Innovative strategies for the discovery of new drugs against androgenetic alopecia
Bibliographic record
Abstract
INTRODUCTION: Androgenetic alopecia (AGA) is the most common cause of hair loss worldwide. The significant psychological and social impact of AGA continues to drive demand for more effective treatments beyond the limited options currently available. AREAS COVERED: The authors review the key components of AGA pathogenesis, as well as current treatments, and therapeutic techniques under development. Innovative strategies for AGA drug discovery are still needed, given the significant unmet medical needs and the limited efficacy of both current and emerging treatments. The authors outline relevant preclinical models, such as hair follicle (HF) cell cultures, 3D spheroids, organoids, follicle explants, and animal models, highlighting their advantages and limitations in AGA research. Finally, they summarize the primary objectives in AGA treatment development, including direct hair growth promotion, interference with androgen signaling, and HF rejuvenation, identifying key pathogenesis intervention points for treatment development. EXPERT OPINION: model could significantly expedite drug discovery. Near future development research should focus on drug delivery improvements. Longer term, treatments targeting AGA's underlying pathophysiology and promoting HF rejuvenation or true regeneration would provide the most benefit to prospective patients.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".