TY  - JOUR
AU  - Chansanroj, Krisanin
AU  - Petrović, Jelena
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
PY  - 2011
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1504
AB  - Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties.
PB  - Elsevier Science BV, Amsterdam
T2  - European Journal of Pharmaceutical Sciences
T1  - Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks
VL  - 44
IS  - 3
SP  - 321
EP  - 331
DO  - 10.1016/j.ejps.2011.08.012
ER  - 

@article{
author = "Chansanroj, Krisanin and Petrović, Jelena and Ibrić, Svetlana and Betz, Gabriele",
year = "2011",
abstract = "Artificial neural networks (ANNs) were applied for system understanding and prediction of drug release properties from direct compacted matrix tablets using sucrose esters (SEs) as matrix-forming agents for controlled release of a highly water soluble drug, metoprolol tartrate. Complexity of the system was presented through the effects of SE concentration and tablet porosity at various hydrophilic-lipophilic balance (HLB) values of SEs ranging from 0 to 16. Both effects contributed to release behaviors especially in the system containing hydrophilic SEs where swelling phenomena occurred. A self-organizing map neural network (SOM) was applied for visualizing interrelation among the variables and multilayer perceptron neural networks (MLPs) were employed to generalize the system and predict the drug release properties based on HLB value and concentration of SEs and tablet properties, i.e., tablet porosity, volume and tensile strength. Accurate prediction was obtained after systematically optimizing network performance based on learning algorithm of MLP. Drug release was mainly attributed to the effects of SEs, tablet volume and tensile strength in multi-dimensional interrelation whereas tablet porosity gave a small impact. Ability of system generalization and accurate prediction of the drug release properties proves the validity of SOM and MLPs for the formulation modeling of direct compacted matrix tablets containing controlled release agents of different material properties.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "European Journal of Pharmaceutical Sciences",
title = "Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks",
volume = "44",
number = "3",
pages = "321-331",
doi = "10.1016/j.ejps.2011.08.012"
}

Chansanroj, K., Petrović, J., Ibrić, S.,& Betz, G.. (2011). Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks. in European Journal of Pharmaceutical Sciences
Elsevier Science BV, Amsterdam., 44(3), 321-331.
https://doi.org/10.1016/j.ejps.2011.08.012

Chansanroj K, Petrović J, Ibrić S, Betz G. Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks. in European Journal of Pharmaceutical Sciences. 2011;44(3):321-331.
doi:10.1016/j.ejps.2011.08.012 .

Chansanroj, Krisanin, Petrović, Jelena, Ibrić, Svetlana, Betz, Gabriele, "Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks" in European Journal of Pharmaceutical Sciences, 44, no. 3 (2011):321-331,
https://doi.org/10.1016/j.ejps.2011.08.012 . .

TY  - JOUR
AU  - Petrović, Jelena
AU  - Chansanroj, Krisanin
AU  - Meier, Brigitte
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
PY  - 2011
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1484
AB  - Various modeling techniques have been applied to analyze fluidized-bed granulation process. Influence of various input parameters (product, inlet and outlet air temperature, consumption of liquid-binder, granulation liquid-binder spray rate, spray pressure, drying time) on granulation output properties (granule flow rate, granule size determined using light scattering method and sieve analysis, granules Hausner ratio, porosity and residual moisture) has been assessed. Both conventional and novel modeling techniques were used, such as screening test, multiple regression analysis, self-organizing maps, artificial neural networks, decision trees and rule induction. Diverse testing of developed models (internal and external validation) has been discussed. Good correlation has been obtained between the predicted and the experimental data. It has been shown that nonlinear methods based on artificial intelligence, such as neural networks, are far better in generalization and prediction in comparison to conventional methods. Possibility of usage of SOMs, decision trees and rule induction technique to monitor and optimize fluidized-bed granulation process has also been demonstrated. Obtained findings can serve as guidance to implementation of modeling techniques in fluidized-bed granulation process understanding and control.
PB  - Elsevier Science BV, Amsterdam
T2  - European Journal of Pharmaceutical Sciences
T1  - Analysis of fluidized bed granulation process using conventional and novel modeling techniques
VL  - 44
IS  - 3
SP  - 227
EP  - 234
DO  - 10.1016/j.ejps.2011.07.013
ER  - 

@article{
author = "Petrović, Jelena and Chansanroj, Krisanin and Meier, Brigitte and Ibrić, Svetlana and Betz, Gabriele",
year = "2011",
abstract = "Various modeling techniques have been applied to analyze fluidized-bed granulation process. Influence of various input parameters (product, inlet and outlet air temperature, consumption of liquid-binder, granulation liquid-binder spray rate, spray pressure, drying time) on granulation output properties (granule flow rate, granule size determined using light scattering method and sieve analysis, granules Hausner ratio, porosity and residual moisture) has been assessed. Both conventional and novel modeling techniques were used, such as screening test, multiple regression analysis, self-organizing maps, artificial neural networks, decision trees and rule induction. Diverse testing of developed models (internal and external validation) has been discussed. Good correlation has been obtained between the predicted and the experimental data. It has been shown that nonlinear methods based on artificial intelligence, such as neural networks, are far better in generalization and prediction in comparison to conventional methods. Possibility of usage of SOMs, decision trees and rule induction technique to monitor and optimize fluidized-bed granulation process has also been demonstrated. Obtained findings can serve as guidance to implementation of modeling techniques in fluidized-bed granulation process understanding and control.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "European Journal of Pharmaceutical Sciences",
title = "Analysis of fluidized bed granulation process using conventional and novel modeling techniques",
volume = "44",
number = "3",
pages = "227-234",
doi = "10.1016/j.ejps.2011.07.013"
}

Petrović, J., Chansanroj, K., Meier, B., Ibrić, S.,& Betz, G.. (2011). Analysis of fluidized bed granulation process using conventional and novel modeling techniques. in European Journal of Pharmaceutical Sciences
Elsevier Science BV, Amsterdam., 44(3), 227-234.
https://doi.org/10.1016/j.ejps.2011.07.013

Petrović J, Chansanroj K, Meier B, Ibrić S, Betz G. Analysis of fluidized bed granulation process using conventional and novel modeling techniques. in European Journal of Pharmaceutical Sciences. 2011;44(3):227-234.
doi:10.1016/j.ejps.2011.07.013 .

Petrović, Jelena, Chansanroj, Krisanin, Meier, Brigitte, Ibrić, Svetlana, Betz, Gabriele, "Analysis of fluidized bed granulation process using conventional and novel modeling techniques" in European Journal of Pharmaceutical Sciences, 44, no. 3 (2011):227-234,
https://doi.org/10.1016/j.ejps.2011.07.013 . .