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The prediction of spray drying formu...

Wendel, Susan Carol.

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The prediction of spray drying formulations and processes for pharmaceutical powders.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	The prediction of spray drying formulations and processes for pharmaceutical powders./
Author:	Wendel, Susan Carol.
Description:	221 p.
Notes:	Director: Nicholas G. Lordi.
Contained By:	Dissertation Abstracts International62-10B.
Subject:	Health Sciences, Pharmacy. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3027972
ISBN:	0493401881

The prediction of spray drying formulations and processes for pharmaceutical powders.
Wendel, Susan Carol.

The prediction of spray drying formulations and processes for pharmaceutical powders. - 221 p.

Director: Nicholas G. Lordi.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2001.

Model spray dried materials were used in order to determine if it is possible to develop a system capable of predicting spray drying formulations and processes for a variety of materials. These model materials were maltodextrin, colloidal silicon dioxide, dextrose, sorbitol, lactose, and povidone.

ISBN: 0493401881Subjects--Topical Terms:

1017737
Health Sciences, Pharmacy.

The prediction of spray drying formulations and processes for pharmaceutical powders.
LDR:03189nam 2200313 a 45 001 929892
005 20110427
008 110427s2001 eng d
020 $a 0493401881
035 $a (UnM)AAI3027972
035 $a AAI3027972
040 $a UnM $c UnM
100 1 $a Wendel, Susan Carol. $3 1253377
245 1 0 $a The prediction of spray drying formulations and processes for pharmaceutical powders.
300 $a 221 p.
500 $a Director: Nicholas G. Lordi.
500 $a Source: Dissertation Abstracts International, Volume: 62-10, Section: B, page: 4492.
502 $a Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2001.
520 $a Model spray dried materials were used in order to determine if it is possible to develop a system capable of predicting spray drying formulations and processes for a variety of materials. These model materials were maltodextrin, colloidal silicon dioxide, dextrose, sorbitol, lactose, and povidone.
520 $a Extensive experimentation revealed that four of the six selected model materials were able to be spray dried. While some of the experimental results were quite predictable, other results did not follow expected trends and may have been influenced by material factors that are not addressed by theoretical relationships. Consequently, development of a predictive model for the spray drying process needed to address material factors in addition to process parameters.
520 $a Several critical physical characterization factors of each model raw material were selected for inclusion in the feasibility prediction process. Each factor had a direct effect on the ability of the material to be spray dried, which was expressed in a decision tree format. Additionally, a method for simulating the evaporation stage of the spray drying process was developed using DSC instrumentation.
520 $a Six material characteristics and six processing parameters were found to be predictive of the physical characteristics of the spray dried materials produced using both nozzle configurations. Artificial neural networks (ANN) were shown a suitable tool for the determination of these critical independent variables. Also, ANN were capable of building models which describe and predict the physical characteristics of spray dried powders. Specifically, a three-layer backpropagation network was shown to effectively model the process. Genetic algorithms used in conjunction with these ANN were capable of predicting the optimal processing parameters required to achieve specified powder characteristics.
520 $a It was determined that a predictive system composed of a database, decision trees, neural networks, and genetic algorithms can be constructed for evaluating the feasibility of a formulation to be spray dried and for calculating the optimal processing conditions required to produce a spray dried powder with user-specified physical characteristics.
590 $a School code: 0190.
650 4 $a Health Sciences, Pharmacy. $3 1017737
690 $a 0572
710 2 0 $a Rutgers The State University of New Jersey - New Brunswick. $3 1017590
773 0 $t Dissertation Abstracts International $g 62-10B.
790 $a 0190
790 1 0 $a Lordi, Nicholas G., $e advisor
791 $a Ph.D.
792 $a 2001
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3027972