A population pharmacokinetic model for R- and S-citalopram and desmethylcitalopram in Alzheimer’s disease patients with agitation
Bibliographic record
Abstract
The citalopram for Alzheimer's disease trial evaluated citalopram for the management for agitation in Alzheimer's disease patients. Sparse data was available from this elderly patient population. A nonlinear mixed effects population pharmacokinetic modeling approach was used to describe the pharmacokinetics of R- and S-citalopram and their primary metabolite (desmethylcitalopram). A structural model with 4 compartments (one compartment/compound) with linear oral absorption and elimination described the data adequately. Overall, the model showed that clearance of the R-enantiomer was slower than the clearance of the S-enantiomer. Without accounting for any patient-specific covariates, the population estimate of the metabolic clearance of citalopram was 8.6 (R-citalopram) and 14 L/h (S-citalopram). The population estimate of the clearance of desmethylcitalopram was 23.8 (R-Dcit) and 38.5 L/h (S-Dcit). Several patient-specific covariates were found to have a significant effect on the pharmacokinetics of R,S-citalopram and desmethylcitalopram. A significant difference in the metabolic clearance of R-citalopram between males and females (13 vs 9.05 L/h) was identified in this analysis. Both R- and S-citalopram metabolic clearance decreased with age. Additionally, consistent with literature reports S-citalopram metabolic clearance increased with increasing body weight and was significantly influenced by CYPC19 genotype, with a difference of 5.8 L/h between extensive/rapid and intermediate/poor metabolizers. R,S-desmethylcitalopram clearance increased with increasing body weight. This model may allow for the opportunity to delineate the effect of R- and S-citalopram on pharmacodynamics outcomes related to the management of agitation in Alzheimer's disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".