Quantifying Intraindividual Variations in Plasma Clozapine Levels
Bibliographic record
Abstract
OBJECTIVE: Clozapine has strong recommendations for therapeutic drug monitoring. While factors that influence interindividual variation in plasma clozapine levels have been extensively reported, intraindividual variation remains poorly studied. We employed a population pharmacokinetic approach to assess intraindividual variations in plasma levels of both clozapine and N-desmethylclozapine, as well as the impact of smoking on this variability. METHODS: Patients who were initiated on clozapine from January 2009 to December 2010 and who provided at least 2 plasma samples were included in this study. The observed concentrations of clozapine and N-desmethylclozapine were applied in a Bayesian pharmacokinetic modeling approach by using a previously published pharmacokinetic model from an independent sample to compute a predicted concentration. The predicted concentrations of clozapine and N-desmethylclozapine were then compared with the observed concentrations in the form of a ratio: predicted-to-observed concentration ratio (Cpred/Cobs). The coefficient of variation of the Cpred/Cobs ratios was taken as a measure of intraindividual variation. RESULTS: A total of 723 plasma levels from 61 patients were included in this analysis. The coefficient of variation of Cpred/Cobs ratios for clozapine and N-desmethylclozapine were 29.8% (SD = 17.2%) and 27.4% (SD = 16.4%), respectively. Though values were higher, smoking did not have a significant effect on coefficients of variation of clozapine (33.5% vs 26.3%, P = .184) or N-desmethylclozapine (30.7% vs 24.2%, P = .100). CONCLUSIONS: Clinicians need to be aware of intraindividual variability and not assume that plasma levels are static. If plasma levels are used to guide dosing of clozapine, serial measurements rather than a single level might be necessary to make an informed clinical decision. The clinical implications of intraindividual variability in plasma clozapine levels need further study.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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 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".