Processes 2023, 11, 22. https://doi.org/10.3390/pr11010022 www.mdpi.com/journal/processes Article Real-Time Cell Growth Control Using a Lactate-Based Model Predictive Controller Kathleen Van Beylen 1,2 , Janne Reynders 1,2 , Ahmed Youssef 1,3 , Alberto Peña Fernández 1 , Ioannis Papantoniou 2,4 and Jean-Marie Aerts 1, * 1 Division of Animal and Human Health Engineering, Department of Biosystems, Faculty of Bioscience Engineering, KU Leuven, 3001 Leuven, Belgium 2 Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, 3000 Leuven, Belgium 3 Skeletal Biology and Engineering Research Centre, Department of Development and Regeneration, KU Leuven, 3000 Leuven, Belgium 4 Johnson and Johnson Pharmaceutical Research and Development Belgium: Janssen Research and Development Beerse, 2340 Beerse, Belgium * Correspondence: jean-marie.aerts@kuleuven.be Abstract: Providing a cost-efficient feeding strategy for cell expansion processes remains a challeng- ing task due to, among other factors, donor variability. The current method to use a fixed medium replacement strategy for all cell batches results often in either over- or underfeeding these cells. In order to take into account the individual needs of the cells, a model predictive controller was devel- oped in this work. Reference experiments were performed by expanding human periosteum de- rived progenitor cells (hPDCs) in tissue flasks to acquire reference data. With these data, a time- variant prediction model was identified to describe the relation between the accumulated medium replaced as the control input and the accumulated lactate produced as the process output. Several forecast methods to predict the cell growth process were designed using multiple collected datasets by applying transfer function models or machine learning. The first controller experiment was per- formed using the accumulated lactate values from the reference experiment as a static target func- tion over time, resulting in over- or underfeeding the cells. The second controller experiment used a time-adaptive target function by combining reference data as well as current measured real-time data, without over- or underfeeding the cells. Keywords: real-time model predictive control; cell expansion; lactate; cell-based therapies 1. Introduction Recent years are seeing a constant increase of cell-based therapeutic products creat- ing thus the need for the development of efficient cell expansion methods [1]. Cells are the core element in these therapies; hence, efficient, well-monitored and controlled pro- cesses are needed [2]. For autologous therapies, cells isolated from biopsies require con- siderable expansion in order to reach clinically relevant numbers for treatments requiring up to 10 7 –10 8 cells per treatment [3,4]. Due to the increasing demand for cells, the need for improving cell expansion process efficiency becomes critical. The urge to manage process variability motivates the sector to adopt QbD (Quality by Design) principles under which the process conditions might vary (within validated limits, e.g., to compensate for differences in the starting cell material), but where the final product and its effect in the patient are robust and reproducible [5]. A significant impact on the cell expansion process is the concentration of waste products, nutrients and other soluble growth factors present in the medium. Without medium replacements, the cell proliferation is inhibited by a combination of several influences such as lactate inhibition [6], acidification of the medium [7], energy sources depletion [8] and the presence or Citation: Van Beylen, K.; Reynders, J.; Youssef, A.; Fernández, A.P.; Papantoniou, I.; Aerts, J.-M. Real-Time Cell Growth Control Using a Lactate-Based Model Predictive Controller. Processes 2023, 11, 22. https://doi.org/10.3390/pr11010022 Received: 21 November 2022 Revised: 15 December 2022 Accepted: 19 December 2022 Published: 22 December 2022 Copyright: © 2022 by the author. Li- censee MDPI, Basel, Switzerland. This article is an open access article distrib- uted under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecom- mons.org/licenses/by/4.0/).