R‐ and S‐citalopram concentrations have differential effects on neuropsychiatric scores in elders with dementia and agitation
Bibliographic record
Abstract
AIMS: The aim was to determine the relationship between (R) and (S)-citalopram enantiomer exposure (AUC(0,24 h)) and therapeutic response in agitated individuals greater than 60 years old with Alzheimer's dementia (AD). METHODS: Citalopram enantiomer exposures (AUC(0,24 h)) derived from an established population pharmacokinetic analysis were utilized to explore the relationship between (R)- and (S)-citalopram area under the curve (AUC(0,24 )) and Mini-Mental State Examination (MMSE), Neurobehavioural Rating Scale-Agitation Subscale (NBRS-A), modified Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change (mADCS-CGIC) and Neuropsychiatric Inventory Agitation subscale (NPIA) scores. Time dependent changes in these scores (disease progression) were accounted for prior to exploring the exposure effect relationship for each enantiomer. These relationships were evaluated using a non-linear-mixed effects modelling approach as implemented in nonmem v7.3. RESULTS: (S)-AUC(0,24 h) and (R)-AUC(0,24 h) each contributed to improvement in NBRS-A scores (k3(R) -0.502; k4(S) -0.712) as did time in treatment. However, increasing (R)-AUC(0,24 h) decreased the probability of patient response (maximum Δ -0.182%/AUC(0,24 h)) based on the CGIC while (S)-AUC(0,24 h) improved the probability of response (maximum Δ 0.112%/AUC(0,24 h)). (R)-AUC(0,24 h) was also associated with worsening in MMSE scores (-0.5 points). CONCLUSIONS: Our results suggest that citalopram enantiomers contributed differentially to treatment outcomes. (R)-citalopram accounted for a greater proportion of the adverse consequences associated with racemic citalopram treatment in patients with AD including a decreased probability of treatment response as measured by the CGIC and a reduction in MMSE scores. The S-enantiomer was associated with increased probability of response based on the CGIC.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".