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Microscale bioprocessing platform for the evaluation of membrane filtration processes for primary recovery

Rayat, A.C.M.E.; (2011) Microscale bioprocessing platform for the evaluation of membrane filtration processes for primary recovery. Doctoral thesis, UCL (University College London). Green open access

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Abstract

An automated microscale bioprocessing platform for membrane filtration processes was established to identify key process issues early and aid the rapid design of robust and scaleable filtration processes. To demonstrate the utility of this platform, it was used to investigate the impact of upstream operations on microfiltration performance. The primary recovery of humanised antibody Fab’ fragments from Escherichia coli (supplied courtesy of UCB Celltech) were used as a case study to evaluate the microfiltration methodologies and devices created in this work. Initially, the methodology associated with the microscale dead-end filtration device previously created and investigated by Jackson et al. (2006) has been improved by reducing the required volume by 50% (~500 \mu L). This improved method demonstrated reproducibility and sensitivity to changes in feed preparation. The method was then applied in the study of the influence of various cell disruption operations on subsequent solid-liquid separation and hence, Fab’ product recovery. Results showed that the heat extracted cells showed better dead-end microfiltration performance in terms of permeate flux and specific cake resistance. In contrast, the cell suspensions prepared by homogenisation and sonication showed more efficient product release but with lower product purity and poorer microfiltration performance. Having established the various microscale methods, the linked sequence was automated on the deck of the Tecan™ robotic platform and used to illustrate how different conditions during thermo-chemical extraction impacted on the optimal performance of the linked unit operations of product release by extraction and subsequent recovery by microfiltration. The microscale approach was then extended for crossflow operations. A microscale crossflow filtration device was designed to enable integration also within the Tecan™ platform for automated processing. The device has an effective membrane area of 0.001 m2, which is a hundred-fold smaller than the larger scale Pellicon-2™ membrane module used for scale translation studies, and has two independent membrane channels for parallel analysis. The device was first characterised by determining the normalised water permeability (NWP) of a Poly(vinylidene fluoride) membrane and compared this with the NWP of the membrane by dead-end filtration. NWP is an inherent membrane property and as expected, the NWP values derived from crossflow filtration experiments match the values derived from dead-end filtration to within 90%. For scale translation studies, two types of feeds were used: a model feed, which is resuspended active dry yeast and Bovine Serum Albumen in phosphate buffer, and the antibody fragment expressing E. coli strain. Results showed, that at matched optimal shear rates and transmembrane pressure, the percentage differences between microscale and large scale values were up to ± 25% for the permeate flux, ± 10% for Fab’ and total protein yields. These scale-up predictions were achieved with a ten-fold reduction in feed material requirement for crossflow operation. Overall, the results illustrate the power of microscale techniques to identify and enable the understanding of key process performance attributes in a bioprocess sequence. The broader implications derived from using these microscale membrane devices, further applications and recommendations for future research are also discussed.

Type:Thesis (Doctoral)
Title:Microscale bioprocessing platform for the evaluation of membrane filtration processes for primary recovery
Open access status:An open access version is available from UCL Discovery
Language:English
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Biochemical Engineering

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