Process Analytical Technology for Advanced Process Control in Biologics Manufacturing with the Aid of Macroscopic Kinetic Modeling

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.

[1]  J. Strube,et al.  Integration of Upstream and Downstream in Continuous Biomanufacturing , 2017 .

[2]  Michael W. Glacken,et al.  Catabolic Control of Mammalian Cell Culture , 1988, Bio/Technology.

[3]  Jochen Strube,et al.  Process Analytical Approach towards Quality Controlled Process Automation for the Downstream of Protein Mixtures by Inline Concentration Measurements Based on Ultraviolet/Visible Light (UV/VIS) Spectral Analysis , 2017, Antibodies.

[4]  Brandon Berry,et al.  Cross‐scale predictive modeling of CHO cell culture growth and metabolites using Raman spectroscopy and multivariate analysis , 2015, Biotechnology progress.

[5]  Jochen Strube,et al.  Design and Operation of Continuous Countercurrent Chromatography in Biotechnological Production , 2014 .

[6]  Volker C. Hass,et al.  Advanced Process and Control Strategies for Bioreactors , 2017 .

[7]  Cleo Kontoravdi,et al.  Towards the implementation of quality by design to the production of therapeutic monoclonal antibodies with desired glycosylation patterns , 2010, Biotechnology progress.

[8]  C. Goudar Computer programs for modeling mammalian cell batch and fed-batch cultures using logistic equations , 2012, Cytotechnology.

[9]  Qun Ma,et al.  Optimization of Parameter Selection for Partial Least Squares Model Development , 2015, Scientific Reports.

[10]  Ana P. Teixeira,et al.  Advances in on-line monitoring and control of mammalian cell cultures: Supporting the PAT initiative. , 2009, Biotechnology advances.

[11]  Wei-Shou Hu,et al.  Cell culture technology for pharmaceutical and cell-based therapies , 2005 .

[12]  Christoph Herwig,et al.  Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness , 2016, Bioengineering.

[13]  Elmar Heinzle,et al.  Segmented linear modeling of CHO fed‐batch culture and its application to large scale production , 2016, Biotechnology and bioengineering.

[14]  Dong-Yup Lee,et al.  Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures , 2017, Biotechnology and bioengineering.

[15]  S. P. Asprey,et al.  Modelling of Mammalian Cells and Cell Culture Processes , 2004, Cytotechnology.

[16]  N D Lourenço,et al.  Bioreactor monitoring with spectroscopy and chemometrics: a review , 2012, Analytical and Bioanalytical Chemistry.

[17]  Carl-Fredrik Mandenius,et al.  Soft sensors in bioprocessing: a status report and recommendations. , 2012, Biotechnology journal.

[18]  E. Heinzle,et al.  Macroscopic modeling of mammalian cell growth and metabolism , 2015, Applied Microbiology and Biotechnology.

[19]  Zizhuo Xing,et al.  Modeling kinetics of a large‐scale fed‐batch CHO cell culture by Markov chain Monte Carlo method , 2010, Biotechnology progress.

[20]  Christoph Herwig,et al.  Soft sensor for monitoring biomass subpopulations in mammalian cell culture processes , 2017, Biotechnology Letters.

[21]  S. Yoon,et al.  Substitution of glutamine by glutamate enhances production and galactosylation of recombinant IgG in Chinese hamster ovary cells , 2010, Applied Microbiology and Biotechnology.

[22]  Hervé Broly,et al.  Lactate metabolism shift in CHO cell culture: the role of mitochondrial oxidative activity. , 2013, New biotechnology.

[23]  Alan G. Ryder,et al.  Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review , 2017, Applied spectroscopy.

[24]  Helena Lidén,et al.  Sensor Systems for Bioprocess Monitoring , 1998 .

[25]  Klaus Joeris,et al.  Logistic Equations Effectively Model Mammalian Cell Batch and Fed‐Batch Kinetics by Logically Constraining the Fit , 2005, Biotechnology progress.

[26]  N. Vijayasankaran,et al.  Decreasing lactate level and increasing antibody production in Chinese Hamster Ovary cells (CHO) by reducing the expression of lactate dehydrogenase and pyruvate dehydrogenase kinases. , 2011, Journal of biotechnology.

[27]  Robust parameter estimation during logistic modeling of batch and fed‐batch culture kinetics , 2009, Biotechnology progress.

[28]  Jochen Strube,et al.  Challenges in biotechnology production—generic processes and process optimization for monoclonal antibodies , 2005 .

[29]  Seongkyu Yoon,et al.  In‐line monitoring of amino acids in mammalian cell cultures using raman spectroscopy and multivariate chemometrics models , 2018, Engineering in life sciences.

[30]  C. Herwig,et al.  Quantification of cell lysis during CHO bioprocesses: Impact on cell count, growth kinetics and productivity. , 2015, Journal of biotechnology.

[31]  R. Curi,et al.  Glutamine and glutamate—their central role in cell metabolism and function , 2003, Cell biochemistry and function.

[32]  Dirk C Hinz,et al.  Process analytical technologies in the pharmaceutical industry: the FDA’s PAT initiative , 2006, Analytical and bioanalytical chemistry.

[33]  Ganapathy Subramanian Continuous Biomanufacturing - Innovative Technologies and Methods: Innovative Technologies and Methods , 2017 .

[34]  David E. Ruckerbauer,et al.  What can mathematical modelling say about CHO metabolism and protein glycosylation? , 2017, Computational and structural biotechnology journal.

[35]  Brian McNeil,et al.  In‐situ near infrared spectroscopy to monitor key analytes in mammalian cell cultivation , 2003, Biotechnology and bioengineering.

[36]  Jochen Strube,et al.  Host Cell Proteins in Biologics Manufacturing: The Good, the Bad, and the Ugly , 2017, Antibodies.

[37]  Matthias Otto,et al.  Chemometrics: Statistics and Computer Application in Analytical Chemistry , 1999 .

[38]  Bernd Hitzmann,et al.  Fluorescence Spectroscopy and Chemometric Modeling for Bioprocess Monitoring , 2015, Sensors.

[39]  Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development , Manufacturing , and Quality Assurance , 2004 .

[40]  Urs von Stockar,et al.  Real-time in situ monitoring of freely suspended and immobilized cell cultures based on mid-infrared spectroscopic measurements. , 2002, Biotechnology and bioengineering.

[41]  John P. Barford,et al.  An unstructured kinetic model of macromolecular metabolism in batch and fed-batch cultures of hybridoma cells producing monoclonal antibody , 2000 .

[42]  Brian Glennon,et al.  Process model comparison and transferability across bioreactor scales and modes of operation for a mammalian cell bioprocess , 2013, Biotechnology progress.

[43]  Ian R. Lewis,et al.  Raman spectroscopy as a process analytical technology for pharmaceutical manufacturing and bioprocessing , 2016, Analytical and Bioanalytical Chemistry.

[44]  Philipp Christen,et al.  Biochemie und Molekularbiologie. Eine Einführung in 40 Lerneinheiten , 2016 .

[45]  Zizhuo Xing,et al.  Identifying Inhibitory Threshold Values of Repressing Metabolites in CHO Cell Culture Using Multivariate Analysis Methods , 2008, Biotechnology progress.

[46]  Bernd Hitzmann,et al.  Present Status of Automation for Industrial Bioprocesses , 2017 .

[47]  Nadja Alt,et al.  Determination of critical quality attributes for monoclonal antibodies using quality by design principles. , 2016, Biologicals : journal of the International Association of Biological Standardization.

[48]  Christoph Clemens,et al.  CHO gene expression profiling in biopharmaceutical process analysis and design. , 2010, Biotechnology and bioengineering.

[49]  T. Scheper,et al.  Spectroscopic methods and their applicability for high‐throughput characterization of mammalian cell cultures in automated cell culture systems , 2016 .

[50]  Frederik Rudolph,et al.  Process parameters impacting product quality , 2015, BMC Proceedings.

[51]  C. Clemens,et al.  Advancing biopharmaceutical process development by system-level data analysis and integration of omics data. , 2012, Advances in biochemical engineering/biotechnology.

[52]  F. Wurm Production of recombinant protein therapeutics in cultivated mammalian cells , 2004, Nature Biotechnology.

[53]  C. Goochee,et al.  The effect of ammonia on the O‐linked glycosylation of granulocyte colony‐stimulating factor produced by chinese hamster ovary cells , 1995, Biotechnology and bioengineering.

[54]  Jochen Strube,et al.  Trends in Upstream and Downstream Process Development for Antibody Manufacturing. , 2014, Bioengineering.

[55]  B O Palsson,et al.  Effects of ammonia and lactate on hybridoma growth, metabolism, and antibody production , 1992, Biotechnology and bioengineering.