Is the Safety of Finasteride Correlated With Its Route of Administration: Topical Versus Oral? A Pharmacovigilance Study With Data From the United States Food and Drug Administration Adverse Event Reporting System
Bibliographic record
Abstract
BACKGROUND: The United States Food and Drug Administration (FDA) approved oral finasteride for androgenetic alopecia. In 2022, approximately 2.6 million U.S. men used it for hair loss and prostate conditions. Post-finasteride syndrome (PFS), proposed in 2012, involves persistent sexual and neuropsychiatric adverse events (AEs) after cessation. These AEs are controversial and often lack assessment of contributing risk factors. We analyzed FDA Adverse Event Reporting System (FAERS) data to explore finasteride's administration route and PFS-like AEs. METHODS: Using the information component (IC) method for disproportionality analyses, we assessed signals for 13 PFS-related AEs with topical and oral finasteride and dutasteride across two periods: 2006-2011 (pre-PFS reporting) and 2019-2024 (post-PFS reporting). These periods reflect times before and after formal PFS reporting in 2012. Eight analyses per AE were conducted based on agent, route, and era. RESULTS: Fewer signals for PFS-like AEs were detected with topical finasteride compared to oral finasteride in both eras. No signals were found for topical dutasteride, possibly because its use is very limited. Many reported AEs, such as erectile dysfunction and depression, may be influenced by age, stress, or comorbidities. CONCLUSIONS: Signals for PFS-like AEs were detected with topical finasteride, but were less frequent than with oral finasteride. The high prevalence of these AEs in the general population and the influence of confounding factors, such as psychological stress or nocebo effects, combined with the lack of genotyping, hormonal assessments, or family history data in most reports, suggest caution in attributing causality to finasteride. Topical finasteride may pose a lower risk, but robust evidence is needed to clarify its safety profile.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".