Beyond immunotherapy: new approaches for disease modifying treatments for early Alzheimer’s disease
Bibliographic record
Abstract
INTRODUCTION: Current pharmacological recommendations for the treatment of Alzheimer's disease (AD) include the cholinesterase inhibitors and the N-methyl-D-aspartate antagonist, memantine. However, these medications only manage symptoms of AD, and do not target Aβ plaques and neurofibrillary tangles. As such, there is a need to develop effective and safe disease modifying treatments that directly target AD pathology and alter the course of AD progression. Areas covered: This review evaluates ongoing phase 2 and 3 clinical trials, as well as those completed or published over the past five years. Studies for this review were obtained from clinicaltrials.gov, alzforum.org/therapeutics, and PubMed. Keywords and search criteria included: phase 2, or 3 trials related to Alzheimer's disease, mild cognitive impairment, amyloid-beta and tau. Immunotherapies for AD have not been included as this is beyond the scope of this review. Expert opinion: A substantial number of trials investigating disease modifying drugs in AD target amyloid-beta and tau pathology. However, many of these trials have relatively short treatment duration and do not include combined assessment of biomarkers and clinical outcomes. Future investigations are recommended to include biomarker assessments and clinical outcomes over a minimum treatment duration of 18 months in order to establish disease-modifying effects.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".