ASSESSMENT OF NEW PSYCHOACTIVE SUBSTANCES IN WASTEWATER
Claudia Mariottini Master’s thesis MDP in general toxicology School of Pharmacy University of Eastern Finland March 2018
UNIVERSITY OF EASTERN FINLAND Faculty of Health Sciences, School of Pharmacy Master degree programme in general toxicology MARIOTTINI CLAUDIA: Assessment of new psychoactive substances in wastewater Master’s thesis, 57 p, VIII appendices (28 p) Supervisors: Teemu Gunnar, Aino Kankaanpää, Risto Juvonen March 2018 ___________________________________________________________________ Keywords: new psychoactive substances; wastewater-based epidemiology; ultra-high performance liquid chromatography-tandem mass spectrometry
ABSTRACT Consumption of illicit drugs is a widespread problem in the world. In addition to the most common illegal drugs, such as cannabis, amphetamines, heroin and MDMA, a more recent group of so called new psychoactive substances (NPS) is emerging on the drug market. The amount of different NPS is enormous, they resemble chemically or pharmacologically drugs stated as illegal, but they do not fall under the international drug conventions. The emergence and persistence patterns can vary significantly between countries and regions and information about their toxicity and effects on humans are most often unknown. Wastewater-based epidemiology is a quite novel method for getting near-real-time data on new drug habits and on changes in drug use over time. It consists on the detection of traces of chemicals in municipal wastewater. The forensic toxicology unit of the National Institute for Health and Welfare (THL) in Helsinki is responsible for analyzing wastewater samples collected from different Finnish cities. Until now, mainly traditional illicit drugs, such as cocaine, amphetamine, metamphetamine, and MDMA (“ecstasy”) have been assessed from the samples, but only a limited number of NPS. The aim of this master’s thesis was to develop and validate a good and sensitive analytical method that would allow the detection of also the most common new psychoactive substances that have been encountered in the Finnish drug market during the last years. The analysis was performed by ultra-high performance liquid chromatography coupled to tandem-mass spectrometry, with solid-phase extraction and liquid-liquid extraction as pretreatment steps. The method was validated in part and will be validated entirely afterwards. In general, the results obtained with the new method were good and most of the studied compounds can be determined quantitatively in the future. Some of the substances, however, can be analyzed only qualitatively for the moment, and further method development is needed to determine them quantitatively. The method developed in this thesis will be taken in use by THL for routine analysis of wastewater samples. The results will provide a more complete and detailed view of the drug situation in Finland, giving temporal and spatial information on trends in drug use.
TABLE OF CONTENTS ABBREVIATIONS....................................................................................................... 1 1 INTRODUCTION ..................................................................................................... 1 2 NEW PSYCHOACTIVE SUBSTANCES .................................................................. 3 2.1 Groups of new psychoactive substances and their toxicity ........................................... 4 2.1.1 Synthetic cannabinoids .......................................................................................... 6 2.1.2 Synthetic cathinones ............................................................................................. 7 2.1.3 Ketamine ............................................................................................................... 8 2.1.4 Phenethylamines ................................................................................................... 9 2.1.5 Piperazines...........................................................................................................10 2.1.6 Plant-based substances .......................................................................................11 2.1.7 Other substances .................................................................................................13 2.2 Legal situation of new psychoactive substances .........................................................15 2.3 New psychoactive substances in Finland ....................................................................17
3 WASTEWATER-BASED EPIDEMIOLOGY ........................................................... 18 3.1 Wastewater-based epidemiology approach.................................................................20 3.1.1 Sample collection .................................................................................................20 3.1.2 Chemical analysis.................................................................................................21 3.1.3 Back-calculation of the results ..............................................................................22 3.2 Limitations of wastewater-based epidemiology ...........................................................23 3.3 Wastewater-based epidemiology of new psychoactive substances .............................24
4 UHPLC-MS/MS AND VALIDATION PARAMETERS ............................................. 24 4.1 Sample preparation.....................................................................................................25 4.1.1 Solid-phase extraction ..........................................................................................25 4.1.2 Liquid-liquid extraction ..........................................................................................26 4.2 Liquid chromatography ...............................................................................................27 4.3 Tandem mass spectrometry ........................................................................................27 4.4 Validation ....................................................................................................................29 4.4.1 Selectivity .............................................................................................................30 4.4.2 Linearity................................................................................................................30 4.4.3 Limits of detection and quantification ....................................................................31 4.4.4 Accuracy ..............................................................................................................31 4.4.5 Precision ..............................................................................................................32 4.4.7 Stability.................................................................................................................32 4.4.8 Recovery ..............................................................................................................33 4.4.9 Matrix effects ........................................................................................................33 4.4.10 Measurement uncertainty ...................................................................................34
5 METHOD DEVELOPMENT ................................................................................... 35 5.1 Chemicals and reagents .............................................................................................35 5.2 Instrumentation ...........................................................................................................35 5.3 Method performance ...................................................................................................36 5.3.1 Sample pretreatment ............................................................................................36 5.3.2 Solid-phase extraction and liquid-liquid extraction ................................................36 5.3.4 Ultra-high performance iquid chromatography-tandem mass spectrometry ..........37
6 RESULTS OF METHOD VALIDATION ................................................................. 38 6.1 Linearity ......................................................................................................................39 6.5 Accuracy and precision ...............................................................................................40
7 CONCLUSIONS .................................................................................................... 42 8 AKNOWLEDGMENTS ........................................................................................... 44 9 REFERENCES ...................................................................................................... 45 10 APPENDICES ..................................................................................................... 54 APPENDIX I .....................................................................................................................54 APPENDIX II ....................................................................................................................58 APPENDIX III....................................................................................................................60 APPENDIX IV ...................................................................................................................62 APPENDIX V ....................................................................................................................67 APPENDIX VI ...................................................................................................................68 APPENDIX VII ..................................................................................................................76 APPENDIX VIII .................................................................................................................79
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ABBREVIATIONS α-PVP BZP CJEU CNS DMT EMCDDA ESI EU EWA EWS Fimea GBL IS KET LC LOD LLE LLOQ LOQ mCPP MDMA βk-MDMA MDPV ME 4-MMC MS MS/MS m/z NPS PCP PE PEA r2 RE RSD RT SC S/N SPE STP TFMPP THC THL u U UHPLC ULOQ UNODC WBE WHO
alpha-pyrrolidinopentiophenone 1-benzylpiperazine Court of Justice of the European Union central nervous system dimethyltryptamine European Monitoring Centre for Drugs and Drug Addiction electrospray ionization European Union Early Warning Advisory Early Warning System Finnish Medicines Agency gamma-butyrolactone internal standard ketamine liquid chromatography limit of detection liquid-liquid extraction lower limit of quantification limit of quantification 1-(3-chlorophenyl)piperazine 3,4-methylenedioxymethamphetamine methylone 3,4-methylenedioxypyrovalerone matrix effect mephedrone mass spectrometry tandem mass spectrometry mass-to-charge ratio new psychoactive substances phencyclidine process efficiency phenethylamines coefficient of determination recovery efficiency relative standard deviation retention time synthetic cannabinoids signal-to-noise ratio solid-phase extraction sewage treatment plant 1-(3-trifluoromethylphenylpiperazine) Δ9-tetrahydrocannabinol National Institute for Health and Welfare measurement uncertainty expanded uncertainty ultra-high performance liquid chromatography upper limit of quantification United Nations Office on Drugs and Crime wastewater-based epidemiology World Health Organization
Abbreviations of the compounds studied in this thesis are listed in Appendix IV.
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1 INTRODUCTION Consumption of illicit drugs is a widespread problem in the world. According to official statistics, it is estimated that 1 in every 20 adults aged 15-64, that is 250 million people, has used at least one drug in 2015 (UNODC 2017). Cannabis, amphetamines, heroin, and other opioids have been and are still the drugs that pose the major public health concern. However, there are also newer emerging drug classes - one of them is the group of new psychoactive substances (NPS), which have been steadily increasing in the global drug market. NPS compose a very complex and problematic group of drugs, as the number of different substances is enormous, and the emergence and persistence patterns vary significantly between countries and regions. Also, information about their toxicity and effects on humans are most often unknown. This situation brings challenges to prevention, treatment, control, and identification efforts.
The estimation of trends in drug use has usually been done following epidemiological indicators, such as general population surveys, problems caused by use of drugs, demand of treatment of drug related health problems, drug-related deaths and mortality, and drug-related infectious diseases. However, since the 2000s a new method has been taken into use by different laboratories around Europe. It is the wastewater-based epidemiology (WBE) or wastewater analysis, which detects traces of chemicals in municipal wastewater and gives near-real-time data on new drug habits and on changes in drug use over time, if it is done regularly and continuously.
Since 2012 the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) has been recollecting data on wastewater-based epidemiology from member countries to get a general overview on drug consumption in Europe, giving importance mainly to the most common illicit drugs, i.e. cocaine, amphetamine, metamphetamine and MDMA (“ecstasy”). Since then, the Finnish cities of Helsinki, Espoo, Tampere and Turku have participated to the European-wide annual wastewater campaigns undertaken by the Sewage Analysis Core Group Europe (SCORE). In addition, the National Institute for Health and Welfare (THL) follows the drug trends at a national level by analyzing every second year wastewater samples from 14 Finnish cities (Helsinki, Espoo, Lahti, Turku, Tampere, Jyväskylä, Lappeenranta, Kuopio, Oulu,
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Rovaniemi, Kotka, Joensuu, Vaasa and Savonlinna), which cover the 45% of the population (Gunnar & Vuori 2017; THL 2018). Wastewater samples are collected daily during one complete week in spring and one week in autumn. The main findings have been that in the few past years the amount of metamphetamine detected has been roughly increasing, becoming almost as prevalent as amphetamine (Gunnar & Vuori 2017; Kankaanpää et al. 2014; Kankaanpää et al. 2016). The amounts of cocaine and MDMA have also been slowly, but constantly increasing. This master’s thesis was done at the forensic toxicology unit of the National Institute for Health and Welfare located in Helsinki, which is responsible for part of the forensic toxicological analysis, and also for the wastewater-based epidemiology, in Finland. The forensic toxicology laboratory of THL has already in use an analytical method to assess the most common illicit drugs and some NPS in wastewater. The aim of the experimental part of this thesis was to develop and validate a new method, which would allow also the detection of a larger range of NPS. The wide range of different NPS with different physicochemical properties, their short appearance times in the drug market and the very low concentrations pose a challenge to develop an enough sensitive and reliable detection method.
The analysis was performed for 200 ml wastewater samples, pretreated with solidphase extraction (SPE) and liquid-liquid extraction (LLE), using ultrahigh-performance liquid chromatography (UHPLC) coupled to tandem-mass spectrometry (MS/MS). Around forty new molecules were added to the list of detectable substances from wastewater samples. In general, the results showed that many NPS can be determined in from wastewater quantitatively above the established detection limits in the future. Some of the substances, however, can be analyzed only qualitatively for the moment, and further method development is needed for a more sensitive analysis.
2 NEW PSYCHOACTIVE SUBSTANCES New psychoactive substances, sometimes also called “designer drugs” or “legal highs”, are defined as “substances of abuse, either in a pure form or in a preparation,
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that are not controlled by the 1961 Single Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances, but which may pose a public health threat comparable to that posed by substances listed in those conventions” (EUR-Lex 2005). The term “new” is not related to anything newly invented, many of the NPS being synthesized for the first time many years ago, but rather to drugs that have only recently become available on the market and misused (EAW 2017). NPS, in fact, have started to emerge as a health problem in several countries not until the first decade of the 2000’s. These substances are also called “psychoactive”, because they usually affect the central nervous system (CNS).
Chemical structures of internationally banned drugs are continuously being slightly modified in a way that the new created compounds cannot be easily and rapidly classified as illicit drugs by law and thus fall outside international drug control conventions (UNODC 2013a; UNODC 2013b). NPS can be distinguished between chemical analogues and mimetics. Analogues are structurally similar to a parent substance and differ often only by small chemical modifications; mimetics, instead, are chemically different from the original compound, but they mimic its pharmacological effects, usually acting on the same brain receptors.
2.1 Groups of new psychoactive substances and their toxicity New psychoactive substances differ widely in their effects and chemistry. Data on many drug related indicators (such as epidemiology of drug use, drug seizures, drugrelated deaths and problematic drug use) are usually collected at a national level, and then reported to and put together by the European Monitoring Centre for Drugs and Drug Addiction and the United Nations Office on Drugs and Crime (UNODC) (Wood et al. 2014a). However, data about the effects and toxicity of NPS produced in humans is very limited, as there has been little research on them. Most studies are based only on animal tests, fatal intoxications in humans or clinical observations in emergency patients, although in many cases of fatal poisoning also other substances have been abused contemporaneously (Dines et al. 2015; Heyerdahl et al. 2014; Wood et al. 2014a; Wood et al. 2014b). The most common side effects vary from seizures to
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agitation, aggression, acute psychosis, and development of dependence. Information on adverse effects or risks associated with long-term use is still largely unknown. In addition, true composition and purity of the products containing NPS are often not exactly known by the users.
In 1997 the EMCDDA launched an Early Warning System (EWS), where information on the manufacture, trafficking and use of NPS in the EU Member States is recollected and presented in the form of Joint Reports, that are then provided to involved authorities (EMCDDA 2007). Based on the Joint Report, the Council of the European Union may request a risk assessment of the health and social risks that NPS are posing. The UNODC launched in 2013 a similar Early Warning Advisory (EWA) system to respond to the emergence of NPS at a global level (EWA 2017). The EWA aims to monitor, analyze, and report trends on NPS, as a basis for effective evidence-based policy responses, also giving information on these substances, such as analytical methodologies, reference documents, mass spectra and trend-analysis data. Between 2008 and 2015, a total of 644 NPS had been reported by 102 countries to the EWA. The majority of NPS consists of synthetic cannabinoid receptor agonists, stimulants, and classic hallucinogens (fig. 1).
Stimulants (35%) Synthetic cannabinoid receptor agonists (35%) Classic hallucinogens (18%) Dissociatives (3%) Opioids (2%) Sedatives/hypnotic (2%) Not yet assigned (5%)
Figure 1. Distribution of new psychoactive substances by effect group (modified from EWA 2017).
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According to the UNODC new psychoactive substances can be categorized into seven main groups, which will be shortly described in the following paragraphs. These are synthetic
cannabinoids,
synthetic
cathinones,
ketamine,
phenethylamines,
piperazines, plant-based substances, and a miscellaneous group for those substances that do not fit in any other group.
2.1.1 Synthetic cannabinoids
Synthetic cannabinoids (SC), commonly known also as “Spice” from an early brand, act as cannabinoid receptor agonists and they mimic the effects produced by Δ9tetrahydrocannabinol (THC), which is the main psychoactive component of the natural cannabis plant (EMCDDA 2016c; EWA 2017; Lovett et al. 2015; UNODC 2013a; UNODC 2013b). Those SC that have similar chemical structure to THC, e.g. HU-210, are called ‘classical cannabinoids’, whilst ‘non-classical cannabinoids’ include cyclohexylphenols
(“CP”-compounds),
and
the
more
recently
emerged
aminoalkylindoles, such as naphtoylindoles (e.g. JWH-018), phenylacetylindoles (e.g. JWH-250), and benzoylindoles (e.g. AM-2233) (fig. 2). In general, all SC tend to be much more potent than THC (EMCDDA 2016c; EWA 2017).
As cannabis, synthetic cannabinoids are used to rise the mood, relax, and alter perceptions (UNODC 2013b). Information about the pharmacology and toxicology of synthetic cannabinoids is very limited and mainly comes from scientific reports and clinical observations. Their common side effects are vomiting, agitation, confusion, and hallucinations (Castaneto et al. 2014; Cohen et al. 2017; EWA 2017; Papanti et al. 2013; Spaderna et al. 2013; UNODC 2013b). Cardiovascular problems, such as tachycardia and hypertension, and psychological disorders may also be present. Some of the SC seem to be also carcinogenic, due to the metabolites formed from the contaminating substances (Lin et al. 2009).
Synthetic analogues of THC have already been available since the 1980s, but none of the SC is yet under the international control of the 1971 Convention on Psychotropic Substances. Synthetic cannabinoids are the most widely used NPS, as many as 169 compounds being detected since 2008 (EMCDDA 2016c; EMCDDA 2017b; UNODC 2013b).
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a)
b)
c)
d)
Figure 2. Chemical structures of a) THC, b) HU-210, c) CP 55,940 and c) JWH-018. HU-210 is a classical cannabinoid, and CP 55,940 and JWH-018 are non-classical cannabinoids. 2.1.2 Synthetic cathinones
Synthetic cathinones are structurally related to cathinone, which is the main psychoactive component in the leaves of the khat plant (Catha edulis) (EMCDDA 2014; UNODC 2013b). Cathinone itself and some of its synthetic analogues, such as cathine, methcathinone, amfepramone and pyrovalerone, are listed in the 1971 Convention on Psychotropic Substances. Synthetic cathinones appeared on the European illicit drug market for the first time in 2005 and at an international level mephedrone (4-MMC), methylone (βk-MDMA) and 3,4-methylenedioxypyrovalerone (MDPV) are the most widely used non-controlled synthetic cathinones (EMCDDA 2015a; Karila et al. 2015) (fig. 3).
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a)
b)
c)
d)
Figure 3. Chemical structures of a) cathinone, b) mephedrone, c) methylone and d) MDPV. Cathinones behave predominantly as stimulants in the CNS, showing similar sympathomimetic effects as amphetamine. Their potency is, however, considered to be lower than that of phenethylamines, probably due to their polar chemical structure which is penetrating less efficiently the blood-brain barrier (EMCDDA 2014; EWA 2017; Karila et al. 2015; UNODC 2013b). Knowledge on toxicity comes mainly from user reports and clinical observations. Desired effects from the use of synthetic cathinones are euphoria, empathy, increased alertness and awareness, mental stimulation, and elevated sociability (Abbott & Smith 2015; Karila et al. 2015; McGraw & McGraw 2012; Prosser & Nelson 2012). Unwanted health effects include increased heart rate, elevated blood pressure, breathing difficulties, deterioration of memory, anorexia, hallucinations, paranoia, and depression.
2.1.3 Ketamine
Ketamine (KET) is a derivative of phencyclidine (PCP), which is internationally controlled by the 1971 Convention (fig. 4) (EMCDDA 2014; EWA 2017; UNODC 2013b). Phencyclidine was developed in the 1950s as an anesthetic, but later withdrawn because of undesired hallucinogenic and delirium effects. Ketamine was then synthesized in 1962 as a medical alternative to PCP. Ketamine is nowadays controlled through general medicine legislations and used mainly in veterinary
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medicine to induce and maintain general anesthesia and, based on clinical experience, it has a good safety profile. a)
b)
Figure 4. Chemical structures of a) phencyclidine and b) ketamine.
However, ketamine can also be misused, as in higher doses it produces psychedelic hallucinations and an out-of-body experience, characterized by a feeling of detachment of the mind from the body (EMCDDA 2002; EWA 2017; Han et al. 2016; UNODC 2013b). Repeated exposure of ketamine has been found to be neurotoxic in rats, but effects in primates including humans have not been studied. The main effects caused by ketamine are neurobehavioral, such as severe confusion, anxiety, changes in the perception of reality and impairment of motor function. Ketamine affects also the cardiovascular system, producing changes in heart rate, blood pressure and cardiac output.
2.1.4 Phenethylamines
The main illicit phenethylamines (PEA), such as amphetamine, methamphetamine, methylphenidate, 3,4-methylenedioxymethamphetamine (MDMA, “ecstasy”) and mescaline, are under the control of the 1971 Convention, but there is still a large number of PEA derivatives not controlled by law (EWA 2017; UNODC 2013a; UNODC 2013b). PEA have usually either stimulant or hallucinogenic effects in the brain. Stimulants affect the dopamine, norepinephrine and/or serotonin systems of the CNS, while the hallucinogens usually modulate specific serotonin-receptor activities and produce hallucinations. Chemical structures of amphetamine, methamphetamine and
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two common derivatives, 4-fluoro-amphetamine and para-methoxymethamphetamine are shown in figure 5. a)
b)
c)
d)
Figure 5. Chemical structures of a) amphetamine, b) methamphetamine, c) 4-fluroamphetamine and d) para-methoxymethamphetamine. At low doses, phenethylamines rise alertness, bring energy, and increase endurance. At higher doses, they usually cause euphoria and induce strong feelings of selfesteem, such as decreased fear, anxiety, and insecurity (EWA 2017; UNODC 2013b). On the other side, PEAs can cause anorexia, high blood pressure, hyperthermia, increased heart rate and hallucinations. Stroke, cardiac arrest, and brain damage are also possible and may eventually lead to death.
2.1.5 Piperazines
The parent substance of this group, piperazine, was introduced in medicine in 1953, as it was found to have antihelmintic properties (fig. 6) (UNODC 2013a; UNODC 2013b). The most common substance of this group is 1-benzylpiperazine (BZP), which was first created as a potential antidepressant drug, but later found to produce similar effects to amphetamine. In addition to BZP, 1-(3-trifluoromethylphenylpiperazine) (TFMPP) and 1-(3-chlorophenyl)piperazine (mCPP) are widespread piperazines. None of these piperazines is yet under the international control of the Convention.
Most piperazines have euphoric and stimulant effects in the brain. Pharmacology and toxicology of BZP has been investigated with animal studies, which have clarified that BZP acts mainly by stimulating the release and inhibiting the reuptake of dopamine,
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serotonin, and noradrenaline (Baumann et al. 2004; Elliott 2011). The use of BZP leads, therefore, to elevated alertness, euphoria, and a general feeling of well-being. Adverse effects include instead repetitive thoughts, elevated blood pressure, tachycardia, nausea, dilation of pupils, nausea, urinary incontinence, chest pain, and hallucinations. At very high doses also acute psychosis, respiratory failure, renal toxicity, and seizures are possible (EMCDDA 2014).
a)
b)
c)
d)
Figure 6. Chemical structures of a) piperazine, b) BZP, c) TFMPP and d) mCPP.
2.1.6 Plant-based substances
Kratom, Salvia divinorum and khat are examples of widely used psychoactive plants that are not under the international control.
Kratom (Mitragyna speciosa) is a 4 to 16 meters high tropical tree cultivated mainly in South-East Asia and used in traditional Thai medicine to treat diarrhea and as an opium substitute (EMCDDA 2014; UNODC 2013b). The kratom plant contains over 40 structurally similar alkaloids. The main psychoactive components in the leaves are mitragynine and 7-hydroxymitragynine, which are selective and full agonists of the μsubtype opioid receptor (fig. 7). Human clinical studies are scarce, but animal studies have showed that kratom is slightly toxic to animals. In general, at low doses kratom is
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used as a stimulant to battle against fatigue during long working days, as it increases physical energy, work capacity and alertness. Regular use may produce dependence and causes weight loss, constipation, tiredness, and hyperpigmentation of cheeks. At higher doses, however, kratom has sedative effects and it is used as a recreational drug, as it decreases physical and emotional pain, and brings a feeling of well-being before eventually developing sedative properties, creating a mixed state of wakefulness, and dreaming. Chronic use cause leads to withdrawal symptoms such as muscle aches, irritability, crying, and diarrhea. Neither Mitragyna speciosa nor the alkaloids contained in the plant are listed in any of the Schedules of the United Nations Drug Conventions. a)
b)
c)
Figure 7. Chemical structures of a) mitragynine and b) 7-hydroxymitragynine, and c) plant of kratom (source: Wikimedia Commons). Salvia divinorum is a psychoactive plant, member of the mint family, growing in a limited area of the highlands of Oaxaca, in Southern Mexico (EMCDDA 2014; UNODC 2013b). The Mazatec Indians used to ingest the fresh leaves of the plant or make preparations for divinatory rituals, healing ceremonies and medical purposes. At low doses it was used to treat ailments like diarrhoea, anaemia, headaches and rheumatism. Its use as a hallucinogen has been spreading since the late 1990s. Smoking the dried and crushed leaves induce short-lived, but intense hallucinatory experiences. Unlike many other drugs, its use often causes dysphoria, and also hypotension, slurred speech, lack of coordination and eventually loss of consciousness. The main psychoactive component of the Salvia divinorum-plant is called salvinorum A (fig. 8).
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a)
b)
Figure 8. Chemical structure of salvinorum A, and b) plant of Salvia divinorum (source: Wikimedia Commons). Khat (Catha edulis) is a flowering evergreen shrub originally from the Horn of Africa and the Arabian peninsula (EMCDDA 2014; UNODC 2013b). Khat contains different alkaloids (mainly cathinone and cathine) and chewing its fresh leaves releases these substances into the saliva (fig. 9). Similarly to amphetamine, cathinone and cathine are CNS stimulants, but have lower potency. The use of khat generates mild euphoria and excitement, and increases talkativeness. Even though khat is not considered to be a seriously addictive drug, it causes also undesired effects. It may affect sleep, leading to rebound effects such as late awakening, day-time sleepiness, and poor work performance. Long-term use may lead to tooth decay, gastrointestinal disorders, and cardiovascular problems. Although cathinone and cathine are listed in the 1971 Convention, Catha edulis still remains without international control. a)
b)
c)
Figure 9. Chemical structures of a) cathinone and b) cathine, and c) plant of khat (source: Wikimedia Commons). 2.1.7 Other substances
Tryptamines, aminoindanes and phencyclidine-type substances are, among others, included in this miscellaneous NPS group.
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Tryptamines have similar effects to those tryptamines that are already controlled by international law, such as psilocybin or dimethyltryptamine (DMT) (fig. 10) (UNODC 2013a). Some tryptamines are natural neurotransmitters, but most of them have psychedelic properties and their use leads to hallucinatory states, with auditory, visual, and temporal distortions of reality. Reported adverse effects include anxiety, agitation, gastrointestinal problems, and muscle tension. Chemical structures of psilocybin, DMT and an uncontrolled tryptamine, alpha-methyltryptamine, are represented in figure 10.
a)
b)
c)
Figure 10. Chemical structures of a) psilocybin, b) DMT and c) alphamethyltryptamine. Aminoindanes were developed in the 1970s for their bronchodilating and analgesic properties (UNODC 2013a). However, they may affect and stimulate the serotonin system of the brain, producing empathogenic and entactogenic effects, similar to MDMA. Chemical structures of two aminoindanes classified as NPS, 2-aminoindane and 5,6-methylenedioxy-2-aminoindane, are shown in figure 11. a)
b)
Figure 11. Chemical structures of a) 2-aminoindane and b) 5,6-methylenedioxy-2aminoindane. The phencyclidine-type substance group has recently appeared in the drug market and consists of compounds that are structurally similar to phencyclidine and ketamine (see section 2.1.3) (UNODC 2013a). PCP-type substances first appeared in Europe in 2010 as “research chemicals” and information of their effects is still very limited. Acute intoxication can induce a wide range of behavioral and psychological effects, including
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mild neurologic and physiologic abnormalities, stupor and eventually coma. Two PCPtype NPS, methoxetamin and 3-methoxyphencyclidine, are represented in figure 12.
a)
b)
Figure 12. Chemical structures of a) methoxetamin and b) 3-methoxyphencyclidine.
2.2 Legal situation of new psychoactive substances New psychoactive substances are not among the illicit drugs listed in the international United Nations conventions, which make it possible to promote and sell them as ‘natural’ or ‘legal’ products, until they are banned either at a national or international level (EMCDDA 2015b; EMCDDA 2016a; EMCDDA 2016b). Generally, when a new psychoactive substance is discovered, the World Health Organization (WHO) assesses the risk to public health and, if needed, the drug may be added to the list of those drugs that are controlled under the criminal law to deter and punish unauthorized trade. However, in recent years, the continuously increasing number of NPS and the speed they emerge into the market have brought about some policy challenges:
1. The primary justification for punitive control measures is that a certain substance poses a public health risk, but in the case of NPS, evidence from scientific research about their risks is often very limited or non-existent.
2. NPS appear and disappear from the market very quickly. Updating the law takes time, and substances are often replaced by newer ones before being identified and put under control by the authorities.
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3. The amount of different NPS is so large, that the manufacturers use the lists in the drug laws simply as exclusions from their potentially vast product range.
4. The addition of NPS to the list of controlled substances would imply the obligation to test for those substances, but for technical and financial reasons complete drug testing is not always possible.
The governments have come across the necessity of finding innovative legal ways to respond to the emerging problems related to the spread of NPS (EMCDDA 2015b; EMCDDA 2016a; EMCDDA 2016b). Mainly three, sometimes overlapping, legal responses can be delineated:
a) Use of medicines legislation: Some of the European Member States, including Finland, have used medicinal product laws to control supply of NPS at national level (Kainulainen et al. 2014; Kotovirta & Pihlainen 2015; Pihlainen 2015; STM 2014a; STM 2014b). Based on an EU directive (EUR-Lex 2001), the definition of medicinal product did not include the requirement of such a product to have beneficial effects on human health, which enabled countries to use medicine legislations to respond to NPS. If classified as a medicinal product, a license is required for any importation, marketing, or distribution of the substance. However, in July 2014 the Court of Justice of the European Union (CJEU) observed that this was not a correct interpretation of the harmonized EU definition and rectified that medicinal products must have beneficial effects on human health (EUR-Lex 2014). This method is, therefore, very limited now.
b) Extension and adaptation to existing laws and processes: Some countries have included NPS under already existing drug legislation, either modifying or extending these laws.
c) Introduction of a new legislation to tackle new substances: New legislations specific to NPS give clear definitions about substances requiring control, list the mechanisms that reduce the time needed to control new substances, and establish the levels of punishment. A substance is usually considered to require control if it has at least two of the tree following elements: it is psychoactive, it has a motive of abuse or intoxication, or it can cause some possible harm or threat to health. Especially those
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countries where a limited number of NPS have emerged, including Finland, classify and list NPS individually (individual listing) (Kainulainen et al. 2014; Kotovirta & Pihlainen 2015; UNODC 2015). Several other countries have instead adopted broader legislative approaches (analogue and generic systems) for a more rapid control of NPS, as listing systems have been extended to cover also substances or clusters of substances, that are not explicitly mentioned in the legislation, but which are similar to the controlled substances in terms of structure, effects, or both (UNODC 2015). The individual listing system is a longer and more complex procedure than the analogue and generic systems, but it has the advantage of not leaving amibiguities about whether a substance is covered by the legislation or not.
2.3 New psychoactive substances in Finland In Finland, the use of NPS has been constantly increasing over the last years (KRP 2016; KRP 2017). NPS are ordered through the internet mainly from Chinese drug laboratories, which then send the drugs by mail or through courier services. The most common designer drug in use in Finland in the last years has been alphapyrrolidinopentiophenone (α-PVP, banned in 2013), but also gamma-butyrolactone (GBL, known as “lakka” in Finland) and synthetic cannabinoids have found their established users. In 2015, in one out of five drug-induced deaths an NPS was detected in post-mortem toxicological analyses, α-PVP and MDPV being the most commonly found (EMCDDA 2017c).
After the CJEU prohibited the use of the Medicines Act (395/1987) to control NPS, the Ministry of Justice, the Finnish Medicine Agency (Fimea), the Office of the Prosecutor General, the Ministry of the Interior, the Customs (Tulli) and the National Institute for Health and Welfare gathered together and updated the Narcotics Act (373/2008) (Kainulainen et al. 2014; Kotovirta & Pihlainen 2015; Pihlainen 2015; STM 2014a; STM 2014b). The act was extended to cover a group of so-called ‘psychoactive substances prohibited on the consumer market’, listed in a new decree (“Government decree on psychoactive substances prohibited on the consumer market” 1130/2014) following risk assessment. Psychoactive substances prohibited on the consumer market are defined in the decree as “substances used for intoxicating purposes that might be a
18
danger to health and that have been decided to be made subject to control in accordance with the decision of the CJEU or are positional isomers of such a substance and are neither medicine nor narcotic drugs”. The evaluation of a new substance is done by Fimea together with THL, Police and Customs, and approval of the inclusion to the list is done by the government. Since December 2014 manufacture, import, storage, holding for sale and disposal of prohibited psychoactive substances are criminalized and punishable by up to a year in prison as an offence endangering health and safety, according to the Criminal Code (39/1189).
3 WASTEWATER-BASED EPIDEMIOLOGY The estimation of the consumption of new psychoactive substances and other illicit drugs has usually been complex and time-consuming, facing methodological challenges and requiring considerable expenses to get reliable results (Griffiths & Mounteney 2010). The common data collection tools follow five epidemiological key indicators: general population surveys, problem drug use, treatment demand, drugrelated deaths and mortality, and drug-related infectious diseases (EMCDDA 2017a; Griffiths et al. 2011). At the beginning of the 2000s Daughton (2001) described a relatively new method, that has been becoming more common to complement and extend the established epidemiological tools for monitoring the use of drugs. This method is based on the analysis of municipal wastewater and it is referred to as ‘wastewater-based epidemiology’ or ‘wastewater analysis’. Most of the chemicals that enter our body are excreted unchanged or as a mixture of metabolites in urine and faeces, which finally end up in the sewer network (fig. 13) (Castiglioni et al. 2014; Daughton 2001). Thus, the traces of chemicals found in the wastewater of municipal water treatment plant indicate most probably that those chemicals have been consumed within the area served by a given sewer network (Castiglioni et al. 2014; Zuccato et al. 2008).
19
Figure 13. The pathway of illicit drugs from the consumer to the sewage treatment plant and the environment (modified from Castiglioni et al. 2011). Wastewater-based epidemiology is a flexible and non-invasive tool that can be used to get near-real-time data on new drug habits and on changes in drug use over time in particular locations, or during special events or holidays. WBE has some advantages over other approaches: results are not exposed to biases associated with self-reported data and it identifies better the real variety of drugs being consumed, as users are not always aware or informed of the chemicals contained in the mix of drugs they are taking (Griffiths & Mounteney 2010). WBE could also be a helpful tool for target health programs and policy initiatives, due to its rapid capacity to detect new trends (EMCDDA 2016e).
The wastewater-based epidemiology approach is currently in use in several countries to follow the trends in the use of the major classes of illicit drugs, such as cocaine, heroin, cannabis, and amphetamines (Jones-Lepp et al. 2004; Kankaanpää et al. 2014; Kankaanpää et al. 2016; Löve et al. 2018; Thomas et al. 2012; van Nuijs et al. 2011; Zuccato et al. 2005; Zuccato et al. 2008). More recently also new psychoactive substances have been identified with this method (Bade et al. 2017; Borova et al. 2015; Castiglioni et al. 2015; Gao et al. 2017; González-Mariño et al. 2016a; GonzálezMariño et al. 2016b; Gunnar & Vuori 2017; Kinya et al. 2015; van Nuijs et al. 2013).
20
3.1 Wastewater-based epidemiology approach The wastewater-based epidemiology approach includes several phases beginning with the collection of the wastewater samples and ending with the back-calculation of drug use within a population, as showed in figure 14. The steps are described more accurately in the following paragraphs.
Figure 14. The main steps of the wastewater-based epidemiology and the data required for each step (modified from Castiglioni et al. 2014). 3.1.1 Sample collection
The first step of wastewater analysis consists of sample collection and it is the fundamental step in the whole process. Representative samples of raw wastewater are collected from a sewage treatment plant (STP). Ort et al. (2010a) have presented
21
a general collection method, that should be preferably followed. This sampling guide gives detailed information on the appropriate sampling mode, frequency, and location, which aims to reduce sampling-related errors and to enhance data quality. The sampling mode is determined by flow variations and the sampling frequency depends on concentration variations (Brewer et al. 2012; Lai et al. 2013). A flow- or volumeproportional sampling manner should be applied if possible to avoid incorrectly weighted samples and biased results (Ort et al. 2010a; Ort et al. 2010b; Ort 2014). A continuous sampling frequency is recommended, but if this mode cannot be applied, there should still be a high sampling frequency (not longer than 5-10 minutes). The best sampling location would be at the influent of the STP, but if it is not possible, samples can also be collected after the primary clarifier, as the concentrations of the dissolved compounds should not be significantly changed. Samples are preferably collected during a 24-hour interval.
3.1.2 Chemical analysis
The collected wastewater samples are analyzed to determine concentrations of the target drug residues. Due to the medium-high polarity and low volatility of the compounds, liquid chromatography (LC) is the technique of choice for the separation of the target residues and mass spectrometry (MS) for their analysis. Ion trap and triple quadrupole mass spectrometers are commonly used (Castiglioni et al. 2011; Castiglioni et al. 2013). Shortly, the LC-separated substances of interest are ionized, fragmented, detected and quantitated by monitoring specific ion mass-to-charge ratios (m/z) of each compound. Every compound under study has its own characteristic precursor ion, which has the most intense transition and which is used for quantification. The other fragmented ions, called qualifiers, are used for confirmation (Watson & Sparkman 2007).
The matrix of sewage water is complex and contains a large number of different substances, that may interfere and compete with the target residues during the ionization process. (Castiglioni et al. 2008; Castiglioni et al. 2011; Postigo et al. 2008a). In addition, the expected substance concentrations are around a thousand-fold lower than in human biological fluids, showing concentrations of nanograms per liter range. In order to ensure an accurate quantification and reliable identification, such matrix
22
effects must be removed, minimized or corrected. This is usually achieved with a sample pre-treatment step, where the samples are cleaned up and the analytes concentrated. Solid-phase extraction is the most widely used pre-treatment method, but also on-line solid-phase extraction and large-volume extraction are sometimes used (Postigo et al. 2008b; Chiaia 2008; Berset et al. 2010). To minimize matrix effects deuterated analogues of the target analytes are usually used as internal standards (ISs). Samples are spiked with ISs as surrogates before processing for more accurate quantification, as they compensate matrix effects and ensure the satisfactory correction for analytical errors associated with sample manipulation and storage. Finally, the retention times of the analytes are compared to those of the reference standards for the identification of the compounds. The analytical method developed in this master’s thesis has been done using liquid chromatography coupled to tandem mass-spectrometry, with solid-phase extraction as pre-treatment step. These methods will be described in more details in the following chapter.
3.1.3 Back-calculation of the results
The back-calculation of drug use (mg/day/1000 population) is normally performed using equation 1, presented by Castiglioni et al. (2014).
DRUG use =
(Ctr × F)×Cf P
(1)
where Ctr is the concentration of each measured target residue (ng/l), F is the corresponding flow rate of sewage (L/day), Cf is the specific correction factor used for each drug and P is the number of population served by each STP (Castiglioni et al. 2014).
The correction factors take into account the average excretion rate of a given analyte and the ratio between the molecular mass ratio of the parent drug and its metabolite (Zuccato et al. 2008). Results are finally normalized to the number of population served by the STP in question to allow a comparison between the estimates obtained in
23
different cities. If a mean dose of a substance is available, also the number of doses of drugs used within a given population can be calculated. The estimation of population size can be calculated using different biological parameters, census data, number of house connections, or the design capacity, but the overall variability among different estimates is generally very high (EMCDDA 2016e).
3.2 Limitations of wastewater-based epidemiology Findings coming from wastewater analysis may be affected by errors. Uncertainties are mainly associated with sample collection, biomarker analysis and stability, backcalculation methods of drug use, and different approaches for estimation of the population size under investigation (Castiglioni et al. 2013; Castiglioni et al. 2016; EMCDDA 2016d; Lai et al. 2015).
Table 1. Typical information provided by epidemiological studies and wastewater analysis (modified from Castiglioni et al. 2014) Main information provided Epidemiological studies Wastewater analysis Drug use estimates Frequency of drug use
Yes
No
Changes in patterns of drug use in time (e.g. daily, weekly, monthly)
No
Yes
Mode of drug use (route of administration, coadministration with other drugs/pharmaceuticals, etc.)
Yes
Yes/No*
Main classes of users (age, sex, race,
Yes
No
Purity and price of drugs
Yes
No
Emerging trends in drug use (e.g. appearance of new drugs)
No
Yes
High costs of studies
Yes
No
Real-time estimates
No
Yes
Retrospective analysis
No
Yes
etc.)
The method
*Subject to availability of characteristic biomarkers.
24
Results from wastewater do not either give information about prevalence, frequency and mode of use, main classes of users or purity of drugs. Furthermore, translating the total consumed amounts into the corresponding number of average doses is complicated, as drugs can be taken by different routes and in amounts and purity levels that vary widely, which is why the method can only complement, but not replace the existing epidemiological monitoring methods (table 1) (Zuccato et al. 2008).
Despite the application of properly validated procedures, the employment of different sampling systems and analytical methods may lead to results potentially affected by bias, thus making it difficult to perform a comparative study between different laboratories. However, several common quality control criteria can be adopted to reduce the potential errors associated with sample manipulation and storage, and to ensure similar evaluations of method performance. In 2010, a Europe-wide network (Sewage analysis CORE group - SCORE) was created to standardize these procedures, and a best-practice consensus document was created in order to minimize the possibility of uncertainties and to ensure similar evaluations of method (appendix I) (Castiglioni et al. 2013; Castiglioni et al. 2014; Thomas et al. 2012).
3.3
Wastewater-based
epidemiology
of
new
psychoactive
substances In addition to detect common illicit drugs, wastewater-based epidemiology could be used as a tool to get information on temporal and regional trends in the use of new psychoactive substances. The wide range of NPS, their rapidity in appearing and disappearing from the market, their relatively small amount of use and the lack of data on pharmacokinetic profiles, however, pose challenges in the analysis.
4 UHPLC-MS/MS AND VALIDATION PARAMETERS The aim of the experimental part of this master’s thesis was to develop and validate a good and sensitive analytical method which allows the detection of new psychoactive substances in wastewater. First, we reviewed already published studies and compared
25
the different methods. Especially, we considered sample size, blank sample matrix and validation parameters (appendix II). As with the analysis of more common drugs, the analytical method of choice is ultra-high performance liquid chromatography coupled to tandem mass spectrometry. The samples were pretreated first using solid-phase extraction and then with liquid-liquid extraction.
4.1 Sample preparation Sample preparation is an essential phase of a chemical analysis and it is generally the most time-consuming step. Wastewater is a complex matrix and sample preparation is needed to homogenize a sample, diminish sample size, and concentrate and purify the sample (Sirén et al. 2009; Wells 2003). On the other hand, sample treatment and purifying can often lead to diminished recovery of the studied analyte.
4.1.1 Solid-phase extraction
Extraction is a common separation technique, where a specific compound is moved from one phase to another according to its chemical and physical properties (Majors 1997; Sirén et al. 2009; Wells 2003). Extraction is performed mainly to purify and concentrate the analyte in question from a complex matrix before its analysis. Extraction is done between two immiscibile phases, that in the case of solid-phase extraction are a mobile liquid phase and a stationary solid phase.
SPE is performed with cartridges containing the stationary phase, which is usually a polar sorbent (Majors 1997; Sirén et al. 2009; Wells 2003). First, the cartridge is conditioned, i.e. it is washed with the proper eluent to evenly wet the sorbent particles (fig. 15). The sample is then added into the cartridge, and the polar analytes of the sample will interact and retain in the solid phase, while the solvent and other non-polar impurities will pass through the cartridge. The cartridge is washed again with an eluent to remove further impurities. Finally, the analyte is eluted out of the column with a polar solvent or a buffer of the appropriate pH.
26
1. Conditioning
2. Sample loading
3. Washing 4. Target compound elution
Eluted interferences
Target compound
Figure 15. General steps of solid-phase extraction (modified from Pharmatutor: http://www.pharmatutor.org/articles/bio-analytical-method-development-validationtransfer-by-using-lc-ms-review).
4.1.2 Liquid-liquid extraction
In liquid-liquid extraction the compounds are separated between two immiscible liquid phases, which are usually a polar water phase and a non-polar organic solvent (Majors 1997; Sirén et al. 2009; Wells 2003). The separation is based on the Nernst distribution law, according to which the ratio of the concentrations of solutes distributed between two immiscible solvents is constant, despite external conditions.
Before LLE, the pH of the sample is often adjusted to reduce problems related with the extraction, such as formation of emulsion, precipitation or other unwanted side reactions (Majors 1997; Sirén et al. 2009; Wells 2003). The appropriate extractant solvent is selected according to its polarity, selectivity and capacity, and then added to the sample. The first step of LLE requires a mixing for an intensive contact of both liquid phases, to enable the transfer of the solutes of interest from the original phase into the second solvent. The second step is the separation of the two liquid phases. The mixing step may be repeated to obtain a more complete separation. At the end, the phase containing the analyte of interest, the extract, is collected and analyzed.
27
4.2 Liquid chromatography Chromatography is a physical separation method, in which different analytes are distributed between two distinct and immiscible phases, namely a stationary phase and a mobile phase (Jaarinen & Niiranen 2008; Loura 2013; Riekkola & Hyötyläinen 2000; Shackman 2013). In the case of liquid chromatography the mobile phase is a liquid solvent, which flows through the stationary phase.
The sample is first dissolved in the same solvent as the mobile phase, or in another which is miscible with it (Jaarinen & Niiranen 2008; Loura 2013; Riekkola & Hyötyläinen 2000; Shackman 2013). It is then injected into the mobile phase, which is pumped by a pump at a certain flow rate to the column (fig. 16). The solutes will distribute between the mobile and the stationary phases according to their distribution constants, only moving when residing in the mobile phase. The time required by an analyte to travel through the column, i.e. the retention time (RT), is determined by the extent of interaction between analyte and stationary phase, meaning that different solutes emerge from the column into the detecting system at distinct times. A mass spectrometer is commonly used as a detector for liquid chromatography.
Control and data system
Injection valve
Detector
Column Solvent reservoir
Pump
To waste
Figure 16. A basic LC system (modified from Shackman 2013).
4.3 Tandem mass spectrometry Mass spectrometry is a microanalytical technique used to detect and determine selectively the amount of a specific analyte (de Hoffman & Stroobant 2007; Jaarinen & Niiranen 2008; Watson & Sparkman 2007; Westman-Brinkmalm & Brinkmalm
28
2009a). The mass spectrometer is a highly sophisticated and computerized instrument, which consists of three basic components: ion source, mass analyzer and ion detector (fig. 17). The molecules in question are firstly brought to a gas phase and then ionized to positive or negative-charged ions. The formed ions are accelerated to the analyzer, where they are being split to smaller fragments, which are then separated and detected according to their mass-to-charge ratio (m/z). Neutral molecules and neutral fragments are not detected by the spectrometer. The resulting mass spectrum is a plot of the (relative) abundance of the generated ions as a function of the m/z.
Magnet Analyzer tube Ion exit slit Ion source
To vacuum pump
Separated ion beam
Ion detector
Figure 17. Schematic diagram of a mass spectrometer (modified from Watson & Sparkman 2007). Tandem mass spectrometry differs from normal MS in that it involves multiple steps of mass selection or analysis, and fragmentation occurs usually between the steps (de Hoffman & Stroobant 2007; Downard 2004; Westman-Brinkmalm & Brinkmalm 2009b). In the first stage of mass spectrometry (MS1) ions are formed in the ion source and separated based on their m/z-ratio (fig. 18). Ions of a particular m/z-ratio, called precursor ions, are selected and directed into a collision chamber. There the precursor ions are dissociated into fragments, the product ions. The product ions are finally separated and detected in a second stage of mass spectrometry (MS2).
29
Ion source
MS1
Dissociation region
MS2 Ion detector
Figure 18. Schematic representation of the steps of the analyte in a tandem mass spectrometry experiment (modified from Downard 2004). In this method electrospray ionization (ESI) was used as ion source. In ESI a solution containing the analyte is forced through a small capillary tube, so that the fluid sprays into a strong electric field, while the presence of a flow of warm nitrogen is present to assist desolvation (de Hoffman & Stroobant 2007; Watson & Sparkman 2007; Westman-Brinkmalm & Brinkmalm 2009a). Very fine droplets are generated, which will possess an excess of positive or negative charges depending on the capillary bias polarity. The charged ions then enter the analyzer.
4.4 Validation Validation is an important part of quality assurance. A validation process consists of certain parameters that the method must fulfill to ensure that it is suitable for the intended applications, and that the obtained results are reliable (FDA 2001; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). Reliable data is a prerequisite for a correct interpretation of findings and also for daily routine work. Unreliable results, instead, could cause over- or underestimation of effects, false interpretations and wrong conclusions. The acceptance criteria of the different parameters can be decided either by the laboratory itself, or they can be set by the client, such as the authorities.
Validation can be performed as a full, partial or cross-validation (FDA 2001). A full validation is performed whenever a method is taken in use for the first time or when new compounds are added to an existing method. A partial method is done when some modifications are done to an already validated method. A cross-validation is a comparison of validation parameters between two or more methods developed for the same study.
30
The main parameters that should be studied at least in a validation process are selectivity, linearity, stability, accuracy, precision and the lower limit of quantification (Peters et al. 2007). Additional parameters as limit of detection, recovery, reproducibility, and ruggedness may be relevant. The method developed in this master’s thesis was validated as a full validation, according to the validation plan “Muuntohuumeiden jätevesitutkimus” (appendix III). All validation parameters included in the validation plan are shortly explained in the following paragraphs. However, for lack of time, only linearity, limits of detection and quantification, accuracy and precision were studied for this thesis. Other parameters will be studied afterwards.
4.4.1 Selectivity
Selectivity is the ability of a method to quantify and differentiate unequivocally a specific analyte in the presence of other sample components, such as metabolites, impurities, decomposition products, endogenous matrix components or exogenous xenobiotics (FDA 2001; ICH 1994; MIKES 2005; Peters et al. 2007). In analytical toxicology, selectivity is usually established by proving the lack of interfering signals in the blank matrix at the lower limit of quantification (LLOQ, see 4.4.3) and an analysis of at least 10-20 sources of blank samples is recommended (Peters et al. 2007).
4.4.2 Linearity
Linearity refers to the ability of a method to obtain an acceptable linear correlation, within a given range, between the concentration of an analyte in the sample and the corresponding instrument response (FDA 2001; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). Linearity should be assessed analyzing at least two replicates of at least five different concentration levels of calibration standards. The calibration curve is plotted and results are evaluated using appropriate statistical methods, such as the least squares regression model. The regression equation is used to calculate the coefficient of determination (r2), which indicates the portion of the total
31
variation in the dependent variable that is explained by variation in the independent variable. Linearity is accepted when r2>0,99 within a certain concentration range.
4.4.3 Limits of detection and quantification
The limit of detection (LOD) is the lowest concentration of an analyte that can be detected, but not necessarily quantified as an exact value, with a specific analytical method (FDA 2001; ICH 1994; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). It has also to differ clearly from the background noise. In chromatography LOD can be quantified based on the signal-to-noise ratio (S/N), which can be defined as the height of the analyte peak (signal) and the amplitude between the highest and lowest point of the baseline (noise) in a certain area around the analyte peak. A S/N equal to or greater than 3 is usually chosen (Peters et al. 2007).
The limit of quantification (LOQ) is the lowest concentration of analyte in a sample that can be quantified with acceptable precision and accuracy (FDA 2001; ICH 1994; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). The LOQ is usually the lowest concentration of the calibration curve. The acceptance criteria of the lowest limit of quantification are a relative standard deviation (RSD) below 20% for precision and a BIAS varying ±20% for accuracy (Peters et al. 2007). The S/N is required to be equal or greater than 10. The upper limit of quantification (ULOQ) is, instead, the highest concentration of analyte that can be determined with acceptable precision and accuracy and it is usually the highest concentration level of the calibration curve.
4.4.4 Accuracy
The accuracy (or bias or trueness) of a method expresses the degree of closeness under specific conditions between the obtained mean test results value and the accepted reference value (FDA 2001; ICH 1994; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). Accuracy is studied by replicate analysis of samples containing known concentrations of analyte and it is usually expressed as a percent deviation from the accepted reference value. The parameter can be calculated using
32
equation 2. Accuracy is accepted when BIAS is within 15% of the accepted reference value (BIAS ± 20% near LLOQ) (Peters et al. 2007).
BIAS =
average of test results−reference value reference value
∗ 100%
(2)
4.4.5 Precision
The precision of a method indicates the degree of scatter, i.e. the closeness of agreement between individual measurements obtained from repeated analysis of a single homogeneous sample under specific conditions (FDA 2001; ICH 1994; Jaarinen & Niiranen 2008; MIKES 2005; Peters et al. 2007). Precision does not relate to reference values and it is usually expressed as the relative standard deviation or coefficient of variation of the measurements. RSD can be calculated with equation 3, where SD is the standard deviation of the replicates’ measurements and x̅ is the average of the measurements. The precision parameter is accepted when RSD does not exceed 15% (RSD ≤ 20% near LLOQ) (Peters et al. 2007).
RSD =
SD x̅
∗ 100%
(3)
There are three types of precision: repeatability, intermediate precision and reproducibility (ICH 1994). Repeatability (or intra-assay precision) determines the precision under the same operating conditions over a short interval of time, while intermediate precision (or inter-assay precision) indicates the precision under varying conditions, such as different days, analysts, equipment and reagents. Reproducibility refers to the precision between distinct laboratories and it is usually studied only if the method needs to be standardized for the use of different laboratories.
4.4.7 Stability
The stability of a compound depends on the storage conditions, its chemical properties, the matrix, and the container system (FDA 2001; ICH 1994; Peters et al. 2007). Stability is important for reliable quantification and it is studied during situations that
33
could be encountered during the whole analytical process, such as sample collection, handling and analysis. In addition, stability is assessed during long-term (usually at frozen temperature) and short-term storage (at room temperature), and after going through freeze and thaw cycles. Stability has to be studied both in sample matrix and processed samples.
4.4.8 Recovery
Recovery refers to the extraction efficiency of a method and it is reported as the percentage of the analyte response after sample extraction and processing, compared to that of a solution containing pure authentic standard, which corresponds to 100% recovery (FDA 2001; MIKES 2005). Recovery is calculated with equation 4, where C1 is the measured concentration of the spiked sample, C2 is the measured concentration of the unspiked neat sample, and C3 is the theoretical concentration of the spiked sample.
Recovery =
C1 −C2 C3
∗ 100%
(4)
4.4.9 Matrix effects
In UHPLC-MS/MS the coeluting compounds may affect analyte ionization, depending mainly on sample matrix, sample preparation procedure, quality of chromatographic separation, mobile phase additives and ionization type. Suppression or enhancement of ionization may affect some validation parameters, such as LOD, LLOQ, linearity, accuracy and precision, and thus it is important to evaluate several sources of blank matrix.
In
analytical
toxicology,
matrix
effects
are
generally
assessed
following
recommendations of Matuszewski et al. (2003). The standard peak area in solvent (A), the standard spiked after extraction (B), and the standard spiked before extraction (C) are compared to determine the matrix effect (ME), the recovery efficiency (RE) of the extraction procedure and overall process efficiency (PE), as shown in equations 5, 6 and 7.
34 𝐵
𝑀𝐸(%) = 𝐴 ∗ 100
(5)
𝐶
𝑅𝐸(%) = 𝐵 ∗ 100 𝐶
𝑃𝐸(%) = 𝐴 ∗ 100 =
(6) (𝑀𝐸∗𝑅𝐸) 100
(7)
4.4.10 Measurement uncertainty
Every step of an analysis, like sampling, sample pretreatment and measurement, includes sources of uncertainty. Measurement uncertainty is a quantitative estimation of the interval around the measured value, where the true value lies with some probability (EURACHEM/CITAC 2012; MIKES 2005). Uncertainty is composed of systematic and random errors.
Measurement uncertainty (u) is usually calculated with equation 8, where u1 expresses systematic error and u2 expresses random error.
u = √u1 2 + u2 2
(8)
Systematic errors (u1) are instrumental, methodological, or personal mistakes that deviate measured values constantly and predictably in one direction from the true values. u1 is calculated using accuracy results (EURACHEM/CITAC 2012; MIKES 2005). Random errors (u2) are caused by uncontrollable and unexpected changes in variables affecting experimental results. Random errors cannot be eliminated, but they can be reduced by increasing the number of measurements. u2 is calculated with the deviation results obtained from repeatability and reproducibility measurements.
Measurement uncertainty is often reported as expanded uncertainty (U), which provides an interval within which the measured value lies with a higher level of confidence (EURACHEM/CITAC 2012; MIKES 2005). U is obtained by multiplying u by a coverage factor k, which is chosen based on the level of confidence desired. For an approximate level of confidence of 95%, k is usually set to 2 (equation 9). U(95%) = 2 ∗ u
(9)
35
5 METHOD DEVELOPMENT In the following paragraphs all the steps related to the development of the analytical method, as well as used chemicals, reagents, and equipment, will be described.
5.1 Chemicals and reagents The new psychoactive substances that were considered in the experimental part of this thesis are shown in appendix IV. They are all compounds that have been encountered in the Finnish drug market in a greater or lesser extent. True purity of all compounds is known, with the exception of 3,4-dimethylmethcathinonen (3,4-DMMC), mephedrone (4-MMC), 6-(2-aminopropyl)benzofuran (6-APB) and ethylphenidate (EPH), as they were samples confiscated by the Customs. The control wastewater, that did not contain any drug residues and that was used during the validation of this method, was collected from one part of the sewage treatment plant of Suomenoja, in Espoo, which recollects the sewage of only 200 inhabitants.
5.2 Instrumentation The analysis was performed with an Agilent liquid-chromatography-tandem mass spectrometer (Agilent 1290 Infinity LC coupled to Agilent 6460 Triple Quad) with electron-spray ionization (fig. 19). The column used in this study was the Waters reversed-phase Acquity CSH C18 column (particle size 1,7 µm, inner diameter 2,1 mm, length 75 mm), with an Acquity CSH C18 VanGuard used as pre-column. MassHunter Workstation (Agilent) was the software used for data analysis.
Figure 19. UHPLC-MS/MS equipment used in this study.
36
5.3 Method performance A stock solution containing known concentrations of each compound (300 ng/ml for 4CEC and α-PHPp, 500 ng/ml for 3,4-CTMP, 4-F-α-PVP and EPH, and 1000 ng/ml for all other compunds) in methanol was prepared and kept at -20°C.
The stock solution was diluted daily to smaller concentrations (Solution I: 10 µg/l, and Solution II: 0,5 µg/l) and used for making calibration curves, according table 2. The sample size of control wastewater, which did not contain any residues of drugs, was 200 ml.
Table 2. Standard preparation guide. Standard concentration Standard preparation Std20,0ng/l
400 µl Solution I + 200 ml control wastewater
Std10,0ng/l
200 µl Solution I + 200 ml control wastewater
Std5,0ng/l
100 µl Solution I + 200 ml control wastewater
Std1,0ng/l
400 µl Solution II + 200 ml control wastewater
Std0,5ng/l
200 µl Solution II + 200 ml control wastewater
Std0,25ng/l
100 µl Solution II + 200 ml control wastewater
Std0
200 ml control wastewater
5.3.1 Sample pretreatment
3 ml phosphate buffer (pH=2,5) was added to a 200 ml sample. The phosphate buffer contained 5 ng/l of internal standard. The sample was centrifuged at 3500 rpm for 5 minutes. The internal standards used for each compound are presented in table 4.
5.3.2 Solid-phase extraction and liquid-liquid extraction
Solid-phase extraction was performed with Bond Elut Plexa PCX-cartridges from Agilent Technologies (fig. 20) with minor modifications to the instructions recommended by the producer (appendix V). The sorbent of the cartridge was first conditioned with 5 ml methanol and equilibrated with 5 ml pure water. It was important
37
that the cartridge did not dry between the conditioning steps. After that, 200 ml of the pre-treated sample were loaded to the cartridge. The cartridge was then washed with 5 ml pure water and 5 ml 0,1 M hydrochloric acid (pH=2), and finally dried with nitrogen gas. At the end, sample analytes were eluted with 10 ml methanol/ammonia-mixture (100:3). 400 µl formic acid was added to the eluted sample and and evaporated at 55°C under nitrogen gas for 30 minutes, when about 500 µl ammonium formiate was left in the test tube.
Figure 20. Performance of SPE with Bond Elut Plexa PCX-cartridges.
Liquid-liquid extraction was performed by adding 0,5 ml 10 M kaliumhydroxide and 3 ml toluene to the ammonium formate. Samples were then mixed and vortexed for at least 30 seconds, and finally centrifuged at 3500 rpm for 5 minutes. The toluene-phase was separated into another tube and 0,1 ml 0,2 M hydrochloric acid added. The test tube was again mixed by vortexing for at least 30 seconds, and centrifuged at 3500 rpm for 5 minutes. Finally, 50 µl of the water-phase were pipetted into a vial.
5.3.4 Ultra-high performance iquid chromatography-tandem mass spectrometry
The extracted sample vials were introduced to the UHPLC-MS/MS and analyzed with the running method “jv_designer_5ul_2017”, which was regulated to have an injection volume of 5 µl, a flow speed 0,5 ml/min and a total run time of 7 minutes. All parameters of the method and detection ions for each compound can be seen in appendix VI.
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6 RESULTS OF METHOD VALIDATION Main results of validation are collected in table 3. More detailed information on each parameter is given in the following paragraphs.
Table 3. Linearity, precision and accuracy results of studied compounds. Compound
Linearity
2-DPMP 4-Cl-α-PVP 4-F-α-PVP 4F-MPH α-PHP CET EPH IPH MDPBP MDPPP 4-MEC α-PVP KET MDMA 3,4-CTMP 4-FMA α-PBP bk-DMBDB MDPV MPH MXE 3,4-DMMC 5-EAPB PD 4-MMC bk-MDEA 3-MeO-PCP MABP MPA 6-APB PMMA α-PHPp 5-IAI bk-MDMA MDA 4-FA MDAI 2-AI AMT 4-CEC PMA Symbol explanation:
+ + + + + + + + + + + + + + + + ‒ + ‒ ‒ ‒ + + + + + + ‒ ‒ + + + ‒ ‒ ‒ ‒ ‒ ‒ ‒ ‒
Precision (high conc) + + + + + + + + + + + + + + + + + + + + + ± + + + + ± + + + + + ‒ ± ‒ ± ‒ ‒ ‒ ‒ ‒
Accuracy (high conc) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ± ‒ ‒ ‒ ± ± ‒ ‒ ‒ ‒
Precision (med conc) + + + + + + + + + + + + + + + + + + + + + + + + + ‒ + + + ‒ + + ± ‒ ± ± ± ± ‒ ‒ ‒
Accuracy (med conc) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ± + + + ‒ ‒ ‒ ‒ ‒ ‒
Precision (low conc) + + + + + + + + + + ± ± ± + + ‒ + + + + + ± ± ± ± + ± ± ± ‒ ‒ ± ‒ ‒ ‒ ‒ ‒ ‒ ± ‒ ‒
Accuracy (low conc) + + + + + + + + + + + + + ± ‒ + + ‒ + + + + ± ± ‒ ‒ ± ± ± ± ± ± ± ‒ ‒ ‒ ‒ ‒ ‒ ‒ ‒
+ = Linearity: r2≥0,99; precision and accuracy: RSD or BIAS ≤20,0% (high and medium concentration) or ≤15,0% (low concentration) ± = Precision and accuracy: RSD or BIAS 20,1-30,0% (high and medium concentration) or 15,1-25,0% (low concentration) ‒ = Linearity: r230,0% (high and medium concentration) or >25,0% (low concentration)
39
6.1 Linearity Linearity was studied at six different concentration levels (20,0; 10,0; 5,0; 1,0; 0,5 and 0,25 ng/l for most compounds) by analyzing three parallel samples at each level. In table 4, as an example, all linearity measurements and calculations are shown for alpha-PVP, which has long been one of the most used NPS in Finland. BIAS and RSD were calculated according to equations 2 and 3.
Table 4. Linearity results for alpha-PVP. Concentration level (ng/l)
Measured concentrations (ng/l) 1
20
Average conc
SD
RSD (%)
BIAS (%)
2
3
(ng/l)
19,89
20,19
20,0
0,21
1,1
0,2
10
10,84
10,23
10,22
10,4
0,36
3,4
4,3
5
5,20
3,90
4,15
4,4
0,69
15,5
-11,6
1
1,18
1,03
1,32
1,2
0,14
12,3
17,4
0,5
0,46
0,46
0,43
0,45
0,02
4,1
-10,2
0,25
0,03
0,13
0,10
0,08
0,05
58,6
-66,1
The results of table 4 indicated that alpha-PVP show linear correlation when its concentration was 0,5 ng/l or higher. Below 0,5 ng/l, both RSD and BIAS values fall outside the accepted ±20% interval. Linearity results of all other studied compounds are presented in appendix VII. RSD, BIAS and r2-values obtained from the regression curves were used to assess the LOQs (table 5). Table 5. Results of linearity. Compound
IS
2-AI 2-DPMP 3,4-CTMP 3,4-DMMC 3-MeO-PCP 4-CEC 4-Cl-α-PVP 4-F-α-PVP 4-FA 4-FMA 4F-MPH 4-MEC 4-MMC 5-EAPB 5-IAI 6-APB α-PBP α-PHP α-PHPp α-PVP AMT
MDMA-d5 MDPV-d8 MDPV-d8 MDPV-d8 MDPV-d8 MDMA-d5 MDPV-d8 α-PVP-d8 MDMA-d5 MDMA-d5 cocaine-d3 4-MMC-d3 4-MMC-d3 4-MMC-d3 ketamine-d4 MDMA-d5 α-PVP-d8 MDPV-d8 MDPV-d8 α-PVP-d8 MDMA-d5
Linearity range (ng/l) 0,5-20 0,5-20 0,5-10 0,5-20 0,5-20 0,3-6 0,25-20 0,25-10 0,5-20 0,5-20 0,25-20 0,25-20 0,5-20 0,5-20 1-20 1-20 0,5-20 0,5-20 0,3-6 0,5-20 1-20
r2 0,9893 0,9932 0,9941 0,9904 0,9916 0,9696 0,9904 0,9913 0,9738 0,9903 0,9947 0,9922 0,9921 0,9915 0,9911 0,9913 0,9875 0,9936 0,9925 0,9911 0,8309
LOQ (ng/l) 0,5 0,5 0,5 0,5 0,5 0,3 0,25 0,25 0,5 0,5 0,25 0,25 0,5 0,5 1 1 0,5 0,5 0,3 0,5 1
Compound
IS
bk-DMBDB bk-MDEA bk-MDMA CET EPH IPH KET MABP MDA MDAI MDMA MDPBP MDPPP MDPV MPA MPH MXE PD PMA PMMA
ketamine-d4 MDPV-d8 ketamine-d4 cocaine-d3 MDPV-d8 MDPV-d8 ketamine-d4 MDPV-d8 MDMA-d5 MDMA-d5 MDMA-d5 ketamine-d4 ketamine-d4 MDPV-d8 MDMA-d5 MDPV-d8 MDPV-d8 ketamine-d4 ketamine-d4 MDMA-d5
Linearity range (ng/l) 1-20 1-20 1-20 0,5-20 0,25-10 0,5-20 0,5-20 0,5-20 1-20 1-20 0,5-20 0,5-20 0,5-20 0,25-20 0,5-20 0,5-20 0,5-20 0,5-20 1-20 1-20
r2 0,9925 0,9909 0,9728 0,9918 0,9932 0,9902 0,9927 0,9877 0,9629 0,9856 0,9925 0,9948 0,9913 0,9896 0,9895 0,9872 0,9831 0,9900 0,7075 0,9863
LOQ (ng/l) 1 1 1 0,5 0,25 0,5 0,5 0,5 1 1 0,5 0,5 0,5 0,25 0,5 0,5 0,5 0,5 1 1
40
Most of the studied compounds fulfilled the linearity criteria because their r2 was higher than 0,99. Linearity was not accepted for 2-AI, 4-CEC, 4-FA, alpha-PBP, AMT, bkMDMA, MABP, MDA, MDAI, MDPV, MPA, MPH, MXE, PMA and PMMA.
6.5 Accuracy and precision Accuracy and precision were studied by analyzing three parallel samples at three different concentration levels (20,0; 5,0 and 1,0 ng/l for most compounds) during five different days. All results are given in the table of appendix VIII, which shows the withinday and between-day precision values (RSD) and the within-day and between-day accuracy values (BIAS) for every compound.
In table 6, again as an example, all measurements and calculations for accuracy and precision are shown for alpha-PVP. BIAS and RSD are calculated according to equations 2 and 3.
The results of alpha-PVP are good. The precision parameter (RSD) is below 15% in all except three cases, where one has a RSD of 18% and two exceeding cases are at the lowest concentration. The accuracy parameter (BIAS) is below 15% in all except one case, which however has a concentration near the LOQ fulfilling its criterion to be below 20%. These results indicate that the developed method is precise and accurate for the assessment of alpha-PVP from wastewater.
To summarize the accuracy and precision results of the remainder compounds, the method is very good or good for the assessment of 2-DPMP, 4-Cl-alpha-PVP, 4-Falpha-PVP, 4-FMA, 4F-MPH, 5-EAPB, alpha-PBP, alpha-PHP, alphaPHPp, EPH, CET, IPH, KET, MDPBP, MDPPP, MDPV, MXE, MPH and MPA. The method is also good for 3,4-CTMP, 3,4-DMMC, 4-MEC, 4-MMC, 6-APB, bk-DMBDB, MABP, MDMA, PD and PMMA at medium-high concentrations, but sensitivity is lost at lower concentrations.
41
Table 6. Example calculations for accuracy and precision of alpha-PVP. alpha-PVP, 20 ng/l
Measured conc (ng/l)
Day1
Day2
Day3
Day4
Day5
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
-*
18,9
22,8
21,1
21,7
22,2
-*
21,1
20,1
19,2
18,9
19,4
20,3
21,2
17,6
Average conc (ng/l)
20,8
21,7
20,6
19,2
19,7
SD
2,8
0,6
0,8
0,2
1,9
RSDwd (%)
13,3
2,7
3,7
1,2
9,4
2,9
-4,2
-1,5
Day3
Day4
Day5
RSDbd (%)
= average of all RSDwd = 6,1
BIASwd (%) BIASbd (%)
4,1
= average of all BIASwd = 4,2
alpha-PVP, 5 ng/l
Measured conc (ng/l)
8,3
Day1
Day2
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
5,7
4,2
4,2
4,8
4,7
5,2
4,5
4,6
4,6
4,9
4,4
5,5
4,9
5,0
4,0
Average conc (ng/l)
4,70
4,87
4,54
4,91
4,61
SD
0,9
0,2
0,1
0,6
0,6
RSDwd (%)
18,1
5,0
1,7
11,4
12,2
-9,2
-1,7
-7,9
Day3
Day4
Day5
RSDbd (%)
= average of all RSDwd = 9,7 average of all BIASwd = 5,5
BIASwd (%) BIASbd (%)
-6,1
= average of all BIASwd = 5,5
alpha-PVP, 1 ng/l
Measured conc (ng/l)
-2,6
Day1
Day2
1
2
3
1
2
3
1
2
3
1
2
3
1
2
3
0,9
0,8
0,9
0,7
1,1
0,7
1,1
1,5
0,8
1,1
1,1
0,9
1,0
0,9
0,9
Average conc (ng/l)
0,89
0,83
1,15
1,01
0,95
SD
0,07
0,22
0,37
0,09
0,07
RSDwd (%)
8,1
26,7
32,3
9,0
7,6
14,7
1,3
-5,3
RSDbd (%) BIASwd (%) BIASbd (%)
= average of all RSDwd = 16,7 -10,7
-16,7
= average of all BIASwd = 9,7
*Data not available. wd=within-day, bd=between-day.
Accuracy and precision results were less good for 2-AI, 3-MeO-PCP, 4-CEC, 4-FA, 5IAI, bk-MDEA and bk-MDMA, as there was huge variation between different measurement days. The method was not enough accurate and precise to assess AMT, MDA, MDAI and PMA.
42
7 CONCLUSIONS The method developed in this master’s thesis was not validated entirely because of lack of time, but the validation tests regarding linearity, accuracy and precision proved that the method is good and reliable for most of the substances that were taken in consideration (table 3). However, further improvement should be done, as in the case of some compounds large variability was seen between different measurement days. These differences may form, for example, because of unstable or deviating conditions (temperature, pH, evaporation time, etc.) during storage or sample pretreatment procedure. Also, the selection of a wider range of internal standards could give better results. The equipment should be maintained, cleaned, and calibrated regularly to avoid measurement errors. Some substances used in this master’s thesis were samples seized by the Customs - the purity of these substances was not always exactly known and there might have been disturbing agents.
Wastewater-based epidemiology is certainly a good new method to obtain a near-realtime picture of the drug situation in a certain area and over a certain time. In Finland, wastewater samples are collected from several cities, which gives extensive data on the use of drugs in the country. In the case of new psychoactive substances, however, the expected concentrations in real wastewater samples are very low and sometimes, or even often, below the limits of detection indicating very little or no use.
Many other European countries also participate to the collection of wastewater data, but there is still a long way before we can have information on drug trends worldwide. Analyzing wastewater samples requires appropriate facilities and expensive equipment. The analysis, and especially the pre-treatment step, may be timeconsuming and requires trained and qualified personnel.
In future, wastewater-based epidemiology will take an important place beside the existing epidemiological tools that are used to estimate the consumption of new psychoactive substances and drugs in general. WBE could also be used to have information of drug use in specific environments, such as prisons, schools or festivals. However, attention should be paid to ethics and the sample size should not be
43
restricted to a too small one. In addition, WBE will have a significant role for targeting health programs and policy initiatives, due to its rapid capacity to detect the new drug trends.
Currently, the wastewater-based epidemiology method is in use to detect not only illicit drugs, but also other substances of interest, such as antibiotics. The method could be extended to detect also any other compounds that could somehow affect humans or the environment.
44
8 AKNOWLEDGMENTS I want to aknowledge especially my supervisors at the National Institute for Health and Welfare: Teemu Gunnar, head of the forensic toxicology unit, and Aino Kankaanpää, development manager. Thank you, Teemu, for giving me this great opportunity to write my thesis at THL and to let me become acquainted with the use of UHPLC-MS/MS. And thank you, Aino, for giving me precious advices especially during the development of the method.
Many thanks to lecturer Risto Juvonen, my supervisor at UEF, for your tips during the writing process. Last but not least, thank you to all people working in the lab and to the ”kesähemmot” for the good company, for the funny things done outside working hours, and for the daily “Hesarin kyssärit”.
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10 APPENDICES APPENDIX I(1). Common protocol of action for monitoring illicit drugs in wastewater.
Common protocol of action for monitoring illicit drugs in wastewater – October 2013
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APPENDIX I(2). Common protocol of action for monitoring illicit drugs in wastewater.
Introduction Wastewater analysis is a rapidly developing scientific discipline with the potential for monitoring real-time population-level trends in illicit drug use. Originally used in the 1990s to monitor the environmental impact of liquid household waste, the method has since been used to estimate illicit drug use in different populations. It involves sampling a source of wastewater, such as a sewage influent to a wastewater treatment plant. This allows scientists to estimate the quantity of drugs consumed in a community by measuring the levels of illicit drugs and their metabolites excreted in urine. In 2010, a Europe-wide network (SCORE - Sewage analysis CORe group - Europe) was established to standardize the wastewater analysis approach and to coordinate international studies through the creation of a common protocol of action. Following the success of an initial study in 19 European cities, a demonstration programme was undertaken in 2012 covering 23 cities from 11 European countries in 2012. This document presents the common protocol of action based on the current understanding of best-practice regarding sample collection, storage and analytical procedure as developed within the SCORE network. It is now being used to conduct investigations at a European scale and it supports the production of homogeneous and comparable data at different sites.
Consensus protocol for the sampling, analysis and reporting The common protocol of action for monitoring illicit drugs in wastewater was agreed at a meeting held at Dublin City University, Dublin, Ireland on the 14th of December 2010. This was revised following experiences of the collaboration in 2011. A sampling questionnaire should also be completed for each sewer network, preferably by means of an interview with plant staff1.
1
Further information on the sampling questionnaire can be obtained via
[email protected].
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APPENDIX I(3). Common protocol of action for monitoring illicit drugs in wastewater. Details of sampling Parameter Sampling point Sample Type Defined day Defined week (obligatory) Optional period Sampling container Sample volume Storage treatment during sampling Storage after sampling
Filtration
Additional parameters to be recorded (sampling questionnaire) Additional analyses (from STW)
Flow data Type of sewage influent Temperature pH
Agreed protocol Further comment st 1 routine influent sampling location at works To be noted 24 h Composite Start/Finish between 8 and 10am
PET or glass (silanised) > 0.5L 3:1). Sample Analysis - Mean concentration of 3 measurements based on 3 individual extractions. A reporting template will be provided.
Freeze/ thaw stability
Processed sample stability
Natural vs. acidified (pH2) water, temp 20, 4, -20°C, time 24h, 48h, 7 and 30 days
5 replicates
Recovery
6 x 2 (10 and 800 ng/l)
4°C and RT (22−24°C) for up to2 days
Triplicate of spiked samples
S/N≥10
- Accuracy: 70-120% - Precision: RSD≤20%
LLOQ
6 replicates of lowest spiked conc (10 ng/l)
S/N≥3
6 replicates of lowest spiked conc (10 ng/l)
S/N≥3
Castiglioni et al. 2015
LOD
6 x 8 (200-80000 ng/l)
Borova et al. 2015
3 replicates of 2 levels (12-120 ng/l)
3 x 7 (0,5-100 ng/l)
Bade et al. 2017
Accuracy (BIAS) and precision (RSD)
Linearity
Selectivity
Validation parameter
IQL-100 μg/l
González-Mariño et al. 2016a
24 h at 4°C, 1 week at 20°C
Spiked ultrapure water Triplicate analysis (pH2) at 3 levels (2, 10, and 20 ng to 50 ml)
S/N≥10
S/N≥3
10 levels (0,1 to 200 ng/ml)
Gao et al. 2017
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APPENDIX II(1). Comparison of validation parameters of published studies.
6 replicates before (control) and 6 replicates after 3 freeze/thaw cycles (treatment)
Freeze/ thaw stability
24 h at 4°C, 1 week at -20°C
Repeated injection of processed samples at certain time intervals
- BIAS: ±20% - RSD: 20% - S/N≥10
Processed sample stability
5 replicates S/N≥10
LLOQ
S/N≥3
Extraction of 5-6 spiked samples; analysis of 56 100% controls
5 replicates S/N≥3
LOD
7 levels
van Nuijs et al. 2013
- Accuracy: 80–120% - Precision: RSD
