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Related Experiment Video

Updated: Jun 3, 2026

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

A novel model for diffusion based release kinetics using an inverse numerical method.

Hadi Mohammadi¹, Walter Herzog

¹The Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada. hadim74@gmail.com

Medical Engineering & Physics

|March 9, 2011

Summary

This study introduces a novel inverse numerical model for drug release dynamics, accurately predicting the entire process. The model enhances experimental precision by minimizing errors in diffusion-based release kinetics.

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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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Area of Science:

Pharmacokinetics and Drug Delivery
Computational Modeling
Biomedical Engineering

Background:

Accurate modeling of drug release kinetics is crucial for effective therapeutic delivery.
Existing models often require separate descriptions for short-term and long-term drug release.
Identifying and reducing experimental errors in diffusion-based release measurements remains a challenge.

Purpose of the Study:

To develop and analyze a unified inverse numerical model for drug release dynamics based on Fick's second law.
To create a model valid for the entire drug release process, overcoming limitations of previous approaches.
To provide a tool for identifying and reducing experimental errors in diffusion-based release kinetics.

Main Methods:

Developed an inverse numerical model utilizing Fick's second law.
Employed Lagrange multiplier methods and least-square algorithms to define and optimize a cost function.
Utilized finite difference methods for the discretization of the cost function.
Validated the model against established methods for accuracy and computational efficiency.

Main Results:

The proposed model accurately describes diffusion-based drug release kinetics for both static and dynamic conditions.
The model is valid for the entire drug release process, unlike previous two-state models.
Achieved comparable accuracy to finite element methods with significantly reduced computational time.
Demonstrated potential for identifying and minimizing experimental errors in release kinetics measurements.

Conclusions:

The developed inverse numerical model offers a unified and efficient approach to simulating drug release dynamics.
This method can improve the accuracy and reliability of experimental measurements in drug delivery research.
The model has broad applicability in drug release procedures and tissue engineering applications requiring controlled oxygenation.