B-275 The Evolving Illicit Opioid Landscape: Insights into Fentanyl Analogs Para-Fluorofentanyl and Ortho-Methylfentanyl
Notice bibliographique
Résumé
Abstract Background The illicit drug landscape has shifted significantly with changes in the availability, use and types of novel psychoactive substances. Two such substances, para-fluorofentanyl (pFF) and ortho-methylfentanyl (OMF), illicit fentanyl analogs, have increasingly surfaced in recent years. Their potency varies across different drug supplies due to the unregulated and clandestine nature of their chemical manufacturing process. This can pose a serious risk to users, as it can increase the likelihood of overdose and fatalities. Despite their presence, there remains a substantial gap in understanding these synthetic fentanyl analogs. This study aims to characterize the prevalence, concentration levels, and co-occurrence of pFF and OMF in urine samples from individuals dependent on opioids. Additionally, it seeks to evaluate the cross-reactivities of these substances with a fentanyl immunoassay. Methods Urine samples were collected and analyzed over an eight-month period for fentanyl, norfentanyl, and pFF, while OMF testing was conducted on samples collected over three months. Immunoassay data was obtained using an Olympus AU480 instrument with the Thermo Scientific DRI Fentanyl assay. Mass spectrometry analysis was performed using an internally developed dynamic multiple reaction monitoring method on an Agilent 6470 triple quadrupole instrument. To assess cross-reactivity, OMF was spiked into urine in triplicate at concentrations of 1, 2, 5, 10, 20, 50, 100, 200, and 500 ng/mL, while pFF was tested at 1, 2, 3, 5, and 10 ng/mL. Results Over an eight-month period, a total of 4,808 urine samples underwent drug testing. Among these, 26% confirmed positive for fentanyl. We then examined the presence of fentanyl analogs, pFF and OMF in the fentanyl positive samples. During this period, 1,267 fentanyl-confirmed samples were analyzed, with 89% testing positive for pFF. pFF concentrations ranged from 2 to 7,300 ng/mL, with an average of 697 ng/mL. Since OMF was introduced later in our testing panel, we analyzed three months of data, totaling 528 fentanyl-confirmed samples. Of these, 75% tested positive for OMF, with concentrations ranging from 1 to 8,600 ng/mL with an average of 242 ng/mL. Among pFF-positive samples, 84% were also positive for fentanyl, 97% for norfentanyl, and 91% for OMF. In OMF-positive samples, 92% tested positive for fentanyl, 98% for norfentanyl, and 72% for pFF. Cross-reactivity analysis showed that pFF had a 41% cross-reactivity with the fentanyl immunoassay, while OMF exhibited minimal cross-reactivity (<2%). Conclusion Our findings highlight the widespread presence of fentanyl analogs in opioid-dependent populations, with pFF and OMF frequently co-occurring with fentanyl. Their high prevalence suggests significant distribution within the illicit drug supply. Cross-reactivity analysis revealed that pFF exhibits moderate immunoassay cross-reactivity, while OMF demonstrates minimal cross-reactivity, underscoring the need for confirmatory mass spectrometry testing. These results reinforce the evolving nature of the illicit opioid landscape and the necessity for continued monitoring and improved detection strategies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».