From microbubbles to macro-control: Linking ultrasound-induced microarchitecture to precise drug release kinetics...

Spatiotemporally controlled drug delivery systems are pivotal for advancing personalized medicine, yet programming precise release kinetics within implantable materials remains a significant challenge. Acoustically responsive scaffolds (ARSs), which utilize ultrasound to trigger payload release from phase-change emulsions embedded within a hydrogel matrix, offer a promising solution. However, the complex interplay of three ultrasound-based effects within an ARS - drug release kinetics, bubble morphology, and matrix mechanics - remains poorly understood. Here, we utilized a 2⁴ full factorial design to evaluate the effects of acoustic pressure, scanning velocity, step size, and fibrin concentration on drug release kinetics, rheological properties, and bubble morphology. This design enabled simultaneous estimation of parameter prioritization and interactions across the ARS process-structure-function response space. Using optimized linear models for twelve response variables, we show that release kinetics and rheological properties were orthogonally governed by acoustic pressure and fibrin concentration, respectively. The number of bubbles within the ARS, an indicator of acoustic droplet vaporization (ADV), correlated directly with acoustic pressure and fibrin concentration, which is consistent with trends of ADV efficiency and bubble coalescence, respectively. At high pressure, release correlated inversely with bubble-derived metrics (i.e., count, cross-sectional area, total surface area), which is consistent with a barrier-like effect of bubbles on drug diffusion. Mechanical properties (i.e., storage/loss moduli) and release kinetics exhibited diverging trends with fibrin concentration, thus revealing the importance of initial matrix concentration. Ultimately, this work helps elucidate critical relationships between process parameters and response variables, providing a quantitative framework for the rational design of personalized therapeutics using ARSs.