Sex, Race, and Smoking Impact Olanzapine Exposure
Bibliographic record
Abstract
Response to antipsychotics is highly variable, which may be due in part to differences in drug exposure. The goal of this study was to evaluate the magnitude and variability of concentration exposure of olanzapine. Patients with Alzheimer's disease (n = 117) and schizophrenia (n = 406) were treated with olanzapine as part of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Combined, these patients (n = 523) provided 1527 plasma samples for determination of olanzapine concentrations. Nonlinear mixed-effects modeling was used to determine the population pharmacokinetics of olanzapine, and patient-specific covariates were evaluated as potential contributors to variability in drug exposure. The population mean olanzapine clearance and volume of distribution were 16.1 L/h and 2150 L, respectively. Elimination of olanzapine varied nearly 10-fold (range, 6.66-67.96 L/h). Smoking status, sex, and race accounted for 26%, 12%, and 7% of the variability, respectively (P < .0001). Smokers cleared olanzapine 55% faster than non/past smokers (P < .0001). Men cleared olanzapine 38% faster than women (P < .0001). Patients who identified themselves as black or African American cleared olanzapine 26% faster than other races (P < .0001). Differences in olanzapine exposure due to sex, race, and smoking may account for some of the variability in response to olanzapine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".