Bench-Scale Characterization of Cleaning Process Design Space for Biopharmaceuticals - A method to evaluate the relative cleanability of new products. - BioPharm International

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Bench-Scale Characterization of Cleaning Process Design Space for Biopharmaceuticals
A method to evaluate the relative cleanability of new products.


BioPharm International
Volume 22, Issue 3


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.


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