Hierarchical Bayesian calibration of mesoscopic models for ultrasound contrast agents from force spectroscopy data

Abstract

Ultrasound-guided drug and gene delivery (USDG) is a promising non-invasive approach for targeted therapeutic applications. Mechanical properties of encapsulated microbubbles (EMBs), which serve as contrast agents, affect their interactions with ultrasound and are, thus, critical to the success of USDG. Accurate calibration of particle-based EMB models is challenging as Bayesian inference with dissipative particle dynamics is prohibitively computationally expensive. We employ a surrogate-accelerated Bayesian calibration workflow that combines deep neural network surrogates and polynomial surrogates, transitional Markov chain Monte Carlo sampling, and hierarchical regularization across EMB diameters. Using this framework, we construct data-informed models of the commercial agents Definity and SonoVue and infer their force field parameters from published compression, indentation, and acoustic experiments. The presented methodology can be used to derive bespoke, data-informed models for a wide range of contrast agents, including gas vesicles, EMBs with diverse capsids consisting of lipids, proteins, or polymers, and functionalized with ligands.