In the early stages of downstream process development there is typically only limited availability of process material. Novel methods to obtain information from fewest experiments are essential to make informed choices between processing alternatives at the earliest stage. Design of chromatographic separation initially involves scouting of appropriate matrix type, mobile phase compositions followed by test runs at lab scale and verified at pilot scale. Traditional small-scale methods for chromatography development focus on the screening of separation media and feedstock conditions. It is still necessary to predict chromatography performance at different scales and operating conditions. In this work a new method has been developed to predict performance of larger scale columns using an ultra scale-down approach. The strategy breaks traditional geometric scaling rules, using models to correct for the differences in performance and also for prediction of the effect of changes in operating conditions. Micro-scale columns were used to scale down lab scale runs further challenging the traditional scale down strategies. The characteristics of antibody fragments in E.coli lysate were identified in terms of pH, precipitation and ionic strength to determine good binding conditions. Chromatography studies were carried out at laboratory scale (1 mL) to investigate the flowrate effects on the adsorption of antibody fragments on a strong cation exchange resin. The effect was successfully predicted using a general rate model, which describes the physical and chemical forces of resin-protein interactions but with modifications to allow for deviations noted in experimental performance possibly due to fouling and long loading times changing the rate of protein transfer. Further studies were carried out using micro-scale tip chromatography, mimicking the results obtained at 1 mL scale. A similar effect of flowrate was observed and the scale up factor to predict the performance of laboratory 1 mL scale from 40 μL micro-scale was investigated.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:565681 |
| Date | January 2012 |
| Creators | Tang, A. |
| Publisher | University College London (University of London) |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://discovery.ucl.ac.uk/1352728/ |
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