Multiscale modeling and simulation approaches for biomedical ultrasonic applications
Ultrasound-guided drug and gene delivery (USDG) enables controlled and spatially precise delivery of drugs and macromolecules, encapsulated in microbubbles (MBs) and submicron gas vesicles (GVs), to target areas such as cancer tumors. It is a non-invasive, high precision, low toxicity process with drastically reduced drug dosage. These advantages open doors to numerous biomedical applications, from sonothrombolysis to blood–brain barrier opening. However, the progress and deployment of this technology is subject to extensive experimentation and heuristics. The proposal aims to develop a virtual environment to quantify and optimize USDG and in particular the MBs and GVs utilized as drug carriers and contrast agents. Their type and concentration, and interface with ultrasound (US) are critical to the success and efficiency of USDG. State-of-the-art USDG systems operate in a narrow range of empirically-tuned US parameters. This empiricism entails severe risks and limitations for clinical applications and delays the adoption of this potent technology. I propose a computational framework that would allow for controlled testing, data-driven quantification of uncertainties, and a rational optimization of experimental US parameters. The framework will rely on submicron resolution modeling and simulation of cavitating MBs and GVs interacting with US. Limitations of existing models based on continuum theory preclude an accurate description of cavitation, drastically degrading the prediction of drug delivery outcomes. I will develop new, data-informed mesoscopic models of US contrast agents, capturing their rheological and acoustic behavior. Specific interactions of US and agents at a submicron level will be included by harnessing novel multiscale methods that enable seamless propagation of US from the macro to microscopic level. The proposed framework will be integrated with experimental efforts to advance USDG across biomedical applications.