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首页> 外文期刊>European journal of pharmaceutics and biopharmaceutics: official journal of Arbeitsgemeinschaft fuer Pharmazeutische Verfahrenstechnik e.V >Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models
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Investigation of the effects of process variables on derived properties of spray dried solid-dispersions using polymer based response surface model and ensemble artificial neural network models

机译：使用基于聚合物的响应面模型和集成人工神经网络模型研究工艺变量对喷雾干燥固体分散体衍生性质的影响

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摘要

The objective of this study was to use different statistical tools to understand and optimize the spray drying process to prepare solid dispersions. In this study we investigated the relationship between input variables (inlet temperature, feed concentration, flow rate, solvent and atomization parameters) and quality attributes (yield, outlet temperature and mean particle size) of spray dried solid dispersions (SSDs) using response surface model and ensemble artificial neural network. The Box Behnken design was developed to investigate the effect of various input variables on quality attributes of final products. Moreover, Pearson correlation analysis, self organizing map, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated. Final validation of prepared models was done using binary SSDs of six model drugs with PVP. Results demonstrated the effectiveness of proposed PVP based model which can help scientists to gain detailed understanding of spray drying process of solid dispersion using minimal resources and time during early formulation development stage. It will also help them to ensure consistent quality of SSDs using broad range of input variables.
机译：这项研究的目的是使用不同的统计工具来理解和优化喷雾干燥过程以制备固体分散体。在这项研究中，我们使用响应表面模型研究了喷雾干燥的固体分散体（SSD）的输入变量（入口温度，进料浓度，流速，溶剂和雾化参数）与质量属性（产率，出口温度和平均粒径）之间的关系。和集成人工神经网络。 Box Behnken设计的开发目的是调查各种输入变量对最终产品质量属性的影响。此外，皮尔逊相关分析，自组织图，等高线图和响应面图被用来说明输入变量和质量属性之间的关系。还研究了溶剂的不同理化性质对喷雾干燥产品质量属性的影响。使用具有PVP的六种模型药物的二进制SSD对准备好的模型进行最终验证。结果证明了所提出的基于PVP的模型的有效性，该模型可以帮助科学家在早期的制剂开发阶段使用最少的资源和时间来详细了解固体分散体的喷雾干燥过程。它还将帮助他们使用各种输入变量来确保SSD的稳定质量。

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