Concomitant and controlled release of furazolidone and bismuth(III) incorporated in a cross-linked sodium alginate-carboxymethyl cellulose hydrogel.

The combination of bismuth(III) citrate and the antibiotic furazolidone (FDZ) results in a synergetic effect on Helicobacter pylori eradication. However, the problems associated with their oral administration are challenges to overcome. Thus, in the present study, sodium alginate (SA)/carboxymethyl cellulose (CMC) blend hydrogels (SC) were developed for concomitant and controlled release of furazolidone and bismuth(III). The blank formulation (SCblank) and the three drug-loaded hydrogels (SCFDZ, SCBi, and SCFDZ-Bi) were prepared by casting method and characterized by infrared spectroscopy, scanning electron microscopy, differential scanning calorimetry, and X ray powder diffraction analyses. The swelling equilibrium and cumulative release amounts of FDZ and Bi3+ have indicated distinct behaviors of the hydrogels to different pH values. The bismuth-containing sample (SCFDZ-Bi) presents more resistance to degradation on a neutral solution and shows more suitable properties for controlled drug release than the sample without bismuth (SCFDZ). Microbiological studies, using Escherichia coli as a model, show bacteria viability reduction in presence of the drug-loaded samples. The developed system containing furazolidone and bismuth(III) appears to be promising for oral administration with concomitant and controlled release of these drugs aimed at the pharmacological treatment of gastrointestinal disorders.

Vacuum Thermoforming Process: An Approach to Modeling and Optimization Using Artificial Neural Networks.

In the vacuum thermoforming process, the group effects of the processing parameters, when related to the minimizing of the product deviations set, have conflicting and non-linear values which make their mathematical modelling complex and multi-objective. Therefore, this work developed models of prediction and optimization using artificial neural networks (ANN), having the processing parameters set as the networks' inputs and the deviations group as the outputs and, furthermore, an objective function of deviation minimization. For the ANN data, samples were produced in experimental tests of a product standard in polystyrene, through a fractional factorial design (2k-p). Preliminary computational studies were carried out with various ANN structures and configurations with the test data until reaching satisfactory models and, afterwards, multi-criteria optimization models were developed. The validation tests were developed with the models' predictions and solutions showed that the estimates for them have prediction errors within the limit of values found in the samples produced. Thus, it was demonstrated that, within certain limits, the ANN models are valid to model the vacuum thermoforming process using multiple parameters for the input and objective, by means of reduced data quantity.