Final filtration with a 0.2-μm filter is considered one of the simplest unit operations. Because it is the last step in the process, however, it is a critical step for successful manufacturing. Filter clogging can occur primarily because of molecular aggregation and can result in deterioration of product quality and longer processing times and thus, loss of plant capacity. This article is the 11th in the "Elements of Biopharmaceutical Production" series. It presents a case study investigating the root cause of failures in sterile filtration by evaluating the effects and interactions of four operating parameters: hold temperature, concentration, hold time, and pH. It is shown that the use of statistical modeling can be effective in solving such problems.
The problem of protein aggregation and filter fouling has been examined extensively using bovine serum albumin (BSA) as a model molecule.1–6 It has been shown that the mechanism behind aggregation in filters is governed by the unique properties of the molecule and its interaction with the membrane.1,3,7,8 Factors that can influence fouling include isoelectric point (IEP), surface chemical groups, and membrane charge of the protein, as well as environmental conditions. For example, a molecule with more free thiol surface groups than the average protein may be more likely to aggregate and foul the filter.3 Saksena and Zydney examined the separation of immunoglobulins and albumin and found that the solution environment has a strong impact on the electrostatic interactions between proteins and the filter.9 Andya, et al., have discussed aggregate formation and stabilization of a humanized monoclonal antibody (MAb) formulation.7Maa and Hsu have investigated fouling during sterile filtration of recombinant human growth hormone.10,11 They examined several fouling mechanisms including pore constriction, adsorption because of nonspecific binding between protein and membrane, shear-induced adsorption, and hydrophobic interface-induced aggregation and adsorption. The results suggested that hydrophobic interface-induced aggregation and adsorption was the dominant cause of filter fouling. Adding of a surfactant or increasing the pH alleviated the problem, whereas increase in ionic strength resulted in more fouling. Many conditions pertaining to the separation process can have an effect on the filterability of a given solution. These conditions include, but are not limited to, concentration, pH or ionic strength, component interactions, charge effects, membrane pore size, membrane consistency, and temperature.8,12
This article investigates the impact on filter throughput of four operating parameters: hold temperature, concentration, hold time, and pH. Theoretical models and statistical analysis have been used to gain insight into the performance of the filtration step.
The intermediate blocking law for fitting constant flow rate experimental curves is often used to model normal flow filtration.12,13 This is especially applicable to the application presented here involving sterile filtration with pore blocking because of aggregation.12 It assumes that particles can deposit on any part of the membrane surface and that any particle depositing on a pore plugs the pore completely. The decrease in free surface, dS, is proportional to the free surface, S, and the reduction of the free surface of pores is identical to the probability for a pore to get blocked:
Equation 1 can be integrated as follows:
Further, Darcy's law states that
Equations 2 and 3 can be combined as
in which, ΔP is the pressure differential (psig), ΔP0 is the initial pressure differential (psig), V/A is the throughput (L/m2), ε is the porosity, and σ is the clogging coefficient (m-1).
For our case, we performed a nonlinear least squares fit using Microsoft Excel Solver. A porosity of 0.7 was assumed per the membrane specification from the vendor. The clogging coefficient and initial pressure fitting parameters were determined from the equations mentioned above. The maximum throughput for this study was defined as the value when the pressure differential endpoint reached 30 psig. By rearranging Equation 4, this maximum throughput can be determined as follows:
If data on filter performance is available, Equation 5 also can be used to predict the maximum throughput that can be achieved before the pressure drop reaches 30 psig.