Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment

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 and ML predictions, researchers identified an optimal material composition for MNs that achieves high hardness and rapid dissolution. These MNs demonstrated sustained release of growth factors, over 90% bacterial inhibition, reactive oxygen species scavenging, and enhanced proliferation of 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 of synthetic materials. This framework could expedite clinical translation of biomaterials like MNs.