Artificial neural networks and their application in the optimization of carbamazepine solid dispersions

The aim of this study was to examine the possibility of using artificial neural networks in the optimization of solid dispersions with carbamazepine. Artificial neural networks of the Generalized regression neural network type with four layers, gave models that describe the effect of components in s...

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Published inActa Poloniae pharmaceutica Vol. 79; no. 4; pp. 551 - 555
Main Authors Vojinovic, Tanja, Potpara, Zorica, Vukmirovic, Mihailo, Turkovic, Nemanja, Ibric, Svetlana
Format Journal Article
LanguageEnglish
Published 21.11.2022
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Summary:The aim of this study was to examine the possibility of using artificial neural networks in the optimization of solid dispersions with carbamazepine. Artificial neural networks of the Generalized regression neural network type with four layers, gave models that describe the effect of components in solid dispersions carbamazepine-Neusilin ® UFL2 (magnesium aluminosilicate)-Collidon ® VA64 (vinylpyrrolidone-vinyl acetate) and dissolved carbamazepine value (%) after 10 (Q10) and 30(Q30) minutes of carbamazepine testing. After the learning process, root mean square error (RMS) values of 0.0029 were obtained for the training data set, and 0.1185 for the test training data, which is an excellent prediction of the neural network.
ISSN:0001-6837
DOI:10.32383/appdr/154044