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Mathematical model for controlled diffusional release of dispersed solute drugs from monolithic implants
R Collins1, N Jinuntuya, P Petpirom
1Wright State University, Department of Biomedical Engineering, Dayton, Ohio 45435, USA.
Annals of the New York Academy of Sciences
|January 26, 1999
Summary
New mathematical models predict drug release from implants when initial drug concentration exceeds solubility. This aids in designing predictable drug delivery systems for various therapeutic needs.
Area of Science:
- Pharmacokinetics and Mathematical Modeling
- Materials Science and Drug Delivery
Background:
- Drug release from matrices often involves complex diffusion dynamics.
- High initial drug loading (c0 > solubility limit, cs) presents unique challenges in predicting release profiles.
- Understanding these dynamics is crucial for developing effective controlled-release drug formulations.
Purpose of the Study:
- To develop mathematical models for solute diffusion from multi-layered matrices with high initial drug loading.
- To provide analytical solutions for predicting cumulative mass release over time.
- To establish a computational tool for designing implantable drug delivery systems.
Main Methods:
- Formulation of new mathematical models incorporating a Stefan problem with moving boundaries.
- Analytical solutions derived for both non-erodible and biodegradable matrices.
- Identification of an inward moving diffusional front separating undissolved drug regions from extracted regions.
Main Results:
- The cumulative mass released is accurately determined as a function of time.
- Models account for the presence of undissolved drug and moving boundaries.
- The study provides a framework for analyzing diffusional release under supersaturation conditions.
Conclusions:
- The developed models and solutions offer a reliable design tool for specialized implantable drug delivery systems.
- This approach enables the fabrication of devices delivering prespecified and reproducible dosages.
- The mathematical framework can predict suitable dosages for new drugs, potentially reducing animal testing.