Innovative strategies for the discovery of new drugs against androgenetic alopecia.

Kevin J McElwee, John P Sundberg
Author Information
  1. Kevin J McElwee: Department of Dermatology and Skin Science, University of British Columbia, Vancouver, Canada. ORCID
  2. John P Sundberg: The Jackson Laboratory, Bar Harbor, ME, USA. ORCID

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: Developing better models, possibly using induced pluripotent stem cell (iPSC) systems, could greatly accelerate drug discovery. Similarly, a superior 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.

Keywords

MeSH Term

Humans
Alopecia
Animals
Drug Discovery
Hair Follicle
Drug Development
Disease Models, Animal
Induced Pluripotent Stem Cells
Drug Delivery Systems
Androgens

Chemicals

Androgens

Word Cloud

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