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首页> 外文学位 >To understand and correlate various parameters involved in preparation of spray-dried solid dispersions using polymer based response surface models and ensemble artificial neural networks.
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To understand and correlate various parameters involved in preparation of spray-dried solid dispersions using polymer based response surface models and ensemble artificial neural networks.

机译：了解并关联使用基于聚合物的响应表面模型和集成人工神经网络制备喷雾干燥的固体分散体所涉及的各种参数。

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摘要

In the current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. The obtained powders were analyzed by modulated differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, polarized light microscopy, and particle size analysis. Moreover, Pearson correlation analysis, Kohonen's self-organizing map, contribution plot, 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 for PVP based systems. Final validation of prepared models was done using binary SSDs of different model drugs with polymer based on root mean square error and mean absolute error for each quality attribute. large quantities of API and long development time. Moreover, developing a mathematical model of a spray drying process is difficult task, for instance, modeling the rapid heat and mass transfer that occurs between the droplet phase and the liquid phase; the moving boundary of the droplet, and the presence of multiphase flows. In current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. The obtained powders were analyzed by modulated differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, polarized light microscopy, and particle size analysis. Moreover, Pearson correlation analysis, Kohonen's self-organizing map, contribution plot, 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 for PVP based systems. Final validation of prepared models was done using binary SSDs of different model drugs with polymer based on root mean square error and mean absolute error for each quality attribute. iilarge quantities of API and long development time. Moreover, developing a mathematical model of a spray drying process is difficult task ,for instance, modeling the rapid heat and mass transfer that occurs between the droplet phase and the liquid phase; the moving boundary of the droplet, and the presence of multiphase flows. In current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. (Abstract shortened by UMI.).
机译：在当前的研究中，使用聚合物作为安慰剂配方开发了用于喷雾干燥过程的模型，以预测二元固体分散体（SD）的质量属性（过程产率，出口温度和粒度）。评价的不同聚合物是聚乙烯吡咯烷酮（PVP-K 29/32）和羟丙基甲基纤维素乙酸琥珀酸酯（HPMC-AS-HF）。通过考虑不同配方（进料浓度和所用溶剂）和工艺参数（流速，入口温度，所用喷嘴尺寸，雾化压力）的组合，设计实验以更好地理解喷雾干燥过程。根据实验数据，开发了响应面模型和集成人工神经网络来预测不同的质量属性。通过调制差示扫描量热法，热重分析，X射线衍射，偏振光显微镜和粒度分析来分析获得的粉末。此外，通过皮尔逊相关分析，Kohonen的自组织图，贡献图，等高线图和响应面图来说明输入变量与质量属性之间的关系。对于基于PVP的系统，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。根据不同质量属性的均方根误差和绝对绝对误差，使用带有聚合物的不同模型药物的二元SSD对最终制备的模型进行最终验证。大量的API和较长的开发时间。此外，建立喷雾干燥过程的数学模型是一项艰巨的任务，例如，对液滴相和液相之间发生的快速传热和传质建模；液滴的运动边界以及多相流的存在。在当前的研究中，使用聚合物作为安慰剂配方开发了用于喷雾干燥过程的模型，以预测二元固体分散体（SD）的质量属性（过程产率，出口温度和粒径）。评价的不同聚合物是聚乙烯吡咯烷酮（PVP-K 29/32）和羟丙基甲基纤维素乙酸琥珀酸酯（HPMC-AS-HF）。通过考虑不同配方（进料浓度和所用溶剂）和工艺参数（流速，入口温度，所用喷嘴尺寸，雾化压力）的组合，设计实验以更好地理解喷雾干燥过程。根据实验数据，开发了响应面模型和集成人工神经网络来预测不同的质量属性。通过调制差示扫描量热法，热重分析，X射线衍射，偏振光显微镜和粒度分析来分析获得的粉末。此外，通过皮尔逊相关分析，Kohonen的自组织图，贡献图，等高线图和响应面图来说明输入变量与质量属性之间的关系。对于基于PVP的系统，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。根据不同质量属性的均方根误差和绝对绝对误差，使用带有聚合物的不同模型药物的二元SSD对最终制备的模型进行最终验证。 ii大量的API和长的开发时间。此外，建立喷雾干燥过程的数学模型是一项艰巨的任务，例如，对液滴相和液相之间发生的快速传热和传质进行建模；液滴的运动边界以及多相流的存在。在当前的研究中，使用聚合物作为安慰剂配方开发了用于喷雾干燥过程的模型，以预测二元固体分散体（SD）的质量属性（过程产率，出口温度和粒径）。评估的不同聚合物是聚乙烯吡咯烷酮（PVP-K 29/32）和羟丙基甲基纤维素乙酸琥珀酸酯（HPMC-AS-HF）。通过考虑不同配方（进料浓度和所用溶剂）和工艺参数（流速，入口温度，所用喷嘴尺寸，雾化压力）的组合，设计实验以更好地理解喷雾干燥过程。根据实验数据，开发了响应面模型和集成人工神经网络来预测不同的质量属性。 （摘要由UMI缩短。）。

著录项

  • 作者

    Patel, Ashwinkumar D.;

  • 作者单位

    Long Island University, The Brooklyn Center.;

  • 授予单位 Long Island University, The Brooklyn Center.;
  • 学科 Health Sciences Pharmacy.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 262 p.
  • 总页数 262
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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