Drug repurposing for Alzheimer’s disease: a Delphi consensus and stakeholder consultation
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is an escalating global challenge, with more than 40 million people affected, and this number is projected to increase to more than 100 million by 2050. While amyloid-targeting antibody treatments (lecanemab and donanemab) are a significant step forward, the benefits of these therapies remain limited. This highlights the necessity for safe and effective compounds that offer greater therapeutic benefits to the majority of individuals with or at risk of AD. Drug repurposing allows for a cost-effective, time-efficient strategy to accelerate the availability of treatments, owing to the availability of safety information. METHOD: This study focuses on the third iteration of the Delphi consensus programme aimed at identifying new high-priority drug candidates for repurposing in AD. An international expert panel comprising academics, clinicians and industry representatives was convened. Through a combination of anonymized drug nominations, systemic evidence reviews, iterative consensus rankings, and lay advisory inputs, drug candidates were evaluated and ranked based on rational, non-clinical, and clinical evidence and overall safety profiles. RESULTS: Among the 80 candidates that were nominated by the expert panel, seven underwent review, with only three candidates meeting the following consensus criteria of relevant mechanisms for targeting neurodegenerative pathways, non-clinical efficacy, and tolerability in older individuals. The three agents were: [1] the live attenuated herpes zoster (HZ) vaccine (Zostavax) [2], sildenafil, a phosphodiesterase-5 (PDE-5) inhibitor, and [3] riluzole, a glutamate antagonist. The HZ vaccine additionally offers potential for population-level dementia risk reduction. CONCLUSION: This Delphi consensus identified three high-priority drug repurposing candidates for AD with favourable safety profiles and mechanistic plausibility, which are considered suitable for pragmatic clinical trials, including remote or hybrid designs. The PROTECT platform, which supports international cohorts in the UK, Norway, and Canada, offers a well-established means to conduct such trials effectively, thus helping to accelerate the evaluation and potential deployment of these drug candidates to benefit individuals with or at risk for AD.
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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 | 0.231 | 0.151 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".