Paroxetine: population pharmacokinetic analysis in late‐life depression using sparse concentration sampling
Bibliographic record
Abstract
AIM: To develop a population pharmacokinetic (PK) model using sparse sampling of long-term treatment with paroxetine in elderly depressed subjects, incorporating CYP2D6 genotype as well as other covariates. METHODS: Elderly subjects (age>or=70 years) with nonpsychotic, nonbipolar major depressive disorder from the inpatient and outpatient clinic were treated with paroxetine in a 5-year clinical trial investigating 'Maintenance Therapies in Late-Life Depression' (MTLD-2). Plasma concentrations were collected during regular visits. CYP2D6 genotype was determined using polymerase chain reaction (PCR) for each individual. A nonlinear mixed-effects model was developed with NONMEM for these subjects who received 10-40 mg day-1 of paroxetine during treatment. One- and two-compartment models with linear and nonlinear elimination (Michaelis-Menten) were evaluated. PK parameters as well as interindividual and residual variability were estimated. The effects of age, weight, sex, race and CYP2D6 genotypes on the pharmacokinetics of paroxetine were evaluated. RESULTS: One hundred and seventy-one subjects with a mean age of 77 years (range 69-95) and a mean weight of 72.0 kg (range 32.9-137.0) were enrolled in the MTLD-2 clinical trial. A total of 1970 paroxetine concentrations were available for population PK analyses. Approximately 10 samples were taken per subject. A two-compartment nonlinear PK model with additive and proportional error provided the best base model for description of the data. Weight and CYP2D6 polymorphisms were found to have a significant effect on maximal velocity (Vm), whereas sex had an effect on volume of distribution of the central compartment. The Vm estimates in each of the CYP2D6 phenotypic groups were: 125 microg h-1 in poor metabolizer (n=1), 182 microg h-1 in intermediate metabolizers (n=28), 454 microg h-1 in extensive metabolizers (n=36) and 3670 microg h-1 in ultra-rapid metabolizers (n=5). CONCLUSIONS: The population PK model adequately described paroxetine data in this elderly depressed population. The data indicate that female and male subjects with different CYP2D6 polymorphisms have different elimination rates and therefore may need to be dosed differently based on metabolizer genotype.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".