Emerging therapies for treatment of agitation, psychosis, or apathy in Alzheimer’s disease
Bibliographic record
Abstract
INTRODUCTION: Agitation, psychosis, and apathy are prevalent and highly distressing neuropsychiatric symptoms (NPS) of Alzheimer's disease (AD) that have been linked to numerous negative outcomes, including increased mortality, worsened cognitive decline, and caregiver burden. Current treatments for AD-associated agitation, namely atypical antipsychotics, provide some benefits but may increase the risk of serious adverse events and death. Meanwhile, no pharmacotherapies have been approved by regulatory agencies for the treatment of psychosis or apathy in AD. Over the past decade, many new and repurposed drugs have emerged as potential therapeutic options for managing these challenging NPS. AREAS COVERED: This review aims to provide a comprehensive summary of pharmacotherapies that have recently been investigated in phase 2 and 3 clinical trials for the treatment of agitation, psychosis, or apathy in AD. EXPERT OPINION: Novel atypical antipsychotics, serotonergic antidepressants, cannabinoids, and dextromethorphan combination drugs have shown promising results for alleviating agitation. Pimavanserin appears to be the most effective emerging therapy for psychosis, while methylphenidate has demonstrated good efficacy for apathy. Further research on biomarkers of NPS severity and treatment response, as well as continued improvements in methodological approaches are needed to advance the field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".