B-275 The Evolving Illicit Opioid Landscape: Insights into Fentanyl Analogs Para-Fluorofentanyl and Ortho-Methylfentanyl
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".