Toxicological Oral Fluid Results Among Spanish Drivers Testing Positive On On-site Drug Controls From 2013 To 2015

Toxicological Oral Fluid Results Among Spanish Drivers Testing Positive On On-site Drug Controls From 2013 To 2015. José Ángel Lema-Atán, Ana de Castro, Elena Lendoiro, Manuel López-Rivadulla, Angelines Cruz Servicio de Toxicología, Instituto de Ciencias Forenses, Universidad de Santiago de Compostela, C/San Francisco, s/n, Santiago de Compostela, Spain José Ángel Lema-Atán: [email protected] Ana de Castro: [email protected] Elena Lendoiro: elena.lendoi[email protected] Manuel López-Rivadulla: [email protected] Angelines Cruz: [email protected] Corresponding author: Angelines Cruz: [email protected] Abstract Background: Driving under the influence of drugs (DUID) increases the risk of serious injury or death in traffic accidents. The aim of this study was to provide information about DUID in Spanish drivers. Methods: 10,064 oral fluid samples were collected from Spanish drivers that tested positive on the roadside using the Dräger DrugTest 5000 (DDT5000) between 2013 and 2015. Samples were collected using QuantisalTM and analysed by LC-MS/MS at the Toxicology Laboratory of the Institute of Forensic Science of the University of Santiago de Compostela. Results: Drivers were mainly young men (85.1% male, 29.7 ± 8.1 years old). In 98.5% of cases, LC-MS/MS results confirmed at least one of the positive results detected on the roadside. Cannabis (82.4%) and cocaine (42.1%) were the most commonly detected drugs. Poly-drug use was observed in 42.7% of drivers, mostly for all illicit drugs (>80%) except for cannabis (42.6%). Illicit drug and single-drug use was more frequent among drivers under 35 years old, and medicines and poly-drug use more common among drivers older than 35 years old. The on-site device performance was calculated using both the DDT5000 cut-offs and the LC-MS/MS method LOQs. Sensitivity (>73% vs >58%), specificity [>94% for all the compounds regardless the cut-offs used, except for cannabis (71 %)] and accuracy (>87.5% with both cut-offs) fulfilled the DRUID Project requirements in all cases. Conclusion: LC-MS/MS confirmation result was negative in only 1.5% of the cases. The DUID driver profile was a young man, consuming cannabis or a combination of cannabis and cocaine. Keywords: oral fluid, drugs of abuse, medicines, driving under influence Highlights 1. Oral fluid samples were collected from drivers positive for illicit drugs on-site 2. In 98.5% of the cases at least one on-site result was confirmed by LC-MS/MS 3. DDT5000 sensitivity, accuracy and specificity fulfilled DRUID Project requirements 4. Young men drivers, and cannabis and cocaine use were the most common trends 5. Poly-drug use was >80% for all the drugs, except for cannabis (42.7%) 1. Introduction The use of illicit psychoactive substances shows a general prevalence about 5% of the adult population worldwide aged 15-64 years (UNODC, 2017). Cannabis is the most common illicit drug (2.7%-4.9%, depending on the country), followed by amphetamines (0.3%- 1.24%), opioids (0.6%-0.9%), opiates (0.27%-0.49%), “ecstasy” (0.19%-0.71%) and cocaine (0.27%-0.46%). Consumption of illicit drugs in Europe is even higher, and Spain is one of the European countries with the highest prevalence of illicit drug use, especially for cannabis (9.5%) and cocaine (2%) (OEDA, 2017). Driving under the influence of drugs (DUID) impairs essential cognitive and psychomotor skills (perception, attention, coordination, reaction time, information processing or visual function), and is associated with behavioural changes (aggressiveness, impatience, competitiveness, excessive self-confidence, recklessness). DUID increases the risk of serious injury or death in traffic accidents, being a leading cause of global injury mortality and the second cause of preventable traffic death, after speeding (WHO, 2015). DRUID (DRiving Under the Influence of Drugs, alcohol and medicines) Project was the most important research project related to drugs and driving in the EU to date. DRUID Project results showed that 7.4% of European drivers tested positive for any psychoactive substance, 3.5% for alcohol, 1.9% for illicit drugs and 1.4% for medicines. These figures in Spanish drivers were 17%, 6.6%, 10.9% and 2%, respectively (Gómez-Talegón et al., 2012). Spain introduced mandatory on-site oral fluid (OF) drug controls in 2010, with a zerotolerance law. If the on-site screening test is positive or the driver has signs of drug impairment, a second OF sample must be collected and analysed using GC-MS or LCMS/MS. The Laboratory of Toxicology of the Institute of Forensic Sciences of the University of Santiago de Compostela (USC) was responsible for the confirmation analysis until 2015, so valuable epidemiological information about drug use on Spanish roads is currently available. The main aim of this study was to provide recent information about DUID among Spanish drivers by analysing 10,064 OF samples that previously tested positive on-site using the Dräger DrugTest 5000 (DDT5000) (Dräger Safety AG and Co. KGaA, Lübeck, Germany), in order to: a) know demographic characteristics of the Spanish drivers who tested positive on the on-site OF drug test; b) analyse the prevalence of the different psychoactive drugs detected and the pattern of drug use; and c) assess the performance of the on-site drug test device. 2. Material and methods 2.1. Participants Between December 2013 and February 2015, Spanish traffic police officers performed roadside OF drug tests to drivers suspected of DUID, using the DDT5000. Drivers with a positive on-site result donated a second OF specimen, which was collected with the QuantisalTM device (Inmunalysis, Pomona, CA, EE.UU). OF specimens were sent to the Laboratory of Toxicology of the Institute of Forensic Sciences of the USC under cold conditions and keeping the chain of custody. 2.2. LC-MS/MS method A previously published method, with minor modifications, was used for confirmation purposes (Concheiro et al., 2008). The method allows the identification of the main illicit drugs and common psychoactive medicines that cause driving impairment, including morphine, codeine, 6-monoacetylmorphine (6-AM), amphetamine, methamphetamine, 3,4methylenedioxyamphetamine (MDA), 3,4-methylenedioxymethamphetamine (MDMA), 3,4-methylenedioxy-N-ethylamphetamine (MDEA), benzoylecgonine (BE), cocaine, delta9-tetrahydrocannabinol (THC), ketamine, methadone, zolpidem, zopiclone, alprazolam, clonazepam, oxazepam, nordiazepam, lorazepam, flunitrazepam, diazepam. Briefly, solid phase extraction (SPE) was performed with Strata X cartridges (Phenomenex, Torrence, CA, USA). After loading the sample, two consecutive washes with water:methanol (95:5, v/v) and water:methanol:NH4OH (70:29.5:0.5, v/v) were applied. Elution was performed with dichloromethane:2-propanol (75:25, v/v), and dried extracts were reconstituted in a mixture of 0.1% formic acid:acetonitrile (90:10, v/v). Chromatographic separation was performed using an Atlantis T3 (2.1 mm x 50 mm, 3 μm) column and a gradient with acetonitrile and 0.1% formic acid in water. Detection was conducted using a Quattro MicroTM API ESCI tandem mass spectrometer (Waters Corporation, Milford, MA, USA), operating in electrospray in positive mode (ESI+), and two MRM transitions were monitored for each compound. The method was fully validated, and a limit of quantification (LOQ) of 1 ng/mL was applied for all the analytes. 2.3 Comparison of on-site and LC-MS/MS confirmation results On-site screening results (when available) were compared with the laboratory confirmation results in order to evaluate the DDT5000 performance. Calculated parameters included True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Sensitivity, Specificity, and Accuracy, Positive Predictive Value (PPV) and Negative Predictive Value (NPV), when possible (Blencowe, 2010). To consider a positive OF sample, both DDT5000 cut-offs (Table 1) and LC-MS/MS LOQs (1 ng/mL) were used. Analytes identified with the LC-MS/MS method and not included on the on-site screening device panel were designated as additional findings (AF). 2.4 Statistical analysis An excel database was created with the information available, including the demographic data (sex, age), sampling date, day of the week, working day or holiday according to the Spanish working schedule, and on-site screening result (positive/negative) and LC-MS/MS (concentration) confirmation results. IBM SPSS Statistics 20.0 software (IBM Inc., IBM Corporation, Armonk, NY, USA) was used to carry out the statistical analysis. For the analysis of quantitative variables (age and analyte concentrations), mean and median values were used as central measures, and standard deviation (SD), maximum and minimum values as measures of dispersion. Qualitative variables were expressed as number of events and frequency (%). In the bivariate analysis, Chi-Square test (χ2, for qualitative variables), Student's t-test (for quantitative variables with normal distribution), and Mann-Whitney test (for quantitative variables with no normal distribution) were used. Kolmogorov-Smirnov and Shapiro-Wilk tests were employed to check the fit to a normal distribution. The level of significance was set at p< 0.05. 3. Results 3.1. Participants The laboratory analysed 10,064 OF specimens during the 15-month period of study. On-site screening results were available in 98% of the cases (n=9,868). Men accounted for 85.1% (n= 8,561) of drivers and women for 3.5% (n= 351); sex was unknown in 11.4% of the cases (n= 1,152). Drivers mean age was 29.7±8.1 years old (median= 28; range= 15-83), with 64.2% of the drivers between 15-34 years old (26.9% ≤ 24 years old; 37.3% between 25-34 years old; 18.4% between 35-49%; and 2% ≥50 years old). Age was unknown in 15.1% of the cases. Age was similar between men (29.7±8.1 years) and women (30.2±8.3 years) (p= 0.197). 3.2. Analytical results 3.2.1. LC-MS/MS results Confirmation of at least one of the on-site positive results detected was possible in 98.5% (n=9,912) of the 10,064 OF specimens analysed. Among these drivers, 91.9% used only illicit drugs (n=9,106), 0.35% (n=35) only psychoactive medicines, and 7.8% (n=771) both illicit drugs and medicines. Among the total number of drivers (n=10,064), the most prevalent drug was cannabis (82.4%, n=8,294), followed by cocaine (42.1%, n= 4,237), amphetamines (14.2%, n=1,431), heroin (confirmed by the presence of 6-AM) (7.9%, n=796) and ketamine (2.1%, n=215). For cocaine positive specimens, the presence of cocaine and its metabolite benzoylecgonine (BE) were confirmed in nearly all cases, while cocaine was the only analyte detected in 2 specimens and BE in 1 specimen. For amphetamine and derivatives, MDMA (9.9%, n=993), amphetamine (8.5%, n=857) and MDA (6.4%, n=646) were the most frequent analytes, and to a lesser extent methamphetamine (0.6%, n=64) and MDEA (0.1%, n=11). Regarding opiates other than heroin, 55 cases tested positive for codeine, 9 for morphine and 12 for both codeine and morphine (3 cases due to codeine use as morphine/codeine ratio was <0.1, 4 to morphine use and 5 of unknown origin as similar concentrations for morphine and codeine were measured) (Gasche et al., 2004; Jones et al., 2008). AF to other psychoactive medicines not included in the DDT5000 drug panel were detected in 7.5% of drivers (n=757), methadone being the most frequent analyte detected (4.9%, n=493), followed by benzodiazepines (3.8%, n=387) and zolpidem (0.1%, n=10). Among benzodiazepines, the most prevalent were nordiazepam (2.1%, n=210) and alprazolam (1.7%, n=169) and, with a frequency below 1%, diazepam, lorazepam, oxazepam, clonazepam and flunitrazepam. Zopiclone was not detected in any specimen. Table 2 shows OF concentrations for the analytes identified by LC-MS/MS. Median concentrations ranged between 100-650 ng/mL for most illicit drugs, except for MDA, MDEA and ketamine, which were in the range of 15-50 ng/mL. Minimum concentrations usually corresponded with the method LOQ (1 ng/mL). 3.2.2. Performance of DDT5000 screening device DDT5000 performance was evaluated considering only the OF specimens with on-site screening results available (n= 9,868; 98% of the total). To evaluate PPV and NPV, prevalence of substance use among the tested drivers was estimated by multiplying prevalence of each drug among the confirmed cases by 0.8. Tables 3 and 4 show the parameters for the evaluation of the on-site screening device using the DDT5000 cut-offs and the LC-MS/MS method LOQs, respectively. The lack of agreement between the number of known on-site results (n=9,868) and those shown in the Tables 3 and 4 was due to the presence of a variable number of on-site “invalid” results (0.3% to 2.1% of the known onsite results, depending on the drug). Using the DDT5000 cut-offs, good sensitivity was obtained for all drug groups (81%-95.3%) except for methamphetamine and derivatives (73.5%). Specificity was high for all drugs (≥94%), except for cannabis (71%), and accuracy ranged from 87.5% for cannabis to 97.7% for opiates. Using the LC-MS/MS LOQs, sensitivity decreased (ranging from 58% for methamphetamine to 88.7% for cannabis and opiates), and specificity increased for all drugs, especially for cannabis (95.4%). Positive predictive value (PPV) using the DDT5000 cut-offs ranged between 77.3% (opiates) to 87.7% (cocaine) for all drugs except for amphetamine (65%), while higher values were observed using the LC-MS/MS LOQs, especially for the most prevalent drugs (cannabis= 97.4%, and cocaine= 91.0%). Negative predictive values (NPV) ranged from 88.6% (cannabis) to 99.4% (opiates) using the DDT5000 cut-offs, while lower values (ranging from 81.3% for cannabis to 99.2% for opiates) were achieved using the LC-MS/MS LOQs. With the DDT5000 cut-offs, the highest %false positive results (%FP) were for cannabis (9.3%) and the lowest for methamphetamine (1.4%), while the highest %false negative results (%FN) were for cocaine (6.1%) and the lowest for opiates (0.7%). Using the LCMS/MS LOQs, the %FP decreased for all drugs (ranging from 2.7% for amphetamine to 0.8% for cannabis) while, inversely, the %FN increased (ranging from 9.3% for cannabis to 1% for opiates). 3.3. Patterns of drug consumption 3.3.1. Single-drug use vs poly-drug use Detection of single-drug use was frequent (57.3%, n=5,679) in those OF specimens with a positive LC-MS/MS result (n=9,912). Cannabis was the most common drug in single-drug users (83.7%; n=4754), followed by cocaine (12.8%; n=727), amphetamine and derivatives (2.5%; n=140), heroin (0.4%; n=22), codeine (0.4%; n=24), benzodiazepines (0.14%; n=8), ketamine (0.05%; n=3) and methadone (0.02; n=1). Poly-drug use was observed in 42.7% (n=4233) of the positive cases. Association of two (28.8%; n=2853) or three (10.4%; n=1031) drugs was the common trend. Nevertheless combinations of 4 (2.9%, n=286), 5 (0.6%, n= 61) or even 6 (0.02%, n= 2) drugs were also observed. Except for cannabis (42.6%) and codeine (56.9%), poly-drug use clearly predominated for all the substances (≥82% of the positive cases) (Table 5). Table 6 shows the frequency of different drug combinations in poly-drug users. The majority of the drugs were consumed in association with cannabis and/or cocaine. Nevertheless, methadone was usually associated with heroin and cocaine, and ketamine was frequently associated with amphetamines. Finally, the most popular trends in this population were consumption of cannabis alone (48%, n=4754), cannabis and cocaine association (19.1%, n=1889), and cocaine alone (7.3%, n=727). Other common associations were cannabis, cocaine and amphetamine (4.6%, n=459), and cannabis and amphetamines (4.6%, n=456). 3.3.2. Working days vs holidays On-site drug tests were performed on national holidays in 35.3% (n=3548) of the cases. No significant differences were found on the illicit drugs pattern detected on working days and holidays. However, benzodiazepines and methadone were more frequently detected on working days than on holidays (71.3% vs 28.4%, p= 0.016; and 79.1% vs 20.9%, p< 0.001, respectively). 3.3.3. Men vs women Illicit drug use was similar in men and women (98.2% vs 97.7%, p= 0.837). On the contrary, benzodiazepines and methadone use was more common in women than in men (5.7% vs 3.9%, p= 0.032; and 8.8% vs 5.0%, p< 0.001, respectively). Poly-drug use was also more common among women (51.2% vs 43.2%), but no statistically significant differences were observed. No statistically differences between men and women were neither observed in the number of associated drugs (30% men and 29% women consumed two drugs; and 21.1% men and 14.2% women combined 3 or more drugs). Figure 1 shows psychoactive substances frequency within men and women. Cannabis was the most frequent drug in both sexes, followed by cocaine, amphetamines and heroin. However, statistically significant differences on the pattern of drug use between men and women were observed for amphetamine and derivatives (14.3% men vs 28% women, p<0.001), and for heroin (8.4% men vs 12.8% women, p<0.001). 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Analyte group DDT5000 cutoff (ng/mL) Confirmed analyte Cannabis 25 THC Cocaine 20 Cocaine BE Amphetamine 50 Amphetamine Methamphetamine 35 Methamphetamine MDMA MDA MDEA Opioids 20 6-AM Morfine Codeine THC: delta-9-tetrahydrocannabinol; BE: benzoylecgonine; MDMA: 3,4methylenedioxymethamphetamine; MDA: 3,4-methylenedioxyamphetamine, MDEA: 3,4-methylenedioxy-N-ethylamphetamine; 6-AM: 6-acetylmorphine Table 2. Mean, median, maximum and minimum concentrations (ng/mL), and standard deviation (SD) observed in the oral fluid specimens analysed by the LC-MS/MS confirmation method. Drug N Mean SD Median Maximum Minimum THC 8,294 346.5 1418.2 121.0 107886.1 1.0 BE 4,235 766.6 2981.7 107.6 100096.7 1.0 Cocaine 4,236 2567.6 8022.8 195.6 131456.9 1.7 Amphetamine 857 3049.8 10107.5 604.4 213511.2 1.5 Methamphetamine 64 1074.3 2273.6 125.0 11226.4 1.5 MDA 646 92.8 325.8 23.7 5795.8 1.0 MDMA 993 2128.5 5905.8 286.2 94324.7 1.0 MDEA 11 315.7 940.0 15.8 3147.5 1.3 6-AM 796 6411.7 50968.9 255.0 1323921.0 1.0 Morphine 816 2488.1 10212.0 293.4 216145.8 1.1 Codeine 703 213.2 1344.7 34.4 32166.2 1.0 Ketamine 215 516.1 1316.6 50.4 10073.4 1.0 Methadone 493 456.7 1735.1 127.3 26542.0 1.0 Clonazepam 9 161.3 304.3 4.4 931.2 1.9 Flunitrazepam 4 3.7 1.0 3.3 5.0 3.1 Alprazolam 169 362.3 3343.5 7.2 42865.8 1.0 Oxazepam 32 6.4 6.9 2.9 25.1 1.0 Lorazepam 43 123.3 481.4 7.0 2655.8 1.2 Nordiazepam 210 30.6 139.0 7.5 1828.3 1.1 Diazepam 61 239.2 1463.6 4.1 10891.9 1.0 Zolpidem 10 41.6 59.2 17.0 181.2 2.7 THC: delta-9-tetrahydrocannabinol; BE: benzoylecgonine; MDMA: 3,4methylenedioxymethamphetamine; MDA: 3,4methylenedioxyamphetamine, MDEA: 3,4-methylenedioxy-N-ethylamphetamine; 6-AM: 6-monoacetylmorphine; N: number of positive cases for each drug Table 3. Assessment of the Dräger DrugTest 5000 (DDT5000) performance using the DDT5000 cut-offs. Drug Group n* TP FP FN TN Sensitivity (%) Specificity (%) Accuracy PPV (%) NPV (%) CAN 9842 6371 916 316 2239 95.3 71.0 87.5 86.4 88.6 COC 9660 3209 352 595 5504 84.4 94.0 90.2 87.7 92.2 AMP 9749 567 283 133 8766 81.0 96.9 95.7 65.0 98.6 MAMP 9721 547 133 197 8844 73.5 98.5 96.6 80.5 97.7 OPI 9659 682 160 64 8753 91.4 98.2 97.7 77.3 99.4 CAN: cannabis; COC: cocaine; OPI: opioids; AMP: amphetamine and derivatives; MAMP: methamphetamine and derivatives; TP: true positive; FP: false positive; FN: false negative; TN: true negative; PPV: positive predictive value; NPV: negative predictive value. PPV and NPV were calculated estimating 80% prevalence of positive on-site results. *For each analyte, invalid DDT5000 results were observed in 9868-n cases. Table 4. Assessment of the DDT5000 performance using the LC-MS/MS LOQs (1 ng/mL). Drug Group n* TP FP FN TN Sensi tivity (%) Specifici ty (%) Accur acy PPV (%) NPV (%) CAN 9842 7208 79 917 1638 88.7 95.4 89.9 97.4 81.3 COC 9660 3332 229 766 5333 81.3 95.9 89.7 91.0 91.0 AMP 9749 587 263 251 8648 70.0 97.0 94.7 67.9 97.3 MAMP 9721 570 110 413 8628 58.0 98.7 94.6 79.1 96.5 OPI 9659 753 89 96 8721 88.7 99.0 98.1 85.6 99.2 CAN: cannabis; COC: cocaine; OPI: opioids; AMP: amphetamine and derivatives; MAMP: methamphetamine and derivatives; TP: true positive; FP: false positive; FN: false negative; TN: true negative; PPV: positive predictive value; NPV: negative predictive value. PPV and NPV were calculated estimating 80% prevalence of positive on-site results. *For each analyte, invalid DDT5000 results were observed in 9868-n cases. Table 5. Single vs poly-drug users (%) and frequency of combinations (%) for each drug group. Drug Group Single-drug use (%) Poly-drug use (%) 2 substances (%) ≥ 3 substances (%) Cannabis 57.4 42.6 29.8 12.8 COC 17.2 82.8 52.6 30.2 AMP 9.8 90.2 43.7 46.5 Heroin 2.7 97.3 22 75.3 Ketamine 1.4 98.6 8.4 90.2 Methadone 0.2 99.8 11 88.8 BZD 2.1 97.9 27.1 70.8 Zolpidem - 100 20 80 Codeine 43.1 56.9 39.7 17.2 COC: cocaine; AMP: amphetamine and derivatives; BZD: benzodiazepines; Heroin: cases with 6-monoacetylmorphine detection Table 6. Frequency of drug associations in poly-drug users for each drug group (%). Data were calculated related to the total number of positive cases for each drug group. Associated with (%) Cannabis COC AMP Heroin Ketamine MTD BZD Zolpidem Codeine Cannabis (n=8,294) - 34.6 13.1 4.3 2.1 2.6 2.9 0.1 0.3 COC (n=4,237) 67.7 - 18.2 16.2 3.8 9.6 6.3 0.2 0.3 AMP (n=1,431) 75.8 53.9 - 1.5 11.8 1.0 2.4 0.1 0.3 Heroin (n=796) 44.6 86.2 2.8 - 1.0 51 21.9 0.1 None Ketamine (n=215) 79.5 74.9 78.6 3.7 - None 4.2 None None MTD (n=493) 43.8 82.8 2.8 82.4 None - 26.2 0.4 None BZD (n= 387) 62.8 69.5 8.3 45.0 2.3 33.3 - 1.0 1.6 Zolpidem (n=10) 50.0 90.0 20.0 10.0 None 20.0 40.0 - None Codeine (n=58) 41.4 20.7 6.9 None None None 10.3 None - COC: cocaine; AMP: amphetamine and derivatives; MTD: Methadone; BZD: benzodiazepines; Heroin: cases with 6-monoacetylmorphine detection; n= number of positive cases for each drug Figures Figure 1. Prevalence (%) of drug consumption by sex (Nmen=8434; Nwomen=344). [BDZs: benzodiazepines]. 0 10 20 30 40 50 60 70 80 90 83.3 43.6 14.3 8.1 2.3 5.1 4 75.2 45.5 28 12.8 3.5 95,8 Men Women (%)