Lifetime Studies for Membrane Reuse: Principles and Case Studies - - BioPharm International


Lifetime Studies for Membrane Reuse: Principles and Case Studies

BioPharm International
Volume 21, Issue 9

Figure 2. Data from membrane reuse studies for prospective validation: A) data from reuse studies; B) data from blank runs for carryover studies; C) flux; and D) normalized water permeability
In prospective validation, cycling studies are performed at small scale. Once the target lifetime has been established at small scale, it is considered confirmed and validated at full scale. Although this approach entails substantial work at small scale, it reduces the risk of failure at full scale and the resulting loss of batches that could occur if lifespan were solely determined at large scale. To establish 100 reuses at large scale, a strategy for this approach could be to perform cycling studies at small scale to establish 120 reuses, with evaluation of carryover and performance every 20 runs. Once the small-scale data are in place, monitoring could be performed during manufacturing, with evaluation of carryover and performance performed every 20 runs.

The following two case studies discuss examples of processes during which these two approaches have been applied successfully.


This case study for concurrent validation presents the performance of a UF/DF step in a process for a high-volume monoclonal antibody product. The membrane under consideration had been successfully used more than 100 times for a similar step in similar process. Hence, a decision was made to undertake concurrent validation with a target of 100 reuses.

Three criteria—step yield, conductivity, and pH of the product retentate pool—were used to demonstrate acceptable process performance of the membranes throughout their lifetimes. These parameters were monitored for every lot. Cleanability, defined as acceptable lot-to-lot protein carryover and microbial control, was determined based on NWP, bioburden, endotoxin, and protein determination for every lot. Protein carryover was assessed by collecting retentate samples during membrane equilibration, and by testing with specific enzyme-linked immunosorbent assays.

Figure 1 presents data from the reuse study. All performance parameters were within the predetermined acceptance criteria range throughout the membrane lifetime study. Membrane performance with respect to step yield (Figure 1B) and conductivity (Figure 1D) was found to be acceptable. NWP (Figure 1A) and endotoxin levels (Figure 1C) illustrate the effective cleaning of the membrane. Bioburden remained below detection limits throughout the study (data not shown). Further, periodic blank runs were performed and the results were found to be within acceptance criteria (data not shown). The collective set of acceptable performance parameters for the UF/DF membrane system showed that the procedures used to operate, clean, store, and maintain the systems provide effective and controlled performance for at least 100 uses.


This case study for prospective validation presents the performance of a UF/DF step in a process for a low-volume microbial product. The UF/DF step occurred early in the process. Hence, there was concern about the effectiveness of the cleaning procedure. To support a 10-lot campaign at the manufacturing scale, 16 runs were performed at small scale.

The first step with this approach was to establish and qualify the scale-down model.3 Some parameters commonly used to evaluate performance of the scale-down model include NWP, product yield, purity, impurity clearance, flux-versus-time curves, and product carryover. It is important to choose an appropriate cleaning method for both efficacy and for membrane performance with respect to leachables and extractables. Feed material must be representative of what will be produced at manufacturing scale.

Scale down is typically performed at operating conditions identical to those at manufacturing scale with respect to parameters such as product loading per unit membrane area, buffer components and properties (e.g., pH and ionic strength), temperature, TMP, and crossflow rate. Finally, the scale-down model should be compared with the equipment and conditions used at manufacturing scale. Although performance indicators like step recovery can differ at the two scales, depending on equipment setup, it is important to understand what the differences are and to treat the small-scale data appropriately when applying the resulting conclusions to manufacturing scale.

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