Determination of Clozapine and its Metabolite, Norclozapine in Various Biological Matrices Using High-Performance Liquid Chromatography
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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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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