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Continuous biomarker monitoring and on-demand therapy are essential for chronic disease management, with diabetes being a key example. Although continuous glucose monitoring (CGM) and insulin pumps are moving toward a closed-loop system, it remains difficult to predict how device design influences system performance. Traditional optimization methods are inefficient and empirical: Data-driven algorithms lack interpretability, trial-and-error design is time-consuming, and first-principle models are difficult to integrate at the system level. To address these challenges, we introduce a physics-based framework which integrates microneedle (MN) designs for sensing and therapy with a physiological model of glycemic control. The framework builds compact relationships showing how material, chemical, and geometrical features affect key metrics such as response time, extraction flux, and insulin delivery rate. Specifically, we develop a theory for three sensing MN types (hollow, porous/swellable, and nanostructured MNs) and one therapeutic patch (electro-termo-mechanical device) to create a predictive model for glycemic regulation. By linking device design to system behavior across time and spatial scales, we examine the framework in three cases: How disease progression, MN design, and MN sensitivity affect plasma glucose levels and time-in-range metrics. This work establishes the foundation for a physics-based digital twin for diabetes management, complementing ML, experiments, and numerical models. More broadly, it streamlines patch design, minimizing glycemic events and trial-and-error in next-generation CGM technology.
Fratus et al. (Mon,) studied this question.