Optimization of pH-independent chronotherapeutic release of verapamil HCl from three-layer matrix tablets.
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.
Faculty of Pharmaceutical Sciences, Hashemite University, Zarqa, Jordan. Electronic address: nzoubi@hu.edu.jo.
Nizar
N
Al-Zoubi
Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, The University of Jordan, Amman, Jordan.
Hatim S
HS
Alkhatib
Department of Pharmaceutical Sciences and Pharmaceutics, Faculty of Pharmacy, Applied Science University, Amman, Jordan.
Ghadah
G
Alobaidi
Department of Pharmaceutical Sciences and Pharmaceutics, Faculty of Pharmacy, Applied Science University, Amman, Jordan.
Safwan
S
Abdel-Rahim
Department of Pharmaceutical Technology, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan.
Wasfy
W
Obeidat
Department of Pharmaceutical Technology, School of Pharmacy, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Stavros
S
Malamataris
D000464:Alginates / Q000737:chemistry; D002626:Chemistry, Pharmaceutical; D003692:Delayed-Action Preparations / Q000737:chemistry*; D055870:Drug Chronotherapy; D065546:Drug Liberation; D020723:Glucuronic Acid / Q000737:chemistry; D006603:Hexuronic Acids / Q000737:chemistry; D006863:Hydrogen-Ion Concentration; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011135:Polysaccharides, Bacterial / Q000737:chemistry; D013607:Tablets / Q000737:chemistry*; D014700:Verapamil / Q000737:chemistry*
D000464:Alginates; D003692:Delayed-Action Preparations; D006603:Hexuronic Acids; D011135:Polysaccharides, Bacterial; D013607:Tablets; D020723:Glucuronic Acid; D014700:Verapamil; C002563:xanthan gum
Artificial neural networks; Chronodelivery; Sodium alginate; Three-layer matrix tablets; Xanthan gum