Using Compartments to Model Drug Delivery from Biodegradable Polymers.

Area of Science:

• Pharmacology and Pharmaceutical Sciences
• Biomedical Engineering
• Computational Modeling

Background:

• Polymeric drug delivery systems offer controlled release, enhancing therapeutic outcomes and patient compliance.
• Mathematical models are crucial for designing and optimizing these complex delivery systems.
• Existing models often rely on diffusion equations, which may not be intuitive for all pharmaceutical professionals.

Purpose of the Study:

• To introduce a novel compartmental mathematical model for predicting drug release from polymeric systems.
• To provide a model that integrates polymer degradation and drug diffusion processes.
• To adapt the model for various delivery geometries (membranes, fibers, particles) and incorporate specific interactions.

Main Methods:

• Development of a compartmental model incorporating polymer degradation and drug diffusion.
• Adaptation of the model to membrane, fiber, and particle geometries.
• Derivation of model parameters from diffusion coefficients, including polymer-drug binding and size distributions.
• Validation against diffusion equation-based solutions and experimental data.

Main Results:

• The compartmental model accurately predicts drug release, comparable to diffusion-based models.
• The model successfully incorporates polymer-drug binding and size distribution effects.
• Integration with distribution models allows prediction of in vivo plasma drug concentrations.
• A user-friendly Python implementation is available.

Conclusions:

• The developed compartmental model offers a practical and effective tool for polymeric drug delivery system design.
• The model's flexibility across geometries and inclusion of key interactions enhance its applicability.
• This approach facilitates the prediction of drug release profiles and in vivo performance.