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How to adjust dexamethasone mobility in silicone matrices: A quantitative treatment
M Gehrke1, J Sircoglou2, C Vincent2
1Univ. Lille, F-59000 Lille, France; INSERM U1008, 3 Rue du Prof. Laguesse, F-59006 Lille, France.
Abstract:
Silicone-based drug delivery systems offer a great potential to improve the therapeutic efficacy and safety of a large variety of medical treatments, e.g. allowing for local long-term delivery of active agents to the inner ear. Different formulation parameters can be varied to adjust desired drug release kinetics. However, often only qualitative information is available on their effects, and product optimization is cumbersome. The aim of this study was to provide a quantitative analysis, allowing also for theoretical predictions of the impact of the device design on system performance. Dexamethasone was incorporated into thin films based on different types of silicones (e.g. varying in the type of side chains and contents of amorphous silica), optionally containing different types and amounts of poly(ethylene glycol) (PEG) (5% or 10%). Furthermore, the initial drug content was altered (from 10% to 50%). In most cases, an analytical solution of Fick's second law could be used to describe the resulting drug release kinetics from the films and to determine the respective "apparent" diffusion coefficient of the drug (which varied from 2×10(-14) to 2×10(-12)cm(2)/s, depending on the system's composition). Thus, the impact of the investigated formulation parameters on drug mobility in the polymeric matrices could be quantitatively described. Importantly, the knowledge of the "apparent" drug diffusivity can be used to theoretically predict the resulting release kinetics from dosage forms of arbitrary size and shape. For instance, dexamethasone release was theoretically predicted from cylindrical extrudates based on a selection of different silicone types. Interestingly, these predictions could be confirmed by independent experiments. Hence, this type of quantitative analysis can replace time-consuming and cost-intensive series of trial-and-error experiments during product optimization. This is particularly helpful, if long-term drug release (e.g., during several weeks, months or years) is targeted.
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