LC-MS/MS determination of 27 antipsychotics and metabolites in plasma for medication management monitoring
Why this work is in the frame
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Bibliographic record
Abstract
With the increasing prevalence and escalating complexity of mental disorders, precise medication has become critically important. This necessitates an efficient, accurate, and convenient method for drug concentration monitoring to support laboratory personnel and clinicians. In this study, three liquid chromatography-tandem mass spectrometry methods were developed and validated for simultaneously determining and quantifying 27 antipsychotics and related metabolites in human plasma. The plasma samples were subjected to protein precipitation using methanol, with isotope-labelled internal standards (ISs), followed by separation via isocratic elution on a BEH C18 column. Mass spectrometric analysis was performed using electrospray ionisation in positive ionisation mode with multiple reaction monitoring for quantitative detection. The analytes demonstrated high separation efficiency, with a single sample run time of 3.0 min. The method exhibited a wide linear range with excellent linearity across the concentration range. The intra- and inter-batch precision were ≤10.00%, the accuracy was 88.67–113.29%. Accurate quantification of antipsychotics remained unaffected under various storage conditions: 72 h at room temperature, 7 d at 4 °C refrigeration, and 14 d at −80 °C freezing. This validated methodology has been successfully applied to plasma samples from patients with psychiatric disorders, demonstrating its practical utility for accurate quantification of antipsychotics in large-scale and complex matrices containing multiple analytes.
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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 | 0.000 | 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