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Zheng H, Harcum SW, Pei J, Xie W. Stochastic biological system-of-systems modelling for iPSC culture. Commun Biol 2024; 7:39. [PMID: 38191636 PMCID: PMC10774284 DOI: 10.1038/s42003-023-05653-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.
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Affiliation(s)
- Hua Zheng
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | | | - Jinxiang Pei
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Wei Xie
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA.
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2
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Survyla A, Urniezius R, Simutis R. Viable cell estimation of mammalian cells using off-gas-based oxygen uptake rate and aging-specific functional. Talanta 2023; 254:124121. [PMID: 36462281 DOI: 10.1016/j.talanta.2022.124121] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/17/2022] [Accepted: 11/19/2022] [Indexed: 11/27/2022]
Abstract
This study developed an estimation routine for counting the viable cells in an in vitro fed-batch Chinese hamster ovary cultivation that relies on off-gas information and inlet gas mixture knowledge. We computed the oxygen uptake rate bound to the bioreactor exhaust gas outlet when the inlet gas mixture was stationary. Our mammalian biosynthesis analysis determined the stoichiometric parameters as a function of the average population age. We cross-validated an identical algorithm for mammalian and microbial cultivations and found that the' 99% confidence band of the model generally overlapped with the error bars defined from observations. The resulting RMSE and MAE averages were 0.188 and 0.14e9cells L-1, respectively, when estimating the viable mammalian cell count. The validation for the estimation of total bacterial biomass yielded an MAE and RMSE of 1.78 g L-1 and 2.53 g L-1, respectively. Moreover, our proposed approach provides an online estimation of the average population age for both aerobically cultivated microorganisms.
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Affiliation(s)
- Arnas Survyla
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania
| | - Renaldas Urniezius
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania.
| | - Rimvydas Simutis
- Department of Automation, Kaunas University of Technology, Studentu 48, LT-51367, Kaunas, Lithuania
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3
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The Role of Process Systems Engineering in Applying Quality by Design (QbD) in Mesenchymal Stem Cell Production. Comput Chem Eng 2023. [DOI: 10.1016/j.compchemeng.2023.108144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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4
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Bioengineering Outlook on Cultivated Meat Production. MICROMACHINES 2022; 13:mi13030402. [PMID: 35334693 PMCID: PMC8950996 DOI: 10.3390/mi13030402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/21/2022] [Accepted: 02/23/2022] [Indexed: 02/04/2023]
Abstract
Cultured meat (also referred to as cultivated meat or cell-based meat)—CM—is fabricated through the process of cellular agriculture (CA), which entails application of bioengineering, i.e., tissue engineering (TE) principles to the production of food. The main TE principles include usage of cells, grown in a controlled environment provided by bioreactors and cultivation media supplemented with growth factors and other needed nutrients and signaling molecules, and seeded onto the immobilization elements—microcarriers and scaffolds that provide the adhesion surfaces necessary for anchor-dependent cells and offer 3D organization for multiple cell types. Theoretically, many solutions from regenerative medicine and biomedical engineering can be applied in CM-TE, i.e., CA. However, in practice, there are a number of specificities regarding fabrication of a CM product that needs to fulfill not only the majority of functional criteria of muscle and fat TE, but also has to possess the sensory and nutritional qualities of a traditional food component, i.e., the meat it aims to replace. This is the reason that bioengineering aimed at CM production needs to be regarded as a specific scientific discipline of a multidisciplinary nature, integrating principles from biomedical engineering as well as from food manufacturing, design and development, i.e., food engineering. An important requirement is also the need to use as little as possible of animal-derived components in the whole CM bioprocess. In this review, we aim to present the current knowledge on different bioengineering aspects, pertinent to different current scientific disciplines but all relevant for CM engineering, relevant for muscle TE, including different cell sources, bioreactor types, media requirements, bioprocess monitoring and kinetics and their modifications for use in CA, all in view of their potential for efficient CM bioprocess scale-up. We believe such a review will offer a good overview of different bioengineering strategies for CM production and will be useful to a range of interested stakeholders, from students just entering the CA field to experienced researchers looking for the latest innovations in the field.
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Manstein F, Ullmann K, Kropp C, Halloin C, Triebert W, Franke A, Farr CM, Sahabian A, Haase A, Breitkreuz Y, Peitz M, Brüstle O, Kalies S, Martin U, Olmer R, Zweigerdt R. High density bioprocessing of human pluripotent stem cells by metabolic control and in silico modeling. Stem Cells Transl Med 2021; 10:1063-1080. [PMID: 33660952 PMCID: PMC8235132 DOI: 10.1002/sctm.20-0453] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/18/2021] [Accepted: 01/24/2021] [Indexed: 12/13/2022] Open
Abstract
To harness the full potential of human pluripotent stem cells (hPSCs) we combined instrumented stirred tank bioreactor (STBR) technology with the power of in silico process modeling to overcome substantial, hPSC‐specific hurdles toward their mass production. Perfused suspension culture (3D) of matrix‐free hPSC aggregates in STBRs was applied to identify and control process‐limiting parameters including pH, dissolved oxygen, glucose and lactate levels, and the obviation of osmolality peaks provoked by high density culture. Media supplements promoted single cell‐based process inoculation and hydrodynamic aggregate size control. Wet lab‐derived process characteristics enabled predictive in silico modeling as a new rational for hPSC cultivation. Consequently, hPSC line‐independent maintenance of exponential cell proliferation was achieved. The strategy yielded 70‐fold cell expansion in 7 days achieving an unmatched density of 35 × 106 cells/mL equivalent to 5.25 billion hPSC in 150 mL scale while pluripotency, differentiation potential, and karyotype stability was maintained. In parallel, media requirements were reduced by 75% demonstrating the outstanding increase in efficiency. Minimal input to our in silico model accurately predicts all main process parameters; combined with calculation‐controlled hPSC aggregation kinetics, linear process upscaling is also enabled and demonstrated for up to 500 mL scale in an independent bioreactor system. Thus, by merging applied stem cell research with recent knowhow from industrial cell fermentation, a new level of hPSC bioprocessing is revealed fueling their automated production for industrial and therapeutic applications.
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Affiliation(s)
- Felix Manstein
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Kevin Ullmann
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Christina Kropp
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Caroline Halloin
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Wiebke Triebert
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Annika Franke
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Clara-Milena Farr
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Anais Sahabian
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Alexandra Haase
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Yannik Breitkreuz
- Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany
| | - Michael Peitz
- Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany.,Cell Programming Core Facility, University of Bonn Medical Faculty, Bonn, Germany
| | - Oliver Brüstle
- Institute of Reconstructive Neurobiology, University of Bonn Medical Faculty & University Hospital Bonn, Bonn, Germany
| | - Stefan Kalies
- Institute of Quantum Optics, Leibniz University Hannover, Hannover, Germany.,Lower Saxony Centre for Biomedical Engineering, Implant Research and Development, Hannover, Germany
| | - Ulrich Martin
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Ruth Olmer
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
| | - Robert Zweigerdt
- Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Germany.,REBIRTH Cluster of Excellence, Hannover Medical School, Hannover, Germany
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Nath SC, Harper L, Rancourt DE. Cell-Based Therapy Manufacturing in Stirred Suspension Bioreactor: Thoughts for cGMP Compliance. Front Bioeng Biotechnol 2020; 8:599674. [PMID: 33324625 PMCID: PMC7726241 DOI: 10.3389/fbioe.2020.599674] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 10/30/2020] [Indexed: 12/23/2022] Open
Abstract
Cell-based therapy (CBT) is attracting much attention to treat incurable diseases. In recent years, several clinical trials have been conducted using human pluripotent stem cells (hPSCs), and other potential therapeutic cells. Various private- and government-funded organizations are investing in finding permanent cures for diseases that are difficult or expensive to treat over a lifespan, such as age-related macular degeneration, Parkinson’s disease, or diabetes, etc. Clinical-grade cell manufacturing requiring current good manufacturing practices (cGMP) has therefore become an important issue to make safe and effective CBT products. Current cell production practices are adopted from conventional antibody or protein production in the pharmaceutical industry, wherein cells are used as a vector to produce the desired products. With CBT, however, the “cells are the final products” and sensitive to physico- chemical parameters and storage conditions anywhere between isolation and patient administration. In addition, the manufacturing of cellular products involves multi-stage processing, including cell isolation, genetic modification, PSC derivation, expansion, differentiation, purification, characterization, cryopreservation, etc. Posing a high risk of product contamination, these can be time- and cost- prohibitive due to maintenance of cGMP. The growing demand of CBT needs integrated manufacturing systems that can provide a more simple and cost-effective platform. Here, we discuss the current methods and limitations of CBT, based upon experience with biologics production. We review current cell manufacturing integration, automation and provide an overview of some important considerations and best cGMP practices. Finally, we propose how multi-stage cell processing can be integrated into a single bioreactor, in order to develop streamlined cGMP-compliant cell processing systems.
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Affiliation(s)
- Suman C Nath
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lane Harper
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Derrick E Rancourt
- Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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7
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Van Beylen K, Youssef A, Peña Fernández A, Lambrechts T, Papantoniou I, Aerts JM. Lactate-Based Model Predictive Control Strategy of Cell Growth for Cell Therapy Applications. Bioengineering (Basel) 2020; 7:bioengineering7030078. [PMID: 32698462 PMCID: PMC7552707 DOI: 10.3390/bioengineering7030078] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 07/15/2020] [Accepted: 07/15/2020] [Indexed: 12/24/2022] Open
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.
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Affiliation(s)
- Kathleen Van Beylen
- Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; (K.V.B.); (A.Y.); (A.P.F.); (T.L.)
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1, Herestraat 49, 3000 Leuven, Belgium;
| | - Ali Youssef
- Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; (K.V.B.); (A.Y.); (A.P.F.); (T.L.)
| | - Alberto Peña Fernández
- Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; (K.V.B.); (A.Y.); (A.P.F.); (T.L.)
| | - Toon Lambrechts
- Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; (K.V.B.); (A.Y.); (A.P.F.); (T.L.)
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1, Herestraat 49, 3000 Leuven, Belgium;
| | - Ioannis Papantoniou
- Prometheus, Division of Skeletal Tissue Engineering, KU Leuven, Onderwijs en Navorsing 1, Herestraat 49, 3000 Leuven, Belgium;
- Skeletal Biology and Engineering Research Centre, Onderwijs en Navorsing 1, Herestraat 49, 3000 Leuven, Belgium
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology—Hellas (FORTH), 26504 Patras, Greece
| | - Jean-Marie Aerts
- Department of Biosystems, Division Animal and Human Health Engineering, M3-BIORES: Measure, Model & Manage Bioresponses Laboratory, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium; (K.V.B.); (A.Y.); (A.P.F.); (T.L.)
- Correspondence:
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