Predicting Drug Release From Degradable Hydrogels Using Fluorescence Correlation Spectroscopy and Mathematical...

Area of Science:

• Biomaterials Science
• Chemical Engineering
• Computational Modeling

Background:

• Predicting solute release from degradable hydrogels is crucial for drug delivery and tissue engineering but remains challenging.
• Existing models often struggle with time-varying hydrogel properties and complex degradation mechanisms.

Purpose of the Study:

• To develop a simple mathematical and computational model for predicting solute release profiles from degradable hydrogels.
• To incorporate time-varying diffusivity and geometry into the predictive model.
• To validate the model's predictions against experimental release data.

Main Methods:

• Utilized fluorescence correlation spectroscopy (FCS) to measure protein diffusivity (BSA, IgG) over hydrogel degradation.
• Monitored changes in hydrogel geometry experimentally over time.
• Developed a computational model using curve fits of diffusivity and geometry data as inputs.

Main Results:

• The computational model accurately predicted solute release profiles from degradable hydrogels.
• Model predictions were validated using conventional bulk release experiments.
• The model successfully accounted for time-varying diffusivity and geometry.

Conclusions:

• The developed model offers a valuable tool for predicting release from degradable hydrogels, even when degradation mechanisms are unknown.
• This 'black box' approach enhances the model's applicability across diverse hydrogel systems.
• The findings support advancements in controlled drug delivery and tissue engineering applications.