Transforma)ons of Illicit Drugs in Bodies and Sewers
Pros and Cons of Biomarker Consolida)on in Sewage Epidemiology 1 1 2 Kevin J. Bisceglia , Amanda R. Stashin , Katrice A. Lippa 1Department of Chemistry, Hofstra University, Hempstead, NY 11549 2Chemical Sciences Division, NIST, Gaithersburg, MD 20899
Introduc)on:
Methodology:
• Sewage epidemiology (SE) aSempts to monitor community-‐level drug use by tracking the occurrence of relevant biomarkers in municipal sewage. SE may represent a valuable public health surveillance tool, but first its reliability must be established.
Biomarker stability in wastewater
• Consensus prac<ce in SE is to use only the dominant drug metabolite as biomarker. When doing so, sources of uncertainty that depend on biomarker iden<ty are o^en two-‐to-‐five <mes larger than those influenced by study area1.
•
•
• This occurs because catchment-‐specific sources of uncertainty can be minimized by careful study design, while biomarker-‐specific uncertain<es are both more poorly characterized and more difficult to control.
•
• Here, we evaluate the poten<al of composite measurands – which capture most or all major metabolites of a target compound – to reduce two biomarker-‐specific sources of uncertainty:
Drug and (for cocaine only) metabolite transforma<on was inves<gated in sewage influent at 9, 23, and 31 °C and circumneutral pH, under condi<ons designed to s<mulate growth of suspended aerobic bacteria. Reactors (in triplicate) were spiked simultaneously with all compounds at nM concentra<ons, shaken (180 rpm) in the dark, and sampled repeatedly over 24 h. Samples were analyzed by direct injec<on liquid chromatography isotopic dilu<on mass spectrometry (LC-‐IDMS/MS)2. Cociaine and its metabolites were fit to an ester hydrolysis model using nonlinear least squares regression. Other drugs were fit using nonspecific pseudo-‐first order kine<cs.
Analysis of metabolic excre)on profiles
• Mean excre<on frac<ons for composite measurands were computed as the sum of their components; uncertain<es were computed as the root sum of the squares (RSS) of component SDs.
Independent urine samples: •
Rela<ve abundances of drug and drug metabolites in independently collected, but concurrently analyzed, urine samples was used as another means of evalua<ng excre<on-‐related variability.
•
Uncertainty was evaluated as the SD among all urine samples, and from an analysis of variance/covariance (ANCOVA) among measurements within each sample. Normal distribu<ons were assumed, and evaluated when possible (K–S test).
Var X + Y = Var X + Var ! + 2Cov X, Y
Controlled dose studies:
(1) variability in the metabolic excre<on profiles of drugs of abuse, which are thought to arise from gene<c and lifestyle differences within the popula<on
•
(2) variability in biomarker transforma<ons that may occur during transport municipal sewer systems.
1 Var X = n−1
For each drug, subject-‐weighted, mean excre<on frac<ons were computed from controlled dose studies. Only studies that administered radio-‐labeled compound and/or monitored > 80% of known metabolites were included. Standard devia<ons (SD) were computed as the root mean square error (RMSE) from an analysis of variance (ANOVA)
!
X! − X !!!
!
1 Cov X, Y = n−1
!
X! − X Y! − Y !!!
Results: Cocaine
Stability of cocaine & metabolites in municipal sewage
Frac)on of a cocaine dose (and associated uncertainty) captured by different biomarkers
Poten)al metabolic transforma)on pathways for cocaine
20
70
Cocaine
Concentra)on (nM)
8
Controlled dose data are from a radio-‐labeled study (n = 14)3; urine data are from
Smith (n = 30)4. Uncertainty is expressed as SD (error bars) and rela<ve SD (%RSD). Absolute excre<on of COCtot could not be es<mated from independent urine samples. Note that RSD for BE, the concensus SE biomarker for cocaine, is 5-‐10 )mes larger than for composite measurands.
Covariance matrix (n =(n 3=0) the rela)ve abundance Variance/Covariance matrix 30)for for the relative abundance (unitless) of cocaine and its metabolites in urine (unitless) of cocaine and its major metabolits in urine a
a
COC CE EME EEE BE mOHBE EC
“H2O” denotes hydrolysis; “Ox” is oxida<on; “EtOH” is trans-‐esterifica<on from ethanol co-‐inges<on; “pyrolysis” metabolites form during smoking crack cocaine
a
CE
EME
EEE
BE
mOHBE
EC
3.1 9.4 8.4 -10.3 -0.2 -11.6
133.2 28.7 -43.8 -0.3 -138.1
31.8 -32.8 -0.5 -35.0
341.7 0.1 -182.7
0.6 0.7
424.2
4 0 0
6
12
18
24
30
60
200
50
150
40
100
0
8
130
6
115
4
100
2
85
Ecgonine0 Methyl0Ester
0 0
6
12
• N-‐demethyla<on to amphetamine (AM) deriva<ves is common and may represent a substan<al por<on of an original MA load. However, AM has legal uses and mul<ple precursors; its inclusion is unlikely to reduce uncertainty.
70 0
5 10 15 20 25 30
6
12
Simulated in-‐sewer Simulated In-Sewer Accumulation accumula)on of BE of Benzoylecgonine
Methamphetamine (MA)
S
15.0# 10.0# 5.0# 0.0# 5#
10#
15#
20#
Time#(hrs)#
25#
30#
1.6#
6.0# 4.0# 2.0# 0.0# 10#
15#
20#
25#
18
24
30
24
30
Cocaine and metabolites possessing an alkyl ester are readily hydrolyzed at pH 7.2 and 23 °C. W h i l e b e n z o y l e c g o n i n e appears stable, in-‐sewer hydrolysis of cocaine and cocaethylene can cause reasonable accumula)on (~10-‐20%) during transport in sewer systems.
HO HO
O O OH
O
HMMA-glucuronide
Darker arrows represent dominant metabolic pathways in humans. A^er demethyla<on and cleavage of the methylenedioxy ring, a majority of the MDMA dose ubdergoes conjuga<on with glucuronides and sulfates. T h e r e d d a s h e d l i n e e n c l o s e s metabolites included in the ΣDHMA composite measurand, while those inside of the black dashed line are represented by ΣHMMA.
Stability of MDMA in municipal sewage
8.0#
5#
12
Frac)on of a MDMA dose (and associated uncertainty) captured by different biomarkers
AM#
0#
H N
HO
HO
Concentra3on#(nM)#
Concentra3on#(nM)#
Concentra0on#(nM)#
20.0#
HMMA-sulfate
O
H N
glucuronide & sulfate conjugates
10.0#
MA#
H N
S
HO
DHMA-3-sulfate
25.0#
Conclusions:
O
4-hydroxyamphetamine (OHMA)
glucuronide & sulfate conjugates
O
4-Hydroxy-3-methoxymethamphetamine (HMMA)
3,4-Dihydroxymethamphetamine (DHMA)
O
Stability of methamphetamine and amphetamine in municipal sewage
0#
H N
O
Frac)on of a methamphetamine dose (and associated uncertainty) captured by different biomarkers
4-Hydroxy-3-methoxyamphetamine (HMA)
HO
NH2
4-hydroxymethamphetamine (OHMA)
NH 2
O
HO
HN
O
HO
O
HO
3,4-Methylenedioxymethamphetamine (MDMA) HO
NH 2
3,4-Dihydroxyamphetamine (DHA)
H N
HO
O
6
O
Amphetamine (AM)
0
Note that the sum of all metabolites (as represented by the mass balance) remains constant, however.
HO
3,4-Methylenedioxyamphetamine (MDA)
O
18
Time (h)
HO
NH 2
O
H N
30
Poten)al metabolic transforma)on pathways for MDMA O
NH2
HN
• Finally, substan<al frac<ons of MA and AM form nonspecific compounds (e.g., hydroxybenzoic acid) that are ill-‐suited for SE.
Only one controlled dose study (n = 24)6 and one urine study (n = 25)7 has considered conjugate forma<on. Inclusion of hydroxylated MA metabolites reduces uncertainty, but the effect is not large.
MDMA
24
S t a b i l i t y h a s i m p o r t a n t implica<ons for the selec<on of metabolites for monitoring drug abuse.
Abundances of EC, BE, EME, and COC are all highly nega)vely correlated in urine The black dashed line encloses metabolites in the composite measurand COCtot; The red samples. This causes uncertainty in composite measurands that consolidate these dashed line encloses those in the measurand Echyd. metabolites to be reduced, even in independent sample sets (above).
Poten)al m etabolic t ransforma)on Methamphetamine (MA) metabolism is complicated by several factors. Licit use & illicit pathways for precursors • The frac<on excreted as MA can vary from 2-‐76% depending on urinary methamphetamine pH5, which is seldom reported.
18
Ecgonine
See transformation diagram for abbreviations.
Methamphetamine
Mass Balance
16 12
COC 15.5 4.4 24.7 7.3 -16.9 -0.3 -32.6
250
Benzoylecgonine
30#
Time#(hrs)#
Composite biomarkers reduce metabolism-‐related uncertainty via a “funneling effect” wherein a group of interrelated metabolites coalesce into a single measurand. • Effects of consolida<on are par<cularly pronounced when dominant metabolic pathways involve readily hydrolysable func<onal groups (e.g., esters and phase II conjugates) and structural backbones that are stable in wastewater. • Cocaine and MDMA provide two examples. Consolida<on reduces metabolism-‐related uncertainty for both to ≈ 10 % RSD, a value in keeping with uncertain<es that are not biomarker-‐specific (e.g., flow and loading variability). • Cocaine and MDMA monitoring can be streamlined by pre-‐trea<ng samples (via acid-‐ or base-‐catalyzed hydrolysis) to reduce the number of analytes to one (EChyd) and three (MDMA, DHMAhyd, and HMMAhyd), respec<vly. • Pre-‐treatment may also minimize concern about environmental transforma<ons (e.g., in-‐sewer produc<on of BE) • Improvements in precision come at the cost of reduced informa<on about metabolite ra<os • There is a dire need for addi<onal controlled dose studies for drugs of abuse, to help iden<fy other composite biomarkers and to beSer characterize metabolism-‐related uncertainty in general.
MDMA#
1.2# 0.8# 31#C# 23#C# 9#C#
0.4# 0.0# 0#
5#
10#
15#
20#
25#
30#
Time#(hrs)#
MDMA is stable in municipal wastewater under all condi<ons inves<gated.
Only one controlled dose study (n = 20)8 and one urine study (n = 25)9 has considered conjugate forma<on. As with MA, MDMA metabolism and excre<on are extremely sensi<ve to urinary pH, which was not controlled or recorded in these studies. RSD, though large, does decrease by a factor of 2-‐3 a^er inclusion of conjugates, however, a finding that is predicted by ANCOVA results (not shown). Conversely, addi<on of MDA does nou reduce variability.
Acknowledgements References We wish to thank A. Lynn Roberts (Johns Hopkins) for technical guidance, and Seth Guikema (Hopkins) and D a v e D u e w e r ( N I S T ) f o r a s s i s t a n c e w i t h environmental data distribu<ons. Funding for A. Stashin was provided by a Lister Endowed Fellowship in Chemistry Research, awarded by Dr. Bruce and Doris Lister through Hofstra University.
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