Paper Spray Mass Spectrometry for the Quantitation of Drugs of Abuse in Biofluids and Street Drug Samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Paper spray mass spectrometry (PS-MS) has been developed as a tool for the analysis of drugs of abuse (DoA) in street drug samples, urine, and oral fluid. PS-MS is presented as a viable alternative to the traditional gas chromatography and liquid chromatography-mass spectrometry methods. PS-MS achieves sensitive and quantitative results in as little as 1-2 minutes with little to no sample preparation. Initial research presented in this thesis illustrates how PS-MS was developed for the analysis of fentanyl and related analogs of powdered drug slurries acting as a proxy for street drug samples, as a proof of concept for real world drug checking. Analysis of DoA in these pseudo-drug samples demonstrated the potential for both quantitative and qualitative analysis of fentanyl analogs. PS-MS was then demonstrated and evaluated for its effectiveness for real world drug checking applications during a world first demonstration of PS-MS for on-site drug checking in the Downtown Eastside of Vancouver, British Columbia, a recognized epicenter of the opioid overdose crisis. During the pilot test, 113 samples were submitted for analysis and successfully quantified using PS-MS, which targeted and quantified 49 different drug targets. Of these 113 drug samples, 44% of all samples were found to contain fentanyl, with a median concentration of 3.6% (w/w). The benzodiazepine etizolam was detected in 10 samples, none of the people who submitted these 10 samples expected a benzodiazepine to be present in their sample. It was later found that other drug checking technologies in use were underreporting the presence of etizolam or other benzodiazepines present in drug samples. These results, coupled with the quantitative capabilities and low levels of detection observed during the pilot test of PS-MS for drug checking demonstrated the efficacy of PS-MS and inspired further development of the application. PS-MS was then implemented by the Vancouver Island Drug Checking Project for the routine quantitative measurement of thousands of drug samples. During the span of this routine measurement, two unidentified compounds began appearing in carfentanil-containing drug samples. High resolution accurate mass (HRAM) mass spectrometry was used to determine the chemical composition of these two unknowns as C23H29N3O2 (m/z 380.2333) and C23H29N2O3 (m/z 381.2137). Further tandem mass spectrometry experiments were used for structural elucidation and the unknowns were putatively identified as desmethylcarfentanil amide and desmethylcarfentanil acid. LC-MS data on different drug samples containing the same compounds further supported the identification of these carfentanil structural analogs. µ-Opioid receptor binding modeling determined that the binding poses, and binding energies of these structural analogs were nearly identical to that of carfentanil, suggesting potentially similar activities/toxicities. PS-MS was further applied to the analysis of cannabinoids in urine and oral fluid samples. Due to the inherently low ionization efficiencies and sensitivity to cannabinoids observed with PS-MS, a reactive paper spray ionization method utilizing a diazonium salt as a on-paper derivatization reagent was developed. The derivatization scheme dramatically lowered the limits of detection for tetrahydrocannabinol (THC) in oral fluid and THC metabolite in urine, to levels able to meet forensic and clinical cutoff values (low parts per billion). The quantitative results were compared to a LC-MS results from a commercial clinical laboratory, demonstrating good agreement between the two methods. The results presented herein demonstrate the applicability and dramatic benefits of PS-MS for drug checking applications, as well as for cannabinoids in oral fluid and urine. High resolution mass spectrometry is demonstrated for the structural elucidation and identification of unknown drug compounds in an ever-changing street drug supply.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it