LC-MS/MS determination of 27 antipsychotics and metabolites in plasma for medication management monitoring
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".