Molecular Therapeutics in Development to Treat Alzheimer’s Disease
Bibliographic record
Abstract
Until recently, only symptomatic therapies, in the form of acetylcholine esterase inhibitors and NMDA-receptor antagonists, have been available for the treatment of Alzheimer's disease. However, advancements in our understanding of the amyloid cascade hypothesis have led to a development of disease-modifying therapeutic strategies. These include immunotherapies based on an infusion of monoclonal antibodies against amyloid-β, three of which have been approved for the treatment of Alzheimer's disease in the USA (one of them, lecanemab, has also been approved in several other countries). They all lead to a dramatic reduction of amyloid plaques in the brain, whereas their clinical effects have been more limited. Moreover, they can all lead to side effects in the form of amyloid-related imaging abnormalities. Ongoing developments aim at facilitating their administration, further improving their effects and reducing the risk for amyloid-related imaging abnormalities. Moreover, a number of anti-tau immunotherapies are in clinical trials, but none has so far shown any robust effects on symptoms or pathology. Another line of development is represented by gene therapy. To date, only antisense oligonucleotides against amyloid precursor protein/amyloid-β and tau have reached the clinical trial stage but a variety of gene editing strategies, such as clustered regularly interspaced short palindromic repeats/Cas9-mediated non-homologous end joining, base editing, and prime editing, have all shown promise on preclinical disease models. In addition, a number of other pharmacological compounds targeting a multitude of biochemical processes, believed to be centrally involved in Alzheimer's disease, are currently being evaluated in clinical trials. This article delves into current and future perspectives on the treatment of Alzheimer's disease, with an emphasis on immunotherapeutic and gene therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".