Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes
Abstract
The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R-2 for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180 degrees C inlet air te...mperature, 10% pump speed).
Keywords:
Aripiprazole / Artificial neural network / Cyclodextrins / Design of experiments / Spray dryingSource:
Drying Technology, 2014, 32, 2, 167-179Publisher:
- Taylor & Francis Inc, Philadelphia
Funding / projects:
DOI: 10.1080/07373937.2013.811593
ISSN: 0737-3937
WoS: 000328930300008
Scopus: 2-s2.0-84891313227
Collections
Institution/Community
PharmacyTY - JOUR AU - Miletić, Tijana AU - Ibrić, Svetlana AU - Đurić, Zorica PY - 2014 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2188 AB - The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R-2 for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180 degrees C inlet air temperature, 10% pump speed). PB - Taylor & Francis Inc, Philadelphia T2 - Drying Technology T1 - Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes VL - 32 IS - 2 SP - 167 EP - 179 DO - 10.1080/07373937.2013.811593 ER -
@article{ author = "Miletić, Tijana and Ibrić, Svetlana and Đurić, Zorica", year = "2014", abstract = "The aim of this study was to investigate the usefulness of combined application of quality by design tools such as central composite design (CCD), response surface methodology (RSM), and artificial neural networks (ANN) in the characterization, modeling, and optimizaton of spray drying of a poorly soluble drug : cyclodextrin complex. Models were developed by RSM and ANN from different pools of data. The model with best predictability was the ANN multilayer perceptron (MLP)1 model developed from the largest group of data (R-2 for response yield 0.854, moisture content 0.886). On the other hand, analysis of equations derived from the application of RSM contributed in better understanding the complex relationships between input and output variables. By application of a desirability function approach, optimal process parameters that resulted in the best process yield (86%) and minimal moisture content in the powder (3.3%) were established (25% feed concentration, 180 degrees C inlet air temperature, 10% pump speed).", publisher = "Taylor & Francis Inc, Philadelphia", journal = "Drying Technology", title = "Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes", volume = "32", number = "2", pages = "167-179", doi = "10.1080/07373937.2013.811593" }
Miletić, T., Ibrić, S.,& Đurić, Z.. (2014). Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes. in Drying Technology Taylor & Francis Inc, Philadelphia., 32(2), 167-179. https://doi.org/10.1080/07373937.2013.811593
Miletić T, Ibrić S, Đurić Z. Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes. in Drying Technology. 2014;32(2):167-179. doi:10.1080/07373937.2013.811593 .
Miletić, Tijana, Ibrić, Svetlana, Đurić, Zorica, "Combined Application of Experimental Design and Artificial Neural Networks in Modeling and Characterization of Spray Drying Drug: Cyclodextrin Complexes" in Drying Technology, 32, no. 2 (2014):167-179, https://doi.org/10.1080/07373937.2013.811593 . .