Locating optimal protein precipitation conditions for complex biological feed materials is problematic. This article describes the application of a series of high-throughput platforms for the rapid identification and selection of conditions for the precipitation of an IgG(4) monoclonal antibody (mAb) from a complex feedstock using only microliter quantities of material. The approach uses 96-microwell filter plates combined with high-throughput analytical methods and a method for well volume determination for product quantification. The low material, time and resource requirements facilitated the use of a full factorial Design of Experiments (DoE) for the rapid investigation into how critical parameters impact the IgG(4) precipitation. To aid the DoE, a set of preliminary range-finding studies were conducted first. Data collected through this approach describing Polyethylene Glycol (PEG) precipitation of the IgG(4) as a function of mAb concentration, precipitant concentration, and pH are presented. Response surface diagrams were used to explore interactions between parameters and to inform selection of the most favorable conditions for maximum yield and purification. PEG concentrations required for maximum yield and purity were dependant on the IgG(4) concentration; however, concentrations of 14 to 20% w/v, pH 6.5, gave optimal levels of yield and purity. Application of the high-throughput approach enabled 1,155 conditions to be examined with less than 1 g of material. The level of insights gained over such a short time frame is indicative of the power of microwell experimentation in allowing the rapid identification of appropriate processing conditions for key bioprocess operations.
AbctractFlocculation unit operations are being revisited as a strategy to ease the burden posed on clarification and purification operations by the increasingly high cell density cultures used in the biopharmaceutical industry. The purpose of this study was to determine the key process parameters impacting flocculation scale-up and use this understanding to develop an automated ultra-scale down (USD) method for the rapid characterization of flocculation at the microliter scale. The conditions under which flocculation performance of a non-geometrically similar vessel three orders of magnitude larger can be mimicked by the USD platform are reported. Saccharomyces cerevisiae clarified homogenate was flocculated with poly(ethyleneimine) (PEI) to remove the residual solids remaining in the centrate. Flocculant addition time modulated flocculation performance depending on the predominant mixing time scale (i.e. macro-, meso- or micromixing). Particle growth and breakage was mimicked at the two flocculation scales by the average turbulent energy dissipation (εavg) and impeller tip speed (vtip) scale-up bases. The results obtained were used to develop an USD method. The USD method proposed uses constant εavg as the scale-up basis under a micromixing controlled regime. These conditions mimicked the STR flocculation performance within a ±5% error margin. Operation in the mesomixing regime led to particle size deviations between the flocculation scales of ≤50 %. These results, in addition to the microscopic observations made, demonstrate the USD system presented in this work can produce process-relevant flocculated material at the microliter scale under the correct operating conditions.
Ultra scale-down approaches represent valuable methods for chromatography development work in the biopharmaceutical sector, but for them to be of value, scale-down mimics must predict large-scale process performance accurately. For example, one application of a scale-down model involves using it to predict large-scale elution profiles correctly with respect to the size of a product peak and its position in a chromatogram relative to contaminants. Predicting large-scale profiles from data generated by small laboratory columns is complicated, however, by differences in dispersion and retention volumes between the two scales of operation. Correcting for these effects would improve the accuracy of the scale-down models when predicting outputs such as eluate volumes at larger scale and thus enable the efficient design and operation of subsequent steps. This paper describes a novel ultra scale-down approach which uses empirical correlations derived from conductivity changes during operation of laboratory and pilot columns to correct chromatographic profiles for the differences in dispersion and retention. The methodology was tested by using 1 mL column data to predict elution profiles of a chimeric monoclonal antibody obtained from Protein A chromatography columns at 3 mL laboratory- and 18.3 L pilot-scale. The predictions were then verified experimentally. Results showed that the empirical corrections enabled accurate estimations of the characteristics of larger-scale elution profiles. These data then provide the justification to adjust small-scale conditions to achieve an eluate volume and product concentration which is consistent with that obtained at large-scale and which can then be used for subsequent ultra scale-down operations.
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