temporal prevalence of drug use, catchment population, and sampling frequency have been characterized in detail (Mathieu et al. 2011; Lai et al. 2011; Castiglioni et al. 2013), and generally do not depend on the choice of metabolite(s). Other sources of uncertainty do depend on the choice of metabolite being monitored. These include analytical uncertainty, variability in the fraction of the parent drug that is excreted by humans as specific metabolite(s), and uncertainty
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regarding the partitioning and transformation of metabolite(s) in the wastewater matrix. Cocaine (COC) has received the most attention from the sewage epidemiology community, to date, and consensus is that benzoylecgonine (BE, its principal metabolite) should be used to estimate levels of cocaine utilization (Castiglioni et al. 2013). Partitioning to wastewater solids is unlikely to be important for cocaine and its metabolites (Langford et al. 2011; Baker and Kasprzyk-Hordern 2011; Plósz et al. 2013). In contrast, transformation of cocaine and its metabolites is a concern during sewer transport, sample collection, and sample processing. Castiglioni et al. (2013) reviewed seven investigations of cocaine stability in sewage (including unpublished data now presented herein). They concluded that COC and alkyl ester-containing metabolites (e.g., ecgonine methyl ester, EME) are readily transformed in wastewater at circumneutral pH and temperatures of 4 and 23 °C. A more recent study (Plósz et al. 2013) confirmed these findings and also concluded that transformation rates do not depend on the presence of dissolved oxygen. All studies found BE to be relatively stable in wastewater under the same conditions, but that hydrolysis of co-occuring COC caused concentrations of BE to increase over time. As a result, back-calculations of cocaine utilization from BE are biased by production that occurs in-sewer and during sample collection. Sample collection and processing often exceeds 24 h, although pH and temperature can be manipulated to minimize transformation rates (Gheorghe et al. 2008). Castiglioni et al. (2013) assert that uncertainty from in-sewer BE production is likely to be small (≈2–10 %), as BE concentrations are typically double COC concentrations, and hydraulic residence times in most sewers are short (≤12 h). On the other hand, Plosz et al (2013) note that in-sewer BE production is dependent upon COC loading patterns. They state that failure to account for weekday/weekend loading variations can bias utilization estimates by 18 % and that special events and festivals can bias estimates by 50 %. Recent sewage epidemiology investigations (Plósz et al. 2013; Castiglioni et al. 2013) have stressed the need for additional data on COC (bio)transformation under realistic conditions. COC and EME transformation rates can vary while the wastewater source, temperature, and pH remain unchanged, presumably due to temporal variations in microbial and chemical composition (Castiglioni et al. 2011). The influence of temperature on cocaine transformation has been specifically highlighted as a knowledge gap (Plósz et al. 2013). Moreover, while there is strong evidence that ester hydrolysis is the dominant transformation mechanism in wastewater (Plósz et al. 2013; Castiglioni et al. 2013), and limited evidence that N -demethylation may also occur (Castiglioni et al. 2011), no published study has attempted to close a mass balance on COC and its transformation products. Only one study (Castiglioni et al. 2011) has investigated
whether ecgonine (EC), the final hydrolysis product of cocaine, is stable in municipal sewage. Variability in metabolic excretion has been repeatedly highlighted as a major source of uncertainty in backcalculating estimates of drug utilization via the sewer epidemiology approach (Khan and Nicell 2011; Lai et al. 2011; Castiglioni et al. 2013)—perhaps the second largest source after catchment population (Castiglioni et al. 2013). Metabolic excretion profiles for drugs of abuse are known to vary widely based on age, gender, urine pH, health status, route, frequency, and amount of ingestion, and many other genetic and lifestyle factors (Gibson and Skett 2001). Meta-analysis of ten controlled cocaine administration studies found that excretion is highly dependent upon route of administration (ROA) and that 27.1±11.4 % (RSD=42 %) of a COC dose is excreted in urine as BE when all ROAs are considered equally (Castiglioni et al. 2013). BE excretion variability may be lowered if COC ingestion patterns are known. Castiglioni et al. (2013) assume the following consumption pattern for Europe: 95 % via nasal insufflation (NI), 4 % smoked (SM), and 2 % intravenously (IV). This lowers variability in BE excretion to 26 % RSD. However, they also note that information about ROI patterns is generally scarce, and varies geographically and over time. In the USA, for example, smoking cocaine (as crack) is more prevalent in urban than rural areas (Kandel et al. 1997). In Baltimore, MD, however, it may be more common to ingest COC intravenously (and concurrently with heroin) than via smoking or snorting (Harrell et al. 2012). An alternative means of estimating cocaine utilization would be to use measurement endpoints that pool all of its major metabolites, such as total cocaine (COCtot, defined as the sum of cocaine and all of its measurable metabolites) or post-hydrolysis ecgonine (EChyd). In determining EChyd, a simple hydrolysis procedure converts cocaine and all of its tropane-containing metabolites into ecgonine with >98 % efficiency (Bisceglia et al. 2012). Such endpoints include all of the major cocaine metabolites, capturing >60 % of an administered dose (Jeffcoat et al. 1989; Baselt 2004; Khan and Nicell 2011) via a “funneling effect,” wherein a group of metabolites coalesce into a single measurand. As a result, their composite variance is likely to be much lower than for any single metabolite. Preliminary evidence suggests that EC and anhyroecgonine (AEC, the 2-tropene hydrolysis end-product of pyrolized cocaine) are stable in wastewater (Castiglioni et al. 2011; Bisceglia et al. 2012). EC also appears to be the stable end-product for cocainic metabolites in human urine, and has been suggested as a more reliable and longer-lasting alternative to BE for conducting urine-based drug tests (Smith et al. 2010). In this paper, we examine the stability of cocaine (COC) and 11 of its metabolites [benzoylecoginine (BE), ecgonine methyl ester (EME), cocaethylene (CE), ecgonine ethyl ester
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(EEE), ecgonine (EC), m -hydroxybenzoylecgonine (mOHBE), p -hydroxybenzoylecgonine (pOHBE), anhydroecgonine methyl ester (AEME), anhydroecgonine (AEC), norcocaine (NC), and norbenzoylecgonine (NBE); see Figure S1 in Supplementary Materials] in municipal wastewater incubated under semi-stagnant, gently aerated conditions at 9, 23, and 31 °C. Results from the stability investigation are used in conjunction with an analysis of the variability in urinary excretion profiles to evaluate the uncertainty that might be associated with estimating cocaine utilization from measured concentrations of benzoylecgonine, or from groups of metabolites (i.e., COCtot and EChyd).
aliquots, adjusting to pH 2.0 with 0.12 mL of 0.1 M HCl (the volume was determined experimentally on a separate subsample), and adding 0.12 mL of an acetonitrile solution containing isotopically labeled surrogate standards (Bisceglia et al. 2010) at concentrations comparable to the spiking conditions. Previous studies indicate that cocaine hydrolysis rates are minimized at pH 2.0 (Gheorghe et al. 2008; Bisceglia et al. 2012). The samples were stored at −20 °C for 4 days until analysis could be completed by direct injection reversedphase liquid chromatography tandem mass spectrometry (RPLC/MS/MS) using a pentafluorophenyl-propyl column. Detailed analytical conditions and quality assurance/quality control steps are provided elsewhere (Bisceglia et al. 2010).
Materials and methods
Kinetic modeling
Chemicals
Kinetic modeling of the stability data was performed with Scientist version 2.01 (Micromath, St. Louis, MO). Time courses of COC, CE, NC, AEME, and their respective metabolites were fit by nonlinear least squares regression to an ester hydrolysis model (reaction schematic and rate equations are provided as Supplemental Material). Pseudo-first-order kinetics was assumed. Equations were normalized to initial concentrations, in order to prevent biases in fitting that would be caused by large differences in initial concentrations (and hence, large differences in residual errors) among the various metabolites. Only the data from the first 13 h were used in fitting EC and AEC transformations.
Details on the procurement and handling of all chemicals and on the preparation of stock and calibration solutions are listed elsewhere (Bisceglia et al. 2010). Stability experiments Transformation of cocaine and its metabolites was investigated in batch reactors at 9, 23, and 31 °C. Grab samples of untreated wastewater influent (pH=7.3) were obtained from the Back River Wastewater Treatment Plant (BRWWTP) in Baltimore, MD on May 19 and 21, 2009, and transported on ice to our lab. Samples were immediately passed through an 11-μm filter (Whatman No. 1, Piscataway, NJ) to remove coarse particulates but retain suspended bacteria (Jonas and Tuttle 1990). Samples were spiked with cocaine and metabolites within 1 h of collection so that reactor concentrations were three to five times greater than background occurrence (as determined from a separate analysis of the same wastewater stream). Because of analytical constraints, spiking concentrations for EC and AEC were 20 and 600 times greater than background, respectively. Additional stability studies were conducted on four opioids (6acetylmorphine, morphine, hydrocodone, and oxycodone), five phenylamine drugs (amphetamine, methamphetamine, 3 , 4- m e t hy l e n e d i o x ym e t h am p he t am i n e (M D M A ) , methylbenzodioxolylbutanamine (MBDB), and 3,4methylenedioxy-N-ethylamphetamine (MDEA)), and two human use markers (creatinine and cotinine); results are provided elsewhere (Bisceglia 2010). After spiking, the samples were split into 250-mL aliquots (three per temperature investigation). Each aliquot was transferred to a 1-L Erlenmeyer flask, plugged with a foam stopper to allow air transfer, and placed in a temperature-controlled incubator. In all cases, the reactors were constructed within 3 h of sample collection. Reactors were shaken at 180 rpm in the dark and sampled repeatedly over 24 h by removing 1-mL
Results and discussion Stability of cocaine and its metabolites in municipal wastewater Time courses for COC, CE, and their tropane metabolites are presented in Fig. 1, and for the nortropane- and 2-tropene (pyrolysis)-related metabolites in Fig. 2. Rapid degradation was observed at 31 and 23 °C for almost all compounds containing an alkyl ester. AEME (Fig. 2) is an exception to this generalization, possibly because its carbonyl is in conjugation with the π system on the 2-tropene ring. In contrast, compounds containing a benzoyl substituent as the sole ester appeared stable at all temperatures within the time frame investigated, with BE and NBE exhibiting modest to moderate accumulation attributed to the degradation of their precursors. Finally, EC and AEC, which lack ester moieties, appeared stable at all temperatures within the first 13 h. After 13 h, however, both compounds exhibited an apparent shift in kinetic regime, and degradation could be discerned at 31 and 23 °C. Data were successfully fit by a hydrolysis model wherein the alkyl and benzoyl esters on each compound were
Concentration (nM)
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Time (h) Fig. 1 Stability profiles for cocaine, cocaethylene, and their metabolites in untreated municipal wastewater. Solid, dashed (− − − −), and dotted (·········) lines represent model fits for the hydrolysis of all chemicals to
products shown in Figure S1 in Supplementary Material at 31 °C (○), 23 °C (▲), and 9 °C (□), respectively. Error bars denote 95 % confidence intervals for the analysis of three replicates. Consult text for abbreviations
considered labile. A schematic (Figure S1), rate equations, and fitted pseudo-first-order rate constants (Table S1) are provided as Supplementary Material. We chose to ignore data for EC and AEC after 13 h when performing model fittings, and no degradation rates are reported for either compound. EC and AEC are close structural analogs, and it is possible that their combined concentration (which, by analytical necessity, was≈60 times greater than their combined concentration in unamended wastewater; Bisceglia et al. 2010; Castiglioni et al. 2011) was sufficient to induce bacterially mediated degradation by a specific, albeit unknown, pathway. While it remains uncertain whether this pathway is relevant at concentrations (1–5 nM) and residence times (≈12 h) that are common to municipal sewers (Castiglioni et al. 2013), both
chemicals were found to be stable for at least 2 h in untreated municipal sewage at elevated temperature and alkaline pH (Bisceglia et al. 2012), and almost indefinitely in urine samples stored at −20 °C (Smith et al. 2010). The good mass balances (>99 % over the first 13 h) observed for COC, CE, and their hydrolysis products (represented as “Sum of EC and Precursors” in Fig. 1) during incubations provide strong support for hydrolysis as a dominant transformation mechanism for COC and its metabolites in municipal wastewater. Plosz et al. (2013) also successfully applied a hydrolysis model to the biotransformation of COC in unfiltered municipal wastewater. The pseudo-first-order rate constant for the overall hydrolysis of COC (k1 +k2 in Figure S1) was twice as large in their study as ours; both
Fig. 2 Stability profiles for the pyrolysis and norcocainic metabolites in untreated municipal wastewater. Solid, dashed (− − − −), and dotted (·········) lines represent model fits for the hydrolysis of the pyrolysis metabolites to AEC, and for the hydrolysis of the norcocainic metabolites to norecgonine (NEC, not shown) at 31 °C (○), 23 °C (▲), and 9 °C (□), respectively. Error bars denote 95 % confidence intervals for the analysis of three replicates. Consult text for abbreviations
Concentration (nM)
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Time (h) wastewater values were≈10 times greater than the value in deionized water (Garrett and Seyda 1983; see Table 1). Plosz et al.'s value could be larger because of inherent differences in microbial ecology or because their wastewater was not filtered and likely contained more degrading bacteria. Regardless, the difference highlights the importance of characterizing biological activity during transformation studies and of using caution when comparing rates among studies. Interestingly, respective constants for BE and EME hydrolysis (the only COC metabolites common to all studies) agree to within a factor of three. All studies indicate that BE hydrolyzes slow enough to be considered stable at≈pH 7.5 and 20 °C, while EME is readily hydrolyzed under the same conditions, perhaps even faster than cocaine. Variability in urinary excretion of BE relative to composite measurement endpoints Subject-weighted, mean excretion values and associated uncertainties (as % RSD) are presented in Table 2 (Controlled dose studies) for BE and two composite measurement endpoints, COCtot and EChyd. Three ROAs (IV, IN, and SM) are considered, as are two mixed ROA scenarios: (1) equal prevalence of IV, IN, and SM, (2) the European consumption described in the Introduction. BE excretion data are reproduced from Castiglioni et al. (2013). Values for COCtot
and EChyd were determined as part of the current study from Jeffcoat et al. (1989), the only published controlled dose study to administer radio-labeled cocaine while also monitoring EC. For ROA, COCtot was computed as the total fraction of 14Clabeled cocaine detected in urine (U), and urine and feces (U+ F); EChyd was computed as the sum of COC, BE, EME, and EC detected in urine. Uncertainties for COCtot (U+F) and EChyd (U) were computed as the root sum of the squares (RSS) of the component standard deviations (SD). Additional means of evaluating excretion-related variability in COCtot and EChyd were sought for comparison purposes. The most recent cocaine administration study includes EC (Smith et al. 2010), but does not provide enough information to compute metabolic excretion fractions. Castiglioni et al. (2011) compared maximum concentrations (Cmax) from this study, but Cmax does not necessarily correlate with cumulative excretion, in part because such values occur at different times for different metabolites, and thus are not determined from the same sample. Instead, occurrence data from independently collected, but concurrently analyzed, urine samples (Paul et al. 2005; n =30) were used to compute relative excretion fractions for BE and EChyd (as percent of total measured cocaine; Table 2, Independent urine samples). Mean ± SD for all samples is reported for BE. EChyd was computed as the sum of COC, CE, BE, EME, EEE, mOHBE, and EC. Uncertainty was evaluated by computing SD for all urine
Environ Sci Pollut Res Table 1 Pseudo-first-order transformation rate constants (day−1) for cocaine and metabolites in different media a
Assuming active biomass= 150 mg/L
b
Garrett et al. (1994)
c
Not investigated
d
From first 13 h of data
DI water pH 7.5, 19.4 °C (Garrett and Seyda 1983; Garrett et al. 1994)
Unfiltered wastewatera pH 7.5, 19.4 °C (Plósz et al. 2013)
11-μm filtered wastewater pH 7.4, 23.0 °C (Present study)
COC
0.23
3.3
1.4
BE EME EC
0.00 0.51b NAc
0.0 1.3 NAc
0.0 1.7 0d
samples and by the analysis of variance/covariance among measurements within the samples (Table S2 in Supplementary Material); both estimates of uncertainty were in good agreement. COCtot represents 65–70 % of administered drug in urine, and 70–75 % in urine and feces, across ROAs. Mean urinary recovery from studies that did not monitor for EC or use radiolabeled cocaine is≈60 % (Baselt 2004; Khan and Nicell 2011), although some report that an additional 5–12 % is excreted as “polar, unidentified metabolites” (Cone et al. 1998). More importantly, COCtot does not seem to be as dependent on ROA as BE (ranges in excretion: 64–69 and
15–37 %, respectively), and associated uncertainties (as % RSD) are four times smaller for COCtot under EU ROA patterns. In both the controlled-dose study and spot urine samples, EChyd represents≈90 % of urinary cocaine and likely captures≈60 % of total administered cocaine. Interestingly, although metabolite abundance in spot urine samples should not necessarily reflect cumulative excretion urinary fractions, mean BE and EChyd in urine samples (as percent of measured COCtot; Table 2, Independent urine samples) are within 5 % of those in controlled-dose studies (Table 2, Controlled dose studies). Similar to COCtot, EChyd is less dependent on ROA (range 51–61 % of dose) than BE and has less uncertainty
Table 2 Percent of cocaine (mean ± standard deviation (RSD))detected in urine as COCtot, BE, and EChyd for different routes of administration (ROA) during controlled dose studies and in independent urine samples Controlled dose studiesa ROA IV IN SM Assumed ROA patterns: Equal weightd EU weighte
COCtot (U) 69.0±10.0 (14.5) 68.2±4.6 (6.7) 64.0±7.2 (11.3)
COCtot (U+F)b 74.5±10.1 (13.6) 72.1±5.1 (7.1) 75.1±7.7 (10.2)
BE (U) 37.3±9.6 (25.8) 29.4±7.4 (25.3) 14.8±5.8 (39.1)
EChyd(U)c 58.7±6.0 (10.2) 60.7±8.2 (14.0) 51.2±8.8 (17.1)
67.1±2.7 (4.0) 68.1±4.8 (7.1)
73.9±1.6 (2.1) 72.3±5.3 (7.4)
27.1±11.4 (41.9) 29.2±7.8 (26.5)
56.9±5.0 (8.8) 60.9±8.4 (13.8)
BE (U) 46.9±18.5 (39.0)
EChyd(U)h 89.1±10.4 (12.0)
31.4±12.5 (40.0)i 31.9±13.0 (40.8)i
59.8±7.4 (12.3)i 60.6±8.2 (13.6)i
Independent urine samplesf g
As percent of measured COCtot (Dose and ROA unknown) Assumed ROA patterns: Equal weight EU weight
IV intraveinous, IN nasal insufflations, SM smoked as crack cocaine a
COCtot and EChyd data from Jeffcoatet al. (1989), BE data from Castiglioniet al. (2013)
b
Urine + Feces; SD computed as root sum of squares (RSS) of urine and fecal SDs
c
SD computed as RSS of reported SDs for EC precursors
d
Equal utilization via IV, IN, SM
e
2 % utilized via IV, 94 % via IN, 4 % via SM (Castiglioni et al. 2013)
f
Data from Paul et al. (2005); n =30
g
Percent of measured urinary cocaine present as BE and as EC precursors
h
SD computed from covariance of EC precursors (see text for details)
i
Adjusted for fraction of administered dose detected in urine as BE and EC precursors by Jeffcoat et al.; SD computed as RSS of SDs from Jeffcoat et al. and Paul et al
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associated with its metabolic excretion fraction, although % RSD is only two times smaller than BE under EU ROA patterns. As would be expected if hydrolysis is the dominant transformation pathway, COC and its hydrolytic metabolites are highly negatively correlated in urine samples (Table S2). Correlations between EC and BE, EC and EME, and EC and COC are particularly strong. Together, these four chemicals account for more than 80 % of covariance among metabolites in urine samples, and are the primary reason for the reduced variability in the urinary excretion fractions of COCtot, EChyd, and presumably other composite measurement endpoints that include them. Moreover, if hydrolytic transformations are also dominant in municipal wastewater, as indicated by Plosz et al. (2013) and the present study, then uncertainty arising from in-sewer and in-sample transformations should be correspondingly lower for COCtot and EChyd than BE as well.
Conclusion As noted by others (van Nuijs et al. 2012; Plósz et al. 2013; Castiglioni et al. 2013) and confirmed here, the hydrolysis of precursors will positively bias estimates of COC utilization derived from BE alone, and reliable estimates require knowledge of COC loading and ROA patterns (Plósz et al. 2013). In the absence of such information, it may be worth pursuing composite measurands that include a broader array of metabolites, including COC, EME, and the ultimate tropane (EC), nortropane (NEC), and 2-tropene (AEC) backbones. COCtot and EChyd are two such measurands, and include—at a minimum—COC, BE, EME, and EC. COCtot may allow researchers to monitor ROA, alcohol coingestion, and sources of loading through metabolite ratios (Bisceglia et al. 2010; Castiglioni et al. 2011; Castiglioni et al. 2013). Its determination is more analytically challenging than BE and COC alone, however, and a pre-concentration step is still required. Alternatively, EChyd captures as much as 90 % of urinary cocaine (≈60 % of administered cocaine) in a single metabolite (EC), with only minimal reduction in analytical precision (Castiglioni et al. 2011; Bisceglia et al. 2012). Preconcentration is generally not required for measuring EChyd via LC/MS/MS, and doing so may enable measurement via LC/MS (Bisceglia et al. 2012). Processing and analysis of samples for EChyd takes about 3 h—considerably less than conventional SPE—and does not require specialized equipment (such as is needed for online pre-concentration) or disposable supplies. It is important to note that EChyd is unable to differentiate between cocaine that is consumed and cocaine that enters the sewer system via other means (hence the term “utilization”). COC/BE ratios in wastewater are larger than would be
predicted from metabolism alone, suggesting alternative routes of entry (Castiglioni et al. 2013). The fraction of “excess” cocaine in wastewater is small and relatively consistent across studies (Castiglioni et al. 2013), however, so researchers wishing to estimate consumption via EChyd might consider applying a correction factor. Alternatively, they may prefer to monitor cocaine utilization fairly precisely via EChyd, than to monitor consumption (via BE or EChyd) with substantially greater uncertainty. Provided that the tropane, 2-tropene, and nortropane backbones of COC are stable under realistic sewer conditions, both COCtot and EChyd would largely eliminate concerns regarding the hydrolytic (and, for COCtot, possibly N-demethylation) transformations of individual cocaine metabolites. Both endpoints are likely to reduce uncertainty arising from variability in metabolic excretion and in-sewer transformation to≈10 % RSD. While further studies are required to refine these numbers, this represents more than a twofold reduction relative to uncertainty estimates derived for BE alone and roughly equivalent to reported uncertainties from sources that are not metabolite-specific (e.g. sampling frequency, flow variability). COCtot, EChyd, and other composite measurement endpoints merit further consideration from the sewage epidemiology community. Such efforts should begin by evaluating the stability of the total cocaine load under realistic sewer conditions. Acknowledgements We wish to thank Nick Frankos at the BRWWTP for providing us with wastewater samples and Lynn Roberts for providing technical guidance. We also wish to thank David Duewer and Seth Guikema for helpful discussions regarding environmental data distributions. Conflict of interest Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.
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Jonas RB, Tuttle JH (1990) Bacterioplankton and organic carbon dynamics in the lower mesohaline Chesapeake Bay. Appl Environ Microbiol 56:747–757 Kandel D, Chen K, Warner LA et al (1997) Prevalence and demographic correlates of symptoms of last year dependence on alcohol, nicotine, marijuana and cocaine in the U.S. population. Drug Alcohol Depend 44:11–29. doi:10.1016/S0376-8716(96)01315-4 Khan U, Nicell J a (2011) Refined sewer epidemiology mass balances and their application to heroin, cocaine and ecstasy. Environ Int 37: 1236–1252. doi:10.1016/j.envint.2011.05.009 Lai FY, Ort C, Gartner C et al (2011) Refining the estimation of illicit drug consumptions from wastewater analysis: co-analysis of prescription pharmaceuticals and uncertainty assessment. Water Res 45:4437– 4448. doi:10.1016/j.watres.2011.05.042 Langford KH, Reid M, Thomas KV (2011) Multi-residue screening of prioritised human pharmaceuticals, illicit drugs and bactericides in sediments and sludge. J Environ Monit 13:2284–2291 Mathieu C, Rieckermann J, Berset J-D et al (2011) Assessment of total uncertainty in cocaine and benzoylecgonine wastewater load measurements. Water Res 45:6650–6660. doi:10.1016/j.watres.2011.09.049 Van Nuijs ALN, Abdellati K, Bervoets L et al (2012) The stability of illicit drugs and metabolites in wastewater, an important issue for sewage epidemiology? J Hazard Mater 239–240:19–23. doi:10. 1016/j.jhazmat.2012.04.030 Paul BD, Lalani S, Bosy T et al (2005) Concentration profiles of cocaine, pyrolytic methyl ecgonidine and thirteen metabolites in human blood and urine: determination by gas chromatography-mass spectrometry. Biomed Chromatogr 19:677–688 Plósz BG, Reid MJ, Borup M et al (2013) Biotransformation kinetics and sorption of cocaine and its metabolites and the factors influencing their estimation in wastewater. Water Res 47(7):2129–2140. doi:10. 1016/j.watres.2012.12.034 Smith ML, Shimomura E, Paul BD et al (2010) Urinary excretion of ecgonine and five other cocaine metabolites following controlled oral, intravenous, intranasal, and smoked administration of cocaine. J Anal Toxicol 16:57–63 Zuccato E, Chiabrando C, Castiglioni S et al (2005) Cocaine in surface waters: a new evidence-based tool to monitor community drug abuse. Environ Health 4:14
RESEARCH ARTICLE
Stability of cocaine and its metabolites in municipal wastewater – the case for using metabolite consolidation to monitor cocaine utilization Kevin J. Bisceglia & Katrice A. Lippa
Received: 5 September 2013 / Accepted: 25 November 2013 # Springer-Verlag Berlin Heidelberg (outside the USA) 2013
Abstract Transformations of cocaine and eleven of its metabolites were investigated in untreated municipal sewage at pH≈7 and 9, 23, and 31 °C. Results indicated that hydrolysis—possibly bacterially mediated—was the principal transformation pathway. Residues possessing alkyl esters were particularly susceptible to hydrolysis, with pseudo-first-order rate constants varying from 0.54 to 1.7 day−1 at 23 °C. Metabolites lacking esters or possessing only a benzoyl ester appeared stable. Residues lacking alkyl esters did accumulate through hydrolysis of precursors, however. As noted previously, this may positively bias cocaine utilization estimates based on benzoylecgonine alone. Reported variability in metabolic excretion was used in conjunction with transformation data to evaluate different approaches for estimating cocaine loading. Results indicate that estimates derived from measurands that capture all major cocaine metabolites, such as COCtot (the sum of all measurable metabolites) and EChyd
(the sum of all metabolites that can be hydrolyzed to ecgonine), may reduce uncertainty arising from variability in metabolite transformation and excretion, possibly to≈10 % RSD. This is more than a two-fold reduction relative to estimates derived from benzoylecgonine (>26 % RSD), and roughly equivalent to reported uncertainties from sources that are not metabolite-specific (e.g., sampling frequency, flow variability). They and other composite measurands merit consideration from the sewage epidemiology community, beginning with efforts to evaluate the stability of the total cocaine load under realistic sewer conditions. Keywords Cocaine . Ecgonine . Benzoylecgonine illicit drugs . Wastewater . Stability . Sewage epidemiology . Sewer information mining
Introduction
Responsible editor: Philippe Garrigues Electronic supplementary material The online version of this article (doi:10.1007/s11356-013-2403-5) contains supplementary material, which is available to authorized users. K. J. Bisceglia Department of Geography and Environmental Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218, USA K. J. Bisceglia : K. A. Lippa Chemical Sciences Division, National Institute of Standards and Technology, 100 Bureau Drive, Stop 8392, Gaithersburg, MD 20899-8392, USA K. J. Bisceglia (*) Department of Chemistry, Hofstra University, Hempstead, NY 11549, USA e-mail: [email protected]
Monitoring illicit drugs and their metabolites in municipal wastewater (often called sewage epidemiology) is becoming increasingly attractive as a means for conducting preliminary assessments of drug use within municipalities (Castiglioni et al. 2013). The procedure for converting measurements of occurrence into estimates of per capita drug utilization was developed by Zuccato et al. (2005). Conversions are simple to perform, but they entail several important sources of uncertainty. Uncertainties arising from variations in wastewater flow, spatial and temporal prevalence of drug use, catchment population, and sampling frequency have been characterized in detail (Mathieu et al. 2011; Lai et al. 2011; Castiglioni et al. 2013), and generally do not depend on the choice of metabolite(s). Other sources of uncertainty do depend on the choice of metabolite being monitored. These include analytical uncertainty, variability in the fraction of the parent drug that is excreted by humans as specific metabolite(s), and uncertainty
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regarding the partitioning and transformation of metabolite(s) in the wastewater matrix. Cocaine (COC) has received the most attention from the sewage epidemiology community, to date, and consensus is that benzoylecgonine (BE, its principal metabolite) should be used to estimate levels of cocaine utilization (Castiglioni et al. 2013). Partitioning to wastewater solids is unlikely to be important for cocaine and its metabolites (Langford et al. 2011; Baker and Kasprzyk-Hordern 2011; Plósz et al. 2013). In contrast, transformation of cocaine and its metabolites is a concern during sewer transport, sample collection, and sample processing. Castiglioni et al. (2013) reviewed seven investigations of cocaine stability in sewage (including unpublished data now presented herein). They concluded that COC and alkyl ester-containing metabolites (e.g., ecgonine methyl ester, EME) are readily transformed in wastewater at circumneutral pH and temperatures of 4 and 23 °C. A more recent study (Plósz et al. 2013) confirmed these findings and also concluded that transformation rates do not depend on the presence of dissolved oxygen. All studies found BE to be relatively stable in wastewater under the same conditions, but that hydrolysis of co-occuring COC caused concentrations of BE to increase over time. As a result, back-calculations of cocaine utilization from BE are biased by production that occurs in-sewer and during sample collection. Sample collection and processing often exceeds 24 h, although pH and temperature can be manipulated to minimize transformation rates (Gheorghe et al. 2008). Castiglioni et al. (2013) assert that uncertainty from in-sewer BE production is likely to be small (≈2–10 %), as BE concentrations are typically double COC concentrations, and hydraulic residence times in most sewers are short (≤12 h). On the other hand, Plosz et al (2013) note that in-sewer BE production is dependent upon COC loading patterns. They state that failure to account for weekday/weekend loading variations can bias utilization estimates by 18 % and that special events and festivals can bias estimates by 50 %. Recent sewage epidemiology investigations (Plósz et al. 2013; Castiglioni et al. 2013) have stressed the need for additional data on COC (bio)transformation under realistic conditions. COC and EME transformation rates can vary while the wastewater source, temperature, and pH remain unchanged, presumably due to temporal variations in microbial and chemical composition (Castiglioni et al. 2011). The influence of temperature on cocaine transformation has been specifically highlighted as a knowledge gap (Plósz et al. 2013). Moreover, while there is strong evidence that ester hydrolysis is the dominant transformation mechanism in wastewater (Plósz et al. 2013; Castiglioni et al. 2013), and limited evidence that N -demethylation may also occur (Castiglioni et al. 2011), no published study has attempted to close a mass balance on COC and its transformation products. Only one study (Castiglioni et al. 2011) has investigated
whether ecgonine (EC), the final hydrolysis product of cocaine, is stable in municipal sewage. Variability in metabolic excretion has been repeatedly highlighted as a major source of uncertainty in backcalculating estimates of drug utilization via the sewer epidemiology approach (Khan and Nicell 2011; Lai et al. 2011; Castiglioni et al. 2013)—perhaps the second largest source after catchment population (Castiglioni et al. 2013). Metabolic excretion profiles for drugs of abuse are known to vary widely based on age, gender, urine pH, health status, route, frequency, and amount of ingestion, and many other genetic and lifestyle factors (Gibson and Skett 2001). Meta-analysis of ten controlled cocaine administration studies found that excretion is highly dependent upon route of administration (ROA) and that 27.1±11.4 % (RSD=42 %) of a COC dose is excreted in urine as BE when all ROAs are considered equally (Castiglioni et al. 2013). BE excretion variability may be lowered if COC ingestion patterns are known. Castiglioni et al. (2013) assume the following consumption pattern for Europe: 95 % via nasal insufflation (NI), 4 % smoked (SM), and 2 % intravenously (IV). This lowers variability in BE excretion to 26 % RSD. However, they also note that information about ROI patterns is generally scarce, and varies geographically and over time. In the USA, for example, smoking cocaine (as crack) is more prevalent in urban than rural areas (Kandel et al. 1997). In Baltimore, MD, however, it may be more common to ingest COC intravenously (and concurrently with heroin) than via smoking or snorting (Harrell et al. 2012). An alternative means of estimating cocaine utilization would be to use measurement endpoints that pool all of its major metabolites, such as total cocaine (COCtot, defined as the sum of cocaine and all of its measurable metabolites) or post-hydrolysis ecgonine (EChyd). In determining EChyd, a simple hydrolysis procedure converts cocaine and all of its tropane-containing metabolites into ecgonine with >98 % efficiency (Bisceglia et al. 2012). Such endpoints include all of the major cocaine metabolites, capturing >60 % of an administered dose (Jeffcoat et al. 1989; Baselt 2004; Khan and Nicell 2011) via a “funneling effect,” wherein a group of metabolites coalesce into a single measurand. As a result, their composite variance is likely to be much lower than for any single metabolite. Preliminary evidence suggests that EC and anhyroecgonine (AEC, the 2-tropene hydrolysis end-product of pyrolized cocaine) are stable in wastewater (Castiglioni et al. 2011; Bisceglia et al. 2012). EC also appears to be the stable end-product for cocainic metabolites in human urine, and has been suggested as a more reliable and longer-lasting alternative to BE for conducting urine-based drug tests (Smith et al. 2010). In this paper, we examine the stability of cocaine (COC) and 11 of its metabolites [benzoylecoginine (BE), ecgonine methyl ester (EME), cocaethylene (CE), ecgonine ethyl ester
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(EEE), ecgonine (EC), m -hydroxybenzoylecgonine (mOHBE), p -hydroxybenzoylecgonine (pOHBE), anhydroecgonine methyl ester (AEME), anhydroecgonine (AEC), norcocaine (NC), and norbenzoylecgonine (NBE); see Figure S1 in Supplementary Materials] in municipal wastewater incubated under semi-stagnant, gently aerated conditions at 9, 23, and 31 °C. Results from the stability investigation are used in conjunction with an analysis of the variability in urinary excretion profiles to evaluate the uncertainty that might be associated with estimating cocaine utilization from measured concentrations of benzoylecgonine, or from groups of metabolites (i.e., COCtot and EChyd).
aliquots, adjusting to pH 2.0 with 0.12 mL of 0.1 M HCl (the volume was determined experimentally on a separate subsample), and adding 0.12 mL of an acetonitrile solution containing isotopically labeled surrogate standards (Bisceglia et al. 2010) at concentrations comparable to the spiking conditions. Previous studies indicate that cocaine hydrolysis rates are minimized at pH 2.0 (Gheorghe et al. 2008; Bisceglia et al. 2012). The samples were stored at −20 °C for 4 days until analysis could be completed by direct injection reversedphase liquid chromatography tandem mass spectrometry (RPLC/MS/MS) using a pentafluorophenyl-propyl column. Detailed analytical conditions and quality assurance/quality control steps are provided elsewhere (Bisceglia et al. 2010).
Materials and methods
Kinetic modeling
Chemicals
Kinetic modeling of the stability data was performed with Scientist version 2.01 (Micromath, St. Louis, MO). Time courses of COC, CE, NC, AEME, and their respective metabolites were fit by nonlinear least squares regression to an ester hydrolysis model (reaction schematic and rate equations are provided as Supplemental Material). Pseudo-first-order kinetics was assumed. Equations were normalized to initial concentrations, in order to prevent biases in fitting that would be caused by large differences in initial concentrations (and hence, large differences in residual errors) among the various metabolites. Only the data from the first 13 h were used in fitting EC and AEC transformations.
Details on the procurement and handling of all chemicals and on the preparation of stock and calibration solutions are listed elsewhere (Bisceglia et al. 2010). Stability experiments Transformation of cocaine and its metabolites was investigated in batch reactors at 9, 23, and 31 °C. Grab samples of untreated wastewater influent (pH=7.3) were obtained from the Back River Wastewater Treatment Plant (BRWWTP) in Baltimore, MD on May 19 and 21, 2009, and transported on ice to our lab. Samples were immediately passed through an 11-μm filter (Whatman No. 1, Piscataway, NJ) to remove coarse particulates but retain suspended bacteria (Jonas and Tuttle 1990). Samples were spiked with cocaine and metabolites within 1 h of collection so that reactor concentrations were three to five times greater than background occurrence (as determined from a separate analysis of the same wastewater stream). Because of analytical constraints, spiking concentrations for EC and AEC were 20 and 600 times greater than background, respectively. Additional stability studies were conducted on four opioids (6acetylmorphine, morphine, hydrocodone, and oxycodone), five phenylamine drugs (amphetamine, methamphetamine, 3 , 4- m e t hy l e n e d i o x ym e t h am p he t am i n e (M D M A ) , methylbenzodioxolylbutanamine (MBDB), and 3,4methylenedioxy-N-ethylamphetamine (MDEA)), and two human use markers (creatinine and cotinine); results are provided elsewhere (Bisceglia 2010). After spiking, the samples were split into 250-mL aliquots (three per temperature investigation). Each aliquot was transferred to a 1-L Erlenmeyer flask, plugged with a foam stopper to allow air transfer, and placed in a temperature-controlled incubator. In all cases, the reactors were constructed within 3 h of sample collection. Reactors were shaken at 180 rpm in the dark and sampled repeatedly over 24 h by removing 1-mL
Results and discussion Stability of cocaine and its metabolites in municipal wastewater Time courses for COC, CE, and their tropane metabolites are presented in Fig. 1, and for the nortropane- and 2-tropene (pyrolysis)-related metabolites in Fig. 2. Rapid degradation was observed at 31 and 23 °C for almost all compounds containing an alkyl ester. AEME (Fig. 2) is an exception to this generalization, possibly because its carbonyl is in conjugation with the π system on the 2-tropene ring. In contrast, compounds containing a benzoyl substituent as the sole ester appeared stable at all temperatures within the time frame investigated, with BE and NBE exhibiting modest to moderate accumulation attributed to the degradation of their precursors. Finally, EC and AEC, which lack ester moieties, appeared stable at all temperatures within the first 13 h. After 13 h, however, both compounds exhibited an apparent shift in kinetic regime, and degradation could be discerned at 31 and 23 °C. Data were successfully fit by a hydrolysis model wherein the alkyl and benzoyl esters on each compound were
Concentration (nM)
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Time (h) Fig. 1 Stability profiles for cocaine, cocaethylene, and their metabolites in untreated municipal wastewater. Solid, dashed (− − − −), and dotted (·········) lines represent model fits for the hydrolysis of all chemicals to
products shown in Figure S1 in Supplementary Material at 31 °C (○), 23 °C (▲), and 9 °C (□), respectively. Error bars denote 95 % confidence intervals for the analysis of three replicates. Consult text for abbreviations
considered labile. A schematic (Figure S1), rate equations, and fitted pseudo-first-order rate constants (Table S1) are provided as Supplementary Material. We chose to ignore data for EC and AEC after 13 h when performing model fittings, and no degradation rates are reported for either compound. EC and AEC are close structural analogs, and it is possible that their combined concentration (which, by analytical necessity, was≈60 times greater than their combined concentration in unamended wastewater; Bisceglia et al. 2010; Castiglioni et al. 2011) was sufficient to induce bacterially mediated degradation by a specific, albeit unknown, pathway. While it remains uncertain whether this pathway is relevant at concentrations (1–5 nM) and residence times (≈12 h) that are common to municipal sewers (Castiglioni et al. 2013), both
chemicals were found to be stable for at least 2 h in untreated municipal sewage at elevated temperature and alkaline pH (Bisceglia et al. 2012), and almost indefinitely in urine samples stored at −20 °C (Smith et al. 2010). The good mass balances (>99 % over the first 13 h) observed for COC, CE, and their hydrolysis products (represented as “Sum of EC and Precursors” in Fig. 1) during incubations provide strong support for hydrolysis as a dominant transformation mechanism for COC and its metabolites in municipal wastewater. Plosz et al. (2013) also successfully applied a hydrolysis model to the biotransformation of COC in unfiltered municipal wastewater. The pseudo-first-order rate constant for the overall hydrolysis of COC (k1 +k2 in Figure S1) was twice as large in their study as ours; both
Fig. 2 Stability profiles for the pyrolysis and norcocainic metabolites in untreated municipal wastewater. Solid, dashed (− − − −), and dotted (·········) lines represent model fits for the hydrolysis of the pyrolysis metabolites to AEC, and for the hydrolysis of the norcocainic metabolites to norecgonine (NEC, not shown) at 31 °C (○), 23 °C (▲), and 9 °C (□), respectively. Error bars denote 95 % confidence intervals for the analysis of three replicates. Consult text for abbreviations
Concentration (nM)
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Time (h) wastewater values were≈10 times greater than the value in deionized water (Garrett and Seyda 1983; see Table 1). Plosz et al.'s value could be larger because of inherent differences in microbial ecology or because their wastewater was not filtered and likely contained more degrading bacteria. Regardless, the difference highlights the importance of characterizing biological activity during transformation studies and of using caution when comparing rates among studies. Interestingly, respective constants for BE and EME hydrolysis (the only COC metabolites common to all studies) agree to within a factor of three. All studies indicate that BE hydrolyzes slow enough to be considered stable at≈pH 7.5 and 20 °C, while EME is readily hydrolyzed under the same conditions, perhaps even faster than cocaine. Variability in urinary excretion of BE relative to composite measurement endpoints Subject-weighted, mean excretion values and associated uncertainties (as % RSD) are presented in Table 2 (Controlled dose studies) for BE and two composite measurement endpoints, COCtot and EChyd. Three ROAs (IV, IN, and SM) are considered, as are two mixed ROA scenarios: (1) equal prevalence of IV, IN, and SM, (2) the European consumption described in the Introduction. BE excretion data are reproduced from Castiglioni et al. (2013). Values for COCtot
and EChyd were determined as part of the current study from Jeffcoat et al. (1989), the only published controlled dose study to administer radio-labeled cocaine while also monitoring EC. For ROA, COCtot was computed as the total fraction of 14Clabeled cocaine detected in urine (U), and urine and feces (U+ F); EChyd was computed as the sum of COC, BE, EME, and EC detected in urine. Uncertainties for COCtot (U+F) and EChyd (U) were computed as the root sum of the squares (RSS) of the component standard deviations (SD). Additional means of evaluating excretion-related variability in COCtot and EChyd were sought for comparison purposes. The most recent cocaine administration study includes EC (Smith et al. 2010), but does not provide enough information to compute metabolic excretion fractions. Castiglioni et al. (2011) compared maximum concentrations (Cmax) from this study, but Cmax does not necessarily correlate with cumulative excretion, in part because such values occur at different times for different metabolites, and thus are not determined from the same sample. Instead, occurrence data from independently collected, but concurrently analyzed, urine samples (Paul et al. 2005; n =30) were used to compute relative excretion fractions for BE and EChyd (as percent of total measured cocaine; Table 2, Independent urine samples). Mean ± SD for all samples is reported for BE. EChyd was computed as the sum of COC, CE, BE, EME, EEE, mOHBE, and EC. Uncertainty was evaluated by computing SD for all urine
Environ Sci Pollut Res Table 1 Pseudo-first-order transformation rate constants (day−1) for cocaine and metabolites in different media a
Assuming active biomass= 150 mg/L
b
Garrett et al. (1994)
c
Not investigated
d
From first 13 h of data
DI water pH 7.5, 19.4 °C (Garrett and Seyda 1983; Garrett et al. 1994)
Unfiltered wastewatera pH 7.5, 19.4 °C (Plósz et al. 2013)
11-μm filtered wastewater pH 7.4, 23.0 °C (Present study)
COC
0.23
3.3
1.4
BE EME EC
0.00 0.51b NAc
0.0 1.3 NAc
0.0 1.7 0d
samples and by the analysis of variance/covariance among measurements within the samples (Table S2 in Supplementary Material); both estimates of uncertainty were in good agreement. COCtot represents 65–70 % of administered drug in urine, and 70–75 % in urine and feces, across ROAs. Mean urinary recovery from studies that did not monitor for EC or use radiolabeled cocaine is≈60 % (Baselt 2004; Khan and Nicell 2011), although some report that an additional 5–12 % is excreted as “polar, unidentified metabolites” (Cone et al. 1998). More importantly, COCtot does not seem to be as dependent on ROA as BE (ranges in excretion: 64–69 and
15–37 %, respectively), and associated uncertainties (as % RSD) are four times smaller for COCtot under EU ROA patterns. In both the controlled-dose study and spot urine samples, EChyd represents≈90 % of urinary cocaine and likely captures≈60 % of total administered cocaine. Interestingly, although metabolite abundance in spot urine samples should not necessarily reflect cumulative excretion urinary fractions, mean BE and EChyd in urine samples (as percent of measured COCtot; Table 2, Independent urine samples) are within 5 % of those in controlled-dose studies (Table 2, Controlled dose studies). Similar to COCtot, EChyd is less dependent on ROA (range 51–61 % of dose) than BE and has less uncertainty
Table 2 Percent of cocaine (mean ± standard deviation (RSD))detected in urine as COCtot, BE, and EChyd for different routes of administration (ROA) during controlled dose studies and in independent urine samples Controlled dose studiesa ROA IV IN SM Assumed ROA patterns: Equal weightd EU weighte
COCtot (U) 69.0±10.0 (14.5) 68.2±4.6 (6.7) 64.0±7.2 (11.3)
COCtot (U+F)b 74.5±10.1 (13.6) 72.1±5.1 (7.1) 75.1±7.7 (10.2)
BE (U) 37.3±9.6 (25.8) 29.4±7.4 (25.3) 14.8±5.8 (39.1)
EChyd(U)c 58.7±6.0 (10.2) 60.7±8.2 (14.0) 51.2±8.8 (17.1)
67.1±2.7 (4.0) 68.1±4.8 (7.1)
73.9±1.6 (2.1) 72.3±5.3 (7.4)
27.1±11.4 (41.9) 29.2±7.8 (26.5)
56.9±5.0 (8.8) 60.9±8.4 (13.8)
BE (U) 46.9±18.5 (39.0)
EChyd(U)h 89.1±10.4 (12.0)
31.4±12.5 (40.0)i 31.9±13.0 (40.8)i
59.8±7.4 (12.3)i 60.6±8.2 (13.6)i
Independent urine samplesf g
As percent of measured COCtot (Dose and ROA unknown) Assumed ROA patterns: Equal weight EU weight
IV intraveinous, IN nasal insufflations, SM smoked as crack cocaine a
COCtot and EChyd data from Jeffcoatet al. (1989), BE data from Castiglioniet al. (2013)
b
Urine + Feces; SD computed as root sum of squares (RSS) of urine and fecal SDs
c
SD computed as RSS of reported SDs for EC precursors
d
Equal utilization via IV, IN, SM
e
2 % utilized via IV, 94 % via IN, 4 % via SM (Castiglioni et al. 2013)
f
Data from Paul et al. (2005); n =30
g
Percent of measured urinary cocaine present as BE and as EC precursors
h
SD computed from covariance of EC precursors (see text for details)
i
Adjusted for fraction of administered dose detected in urine as BE and EC precursors by Jeffcoat et al.; SD computed as RSS of SDs from Jeffcoat et al. and Paul et al
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associated with its metabolic excretion fraction, although % RSD is only two times smaller than BE under EU ROA patterns. As would be expected if hydrolysis is the dominant transformation pathway, COC and its hydrolytic metabolites are highly negatively correlated in urine samples (Table S2). Correlations between EC and BE, EC and EME, and EC and COC are particularly strong. Together, these four chemicals account for more than 80 % of covariance among metabolites in urine samples, and are the primary reason for the reduced variability in the urinary excretion fractions of COCtot, EChyd, and presumably other composite measurement endpoints that include them. Moreover, if hydrolytic transformations are also dominant in municipal wastewater, as indicated by Plosz et al. (2013) and the present study, then uncertainty arising from in-sewer and in-sample transformations should be correspondingly lower for COCtot and EChyd than BE as well.
Conclusion As noted by others (van Nuijs et al. 2012; Plósz et al. 2013; Castiglioni et al. 2013) and confirmed here, the hydrolysis of precursors will positively bias estimates of COC utilization derived from BE alone, and reliable estimates require knowledge of COC loading and ROA patterns (Plósz et al. 2013). In the absence of such information, it may be worth pursuing composite measurands that include a broader array of metabolites, including COC, EME, and the ultimate tropane (EC), nortropane (NEC), and 2-tropene (AEC) backbones. COCtot and EChyd are two such measurands, and include—at a minimum—COC, BE, EME, and EC. COCtot may allow researchers to monitor ROA, alcohol coingestion, and sources of loading through metabolite ratios (Bisceglia et al. 2010; Castiglioni et al. 2011; Castiglioni et al. 2013). Its determination is more analytically challenging than BE and COC alone, however, and a pre-concentration step is still required. Alternatively, EChyd captures as much as 90 % of urinary cocaine (≈60 % of administered cocaine) in a single metabolite (EC), with only minimal reduction in analytical precision (Castiglioni et al. 2011; Bisceglia et al. 2012). Preconcentration is generally not required for measuring EChyd via LC/MS/MS, and doing so may enable measurement via LC/MS (Bisceglia et al. 2012). Processing and analysis of samples for EChyd takes about 3 h—considerably less than conventional SPE—and does not require specialized equipment (such as is needed for online pre-concentration) or disposable supplies. It is important to note that EChyd is unable to differentiate between cocaine that is consumed and cocaine that enters the sewer system via other means (hence the term “utilization”). COC/BE ratios in wastewater are larger than would be
predicted from metabolism alone, suggesting alternative routes of entry (Castiglioni et al. 2013). The fraction of “excess” cocaine in wastewater is small and relatively consistent across studies (Castiglioni et al. 2013), however, so researchers wishing to estimate consumption via EChyd might consider applying a correction factor. Alternatively, they may prefer to monitor cocaine utilization fairly precisely via EChyd, than to monitor consumption (via BE or EChyd) with substantially greater uncertainty. Provided that the tropane, 2-tropene, and nortropane backbones of COC are stable under realistic sewer conditions, both COCtot and EChyd would largely eliminate concerns regarding the hydrolytic (and, for COCtot, possibly N-demethylation) transformations of individual cocaine metabolites. Both endpoints are likely to reduce uncertainty arising from variability in metabolic excretion and in-sewer transformation to≈10 % RSD. While further studies are required to refine these numbers, this represents more than a twofold reduction relative to uncertainty estimates derived for BE alone and roughly equivalent to reported uncertainties from sources that are not metabolite-specific (e.g. sampling frequency, flow variability). COCtot, EChyd, and other composite measurement endpoints merit further consideration from the sewage epidemiology community. Such efforts should begin by evaluating the stability of the total cocaine load under realistic sewer conditions. Acknowledgements We wish to thank Nick Frankos at the BRWWTP for providing us with wastewater samples and Lynn Roberts for providing technical guidance. We also wish to thank David Duewer and Seth Guikema for helpful discussions regarding environmental data distributions. Conflict of interest Certain commercial equipment, instruments, or materials are identified in this paper to specify adequately the experimental procedure. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.
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