Big data analytics streamlining therapeutic drug development from micro-scale through to industrial-scale (cell culture)

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The aim of this EngD is to generate a data-driven decisional tool to speed up the delivery of novel therapeutics from cell line development to full-scale manufacturing. The project will focus on streamlining the data management, visualisation and analytics of all available on-line, off-line and product quality data generated by cell culture operations recorded across multiple facilities and scales. The project will develop advanced machine learning algorithms and apply them to novel applications related to industrially-relevant cell culture datasets. The application of these algorithms will enable exploratory analysis to test hypotheses, identify patterns and correlations and uncover trends unlocking novel insights that will directly translate into the faster delivery of novel therapeutic proteins. The project will initially focus on cell line development to evaluate the key performance drivers defining robust and stable cell lines that perform as expected as they scale-up from micro-scale (AMBR (15 ml) to lab-scale (3-7 L) and ultimately to commercial manufacturing (15,000 L). The project also aims to quantify the variability and assess the performance of each cell line during scale-up to better forecast any potential failures or expected process issues at the larger scales. The project aims to provide an opportunity to investigate different big data and machine learning algorithms through the use of software's such as Python and R to analyse data of high dimensions. Other skills to be gained also focus on mammalian cell culture and laboratory automation as well as proficiency in data science and mechanistic modelling.