Blendas formadas pela polimerização eletroquímica de pirrol em matriz de acrilamida (hidrogel) foram utilizadas, neste trabalho, como dispositivos para a liberação controlada de drogas. A influência de vários parâmetros nesta síntese, como tipo de matriz de hidrogel e condições de polimerização, foi estudada empregando-se planejamento fatorial fracionário. O objetivo final consistiu na obtenção de um material adequado para liberação baseada no controle de potencial. Para os testes de liberação foi utilizada Safranina, como substância modelo. As curvas de liberação demonstram a potencialidade do uso de tais dispositivos. Estes dispositivos foram também caracterizados por espectroscopia Raman e voltametria cíclica.

Blends formed by electrochemical polymerization of polypyrrole (PPy) into polyacrylamide (PAAm) hydrogels were used as devices for controlled drug release. The influence of several parameters in the synthesis, such as type of hydrogel matrix and polymerization conditions was studied by using a fractional factorial design. The final goal was to obtain an adequate device for use in controlled release tests, based on electrochemical potential control. For controlled release tests, Safranin was used as model drug and release curves (amount of drug vs. time) have shown that these blends are promising materials for this use. The optimized blends obtained were characterized by cyclic voltammetry and Raman spectroscopy.

In this work, the artificial neural networks (ANN) and partial least squares (PLS) regression were applied to UV spectral data for quantitative determination of thiamin hydrochloride (VB1), riboflavin phosphate (VB2), pyridoxine hydrochloride (VB6) and nicotinamide (VPP) in pharmaceutical samples. For calibration purposes, commercial samples in 0.2 mol L-1 acetate buffer (pH 4.0) were employed as standards. The concentration ranges used in the calibration step were: 0.1 - 7.5 mg L-1 for VB1, 0.1 - 3.0 mg L-1 for VB2, 0.1 - 3.0 mg L-1 for VB6 and 0.4 - 30.0 mg L-1 for VPP. From the results it is possible to verify that both methods can be successfully applied for these determinations. The similar error values were obtained by using neural network or PLS methods. The proposed methodology is simple, rapid and can be easily used in quality control laboratories.