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Modeling and Simulations of Olfactory Drug Delivery with Passive and Active Controls of Nasally Inhaled Pharmaceutical Aerosols

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Modeling of diffusion controlled drug delivery.

Juergen Siepmann¹, Florence Siepmann

¹University of Lille, College of Pharmacy, 3 Rue du Prof. Laguesse, 59006 Lille, France. juergen.siepmann@univ-lille2.fr

Journal of Controlled Release : Official Journal of the Controlled Release Society

|October 25, 2011

Summary

This summary is machine-generated.

Mathematical modeling aids drug development by predicting drug release kinetics from delivery systems. This overview guides selecting appropriate diffusion-controlled release models for various devices, accelerating product innovation.

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Area of Science:

Pharmacology
Chemical Engineering
Materials Science

Background:

Mathematical modeling accelerates drug development and elucidates release mechanisms from advanced drug delivery systems.
In silico simulations offer quantitative prediction of formulation and processing impacts on drug release kinetics.

Purpose of the Study:

To provide a comprehensive overview of the current state-of-the-art in modeling drug release from delivery systems.
To guide the selection of appropriate mathematical models for diffusion-controlled drug release.

Main Methods:

Review of mathematical models for drug release, focusing on diffusion-controlled mass transport.
Categorization of models based on device structure, drug concentration-solubility ratio, and geometry (slabs, spheres, cylinders).
Inclusion of a "road map" for identifying suitable equations for specific drug delivery systems.

Main Results:

Identification of key factors influencing model selection: inner structure, initial drug concentration to solubility ratio, and device geometry.
Presentation of relevant equations for reservoir and matrix systems, with or without an initial drug excess.
Discussion of model assumptions and limitations for various drug delivery devices.

Conclusions:

Mathematical modeling is crucial for understanding and optimizing diffusion-controlled drug release.
The provided framework aids in selecting appropriate models, enhancing the efficiency of drug delivery system development.
The importance of in silico optimization is expected to grow with advancements in information technology.