1
|
Jones W, Gerogiorgis DI. Dynamic optimization of an integrated cultivation-aggregation model for mAb production. Biotechnol Bioeng 2024; 121:2716-2727. [PMID: 38822680 DOI: 10.1002/bit.28761] [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: 09/25/2023] [Revised: 05/18/2024] [Accepted: 05/21/2024] [Indexed: 06/03/2024]
Abstract
Due to their proteinaceous structure, monoclonal antibodies (mAbs) are susceptible to irreversible aggregation, with harmful consequences on drug efficacy and patient safety. To mitigate this risk in modern biopharmaceutical processes, it is critical to comply with current good manufacturing practices (cGMP) and pursue operating strategies minimizing irreversible aggregation whilst also maximizing mAb throughput. These conflicting objectives are targeted in this study by formulating and analyzing an integrated dynamic model accounting for both cultivation and aggregation of mAbs from a Chinese Hamster Ovary (CHO) cell line. Two manipulated dynamic variables are considered here in simulation studies: firstly temperature manipulation within a batch reactor, and secondly feed flow manipulation within a series of isothermal fed-batch reactors. Following this, dynamic optimization investigations have been conducted, firstly with the single objective of maximizing mAb throughput and secondly with multiple (two) objectives of maximizing mAb throughput while also minimizing irreversible aggregate content, simultaneously. The study provides key insight into tradeoffs of how simultaneous temperature and feed flowrate manipulation affects mAb throughput and aggregation inside bioreactors.
Collapse
Affiliation(s)
- Wil Jones
- School of Engineering, Institute for Materials and Processes (IMP), University of Edinburgh, Edinburgh, Scotland, UK
| | - Dimitrios I Gerogiorgis
- School of Engineering, Institute for Materials and Processes (IMP), University of Edinburgh, Edinburgh, Scotland, UK
| |
Collapse
|
2
|
Lane AN, Higashi RM, Fan TWM. Challenges of Spatially Resolved Metabolism in Cancer Research. Metabolites 2024; 14:383. [PMID: 39057706 PMCID: PMC11278851 DOI: 10.3390/metabo14070383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 06/28/2024] [Accepted: 07/07/2024] [Indexed: 07/28/2024] Open
Abstract
Stable isotope-resolved metabolomics comprises a critical set of technologies that can be applied to a wide variety of systems, from isolated cells to whole organisms, to define metabolic pathway usage and responses to perturbations such as drugs or mutations, as well as providing the basis for flux analysis. As the diversity of stable isotope-enriched compounds is very high, and with newer approaches to multiplexing, the coverage of metabolism is now very extensive. However, as the complexity of the model increases, including more kinds of interacting cell types and interorgan communication, the analytical complexity also increases. Further, as studies move further into spatially resolved biology, new technical problems have to be overcome owing to the small number of analytes present in the confines of a single cell or cell compartment. Here, we review the overall goals and solutions made possible by stable isotope tracing and their applications to models of increasing complexity. Finally, we discuss progress and outstanding difficulties in high-resolution spatially resolved tracer-based metabolic studies.
Collapse
Affiliation(s)
- Andrew N. Lane
- Department of Toxicology and Cancer Biology and Markey Cancer Center, University of Kentucky, 789 S. Limestone St., Lexington, KY 40536, USA; (R.M.H.); (T.W.-M.F.)
| | | | | |
Collapse
|
3
|
Karimi Alavijeh M, Lee YY, Gras SL. A perspective-driven and technical evaluation of machine learning in bioreactor scale-up: A case-study for potential model developments. Eng Life Sci 2024; 24:e2400023. [PMID: 38975020 PMCID: PMC11223373 DOI: 10.1002/elsc.202400023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 07/09/2024] Open
Abstract
Bioreactor scale-up and scale-down have always been a topical issue for the biopharmaceutical industry and despite considerable effort, the identification of a fail-safe strategy for bioprocess development across scales remains a challenge. With the ubiquitous growth of digital transformation technologies, new scaling methods based on computer models may enable more effective scaling. This study aimed to evaluate the potential application of machine learning (ML) algorithms for bioreactor scale-up, with a specific focus on the prediction of scaling parameters. Factors critical to the development of such models were identified and data for bioreactor scale-up studies involving CHO cell-generated mAb products collated from the literature and public sources for the development of unsupervised and supervised ML models. Comparison of bioreactor performance across scales identified similarities between the different processes and primary differences between small- and large-scale bioreactors. A series of three case studies were developed to assess the relationship between cell growth and scale-sensitive bioreactor features. An embedding layer improved the capability of artificial neural network models to predict cell growth at a large-scale, as this approach captured similarities between the processes. Further models constructed to predict scaling parameters demonstrated how ML models may be applied to assist the scaling process. The development of data sets that include more characterization data with greater variability under different gassing and agitation regimes will also assist the future development of ML tools for bioreactor scaling.
Collapse
Affiliation(s)
- Masih Karimi Alavijeh
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
| | | | - Sally L. Gras
- Department of Chemical EngineeringThe University of MelbourneParkvilleVictoriaAustralia
- The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVictoriaAustralia
| |
Collapse
|
4
|
Singh VK, Jiménez del Val I, Glassey J, Kavousi F. Integration Approaches to Model Bioreactor Hydrodynamics and Cellular Kinetics for Advancing Bioprocess Optimisation. Bioengineering (Basel) 2024; 11:546. [PMID: 38927782 PMCID: PMC11200465 DOI: 10.3390/bioengineering11060546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/17/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Abstract
Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of "Industry 4.0" concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads.
Collapse
Affiliation(s)
- Vishal Kumar Singh
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
| | - Ioscani Jiménez del Val
- School of Chemical & Bioprocess Engineering, University College Dublin, D04 V1W8 Dublin, Ireland;
| | - Jarka Glassey
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
- School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Fatemeh Kavousi
- Process and Chemical Engineering, School of Engineering and Architecture, University College Cork, T12 K8AF Cork, Ireland;
| |
Collapse
|
5
|
Sun T, Xiang Y, Turner F, Bao X. Integrated Experimental and Mathematical Exploration of Modular Tissue Cultures for Developmental Engineering. Int J Mol Sci 2024; 25:2987. [PMID: 38474234 DOI: 10.3390/ijms25052987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 02/06/2024] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
Developmental engineering (DE) involves culturing various cells on modular scaffolds (MSs), yielding modular tissues (MTs) assembled into three-dimensional (3D) tissues, mimicking developmental biology. This study employs an integrated approach, merging experimental and mathematical methods to investigate the biological processes in MT cultivation and assembly. Human dermal fibroblasts (HDFs) were cultured on tissue culture plastics, poly(lactic acid) (PLA) discs with regular open structures, or spherical poly(methyl methacrylate) (PMMA) MSs, respectively. Notably, HDFs exhibited flattened spindle shapes when adhered to solid surfaces, and complex 3D structures when migrating into the structured voids of PLA discs or interstitial spaces between aggregated PMMA MSs, showcasing coordinated colonization of porous scaffolds. Empirical investigations led to power law models simulating density-dependent cell growth on solid surfaces or voids. Concurrently, a modified diffusion model was applied to simulate oxygen diffusion within tissues cultured on solid surfaces or porous structures. These mathematical models were subsequently combined to explore the influences of initial cell seeding density, culture duration, and oxygen diffusion on MT cultivation and assembly. The findings underscored the intricate interplay of factors influencing MT design for tissue assembly. The integrated approach provides insights into mechanistic aspects, informing bioprocess design for manufacturing MTs and 3D tissues in DE.
Collapse
Affiliation(s)
- Tao Sun
- Department of Chemical Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Yu Xiang
- Department of Materials, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Freya Turner
- Department of Chemical Engineering, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| | - Xujin Bao
- Department of Materials, Loughborough University, Epinal Way, Loughborough LE11 3TU, UK
| |
Collapse
|
6
|
Reddy JV, Raudenbush K, Papoutsakis ET, Ierapetritou M. Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation. Biotechnol Adv 2023; 67:108179. [PMID: 37257729 DOI: 10.1016/j.biotechadv.2023.108179] [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: 11/27/2022] [Revised: 05/18/2023] [Accepted: 05/21/2023] [Indexed: 06/02/2023]
Abstract
In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.
Collapse
Affiliation(s)
- Jayanth Venkatarama Reddy
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Katherine Raudenbush
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA
| | - Eleftherios Terry Papoutsakis
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA; Delaware Biotechnology Institute, Department of Biological Sciences, University of Delaware, USA.
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716-3196, USA.
| |
Collapse
|
7
|
Park JU, Han HJ, Baik JY. Energy metabolism in Chinese hamster ovary (CHO) cells: Productivity and beyond. KOREAN J CHEM ENG 2022. [DOI: 10.1007/s11814-022-1062-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
8
|
Chen Y, Liu X, Anderson JYL, Naik HM, Dhara VG, Chen X, Harris GA, Betenbaugh MJ. A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures. Biotechnol Bioeng 2021; 119:435-451. [PMID: 34811743 DOI: 10.1002/bit.27994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 10/26/2021] [Accepted: 11/13/2021] [Indexed: 11/09/2022]
Abstract
Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.
Collapse
Affiliation(s)
- Yiqun Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiao Liu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Harnish Mukesh Naik
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Venkata Gayatri Dhara
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xiaolu Chen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Glenn A Harris
- Research and Development, 908 Devices Inc., Boston, Massachusetts, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| |
Collapse
|
9
|
São Pedro MN, Silva TC, Patil R, Ottens M. White paper on high-throughput process development for integrated continuous biomanufacturing. Biotechnol Bioeng 2021; 118:3275-3286. [PMID: 33749840 PMCID: PMC8451798 DOI: 10.1002/bit.27757] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/15/2021] [Accepted: 03/12/2021] [Indexed: 12/25/2022]
Abstract
Continuous manufacturing is an indicator of a maturing industry, as can be seen by the example of the petrochemical industry. Patent expiry promotes a price competition between manufacturing companies, and more efficient and cheaper processes are needed to achieve lower production costs. Over the last decade, continuous biomanufacturing has had significant breakthroughs, with regulatory agencies encouraging the industry to implement this processing mode. Process development is resource and time consuming and, although it is increasingly becoming less expensive and faster through high-throughput process development (HTPD) implementation, reliable HTPD technology for integrated and continuous biomanufacturing is still lacking and is considered to be an emerging field. Therefore, this paper aims to illustrate the major gaps in HTPD and to discuss the major needs and possible solutions to achieve an end-to-end Integrated Continuous Biomanufacturing, as discussed in the context of the 2019 Integrated Continuous Biomanufacturing conference. The current HTPD state-of-the-art for several unit operations is discussed, as well as the emerging technologies which will expedite a shift to continuous biomanufacturing.
Collapse
Affiliation(s)
| | - Tiago C. Silva
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| | - Rohan Patil
- Global CMC DevelopmentSanofiFraminghamMassachusettsUSA
| | - Marcel Ottens
- Department of BiotechnologyDelft University of TechnologyDelftThe Netherlands
| |
Collapse
|
10
|
Ben Yahia B, Malphettes L, Heinzle E. Predictive macroscopic modeling of cell growth, metabolism and monoclonal antibody production: Case study of a CHO fed-batch production. Metab Eng 2021; 66:204-216. [PMID: 33887460 DOI: 10.1016/j.ymben.2021.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 03/08/2021] [Accepted: 04/07/2021] [Indexed: 12/31/2022]
Abstract
We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.
Collapse
Affiliation(s)
- Bassem Ben Yahia
- Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbrücken (Germany); Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l'Industrie, Braine l'Alleud B-1420 (Belgium)
| | - Laetitia Malphettes
- Upstream Process Sciences Biotech Sciences, UCB Pharma S.A., Avenue de l'Industrie, Braine l'Alleud B-1420 (Belgium)
| | - Elmar Heinzle
- Biochemical Engineering Institute, Saarland University, Campus A1.5, D-66123 Saarbrücken (Germany).
| |
Collapse
|
11
|
Pérez-Fernández BA, Fernandez-de-Cossio-Diaz J, Boggiano T, León K, Mulet R. In-silico media optimization for continuous cultures using genome scale metabolic networks: The case of CHO-K1. Biotechnol Bioeng 2021; 118:1884-1897. [PMID: 33554345 DOI: 10.1002/bit.27704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 11/18/2020] [Accepted: 01/21/2021] [Indexed: 01/12/2023]
Abstract
The cell culture is the central piece of a biotechnological industrial process. It includes upstream (e.g. media preparation, fixed costs, etc.) and downstream steps (e.g. product purification, waste disposal, etc.). In the continuous mode of cell culture, a constant flow of fresh media replaces culture fluid until the system reaches a steady state. This steady state is the standard operation mode which, under very general conditions, is a function of the ratio between the cell density and the dilution rate and depends on the media supplied to the culture. To optimize the production process it is widely accepted that the concentration of the metabolites in this media should be carefully tuned. A poor media may not provide enough nutrients to the culture, while a media too rich in nutrients may be a waste of resources because, either the cells do not use all of the available nutrients, or worse, they over-consume them producing toxic byproducts. In this study, we show how an in-silico study of a genome scale metabolic network coupled to the dynamics of a chemostat could guide the strategy to optimize the media to be used in a continuous process. Given a known media we model the concentrations of the cells in a chemostat as a function of the dilution rate. Then, we cast the problem of optimizing the production process within a linear programming framework in which the goal is to minimize the cost of the media keeping fixed the cell concentration for a given dilution rate in the chemostat. We evaluate our results in two metabolic models: first a simplified model of mammalian cell metabolism, and then in a realistic genome-scale metabolic network of mammalian cells, the Chinese hamster ovary cell line. We explore the latter in more detail given specific meaning to the predictions of the concentrations of several metabolites.
Collapse
Affiliation(s)
- Bárbara A Pérez-Fernández
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, Havana, Cuba
| | - Jorge Fernandez-de-Cossio-Diaz
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, Havana, Cuba.,Systems Biology Department, Center of Molecular Immunology, Havana, Cuba
| | - Tammy Boggiano
- Systems Biology Department, Center of Molecular Immunology, Havana, Cuba
| | - Kalet León
- Systems Biology Department, Center of Molecular Immunology, Havana, Cuba
| | - Roberto Mulet
- Group of Complex Systems and Statistical Physics, Department of Theoretical Physics, Physics Faculty, University of Havana, Havana, Cuba
| |
Collapse
|
12
|
Abstract
In recent years process modelling has become an established method which generates digital twins of manufacturing plant operation with the aid of numerically solved process models. This article discusses the benefits of establishing process modelling, in-house or by cooperation, in order to support the workflow from process development, piloting and engineering up to manufacturing. The examples are chosen from the variety of botanicals and biologics manufacturing thus proving the broad applicability from variable feedstock of natural plant extracts of secondary metabolites to fermentation of complex molecules like mAbs, fragments, proteins and peptides.Consistent models and methods to simulate whole processes are available. To determine the physical properties used as model parameters, efficient laboratory-scale experiments are implemented. These parameters are case specific since there is no database for complex molecules of biologics and botanicals in pharmaceutical industry, yet.Moreover, Quality-by-Design approaches, demanded by regulatory authorities, are integrated within those predictive modelling procedures. The models could be proven to be valid and predictive under regulatory aspects. Process modelling does earn its money from the first day of application. Process modelling is a key-enabling tool towards cost-efficient digitalization in chemical-pharmaceutical industries.
Collapse
|
13
|
Júlvez J, Oliver SG. A unifying modelling formalism for the integration of stoichiometric and kinetic models. J R Soc Interface 2020; 17:20200341. [PMID: 32752999 DOI: 10.1098/rsif.2020.0341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Current research on systems and synthetic biology relies heavily on mathematical models of the systems under study. The usefulness of such models depends on the quantity and quality of biological data, and on the availability of appropriate modelling formalisms that can gather and accommodate such data so that they can be exploited properly. Given our incomplete knowledge of biological systems and the fact that they consist of many subsystems, biological data are usually uncertain and heterogeneous. These facts hinder the use of mathematical models and computational methods. In the scope of dynamic biological systems, e.g. metabolic networks, this difficulty can be overcome by the novel modelling formalism of flexible nets (FNs). We show that an FN can combine, in a natural way, a stoichiometric model and a kinetic model. Moreover, the resulting net admits nonlinear dynamics and can be analysed in both transient and steady states.
Collapse
Affiliation(s)
- Jorge Júlvez
- Department of Computer Science and Systems Engineering, University of Zaragoza, Zaragoza, Spain
| | - Stephen G Oliver
- Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| |
Collapse
|
14
|
A PSE perspective for the efficient production of monoclonal antibodies: integration of process, cell, and product design aspects. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.01.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
15
|
Möller J, Hernández Rodríguez T, Müller J, Arndt L, Kuchemüller KB, Frahm B, Eibl R, Eibl D, Pörtner R. Model uncertainty-based evaluation of process strategies during scale-up of biopharmaceutical processes. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2019.106693] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
16
|
Tang P, Xu J, Louey A, Tan Z, Yongky A, Liang S, Li ZJ, Weng Y, Liu S. Kinetic modeling of Chinese hamster ovary cell culture: factors and principles. Crit Rev Biotechnol 2020; 40:265-281. [DOI: 10.1080/07388551.2019.1711015] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Peifeng Tang
- Department of Paper and Bioprocess Engineering, SUNY-ESF, Syracuse, NY, USA
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, MA, USA
| | - Jianlin Xu
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, MA, USA
| | - Alastair Louey
- Elpiscience Biopharma, Cayman Islands George Town, Grand Cayman, UK
| | - Zhijun Tan
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, MA, USA
| | - Andrew Yongky
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, MA, USA
| | - Shaoyan Liang
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, CT, USA
| | - Zheng Jian Li
- Global Product Development and Supply, Bristol-Myers Squibb Company, Devens, MA, USA
| | - Yongyan Weng
- Department of Civil Engineering, University of Nottingham, Nottingham, UK
| | - Shijie Liu
- Department of Paper and Bioprocess Engineering, SUNY-ESF, Syracuse, NY, USA
| |
Collapse
|
17
|
Menshutina NV, Guseva EV, Safarov RR, Boudrant J. Modelling of hollow fiber membrane bioreactor for mammalian cell cultivation using computational hydrodynamics. Bioprocess Biosyst Eng 2019; 43:549-567. [PMID: 31786664 DOI: 10.1007/s00449-019-02249-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 11/06/2019] [Indexed: 11/30/2022]
Abstract
The hollow fiber membrane bioreactor (HFMB) has been investigated for the cultivation of mammalian Chinese hamster ovary cell expansion. The experiments were carried out in Petri's dishes and in the hollow fiber membrane bioreactor having 20 fibers (S2025 from FiberCell Systems). The approach to HFMB modelling which combines the model of cell growth kinetics and hydrodynamics has been proposed. The hydrodynamic model is made using ANSYS Fluent software. The mathematical model of HFMB was developed, allowing the study of the hydrodynamics into the lumen and the extracapillary spaces, the filtration through the membrane fiber with the cell expansion on outer membrane surface. The direct nutrient medium flow variant into the extracapillary space was suggested. Based on the numerical simulations, the optimal parameters were selected for daily changes in the medium flow-rate into the lumen space. The HFMB scaling up was performed for the larger size HFMB (60 fibers).
Collapse
Affiliation(s)
- Natalia V Menshutina
- Department of Cybernetics of Chemical Processes, Faculty of Information Technologies and Chemical Engineering, Mendeleev University of Chemical Technology of Russia, Miusskaya Sq., 9, 125047, Moscow, Russia
| | - Elena V Guseva
- Department of Cybernetics of Chemical Processes, Faculty of Information Technologies and Chemical Engineering, Mendeleev University of Chemical Technology of Russia, Miusskaya Sq., 9, 125047, Moscow, Russia.
| | - Ruslan R Safarov
- Department of Cybernetics of Chemical Processes, Faculty of Information Technologies and Chemical Engineering, Mendeleev University of Chemical Technology of Russia, Miusskaya Sq., 9, 125047, Moscow, Russia
| | - Joseph Boudrant
- Laboratoire Réactions et Génie des Procédés, University of Lorraine, CNRS, LRGP, 54000, Nancy, France
| |
Collapse
|
18
|
Early integration of Design of Experiment (DOE) and multivariate statistics identifies feeding regimens suitable for CHO cell line development and screening. Cytotechnology 2019; 71:1137-1153. [PMID: 31705334 DOI: 10.1007/s10616-019-00350-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 10/25/2019] [Indexed: 12/12/2022] Open
Abstract
In Chinese Hamster Ovary (CHO) cell lines, the establishment of the ideal fed-batch regimen promotes metabolic conditions advantageous for the bioproduction of therapeutic molecules. A tailored, cell line-specific feeding scheme is typically defined during process development (PD) activities, through the incorporation of Design of Experiment (DOE) and late stage cell culture approaches. The feeding during early stage cell line development (CLD) was a simplified "one-fits-all" design, inherited from PD lab, that didn't account for CLD needs of throughput and streamlined workflow. The "one-fits-all" efficiency was not routinely verified when novel technologies were incorporated in CLD and sub-optimal feeding carried the risk of not selecting the most desirable cell lines amenable to late stage PD. In our work we developed the DOE-feed method; a streamlined, three-stages framework for identifying efficient feeding schemes as the CLD technologies evolved. We combined early stage cell culture input data with late-stage techniques, such as statistical modelling, principal component analysis (PCA), DOE and Prediction Profiler. Novel in our DOE-feed work, we deliberately anticipated the application of statistics and approached the method development as an early-stage, continuously updated process, by building iterative datasets and statistically interpreting their responses. We capitalized on the statistical models defined by the DOE-feed methodology to study the influence of feeds on daily productivity and growth and to extrapolate feeding-schemes that improved the cell line screening. The DOE-feed became a methodology suited for CLD needs at AbbVie, and optimized the early stage screening, reduced the operational hands-on time and improved the overall workstream efficiency.
Collapse
|
19
|
Narayanan H, Luna MF, Stosch M, Cruz Bournazou MN, Polotti G, Morbidelli M, Butté A, Sokolov M. Bioprocessing in the Digital Age: The Role of Process Models. Biotechnol J 2019; 15:e1900172. [DOI: 10.1002/biot.201900172] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/15/2019] [Indexed: 12/20/2022]
Affiliation(s)
- Harini Narayanan
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | - Martin F. Luna
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
| | | | - Mariano Nicolas Cruz Bournazou
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Gianmarco Polotti
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Massimo Morbidelli
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Alessandro Butté
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| | - Michael Sokolov
- Institute for Chemical and Bioengineering ETHZ Zurich Switzerland
- DataHow AGc/o ETH ZurichHCI, F137Vladimir‐Prelog‐Weg 1 8093 Zurich Switzerland
| |
Collapse
|
20
|
Paul K, Rajamanickam V, Herwig C. Model-based optimization of temperature and pH shift to increase volumetric productivity of a Chinese hamster ovary fed-batch process. J Biosci Bioeng 2019; 128:710-715. [PMID: 31277910 DOI: 10.1016/j.jbiosc.2019.06.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/10/2019] [Accepted: 06/06/2019] [Indexed: 01/24/2023]
Abstract
Industrial production processes, which utilize mammalian cells for the production of therapeutic proteins are routinely designed to include temperature and pH shifts. The process conditions are shifted away from growth promoting conditions towards a state of decreased metabolic activity. This results in the extension of the cultivation duration and therefore in a higher final product concentration. Although the correct timing of these shifts is essential for peak process performance, not many studies have been investigating this topic. In this work temperature and pH shift were optimized with a mechanistic model to increase the final product concentration in comparison to an established industrial fed-batch process. The major advantage of the mechanistic in comparison to a data-driven approach lies in the reduced number of experiments, which is needed. Therefore process development is faster, which decreases the time of the product to the market. Based on the optimization, an increased final product concentration of 14% was achieved in comparison to an already established industrial fed-batch process with the same cell line. Furthermore, the space-time-yield of the process did increase in comparison, resulting in a 20% increase of the final volumetric product concentration.
Collapse
Affiliation(s)
- Katrin Paul
- Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria; Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Vignesh Rajamanickam
- Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria; Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria
| | - Christoph Herwig
- Institute of Chemical, Environmental and Bioscience Engineering, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria; Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses, TU Wien, Gumpendorfer Straße 1a, 1060 Vienna, Austria.
| |
Collapse
|
21
|
Abstract
Intensified and accelerated development processes are being demanded by the market, as innovative biopharmaceuticals such as virus-like particles, exosomes, cell and gene therapy, as well as recombinant proteins and peptides will possess no available platform approach. Therefore, methods that are able to accelerate this development are preferred. Especially, physicochemical rigorous process models, based on all relevant effects of fluid dynamics, phase equilibrium, and mass transfer, can be predictive, if the model is verified and distinctly quantitatively validated. In this approach, a macroscopic kinetic model based on Monod kinetics for mammalian cell cultivation is developed and verified according to a general valid model validation workflow. The macroscopic model is verified and validated on the basis of four decision criteria (plausibility, sensitivity, accuracy and precision as well as equality). The process model workflow is subjected to a case study, comprising a Chinese hamster ovary fed-batch cultivation for the production of a monoclonal antibody. By performing the workflow, it was found that, based on design of experiments and Monte Carlo simulation, the maximum growth rate µmax exhibited the greatest influence on model variables such as viable cell concentration XV and product concentration. In addition, partial least squares regressions statistically evaluate the correlations between a higher µmax and a higher cell and product concentration, as well as a higher substrate consumption.
Collapse
|
22
|
Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin? Processes (Basel) 2019. [DOI: 10.3390/pr7020094] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Innovative biologics, including cell therapeutics, virus-like particles, exosomes,recombinant proteins, and peptides, seem likely to substitute monoclonal antibodies as the maintherapeutic entities in manufacturing over the next decades. This molecular variety causes agrowing need for a general change of methods as well as mindset in the process development stage,as there are no platform processes available such as those for monoclonal antibodies. Moreover,market competitiveness demands hyper-intensified processes, including accelerated decisionstoward batch or continuous operation of dedicated modular plant concepts. This indicates gaps inprocess comprehension, when operation windows need to be run at the edges of optimization. Inthis editorial, the authors review and assess potential methods and begin discussing possiblesolutions throughout the workflow, from process development through piloting to manufacturingoperation from their point of view and experience. Especially, the state-of-the-art for modeling inred biotechnology is assessed, clarifying differences and applications of statistical, rigorousphysical-chemical based models as well as cost modeling. “Digital-twins” are described and effortsvs. benefits for new applications exemplified, including the regulation-demanded QbD (quality bydesign) and PAT (process analytical technology) approaches towards digitalization or industry 4.0based on advanced process control strategies. Finally, an analysis of the obstacles and possiblesolutions for any successful and efficient industrialization of innovative methods from processdevelopment, through piloting to manufacturing, results in some recommendations. A centralquestion therefore requires attention: Considering that QbD and PAT have been required byauthorities since 2004, can any biologic manufacturing process be approved by the regulatoryagencies without being modeled by a “digital-twin” as part of the filing documentation?
Collapse
|
23
|
Dynamic metabolic network modeling of mammalian Chinese hamster ovary (CHO) cell cultures with continuous phase kinetics transitions. Biochem Eng J 2019. [DOI: 10.1016/j.bej.2018.11.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
24
|
Hagrot E, Oddsdóttir HÆ, Mäkinen M, Forsgren A, Chotteau V. Novel column generation-based optimization approach for poly-pathway kinetic model applied to CHO cell culture. Metab Eng Commun 2018; 8:e00083. [PMID: 30809468 PMCID: PMC6376161 DOI: 10.1016/j.mec.2018.e00083] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 10/30/2018] [Accepted: 12/08/2018] [Indexed: 11/26/2022] Open
Abstract
Mathematical modelling can provide precious tools for bioprocess simulation, prediction, control and optimization of mammalian cell-based cultures. In this paper we present a novel method to generate kinetic models of such cultures, rendering complex metabolic networks in a poly-pathway kinetic model. The model is based on subsets of elementary flux modes (EFMs) to generate macro-reactions. Thanks to our column generation-based optimization algorithm, the experimental data are used to identify the EFMs, which are relevant to the data. Here the systematic enumeration of all the EFMs is eliminated and a network including a large number of reactions can be considered. In particular, the poly-pathway model can simulate multiple metabolic behaviors in response to changes in the culture conditions. We apply the method to a network of 126 metabolic reactions describing cultures of antibody-producing Chinese hamster ovary cells, and generate a poly-pathway model that simulates multiple experimental conditions obtained in response to variations in amino acid availability. A good fit between simulated and experimental data is obtained, rendering the variations in the growth, product, and metabolite uptake/secretion rates. The intracellular reaction fluxes simulated by the model are explored, linking variations in metabolic behavior to adaptations of the intracellular metabolism. Novel method to model multiple states by a poly-pathway kinetic model. EFMs relevant to data identified by column generation (CG)-based optimization. CG optimization enables use of networks much larger than systematic enumeration. A kinetic model simulates changes in metabolic rates linked to available amino acids. The flux distribution of each metabolic state is visualized in the original network.
Collapse
Affiliation(s)
- Erika Hagrot
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden
| | - Hildur Æsa Oddsdóttir
- Department of Mathematics, Division of Optimization and Systems Theory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Meeri Mäkinen
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden
| | - Anders Forsgren
- Department of Mathematics, Division of Optimization and Systems Theory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Véronique Chotteau
- Cell Technology Group, Department of Industrial Biotechnology/Bioprocess Design, School of Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.,AdBIOPRO, VINNOVA Competence Centre for Advanced Bioproduction by Continuous Processing, Sweden.,WCPR, Wallenberg Centre for Protein Research, Sweden
| |
Collapse
|
25
|
Sha S, Huang Z, Wang Z, Yoon S. Mechanistic modeling and applications for CHO cell culture development and production. Curr Opin Chem Eng 2018. [DOI: 10.1016/j.coche.2018.08.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
26
|
Möller J, Korte K, Pörtner R, Zeng AP, Jandt U. Model-based identification of cell-cycle-dependent metabolism and putative autocrine effects in antibody producing CHO cell culture. Biotechnol Bioeng 2018; 115:2996-3008. [DOI: 10.1002/bit.26828] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 08/20/2018] [Accepted: 08/30/2018] [Indexed: 02/06/2023]
Affiliation(s)
- Johannes Möller
- Hamburg University of Technology, Institute of Bioprocess and Biosystems Engineering; Hamburg Germany
| | - Katrin Korte
- Hamburg University of Technology, Institute of Bioprocess and Biosystems Engineering; Hamburg Germany
| | - Ralf Pörtner
- Hamburg University of Technology, Institute of Bioprocess and Biosystems Engineering; Hamburg Germany
| | - An-Ping Zeng
- Hamburg University of Technology, Institute of Bioprocess and Biosystems Engineering; Hamburg Germany
| | - Uwe Jandt
- Hamburg University of Technology, Institute of Bioprocess and Biosystems Engineering; Hamburg Germany
| |
Collapse
|
27
|
Ye K, Kaplan DL, Bao G, Bettinger C, Forgacs G, Dong C, Khademhosseini A, Ke Y, Leong K, Sambanis A, Sun W, Yin P. Advanced Cell and Tissue Biomanufacturing. ACS Biomater Sci Eng 2018; 4:2292-2307. [PMID: 33435095 DOI: 10.1021/acsbiomaterials.8b00650] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
This position paper assesses state-of-the-art advanced biomanufacturing and identifies paths forward to advance this emerging field in biotechnology and biomedical engineering, including new research opportunities and translational and corporate activities. The vision for the field is to see advanced biomanufacturing emerge as a discipline in academic and industrial communities as well as a technological opportunity to spur research and industry growth. To navigate this vision, the paths to move forward and to identify major barriers were a focal point of discussions at a National Science Foundation-sponsored workshop focused on the topic. Some of the major needs include but are not limited to the integration of specific scientific and engineering disciplines and guidance from regulatory agencies, infrastructure requirements, and strategies for reliable systems integration. Some of the recommendations, major targets, and opportunities were also outlined, including some "grand challenges" to spur interest and progress in the field based on the participants at the workshop. Many of these recommendations have been expanded, materialized, and adopted by the field. For instance, the formation of an initial collaboration network in the community was established. This report provides suggestions for the opportunities and challenges to help move the field of advanced biomanufacturing forward. The field is in the early stages of effecting science and technology in biomanufacturing with a bright and important future impact evident based on the rapid scientific advances in recent years and industry progress.
Collapse
Affiliation(s)
- Kaiming Ye
- Department of Biomedical Engineering, Center of Biomanufacturing for Regenerative Medicine, Watson School of Engineering and Applied Science, Binghamton University, State University of New York (SUNY), Binghamton, New York 13902, United States
| | - David L Kaplan
- Department of Biomedical Engineering, School of Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Gang Bao
- Department of Bioengineering, School of Engineering, Rice University, Houston, Texas 77005, United States
| | - Christopher Bettinger
- Department of Materials Science and Engineering, College of Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Gabor Forgacs
- Department of Bioengineering, College of Engineering, University of Missouri, Columbia, Missouri 65211, United States.,Modern Meadow, Inc., 340 Kingsland Street, Nutley, New Jersey 07110, United States
| | - Cheng Dong
- Department of Biomedical Engineering, College of Engineering, Penn State University, University Park, Pennsylvania 16802, United States
| | - Ali Khademhosseini
- Department of Bioengineering, University of California, Los Angeles, California 90095, United States
| | - Yonggang Ke
- Department of Biomedical Engineering, College of Engineering, Georgia Tech, Atlanta, Georgia 30332, United States
| | - Kam Leong
- Department of Biomedical Engineering, School of Engineering and Applied Science, Columbia University, New York City, New York 10027, United States
| | | | - Wei Sun
- Department of Mechanical Engineering and Mechanics, College of Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States.,Department of Mechanical Engineering, Tsinghua University, Beijing, China
| | - Peng Yin
- Department of Systems Biology, Harvard Medical School, Cambridge, Massachusetts 02138, United States
| |
Collapse
|
28
|
Paim Á, Cardozo NSM, Tessaro IC, Pranke P. Relevant biological processes for tissue development with stem cells and their mechanistic modeling: A review. Math Biosci 2018; 301:147-158. [PMID: 29746816 DOI: 10.1016/j.mbs.2018.05.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 04/27/2018] [Accepted: 05/04/2018] [Indexed: 02/07/2023]
Abstract
A potential alternative for tissue transplants is tissue engineering, in which the interaction of cells and biomaterials can be optimized. Tissue development in vitro depends on the complex interaction of several biological processes such as extracellular matrix synthesis, vascularization and cell proliferation, adhesion, migration, death, and differentiation. The complexity of an individual phenomenon or of the combination of these processes can be studied with phenomenological modeling techniques. This work reviews the main biological phenomena in tissue development and their mathematical modeling, focusing on mesenchymal stem cell growth in three-dimensional scaffolds.
Collapse
Affiliation(s)
- Ágata Paim
- Department of Chemical Engineering, Universidade Federal do Rio Grande do Sul (UFRGS), R. Eng. Luis Englert, s/n Porto Alegre, Rio Grande do Sul 90040-040, Brazil; Faculty of Pharmacy, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Ipiranga, 2752. Porto Alegre, Rio Grande do Sul 90610-000, Brazil.
| | - Nilo S M Cardozo
- Department of Chemical Engineering, Universidade Federal do Rio Grande do Sul (UFRGS), R. Eng. Luis Englert, s/n Porto Alegre, Rio Grande do Sul 90040-040, Brazil
| | - Isabel C Tessaro
- Department of Chemical Engineering, Universidade Federal do Rio Grande do Sul (UFRGS), R. Eng. Luis Englert, s/n Porto Alegre, Rio Grande do Sul 90040-040, Brazil
| | - Patricia Pranke
- Faculty of Pharmacy, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Ipiranga, 2752. Porto Alegre, Rio Grande do Sul 90610-000, Brazil; Stem Cell Research Institute, Porto Alegre, Rio Grande do Sul, Brazil
| |
Collapse
|
29
|
Hille R, Budman HM. Simultaneous identification and optimization of biochemical processes under model-plant mismatch using output uncertainty bounds. Comput Chem Eng 2018. [DOI: 10.1016/j.compchemeng.2018.03.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
30
|
Stacey AJ, Cheeseman EA, Glen KE, Moore RL, Thomas RJ. Experimentally integrated dynamic modelling for intuitive optimisation of cell based processes and manufacture. Biochem Eng J 2018. [DOI: 10.1016/j.bej.2018.01.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
|
31
|
Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling. Bioengineering (Basel) 2018; 5:bioengineering5010025. [PMID: 29547557 PMCID: PMC5874891 DOI: 10.3390/bioengineering5010025] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 03/13/2018] [Accepted: 03/14/2018] [Indexed: 11/20/2022] Open
Abstract
Productivity improvements of mammalian cell culture in the production of recombinant proteins have been made by optimizing cell lines, media, and process operation. This led to enhanced titers and process robustness without increasing the cost of the upstream processing (USP); however, a downstream bottleneck remains. In terms of process control improvement, the process analytical technology (PAT) initiative, initiated by the American Food and Drug Administration (FDA), aims to measure, analyze, monitor, and ultimately control all important attributes of a bioprocess. Especially, spectroscopic methods such as Raman or near-infrared spectroscopy enable one to meet these analytical requirements, preferably in-situ. In combination with chemometric techniques like partial least square (PLS) or principal component analysis (PCA), it is possible to generate soft sensors, which estimate process variables based on process and measurement models for the enhanced control of bioprocesses. Macroscopic kinetic models can be used to simulate cell metabolism. These models are able to enhance the process understanding by predicting the dynamic of cells during cultivation. In this article, in-situ turbidity (transmission, 880 nm) and ex-situ Raman spectroscopy (785 nm) measurements are combined with an offline macroscopic Monod kinetic model in order to predict substrate concentrations. Experimental data of Chinese hamster ovary cultivations in bioreactors show a sufficiently linear correlation (R2 ≥ 0.97) between turbidity and total cell concentration. PLS regression of Raman spectra generates a prediction model, which was validated via offline viable cell concentration measurement (RMSE ≤ 13.82, R2 ≥ 0.92). Based on these measurements, the macroscopic Monod model can be used to determine different process attributes, e.g., glucose concentration. In consequence, it is possible to approximately calculate (R2 ≥ 0.96) glucose concentration based on online cell concentration measurements using turbidity or Raman spectroscopy. Future approaches will use these online substrate concentration measurements with turbidity and Raman measurements, in combination with the kinetic model, in order to control the bioprocess in terms of feeding strategies, by employing an open platform communication (OPC) network—either in fed-batch or perfusion mode, integrated into a continuous operation of upstream and downstream.
Collapse
|
32
|
Galkin OY. SPECIFICITY OF MANIFACTURING PROCESS VALIDATION FOR DIAGNOSTIC SEROLOGICAL DEVICES. BIOTECHNOLOGIA ACTA 2018. [DOI: 10.15407/biotech11.01.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
|
33
|
Goldrick S, Lee K, Spencer C, Holmes W, Kuiper M, Turner R, Farid SS. On-Line Control of Glucose Concentration in High-Yielding Mammalian Cell Cultures Enabled Through Oxygen Transfer Rate Measurements. Biotechnol J 2018; 13:e1700607. [DOI: 10.1002/biot.201700607] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Revised: 11/27/2017] [Indexed: 12/25/2022]
Affiliation(s)
- Stephen Goldrick
- The Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering; University College London; Gower Street, WC1E 6BT London United Kingdom
- MedImmune; Milstein Building, Granta Park Cambridge, CB21 6GH United Kingdom
| | - Kenneth Lee
- MedImmune LLC; Gaithersburg Headquarters Gaithersburg MD 20878 USA
| | - Christopher Spencer
- MedImmune; Milstein Building, Granta Park Cambridge, CB21 6GH United Kingdom
| | - William Holmes
- MedImmune; Milstein Building, Granta Park Cambridge, CB21 6GH United Kingdom
| | - Marcel Kuiper
- MedImmune; Milstein Building, Granta Park Cambridge, CB21 6GH United Kingdom
| | - Richard Turner
- MedImmune; Milstein Building, Granta Park Cambridge, CB21 6GH United Kingdom
| | - Suzanne S. Farid
- The Advanced Centre of Biochemical Engineering, Department of Biochemical Engineering; University College London; Gower Street, WC1E 6BT London United Kingdom
| |
Collapse
|
34
|
Ulonska S, Kroll P, Fricke J, Clemens C, Voges R, Müller MM, Herwig C. Workflow for Target-Oriented Parametrization of an Enhanced Mechanistic Cell Culture Model. Biotechnol J 2017; 13:e1700395. [DOI: 10.1002/biot.201700395] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 10/19/2017] [Indexed: 01/22/2023]
Affiliation(s)
- Sophia Ulonska
- Institute of Chemical, Environmental and Biological Engineering; TU Wien 1060 Wien Austria
| | - Paul Kroll
- Institute of Chemical, Environmental and Biological Engineering; TU Wien 1060 Wien Austria
- CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses; TU Wien 1060 Wien Austria
| | - Jens Fricke
- Institute of Chemical, Environmental and Biological Engineering; TU Wien 1060 Wien Austria
- CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses; TU Wien 1060 Wien Austria
| | | | - Raphael Voges
- Boehringer Ingelheim Pharma GmbH & Co. KG; 88400 Biberach Germany
| | - Markus M. Müller
- Boehringer Ingelheim Pharma GmbH & Co. KG; 88400 Biberach Germany
| | - Christoph Herwig
- Institute of Chemical, Environmental and Biological Engineering; TU Wien 1060 Wien Austria
- CD Laboratory on Mechanistic and Physiological Methods for Improved Bioprocesses; TU Wien 1060 Wien Austria
| |
Collapse
|
35
|
Kyriakopoulos S, Ang KS, Lakshmanan M, Huang Z, Yoon S, Gunawan R, Lee DY. Kinetic Modeling of Mammalian Cell Culture Bioprocessing: The Quest to Advance Biomanufacturing. Biotechnol J 2017; 13:e1700229. [DOI: 10.1002/biot.201700229] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 09/27/2017] [Accepted: 10/11/2017] [Indexed: 12/15/2022]
Affiliation(s)
- Sarantos Kyriakopoulos
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Kok Siong Ang
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Meiyappan Lakshmanan
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
| | - Zhuangrong Huang
- Department of Chemical Engineering; University of Massachusetts Lowell; Lowell MA USA
| | - Seongkyu Yoon
- Department of Chemical Engineering; University of Massachusetts Lowell; Lowell MA USA
| | - Rudiyanto Gunawan
- Institute for Chemical and Bioengineering; ETH Zurich; Zurich Switzerland
| | - Dong-Yup Lee
- Bioprocessing Technology Institute, Agency for Science; Technology and Research (A*STAR); Singapore
- Department of Chemical and Biomolecular Engineering; National University of Singapore; Singapore
| |
Collapse
|
36
|
Fernandez-de-Cossio-Diaz J, Leon K, Mulet R. Characterizing steady states of genome-scale metabolic networks in continuous cell cultures. PLoS Comput Biol 2017; 13:e1005835. [PMID: 29131817 PMCID: PMC5703580 DOI: 10.1371/journal.pcbi.1005835] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/27/2017] [Accepted: 10/20/2017] [Indexed: 12/15/2022] Open
Abstract
In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced.
Collapse
Affiliation(s)
- Jorge Fernandez-de-Cossio-Diaz
- Systems Biology Department, Center of Molecular Immunlogy, Havana, Cuba
- Group of Complex Systems and Statistical Physics. Department of Theoretical Physics, Physics Faculty, University of Havana, Cuba
| | - Kalet Leon
- Systems Biology Department, Center of Molecular Immunlogy, Havana, Cuba
| | - Roberto Mulet
- Group of Complex Systems and Statistical Physics. Department of Theoretical Physics, Physics Faculty, University of Havana, Cuba
| |
Collapse
|
37
|
Solle D, Hitzmann B, Herwig C, Pereira Remelhe M, Ulonska S, Wuerth L, Prata A, Steckenreiter T. Between the Poles of Data-Driven and Mechanistic Modeling for Process Operation. CHEM-ING-TECH 2017. [DOI: 10.1002/cite.201600175] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Dörte Solle
- Leibniz University Hannover; Institute of Technical Chemistry; Callinstraße 5 30167 Hannover Germany
| | - Bernd Hitzmann
- University of Hohenheim; Institue of Food Science and Biotechnology; Department of Process Analytics and Cereal Science; Garbenstraße 23 70599 Stuttgart Germany
| | - Christoph Herwig
- TU Wien; Institute of Chemical Environmental and Biological Engineering; Christian Doppler Laboratory for Mechanistic and Physiological Methods for Improved Bioprocesses; Gumpendorfer Straße 1a 1060 Vienna Austria
| | | | - Sophia Ulonska
- TU Wien; Institute of Chemical, Environmental and Biological Engineering; Research Division Biochemical Engineering; Gumpendorfer Straße 1a 1060 Vienna Austria
| | - Lynn Wuerth
- Bayer AG; Kaiser-Wilhelm-Allee 51373 Leverkusen Germany
| | - Adrian Prata
- Bayer AG; Kaiser-Wilhelm-Allee 51373 Leverkusen Germany
| | | |
Collapse
|
38
|
Galvanauskas V, Grincas V, Simutis R, Kagawa Y, Kino-oka M. Current state and perspectives in modeling and control of human pluripotent stem cell expansion processes in stirred-tank bioreactors. Biotechnol Prog 2017; 33:355-364. [DOI: 10.1002/btpr.2431] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 12/10/2016] [Indexed: 01/02/2023]
Affiliation(s)
| | - Vykantas Grincas
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Rimvydas Simutis
- Department of Automation; Kaunas University of Technology; Kaunas Lithuania
| | - Yuki Kagawa
- Department of Biotechnology; Osaka University; Osaka Japan
| | | |
Collapse
|
39
|
Ben Yahia B, Gourevitch B, Malphettes L, Heinzle E. Segmented linear modeling of CHO fed-batch culture and its application to large scale production. Biotechnol Bioeng 2016; 114:785-797. [PMID: 27869296 PMCID: PMC5324675 DOI: 10.1002/bit.26214] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Revised: 08/16/2016] [Accepted: 11/02/2016] [Indexed: 12/21/2022]
Abstract
We describe a systematic approach to model CHO metabolism during biopharmaceutical production across a wide range of cell culture conditions. To this end, we applied the metabolic steady state concept. We analyzed and modeled the production rates of metabolites as a function of the specific growth rate. First, the total number of metabolic steady state phases and the location of the breakpoints were determined by recursive partitioning. For this, the smoothed derivative of the metabolic rates with respect to the growth rate were used followed by hierarchical clustering of the obtained partition. We then applied a piecewise regression to the metabolic rates with the previously determined number of phases. This allowed identifying the growth rates at which the cells underwent a metabolic shift. The resulting model with piecewise linear relationships between metabolic rates and the growth rate did well describe cellular metabolism in the fed‐batch cultures. Using the model structure and parameter values from a small‐scale cell culture (2 L) training dataset, it was possible to predict metabolic rates of new fed‐batch cultures just using the experimental specific growth rates. Such prediction was successful both at the laboratory scale with 2 L bioreactors but also at the production scale of 2000 L. This type of modeling provides a flexible framework to set a solid foundation for metabolic flux analysis and mechanistic type of modeling. Biotechnol. Bioeng. 2017;114: 785–797. © 2016 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Bassem Ben Yahia
- Biochemical Engineering InstituteSaarland UniversityCampus A1.5, D‐66123 SaarbrückenGermany
- Upstream Process Sciences Biotech SciencesUCB Pharma S.A.Avenue de l'Industrie, Braine l'Alleud B‐1420Belgium
| | - Boris Gourevitch
- Institut de NeuroScience Paris‐Saclay (NeuroPSI)UMR CNRS 9197
- Université Paris‐SudOrsay Cedex 91405France
| | - Laetitia Malphettes
- Upstream Process Sciences Biotech SciencesUCB Pharma S.A.Avenue de l'Industrie, Braine l'Alleud B‐1420Belgium
| | - Elmar Heinzle
- Biochemical Engineering InstituteSaarland UniversityCampus A1.5, D‐66123 SaarbrückenGermany
| |
Collapse
|
40
|
Richelle A, Gziri KM, Bogaerts P. A methodology for building a macroscopic FBA-based dynamical simulator of cell cultures through flux variability analysis. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2016.06.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|