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MANUFACTURING
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validation study. Typical evaluation factors include (but are not limited to):
• •
• •
Solubility of active
Cleanability of the active concentration
Toxicity
Complexity of the cleaning procedure
Figure 1. Revised FDA process validation guidance.
Critical Process Parameters should be established during risk assessment exercises. In addition to Risk Assessment exercises, experiments should be conducted to attain data about cleaning process being developed and understood. These Stage 1: Process Design studies should preferably utilize a statistical Design of Experiments (DoE) where appropriate. DoE is “a structured, organized method for determining the relationship between factors aff ecting a process and the output of that process.”4
As stated in the FDA Process Validation Guidance, “risk analysis tools can be used to screen potential variables for DoE studies to minimize the total number of experiments conducted while maximizing knowledge gained.” Further, this guidance says “the results of DoE studies can provide justifi cation for establishing ranges of incoming component quality, equipment parameters, and in-process material quality attributes.”4
At the time of the Process Design,
it is generally recognized that not all possible sources of variability will be known, however; if risk management is exercised to develop insightful DoE studies, they should help developing low variability process.
Additionally, comparative cleanability studies could be performed to compare products. For instance, Figure 2 shows Total Organic Carbon (TOC) results from such a study of 4 products. The study, the results of which
26 | | January/February 2015 Figure 2. Total organic carbon results from comparative cleanability studies.
are illustrated in Figure 2, was performed side-by-side by spiking coupons with products’ residuals and cleaning them using a worst case cleaning method used for all 4 products. The time taken to remove residuals was measured and compared. Prior to the experiment, all 4 products were evaluated by applying the fi rm’s theoretical “Worst Case” product matrix. Companies often use these matrices to determine the worst case product which ultimately will be used for a cleaning
The fi rst 2 products (Product A and Product B) during this theoretical evaluation showed very similar scores. It should be noted here that active pharmaceutical ingredients (API) used in these products were very similar compounds, had similar solubility, and therefore were expected to show similar results for their cleanability. However, as seen in Figure 2, the study results were strikingly diff erent. In order to conduct a side-by- side evaluation, the data for all products were normalized. This example shows how important it is to evaluate and compare all of the parameters by normalizing the data and plotting them on the same plane. What Figure 2 shows is that although Product B contains almost twice the concentration of an API, which, as we said earlier was theoretically very similar in solubility to Product A, it took approximately half the time to clean, as was measured by visual observation. In this case, although initial evaluation of the
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