Cross-reactivity in urine of 53 cannabinoid analogs and metabolites using a carboxylic acid enzyme-linked immunosorbent assay (ELISA) and homogenous enzyme immunoassay (HEIA) kit and immunalysis synthetic cannabinoid HEIA kits
Bibliographic record
Abstract
Advancing knowledge of endocannabinoid receptor agonists and the federal legalization of hemp has created a cannabinoid market of naturally abundant phytocannabinoids to a wide array of semi-synthetic and synthetic cannabinoid analogs. Public safety and toxicological concerns exist from lack of regulation, limited pharmacological and metabolomic data, and minimal knowledge of detection ability. Structural similarities of the cannabinoid analogs may allow detection on immunoassays including enzyme-linked immunosorbent assays (ELISA) and homogenous enzyme immunoassays (HEIA), screening platforms in forensic toxicology laboratories for rapid presumptive testing. The cross-reactivity of 27 cannabinoid analogs and 26 commercially available metabolites was evaluated using the Medica EasyRA Enzymatic Immunoassay Analyzer with the Immunalysis Cannabinoids (THC) and Synthetic Cannabinoids 1-3 kits. These analogs were also evaluated using the Dynex DSX Automated ELISA System with the OraSure Technologies Cannabinoids Intercept Microplate EIA. The cannabinoid kits target 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (Δ9-THCCOOH) at a 50 ng/mL cutoff, and the synthetic cannabinoid kits target the N-pentanoic acid metabolite of JWH-018, UR-144, and AB-PINACA at a 10 ng/mL cutoff. Cross-reactivity was evaluated at concentrations of 20, 50, 100, 500, and 1000 ng/mL in urine in triplicate. Absence of cross-reactivity at 1000 ng/mL was considered undetectable. No cross-reactivity was detected on the synthetic cannabinoid kits. Cross-reactivity to Δ9-THCCOOH kits was variable with Δ8-THCCOOH and R-HHCCOOH cross-reacting at the cutoff on the ELISA, with several additional phase I metabolites cross-reacting at 100 ng/mL on both platforms. Analogs lacking the Δ9-THC tricyclic structure and pyran ring cyclization including cannabidiol were undetectable. Alicyclic bond location and alkyl chain length variably affected cross-reactivity, with alkyl lengths 2-4 having increased cross-reactivity comparatively. Compound chirality was also observed to effect instrumental response, with the ELISA having increased cross-reactivity and instrumental response to R-isomers. As knowledge and prevalence of analogs increases, it is crucial to understand the impact on utilized testing platforms.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".