Towards safer risperidone prescribing in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background In the treatment of psychosis, agitation and aggression in Alzheimer’s disease (AD), guidelines emphasise the need to ‘use the lowest possible dose’ of antipsychotic drugs, but provide no information on optimal dosing. Aims This analysis investigated the pharmacokinetic profiles of risperidone and active metabolite, 9-hydroxy (OH)-risperidone, and how this related to emergent extrapyramidal side effects (EPS), using data from The Clinical Antipsychotic Trials of Intervention Effectiveness-AD study. Method A statistical model, which described the concentration-time course of risperidone and 9-OH-risperidone, was used to predict peak, trough and average concentrations of risperidone, 9-OH-risperidone and ‘active moiety’ (combined concentrations) (108 CATIE-AD participants). Logistic regression was used to investigate the associations of pharmacokinetic biomarkers with EPS. Model based predictions were used to simulate the dose adjustments needed to avoid EPS. Results The model showed an age-related reduction in risperidone clearance (p<0.0001), and estimated that 22% of patients had slower active moiety clearance (concentration-to-dose ratio 20.2±7.2 versus 7.6±4.9 ng/mL per mg/day, Mann Whitney U, p <0.0001). Higher average and trough 9-OH-risperidone concentrations ( p <0.0001), and lower Mini-Mental State Examination (MMSE) scores (p<0.0001), were associated with EPS. Model based predictions suggest the optimum dose ranged from 0.25mg/day in those aged 85 years with MMSEs of 5, to 1mg/day in those aged 75 years with MMSEs of 15, with alternate day dosing required for those with slower drug clearance. Conclusions Our findings argue for age- and MMSE -related dose adjustments and suggest that a single plasma sample could be used to identify those with slower drug clearance.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".