Khan, Muazzam Ali; (2018) Increasing the efficiency of antibody purification process by high throughput technology and intelligent design of experiment. Doctoral thesis (Eng.D), UCL (University College London).

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Abstract

Design of experiments (DoE) is used in process development to optimise the operating conditions of unit operations in a cost-effective and time-saving manner. Along with high throughput technologies, the modern high throughput process development lab can turnover a tremendous amount of data with minimal feedstock. These benefits are most useful when applied to the purification bottleneck, which accounts for up to 80% of the total process operating costs. However due to complexities of biochemical reactions and the large number interacting factors in unit operations (which usually cross interact with each other), even carefully planned DoE experiments on high throughput platforms can become difficult to manage and/or not provide useful information. This thesis examines the simplex search method and develops a set of protocols for use of the search method in combination with traditional DoE experimental design protocols. It is that is demonstrated in the developed in chapter 3 whilst also optimising a ammonium sulphate based precipitation step of an industrially relevant feedstock. Comparisons were drawn between a high resolution brute force study, a response surface DoE, the simplex method and then a combination of DoE and the simplex method. Various strategies were demonstrated that get the most out of the simplex method and mitigate against potential pitfalls. The precipitation step was optimised for yield and purity over the 3 factors, pH, ammonium sulphate concentration and initial MAb concentration and the results showed the simplex method was capable of rapidly identifying the optimum conditions in a very large 3 factor design space on an average of 18 experiments. The expansive study not only served as a testing ground for the methods comparison but demonstrated precipitation as a high throughput, low cost substitute for the expensive Protein A step. The DoE –simplex search protocols are then refined in two complex case studies in chapter 4, a PEG precipitation primary capture step and an ammonium sulphate precipitation and centrifugation sequence. The five factor precipitation and centrifugation sequence was especially complicated and utilised ultrascale down models provide accurate scale up data. This involved calibrating an acoustic device to provide shear treatment to the precipitate pre-centrifugation and using jet mixing equations to correlate precipitate conditioning between the TTecan robot’s tips and an impeller in a stirred tank. The techniques developed were all applicable to microscale and high throughput. In both instances, the combined DoE-simplex approach retuned superior results both in terms of experimental savings and generating information-rich data from the final local regions DoE around the simplex located optimums. A microscale chromatography protocol was developed on the Tecan liquid handling robot and demonstrated on screening work with different Protein A and cation exchange media. The caveats encountered when creating the running methods and the analytical methods supporting it for the Atoll robocolumns were highlighted and mitigation solutions implemented. The automated microscale Protein A method was successfully scaled up 50x from a 200 µL robocolumn to a conventional 10 mL labscale column. After selecting a cation exchange resin for developing an aggregate removal step, the DoE-simplex methodology was applied to an antibody product with an extremely high aggregate level and a comparison optimisation was made with a central composite design DoE. The difficult four factor design space overwhelmed the DoE and having used more experiment numbers than the DoE-simplex methodology, only went as far to show the high levels of curvature in the system and offer a poor prediction of the surface. The DoE-simplex methodology was able to provide a general model of the whole surface from the DoE, locate the optimum with the simplex in fewer experiment numbers. This subsequently allowed a local DoE to be applied to the optimum region to determine a robust operating range for the cation exchange step.

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