Date Published: June 29, 2017
Publisher: Public Library of Science
Author(s): Liangshan Ming, Zhe Li, Fei Wu, Ruofei Du, Yi Feng, Mauro Villarini.
Various modeling techniques were used to understand fluidized bed granulation using a two-step approach. First, Plackett-Burman design (PBD) was used to identify the high-risk factors. Then, Box-Behnken design (BBD) was used to analyze and optimize those high-risk factors. The relationship between the high-risk input variables (inlet air temperature X1, binder solution rate X3, and binder-to-powder ratio X5) and quality attributes (flowability Y1, temperature Y2, moisture content Y3, aggregation index Y4, and compactability Y5) of the process was investigated using response surface model (RSM), partial least squares method (PLS) and artificial neural network of multilayer perceptron (MLP). The morphological study of the granules was also investigated using a scanning electron microscope. The results showed that X1, X3, and X5 significantly affected the properties of granule. The RSM, PLS and MLP models were found to be useful statistical analysis tools for a better mechanistic understanding of granulation. The statistical analysis results showed that the RSM model had a better ability to fit the quality attributes of granules compared to the PLS and MLP models. Understanding the effect of process parameters on granule properties provides the basis for modulating the granulation parameters and optimizing the product performance at the early development stage of pharmaceutical products.
Granulation is defined as a process for size enlargement. In this process, small powder particles are brought into contact with each other to form a semi-permanent aggregation in which the original particles can still be distinguished . Unlike high-shear granulation and screw granulation, in which the wet granules are transferred to a drying unit, fluidized bed granulation is a one-step continuous operation including dry mixing, wetting, and drying. Therefore, it reduces the number of unit processes, thus improving the production efficiency, reducing the cost, and satisfying the cGMP requirements . Fluidized bed granulation has many advantages such as simple process and cost saving. Therefore, it is used widely in the chemical and pharmaceutical industries, as well as in agriculture, but its applications are still guided mostly by the empirical trial-and-error methods . To better understand fluidized bed granulation and achieve the desired target product profile, a quality by design (QbD) framework in which the quality is a built-in property rather than a measured test of the final product, should be emphasized and carried out. To provide a mechanistic basis for process understanding, a design of experiment (DoE) approach is recommended. In this approach, a design space with the desired characteristics is established, and the high-risk variables critical to the final product quality are identified . Granulation parameters should be properly controlled in order to obtain high-quality granules. During fluidized bed granulation, the output is highly dependent upon the energy and binder input, in that higher inlet air temperature and velocity and a lower binder addition rate result in spray-drying rather than agglomeration. Conversely, with lower inlet air temperature and velocity and a higher binder addition rate, there is a transition from pneumatic delivery to de-fluidization. Improperly setting parameters, either spray-drying or coating, may result in uncontrollable granulation behavior. Therefore, understanding the operating mechanisms is a prerequisite for reliably obtaining proper quality granules in granulation.
The experimental data obtained in this study show that the high-risk factors in fluidized bed granulation were successfully screened and identified using PBD and BBD. Furthermore, multivariate modeling was an efficient tool in the mechanistic understanding of the influence of the investigated variables on the quality attribute of the prepared granules. A study on fluidized bed granulation was successfully conducted using a two-step approach.