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For almost two decades it is claimed that cell-based therapies will revolutionize the field of medicine. Albeit major scientific breakthroughs that present cells as the active ingredient of a clinical therapy are succeeding each other at increasing speed, today only few of these research successes are able to materialise their full clinical potential and develop into a widely available commercial cell-based treatment. Besides the remaining scientific challenges (e.g. exact mechanisms of action), costly product development, and a complex regulatory and reimbursement landscape, it is hypothesised that the lack of automated, controlled and cost-effective production strategies forms a major hurdle towards a wide-spread clinical translation of cell based therapies. This work therefore aims at creating enabling tools and knowledge for monitored and controlled large-scale stem cell production, with the ultimate goal of facilitating the manufacturing of qualitative and cost-effective cell-based therapies. Flask-based cell expansion processes are currently still the gold standard despite the disadvantage of their limited scale-up and automation potential. In a first phase the translation from flask-based cell production processes to a bioreactor-based process was investigated, without adversely influencing the properties of the cells. In parallel to the bioreactor-based scale-up, this work describes how the data from these bioreactor processes can be utilised to non-invasively monitor critical process parameters in real-time, then utilise this information for process control strategies that enable more informed process decisions, ultimately leading to an improved product quality.

Theses

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Cleanroom environments are areas that comprise several procedures to reduce intrusion of bacteria and dust. Consequently, cell culture researchers working in such environments are required to change gloves whenever taking notes, resulting in significant time delays. This thesis proposes a solution to this problem by introducing automatic speech recognition (ASR) into the researcher’s workflow. Instead of taking notes by hand, the researchers use voice-based commands, thus avoiding glove replacement. The cleanroom environments for cell culture research contain highly challenging acoustic conditions, implying the need of an ASR system which is robust to noise and reverberation.