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Design Space Approach in Optimization of Fluid Bed Granulation and Tablets Compression Process

机译:流化床制粒和片剂压缩工艺优化的设计空间方法

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摘要

The aim of this study was to optimize fluid bed granulation and tablets compression processes using design space approach. Type of diluent, binder concentration, temperature during mixing, granulation and drying, spray rate, and atomization pressure were recognized as critical formulation and process parameters. They were varied in the first set of experiments in order to estimate their influences on critical quality attributes, that is, granules characteristics (size distribution, flowability, bulk density, tapped density, Carr's index, Hausner's ratio, and moisture content) using Plackett-Burman experimental design. Type of diluent and atomization pressure were selected as the most important parameters. In the second set of experiments, design space for process parameters (atomization pressure and compression force) and its influence on tablets characteristics was developed. Percent of paracetamol released and tablets hardness were determined as critical quality attributes. Artificial neural networks (ANNs) were applied in order to determine design space. ANNs models showed that atomization pressure influences mostly on the dissolution profile, whereas compression force affects mainly the tablets hardness. Based on the obtained ANNs models, it is possible to predict tablet hardness and paracetamol release profile for any combination of analyzed factors.
机译:这项研究的目的是使用设计空间方法来优化流化床制粒和片剂压缩过程。稀释剂的类型,粘合剂浓度,混合过程中的温度,制粒和干燥,喷雾速率和雾化压力被认为是关键的配方和工艺参数。为了评估其对关键质量属性(即颗粒特征(粒度分布,流动性,堆积密度,堆积密度,卡尔指数,豪斯纳比和含水量))的影响,在第一组实验中对它们进行了更改。布尔曼实验设计。选择稀释剂的类型和雾化压力作为最重要的参数。在第二组实验中,开发了工艺参数(雾化压力和压缩力)及其对片剂特性的影响的设计空间。确定对乙酰氨基酚的释放百分比和片剂硬度是关键的质量属性。为了确定设计空间,应用了人工神经网络(ANN)。人工神经网络模型表明,雾化压力主要影响溶出曲线,而压缩力主要影响片剂的硬度。基于获得的ANNs模型,可以针对分析因素的任何组合预测片剂硬度和对乙酰氨基酚释放曲线。

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