Investigation of design space for freeze-drying injectable ibuprofen using response surface methodology

Abstract

This study explores the use of a statistical model to build a design space for freeze-drying two formulations with ibuprofen. A 2 × 3 factorial experimental design was used to evaluate independent variables (filling volume and annealing time) and responses as residual moisture content, specific surface area and reconstitution time. A statistical model and response surface plots were generated to define the interactions among the selected variables. The models constructed for both formulations suggest that 1 mL of filled volume and no annealing should be used to achieve optimal residual moisture content, specific surface area and reconstitution time. The proposed models were validated with additional experiments, in which the responses observed were mainly in close agreement with the predicted ones. Additionally, the established models demonstrate the reliability of the evaluation procedure in predicting the selected responses.

Experimental modelling studies on the removal of dyes and heavy metal ions using ZnFe2O4 nanoparticles

“…It should be noted that a high R 2 value does not necessarily show that the model is good, as R 2 increases with the addition of variables to the model (regardless of whether the variables are important or not). On the contrary to the coefficient of determination, the Adjusted coefficient (R 2 -Adj) has more validity to examine the adequacy of the model 32 . The R 2 -Adj value for all models was greater than 98.70%.…”

Application of Quality by Design Principles to the Development of Oral Lyophilizates Containing Olanzapine

“…The value of R 2 (adj) means that 13.48% of all variations cannot be explained by the set model. The R 2 (pre) was 81.19%, which represents the degree of model prediction of the redispersion time in the case of altered factors, such as the total amount of excipients, concentration of gelatin, mannitol, or PVP K25 in OLs [ 13 ]. The influence of factors on the response is more clearly explained by Figure 4 and Figure 5 .…”

“…DoE is an important part of QbD and is based on the results of risk assessment [ 11 ]. It facilitates the establishment of the design space, i.e., the multidimensional combination and interaction of factors that have been shown to provide quality, and specifies information on the values of the factors within which the desired values of responses can be expected [ 12 , 13 ]. Quality risk management is a systematic process of assessing, monitoring, communicating, and reviewing pharmaceutical product quality risks throughout their entire life cycle and begins with risk assessment [ 11 , 14 ].…”

Effect of Concentration and Suspension Agent (HPMC) on Properties of Coal Gangue and Fly Ash Cemented Filling Material

“…Response surface method includes many experimental design and data processing techniques, such as experimental design, regression equation modeling, model significance test, and factor combination condition optimization. By fitting the functional relationship between the response and various factors, drawing the response surface and contour line, the response value corresponding to each factor level can be easily obtained; then the optimal response value corresponding to the level of each factor can be found out [25][26][27][28]. e RSM has several advantages, such as the efficiency to predict the model for each response, to construct a robust model with a small number of experimental data points, to assess the interaction effect between the factors, and to locate the optimal response [29][30][31].…”