Identifying new psychoactive substances (NPS) using drug checking services: An innovative observational study design

doi:10.21203/rs.3.rs-2081925/v1

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Research Article

Identifying new psychoactive substances (NPS) using drug checking services: An innovative observational study design

https://doi.org/10.21203/rs.3.rs-2081925/v1

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Background

Despite the proliferation of new psychoactive substances (NPS) in recent years, there are many gaps in our understanding of the phenomena. Current methodologies still fail to provide timely and scientifically reliable knowledge between controlled trials and online data reports that lack analytical confirmation of the reported substances. This study tests a new online, collaborative, observational methodology for tracking the emergence of NPS. It involves conducting laboratory analysis of community-submitted NPS samples using a naturalistic approach.

Methods

Participants were recruited from online communities of people who claim to use NPS. They were asked to send and report on the substances they would have taken independently of the study participation. They received free drug-checking services for the samples sent during the data collection. Participants were also asked to complete multiple online questionnaires to provide information about the analysed NPS samples. Comprehensive literature reviews of the analysed samples were conducted to assess the importance of the results.

Results

Participants sent 136 sample submission questionnaires. Ninety-two were followed by the laboratory analysis of a submitted NPS sample. Only 54 of those analysed NPS samples completed all the study protocols. Forty different NPS were identified in the laboratory analysis. Of those, 13 substances had no previous reports in the scientific literature as new psychoactive substances. Another 12 substances had no available peer-reviewed data regarding their subjective effects. Only one opioid and no synthetic cannabinoids were detected.

Conclusions

The confirmed identification of 13 previously unreported NPS demonstrates the capacity of the current methodology to identify potentially emerging NPS. Despite the vast diversity of substances submitted, the most common NPS types (e.g., cannabinoids and opioids) were not found. Altogether, these results warrant further research to consolidate the favourable impact of monitoring the NPS market using this innovative methodology.

Citizen Science

New Psychoactive Substances

Epidemiological Monitoring

Designer Drugs

Harm Reduction

Drug-Checking

The phenomenon of new psychoactive substances (NPS) represents an unprecedented and still unsolved challenge in the field of drug use as well as a fast-growing problem from social, cultural, legal, and political perspectives (1). NPS have become a global phenomenon, with 136 countries from all regions of the world have reported 1,127 NPS. Since 2018, the United Nations early warning advisory on NPS has been monitoring toxicology data to help identify the greatest threat to public health posed by NPS (2). Between 2009 and 2020, 1,047 different NPS were identified in 126 countries or regions (3–5). Specifically, in the European Union (EU), by the end of 2020, the number of monitored NPS was over 830, of which 400 were notified after 2015 (3, 5).

According to the United Nations (UN), NPS are substances of abuse not controlled by the 1961 Single Convention on Narcotic Drugs or the 1971 Convention on Psychotropic Substances, with the potential to pose a public health threat. Different authors have previously referred to them (partially or totally) as "designer drugs", "legal highs", "herbal highs", "bath salts", and "research chemicals", usually with different connotations (6). Moreover, the term “new” does not necessarily refer to new inventions but to substances that have recently emerged on the NPS market (7–9).

The speed of technological innovation and the ease of synthesising NPS present substantial challenges to regulatory authorities, researchers, and clinicians as these new substances continue to be detected and identified (10, 11). Also, the emergence of NPS has contributed to a shift from a street-based drug market to a web-based drug market. These changes warrant developing new methodologies to monitor drug markets in line with this new reality (12).

Despite the high number of publications about NPS during the last 20 years, especially after a sharp increase in 2010, there are still unmet challenges and concerning gaps in our understanding of the phenomenon (10, 13). To address this lack of knowledge, (10)published an evidence map of the NPS research, highlighting three main vital points (10). First, most of the studies have been performed in the general hospital population (n = 294, 40.1%) or specialist settings (n = 134, 18.2%), mostly reporting severe intoxications or other acute NPS health-related problems. Second, the most frequent study design reported in the indexed peer-reviewed literature was case series or reports (n = 367), followed by the literature review (n = 243), the survey (n = 130), and the secondary quantitative data analysis (n = 99), with only 13 existing randomised controlled trial (RCT), six prospective cohort studies and one case-control study (10, 11). Third, most of the studies that used questionnaires, surveys, or interviews that assess the effects of NPSs are based on self-reported data without any analytical confirmation that the substance allegedly ingested was previously present in the sample. These results make the data hard to interpret, as the participants might or might not have been answering about the characteristics or effects of a different reported substance. Additionally, the EU drug action plan 2021–2025 defines as a strategic priority the improvement of NPS research and monitoring using new technologies, prioritising knowledge gaps, and creating synergies across different communities (14).

Aside from the difficulties associated with monitoring the NPS market, there are ethical challenges to address in the research field. On the one hand, there are innovative and promising methodologies such as automated data scraping from online communities where NPS use is discussed. Those raise ethical concerns as the data is gathered without the consent of the people who produced it. Additionally, some authors consider that the practice violates the privacy of the community members (15). On the other hand, innovative methodologies that refrain from using the dominant medico-legal discourse and interact with web-based drug forums using alternative discourses regarding drug use show a greater understanding of biomedical research ethics. Integrating the participants' discourse into the study increases transparency and allows for their inclusion in somehow egalitarian discussions with the researchers. The benefits of inclusivity and transparency within the studied community have proven to go beyond ethics, improving the scientific process in several different stages, from study design to data interpretation (16–19).

To address the lack of data gathered outside the general hospital population and the need to include other perspectives, some researchers have pointed to the engagement of online communities of people who claims using NPS as a critical niche and potentially a sentinel population to monitor new trends. Their recommendations include gathering more in-depth data from people with extensive experience using NPS and NPS community representatives (20). Other authors have also seen the potential of conducting studies with populations of people with extensive experience and high curiosity about consuming emerging NPS (3, 18). These populations also happen to be well connected with established drug checking services.

As pointed out by Maghsoudi et al.(13)European studies reported increasing detection of NPS by drug checking services (DCS) over time (13, 21–32). Drug checking services (DCS) have been used to increase our knowledge of new substances and determine potentially harmful effects and adulterations by analysing samples submitted by people who use drugs. These services are well established in Europe, becoming increasingly frequent in North America and play an essential role in drug market monitoring and harm reduction activities. Drug checking services aim to prevent potential harms of NPS, including synthetic opioids (13).

There is strong evidence that drug checking services provide a unique form of drug market monitoring by providing information on the level of concordance between the expected content of the samples provided by people who use these services and the actual drug content (13,21,33). Actual evidence remarks that DCS can detect NPS and other drugs of concern. As DCS gains popularity, ongoing scientific evaluations across diverse settings are critical to understanding the impact and limitations of this intervention (13,34). Additionally, some DCS, such as Energy Control^{^[1]} (EC-ABD) have a well-regarded institutional presence and communication channels in the online NPS communities (21,35–38). On top of these methodological challenges, some authors have noted that the COVID-19 pandemic has limited the capacity to conduct certain types of NPS studies while probably increasing the harms caused by NPS (39).

A concerning evidence gap has been identified in detecting the constantly emerging NPS. Traditional methodologies have failed to produce the desired information for policymakers, failing to balance the timely identification of new trends with the reliability of peer-reviewed controlled trials.

The main goal of this study is to test the potential of a new methodology to produce data to cover the mentioned evidence gap. For this purpose, an online, worldwide, observational study inclusive of the online communities of people who use NPS has been designed. The study demanding methodology combines online surveys and drug checking services to analyse the reported information. The study aims to provide timely and reliable data to identify and characterise previously unreported NPS in the scientific literature.

[1] Energy Control is a project from the non-governmental organization “Associació Benestar i Desenvolupament (ABD).

Overview

The current paper presents the GRASP study's first results (Global Research and Analysis of New Substances Project). The GRASP study was conducted using an observational and new internet-based methodology, the details of which can be found elsewhere (40). This study follows an analytical, observational, cross-sectional structure and a naturalistic approach to collecting participants' data.

The study has been designed and reported according to established guidelines (Cochrane, Centre for Evidence-Based Medicine of Oxford University, and STROBE extensions). The studied population were NPS intended to be consumed by people who both use drugs and participate in online communities that discussed the use of NPS. The intervention consisted of requesting online surveys about information about the acquired substance intended to be consumed, followed by requesting a physical sample of the reported substance and conducting a chemical analysis of it.

Comparisons were made between two groups of datasets: one from substance samples that could be related to the completion of all the study surveys; and another corresponding to substances from characteristic surveys and chemical analyses only. The same groups were made when assessing the expectance of the substance reported by the participants and the actual analysis results.

Finally, differences between the main chemical results of both groups were compared to scientific literature evidence from a selected database that identified the substances found as NPS. (41–43)

The study's main objective was to identify substances previously not reported as new psychoactive substances in the scientific literature and evaluate the feasibility of the proposed new methodology for NPS identification, assessing possible biases and offering an exploratory overview of the analysed data.

Ethical considerations

The study protocol was approved by the Clinical Research Ethics Committee (Parc de Salut Mar, Barcelona, Spain, ref. 2018/8283/I) in January 2019. Recruitment and data collection started on August 2019 and lasted 14 months until October 2020. Completed informed consent documents were received by email. Following the updated and accepted literature recommendations to preserve participants' pseudo-anonymity, the informed consent documents were formatted as PDFs completed by writing the participant username in the designed textbox (18, 40).

The study was not pre-registered in a public database because its innovative design prevented proper classification and reporting. Most of the procedures and resources were focused on the pre-registration of clinical trials and experimental studies, with far less developed procedures for the pre-registration of observational studies and other non-experimental designs (44). Nevertheless, the study protocol was submitted for publication in a later attempt to maximise compliance with all known guidelines. The protocol was submitted on September 2020 before starting data analysis (40).

The protocol was designed according to recommendations of the current guidelines developed by the leading researchers in the field and the ones developed by members of different communities of people who use NPS (45–48). Numerous minor actions, such as contacting each community moderators' team, were conducted to follow these recommendations. Those will be described in their corresponding methodology subsections. However, the three main measures regarding ethics, inclusivity and stigmatisation merit further explanation in this section (49)

As a starting point, the authors have acquired the capacity to communicate with the alternative discourse used by the communities that met the inclusion criteria for recruitment. This communication has required a significant effort to suspend judgment regarding the contradictions with the dominant discourse in academic research and finally integrate both narratives into a compatible perspective (16–19).

Completing the interventions to ensure participants' inclusivity, members of online communities where NPS use is discussed have participated in all stages of the study, from inception and design to interpreting results and writing the paper. This procedure aligns with the EMCDDA drug strategy of promoting human rights and including civil society with their meaningful participation and involvement in the development and implementation of drug policies at national, European, and international levels (50)

Including and reporting their contribution and integrating the systematically missing positive narrative of the stigmatised communities was considered the necessary action to ensure the development of an inclusive and ethical study depending on data provided by this population. Finally, we comply with recent insights to increase the scientific quality of studies regarding illicit psychoactive substances (17). Despite the authors' pact they might have on the authors, this publication intends to add to this new methodological standard of reporting the authors' personal experience with the studied phenomena to increase transparency in their potential biases.

To ensure the consent of all authors, we report that a minority of them are part of the population from which participants were recruited. In contrast, another minority reports lacking any personal experience regarding the use of NPS or participation in online communities where its use is discussed. Hopefully, this will help to progressively recognise the systematically unacknowledged scientific contributions of stigmatised populations that never disclosed their proximity to their area of research (17, 51).

Recruitment and sample collection

Participants were recruited from a population of people who use NPS and interact through online forums where drug use is discussed, and drug checking and harm reduction services are provided. The services mentioned earlier were required to be provided by Energy Control, a previously present organisation recognised by the users’ forums (40). The identified online sites were Bluelight, r/rchemicals subreddit, Drugs-Forum, and DNStar VIP.

Recruitment was conducted using an online advertisement posted in the contacted forums, allowing potential candidates to contact the research team anonymously. Contact with the identified online forums' moderators was conducted to provide information on the study protocol and determine its participation as a recruitment platform for the study. When reached, potential candidates were given the study information sheet and a channel to discuss their concerns with the authorised researcher (MG). As part of the screening process to determine their enrollment as participants in the study, candidates were requested to answer a short online survey to assess health disorders or other incompatibilities and provide a signed informed consent document according to the pseudo-anonymity procedures of the study. Lastly, the remaining candidates were required to register in the GRASP online forum, ensuring that registration data could not be related to an individual.

Possible study influence on the natural behaviour of the participants was assessed using indirect methods included in the screening online survey to ensure their previous intention of using NPS during the study's data collection period. The study GRASP online forum setting was also designed to allow the same interactions that may be conducted on any recruitment site. Still, discussions regarding the acquisition of NPS or sources providing psychoactive substances were explicitly and severely banned. This information was never given to the researchers outside the screening surveys. This observational and non-interventionist approach was based on previously published research developed in our institution (52–55).

Participants provided the study sample by sending a substance sample, using conventional mailing services, and reporting an online substance survey of a corresponding substance sample.

Legal Considerations

Participants received no monetary compensation for their participation but compensation for chemically analysing their submitted samples without economic cost. Otherwise, this service costs around 90 EUR if contracted independently through the Energy Control international drug checking service.

Substance sample collection procedures were legally addressed by giving legal information to the participants clarifying that sending a sample for analysis could not be interpreted as manufacture, trade, use, or participation in criminal activities under the UN conventions' investigative and control techniques. (56, 57)The UN conventions also stress the importance of designing methods to detect illicit consignments of narcotic or psychotropic substances, using all possible organisations in their early warning system.

Energy Control was included in the study to ensure the legal framework provided by the European Regulation (EU 2017/2101 of the European Parliament; Council of 15 November 2017 amending Regulation (EC) No. 1920/2006) as regards information exchange on and an early warning system and risk assessment of new psychoactive substances. (57, 58)

Compliance with the organisation is ensured as it works within the Spanish Reitox national focal point of the EMCDDA early warning system (EWS). These focal points provide national data on detected illicit or new psychoactive substances and fulfil each EU country's national responsibility to develop timely NPS detection. Based on the information reported through the Reitox national focal points, the EMCDDA generates several outputs, such as The European Database on New Drugs and the risk communications. The existence of a legal framework that supports the establishment and operation of an EWS. In many cases, regulations can often be a tool to safeguard (good) compliance with specific procedures, mechanisms, or actions (59).

Finally, to further improve participants' security, those were instructed to send the sample anonymously, without any connection to their individual, with the study code as the only identification. Also, they were advised to send samples of substances not scheduled in their location, the EU or Spain. Nevertheless, the legal status of the sample remained unknown until the laboratory analysis was performed.

After assessing the participants' involvement in the study as a minimal legal risk while providing perceived valuable health services, their participation was considered under the Helsinki declaration.(60)

All the data collected online on the participants were encrypted according to the European and Spanish data protection regulations.^{^[2]}

Data Collection

Online surveys

Enrolled participants were asked to fill out a sample submission questionnaire to send a sample by conventional mail to the Energy Control laboratory for chemical analysis. Before sending the sample, the participants had to wait for the principal investigator (MG) review and approval, based on the confirmation that the online survey was correctly completed. The online survey regarding the substance samples requested the corresponding sample's characteristics known by the participant, such as who was obtained and which was the acquisition setting. After reported surveys were approved, participants received a specifically generated sample code with instructions on sending the corresponding sample and other previously mentioned legal information. When received, analysed, and stored in the laboratory, the composition's sample results were sent back to the participant with specific harm reduction advice when the expected sample content did not match the laboratory results. If the participants decided to consume the samples, they were asked to complete a baseline, and subjective drug effects online survey. The published protocol includes a complete description of this process and questionnaires administered (40).

Laboratory analysis

The chemical identification was conducted after receiving the submitted samples corresponding to a valid reported substance-characteristics survey. Energy Control's laboratory team prepared the received samples and applied a well-defined sequential method to qualitatively determine what active compounds were present in the received samples. Those procedures were made at the Hospital del Mar Research Institute (IMIM) in Barcelona, which was accessible due to previous collaboration between EC-ABD and the health care and research centre.

A screening method was applied to all the samples to determine their chemical composition. This method consisted of several techniques, which were conducted accordingly to previous literature. The primary identification technique used gas chromatography (GC) for scanning and mass spectrometry (MS) to determine the active compounds. Subsequently, the GC/MS process outcomes were compared to four different and extensively used analytical libraries.

This method included other screening techniques to differentiate positional isomers, distinguishing between 2-,3- and 4- MMC substances. Furthermore, specific adjustments to the process were conducted if any fentanyl derivative was expected to be identified.

Some samples (those expected to contain ETH-LAD, MIPLA, 25E-NBOH, DMT, bufotenine, or 5-Meo-DMT) followed a method based on liquid chromatography and mass spectrometry (LC/MS)(61, 62). More detailed information on the procedures can be found in additional material 1.

Data management and statistical analysis

Data collected from the online surveys were managed to generate different database files containing the participant's answers and the laboratory results centralised by the research team. Reported self-administration surveys' data were not included in the main files, but the complete surveys and related participants' numbers were incorporated to analyse the feasibility of the study. Simultaneously, an independent statistician was requested to generate database files from the same collected data to address possible errors when aggregating all the surveys and laboratory results.

Two groups were defined and compared from the generated database files as an exploratory analysis of the possible difference between them. The completed study's sample group was defined as those NPS that were chemically identified and had data related both to a valid substance characteristics survey and a substance self-administration report. The other group consisted of those NPS from which all relevant data for the study's main objective was collected but could not be related to a self-administration report, therefore not completing all the closing study's procedures.

The selected variables to compare between groups were the information provided about the substance sample precedence, the provider of the sample and the type of acquisition setting, the expected composition of the sending sample, and the chemical results of the laboratory analyses. Fisher's exact test was selected as the primary statistical to provide information about the difference significance between each group's results, assessing the study's complete procedures bias on the results and differences between participants' expected composition and the actual composition of the samples.

Literature review

A literature review was performed on the reporting results from peer-reviewed published studies. The review was performed once the data collection period ended and the laboratory results were available. All searches were conducted only in the PubMed database during April 2021, following the PRISMA guidelines for reporting the study’s results (63). A design summary can be found below, while specific details are reported in the additional material 2.

The search terms were defined for each identified substance included in the sample group of submitted samples that fulfilled all the study procedures. Standard chemical nomenclature of those substances and up to three commonly used scientific abbreviations or street names were introduced using the database search engine. Studies’ inclusion criteria to be eligible for the review were: English as a written language; definition of the studied samples potentially used as NPS; and the data sources from where the study data was collected. The included studies were categorised as those that use non-human samples to provide the results and those that the sample consisted of directly obtained reported information from living people.

Publications containing the same author's datasets were discarded in favour of later publications of the same author, expanding the original dataset and previous studies presenting datasets included in the literature review process.

[2] 2016/679 European Parliament General Data Protection Regulation (GDPR) and 27/4/16 “Reglamento general de protección de datos” (RGPD) Spanish Royal Decree.

Study feasibility

Figure 1 shows that four NPS-related forums (Bluelight, r/researchchemicals subreddit) were contacted to identify possible candidates. Two of them (Drugs-Forum, DNStar VIP; 50%) could not collaborate with the study research team. One of them (DNStar VIP) was shot down due to the application of law enforcement resolutions, and the other refused explicitly to post any advertisement related to the study. Following the study advertisement publication, 186 candidates were identified, and 99 (53.2%) of them contacted the researchers. Those candidates were provided with a brief online survey to evaluate their eligibility as participants, excluding people who did not meet the inclusion criteria. The remaining 45 (24.2%) candidates successfully registered on the GRASP online forum site, and 35 (18.8%) enrolled as study participants.

Participants of the study provided 136 (100%) online survey reports of characteristics that referred to a corresponding substance sample. A total of 118 (86.8%) survey reports were validated after assessing their compliance with the study procedures. Of those valid surveys, 92 (67.6%) corresponded to a substance sample received by the researchers. In the end, 23 (65.7%) of the initial 35 (100%) participants remained as the primary providers of the surveys corresponding to a collected substance sample.

Finally, the received substance samples were assessed to determine the number of survey reports and substance samples, and participants could complete all the study procedures. As exposed in Fig. 1, from 92 of the included survey reports and received substance samples, 54 (39.7%) also were associated with a self-administration report of the sample, provided by 17 (48.6%) of the enrolled participants (n = 35)

Sample characteristics

Table 1 shows the substance sample characteristics reported by the participants when completing the substance characteristics online survey. Two comparison groups were established from reported data to assess protocol completion biases. The first group consisted of participants' surveyed claims of the substance characteristics of 54 (58.7%) samples corresponding to a self-administration survey and analytical identification study reports compared to the 92 (100%) reported answers corresponding to a submitted sample. No statistically significant differences among the two groups were found in any of the

measurements when Fisher's exact tests were used to determine the likelihood of the statistical hypothesis. Most of the analysed samples were claimed to be acquired from Europe (n = 52; 56.5%), referring the provider to be a friend or trusted dealer (n = 51; 55.4%), and using the regular surface web (n = 61; 66.3%) as the primary setting to acquiring the substance. Detailed results can be found in Table 1.

Table 1

Comparison of reported NPS samples’ geographical origin, provider and acquisition setting according to their protocol completion status
	Submitted and analysed samples with a corresponding self-administration survey		Submitted and analysed samples
Origin of the substance	n = 54	100%	n = 92	100%
Europe	33	61.1%	52	56.5%
Belgium	0	0%	1	1.1%
Finland	0	0%	1	1.1%
Germany	4	7.4%	4	4.4%
Netherlands	21	38.9%	37	40.2%
Spain	4	7.4%	4	4.4%
UK	1	1.9%	2	2.2%
Eastern Europe	3	5.6%	3	3.3%
North America	16	29.6%	31	33.7%
Canada	8	14.8%	17	18.5%
USA	8	14.8%	14	15.2%
Asia	3	5.6%	6	6.5%
China	3	5.6%	6	6.5%
Unknown	1	1.9%	1	1.1%
Not answered	1	1.9%	2	2.2%
Fisher's Exact Test p-value				0.988
Provider of the substance	n = 54	100%	n = 92	100%
Friend or trusted dealer	32	59.3%	51	55.4%
Unknown dealer	17	31.5%	36	39.1%
Other	5	9.3%	5	5.4%
Fisher's Exact Test p-value				0.651
Acquisition setting	n = 54	100%	n = 92	100%
Surface web	36	66.7%	61	66.3%
Darknet	12	22.2%	20	21.7%
Other	5	9.3%	10	10.9%
Not answered	1	1.9%	1	1.1%
Fisher’s Exact Test p-value				1

Drug checking findings

Table 2 shows that 79 (85.9%) of the analysed samples contained only substances expected by the participants. A minority of the analytical identification results consisted of 6 samples (6.5%) containing the expected substance and other compounds and six other samples (6.5%) containing only other substances than the one expected by the participant.

Analytical identification processes also show that most substances were detected only in one sample (n = 68; 73.9%). Hence, a minority of identified substances (n = 24; 26.1%) were identified multiple times in different samples. Sixteen (66.7%) were expected to contain only the same detected active compound found, identifying 2 (8.3%) substances from samples that were expected to contain another substance and 1 (4.2%) that was found as an adulterant in another sample also containing the participant's expected substance. In 5 (20.8%) of the cases in more of one sample contained the same substance (n = 24), only one substance (4.2%) was identified as substituted, and the other (n = 4; 16.7%) were present as adulterants of other identified and expected substances.

From the sample group of substance samples categorised as those corresponding to the fully completed study's procedures (n = 54), 40 (74.1%) different NPS were detected during the analytical identification process.

Expected and actual identified substances

Most participants expected their samples to contain only the primary compounds they had been told (n = 77; 83.7%). The laboratory analysis was consistent with their expectations, finding that most samples contained only substances expected by the participants (n = 79; 85.9%). More details are shown in Table 3, where a statistical comparison between data from the NPS completed study sample and substances' data of the sample without a corresponding self-administration report, accounting for expected and confirmed adulteration.

No statistically significant difference was found between the two groups when Fisher's exact test was conducted, as in the statistical results shown in Table 1. although less adulteration was expected within the sample group of substances that fulfilled all the study's procedures (7.4%) compared to the other (16.3%).

Table 3

Comparison of the expected and confirmed NPS sample adulteration according to their protocol completion status
	Adulteration expectance and laboratory results from analysed and reportedly consumed samples		Adulteration expectance and laboratory results from analysed samples
Expected adulteration	n = 54	100%	n = 92	100%
No	50	92.6%	77	83.7%
Yes	4	7.4%	15	16.3%
p-value			0.137
Laboratory results	n = 54	100%	n = 92	100%
Adulterated/Substituted or Unknown	4	7.4%	13	14.1%
Expected	50	92.6%	79	85.9%
p-value			0.515

Table 4, on the other hand, shows a statistical significance between the two sample groups on participant substance compounds' expectance and the laboratory results of its active compounds. A Fisher's exact test (p-value = 0.048) implies that the predicted adulteration and the actual adulteration may not be explained by chance.

Table 4

Crossing participants’ expectation of NPS sample adulteration with the laboratory confirmation of the sample adulteration.
					Substances' samples expected to be adulterated by the participants
	Total substances		n = 92	100%	No		Yes
	Total substances		n = 92	100%	n = 77	100%	n = 15	100%
Substances’ samples that contained other or another substance than the expected		Yes	n = 13	14.1%	8	11.7%	5	33.3%
		No	n = 79	85.9%	69	88.3%	10	66.7%
p-value								0.048*
*Statistically significant for a 95% confidence interval

Literature reviews

The screening process of the literature yielded 1984 (100%) studies that may be eligible to conduct the review across the 40 (74.1%) different substances found from the 54 (100%) analysed samples that fulfilled the study's procedures. A total of 188 (9.5%) studies were selected. Most of the reviewed studies that collected information from non-human data sources according to the studies' goals (n = 90; 47.3%) consisted mainly of the analytical characterisation of specimen samples (n = 69; 36.7%) (see Table 5). A minority of the studies collecting information from human sources (n = 98; 52.7%) did so at non-controllable settings, such as analyzing online forums (n = 11; 11.2%) or conducting surveys (n = 16; 16.3%). On the other

hand, most of the reviewed studies collecting information from human sources did so in controlled settings, such as case reports consisting of reported information from patients affected by medical conditions or forensic studies involving biological compounds. Others were cohort studies, based on information collected from a sample of a defined population and compared to similar information collected from a different sample of the same population ulteriorly; and clinical trials where living participants were involved (n = 71; 72.4%) and a clinician or researcher was present, able to corroborate reported information or able to make appropriate interventions. Furthermore, only case reports, cohort studies, and clinical trial methodologies were found in those studies.

Table 5

Screened and selected studies for each reviewed substance classified by main source of data collection.
Substances detected in the 54 analyzed, and reportedly self-administered samples		Studies yielded by search criteria	Studies involving non-human data sources according to study goal		Studies involving human data sources according to data collection			Total selected studies	Studies reporting subjective effects
		Studies yielded by search criteria	Analytical characterization of specific NPS	Prevalence estimation of specific NPS	Live participants	Online forums	Surveys and interviews	Total selected studies	Studies reporting subjective effects
O-PCE		11	0	1	3	0	0	4	2
O-Desmethyltramadol*		190	0	0	0	0	0	0	0
MXPr*		1	0	0	0	0	0	0	0
MiPT*		299	0	0	0	0	0	0	0
Methoxetamine		25	2	2	3	2	0	9	3
Methallylescaline*		1	0	0	0	0	0	0	0
MEAI*		2	0	0	0	0	0	0	0
ETH-LAD		4	1	0	0	0	1	2	1
DOPr**		133	0	0	1	0	0	1	0
DOiP*		6	0	0	0	0	0	0	0
DOF*		1	0	0	0	0	0	0	0
DOC**		10	0	0	1	0	0	1	0
BOD*		2	0	0	0	0	0	0	0
AMT		24	0	0	2	1	1	4	2
Alpha-PHP		33	3	0	5	0	0	8	3
6-APB		87	7	5	5	1	1	19	5
5-MeO-MiPT		14	0	0	1	0	0	1	1
5-MeO-DMT		25	0	0	3	0	3	6	5
5-MeO-DALT		21	0	1	2	0	0	3	2
4-HO-MiPT		3	0	0	1	0	0	1	1
4-HO-MET		73	1	1	1	2	0	5	1
4F-MPH**		6	1	0	2	0	0	3	0
4-Chlorodiazepam**		17	1	0	0	0	0	1	0
4C-D*		46	0	0	0	0	0	0	0
4-ACO-MET*		2	0	0	0	0	0	0	0
4-ACO-MALT*		1	0	0	0	0	0	0	0
3-MMC**		249	10	4	11	2	2	29	0
3-MeO-PCP		28	6	0	11	1	0	18	7
3-MeO-PCE**		5	1	0	0	0	0	1	0
3-HO-PCE*		0	0	0	0	0	0	0	0
3-FPM		13	0	0	4	0	0	4	3
2-FMA**		5	0	0	1	0	0	1	0
2-FDCK		3	2	0	0	0	0	2	0
2-FA**		294	2	0	0	0	0	2	0
2C-T-7**		34	10	0	0	1	0	11	0
2C-E		55	6	0	3	0	1	10	3
2C-D**		94	4	0	0	0	0	4	0
2C-C**		65	1	0	1	0	0	2	0
2C-B		128	11	7	10	1	7	36	5
25E-NBOH*		1	0	0	0	0	0	0	0
Total	n = 40	n = 1948	n = 69	n = 21	n = 71	n = 11	n = 16	n = 188	n = 44
Total	n = 40	n = 1948	n = 90		n = 98			n = 188	n = 44
* Substances which no previously peer-reviewed studies were found **Substances which no previously peer-reviewed studies that reported subjective effects of the drug were found

Of the 40 (100%) different analysed substances, only 27 (67.5%) had previous peer-reviewed scientific publications that defined the substance studied as a new psychoactive substance. The remaining 13 (32.5%) substances identified using the present research methodology were not present in any study of the screened database. Moreover, no studies reported data regarding the subjective effects of 12 (30%) of the NPS identified in this study. The current peer-reviewed literature did not include information on 25 (62.5%) of the analysed and reportedly self-administered substances that acknowledged their use as NPS or their subjective effects. See Table 6 for a detailed list of the reported substances with no previous information in the analysed literature.

Table 6

Summary of the most relevant analysed substances according to the previous peer-reviewed publications.
NPS without identified previous peer-reviewed publications on the conducted review			NPS without identified previous publications reporting their subjective effects on the conducted review
Abbreviation	Alternative Name		Abbreviation	Alternative Name
O-Desmethyltramadol	O-Desmethyltramadol		DOPr	2,5-Dimethoxy-4-propylamphetamine
MXPr	Methoxpropamine		DOC	4-Chloro-2,5-dimethoxyamphetamine
MiPT	N-Methyl-N-isopropyltryptamine		4F-MPH	4-Fluoromethylphenidate
Methallylescaline	4-Methallyloxy-3,5-dimethoxyphenethylamine		4-Chlorodiazepam	4-Chlorodiazepam
MEAI	5-methoxy-2-aminoindane or Chaperon		3-MMC	3-Methylmethcathinone
DOiP	2,5-Dimethoxy-4-isopropylamphetamine		3-MeO-PCE	3-Methoxyeticyclidine
DOF	2,5-Dimethoxy-4-fluoroamphetamine		2-FMA	2-Fluoromethamphetamine
BOD	beta-Methoxy-2C-D		2-FDCK	2-Fluorodeschloroketamine
4C-D	Ariadne		2-FA	2-Fluoroamphetamine
4-ACO-MET	4-Acetoxy-N-ethyl-N methyltryptamine		2C-T-7	2,5-Dimethoxy-4-(propylthio)phenethylamine
4-ACO-MALT	4-Acetoxy-MALT		2C-D	2,5-Dimethoxy-4-methylphenethylamine
3-HO-PCE	3-Hydroxyeticyclidine		2C-C	4-Chloro-2,5-dimethoxyphenethylamine
25E-NBOH	2C-E-NBOH
Total identified substances		n = 13	Total identified substances		n = 12
Total identified substances without previous data according to the study’s literature reviews criteria						n = 25

Two main findings of the study and their key implication are discussed below before discussing the results. The first main result of the study is that it is feasible to collect the intended information using the described inclusive methodology: an observational study combining information laboratory analysis of NPS samples and data from online surveys, both provided by online-recruited participants from a stigmatised population in a naturalistic setting. The second main finding of the study is that up to 13 different NPS were identified that could not be related to any previous peer-reviewed study from the studied literature database. The literature review was conducted after analysing the study results and included only the publications that mentioned the substances as NPS, sometimes discarding a significant number of publications providing relevant knowledge about substance pharmacology. To scale the results, 541 NPS were reported to the UNODC early warning advisory on new psychoactive substances in 2019. However, the number of newly identified NPS at a global level amounted only to 71 (including five whose effect has not yet been determined).

These two findings are further discussed during this section, specifying the actual study feasibility and commenting on the evidence obtained to provide further insight into its potential use.

After discussing the study's main findings, the reported results will be discussed in the same general order as they have been presented. The study participants, the study sample of analysed NPS with reported use by the participants, and the general feasibility of the study are discussed below.

The study procedure provided samples similar to the sizes reported when applying clinical methodologies. Most of the screened candidates (81.2%) failed to become enrolled participants (n = 35 18.2%). Nearly half of those enrolled (n = 35 100%) (n = 17; 48.6%) could fulfil all the designed procedures. This result puts the losses from the screening process into perspective, as the participant retention is acceptable considering the high demands from the study and its only reward being a limited free sample testing with a drug checking service. In the most similar online surveys studied, the response rates were close to 20% (increasing up to 80% in certain conditions not applicable in this context). On the contrary, our study was more demanding than the typical online survey with comparable characteristics described in the literature, leaving candidates' losses among the expected or better (64).

The participants of our study reported the same socio-demographic characteristics as the consistent descriptions of the population of e-psychonauts described in the literature (1, 65–68). Nonetheless, as the research's main goal was centred on combining reported information collected using online techniques and analytically confirming the reported expectations of the samples' content, the study was designed in a way that the participants mentioned above' characteristics outcomes were part of the screening and eligibility process (40).

Including and giving visibility to the contributions of people who use drugs is also essential to reduce stigma. The stigma is reduced when positive representations of the stigmatised reality become more prevalent. The importance of reducing stigma and its adverse effects has been well established (69–71). The statement mentioned above, however, might be controversial, as the generic negative representation of drug use has been used as a universal prevention strategy for drug use.

The designed methodology also intended to further participants promote a symmetrical dialogue with the researchers, which might increase internal data validity by contrasting the research framework with the participants' narrative (47, 70, 72). Inclusivity also facilitates knowledge empowerment of the study participants and promotes de development of citizen science, a goal set by the European Union to promote civil society participation. Increased participation would also accelerate the introduction of patient-reported outcomes that have shown to improve the obtention of conventional methodologies outcomes obtained in clinic settings (15, 73, 74).

Changing the focus from the participants' sample size to the primary sample of the research, we observe similar results. Of the total surveys (n = 136; 100%) that were intended to correspond to a submitted substance sample, more than two-thirds (n = 92; 67.6%) were received, and its correspondence to a valid substance characteristics survey was successfully determined. This percentage also represents the proportion of samples that were expected to be submitted and finally could be analytically identified and conformed to the comparison sample group for assessing biases due to procedure fulfilment.

Finally, the sample group that was defined by identified substances corresponding to fully reported surveys (both substance characteristics and self-administration surveys) consisted of 54 samples. By meeting the criterion mentioned earlier, we considered that more than half (58.7%) of the analysed samples could be labelled as confirmed NPS (comparing the reported expected content with the actual analysis result of a given sample). From these 54 confirmed NPS, 40 (74.1%) different substances were identified. We considered this a very high number of different identified NPS as it is not so far from the new NPS discovered yearly. For example, in 2020, 70 emerging NPS were identified for the first time (75).

No statistically significant differences in the data collected were detected (n = 54 and n = 92) when exploration of possible intervention biases due to reported analytical results back to the participant and receiving a fulfilled self-administration report. That suggests the absence of selection or completion bias and allows the reasonable discussion of all analysed samples' characteristics.

Regarding the reported sample origin, the most commonly reported was Europe, where point-of-care drug checking services are not as prevalent as in North America (76, 77). It is also remarkable that none of the samples allegedly obtained from the United States was detected as synthetic opioids, contrasting with the prevalence of these NPS observed using seizure data (4)

The most common substance provider was a trusted dealer (55.6%). Some studies report consistent findings, such as high levels of trust regarding regular "dealers" (78). However, other studies report inconsistent results, such as identifying distrust in the drug market as the primary motivation to use drug checking services and questioning the motivational effect of offering drug checking services to a group of participants that mostly report trusting their dealers (13, 79).

Regarding the setting in which the samples were acquired, the clear predominance (66.7%) of traditional internet websites (surface web) is inconsistent with previous reports describing the everyday use of encryption technology (darknet) and cryptocurrency as the usual acquisition settings (80). However, our results are consistent with previous research from drug checking services, showing the predominant use of surface websites to purchase emerging NPS (37).

This data proves that this study design is feasible, as in possible to execute. The narrow interpretations of the data discussed to this point would not encourage further research to consolidate this exploratory methodology. Thus, the drug checking service results and the literature review will be discussed afterwards.

Drug analysis laboratories are the key source of reliable and sound scientific information and data (81). Therefore, in this study, an off-site fixed laboratory equipped with GC/MS LC/MS was used to provide the additional reliability needed to cover the evidence gap, compromising in a delayed report of the analysis result to the user compared with other drug checking methodologies (34).

Most of the samples sent and analysed at the laboratory (n = 79; 84.8%) contained only the active compound that the participants expected, suggesting that the purchased NPS of the study participants seemed to be less adulterated than other participants in other settings, such as general users of drug checking services expecting to analyse traditional drugs. The adulteration of a traditional drug like MDMA, for example, is among the lowest, ranging from 10 to 50% (21, 82, 83). Our research found that only 13% of the total analysed samples were adulterated with other interfering active compounds or the expected substance was not present.

Reported information on the surveyed expectation of the participant samples' composition was also compared between both sample groups. Statistical Fisher's exact test did not indicate any difference, but it was noticeable that most participants expected the analysis to confirm their idea about the composition of their submitted sample.

When conducting the same statistical procedure in a correlation table, it pointed out that the given answers could not be entirely explained by randomness. Within a confidence interval of 95%, participants seemed able to predict if the sample contained only the substance they expected or was adulterated in some way.

However, more robust statistical methods could not be applied due to the limitations of the responsible data, and interpretation of participants' prediction capabilities needs to be interpreted conservatively. Despite its potential use in many scenarios, such knowledge may provide a false sense of security, as its reliability is still compatible with severe negative consequences.

From this point, we will refer to the previously mentioned 40 different NPS circumscribed to the sample group that fulfilled all the procedures established in the study protocol. The primary process that could provide information on the impact of that outcome was the reported outcomes review of scientific publications incorporating peer-reviewing methods.

Of the 40 different detected NPS, the literature review failed to find previous publications regarding 13 different identified NPS. Furthermore, the literature review also found no previous publications reporting the subjective effects of the reported NPS for another 12 identified NPS.

Even though our literature review did not find previous peer-reviewed publications regarding the identified substances used as NPS, it is widely known that international organisations such as EMCDDA or the UNODC report findings on these concrete fields of general drug use topics. Their contributions have not been acknowledged in this publication until this point. Maintaining the focus on the study goal of covering a well-described evidence gap has been the only reason to postpone their commentary (10, 11). The study results will be discussed in light of these relevant but not peer-reviewed reports. We will focus on the reports from the UNODC, as this is the only agency that provides data worldwide, refraining from commenting on the multiple exciting publications from the EMCDDA to avoid centring what ought to be a global discussion in Europe.

Of the 40 different identified substances, 5 (12,5%) had been previously scheduled by the UN: 2C-B (schedule II), 3-MeO-PCP or 3-methoxyphencyclidine (Schedule II), Etizolam (Schedule IV), 4-FA (schedule II) and DOC (schedule I) (84). None were from the 13 substances with no previous literature found in our review, nor the 12 substances with no previous reports of their subjective effects found in the same literature review. The literature review results suggest that the study has identified 13 previously unknown substances in the scientific community. However, the literature review presents limitations, and the authors might have missed some previously published knowledge. However, the likelihood of considering these 13 substances as previously unreported on official channels increases when considering the following result. EMCDDA early warning systems and UNODC have been searched for any reference to these substances without finding any results. Finding only results on google from vendors and online forums or informative platforms (Directive of the European Parliament and Council, 2004; EMCDDA, 2020; The Council of the European Union, 2004). There is, thus, the possibility that this paper is reporting for the first time in a scientific or formal channel the use of the following substances as NPS: 4C-D, 4-ACO-MALT, BOD, Methallylescaline, 25E-NBOH, 3-HO-PCE, 4-ACO-MET, DOF, DOiP, MEAI, MiPT, MXPr, O-Desmethyltramadol.

It is important to note, however, that precisely the only opioid identified in our study as NPS (e.g., O-Desmethyltramadol) has to be regarded in the light of several facts necessary to provide an adequate contextualisation. The O-Desmethyltramadol (n = 1; 1.1%) has mainly been researched, and numerous publications provide original empirical data regarding its human pharmacology. However, none of the mentioned publications considered the substance as NPS or recreational drug. That is the case because o-desmethyltramadol is an active metabolite of tramadol, a widely prescribed opioid(85, 86). So, in this context, it must be acknowledged that the significant overlapping of knowledge required from the metabolite of approved medication and the knowledge required from a substance being used for its psychoactive properties with its additional risks of abuse, overdose, and addiction. However, some new research is also warranted regarding its use as NPS and its potential new benefits and harms.

This study likely provides the first reports regarding the use of up to 13 substances as NPS, increasing the potential number of detected NPS considerably after its publication. However, this interpretation will have to be tested with potentially unpublished information or publications missed by the literature review. Regardless, a discussion of where this new methodology would be positioned in the current landscape of the NPS research field is warranted.

This study provides another innovative methodology that can benefit from the technological innovations of software development. In this regard, innovation in this direction can be seen as an alternative to developing methodologies such as data scraping. Promoting inclusivity, empowering citizen science, and open-source collaboration instead of raising ethical concerns about privacy and data ownership.

The study has not outperformed the early warning systems as forum data scraping has done almost 4:1 but provides more reliable data and is comparable to the number (more than half) of substances detected yearly by official institutions such as UNODC (75). However, for this methodology to have a real scientific impact, it needs to be coupled with methodologies with external validity. Studies such as wastewater analysis can provide the capacity to make prevalence estimations of the general use of the newly identified NPS, triaging anecdotes from the early detection of new trends (87, 88).

Despite all the potential that can be imagined, it is essential to remember that this study is only an experimental, observational design(34). We hope, however, that at least the study can provide the early methodological guidance that observational studies can provide according to the Cochrane guidelines, informing the development of future designs based on the time-tested strengths and weaknesses (89).

After discussing the potential and dependency of the study, a proper discussion of its limitations is warranted.

The study has several limitations intrinsic to its design, representing choices consciously made by the research team to overcome the critical limitations affecting previous research in the field. Other limitations, however, reflect avoidable shortcomings of the study implementation or execution, from which some might be solved with experience and others with increased funding.

Another restriction of the possible outcomes obtained by this study design is related to the sample size. While consisting of a reasonable number of participants for a clinic setting, generalisation of the results to the original population is unsuitable. Thus, more established NPS-monitoring methodologies that work with larger samples may complement our designed method. These studies are already being used successfully by many research teams and networks around the globe. One of those monitoring methodologies is wastewater analysis, which can provide analytical confirmation of substances from a larger sample of collected data. However, because the sample size also requires other methods to signal which chemical compounds might be present, the screening process may use a narrow range of substance comparison samples (87, 90, 91)

The main limitation regarding the main study finding is the lack of external validity and, thus, the impossibility of extrapolating the presented results from our NPS samples to any significant population. Only time or the contribution to another study design, such as wastewater analysis, can consolidate the relevance of this crucial finding.

Another limitation regarding the study feasibility, the other key finding, is the difficulties that might arise when trying to replicate the study design, as the conversations and inclusivity process done with members of the online communities where NPS use is discussed. Then the participants might have benefitted from some idiosyncrasies poorly translated in the methodology, study protocol and supplementary materials.

The inability to directly observe and confirm the participants' behaviour is a limitation to the reliability of the data from the questionnaires. The researcher's physical absence, however, makes the study feasible for many reasons.

Another limitation is the accumulation of methodological innovation. When the study design is too different from other studies, its capacity to connect its results to the shared pool of scientific knowledge becomes limited, possibly the study's main limitation. Despite being intrinsic to the innovative design, its impact could be reduced by diminishing the amount of methodological experimentation in future similar designs.

Finally, the possibility of open sourcing all the non-confidential coding of the platform and surveys might have increased engagement and even contributed to the automatisation of time-consuming processes. Our experience convinced us that when tools were provided to the study participants, everyone benefited after finishing the data collection period (92).

The primary limitations and the potential policy implications of this study will be briefly discussed before wrapping up this discussion.

First, this study adds to the mounting evidence that positions drug checking services as a necessary intervention to complete the monitoring of the NPS markets. Multiple occasions have proven that agile monitoring, evaluating, and understanding of constantly evolving synthetic drugs markets has delivered successful outcomes (UNODC, 2021b).

Second, these results also support the feasibility, efficacy, and ethical responsibility of involving the diverse people who use drugs in the scientific process (74). Sadly, the best practice guidelines from the Early Warning Advisory on NPS from the UNODC and the European drug prevention quality standards both do not include people who use drugs in the critical aspects of designing or evaluating interventions, despite advocating for networking with all relevant stakeholders (81, 93).

Third, provide better and faster evidence to support science-based policies instead of morals. Both quality of the information and the information gaps will affect the level of uncertainty in the answers to the questions posed. Uncertainty needs to be made explicit in the conclusions of the risk assessment to deal with the limitations of information scientifically (94).

To conclude, the potential to promptly identify potential new trends that can later inform other methodologies that require this information, producing timely and reliable data necessary to produce evidence-based drug policy. Finally, the study might show the path to developing innovative technologies and inclusive methodologies to fill the evidence gap in the NPS literature.

NPS: new psychoactive substances
EU: European Union
UN: United Nations
RCT: randomised controlled trial
DCS: drug checking services
GRASP: Global Research and Analysis of New Substances Project
EMCDDA: European Monitoring Centre for Drugs and Drug Addiction
EC: European Council
EUR: Euro
EWS: Early Warning System
UNODC: United Nation Office on Drugs and Crime
3-MMC: 3-Methilmethcathinone OR 2-(Methylamino)-1-(3-methylphenyl)propane-1-one
4-HO-MET: Metocin or methylcybin or 3-{2-[Ethyl(methyl)amino]ethyl}-1H-indol-4-ol
4-HO-MiPT: Miprocin or 3-{2-[methyl(propan-2-yl)amino]ethyl}-1H-indol-4-ol
2-FDCK: 2-Fluorodeschloroketamine, 2-(2-Fluorophenyl)-2-methylamino-cyclohexanone
C-D: Dimoxamine or BL-3912 or 1-(2,5-Dimethoxy-4-methylphenyl)butan-2-amine
2C-T-7: Blue Mystic or 2-[2,5-Dimethoxy-4-(propylsulfanyl)phenyl]ethan-1-amine
2C-E: Aquarust or 2-(4-Ethyl-2,5-dimethoxyphenyl)ethanamine
2C-D, 2C-M: 2-(2,5-Dimethoxy-4-methylphenyl)ethan-1-amine
2C-B: Bromo Mescaline, 2,5-dimethoxy-4-bromophenethylamine, 2-(4-Bromo-2,5-dimethoxyphenyl)ethanamine
3-MeO-PCP: 3-Methoxyphencyclidine or 1-[1-(3-methoxyphenyl)cyclohexyl]-piperidine
ETH-LAD: N-Ethyl-nor-LSD or (6aR,9R)-N,N-diethyl-7-ethyl-4,6,6a,7,8,9-hexahydroindolo-[4,3-fg]quinoline-9-carboxamide
25E-NBOH, 2C-E-NBOH, NBOH-2C-E”: “2-({[2-(4-ethyl-2,5-dimethoxyphenyl)ethyl]amino}methyl)phenol”
2C-C: 2-(4-Chloro-2,5-dimethoxyphenyl)ethan-1-amine or 4-Chloro-2,5-dimethoxyphenethylamine
2-FA: 2-Fluoroamphetamine or 1-(2-Fluorophenyl)propan-2-amine
2-FMA: 2-Fluoromethamphetamine or (RS)-1-(2-Fluorophenyl)-N-methylpropan-2-amine
3-FPM: 3-Fluorophenmetrazine
3-FPH: 2-(3-Fluorophenyl)-3-methylmorpholine
3-HO-PCE: 3-Hydroxyeticyclidine or 3-[1-(Ethylamino)cyclohexyl]phenol
3-MeO-PCE: 3-Methoxyeticyclidine, Methoxieticyclidine or N-Ethyl-1-(3-methoxyphenyl)cyclohexan-1-amine
4-AcO-MALT: 4-Acetoxy-MALT or 3-(2-(allyl(methyl)amino)ethyl)-1H-indol-4-yl acetate
4-AcO-MET: 4-Acetoxy-MET, Metacetin or 3-{2-[Ethyl(methyl)amino]ethyl}-1H-indol-4-yl acetate
4F-MPH, 4-FMPH: 4-Fluoromethylphenidate or Methyl 2-(4-fluorophenyl)-2-(piperidin-2-yl)acetate
5-MeO-DALT: N,N-Diallyl-5-methoxytryptamine or N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-(prop-2-en-1-yl)prop-2-en-1-amine
5-MeO-DMT: 5-methoxy-N,N-dimethyltryptamine, O-methyl-bufotenin or 2-(5-Methoxy-1H-indol-3-yl)-N,N-dimethylethanamine
5-MeO-MiPT: 5-Methoxy-N-methyl-N-isopropyltryptamine or N-[2-(5-methoxy-1H-indol-3-yl)ethyl]-N-methylpropan-2-amine
6-APB: 6-(2-Aminopropyl)benzofuran, Benzofury or 1-(1-Benzofuran-6-yl)propan-2-amine
α-PHP: alpha-Pyrrolidinohexanophenone or (RS)-1-Phenyl-2-(pyrrolidin-1-yl)hexan-1-one
AMT: alpha-Methyltryptamine, α-Methyltryptamine or 1-(1H-Indol-3-yl)propan-2-amine
BOD: 4-methyl-2,5,β-trimethoxyphenethylamine or 2-(2,5-Dimethoxy-4-methylphenyl)-2-methoxyethan-1-amine
DOC: 2,5-Dimethoxy-4-chloroamphetamine or 1-(4-Chloro-2,5-dimethoxyphenyl)propan-2-amine
DOF: 2,5-Dimethoxy-4-fluoroamphetamine or 1-(4-Fluoro-2,5-dimethoxyphenyl)propan-2-amine
DOiP: 2,5-Dimethoxy-4-isopropylamphetamine or 1-[2,5-Dimethoxy-(propan-2-yl)phenyl]propan-2-amine
DOPr: 2,5-Dimethoxy-4-propylamphetamine or 1-(2,5-Dimethoxy-4-propylphenyl)propan-2-amine
MEAI, 5-MeO-AI: 5-methoxy-2-aminoindane or Chaperon
3-MeO-2'-Oxo-PCE, MXE: Methoxetamine, Mexxy or (R/S)-2-(3-Methoxyphenyl)-2-(ethylamino)cyclohexanone
MiPT: Methylisopropyltryptamine, N-Methyl-N-isopropyltryptamine or N-[2-(1H-indol-3-yl)ethyl]-N-methylpropan-2-amine
MXPr: methoxpropapine, 2-Oxo-3'-methoxy-PCPr or 2-(3-Methoxyphenyl)-2-(propylamino)cyclohexanone
O-DSMT: O-Desmethyltramadol, Desmetramadolor or 3-(2-((dimethylamino)methyl)-1-hydroxycyclohexyl)phenol
O-PCE, 2'-Oxo-PCE: N-ethyldeschloroketamine, eticyclidone or 2-(ethylamino)-2-phenyl-cyclohexanone

Ethics approval and consent to participate

This study was applying the approved protocol on research of new psychoactive substances through online forum recruitment of people who claim using NPS

The study protocol was approved by the Clinical Research Ethics Committee (Parc de Salut Mar, Barcelona, Spain, ref. 2018/8283/I) in January 2019.

All the data collected online on the participants were encrypted according to the European and Spanish data protection regulations: 2016/679 European Parliament General Data Protection Regulation (GDPR) and 27/4/16 “Reglamento general de protección de datos” (RGPD) Spanish Royal Decree.

Completed informed consent documents were received by email

Availability of data and materials

All datasets, database, applied code on statistical procedures, material sent to participants and expanded information on different aspects of this manuscript can be obtained by contacting directly with the corresponding author (FC).

Competing interests

Dr, Monica Barratt is the Executive Director of Bluelght.org, one of the communities that promoted this study

Dr. Marc Grifell, Guiem Mir, Roger Estelrich-Alzubide and Xoán Carbón are or have been non-paid collaborators of Energy Control specific projects.

Ivan Fornís and Cristina Gil are technical laboratory personnel paid by EC-ABD and Dr. Mireia Ventura is chief of department of the Analysis Service of EC-ABD.

Funding

No specific funding was received to conduct this study exclusively. However, the study benefited from grants and funding given to MG as an individual or to broader funding applications from which this study was only a minor project.

MG dedication (e.g. salary) was funded by a 2-years Rio Ortega grant from the public Instituto de Salud Carlos III a grant (CM18/00168)ISCIII/FSE. The grant allowed for mostly full-time dedication to academic research, but that time was divided into several different projects.
New technology development was publicly sponsored by the Catalan government Valuni thanks to an ACC1Ó grant to develop new technologies VALUNI16-1-0011. Less than 10.000 EUR.
Energy Control – Associació Benestar i Desenvolupament, privately funded the cost of the laboratory analysis needed to conduct the study. Less than 10.000 EUR.
Columbia University privately funded the software used to collect sensitive data online (Qualtrics). Less than 1.000 EUR

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions

MG, GM and RE-A had contributed by having made substantial contributions to the conception, design of the work, acquisition, analysis and interpretation of data and the development of this manuscript from the original idea.

MG and XC had contributed during the recruitment stage by assessing participants and implementing the study protocol.

MG and RE-A have had a main role in writing this manuscript, coordinate all authors and provide creative solutions to clarify the complexity of the study.

GM and JG have been participating in the creation of new code scripts used in the statistical analysis and have had a key role in data management, secure data storage and data analysis, including statistical analysis design.

XC has been the main developer of the online platform to run the study and contributed with creative problem solving to diverse implementation challenges.

IF and CG had contributed by elaborating new chemical analyses protocols for specific substances as scientific team of the Analysis Laboratory at EC-ABD.

MV has been developing the main role of supervising, organizing and handling the substance samples sent to EC-ABD.

MR had contributed to the conception and design of the manuscript after the first assessment conducted by senior researchers. Also contributed to review and solve different challenges during the early stages of the manuscript writing.

LG have been supervising and obtaining funds to develop the study along with providing substantial feedback and contributed by revising all previous versions and guiding the first author as senior researchers in the field.

MF, MB, and VP have given substantial feedback and contributed by supervising the study and revising all versions of the manuscript guiding the first author as senior researchers in the field.

FC has been the main mentor of the first author, has supervised the study and given substantial feedback and contributed by revising all previous versions.

All authors declare to have approved the submitted version and to have agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.

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Table 2 is available in the Supplementary Files section.

Competing interest reported. Dr Monica J Barratt is the Executive Director of Bluelght.org, one of the communities that promoted this study.

Dr Marc Grifell, Guiem Mir, Roger Estelrich-Alzubide, and Xoán Carbón have been working members or non-paid collaborators in Energy Control through different projects.

Ivan Fornís and Cristina Gil are technical laboratory personnel paid by EC-ABD.

Dr Mireia Ventura is the chief of the department of the Analysis Service of EC-ABD.

All of the declarations stated previously consist of non-financial competing interests.

Table2.docx
Table 2. Identified compounds of all analysed samples according to participants’ result expectation
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Additionalfile2.docx

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Identifying new psychoactive substances (NPS) using drug checking services: An innovative observational study design

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Abstract

Background

Methods

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Introduction

Methodology

Overview

Ethical considerations

Recruitment and sample collection

Legal Considerations

Data Collection

Online surveys

Laboratory analysis

Data management and statistical analysis

Results

Study feasibility

Sample characteristics

Drug checking findings

Expected and actual identified substances

Discussion

Conclusions

List Of Abbreviations

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References

Tables

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Supplementary Files

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