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利用广义回归神经网络(GRNN)优化压缩多单位粒子系统(MUPS)中的药物释放。

Optimization of drug release from compressed multi unit particle system (MUPS) using generalized regression neural network (GRNN).

机构信息

Galenika a.d., R&D Institute, Batajnicki drum bb 11080 Belgrade, Serbia.

出版信息

Arch Pharm Res. 2010 Jan;33(1):103-13. doi: 10.1007/s12272-010-2232-8. Epub 2010 Feb 27.

Abstract

The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was accomplished using Carbopol 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response--the release of the drug. The investigated factors were X1-the percentage of polymer Carbopol 71 G and X2-crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X1the percentage of polymer Carbopol 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner which enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%.

摘要

本研究旨在开发双氯芬酸钠(一种非甾体类抗炎药)的缓释压缩基质丸,并采用广义回归神经网络(GRNN)进行优化。根据中心复合设计(CCD),制备了 10 种双氯芬酸钠基质片的配方。采用 Carbopol 71G 作为基质物质来实现双氯芬酸钠的缓释。应用直接制粒和随后将丸粒压缩成 MUPS 片剂的工艺,旨在探索基质系统配方的不同方法,并对主要响应(药物释放)的过程因素进行更有效的控制。考察的因素有 X1-聚合物 Carbopol 71G 的百分比和 X2-MUPS 片剂的压碎强度。在 5 个不同的采样时间点选择体外溶解时间曲线作为响应。药物释放研究的结果表明,不同配方的药物释放率不同,溶解时间在 1 小时至 8 小时之间。对药物释放影响最大的因素是 X1-聚合物 Carbopol 71G 的百分比。应用 GRNN 的目的是模拟这两个因果因素对双氯芬酸钠从压缩基质丸中体外释放曲线的影响。研究的目的是以能够在磷酸盐缓冲液中 8 小时内释放 15-40%(1 小时)、25-60%(2 小时)、35-75%(4 小时)和>70%(8 小时)的双氯芬酸钠的方式来优化药物释放。

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