Bench-Scale Characterization of Cleaning Process Design Space for Biopharmaceuticals - A method to evaluate the relative cleanability of new products. - BioPharm International
Figure 3. Dependence of cleaning time on product concentration, using baseline cleaning conditions for temperature, CIP-100
concentration, dirty hold time, and agitation. Cleaning time increases with product concentration.
Protein Concentration. Protein products tend to get more difficult to clean as their concentration increases. Generally, a rise in concentration
is accompanied by an increase in product viscosity and a stronger propensity to form a thicker soilant layer on the surface.
To assess the challenges posed by new high concentration formulations on the cleaning process, it is important to evaluate
how the cleanability is affected by the increase in protein concentration. We studied three different products: A, B, and
H. A range of product concentrations was prepared by diluting the products in their respective buffers and concentrating them
through 10-K centrifugal filters. Figure 3 shows that the cleaning time can be significantly higher for these products as
the concentration is increased. It is also observed that the increase in cleaning time was more significant for the difficult-to-clean
antibody products (A and B) compared to protein product (H).
Cleaning Design Space Characterization
After completion of the single parameter study, an augmented design of experiments was constructed to evaluate the cross interactions
between the two critical parameters: temperature and CIP-100 concentration. Additional experiments were conducted to sample
the two-parameter space. JMP software was used to perform the statistical analysis using various operating parameters as effects
and the cleaning time as a response. Linear regression analysis was used to fit the model to the experimental data. The model
included both first order terms (temperature and concentration), and second order terms (self- and cross-interaction).
Leverage Plots
Table 2. Scaled estimates for various first- and second-order terms of the fitted model
Leverage plots were constructed to graphically view the significance of each term's effect on cleaning time. A P-value analysis
shows a strong correlation between the cleaning time and the cross interaction between temperature and concentration (Figure
4). The cleaning time was modeled as a response to both first- and second-order interactions between temperature and concentration.
Table 2 lists the scaled estimates for this model and provides a more relevant scale-invariant effect size. As shown in Table
2, the cross interaction between temperature and concentration has the maximum effect on cleaning time. This suggests that
the effect of a temperature change on cleaning time is strongly dependent on the value of CIP-100 concentration, and vice-versa.
Figure 4. Leverage plot analysis for dependence of cleaning time on the most significant term: the cross interaction between
temperature (T) and concentration (C)
A similar analysis was performed for the remaining three products (B, E, and H) and the cross interaction term was consistently
found to be the most significant parameter with respect to its effect on cleaning time.
