In silico modeling of in situ fluidized bed melt granulation
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 methodologySource:
International Journal of Pharmaceutics, 2014, 466, 1-2, 21-30Publisher:
- Elsevier Science BV, Amsterdam
Funding / projects:
DOI: 10.1016/j.ijpharm.2014.02.045
ISSN: 0378-5173
PubMed: 24607215
WoS: 000335441600004
Scopus: 2-s2.0-84896537232
Collections
Institution/Community
PharmacyTY - 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 . .