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Motor vehicle crashes (MVCs) are a major global health concern. While alcohol continues to be a significant contributor to MVCs, the role of illicit and prescription drugs has increased in the last 4 decades. Moreover, the proliferation of new psychoactive substances (NPS) in the United States since 2010 has reshaped recreational drug use. Despite this, its contribution to MVCs has not been systematically evaluated. In this study, we report the prevalence of NPS in roadway crash victims in California.
Serum samples from 1000 roadway crash victims were collected and analyzed using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) against a comprehensive database of 1314 drugs, including 1008 NPS, and quantitative analysis was performed using isotope dilution. Alcohol was quantified in an autoanalyzer using an enzymatic method employing alcohol dehydrogenase.
Eight NPS (detection frequency = 26) were confirmed and quantified in 17 cases. Like current nationwide NPS surveillance studies, bromazolam, para-fluorofentanyl, and mitragynine were most frequently detected. NPS were detected in polypharmacy use, with traditional recreational drugs such as fentanyl, methamphetamine, and delta-9 THC most frequently co-detected. The serum geometric means detected for bromazolam (5.41 ng/mL; range: 0.22–26.59), para-fluorofentanyl (0.45 ng/mL; range: 0.28–2.02) and mitragynine (7.02; range: 0.55–90.55) were lower than those reported for overdose and death cases.
This study is the first to report quantitative levels of multiple NPS and multiple NPS classes in a large US roadway crash survey, with the high detection of CNS depressants and their co-occurrence with traditional recreational drugs highlighting the need for expanded NPS testing, roadside testing strategies, and guidelines for determining drug-induced impairment; the quantitative data may be valuable in establishing these guidelines.
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Motor vehicle crashes (MVCs) are a leading cause of morbidity and mortality. The United Nations estimates that one person dies in a road crash every 24 s. That is roughly 1.35 million people each year (
Drugs can impair driving through a combination of different mechanisms. These include impairing cognitive processes that are critical in driving, such as focus, attention, and decision-making. Psychoactive drugs can also compromise psychomotor skills and coordination, which are essential in observing correct lane position and speed while driving. Some drugs can lower inhibition, which can lead to aggressive driving, speeding, and a loosened attitude toward following traffic rules. Other drugs and medications can cause sedation and fatigue that may compromise alertness and delay reaction time to urgent and critical driving situations (
Surveys and studies on the involvement of drugs in impaired driving and MVCs started in the 1980s (
The rapid rise of new psychoactive substances (NPS, aka designer drugs) in the recreational drug market that started in 2010 complicated drug testing in clinical and forensic laboratories. The rapid molecular evolution of the composition of the drug products containing NPS required a paradigm shift in drug testing (
The recognition of NPS as an important component of drug testing in impaired driving and MVCs was reflected in the inclusion of fentanyl analogs as Tier 2 drugs in the updated 2017 recommendations for toxicological investigation of driving under the influence of drugs (DUID) and MVC cases (
In this study, we report the NPS detected in samples collected from roadway crash victims in northern and southern California in 2024. A roadway crash is defined as a crash involving a motor vehicle, bicycle, mini-mobility device, or pedestrian struck by a motor vehicle on a public roadway. A motor vehicle crash is a subtype of roadway crash that is limited to mechanically or electrically powered devices not operated on rails, i.e., motor vehicles. To the best of our knowledge, this is the first formal report of multiple NPS and NPS classes quantified in a large survey of roadway crash cases in the United States.
All patients 18 and older presenting to two urban trauma centers in California within 6 h of a roadway crash who were undergoing a blood draw as part of their routine Emergency Department (ED) care were eligible for inclusion. Patients were excluded if they were not undergoing a blood draw as part of their ED care, if a blood draw was performed without collecting blood for drug and alcohol testing, if the initial blood draw occurred more than 6 h from the time of the crash, or if the patient was in police custody at the time of presentation or was taken into custody during the first 24 h of hospitalization. Enrollment began on 2 January 2024 and is ongoing. This study was approved by the local institutional review boards (IRB number 2056749) with a full waiver of consent and received a certificate of confidentiality from the National Institutes of Health.
Five mL of blood was collected in both a red top tube and a lithium heparin tube by the bedside nurse during a routine lab draw. Red top tubes were then centrifuged at 1300 relative centrifugal force (RCF) for 10 min and serum was aliquoted into cryotubes which were then refrigerated at 1°C–6°C until shipped to the UCSF Clinical Toxicology and Environmental Biomonitoring Laboratory. Lithium heparin tubes were centrifuged and frozen at −20°C until shipped. Shipments occurred once per week. Alcohol analysis was performed at UCSF Health Clinical Laboratories.
We analyzed drugs in serum samples using a modification of our published method (
Mass spectrometry was performed using the TOF/MS mode in one run followed by a QTOF/MS run using auto MS/MS data acquisition. An electrospray ionization source in the positive and negative modes was used to ionize analytes in the extract. The QTOF/MS was run under the following conditions: gas temperature at 225°C; sheath gas temperature at 350°C; drying gas flow at 14L/min; sheath gas flow at 11L/min; nebulizer pressure at 14 psi; voltage cap at 3000 V (positive mode) or −2,500 V (negative mode); and, nozzle voltage at 500 V (positive mode) or −1,500 V (negative mode). Data acquisition was run at 2 GHz in extended dynamic range mode. A TOF-MS scan across the range of 75–1000 m/z was collected at high resolution. Using the Auto MS/MS mode (information-dependent acquisition), a product ion scan (MS/MS) of the three most abundant peaks at high resolution was triggered each time a precursor ion with an intensity of ≥500 counts per second was generated in the TOF-MS scan; active exclusion of previously selected peak was held for 0.5 min. The MS/MS scan range used was 50–1000 m/z.
Quantitative analysis of confirmed NPS was accomplished using isotope dilution. Each sample was run along with a 12-point calibration curve using the LC-TOF/MS analysis method described above. A mixture of 14 internal standards (11-nor-9-carboxy-delta-9-THC-d3, amiodarone-d4, atropine-d3, cocaine-d3, delta-9-THC-d9, doxepin-d3, fentanyl-d5, hydromorphone-d6, JWH-015-d7, loratadine-d4, MDMA-d5, morphine-d6, oxycodone-d6, paroxetine-d6) with retention times that bracket the retention times of all the drugs in the laboratory’s comprehensive drug database was used.
Alcohol was quantified in plasma samples using an enzymatic method employing alcohol dehydrogenase that converts any alcohol in the sample to acetaldehyde (Abbott Diagnostics). The enzymatic reaction is monitored spectrophotometrically at 340 nm. If the ethanol concentration in the sample is < 0.01 g/dL it is reported as negative. If the concentration is ≥0.01 g/dL, the quantitative concentration is reported.
Drug screening was performed on the total ion chromatogram (TIC) obtained from LC-TOF/MS analysis using the Agilent MassHunter Qualitative Analysis software and a comprehensive database consisting of 1314 drugs, of which 1008 are NPS. This consists of alkylamines (3), aminoindanes (6), amphetamines (29), anabolic steroid (1), arylcyclohexylamines (17), designer benzodiazepines (38), lysergamides (2), new synthetic opioids (296), phenethylamines (28), plant-derived opioids (2), piperazines (9), piperidines (9), synthetic cannabinoids (481), synthetic cathinones (67), and tryptamines (20). The database also includes traditional recreational drugs (54), prescription and over-the-counter drugs (215), precursors (5), additives (5) and impurities (1), dietary supplement stimulants (23), other dietary supplement ingredients (1), and nicotine and its metabolites (2).
To screen for presumptive matches, the following criteria were used: mass error within 10 parts per million (ppm), retention time match within 0.15 min, and a target score ≥70 (an indicator of mass spectral isotopic abundance and spacing or isotopic pattern matches). For confirmation of presumptive positive matches, data from the LC-QTOF/MS run was analyzed. A spectral library match score ≥70 (indicator of fragment ion data match) was imposed for confirmation.
The quantitative levels of confirmed NPS were measured using the Agilent MassHunter Quantitative Analysis software. The isotopologue with the closest retention time to the analyte of interest was used as internal standard.
While sample collection is still ongoing, the first 1000 cases included in our present analysis were collected between 2 January 2024 and 25 July 2024. These roadway crash victims originated from Los Angeles and Sacramento, California. NPS was confirmed in 17 cases (1.7%). Ten of the cases were from Sacramento while the remaining seven were from Los Angeles.
The average age of roadway crash victims with confirmed NPS was 34 (range:18–66; median: 38). Of the 17 cases, 11 were males. Seven were passengers, five were drivers, three were bicyclists with two being on electric bikes, one pedestrian struck by a motor vehicle, and one in mini-mobility device. The types of crashes involved in the cases include single vehicle (8), multi-vehicle (5), auto vs. bicycle (2), auto vs. pedestrian (1), and bicyclist (1) (
Characteristics of seventeen NPS-positive roadway crash cases including specific drugs detected. Source locations were not included to protect patient confidentiality*.
Age/Gender/Type of crash | NPS | Conc’n (ng/mL) | Traditional recreational drug (ng/ML) | Precursor, additive, impurity | Over-the-counter or prescription drug | Ethanol (g/dL) |
---|---|---|---|---|---|---|
33 y/o Male |
para-Fluorofentanyl | 0.28 | Negative | 4-ANPP |
Acetaminophen | Negative |
49 y/o Male |
7-Hydroxymitragynine |
8.92 |
Hydrocodone (62) | Negative | Duloxetine | Negative |
27 y/o Female |
Bromazolam |
26.59 |
Beta-hydroxy fentanyl (2.4) |
4-ANPP | Acetaminophen | Negative |
33 y/o Female |
Acetyl fentanyl para-Fluorofentanyl |
0.55 |
Beta-hydroxy fentanyl (1.6) |
4-ANPP |
Acetaminophen | Negative |
21 y/o Male |
Etizolam | 1.21 | Delta-9-THC (2.7) | Negative | Alprazolam |
Negative |
18 y/o Female |
Bromazolam | 10.62 | Benzoylecgonine (60.8) |
Negative | Negative | Negative |
32 y/o Male |
Bromazolam para-Fluorofentanyl |
0.22 |
Benzoylecgonine (55) |
4-ANPP | 7-aminoclonazepam |
Negative |
42 y/o Male |
para-Fluorofentanyl | 2.02 | Benzoylecgonine (3.3) |
4-ANPP | Negative | Negative |
23 y/o Male |
Mitragynine | 0.55 | Negative | Negative | Negative | Negative |
23 y/o Male |
Alpha-hydroxy bromazolam |
10.58 |
4-Hydroxymethamphetamine (0.1) |
4-ANPP |
Acetaminophen | Negative |
22 y/o Female |
Bromazolam | 17.01 | Beta-hydroxy fentanyl (0.5) |
Quinine | Negative | Negative |
54 y/o Male |
Xylazine | 4.10 | 11-nor-9-carboxy-delta-9-THC (70) |
Negative | 1-(3-Chlorophenyl) piperazine |
0.35 |
34 y/o Male |
7-Hydroxymitragynine |
33.09 |
Fentanyl (68.9) |
4-ANPP | Negative | Negative |
66 y/o Female |
Mitragynine | 0.55 | Hydrocodone (3.6) | Negative | 1-(3-Chlorophenyl) piperazine |
Negative |
33 y/o Male |
Bromazolam | 13.53 | Beta-hydroxy fentanyl (0.3) |
4-ANPP | Negative | Negative |
29 y/o Female |
para-Fluorofentanyl |
0.28 |
Beta-hydroxy fentanyl (7.8) |
4-ANPP |
Acetaminophen | Negative |
35 y/o Male |
Bromazolam | 8.87 | 11-nor-9-carboxy-delta-9-THC (61.5) |
Negative | Negative | Negative |
*The limits of quantification (LOQ) for the analytes (in ng/mL) are as follows: 4-Hydroxymethamphetamine (0.1); 7-Hydroxymitragynine (0.39); 11-nor-9-carboxy-delta-9-THC (60); Acetyl fentanyl (0.39); Alpha-hydroxy bromazolam (0.2); Amphetamine (60); Benzoylecgonine (0.39); Beta-hydroxy fentanyl (0.2); Bromazolam (0.2); Cocaine (0.1); Delta-9-THC (1); Etizolam (0.78); Fentanyl (0.78); Hydrocodone (1.6); Methamphetamine (0.39); Mitragynine (0.39); N-Methyl norfentanyl (0.2); Norfentanyl (0.2); para-Fluorofentanyl (0.2); Protonitazene (0.39); Xylazine (1.56). Details of the analytical method used to quantify the analytes are beyond the scope of this paper but they can be requested from the corresponding author.
Of the 1000 cases, 290 were positive for at least one traditional recreational drug (TRD) or NPS (29%). Of the 288 that were positive for TRDs, 15 have NPS (4.9%). There were eight unique NPS detected with an overall detection frequency of 26. Only CNS depressants were detected, including designer benzodiazepines, new synthetic opioids, and the plant-derived opioid, mitragynine. Bromazolam (detection frequency, DF = 7) was the most detected, followed by para-fluorofentanyl (DF = 4) and mitragynine (DF = 3) (
All but one case also had one or more traditional recreational drugs (TRD), of which fentanyl (DF = 9), methamphetamine (DF = 9), and delta-9-THC (DF = 4) were the most detected. Cocaine was detected in 2 cases while the inactive cocaine metabolite, benzoylecgonine, was detected in 3 cases (
Sixteen of the seventeen cases tested negative for ethanol. The sole sample that tested positive for alcohol had a concentration of 0.35 g/dL (
The analysis of the first 1000 cases indicates that NPS in roadway crashes is a cause for concern in the United States. Eight NPS with a detection frequency of 26 were confirmed in 17 cases. Importantly, the NPS detected at the highest frequencies (bromazolam, para-fluorofentanyl, and mitragynine) are all categorized as CNS depressants. These drugs reduce the activity of the brain, resulting in sedation, drowsiness, and impaired cognitive and motor functions (
The second most detected NPS in our study, para-fluorofentanyl (pFF), was the most frequently detected NPS in the United States from 2021 until it was overtaken by bromazolam in 2023 (
Mitragynine, the primary psychoactive ingredient of Kratom, was the third most detected NPS in our study. Although Kratom was first brought to the United States in the 1980s, its recreational use only gained traction in 2010 (
Five other NPS (3 new synthetic opioids, 1 designer benzodiazepine, xylazine) were quantified in six cases. As a class, opioids were detected the most (16, 62%) in the entire cohort (
Detection frequency of NPS parent compound and metabolite by drug class and position in crash.
While alcohol-impaired driving continues to have the most detrimental impact on traffic safety (
The percentage of drivers using multiple drugs has increased from 32.6 percent in 1993 to 45.85 percent in 2010 (
The current fentanyl epidemic in the United States started in 2015, and since then, fentanyl has been detected as an adulterant in common drugs of abuse, initially in heroin and later in stimulants like methamphetamine and cocaine. Seven of the fentanyl detections have either methamphetamine or cocaine. Additionally, NPS are also added to fentanyl drug preparations. The majority of the bromazolam and pFF detections in NPS surveillance in the United States are co-detected with fentanyl (
Furthermore, our research provides a unique insight into not just the identification of NPS detected in roadway crashes, but their quantitative levels too. Previous studies primarily report qualitative data on NPS; in a few studies, quantitative data for a specific NPS like bromazolam was presented (
Our work is the first to conduct a comprehensive targeted analysis of more than 1000 NPS, in combination with suspect and non-targeted screening in roadway collisions across a large population. While there have been reviews across several countries in Europe such as Spain, Germany, France, and Belgium (
Although our analysis is comprehensive, it is not without limitations. First, samples were collected from cases limited to two geographic locations- Los Angeles and Sacramento. Although these two cities may represent a large and medium-sized city in California, they do not represent the rest of the cities and towns in the state. To a greater extent, they do not represent the rest of the United States. There are known geographic differences in drug use patterns so our findings may not accurately be used to extrapolate what may be true for the rest of the country. Furthermore, our cohort does not include subjects from other at-risk populations, such as drivers or pedestrians who may not have been involved in roadway crashes that required an Emergency Department visit. Certain exclusionary criteria, such as the patient having a warrant for their arrest also limit data extrapolation. Additionally, the study was conducted over 7 months, from January to July 2024. The general findings of our larger ongoing study demonstrated differential detection frequency among drugs between months. The detection frequency of most drugs, for example, was trending up towards the summer months (unpublished data). It is difficult to predict the impact that collection from August to December would have had on our data. This time constraint also makes it difficult to establish trends in NPS use over longer periods of time. Longitudinal studies would provide a more comprehensive understanding of NPS use and its evolving impact on impaired driving. Finally, like any other comprehensive LC-QTOF/MS method, our method may not have the best sensitivity for a few NPS. Comprehensive methods are designed to optimize detection and confirmation of as many drugs (NPS) as possible. However, the wide range of structural differences and polarities among various classes of NPS make it impossible to have optimum sensitivity for all NPS classes. Although our method is good at detecting more than 98% of our target analytes at sensitivities appropriate for serum, it does not have good sensitivity to very small, strongly polar (e.g., GHB analogs) and large highly hydrophobic (e.g., delta-9-THC derivatives) compounds, so if the levels of these NPS are low in biological samples, we likely missed their detection. Moreover, substances of which there is no known reference standard or are very uncommon might be excluded which may further limit the scope of the study.
In summary, although the number of cases where we found NPS in our cohort is limited, our findings provide more evidence of the ongoing danger that NPS may pose to road safety and highlight the need for policy change aimed at expanding NPS testing infrastructure, specifically in cases of impaired driving. Future research should continue to look at NPS in roadway collisions, specifically in drivers, as this is the cohort most responsible for injury and death in crashes. If possible, it would also be beneficial to develop a dynamic roadside testing strategy that adapts to the highly detected and impairing NPS reported in nationwide drug surveillance close to, if not in, real-time.
The original contributions presented in the study are included in the article, further inquiries about the results presented can be directed to the corresponding authors.
The studies involving humans were approved by UC Davis Institutional Review Board. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
RG: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review and editing. DT: Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing. DN: Data curation, Investigation, Writing – review and editing. AS: Investigation, Project administration, Writing – review and editing. DF: Formal Analysis, Investigation, Methodology, Project administration, Resources, Writing – review and editing. JT: Investigation, Project administration, Writing – review and editing. JC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing – review and editing.
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the California Office of Traffic Safety grant number DI24026 (PI: James Alan Chenoweth). Analysis of the NPS in the 17 cases was done through the support of DEA TOX funded through the contract number DOJ 15DDHO24A000000 13 (PI, Roy Gerona).
The authors would like to thank all of the staff who have worked diligently on this project. UC Davis–Isabel Anderson, Abhijeet Gorhe, Addy Hammudi, Eric Hansen, Logen Howze, Karina Klein, Nicholeth Santiago, Nam Tran, and Monica Tsui. Lundquist Institute–Khadije Ahmad, Marziyeh Bagheri, Nichole Faucher, Ahmed Ghanem, Sajad Hamal, Elizabeth Hernandez, Jeremy Stark, Dyonne Tetangco, and Santos Vazquez. California Office of Traffic Safety–David Doucette (former deputy director), Moriah Martinez, Nicole Osuna, and Barbara Rooney (former director).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
The author(s) declare that no Gen AI was used in the creation of this manuscript.
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