Beyond expectations: investigating nilotinib’s potential in attenuating neurodegeneration in Alzheimer’s disease
Bibliographic record
Abstract
Neurodegenerative diseases, such as Alzheimer's disease (AD), pose a formidable global challenge. While therapeutic options are available, their limitations are significant, necessitating the development of innovative treatment approaches. Here, we highlight the importance of repurposing drugs and discuss the future of drug treatments for AD. We review the potential of tyrosine kinase inhibitors (TKI) for mitigating AD pathology and symptoms, as well as neurodegenerative processes more broadly. We focus on nilotinib, a selective BCR-ABL tyrosine kinase inhibitor, which has unique mechanisms of action involving the modulation of cell responses and removal of toxic proteins associated with AD pathogenesis. Encouraging studies have demonstrated its efficacy, calling for further investigation through clinical trials to assess its potential in various neurodegenerative conditions. However, despite these promising preclinical findings, no clinical studies have yet conclusively demonstrated its efficacy in treating AD. Considering the future directions in AD research, personalized medicine approaches hold promise by incorporating patient-specific factors, including sex and gender differences, to tailor nilotinib treatment for improved efficacy and safety profiles.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".