Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.748133
Title: Process understanding and design methodology for industrial biotechnology
Author: McLachlan, Kirsty Jane
ISNI:       0000 0004 7233 2151
Awarding Body: Newcastle University
Current Institution: University of Newcastle upon Tyne
Date of Award: 2017
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Abstract:
Many types of knowledge exist within a bioprocess, but the utilisation of this knowledge is not always as straightforward as collecting and analysing data. The Quality by Design initiative (ICH Guideline, 2009) has increased the need for thorough process understanding within bioprocessing. Fundamental process understanding is imperative to adequately implement a QbD approach to a bioprocess. Formalised knowledge capture techniques have been developed previously (West, 1992; Ranjan et al., 2002; Stowell, 2013), but these tend to be designed only to capture information rather than increase understanding. Equally, modelling techniques can be utilised to predict process behaviour and therefore increase understanding, but these rely on the user to have an understanding of the underlying science. This can be problematic in interdisciplinary industries such as bioprocessing, as there are many factors to build into a model. With this in mind, this research considers the Britest tools with respect specifically to biotechnological applications, and formulates a whole bioprocess development methodology. The Britest tools are a suite of qualitative tools and methodologies which were designed to highlight the knowledge gaps within chemical and physical processes, and to promote innovative process design solutions. The tools can help to identify areas where optimisation may be possible, and also increase the understanding of the process as a whole across a range of disciplines. The Britest tools were first considered with respect to four bioprocesses (Monoclonal Antibody production, Insulin production, Waste Water Treatment and Penicillin production), simulated within SuperPro Designer. The range of processes gave an indication of breadth of application, while the depth of information available in the simulations allowed the research to be unhindered by data availability. From here, several gaps within the toolkit were identified, including the potential for variability and the interactions between multiple parameters. v Variability is inherent within a bioprocess, and the reduction of this variability is a key driver for the implementation for QbD. The Reaction/Reagent Transformation Tracker (R2T2) was designed to capture this variability, and allow the user to evaluate the potential for various scenarios to arise. The tool facilitates a whole process view, without the information becoming overwhelming and confusing for the users. Understanding the interactions between Critical Quality Attributes (CQAs) and Critical Process Parameters (CPPs) is essential to the successful implementation of QbD, and was not covered by the original Britest toolkit. To combat this the Interaction Analysis Table (IAT) was created. The tool was designed to be applied in the early stages of process development, to guide the application of Design of Experiments (DoE) approaches when data is in short supply but process knowledge is available. Finally, the IAT was evaluated for sensitivity, to investigate the potential influence of uncertainty/human error on the outcome. The work identified a parameter and a threshold value enabling the user to assess the confidence in the proposed process analysis outcome. This work sought to develop novel knowledge management tools which had been designed specifically for application to bioprocessing. It aimed to establish the applicability of the Britest toolkit for this purpose, as Britest tools have only previously been applied to chemical and physical processes. A Britest toolkit for bioprocessing could be utilised to aid in the adoption of a QbD approach, through tools specifically designed to capture the knowledge of the process. This knowledge would be difficult to adequately represent in statistical models and could be lost between disciplines without a structured methodology to apply. The toolkit can be used to facilitate better communication in an interdisciplinary environment, and provide key information to enable better process design from an early stage.
Supervisor: Not available Sponsor: Britest
Qualification Name: Thesis (D.Eng.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.748133  DOI: Not available
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