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A New Paradigm for Numerical Simulation of Microneedle-Based Drug Delivery Aided by Histology of Microneedle-Pierced Skin

透皮 药物输送 计算机科学 生物医学工程 扩散 软件 药品 简单(哲学) 材料科学 纳米技术 工程类 物理 医学 认识论 精神科 药理学 哲学 程序设计语言 热力学
作者
Tao Han,Diganta Bhusan Das
出处
期刊:Journal of Pharmaceutical Sciences [Elsevier]
卷期号:104 (6): 1993-2007 被引量:9
标识
DOI:10.1002/jps.24425
摘要

Microneedle (MN) is a relatively recent invention and an efficient technology for transdermal drug delivery (TDD). Conventionally, mathematical models of MNs drug delivery define the shape of the holes created by the MNs in the skin as the same as their actual geometry. Furthermore, the size of the MN holes in the skin is considered to be either the same or a certain fraction of the length of the MNs. However, the histological images of the MN-treated skin indicate that the real insertion depth is much shorter than the length of the MNs and the shapes may vary significantly from one case to another. In addressing these points, we propose a new approach for modeling MN-based drug delivery, which incorporates the histology of MN-pierced skin using a number of concepts borrowed from image processing tools. It is expected that the developed approach will provide better accuracy of the drug diffusion profile. A new computer program is developed to automatically obtain the outline of the MNs-treated holes and import these images into computer software for simulation of drug diffusion from MN systems. This method can provide a simple and fast way to test the quality of MNs design and modeling, as well as simulate experimental studies, for example, permeation experiments on MN-pierced skin using diffusion cell. The developed methodology is demonstrated using two-dimensional (2D) numerical modeling of flat MNs (2D). However, the methodology is general and can be implemented for three dimensional (3D) MNs if there is sufficient number of images for reconstructing a 3D image for numerical simulation. Numerical modeling for 3D geometry is demonstrated by using images of an ideal 3D MN. The methodology is not demonstrated for real 3D MNs, as there are not sufficient numbers of images for the purpose of this paper.
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