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.
RESULTS: The fact that GMP attached covalently with the phosphate group of sodium tripolyphosphate (GMP-STP) was disclosed directly by Fourier transform infrared spectroscopy. Furthermore, ultrasound significantly improved the hydrophobicity and solubility of GMP-STP, which could be attributed to the conversion of α-helix to β-sheet, β-turns, and random coils by sonication. The spatial stabilization of the protein phosphorylation process was boosted by ultrasound, making the droplets more dispersed, and thus an improvement in the functional properties of GMP-STP was observed. Water-holding capacity, oil-binding capacity, and emulsifying and foaming properties were best at an ultrasound power of 400 W.
CONCLUSION: Ultrasound-assisted phosphorylation has great potential to modulate the structure-function relationship of proteins. © 2023 Society of Chemical Industry.