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.
PURPOSE: A Central Composite Rotatable Design (CCRD) of Response Surface Methodology (RSM) was used purposely to optimize process parameters conditions for formulating nanoemulsion containing aripiprazole using high emulsification methods.
METHODS: This design is used to investigate the influences of four independent variables (overhead stirring time (A), shear rate (B), shear time (C), and the cycle of high-pressure homogenizer (D)) on the response variable namely, a droplet size (Y) of nanoemulsion containing aripiprazole.
RESULTS: The optimum conditions suggested by the predicted model were: 120 min of overhead stirring time, 15 min of high shear homogenizer time, 4400 rpm of high shear homogenizer rate and 11 cycles of high-pressure homogenizer, giving a desirable droplet size of nanoemulsion containing aripiprazole of 64.52 nm for experimental value and 62.59 nm for predicted value. The analysis of variance (ANOVA) showed the quadratic polynomial fitted the experimental values with F-value (9.53), a low p-value (0.0003) and a non-significant lack of-fit. It proved that the models were adequate to predict the relevance response. The optimized formulation with a viscosity value of 3.72 mPa.s and pH value of 7.4 showed good osmolality value (297 mOsm/kg) and remained stable for three months in three different temperatures (4°C, 25°C, and 45°C).
CONCLUSION: This proven that response surface methodology is an efficient tool to produce desirable droplet size of nanoemulsion containing aripiprazole for parenteral delivery application.