Dual-Diffusivity Stochastic Model for Macromolecule Release from a Hydrogel.

Area of Science:

• Biomaterials Science
• Drug Delivery Systems
• Computational Modeling

Background:

• Hydrogels are widely used for drug encapsulation and controlled release.
• Accurate prediction of macromolecular drug release from hydrogels is crucial for therapeutic efficacy.
• Existing models often simplify hydrogel network heterogeneity, limiting their predictive power.

Purpose of the Study:

• To develop and validate a three-dimensional computational model for multiphase drug release from hydrogels.
• To incorporate network mesh size heterogeneity using varying diffusion coefficients.
• To assess the performance of a dual-diffusivity model against experimental data.

Main Methods:

• Development of a 3D computational model considering heterogeneous network mesh sizes.
• Implementation of a stochastic approach to simulate random diffusion of drug molecules.
• Utilizing a scaling relationship for computationally efficient generation of drug release profiles.
• Experimental validation using chemically cross-linked dextran hydrogels and macromolecular drugs of varying sizes.

Main Results:

• The dual-diffusivity model demonstrated good agreement with experimental drug release profiles.
• The model successfully captured the release behavior of macromolecular drugs with different sizes.
• The dual-diffusivity approach significantly outperformed single-diffusivity models.

Conclusions:

• A novel dual-diffusivity model provides a more accurate representation of macromolecular drug release from heterogeneous hydrogels.
• This model enhances the understanding and prediction of drug delivery from complex hydrogel matrices.
• The findings support the use of this model for designing and optimizing hydrogel-based drug delivery systems.