Drug Repositioning for Alzheimer’s Disease: A Delphi Consensus and Stakeholder Consultation
Bibliographic record
Abstract
Background Alzheimer’s Disease (AD) is an escalating global challenge, with over 40 million people affected and this number is projected to increase to over 100 million by 2050. While amyloid-targeting antibody treatments (lecanumab and donanemab) are a considerable 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 due to the safety profiles already being known. MethodThis 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 comprised of published academics and/or clinicians, or industry representatives was convened. Through a combination of anonymized drug nominations, systemic evidence reviews, iterative consensus ranking, and a lay advisory input, drug candidates were evaluated and ranked based on rational, preclinical and clinical evidence and overall safety profiles.ResultsOut of the 80 candidates that were nominated by the expert panel, seven underwent review with only three candidates meeting the consensus criteria: (1) the live attenuated Herpes Zoster (HZ) vaccine (Zostavax), (2) Sildenafil, a PDE5 inhibitor, and (3) Riluzole, a glutamate antagonist. Each demonstrated relevant mechanisms for targeting neurodegenerative pathways, preclinical efficacy and tolerability in older individuals. The HZ vaccine additionally offers a potential for population-level dementia risk reduction. ConclusionThis Delphi consensus identified 3 high-priority drug repurposing candidates for Alzheimer’s Disease. With their favourable safety profiles and mechanistic plausibility, they 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 the potential deployment of these drug candidates to benefit individuals.
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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 | 0.242 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".