Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems

Agatonovic-Kustrin, Snezana and Alany, Raid G. (2001) Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems. Pharmaceutical Research, 18(7), pp. 1049-1055. ISSN (print) 0724-8741

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Abstract

PURPOSE: A genetic neural network (GNN) model was developed to predict the phase behavior of microemulsion (ME), lamellar liquid crystal (LC), and coarse emulsion forming systems (W/O EM and O/W EM) depending on the content of separate components in the system and cosurfactant nature. METHOD: Eight pseudoternary phase triangles, containing ethyl oleate as the oil component and a mixture of two nonionic surfactants and n-alcohol or 1,2-alkanediol as a cosurfactant, were constructed and used for training, testing, and validation purposes. A total of 21 molecular descriptors were calculated for each cosurfactant. A genetic algorithm was used to select important molecular descriptors, and a supervised artificial neural network with two hidden layers was used to correlate selected descriptors and the weight ratio of components in the system with the observed phase behavior. RESULTS: The results proved the dominant role of the chemical composition, hydrophile-lipophile balance, length of hydrocarbon chain, molecular volume, and hydrocarbon volume of cosurfactant. The best GNN model, with 14 inputs and two hidden layers with 14 and 9 neurons, predicted the phase behavior for a new set of cosurfactants with 82.2% accuracy for ME, 87.5% for LC, 83.3% for the O/W EM, and 91.5% for the W/O EM region. CONCLUSIONS: This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort.

Item Type: Article
Research Area: Chemistry
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > School of Pharmacy and Chemistry
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Depositing User: Sergei O'Farrell
Date Deposited: 05 May 2017 15:14
Last Modified: 05 May 2017 15:14
URI: http://eprints.kingston.ac.uk/id/eprint/31975

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