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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.
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![Figure 1. Model predictive controller scheme [22].](publication/343093902/figure/fig1/AS:915558367256578@1595297533918/Model-predictive-controller-scheme-22_Q320.jpg)
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...
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... reference trajectory for cumulative lactate concentration was assumed to be the cumulative lactate concentrations actually measured for each condition. Figure 3 shows the amount of accumulated lactate produced and the cell number after the five days of cell expansion. These results are summarised in Table 2, and show the average of the triplicates for the different donors and different medium replacement conditions, which were explained in Table 1. ...
Citations
... In this regard, future integration of online metabolite sensors, such as using Raman spectroscopy 83 , 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 flow 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 84,85 . Overall, the advanced bioprocess controls enabled by CAR T-cell culture-on-a-chip could pave the way for future adaptive manufacturing processes 86 that can mitigate starting material variability and result in cell therapies with improved consistency and efficacy for greater patient benefit. ...
The manufacturing of autologous chimaeric antigen receptor (CAR) T cells largely relies either on fed-batch and manual processes that often lack environmental monitoring and control or on bioreactors that cannot be easily scaled out to meet patient demands. Here we show that human primary T cells can be activated, transduced and expanded to high densities in a 2 ml automated closed-system microfluidic bioreactor to produce viable anti-CD19 CAR T cells (specifically, more than 60 million CAR T cells from donor cells derived from patients with lymphoma and more than 200 million CAR T cells from healthy donors). The in vitro secretion of cytokines, the short-term cytotoxic activity and the long-term persistence and proliferation of the cell products, as well as their in vivo anti-leukaemic activity, were comparable to those of T cells produced in a gas-permeable well. The manufacturing-process intensification enabled by the miniaturized perfusable bioreactor may facilitate the analysis of the growth and metabolic states of CAR T cells during ex vivo culture, the high-throughput optimization of cell-manufacturing processes and the scale out of cell-therapy manufacturing.
... It is often challenging to model and directly control the CQAs of cells in process. As a result, alternative objectives such as controlling glucose or lactate have been identified to indirectly control cell growth and quality [67,68]. Compared to measuring cell growth or a quality attribute, in-line measurement of glucose and lactate concentrations is also more feasible with sensor technologies available. ...
... The copyright holder for this preprint (which this version posted April 7, 2023. ; https://doi.org/10.1101/2023.04.07.535939 doi: bioRxiv preprint 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. ...
While adoptive cell therapies have revolutionized cancer immunotherapy, current autologous chimeric antigen receptor (CAR) T cell manufacturing face challenges in scaling to meet patient demands. CAR T cell production still largely rely on fed-batch, manual, open processes that lack environmental monitoring and control, whereas most perfusion-based, automated, closed-system bioreactors currently suffer from large footprints and working volumes, thus hindering process development and scaling-out. Here, we present a means of conducting anti-CD19 CAR T cell culture-on-a-chip. We show that T cells can be activated, transduced, and expanded to densities exceeding 150 million cells/mL in a two-milliliter perfusion-capable microfluidic bioreactor, thus enabling the production of CAR T cells at clinical dose levels in a small footprint. Key functional attributes such as exhaustion phenotype and cytolytic function were comparable to T cells generated in a gas-permeable well. The process intensification and online analytics offered by the microbioreactor could facilitate high-throughput process optimization studies, as well as enable efficient scale-out of cell therapy manufacturing, while providing insights into the growth and metabolic state of the CAR T cells during ex vivo culture.
... A digital twin is a virtual model that accurately reflects a physical system. For example, a lactate-based controller can adapt medium change protocols to the needs of cell batches derived from different donors 119 . Similarly, Raman spectroscopy can be applied to build a data-driven model that predicts the glucose concentration during culture, which can then inform the controller of the automated glucose feed pump 120 . ...
Bioreactors have the potential to advance the clinical application of cell-based therapies. Cell expansion bioreactors have been used commercially for therapeutic applications; however, bioreactor-based engineering of 3D tissue grafts remains challenging owing to the complexity of tissue architectures, cellular heterogeneity and the lack of non-invasive, tissue-specific biomarkers with which to assess graft viability and maturation. Consequently, only a few bioreactor-based start-up companies that engineer patient-specific tissue grafts have emerged. In this Review, we discuss patient-specific bioreactors that can be used to engineer skin, small-diameter arteries and musculoskeletal tissues. We evaluate the impact of precision manufacturing, including 3D bioprinting, automation and non-invasive sensing, on optimizing the biological, chemical and physical parameters of the bioreactors that are required for specific tissue regeneration. We discuss the commercially available tissue-engineering bioreactors and the potential of digital twins and automation, and we outline the scientific and regulatory pathways that must be followed to enable the translation of tissue-specificbioreactors to the clinic. Bioreactors enable the cultivation of mammalian cells in a closely monitored and controlled microenvironment. This Review discusses bioreactor technologies and closed-loop set-ups for producing patient-specific engineered-tissue grafts, including skin, small-diameter arteries and musculoskeletal tissues, with a particular focus on commercialization and regulatory considerations. Tissue-engineering bioreactors have driven major technological innovations in commercialized cell expansion; however, the clinical translation of bioreactor-based cell-based tissue-engineered constructs remains limited.Bioreactors can be designed to engineer autologous cell-based, patient-specific and tissue-specific grafts, including cartilage, tendons, ligament, bone, skin and small-diameter vascular grafts.Several tissue-engineering bioreactors have been commercialized that enable the engineering of large-scale, economically viable and clinically accessible tissues.The biological, chemical and physical parameters of bioreactors need to be optimized to allow automation, non-invasive sensing, 3D bioprinting and computational modelling for patient-specific tissue regeneration.Distinct clinical, biological and regulatory pathways must be followed to allow the clinical translation of bioreactor-based tissue engineering. Tissue-engineering bioreactors have driven major technological innovations in commercialized cell expansion; however, the clinical translation of bioreactor-based cell-based tissue-engineered constructs remains limited. Bioreactors can be designed to engineer autologous cell-based, patient-specific and tissue-specific grafts, including cartilage, tendons, ligament, bone, skin and small-diameter vascular grafts. Several tissue-engineering bioreactors have been commercialized that enable the engineering of large-scale, economically viable and clinically accessible tissues. The biological, chemical and physical parameters of bioreactors need to be optimized to allow automation, non-invasive sensing, 3D bioprinting and computational modelling for patient-specific tissue regeneration. Distinct clinical, biological and regulatory pathways must be followed to allow the clinical translation of bioreactor-based tissue engineering.
... 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). ...
... 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 absence of other soluble factors [9]. Previous work [10,11] and other research [12,13] report an increase in lactate correlated with a decrease in medium exchanges during the cell expansion, where a higher amount or frequency of medium exchanges increases cell proliferation. However, overfeeding the cells is not cost-efficient and underfeeding the cells strongly reduces the cell growth and thus their growth potential. ...
... These cells have been used for tissue engineering applications for the regeneration of long bone defects [21][22][23][24][25]. hPDCs are highly glycolytic in standard serum containing growth medium [26][27][28][29], resulting in a high glucose consumption and lactate production rates. Based on the lactate production of the cells, this work used accumulated lactate produced as an indication for cell growth throughout the cell expansion process, which has also been used in previous work [11]. ...
... Human periosteum derived cells were used, which were acquired from biopsies after obtaining patients' informed consent; the cells were then expanded in tissue flasks. The expansion was performed according to standard protocols as described in a previous work [11]. ...
Providing a cost-efficient feeding strategy for cell expansion processes remains a challenging 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 developed in this work. Reference experiments were performed by expanding human periosteum derived 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 performed using the accumulated lactate values from the reference experiment as a static target function 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.
... The need for automation in CGT manufacturing has been identified early on and is being supported by several advancements towards the development of versatile, automated platform technologies [49]. In this space, Van Beylen et al. [50 ] presented a Model Predictive Control (MPC) framework for the implementation of personalised feeding strategies, monitoring lactate levels in cell therapy manufacturing. ...
Advanced Therapy Medicinal Products (ATMPs) are a novel class of biological therapeutics that utilise ground-breaking clinical interventions to prevent and treat life-threatening diseases. At the same time, viral vector-based and RNA-based platforms introduce a new generation of vaccine manufacturing processes. Their clinical success has led to an unprecedented rise in the demand that, for ATMPs, leads to a predicted market size of USD 9.6 billion by 2026. This paper discusses how mathematical models can serve as tools to assist decision-making in development, manufacturing and distribution of these new product classes. Recent contributions in the space of process, techno-economic and supply chain modelling are highlighted. Lastly, we present and discuss how Process Systems Engineering can be further advanced to support commercialisation of advanced therapeutics and vaccines.
Secreted metabolites are an important class of bio‐process analytical technology (PAT) targets that can correlate to cell conditions. However, current strategies for measuring metabolites are limited to discrete measurements, resulting in limited understanding and ability for feedback control strategies. Herein, a continuous metabolite monitoring strategy is demonstrated using a single‐use metabolite absorbing resonant transducer (SMART) to correlate with cell growth. Polyacrylate is shown to absorb secreted metabolites from living cells containing hydroxyl and alkenyl groups such as terpenoids, that act as a plasticizer. Upon softening, the polyacrylate irreversibly conformed into engineered voids above a resonant sensor, changing the local permittivity which is interrogated, contact‐free, with a vector network analyzer. Compared to sensing using the intrinsic permittivity of cells, the SMART approach yields a 20‐fold improvement in sensitivity. Tracking growth of many cell types such as Chinese hamster ovary, HEK293, K562, HeLa, and E. coli cells as well as perturbations in cell proliferation during drug screening assays are demonstrated. The sensor is benchmarked to show continuous measurement over six days, ability to track different growth conditions, selectivity to transducing active cell growth metabolites against other components found in the media, and feasibility to scale out for high throughput campaigns.
Industrial cell culture processes are inherently expensive, time-consuming, and variable. These limitations have become a critical bottleneck for the industrial translation of human cell and tissue biomanufacturing, as few human cell culture products deliver sufficient benefit, value, and consistency to offset their high manufacturing costs to produce useful clinical or biomedical solutions. Recent advances in biomedical image analysis and computational modelling can enhance the design and operation of high-efficiency tissue biomanufacturing platforms, as well as the high-content characterisation and monitoring of culture performance to enable bioprocess control, optimisation, and automation. These computational technologies aim to maximize culture outcomes while minimizing variability and process development expense. In this review, we outline current resources and approaches which harness biomedical imaging and image-based computational models to design and operate efficient and robust human tissue biomanufacturing platforms.
