FarFaR - Pharmacy Repository
University of Belgrade, Faculty of Pharmacy
    • English
    • Српски
    • Српски (Serbia)
  • English 
    • English
    • Serbian (Cyrillic)
    • Serbian (Latin)
  • Login
View Item 
  •   FarFaR
  • Pharmacy
  • Radovi istraživača / Researchers’ publications
  • View Item
  •   FarFaR
  • Pharmacy
  • Radovi istraživača / Researchers’ publications
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

In silico modeling of in situ fluidized bed melt granulation

Authorized Users Only
2014
Authors
Aleksić, Ivana
Đuriš, Jelena
Ilić, Ilija
Ibrić, Svetlana
Parojčić, Jelena
Srcić, Stanko
Article (Published version)
Metadata
Show full item record
Abstract
Fluidized bed melt granulation has recently been recognized as a promising technique with numerous advantages over conventional granulation techniques. The aim of this study was to evaluate the possibility of using response surface methodology and artificial neural networks for optimizing in situ fluidized bed melt granulation and to compare them with regard to modeling ability and predictability. The experiments were organized in line with the Box-Behnken design. The influence of binder content, binder particle size, and granulation time on granule properties was evaluated. In addition to the response surface analysis, a multilayer perceptron neural network was applied for data modeling. It was found that in situ fluidized bed melt granulation can be used for production of spherical granules with good flowability. Binder particle size had the most pronounced influence on granule size and shape, suggesting the importance of this parameter in achieving desired granule properties. It was... found that binder content can be a critical factor for the width of granule size distribution and yield when immersion and layering is the dominant agglomeration mechanism. The results obtained indicate that both in silico techniques can be useful tools in defining the design space and optimization of in situ fluidized bed melt granulation.

Keywords:
In situ melt granulation / Fluid bed / Experimental design / Artificial neural networks / Response surface methodology
Source:
International Journal of Pharmaceutics, 2014, 466, 1-2, 21-30
Publisher:
  • Elsevier Science BV, Amsterdam
Funding / projects:
  • Advanced technologies for controlled release from solid drug delivery systems (RS-34007)

DOI: 10.1016/j.ijpharm.2014.02.045

ISSN: 0378-5173

PubMed: 24607215

WoS: 000335441600004

Scopus: 2-s2.0-84896537232
[ Google Scholar ]
15
14
URI
https://farfar.pharmacy.bg.ac.rs/handle/123456789/2225
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Pharmacy
TY  - JOUR
AU  - Aleksić, Ivana
AU  - Đuriš, Jelena
AU  - Ilić, Ilija
AU  - Ibrić, Svetlana
AU  - Parojčić, Jelena
AU  - Srcić, Stanko
PY  - 2014
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2225
AB  - Fluidized bed melt granulation has recently been recognized as a promising technique with numerous advantages over conventional granulation techniques. The aim of this study was to evaluate the possibility of using response surface methodology and artificial neural networks for optimizing in situ fluidized bed melt granulation and to compare them with regard to modeling ability and predictability. The experiments were organized in line with the Box-Behnken design. The influence of binder content, binder particle size, and granulation time on granule properties was evaluated. In addition to the response surface analysis, a multilayer perceptron neural network was applied for data modeling. It was found that in situ fluidized bed melt granulation can be used for production of spherical granules with good flowability. Binder particle size had the most pronounced influence on granule size and shape, suggesting the importance of this parameter in achieving desired granule properties. It was found that binder content can be a critical factor for the width of granule size distribution and yield when immersion and layering is the dominant agglomeration mechanism. The results obtained indicate that both in silico techniques can be useful tools in defining the design space and optimization of in situ fluidized bed melt granulation.
PB  - Elsevier Science BV, Amsterdam
T2  - International Journal of Pharmaceutics
T1  - In silico modeling of in situ fluidized bed melt granulation
VL  - 466
IS  - 1-2
SP  - 21
EP  - 30
DO  - 10.1016/j.ijpharm.2014.02.045
ER  - 
@article{
author = "Aleksić, Ivana and Đuriš, Jelena and Ilić, Ilija and Ibrić, Svetlana and Parojčić, Jelena and Srcić, Stanko",
year = "2014",
abstract = "Fluidized bed melt granulation has recently been recognized as a promising technique with numerous advantages over conventional granulation techniques. The aim of this study was to evaluate the possibility of using response surface methodology and artificial neural networks for optimizing in situ fluidized bed melt granulation and to compare them with regard to modeling ability and predictability. The experiments were organized in line with the Box-Behnken design. The influence of binder content, binder particle size, and granulation time on granule properties was evaluated. In addition to the response surface analysis, a multilayer perceptron neural network was applied for data modeling. It was found that in situ fluidized bed melt granulation can be used for production of spherical granules with good flowability. Binder particle size had the most pronounced influence on granule size and shape, suggesting the importance of this parameter in achieving desired granule properties. It was found that binder content can be a critical factor for the width of granule size distribution and yield when immersion and layering is the dominant agglomeration mechanism. The results obtained indicate that both in silico techniques can be useful tools in defining the design space and optimization of in situ fluidized bed melt granulation.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "International Journal of Pharmaceutics",
title = "In silico modeling of in situ fluidized bed melt granulation",
volume = "466",
number = "1-2",
pages = "21-30",
doi = "10.1016/j.ijpharm.2014.02.045"
}
Aleksić, I., Đuriš, J., Ilić, I., Ibrić, S., Parojčić, J.,& Srcić, S.. (2014). In silico modeling of in situ fluidized bed melt granulation. in International Journal of Pharmaceutics
Elsevier Science BV, Amsterdam., 466(1-2), 21-30.
https://doi.org/10.1016/j.ijpharm.2014.02.045
Aleksić I, Đuriš J, Ilić I, Ibrić S, Parojčić J, Srcić S. In silico modeling of in situ fluidized bed melt granulation. in International Journal of Pharmaceutics. 2014;466(1-2):21-30.
doi:10.1016/j.ijpharm.2014.02.045 .
Aleksić, Ivana, Đuriš, Jelena, Ilić, Ilija, Ibrić, Svetlana, Parojčić, Jelena, Srcić, Stanko, "In silico modeling of in situ fluidized bed melt granulation" in International Journal of Pharmaceutics, 466, no. 1-2 (2014):21-30,
https://doi.org/10.1016/j.ijpharm.2014.02.045 . .

DSpace software copyright © 2002-2015  DuraSpace
About FarFaR - Pharmacy Repository | Send Feedback

OpenAIRERCUB
 

 

All of DSpaceCommunitiesAuthorsTitlesSubjectsThis institutionAuthorsTitlesSubjects

Statistics

View Usage Statistics

DSpace software copyright © 2002-2015  DuraSpace
About FarFaR - Pharmacy Repository | Send Feedback

OpenAIRERCUB