Development of AI-assisted 3D-printed degradable hydrogel microneedles for transdermal delivery of progesterone-loaded solid lipid nanoparticles: a novel approach to slowing Alzheimer's disease progression.

Progesterone (PG) is used to slow the progression of neurodegenerative diseases, particularly Alzheimer's disease (AD) in postmenopausal women. However, PG exhibits high lipophilicity, resulting in strong binding to skin tissues and plasma proteins, which may limit its systemic transport via transdermal routes. Solid lipid nanoparticles (SLNs) have been highlighted for their potential to enhance drug solubility and facilitate brain-targeted drug delivery for AD treatment. Microneedles (MNs) offer an advanced microtechnology for transdermal drug delivery, significantly improving drug permeation into the skin. However, traditional MNs fabrication methods face challenges related to shape control, dosage precision, high costs, and time consumption. Recent advancements in 3D printing technology offer a promising solution to these limitations. This study aimed to design and evaluate 3D-printed MNs-loaded with PG-SLNs for AD treatment. Biodegradable resin was utilized to fabricate MNs, aided by a Convolutional Neural Networks (CNNs) prediction model for improved accuracy. Mechanical strength, penetration efficiency, degradation, in vitro and in vivo drug delivery efficiency, cellular toxicity, and stability were evaluated. The optimized MNs, with a height of 756.98 ± 14.78 µm, effectively penetrated the skin barrier. SLNs exhibited a particle size of 308.91 ± 1.66 nm, PDI of 0.19 ± 0.08, and ZP of -30.03 ± 1.19 mV. The MNs retained sufficient mechanical strength post-drug loading, enabled efficient transdermal PG delivery, exhibited no cytotoxicity to neuronal cells, and remained physicochemically stable for up to 3 months. This study highlights the potential of 3D-printed MN patches as a novel transdermal drug delivery system, demonstrating practical feasibility for medical applications.