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The advantage of ranking key process parameters is that it allows the investigator to know which parameter will have the largest or smallest eff ect on the response. The secondary goals of the DOE were to include scale as a DOE factor to facilitate scale-up and commercial process validation, optimize the process, and create a design space.


A DOE was executed for the fi lling process of this case study. Prior experience and knowledge for similar products were considered in selecting the equipment train and identifying potential process parameters. Lab scale experiments were performed to demonstrate equipment feasibility and to fi nd constraints so that the DOE would not allow for conditions that would result in a “net zero” response. By “net zero,” it is intended to mean a result that would not yield information


that


would contribute to the DOE analysis, such as a container with 0 grams fi ll weight. It is not intended to mean a response that was not within acceptable ranges, for such information would still be useful to analyze the experiment and generate a predictive algorithm for fi ll weight.


The purpose of this trial was to investigate the potential critical process parameters of pump volume, vessel pressure, and pump speed by gaining an understanding of main and interaction eff ects of the input variables on the CQA of fi ll weight. Because commercial equipment was available for the experiment, it was not necessary to include scale as a factor. The analysis of the custom response surface design showed no major interaction eff ects within the ranges investigated. Graphically, this is represented in Figure 2 by noting that none of the parameter plots intersect each other at any level explored in the trial.


By analyzing the factor parameter estimates, shown in Figure 3, it is also apparent that there are no signifi cant interaction eff ects; however, there are addition conclusions available with this analysis. All main eff ects (in this case: pump volume, pump speed, and pressure) were confi rmed critical. The criticality of the parameters is justifi ed because varying these inputs has signifi cant potential to aff ect the fi ll weight. This is communicated in Figure 3 with p-values below 0.05.


All parameters were equally signifi cant and therefore impossible to rank in a meaningful


Figure 3. SSL Topical Product: Factor Parameter Estimates.


way for this design. If this were important, some software packages with DOE platform can generate a predictive algorithm with each parameter and its eff ect on the response could be generated with the same software used to design and analyze the experiment. It is worth noting that the interaction of speed and pressure is almost signifi cant, at α = 0.06, in this situation. If desired, one could investigate this further to fi nd out if the signifi cance increases by taking out the more insignifi cant interactions such as volume and speed, or volume and pressure. For this particular case, all interactions were left in the model for optimal fi t. Using the DOE software employed for this case study, a predictive algorithm was generated to predict optimal process parameter ranges that would meet all required quality attributes specifi ed in the model. Graphically, one set of predicted optimal CPP ranges is represented in Figure 4. There are multiple CPP range combinations that combine to yield acceptable


product; this is one advantage of generating a design space, as discussed below.


Process Design Space


ICH Q8 defi nes a design space as “the multidimensional combination and inter- action of input variables (eg, material attributes) and process parameters that have been demonstrated to provide assurance of quality.”2


The execution of the fi lling trial made possible a design space for the fi lling process, shown in Figure 5. The shaded area in Figure 5 represents combinations of machine input variables that are predicted to yield fi ll weights outside of the range specifi ed in the model. The white area represents the combination of input variables that are predicted to yield fi ll weights within the range specifi ed. The advantage of using DOE


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Figure 2. SSL Topical Product: Factor Interaction Eff ects.


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