Off-Label Topical Application of Sirolimus (Rapamycin) for Dermatological Conditions
Bibliographic record
Abstract
This review summarizes the off-label applications of topical sirolimus (rapamycin) for dermatological conditions beyond its approved use for angiofibromas associated with tuberous sclerosis, focusing on efficacy, safety, and reported adverse effects. Off-label use of mammalian target of rapamycin inhibitors like sirolimus has shown promising potential in dermatology. We analyzed the literature on the efficacy and safety of topical sirolimus for a range of dermatological conditions, including inflammatory eruptions, genodermatoses, bullous disorders, neoplasms, and others. Publications on topical sirolimus for other vascular anomalies, such as lymphatic malformations, were excluded to maintain focus on other lesser-known dermatological uses, except for studies involving post-laser combination therapies for vascular lesions. Topical sirolimus demonstrated notable efficacy in several genetic and benign proliferative skin disorders, including pachyonychia congenita, trichilemmomas, trichoepitheliomas, and Kaposi sarcoma, with a generally favorable safety profile and minimal systemic absorption. However, variability in formulations, dosing regimens, and the predominance of small-scale studies limit definitive conclusions. Further research is warranted to standardize treatment protocols and validate these findings.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".