Impact of patient-specific factors on clozapine metabolism in individuals with treatment-resistant schizophrenia or schizoaffective disorder
Bibliographic record
Abstract
Background: There is high inter-individual variability in clozapine metabolism due to genetic and non-genetic differences. Patient-specific factors such as smoking, inflammation indicated by elevated C-reactive protein (CRP), and certain concurrent medications have a significant influence on clozapine metabolism. Aim: To assess which patient-specific factors best explain variability in clozapine metabolism estimated by clozapine concentration to dose (C/D) ratios. Methods: A retrospective cohort analysis using electronic medical data was conducted on 172 inpatients at the BC Psychosis Program. Patients with normal renal and liver function were included if they were on clozapine and had at least one steady-state plasma concentration. The degree of influence of each factor on the variability of clozapine metabolism in the entire cohort and subgroups stratified by fluvoxamine use was evaluated using multiple linear regression analysis of C/D ratios. Results: Model fit testing showed that the entire cohort model accounts for 52.7% of C/D ratio variability, while the no fluvoxamine and fluvoxamine models accounted for 40.8% and 43.8%. In the entire cohort ( n = 172), fluvoxamine use explained the highest variance, and C/D ratios were higher by 30.6% on average. The second strongest predictor was elevated CRP > 10 mg/L, and C/D ratios were higher by 22.9% on average. Subsequently, obesity, nonsmoker status, and female sex explained a significant but modest proportion of variance. Among participants on fluvoxamine ( n = 58), only fluvoxamine dose was associated with an increase, and for every 25 mg increase in dose, C/D ratios increased by 5% on average. Conclusion: In a clinical population, this study replicated the relationship between reduced rate of clozapine metabolism and the use of fluvoxamine, elevated CRP, obesity, nonsmoking status, and female sex; and the magnitude of the effects were large enough to be clinically relevant.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".