Computational-Based Design of Hydrogels with Predictable Mesh Properties.

Hydrogel systems are an appealing class of therapeutic delivery vehicles, though it can be challenging to design hydrogels that maintain desired spatiotemporal presentation of therapeutic cargo. In this work, we propose a different approach in which computational tools are developed that creates a theoretical representation of the hydrogel polymer network to design hydrogels with predefined mesh properties critical for controlling therapeutic delivery. We postulated and confirmed that the computational model could incorporate properties of alginate polymers, including polymer content, monomer composition and polymer chain radius, to accurately predict cross-link density and mesh size for a wide range of alginate hydrogels. Additionally, the simulations provided a robust strategy to determine the mesh size distribution and identified properties to control the mesh size of alginate hydrogels. Furthermore, the model was validated for additional hydrogel systems and provided a high degree of correlation (R2 > 0.95) to the mesh sizes determined for both fibrin and polyethylene glycol (PEG) hydrogels. Finally, a full factorial and Box-Behnken design of experiments (DOE) approach utilized in combination with the computational model predicted that the mesh size of hydrogels could be varied from approximately 5 nm to 5 μm through controlling properties of the polymer network. Overall, this computational model of the hydrogel polymer network provides a rapid and accessible strategy to predict hydrogel mesh properties and ultimately design hydrogel systems with desired mesh properties for potential therapeutic applications