Prevention of cyclophosphamide‐induced alopecia by selegiline in a murine model
Bibliographic record
Abstract
One of the most common and psychologically traumatic side effects of cancer therapy is the induction of apoptosis in hair follicle cells, resulting in alopecia. We have tested various compounds for their ability to modify chemotherapy‐induced apoptosis. An initial in vitro screen showed selegiline, clinically used in the control of Parkinson's disease, had a particularly intriguing profile. Selegiline dose‐dependently prevented cisplatin‐and cytosine arabinoside‐induced cell death in phenotypically normal cells, while also increasing cell death in human cancer cells in response to cisplatin. Dose‐response curves for the two effects were comparable, with a maximally effective concentration in both of 10nM. Selegiline was next tested in vivo using a mouse cyclophosphamide‐induced alopecia model. Anaesthetized animals were depilated with a wax/rosin mixture to induce synchronous hair follicle cycling. Alopecia was induced by the administration of 150mg/kg i.p. cyclophosphamide on days 7, 9, and 11 postdepilation. Selegiline was administered i.p. daily, starting on day 7 post‐depilation, at either 0.1 or 1.0mg/kg. Animals were monitored daily and hair re‐growth assessed on days 12, 19 and 26 postdepilation. At 0.1mg/kg selegiline at least partially prevented alopecia in 14/15 mice, with complete hair re‐growth indistinguishable from controls seen in 8/15. At the higher dose of 1mg/kg selegiline was considerably less effective, despite significantly greater monoamine oxidase‐B (MAO‐B) inhibition. The results suggest that selegiline can prevent the toxicity of clinically used cancer chemotherapeutics through an MAO‐B inhibition independent mechanism. This may relate to the p53/GAPDH‐dependent anti‐apoptotic effects of selegiline that have previously been reported. The in vitro studies further suggest that such an effect may occur without a concomitant decrease in chemotherapy efficacy at cancer cells. Financial support provided by NSERC and CFI.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".