In this case study, product pool pH, conductivity, impurity percentage, and recovery percentage were used to assess step performance.
As Table 1 shows, the performance of the scale-down model is equivalent to that at large scale with respect to the parameters
given, and the performance of the step is comparable across scales. Recovery was primarily used as a consistency indicator
and was greater than 100% because different assays were used to measure product concentration in the load and pool samples.
Small-scale reuse studies were performed using this scale-down model. NWP and flux-versus-time curves were monitored to assess
the cleanability of the membrane. Finally, blank runs were performed after every five runs to assess carryover at small scale
and host cell proteins (HCP), and product concentration via ELISA and DNA were monitored.
Figure 2 shows data from the small-scale reuse study. The membrane performance was acceptable based on pool pH, conductivity,
impurity percentage, and recovery percentage (Figure 2A). Flux-versus-time curves (Figure 2C) overlapped, and NWP (Figure
2D) was stable over the number of membrane reuses. Carryover for the blank runs (2, 7, 12, 15, and 17) was minimal with respect
to HCP, DNA, and product concentration (Figure 2B). Higher values for host cell proteins and ELISA samples for run 15 were
attributed to sampling error, as supported by results for run 17. The small-scale data presented here suggested that 10 reuses
could be validated at commercial scale, and this was eventually proven.
The application should dictate the approach used to establish membrane lifespan. Substantial time and resources are required
to create and qualify a scale-down model and perform small-scale reuse studies, but when a strong likelihood of cleaning failure
exists, these studies are justifiable. Robust routine in-process testing may be substituted for reuse validation, and it may
provide a strong assurance of membrane performance. Because lifetime studies for membrane reuse require sampling and testing
between lots, the studies may have an impact—sometimes a significant one—on production schedules. A cost–benefit analysis
can often substantiate the benefits of validation.
Anurag S. Rathore, PhD, is director of process development at Amgen, Inc., Thousand Oaks, CA, 805.447.1000, firstname.lastname@example.org
R. Samavedam, T. Kichefski, and S. Cote are all senior validation engineers at Amgen, Inc., West Greenwich, RI. R. Morrison is a manager at Commissioning Agents, Inc., Stonington, CT.
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