Drug repurposing for Alzheimer’s disease: Selective serotonin reuptake inhibitors
Bibliographic record
Abstract
Abstract Background Alzheimer’s disease (AD) is a devastating neurodegenerative disorder which affects more than 50 million people across the globe. Unfortunately, the current pharmacotherapy options are very limited and provide only symptomatic relief. Several drug discovery efforts to treat AD have failed in the clinical trials which is a setback in discovering novel anti‐AD therapies. Considering the cost, time and risks involved in bringing a new drug from the bench‐to‐bedside, drug repurposing or discovering novel therapeutic indications for drugs already in the market is an attractive alternative to find novel AD therapies. Targeting the amyloid (Aβ) cascade event in AD is one of the several potential targets to develop novel anti‐AD agents. Our studies looked at the possibility of repurposing marketed selective serotonin reuptake inhibitors (SSRIs) fluvoxamine, fluoxetine, paroxetine, sertraline and escitalopram in AD and investigated their anti‐Aβ aggregation properties. Method Experiments conducted include fluorescence aggregation kinetics using the dye thioflavin T (ThT), determining Aβ morphology in the presence and absence of SSRIs by transmission electron microscopy (TEM) and computational modeling using the software Discovery Studio Structure‐Based‐Design. Result These experiments demonstrated that among the SSRIs tested, paroxetine in particular was able to prevent the aggregation of both Aβ40 and Aβ42 peptides. Computational studies were used to understand the interactions of paroxetine with Aβ40 and Aβ42 oligomer and fibrils models. Conclusion These studies suggest that the antidepressant paroxetine has the potential to target the amyloid cascade in AD. Further studies are required to determine the suitability of repurposing paroxetine to treat 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".