Determination of Clozapine and its Metabolite, Norclozapine in Various Biological Matrices Using High-Performance Liquid Chromatography
Bibliographic record
Abstract
PURPOSE: To develop and validate an HPLC method for the quantitation of clozapine and its metabolite, norclozapine in human plasma, rat plasma, and the various human plasma lipoprotein fractions. METHODS: Using liquid-liquid extraction, clozapine, and norclozapine are extracted from the biological matrix with MTBE. After concentration, the residue was reconstituted in 1mM HCl and injected on to a C6 Phenyl column (3 microm, 2.0 x 150 mm). Mobile phase was 10 mM ammonium acetate, pH 5--acetonitrile--methanol (5:3:2, v/v/v). Loxapine served as the internal standard. Absorbance was measured at 254 nm. RESULTS: Quantitation limits for clozapine and norclozapine was 100 ng/mL and 50 ng/mL, respectively. Recovery for both clozapine and norclozapine was near 100%. Percent accuracy for intraday variability in human plasma, rat plasma, and human TRL, HDL, LDL, and LPDP lipoprotein fraction was between 92 to 113% for both analytes. Intraday precision for the same matrices was less than 9% CV for both analytes. Percent accuracy and precision for interday variability in human plasma was 97 to 103% and less than 10% CV, respectively. Samples were stabile in the autosampler for 80 h at 10 degrees C and on the benchtop for 2 h. It should be noted, Clozapine-N-oxide, which is a known metabolite of Clozapine, was not determined since it is not clinically active. CONCLUSIONS: This method is a simple, fast and robust HPLC assay for the determination of clozapine and norclozapine in various matrices and lipoprotein fractions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".