Optimisation of chromatography for downstream protein processing.
Doctoral thesis, UCL (University College London).
Downstream bioprocessing and especially chromatographic steps, commonly used for the purification of multicomponent systems, are significant cost drivers in the production of therapeutic proteins. Lately, there has been an increased interest in the development of systematic methods where operating conditions are defined and chromatographic trains are selected. Several models have been developed previously, where chromatographic trains were selected under the assumption of 100% recovery of the desired product. Removing this assumption gives the opportunity not only to select chromatographic trains but also determine the timeline in which the product is selected. Initially, a mixed integer non-linear (MINLP) programming mathematical model was developed to tackle that problem and was tested using three illustrative examples. Later on, this model was linearised by applying piecewise linear approximation techniques and computational efficiency was improved. Next, an alternative MILP model was developed by discretising the recovery levels of the product and computational efficiency improved even by 100-fold. Finally, the equilibrium dispersive model was used in a simple 4-protein mixture and the MINLP model was validated. This research represents a significant step towards efficient downstream process operation and synthesis
|Title:||Optimisation of chromatography for downstream protein processing|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Biochemical Engineering|
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