Quantitative determination of opioids in whole blood using fully automated dried blood spot desorption coupled to on‐line SPE‐LC‐MS/MS
Why this work is in the frame
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Bibliographic record
Abstract
Opioids are well known, widely used painkillers. Increased stability of opioids in the dried blood spot (DBS) matrix compared to blood/plasma has been described. Other benefits provided by DBS techniques include point-of-care collection, less invasive micro sampling, more economical shipment, and convenient storage. Current methodology for analysis of micro whole blood samples for opioids is limited to the classical DBS workflow, including tedious manual punching of the DBS cards followed by extraction and liquid chromatography-tandem mass spectrometry (LC-MS/MS) bioanalysis. The goal of this study was to develop and validate a fully automated on-line sample preparation procedure for the analysis of DBS micro samples relevant to the detection of opioids in finger prick blood. To this end, automated flow-through elution of DBS cards was followed by on-line solid-phase extraction (SPE) and analysis by LC-MS/MS. Selective, sensitive, accurate, and reproducible quantitation of five representative opioids in human blood at sub-therapeutic, therapeutic, and toxic levels was achieved. The range of reliable response (R(2) ≥0.997) was 1 to 500 ng/mL whole blood for morphine, codeine, oxycodone, hydrocodone; and 0.1 to 50 ng/mL for fentanyl. Inter-day, intra-day, and matrix inter-lot accuracy and precision was less than 15% (even at lower limits of quantitation (LLOQ) level). The method was successfully used to measure hydrocodone and its major metabolite norhydrocodone in incurred human samples. Our data support the enormous potential of DBS sampling and automated analysis for monitoring opioids as well as other pharmaceuticals in both anti-doping and pain management regimens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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