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Parameters affecting drug release from inert matrices. 1: Monte Carlo simulation.

Rafael Villalobos¹, Hugo Viquez, Beatriz Hernández

¹División de Estudios de Posgrado (Tecnología Farmacéutica), Facultad de Estudios Superiores Cuautitlán, Universidad Nacional Autónoma de México, Cuautitlán Izcalli C.P. 54740, Estado de México, Mexico. yeccanv@yahoo.com

Pharmaceutical Development and Technology

|January 11, 2011

Summary

This study used Monte Carlo simulations to predict drug release from inert matrices. Physical parameters like porosity and tortuosity accurately fit the Weibull model, enabling release prediction.

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Area of Science:

Pharmaceutical Sciences
Computational Modeling
Materials Science

Background:

Drug release from inert matrices is crucial for controlled delivery systems.
Understanding the influence of matrix properties on drug release is essential for formulation design.

Purpose of the Study:

To investigate Monte Carlo simulation for determining drug release properties from cubic inert matrices.
To evaluate the impact of porosity, surface area, and tortuosity on drug release kinetics.
To correlate physical matrix parameters with mathematical drug diffusion models.

Main Methods:

Monte Carlo simulation was employed to model drug release from matrices with varying drug-excipient ratios.
Simulations considered uni-directional (two-face) and omni-directional (six-face) release processes.
Porosity and tortuosity were evaluated using a blind random walk algorithm; release profiles were analyzed using square-root, power, and Weibull models.

Main Results:

Matrix systems exhibited homogeneous or heterogeneous characteristics based on their composition.
Physical parameters, including porosity and tortuosity, were successfully fitted to the 'a' and 'b' constants of the Weibull model.
The study demonstrated a correlation between matrix physical properties and drug release profiles.

Conclusions:

Monte Carlo simulation provides a viable method for predicting drug release from inert matrices.
The Weibull model effectively describes drug diffusion, incorporating key physical parameters.
This predictive approach simplifies formulation development by relying on measurable physical characteristics.