title: 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.
authors: Industrial Pharmacy Research Group, Department of Pharmaceutical Sciences, University of Basel, Mülhauserstrasse 51, 4056 Basel, Switzerland. Krisanin.chansanroj@unibas.ch
authors: Krisanin
authors: K
authors: Chansanroj
authors: Jelena
authors: J
authors: Petrović
authors: Svetlana
authors: S
authors: Ibrić
authors: Gabriele
authors: G
authors: Betz
mesh_terms: D003692:Delayed-Action Preparations; D004952:Esters; D005079:Excipients / Q000737:chemistry*; D057927:Hydrophobic and Hydrophilic Interactions; D008790:Metoprolol / Q000737:chemistry*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D016062:Porosity; D012995:Solubility; D013395:Sucrose / Q000737:chemistry*; D013607:Tablets; D013718:Tensile Strength
chemical_list: D003692:Delayed-Action Preparations; D004952:Esters; D005079:Excipients; D013607:Tablets; D013395:Sucrose; D008790:Metoprolol