Repurposing Sigma-1 Receptor-Targeting Drugs for Therapeutic Advances in Neurodegenerative Disorders
Bibliographic record
Abstract
Neurodegenerative disorders, such as Alzheimer's, Parkinson's, and Huntington's disease, due to their multifaced and complicated nature, remain uncurable and impose substantial financial and human burdens on society. Therefore, developing new innovative therapeutic strategies is vital. In this context, drug repurposing has emerged as a promising avenue to expedite the development of treatments for these challenging conditions. One particularly compelling target in this regard is the chaperone protein sigma-1 receptor (S1R), which has garnered significant attention for its neuroprotective properties. Interestingly, several medications, including fluvoxamine (an antidepressant), dextromethorphan (a cough suppressant), and amantadine (an antiviral), which were initially developed for unrelated indications, have shown encouraging results in neurodegenerative therapy through S1R activation. These findings suggest that existing drugs in pharmacopeias can play an essential role in alleviating neurodegenerative symptoms by modulating S1R, thereby offering a faster route and cost-effective path to clinical applications compared to the de novo development of entirely new compounds. Furthermore, as a synergistic benefit, combining S1R-targeting drugs with other therapeutic agents may also improve treatment efficacy. In this review, we highlight key repurposed drugs targeting S1R and explore their mechanisms of action, shedding light on their emerging therapeutic potential in the fight against neurodegeneration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".