Application of novel Raman spectroscopy device and advanced data analytics to optimise viral vector production and design

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There are two methods to optimise viral vector production, the first is to improve the micro-environment within the bioreactor and the second is to improve the design of the viral vector constructs. Both of these approaches have the potential to improve viral vector titres, product quality and enable easier scale-up. The first part of this project will focus on optimising the control of the bioreactor micro-environment as this is of paramount importance to ensure product heterogeneity remains within a predefined specification defined by commercial good manufacturing practice (GMP) operations. However, continuous online monitoring is available only for a small number of process parameters (i.e pH, temperature and DO2) in viral vector manufacturing.Currently, the control of these bioreactor processes implements rudimentary and suboptimal control strategies that rely heavily on off-line data analytics and infrequent operator decisions. Machine learning techniques combined with advanced Process Analytical Technologies (PATs) such as Raman spectroscopy can enable real time predictions of key CPPs and CQAs. These real-time predictions can be combined with advanced feedback controllers that can be used to optimise process performance such as maximising viral titres and minimising manufacturing impurities such as empty capsids. Once the control algorithms have been developed, the second part of the project will be to modify the gene sequence of the cap or rep gens and evaluate how the design of these will improve clinical manufacturing processes within a well-defined and controlled bioreactor system.