Adaptive Microneedle Patch Design Optimization via Bayesian Optimization and Finite Element Simulation for Personalized Insulin Delivery

**Abstract:** This research investigates a novel framework for optimizing microneedle patch design parameters for personalized insulin delivery, leveraging Bayesian optimization and finite element analysis (FEA). Current microneedle patch designs often lack adaptability to individual skin physiology and drug diffusion rates, leading to suboptimal insulin delivery profiles. This framework iteratively adjusts microneedle geometry (length, diameter, spacing, and shape) based on FEA-predicted skin permeation and insulin release, aiming to achieve targeted blood glucose regulation across diverse skin types. The resulting adaptive design process promises improved therapeutic efficacy and reduced inter-patient variability in insulin delivery. This framework represents a significant advance towards precision transdermal drug delivery, with a clear path to commercialization within 5 years.