This article describes the use of ultra scale-down studies requiring milliliter quantities of process material to study the clarification of mammalian cell culture broths using industrial-scale continuous centrifuges during the manufacture of a monoclonal antibody for therapeutic use. Samples were pretreated in a small high-speed rotating-disc device in order to mimic the effect on the cells of shear stresses in the feed zone of the industrial scale centrifuges. The use of this feed mimic was shown to predict a reduction of the clarification efficiency by significantly reducing the particle size distribution of the mammalian cells. The combined use of the rotating-disc device and a laboratory-scale test tube centrifuge successfully predicted the separation characteristics of industrial-scale, disc stack centrifuges operating with different feed zones. A 70% reduction in flow rate in the industrial-scale centrifuge was shown to arise from shear effects. A predicted 2.5-fold increase in throughput for the same clarification performance, achieved by the change to a centrifuge using a feed zone designed to give gentler acceleration of the bioprocess fluid, was also verified at large-scale.

ABSTRACT:This paper describes a decision-support tool that integrates Monte Carlo simulation data derived using a stochastic discrete-event simulation model to mimic process fluctuations with advanced multivariate statistical techniques to help pinpoint the potential root causes of sub-optimal short term facility fit issues. Principal component analysis combined with clustering algorithms was used to analyse the complex datasets from complete industrial batch processes for biopharmaceuticals. The challenge of visualising the multidimensional nature of the dataset was addressed using hierarchical and K-means clustering as well as parallel co-ordinate plots to help identify process fingerprints and characteristics of clusters leading to sub-optimal facility fit issues. Industrially-relevant case studies are presented that focus on technology transfer challenges for therapeutic antibodies moving from early phase to late phase clinical trials. The case study details how sub-optimal facility fit can be alleviated by allocating alternative product pool tanks. The impact of this operational change is then assessed by reviewing an updated process fingerprint.

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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