SSRI‐antipsychotic combination in psychotic depression: sertraline pharmacokinetics in the presence of olanzapine, a brief report from the STOP‐PD study
Bibliographic record
Abstract
OBJECTIVE: We recently reported an unexpected interaction between olanzapine and sertraline in a population being treated for psychotic depression. Contrary to knowledge of cytochrome p450 interactions sertraline increased apparent clearance of olanzapine by 30%. Here we examined the pharmacokinetics of sertraline in the same population. Existing studies suggest that sertraline apparent clearance is significantly increased in male subjects and suggested an age/sex interaction. METHODS: We studied subjects undergoing combination of sertraline/olanzapine treatment for psychotic depression in the Study of the Pharmacotherapy of Psychotic Depression. Nonlinear mixed effect modelling software was used to examine the sertraline pharmacokinetics, evaluating age, sex, race, and olanzapine exposure as covariates. RESULTS: Eighty-seven subjects (median age 62 years, 28 male subjects, 11 African-Americans) provided 138 samples for sertraline concentration. Olanzapine exposure had a 14.8-fold range. A one compartment model with combined residual error described the sertraline concentration data adequately. Half-life and sex effect on sertraline apparent clearance (males averaging 50% higher (p < 0.005); 96.6 l/h vs 64.8 in female subjects) were similar to previous reports. No other covariate (age, race or olanzapine exposure) had a significant impact on apparent clearance, and no age/sex interaction emerged. CONCLUSION: Sertraline pharmacokinetics were similar to historical descriptions in populations not taking antipsychotics. Unlike our unexpected finding that sertraline increases olanzapine apparent clearance, olanzapine exposure had no impact on sertraline pharmacokinetics. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".