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Title: An ultra scale-down tool for the predictive design of a filtration procedure for preparation of human cell therapies
Author: Longster, C.
ISNI:       0000 0004 8499 2675
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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With the potential to provide a cure as oppose to a treatment, human cell therapies offer an exciting alternative to biopharmaceuticals. However the challenges associated with whole cell bioprocessing are one of the main issues hindering human cell therapies from becoming commonplace in modern medicine. An ultra scale-down approach to dead end filtration for the recovery of adherent cells for therapy is presented. Initial viable cell yields were low and a number of methods were used in an attempt to improve recovery, with little success. It was possible to recover approximately 85% of the cells but only when the number of cells loaded was low (< 1x106 cells), when a higher number of cells were loaded >1x106 cells, the recoveries were significantly lower. A model is proposed which describes two distinct cell populations; surface cells (TSURF) which reside on the surface of the filter after loading and are almost entirely recovered and filtered cells (TFILT) which enter the filter and are extremely difficult to recover. Results showed that on average 35% ± 11% of filtered cells (TFILT) are recovered regardless of the number of cells loaded. Scanning electron microscopy was used to image the cells residing within the filter. The images showed the cells wrapping around and entangling themselves within the fibres of the filter demonstrating why they are difficult to recover. Finally a method is presented which uses a layer of glass beads on the surface of the filter to prevent the cells coming into contact with filter. By stopping the cells becoming trapped within the filter there was a significant increase in the viable cell recoveries. Using this method it was possible to recover on average 84% ± 6%.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available