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.
CONCLUSION: The MLP network could be used as a model for predicting the dissolution profiles of matrix-controlled release theophylline pellet preparation in product development.
MATERIALS & METHODS: Niosomes were prepared using film hydration and ultrasonication methods. Transferrin was coupled to the surface of niosomes via chemical linker. Nanovesicles were characterized for size, zeta potential, morphology, stability and biological efficacy.
RESULTS: When evaluated in MDA-MB-231 cells, entrapment of T3 in niosomes caused 1.5-fold reduction in IC50 value compared with nonformulated T3. In vivo, the average tumor volume of mice treated with tumor-targeted niosomes was 12-fold lower than that of untreated group, accompanied by marked downregulation of three genes involved in metastasis.
CONCLUSION: Findings suggested that tumor-targeted niosomes served as promising delivery system for T3 in cancer therapy.