Commentary on Craft <i>et al</i>.: Drug contaminants and substitutions in illicit vapes represent a major health risk
Bibliographic record
Abstract
The discovery of contaminants in unregulated THC vaping products brings cause for concern due to the additional health harms associated with the heating and inhalation of drugs not designed for this route of administration. Efforts are needed to educate vape users of the harms of unregulated vaping products. In the past decade, nicotine vaping in the UK has changed dramatically. Originally targeted at adults as smoking cessation aids [1], the use of vapes amongst teenagers and young adults has rapidly grown to what has been described as a ‘vaping epidemic’, with a 2023 survey showing that 20% of 11–17 year olds had tried vaping, up from 7% in 2014 [2]. A significant driver of this trend has been cited as the emergence of disposable vapes that are available in a variety of flavours and nicotine strengths [3]. In response to the health and environmental concerns posed by disposable vapes, the UK Government announced that they will be banned from June 2025 [4]. Concurrently, there has been emerging demand for non-nicotine vapes, predominated by those marketed as containing Δ-9-tetrahydrocannabinol (THC) [5], the major psychoactive ingredient in cannabis [6]. Although it is legal to manufacture and sell THC vapes in other countries (e.g. Canada, Germany and certain states in the USA), they remain illegal in the UK [7]. Craft et al. describe a case where an individual submitted seven vapes sold as containing ‘THC-based products’ to a drug and alcohol service in the UK, which upon forensic toxicological analysis were found to contain the synthetic cannabinoid 5F-MDMB-PICA [8]. Synthetic cannabinoids, including 5F-MDMB-PICA, are full agonists of the CB1 receptor [9], and have been linked to several fatal and non-fatal poisonings [10, 11]. The contamination and substitution of illicit THC vapes with other substances has also been observed elsewhere, with the Welsh Emerging Drugs & Identification of Novel Substances (WEDINOS) project [12] – an initiative that tests drug samples submitted by members of the public – detecting a variety of both illicit drugs (e.g. cocaine, heroin, ketamine, synthetic opioids of the nitazene class, ‘street’ benzodiazepine bromazolam, the hallucinogen 25B-NBOH and the synthetic stimulant 4-CEC) and licensed medicines (e.g. aspirin, dihydrocodeine, the local anaesthetic lidocaine, the sedating antihistamine promethazine and the anxiolytic pregabalin) in samples submitted as ‘THC vapes’, ‘THC vape fluid’ or ‘THC vape juice’. The health harms of illicit drugs such as cocaine and nitazenes are well documented and understood [13, 14]. However, the vaping of many drugs – whether illicit substances or licensed medicines – will likely pose additional health harms as drugs are seldom designed to be heated and inhaled as the route of administration. Whereas the risks of vaping potent sedatives such as nitazenes may be more immediately apparent, with rapid systemic absorption by the alveolar epithelium leading to respiratory depression, the addition and/or substitution of common licensed medicines such as aspirin and lidocaine into vape fluid may at first seem fairly innocuous. However, upon heating, aspirin can break down to form salicylic acid and acetic acid [15], which if inhaled can cause lung irritation leading to significant inflammation, and the vaping of lidocaine could cause myocardial infarction as lidocaine can precipitate cardiac arrythmias [16]. Prior to the UK ban of disposable vapes coming into enforcement in June 2025, there needs to be significant investment in harm reduction strategies for people who may then source vapes from alternative unregulated suppliers, with particular focus on reaching younger people. Education initiatives are needed to highlight the risks of obtaining unregulated vape products and to alert users to the adverse effects of contaminated or substituted vapes (e.g. chest pain, difficulty breathing or confusion), to encourage timely medical intervention. This initiative could be expanded to address the wider problem of substituted and contaminated counterfeit drug products from other unregulated sources, such as online pharmacies [17]. Drug checking facilities – such as the one opened in Bristol in January 2024 [18] – would also play a vital role in this remit by helping people to verify the contents of their purchases. Craft et al. have brought to attention the public health issue of THC vape contamination, and also therefore the opportunity to advocate for harm reduction measures to prioritise the safety of vape users. None. No funding source. None. Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".