Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment
August 2025
in “
OPAL (Open@LaTrobe) (La Trobe University)
”
Studysummary This preclinical study found that a machine-learning-driven strategy for fabricating microneedles with optimal hardness and rapid dissolution significantly promoted hair regrowth in androgenetic alopecia mice by activating the Wnt/β-catenin pathway, surpassing the effects of minoxidil. Our plain-language summary of this paper — not a Tressless recommendation.
The study presents a machine-learning-driven strategy to optimize the composition of microneedles (MNs) for treating androgenetic alopecia (AGA) with platelet-rich plasma (PRP). By conducting 18 experiments using orthogonal designs, the researchers identified an optimal material composition that achieves high hardness and rapid dissolution. The resulting MNs demonstrated sustained release of growth factors, over 90% bacterial inhibition, reactive oxygen species scavenging, and enhanced proliferation of dihydrotestosterone-damaged human dermal papilla cells. In vivo studies showed significant hair regrowth in AGA mice via the Wnt/β-catenin pathway, surpassing minoxidil's effects, while avoiding biosafety risks from synthetic materials. This framework could expedite the clinical translation of biomaterials like MNs.
