As the momentum for implementation of Quality by Design progresses in the pharmaceutical industry, more tools are being implemented to create better understanding of process parameters and their impact on critical quality attributes. Utilizing models and statistics have become a key instrument in creating a robust design space and expansive knowledge space of the process trains. While doing one variable at a time experiments can be used to understand the ranges of parameters, it does not exemplify understanding of the interactions across an entire unit operation. Thus, there is a need to utilize design of experiments and statistical modeling within a unit operation to justify ranges on key processing parameters. However, across multiple unit operations, the initial parameters studied can be confounded by the effects of the other unit operations. An example of this is in a high-shear wet granulation (HSWG) process train, where multiple unit operations are needed in order to process the material into the final product, where the critical quality attributes can be tested. Here we present a case study on a design of experiments on a HSWG process train and how the initial outputs from the experiment are linked to the critical quality attributes without being confounded by the unit operations following the granulation. Due to the multi-unit operations needed in the HSWG train, we present a way to map the processing parameters of the granulation step to the critical quality attributes by using the granule characteristics as the linkage. By understanding interactions of the processing parameters on the granule characteristics and the variation of the granule characteristics on the critical quality attributes, the confounding effects across the other unit operations can be eliminated.
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