No abstract

The Predictive Aggregate Transport Model for microfiltration is used in combination with optimum fluid mechanics and electrostatics to maximize recovery of a heterologous immunoglobulin (IgG) from transgenic goat milk. The optimization algorithm involved varying pH (6.8 -9), transmembrane pressure (2 -4.5 psi), milk feed concentration (1 -2X), membrane module type (linear vs. helical design), and axial velocity (Reynolds number: 830-1170). Operation in the pressure-dependent regime at low uniform transmembrane pressures (c2 psi) using permeate circulation in co-flow, at the pI of the protein (9 in this case) was used to increase IgG recovery from less than 1% to over 95%. Sodium dodecyl sulfate polyacrylamide gel electrophoresis and attenuated total reflection Fourier transform infrared spectroscopy of the microfiltration permeate samples confirmed that all the fat globules and most of the casein micelles were retained in the MF membrane whereas a large amount of the target IgG was transported through the membrane. Transmembrane pressure and hence permeation flux was kept low (c15 lmh) to maximize IgG membrane transport and thus recovery, due to a sparse deposit on the membrane which facilitated high solute transport. Next, an analytical method was used to optimize the diafiltration process using the aggregate transport model, experimental target protein sieving coefficients and permeation flux . The methodology reported here should be generalizable to the recovery of target proteins found in other complex suspensions of biological origin using the microfiltration process. B 2004 Wiley Periodicals, Inc.

A methodology, called the aggregate transport model, is presented that can a priori predict both the pressure-independent permeation flux and yield of target species for the microfiltration of poly-disperse solutions. The model captures the phenomenon of critical shear rate. Beyond the critical shear rate (expressed as a ratio of shear rate to permeation flux), the transmission of proteins drops sharply as a result of cake classification. The widely reported benefits of operating at uniform transmembrane pressure and constant wall concentration follow from this method. The methodology is general in nature and can be used predictively to obtain an optimal balance between flux and yield of target species during the microfiltration of many commercial poly-disperse suspensions. In the accompanying paper we test this model for microfiltration of transgenic whole goat milk.

To meet the technical challenge of recovering human IgG fusion protein from transgenic whole goat milk at reasonable cost with high purity and yield, a predictive aggregate transport model for microfiltration has been developed (Baruah and Belfort, 2003). Here, to test the model's predictability of permeate flux and mass transport, a comprehensive series of experiments with varying wall shear rate, feed temperature, feed concentration, and module design are presented. A very good fit was obtained between the model predictions and measurements for a wide variety of experimental conditions. For microfiltration module design comparison, a linear hollow fiber module (representing current commercial technologies) gave lower permeation flux and higher yield than a helical hollow fiber module (representing the latest self-cleaning methodology). These results are easily explained with the model that is now being used to define operating conditions for maximizing performance. The procedure described by the model is generalizable and can be used to obtain optimal filtration performance for applications other than milk.