Covalent and ionic co-cross-linking--an original way to prepare chitosan-gelatin hydrogels for biomedical...

The first goal of this work was to develop a method for obtaining interpenetrating gelatin (G)-chitosan (CS) networks prepared by double cross-linking (covalent followed by ionic) that exhibit hydrogel character. The second goal was to modulate their properties as a function of the preparation parameters by using neural network models. This study was therefore carried out by experiment and simulation. The covalent cross-linking resulted from the reaction between the carbonyl groups of glutaraldehyde with amino groups belonging to both polymers; the ionic cross-linking is based on the interaction between tripolyphosphate anions and protonated amine groups (ammonium ions) of the polymers. The total cross-linking density (indirectly assessed by estimating the water swelling capacity) and the ability to include hydrosoluble bioactive principles are influenced by the following process parameters: the CS/G ratio, the amount of ionic cross-linker, and the ionic cross-linking time. The prepared hydrogels were characterized with respect to their structural, morphological, and some physical properties. The hydrogels ability to load high amounts of water-soluble drugs indicates their potential use as carriers for biologically active principles in the human body. A neural network methodology was applied to model the swelling degree and caffeine loading/release capacity depending on reaction conditions; in addition, applying this method, the optimal preparation conditions have been determined, targeting pre-established values for swelling degree or maximum caffeine value. The accuracy of the results obtained through this technique proves that the neural networks are suitable tools for modeling cross-linking processes taking place complex nonlinear polymers.