Therapeutic Drug Monitoring of Clozapine and Norclozapine in Human Serum Using Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry
Bibliographic record
Abstract
A rapid, sensitive, and specific method was developed and validated using ultra-performance liquid chromatography- tandem mass spectrometry (UPLC-MS-MS) for simultaneous determination of clozapine and its major metabolite norclozapine in human serum. The compounds were extracted from serum by a single step protein precipitation and analyzed using a UPLC-triple-quadrupole detection (TQD) system. Separation of compounds was achieved on a BEH C18 (50 mm x 2.1 mm, 1.7 microm) analytical column using methanol and water (both containing 0.2% ammonium hydroxide) as the mobile phase at a flow rate of 0.40 mL/min. The compounds were ionized in the electrospray ionization ion source of the TQD and were detected in the multiple reaction monitoring (MRM) mode. The MRM transitions m/z 327 --> 270 and m/z 313 --> 192 for clozapine and norclozapine, respectively, were used for the quantification ions. Clozapine transition 327 --> 192 and norclozapine transition 313 --> 270 were used as confirmation ions. Linear calibration curves in human serum were generated over the range of 10-2000 ng/mL for both clozapine and norclozapine with a correlation coefficient (r(2)) > 0.9970. Calibration curves exhibited consistent linearity and reproducibility. Interassay coefficients of variation (CV) (n = 20) were 3.04-4.94% for clozapine and 2.84-6.07% for norclozapine. Intra-assay CVs (n = 6, 20 days) were 0.61-1.26% and 1.62-2.21% for clozapine and norclozapine, respectively. The extraction recoveries were larger than 95% for both clozapine and norclozapine. The method was applied to the quantification of clozapine and norclozapine in the sera of schizophrenic patients, and the data revealed that the concentrations of two compounds varied significantly in the patients treated with clozapine.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".