Soft materials fabrication and simulation for drug delivery and tissue engineering

Abstract

Human civilization has always advanced alongside the development of materials, from the Stone Age to the Silicon Age. Materials are categorized into soft and hard based on stiffness. Hard materials have driven economic progress, but there is growing interest in soft materials for their unique properties like elasticity, flexibility, and biocompatibility. These characteristics make soft materials crucial in fields such as drug delivery, tissue engineering, and medical implants. The advent of advanced manufacturing techniques, like additive manufacturing, is further expanding their applications. Despite their potential, designing soft materials remains a challenging task. For decades, modelling and simulation have helped address these challenges, providing insights and accelerating material design and applications.

To this end, this thesis presents three simulation methods to establish generalised models that assist in applying soft materials in drug delivery and tissue engineering. The mathematical model can quickly and directly acquire the targeted results from the input variables. Domain discretisation enables the acquisition of the dynamic process of evolution from the beginning to the end, allowing all points in the system to be tracked and recorded. The machine learning method provides a data-driven simulation approach, offering higher prediction accuracy and the potential to predict undiscovered materials or drugs.

Simulation methods are useful tools for understanding the soft materials based applications in drug delivery and tissue engineering area. These applications include controlled drug release, cell cultures platform, oxygen therapy and identifying key features to control the drug delivery system. Therefore, understanding the potential of simulation can guide materials design, optimise devices structures, accelerate the development of new therapies, and personalise drug administration for individual cases, ultimately achieving optimal clinical outcomes.