Interaction Plots and Surface Response Profiles
Figure 5. Interaction profiles for cross interaction between temperature and concentration of CIP-100 cleaning solution.
The intersecting profiles show that these two operating parameters are strongly coupled.
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The interaction plots offer a graphical view of any prospective interaction among the various factors. Any evidence of interaction
between effects is depicted as nonparallel lines in the interaction plots. Figure 5 shows that the impact of CIP-100 concentration
varies depending on the temperature of the cleaning fluid. At high temperatures, a high CIP-100 concentration shortens the
cleaning time, whereas at low temperatures, improved cleaning is achieved at lower CIP-100 concentrations. Such interaction
profiles as well as the process response surface may be product specific and should be established for each product during
the development of the cleaning cycle.
Two-Parameter Design Space
Figure 6. Representation of the design space constructed using two critical operating parameters (temperature and concentration)
and the third performance parameter (observed cleaning time)
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Figure 6 shows contour plots for cleaning time dependence on temperature and concentration for each of the four products.
The plots depict the two-dimensional design space constructed for the cleaning process for all four products. The plots exhibit
the effects of cross-interactions between temperature and cleaning agent concentration on the cleaning time as measured through
the bench-scale cleaning model.
Based on these contour plots, it can be concluded that there are certain product-specific and general behaviors in the response
profiles of cleaning process. All of four products exhibited longer cleaning times at high temperature water conditions, and
at low temperature–high CIP-100 concentration conditions. Improved soilant removal is achieved at low temperature–low CIP-100
and high temperature–high CIP-100 values. At ambient temperatures, the cleaning time progressively increases with increase
in CIP-100 concentration. The trend is reversed for temperatures ≥70°C. Although these general trends are common to the four
products, the magnitude of the change, and the specific temperature and concentration values at which the change occurs, are
unique to each product. Among the four products, product A is the most difficult to clean (H is least) under the conditions
of high temperature and high concentration. Product H, however, becomes the most difficult to clean product in ambient temperature
water (0% CIP-100).
These small-scale studies form the foundation for cleaning process characterization work and offer great value in resource
savings with respect to both material and time. However, as described earlier in the article, certain elements of the cleaning
process could be scale- and equipment-specific. These may include, but are not limited to, equipment shape, location in the
bath, spray ball coverage, level of agitation, and fluid flow dynamics. Bridging studies can be conducted to derive the appropriate
scaling factors needed to convert small-scale cleaning times to large-scale. The scaling factors would be specific to the
equipment, the cleaning bath, and large-scale conditions. Historical monitoring data from the cleaning validation and operation
runs also can offer useful information regarding worst-case conditions and suggestions for appropriate parameters for design
space characterization.
CONCLUSIONS
A bench-scale model has been developed and used to perform cleanability assessments of protein drug products. The method evaluates
the cleaning time of protein soilants deposited on stainless steel coupons and cleaned under simulated large-scale conditions.
The model can successfully estimate the relative cleanability of drug products and support a worst-case–based cleaning validation
approach.
The model was also used to characterize the effect of varying the operating parameters over a broad process design space on
process performance. Statistical analysis using leverage plots show that temperature and concentration of the cleaning solution
are critical process parameters, the cross-interaction term being most significant. Self-interaction plot analysis also demonstrates
the strong coupling between these two parameters and the product-specific nature of the coupling. Cleanability trends are
significantly different for the four products when the temperature and chemical conditions are altered. The findings offer
key insights in the significance of various process parameters and the interplay among them that can be useful for both cycle
development and optimization.
ACKNOWLEDGMENTS
The authors are thankful to Abe Germansderfer (Corporate Validation, Amgen), Erwin Freund, Anurag S. Rathore, and Ed Walls
(Process Development, Amgen) for their critical review and valuable suggestions toward this work.
Nitin Rathore is a senior scientist, Cylia Chen is an associate scientist, and Wenchang Ji is a principal scientist, all in drug product and device development at Amgen, Inc., Thousand Oaks, CA, 805.313.6393, nrathore@amgen.com
Wei Qi is a PhD candidate at the department of chemical engineering at the University of Virginia, Charlottesville, VA.
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