Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications

Abstract

Implementing a personalised feeding strategy for each individual batch of a bioprocess could significantly reduce the unnecessary costs of overfeeding the cells. This paper uses lactate measurements during the cell culture process as an indication of cell growth to adapt the feeding strategy accordingly. For this purpose, a model predictive control is used to follow this a priori determined reference trajectory of cumulative lactate. Human progenitor cells from three different donors, which were cultivated in 12-well plates for five days using six different feeding strategies, are used as references. Each experimental set-up is performed in triplicate and for each run an individualised model-based predictive control (MPC) controller is developed. All process models exhibit an accuracy of 99.80% ± 0.02%, and all simulations to reproduce each experimental run, using the data as a reference trajectory, reached their target with a 98.64% ± 0.10% accuracy on average. This work represents a promising framework to control the cell growth through adapting the feeding strategy based on lactate measurements.

High-density microbioreactor process designed for automated point-of-care manufacturing of CAR T cells

“…In this regard, future integration of online metabolite sensors, such as using Raman spectroscopy 61 , could enable in-process monitoring of CAR T cell metabolic fitness. An enhanced process knowledge, such as of the metabolic changes during ex vivo culture from the data gathered in this study, could form the basis of future feedback-driven processes, such as adjusting perfusion rates based on metabolic state instead of a fixed protocol, to provide consistent culture conditions for CAR T cell growth, or even to drive the cells toward a more desirable phenotype, while minimizing the volume of medium used 62,63 . In summary, the advanced bioprocess controls enabled by CAR T cell cultureon-a-chip could pave the way for future adaptive manufacturing processes 64 that can mitigate starting material variability and result in cell therapies with improved consistency and efficacy for greater patient benefit.…”

Improved control strategies for the environment within cell culture bioreactors

“…There are a limited number of applications of MPC to upstream mAb production that are experimental rather than simulation based. Van Beylen et al (2020) built a model of a cell culture process using experimental data and demonstrated, using the model as a process simulation, that a single-input single-output (SISO) MPC was able to offer control benefits for the regulation of cell growth in the process. However, to be effective the MPC required the linear times series model to adapt to accommodate changing dynamics throughout the batch.…”

“…Evolving biopharma-based therapies are characterised by the high cost of media and batch to batch variation through biological variability and yield losses through poor control amounting to considerable losses. For example, Van Beylen et al (2020) described the impacts of variability on viability, causing around 8% out of specification product for a Novartis cell culture product. More generally, estimates of between 10%− 20% losses for some products have been reported (Hippach et al, 2018).…”

Single-use, Metabolite Absorbing, Resonant Transducer (SMART) culture vessels for label-free, continuous cell culture progression monitoring

“…While optical and electrical methods provide a non-invasive way of probing biological systems, measuring secreted metabolites represents an alternative approach that can have minimal impact on cells while providing implications to many biological processes. [32][33][34][35][36] For instance, lactate and glutamate concentrations are often measured from the sampled culture media due to their correlation with metabolism, and thereby cell growth. In addition, colorimetric assays involving tetrazolium salts are used to reflect metabolic activity via reduction reactions by mitochondrial enzymes.…”