Design of Experiments as a Tool to Optimize the Process of Coating Minitablets with Commercial Gastro-Resistant Coating Mixtures

Abstract

According to the Quality by Design (QbD) concept, Design of Experiment (DoE) was used to indicate critical process parameters and optimize the fluid bed coating of minitablets in a laboratory size batch. Full factorial design was employed to increase knowledge of the process for three kinds of minitablet (MT) cores using two commercial gastro-resistant coating mixtures. The statistical analysis showed that different critical process parameters were indicated for the tested minitablets: X3: the coating mixture flow rate for MTs with pantoprazole sodium and Eudragit L; X2: the product temperature; X3 and X4: the spraying pressure for MTs with pantoprazole sodium and Acryl Eze II; and X1 and X2: MTs with diclofenac sodium. Such differences were the result of features, such as the sub-coat, size, and mass of the cores and the core and coating mixture composition. No optimal parameters were found for any of the tested MT types. Therefore, DoE should be considered as a statistical tool to individually optimize the process for the product, equipment, and tested parameters. However, optimization of the fluid bed coating allowed us to predict the values of the process parameters necessary to obtain good-quality products. Therefore, fluid bed coating may be successfully used to obtain modified-release MTs of high quality after applying the statistical tool DoE.

Keywords: design of experiments; enteric coating; fluid bed coating; minitablets; quality by design.

Figure 1

Example images of minitablet (MT) cross-sections obtained using a stereoscopic microscope: (A) MT-PE, (B) MT-PA, (C) MT-DE.

Figure 2

Dissolution profiles of enteric-coated MTs with pantoprazole ((A)—MT-PE; (B)—MT-PA) and diclofenac ((C)—MT-DE).

Figure 3

Pareto charts of the main effects for the fluid bed coating process of MTs.

Figure 4

Desirability plots for response surface prediction of MT-PE showing interactions between factors X1–X4.

Figure 5

Desirability plots for response surface prediction of MT-PA showing interactions between factors X1–X4.

Figure 6

Desirability plots for response surface predictions of MT-DE showing the interactions between factors X1–X4.

Figure 7

Predicted optimal values of factors X1–X4 and desirability of the fluid bed coating processes for all tested MTs.