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.

Keywords: advanced therapy medicinal products; bio-process; cell growth; lactate; model predictive control.

Figure 1

Model predictive controller scheme [22].

Figure 2

Simplified lactate concentration graph during a cell expansion period with medium replacements at time points $k,k+1$ and $k+2$.

Figure 3

Average results with error bars for the triplicate experiments of each individual donor with each specific medium replacement strategy. (a) Total accumulated lactate produced after 5 days of cell culture (mM). (b) Cell numbers counted at the end of the cell culture period.

Figure 4

Lactate concentration over time (days) for the results of donor 1, condition 5 and triplicate 1.

Figure 5

Visualisation of dynamic auto-regressive exogenous (DARX) model for accumalated lactate (mM) compared to the interpolated data for the experiment of donor 1, condition 5 and triplicate 1.

Figure 6

Graphs exhibiting the b_0,t values over time. (a) b_0,t values for triplicates of donor 1 under condition 6, which means 100% medium replacement every 24 h. (b) b_0,t values for triplicates of donor 1 under condition 4, which means only 10% medium replacement every 24 h.

Figure 7

Top graph shows the interpolated output (accumulated lactate (mM)) compared to the simulated output. Bottom graph represents the interpolated input (total replaced medium (mL)) compared to the simulated input. (a) model-based predictive control (MPC) simulation applied to donor 1, condition 1 and triplicate 1; (b) MPC simulation applied to donor 3, condition 6 and triplicate 3.