Drug release control and system understanding of sucrose esters matrix tablets by artificial neural networks
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.
Keywords:
Controlled release / Matrix tablet / Sucrose esters / Neural network / Swelling / Hydrophilic-lipophilic propertySource:
European Journal of Pharmaceutical Sciences, 2011, 44, 3, 321-331Publisher:
- Elsevier Science BV, Amsterdam
Funding / projects:
- Ministry of Science and Technological Development, Republic of Serbia
DOI: 10.1016/j.ejps.2011.08.012
ISSN: 0928-0987
PubMed: 21878388
WoS: 000296930000017
Scopus: 2-s2.0-80053909625
Collections
Institution/Community
PharmacyTY - 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 . .