Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems
Agatonovic-Kustrin, S; Alany, RG
| HERO ID | 4683799 |
|---|---|
| In Press | No |
| Year | 2001 |
| Title | Role of genetic algorithms and artificial neural networks in predicting the phase behavior of colloidal delivery systems |
| Authors | Agatonovic-Kustrin, S; Alany, RG |
| Journal | Pharmaceutical Research |
| Volume | 18 |
| Issue | 7 |
| Page Numbers | 1049-1055 |
| Abstract | <strong>PURPOSE: </strong>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.<br /><br /><strong>METHOD: </strong>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.<br /><br /><strong>RESULTS: </strong>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.<br /><br /><strong>CONCLUSIONS: </strong>This type of methodology can be applied in the evaluation of the cosurfactants for pharmaceutical formulations to minimize experimental effort. |
| Pmid | 11496944 |
| Is Certified Translation | No |
| Dupe Override | No |
| Is Public | Yes |
| Language Text | English |