METHODS: The Multi-Layered Perceptron (MLP) neural network was used to predict the dissolution profiles of theophylline pellets containing different ratios of microcrystalline cellulose (MCC) and glyceryl monostearate (GMS). The concepts of leave-one-out as well as a time-point by time-point estimation basis were used to predict the rate of drug release for each matrix ratio. All the data were used for training, except for one set which was selected to compare with the predicted output. The closeness between the predicted and the reference dissolution profiles was investigated using similarity factor (f2).

RESULTS: The f2 values were all above 60, indicating that the predicted dissolution profiles were closely similar to the dissolution profiles obtained from physical experiments.

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.