The correct selection of depth filters can significantly improve the productivity of any cell culture purification process. However, if the filtration is not optimized early on, problems such as low product recovery, premature plugging, an excess of DNA and Host Cell Protein (HCP), and lack of scalability can occur, resulting in major problems downstream. Optimizing depth-filter performance requires a clear understanding of the specific fluid characteristics and batch-to-batch variability. This article will summarize basic requirements for optimizing selection.
Depth filters remove particles, submicron particles, colloidal material, and soluble material by taking advantage of the depth of a particular media (think of a sponge) to enable high levels of contaminant removal. The fluid must travel through a tortuous path before it is able to reach the other side. Figure 1 illustrates the flow of fluid through depth-filter media.
It is logical to assume that contaminants that are larger than the filter pore size would easily be removed by mechanical filtration. This mechanism is also referred to as sieving, straining, or size exclusion.
However, the removal of contaminants such as DNA and HCP is less intuitive. Another purification mechanism that operates with depth filters is adsorption, attracting the contaminants using either electrokinetic or surface affinity. Figure 1 also illustrates an adsorbed particle within a depth filter.
The electrokinetic effect present in charge-modified depth-filter media makes removal of these submicron particles, colloidal material, and soluble contaminants possible. Depth media are designed and manufactured with various pore structures and surface modifications. Figure 2 shows the results of testing of a charge modified depth media under the same conditions as a non-charged modified media.
The results show that both filters perform equally at the mechanical removal of large particles, while the charge modified filter is more efficient at removal of submicron particles through adsorption. Pore surface chemistry also plays an important role for the removal of contaminants. The removal mechanism works by selecting a filter that has a pore surface energy lower than that of the contaminants. Thereby, the contaminants, driven to reduce their surface energy, would be attached to the pore surface of the depth filter and removed from the liquid stream.
To select the optimum depth filter, it is necessary to research commercially available filter media. There is a wide range of depth-filter media available including but not limited to:
- positively charge modified for removal of negatively charges species
- carbon impregnated for specific adsorptive properties
- high silica for lipid removal
- low pyrogenic for pyrogen-sensitive applications
Depth filters are nominally rated, since there is no one standard contaminant that can be used to challenge all filters and address the complexity of cell culture media compositions. Therefore, it is necessary to test several filters for each application.
In filter optimization, the main goal is to maximize filter capacity. In this regard, dual layer products take advantage of gradient pore size structure within a filter. The larger pore-size filter is placed upstream of the finer pore-size filter. Larger contaminants are captured within the upstream layer while smaller particles are removed by the downstream layer. As a result, a larger distribution of contaminants can be removed in a single step.
The first step in designing a filtration train is determining what needs to be removed or reduced and what should not be removed. Small-scale testing for each process and/or product is important. In many cases for mammalian cell culture, a few pore sizes of charged depth media in various configurations would be tested. In order to measure the effectiveness of the filter, data such as total cell density, viability, turbidity, and titer would be recorded just prior to filtration. During optimization, the differential pressure across each filter, the weight/volume of filtrate, and turbidity of the effluent is monitored until a terminal differential pressure is reached. The turbidity, titer, DNA concentration, and HCP concentration of the pooled filtrate would be recorded and product quality analyzed to determine the optimal filtration scheme.
Reviewing total cell density, viability, and turbidity is a quick way to review batch-to-batch variation. Titer data pre- and post-filtration will show if any product is being retained in the filter. Turbidity of each pool is an indicator of filtrate quality and depth-filter effectiveness, and should be compared qualitatively, especially when a sterilizing membrane filter is used downstream.
If the desired effluent quality is obtained, the next step will be to size the filtration train for a given scale. By analyzing the pressure drop and weight/volume data, throughput is calculated and is used for sizing a system.
Throughput is represented as a normalized value with units of volume per unit area, commonly recorded in liters per meter squared (L/m2). The value for filter throughput capacity is used to determine amount of filter area needed for any given size filtration.
For example, say that one liter is to be filtered with a 25 centimeter squared (cm2) filter. When the experiment is completed, it could be recorded that 0.85 liters (L) was filtered when the terminal pressure drop was reached.
This data could then be used to determine the filtration surface required for a 150-liter batch by dividing 0.85 L by 25 cm2 filter area to get 0.034 L/cm2 or 340 L/m2. Next, divide the desired batch size of 150 L by the 340 L/m2 capacity to obtain 0.44 m2 of required filter area.