Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment
August 2025
in “
PubMed
”
Studysummary This study found that a machine-learning-driven strategy helped develop microneedles with high hardness and rapid dissolution, effectively promoting hair regrowth in androgenetic alopecia mice by activating the Wnt/β-catenin pathway, surpassing minoxidil's effects without biosafety risks.
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The study presents a machine learning-driven approach 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, and promoted 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. This method also mitigates biosafety risks associated with synthetic materials, offering a promising framework for clinical translation of biomaterials like MNs.
