| In the current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. The obtained powders were analyzed by modulated differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, polarized light microscopy, and particle size analysis. Moreover, Pearson correlation analysis, Kohonen's self-organizing map, contribution plot, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated for PVP based systems. Final validation of prepared models was done using binary SSDs of different model drugs with polymer based on root mean square error and mean absolute error for each quality attribute. large quantities of API and long development time. Moreover, developing a mathematical model of a spray drying process is difficult task, for instance, modeling the rapid heat and mass transfer that occurs between the droplet phase and the liquid phase; the moving boundary of the droplet, and the presence of multiphase flows. In current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. The obtained powders were analyzed by modulated differential scanning calorimetry, thermogravimetric analysis, X-ray diffraction, polarized light microscopy, and particle size analysis. Moreover, Pearson correlation analysis, Kohonen's self-organizing map, contribution plot, contour plots and response surface plot were used to illustrate the relationship between input variables and quality attributes. The influence of different physicochemical properties of solvent on the quality attributes of spray dried products was also investigated for PVP based systems. Final validation of prepared models was done using binary SSDs of different model drugs with polymer based on root mean square error and mean absolute error for each quality attribute. iilarge quantities of API and long development time. Moreover, developing a mathematical model of a spray drying process is difficult task ,for instance, modeling the rapid heat and mass transfer that occurs between the droplet phase and the liquid phase; the moving boundary of the droplet, and the presence of multiphase flows. In current study a model for spray drying processes was developed using polymer as a placebo formulation to predict quality attributes (process yield, outlet temperature, and particle size) for binary solid dispersions (SDs). Different polymers evaluated were Polyvinylpyrrolidone (PVP-K 29/32) and Hydroxylpropyl methylcellulose acetate succinate (HPMC-AS-HF). The experiments were designed to achieve a better understanding of the spray drying process by considering combination of different formulation (feed concentration and solvent used) and process parameters (flow rate, inlet temperature, nozzle size used, atomization pressure). On the basis of the experimental data, a response surface model and an ensemble artificial neural network were developed to predict different quality attributes. (Abstract shortened by UMI.). |
