Machine learning and metabolic modelling assisted implementation of a novel process analytical technology in cell and gene therapy manufacturing

Abstract

Abstract Process analytical technology (PAT) has demonstrated huge potential to enable the development of improved biopharmaceutical manufacturing processes by ensuring the reliable provision of quality products. However, the complexities associated with the manufacture of advanced therapy medicinal products have resulted in a slow adoption of PAT tools into industrial bioprocessing operations, particularly in the manufacture of cell and gene therapy products. Here we describe the applicability of a novel refractometry-based PAT system (Ranger system), which was used to monitor the metabolic activity of HEK293T cell cultures during lentiviral vector (LVV) production processes in real time. The PAT system was able to rapidly identify a relationship between bioreactor pH and culture metabolic activity and this was used to devise a pH operating strategy that resulted in a 1.8-fold increase in metabolic activity compared to an unoptimised bioprocess in a minimal number of bioreactor experiments; this was achieved using both pre-programmed and autonomous pH control strategies. The increased metabolic activity of the cultures, achieved via the implementation of the PAT technology, was not associated with increased LVV production. We employed a metabolic modelling strategy to elucidate the relationship between these bioprocess level events and HEK293T cell metabolism. The modelling showed that culturing of HEK293T cells in a low pH (pH 6.40) environment directly impacted the intracellular maintenance of pH and the intracellular availability of oxygen. We provide evidence that the elevated metabolic activity was a response to cope with the stress associated with low pH to maintain the favourable intracellular conditions, rather than being indicative of a superior active state of the HEK293T cell culture resulting in enhanced LVV production. Forecasting strategies were used to construct data models which identified that the novel PAT system not only had a direct relationship with process pH but also with oxygen availability; the interaction and interdependencies between these two parameters had a direct effect on the responses observed at the bioprocess level. We present data which indicate that process control and intervention using this novel refractometry-based PAT system has the potential to facilitate the fine tuning and rapid optimisation of the production environment and enable adaptive process control for enhanced process performance and robustness.