TY  - JOUR
AU  - Petrović, Jelena
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
AU  - Đurić, Zorica
PY  - 2012
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1705
AB  - The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.
PB  - Elsevier Science BV, Amsterdam
T2  - International Journal of Pharmaceutics
T1  - Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees
VL  - 428
IS  - 1-2
SP  - 57
EP  - 67
DO  - 10.1016/j.ijpharm.2012.02.031
ER  - 

@article{
author = "Petrović, Jelena and Ibrić, Svetlana and Betz, Gabriele and Đurić, Zorica",
year = "2012",
abstract = "The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "International Journal of Pharmaceutics",
title = "Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees",
volume = "428",
number = "1-2",
pages = "57-67",
doi = "10.1016/j.ijpharm.2012.02.031"
}

Petrović, J., Ibrić, S., Betz, G.,& Đurić, Z.. (2012). Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees. in International Journal of Pharmaceutics
Elsevier Science BV, Amsterdam., 428(1-2), 57-67.
https://doi.org/10.1016/j.ijpharm.2012.02.031

Petrović J, Ibrić S, Betz G, Đurić Z. Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees. in International Journal of Pharmaceutics. 2012;428(1-2):57-67.
doi:10.1016/j.ijpharm.2012.02.031 .

Petrović, Jelena, Ibrić, Svetlana, Betz, Gabriele, Đurić, Zorica, "Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees" in International Journal of Pharmaceutics, 428, no. 1-2 (2012):57-67,
https://doi.org/10.1016/j.ijpharm.2012.02.031 . .

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 . .

TY  - JOUR
AU  - Ivić, Branka
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
AU  - Đurić, Zorica
PY  - 2010
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1391
AB  - The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol (R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol (R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol (R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%.
PB  - Pharmaceutical Soc Korea, Seoul
T2  - Archives of Pharmacal Research
T1  - Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)
VL  - 33
IS  - 1
SP  - 103
EP  - 113
DO  - 10.1007/s12272-010-2232-8
ER  - 

@article{
author = "Ivić, Branka and Ibrić, Svetlana and Betz, Gabriele and Đurić, Zorica",
year = "2010",
abstract = "The purpose of this study was development of diclofenac sodium extended release compressed matrix pellets and optimization using Generalized Regression Neural Network (GRNN). According to Central Composite Design (CCD), ten formulations of diclofenac sodium matrix tablets were prepared. Extended release of diclofenac sodium was acomplished using Carbopol (R) 71G as matrix substance. The process of direct pelletisation and subsequently compression of the pellets into MUPS tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve more control of the process factors over the principal response - the release of the drug. The investigated factors were X(1) -the percentage of polymer Carbopol (R) 71 G and X(2)- crushing strength of the MUPS tablet. In vitro dissolution time profiles at 5 different sampling times were chosen as responses. Results of drug release studies indicate that drug release rates vary between different formulations, with a range of 1 hour to 8 hours of dissolution. The most important impact on the drug release has factor X(1) -the percentage of polymer Carbopol (R) 71 G. The purpose of the applied GRNN was to model the effects of these two causal factors on the in vitro release profile of the diclofenac sodium from compressed matrix pellets. The aim of the study was to optimize drug release in manner wich enables following in vitro release of diclofenac sodium during 8 hours in phosphate buffer: 1 h: 15-40%, 2 h: 25-60%, 4 h: 35-75%, 8 h: >70%.",
publisher = "Pharmaceutical Soc Korea, Seoul",
journal = "Archives of Pharmacal Research",
title = "Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)",
volume = "33",
number = "1",
pages = "103-113",
doi = "10.1007/s12272-010-2232-8"
}

Ivić, B., Ibrić, S., Betz, G.,& Đurić, Z.. (2010). Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN). in Archives of Pharmacal Research
Pharmaceutical Soc Korea, Seoul., 33(1), 103-113.
https://doi.org/10.1007/s12272-010-2232-8

Ivić B, Ibrić S, Betz G, Đurić Z. Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN). in Archives of Pharmacal Research. 2010;33(1):103-113.
doi:10.1007/s12272-010-2232-8 .

Ivić, Branka, Ibrić, Svetlana, Betz, Gabriele, Đurić, Zorica, "Optimization of Drug Release from Compressed Multi Unit Particle System (MUPS) Using Generalized Regression Neural Network (GRNN)" in Archives of Pharmacal Research, 33, no. 1 (2010):103-113,
https://doi.org/10.1007/s12272-010-2232-8 . .

TY  - JOUR
AU  - Petrović, Jelena
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
AU  - Parojčić, Jelena
AU  - Đurić, Zorica
PY  - 2009
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1263
AB  - The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series. The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results.
PB  - Elsevier Science BV, Amsterdam
T2  - European Journal of Pharmaceutical Sciences
T1  - Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets
VL  - 38
IS  - 2
SP  - 172
EP  - 180
DO  - 10.1016/j.ejps.2009.07.007
ER  - 

@article{
author = "Petrović, Jelena and Ibrić, Svetlana and Betz, Gabriele and Parojčić, Jelena and Đurić, Zorica",
year = "2009",
abstract = "The main objective of this study was to demonstrate the possible use of dynamic neural networks to model diclofenac sodium release from polyethylene oxide hydrophilic matrix tablets. High and low molecular weight polymers in the range of 0.9-5 x 10(6) have been used as matrix forming materials and 12 different formulations were prepared for each polymer. Matrix tablets were made by direct compression method. Fractions of polymer and compression force have been selected as most influential factors on diclofenac sodium release profile. In vitro dissolution profile has been treated as time series using dynamic neural networks. Dynamic networks are expected to be advantageous in the modeling of drug release. Networks of different topologies have been constructed in order to obtain precise prediction of release profiles for test formulations. Short-term and long-term memory structures have been included in the design of network making it possible to treat dissolution profiles as time series. The ability of network to model drug release has been assessed by the determination of correlation between predicted and experimentally obtained data. Calculated difference (f(1)) and similarity (f(2)) factors indicate that dynamic networks are capable of accurate predictions. Dynamic neural networks were compared to most frequently used static network, multi-layered perceptron, and superiority of dynamic networks has been demonstrated. The study also demonstrated differences between the used polyethylene oxide polymers in respect to drug release and suggests explanations for the obtained results.",
publisher = "Elsevier Science BV, Amsterdam",
journal = "European Journal of Pharmaceutical Sciences",
title = "Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets",
volume = "38",
number = "2",
pages = "172-180",
doi = "10.1016/j.ejps.2009.07.007"
}

Petrović, J., Ibrić, S., Betz, G., Parojčić, J.,& Đurić, Z.. (2009). Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets. in European Journal of Pharmaceutical Sciences
Elsevier Science BV, Amsterdam., 38(2), 172-180.
https://doi.org/10.1016/j.ejps.2009.07.007

Petrović J, Ibrić S, Betz G, Parojčić J, Đurić Z. Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets. in European Journal of Pharmaceutical Sciences. 2009;38(2):172-180.
doi:10.1016/j.ejps.2009.07.007 .

Petrović, Jelena, Ibrić, Svetlana, Betz, Gabriele, Parojčić, Jelena, Đurić, Zorica, "Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets" in European Journal of Pharmaceutical Sciences, 38, no. 2 (2009):172-180,
https://doi.org/10.1016/j.ejps.2009.07.007 . .

TY  - JOUR
AU  - Ivić, Branka
AU  - Ibrić, Svetlana
AU  - Betz, Gabriele
AU  - Đurić, Zorica
PY  - 2009
UR  - https://farfar.pharmacy.bg.ac.rs/handle/123456789/1162
AB  - The purpose of the study was to screen the effects of formulation factors on the in vitro release profile of diclofenac sodium from matrix pellets compressed into multiple unit pellet system (MUPS) tablets using design of experiment (DOE). Extended release of diclofenac sodium was accomplished using Carbopol (R) 71G as matrix substance. According to Fractional Factorial Design FFD 2(3-1) four formulations of diclofenac sodium MUPS matrix tablets were prepared. The process of direct pelletization and subsequently compression of the pellets into tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve a better control of the process factors over the principal response - the release of the drug. The investigated factors were X-1-the percentage of polymer Carbopol (R) 71G, X-2-crushing strength of the tablet and X-3-different batches of the diclofenac sodium. In vitro dissolution time profiles at 6 different sampling times were chosen as responses. Results of drug release studies indicated that drug release rates vary between different formulations, with a range of 1 to 8 h to complete dissolution. The most important impact on the drug release hart factor X-1-the percentage of polymer Carbopol (R) 71G. The polymer percentage is suggested as release regulator for diclofenac sodium release from MUPS matrix tablets. All other investigated factors had no significant influence on the release profile of diclofenac sodium.
PB  - Pharmaceutical Soc Japan, Tokyo
T2  - Zdravstveno varstvo
T1  - Evaluation of Diclofenac Sodium Release from Matrix Pellets Compressed into MUPS Tablets
VL  - 129
IS  - 11
SP  - 1375
EP  - 1384
DO  - 10.1248/yakushi.129.1375
ER  - 

@article{
author = "Ivić, Branka and Ibrić, Svetlana and Betz, Gabriele and Đurić, Zorica",
year = "2009",
abstract = "The purpose of the study was to screen the effects of formulation factors on the in vitro release profile of diclofenac sodium from matrix pellets compressed into multiple unit pellet system (MUPS) tablets using design of experiment (DOE). Extended release of diclofenac sodium was accomplished using Carbopol (R) 71G as matrix substance. According to Fractional Factorial Design FFD 2(3-1) four formulations of diclofenac sodium MUPS matrix tablets were prepared. The process of direct pelletization and subsequently compression of the pellets into tablets was applied in order to investigate a different approach in formulation of matrix systems and to achieve a better control of the process factors over the principal response - the release of the drug. The investigated factors were X-1-the percentage of polymer Carbopol (R) 71G, X-2-crushing strength of the tablet and X-3-different batches of the diclofenac sodium. In vitro dissolution time profiles at 6 different sampling times were chosen as responses. Results of drug release studies indicated that drug release rates vary between different formulations, with a range of 1 to 8 h to complete dissolution. The most important impact on the drug release hart factor X-1-the percentage of polymer Carbopol (R) 71G. The polymer percentage is suggested as release regulator for diclofenac sodium release from MUPS matrix tablets. All other investigated factors had no significant influence on the release profile of diclofenac sodium.",
publisher = "Pharmaceutical Soc Japan, Tokyo",
journal = "Zdravstveno varstvo",
title = "Evaluation of Diclofenac Sodium Release from Matrix Pellets Compressed into MUPS Tablets",
volume = "129",
number = "11",
pages = "1375-1384",
doi = "10.1248/yakushi.129.1375"
}

Ivić, B., Ibrić, S., Betz, G.,& Đurić, Z.. (2009). Evaluation of Diclofenac Sodium Release from Matrix Pellets Compressed into MUPS Tablets. in Zdravstveno varstvo
Pharmaceutical Soc Japan, Tokyo., 129(11), 1375-1384.
https://doi.org/10.1248/yakushi.129.1375

Ivić B, Ibrić S, Betz G, Đurić Z. Evaluation of Diclofenac Sodium Release from Matrix Pellets Compressed into MUPS Tablets. in Zdravstveno varstvo. 2009;129(11):1375-1384.
doi:10.1248/yakushi.129.1375 .

Ivić, Branka, Ibrić, Svetlana, Betz, Gabriele, Đurić, Zorica, "Evaluation of Diclofenac Sodium Release from Matrix Pellets Compressed into MUPS Tablets" in Zdravstveno varstvo, 129, no. 11 (2009):1375-1384,
https://doi.org/10.1248/yakushi.129.1375 . .