ABSTRACT
The aim of this work was to evaluate and optimize formulation of three-layer matrix tablets based on xanthan gum (XG) and sodium alginate for chronotherapeutic pH-independent release of verapamil HCl (VH). Artificial neural networks (ANN) were applied in the optimization and compared with multiple linear regression (MLR). A face-centered central composite experimental design was employed with three factors (mass fraction of VH in intermediate layer, X1, and of XG in matrix former of intermediate and outer layers, X2 and X3). The prepared tablets were tested for in vitro release in 0.1 N HCl and phosphate buffer (pH 7.5), tensile strength and friability. Furthermore, swelling observation and release modeling to Weibull function and power law equation of Peppas were employed to help further understanding of release behavior and mechanism. The releases (%) in phosphate buffer (pH 7.5) at 6, 12 and 24 h were selected as responses to depict the mode of release and similarity factor (f2), between release profiles in 0.1N HCl and pH 7.5 during the first 8 h, as response of pH-independence. A desirability function combining the four responses was constructed and overall desirability values were used for the ANN and MLR modeling. Five additional checkpoint formulations, within the experimental domain, were used to validate the external predictability of the models. The constructed ANN model fitted better to the overall desirability than the MLR model (R=0.838 vs. 0.670, for the additional checkpoint formulations) and therefore, was used for prediction of formulation with optimal in vitro drug release.
