Ultra scale‐down approach to correct dispersive and retentive effects in small‐scale columns when predicting larger scale elution profiles

Abstract

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.

Demonstration of the use of windows of operation to visualize the effects of fouling on the performance of a chromatographic step

“…In addition, the fouling effect of other groups of foulants such as lipids or nucleic acids may be investigated using the same systematic method, to generate process performance‐related data, which take into account fouling effects. This method could also be combined with other frameworks27–29 to minimize the number of experiments, which would be required to investigate different process alternates and explore how various unit operations impact on each other's performances.…”

Integration of stochastic simulation with multivariate analysis: Short‐term facility fit prediction

“…Furthermore, when tech transfer activities are planned before the process limits evaluation validation studies during Phase III clinical trials, most of the manufacturing data would be from lab‐scale or small‐scale experiments. Predicting variability in eluate volumes at large scale from data generated by small laboratory columns is complicated by several factors such as differences in dispersion and retention volumes between the two scales of operation as well as fluctuations in operating conditions (e.g. buffer pH and conductivity).…”

Integration of scale-down experimentation and general rate modelling to predict manufacturing scale chromatographic separations

“…Such approaches make scale-up time consuming and expensive as they require a substantial amount of material even at the smallest scale [3]. Additionally, the number of early stage experiments is limited and optimisation of the chromatographic steps is delayed until late on in development when sufficient amount of feed material is available [4].…”