Cell Culture Engineering

Gyun Min Lee, PhD, is Professor at the Department of Biological Sciences at KAIST, South Korea, and heads the Animal Cell Engineering Laboratory. He is also Scientific Director at the Novo Nordisk Foundation Center for Biosustainability at the Technical University of Denmark. Helene Faustrup Kildegaard, PhD, is a senior researcher and Co-PI for the CHO Cell Line Engineering and Design section at the Novo Nordisk Foundation Center for Biosustainability at the Technical University of Denmark (DTU).

• About the Series Editors xvii1 Platform Technology for Therapeutic Protein Production 1Tae Kwang Ha, Jae Seong Lee, and Gyun Min Lee1.1 Introduction 11.2 Overall Trend Analysis 31.2.1 Mammalian Cell Lines 31.2.2 Brief Introduction of Advances and Techniques 51.3 General Guidelines for Recombinant Cell Line Development 61.3.1 Host Selection 61.3.2 Expression Vector 71.3.3 Transfection/Selection 71.3.4 Clone Selection 81.3.4.1 Primary Parameters During Clone Selection 81.3.4.2 Clone Screening Technologies 91.4 Process Development 91.4.1 Media Development 101.4.2 Culture Environment 101.4.3 Culture Mode (Operation) 101.4.4 Scale-up and Single-Use Bioreactor 111.4.5 Quality Analysis 121.5 Downstream Process Development 121.5.1 Purification 121.5.2 Quality by Design (QbD) 131.6 Trends in Platform Technology Development 141.6.1 Rational Strategies for Cell Line and Process Development 141.6.2 Hybrid Culture Mode and Continuous System 151.6.3 Recombinant Human Cell Line Development for Therapeutic Protein Production 161.7 Conclusion 17Acknowledgment 17Conflict of Interest 17References 172 Cell Line Development for Therapeutic Protein Production 23Soo Min Noh, Seunghyeon Shin, and Gyun Min Lee2.1 Introduction 232.2 Mammalian Host Cell Lines for Therapeutic Protein Production 252.2.1 CHO Cell Lines 252.2.2 Human Cell Lines 262.2.3 Other Mammalian Cell Lines 272.3 Development of Recombinant CHO Cell Lines 272.3.1 Expression Systems for CHO Cells 282.3.2 Cell Line Development Process Using CHO Cells Based on Random Integration 282.3.2.1 Vector Construction 292.3.2.2 Transfection and Selection 302.3.2.3 Gene Amplification 302.3.2.4 Clone Selection 312.3.3 Cell Line Development Process Using CHO Cells Based on Site-Specific Integration 322.4 Development of Recombinant Human Cell Lines 342.4.1 Necessity for Human Cell Lines 342.4.2 Stable Cell Line Development Process Using Human Cell Lines 352.5 Important Consideration for Cell Line Development 362.5.1 Clonality 362.5.2 Stability 362.5.3 Quality of Therapeutic Proteins 372.6 Conclusion 38References 383 Transient Gene Expression-Based Protein Production in Recombinant Mammalian Cells 49Joo-Hyoung Lee, Henning G. Hansen, Sun-Hye Park, Jong-Ho Park, and Yeon-Gu Kim3.1 Introduction 493.2 Gene Delivery: Transient Transfection Methods 503.2.1 Calcium Phosphate-Based Transient Transfection 503.2.2 Electroporation 513.2.3 Polyethylenimine-Based Transient Transfection 523.2.4 Liposome-Based Transient Transfection 523.3 Expression Vectors 533.3.1 Expression Vector Composition and Preparation 533.3.2 Episomal Replication 533.3.3 Coexpression Strategies 543.4 Mammalian Cell Lines 543.4.1 HEK293 Cell-Based TGE Platforms 553.4.2 CHO Cell-Based TGE Platforms 563.4.3 TGE Platforms Using Other Cell Lines 583.5 Cell Culture Strategies 583.5.1 Culture Media for TGE 583.5.2 Optimization of Cell Culture Processes for TGE 593.5.3 qp-Enhancing Factors in TGE-Based Culture Processes 593.5.4 Culture Longevity-Enhancing Factors in TGE-Based Culture Processes 593.6 Large-Scale TGE-Based Protein Production 603.7 Concluding Remarks 62References 624 Enhancing Product and Bioprocess Attributes Using Genome-Scale Models of CHO Metabolism 73Shangzhong Li, Anne Richelle, and Nathan E. Lewis4.1 Introduction 734.1.1 Cell Line Optimization 734.1.2 CHO Genome 754.1.2.1 Development of Genomic Resources of CHO 754.1.2.2 Development of Transcriptomics and Proteomics Resources of CHO 754.2 Genome-Scale Metabolic Model 764.2.1 What Is a Genome-Scale Metabolic Model 764.2.2 Reconstruction of GEMs 774.2.2.1 Knowledge-Based Construction 774.2.2.2 Draft Reconstruction 774.2.2.3 Curation of the Reconstruction 774.2.2.4 Conversion to a Computational Format 794.2.2.5 Model Validation and Evaluation 794.3 GEM Application 804.3.1 Common Usage and Prediction Capacities of Genome-Scale Models 824.3.2 GEMs as a Platform for Omics Data Integration, Linking Genotype to Phenotype 834.3.3 Predicting Nutrient Consumption and Controlling Phenotype 844.3.4 Enhancing Protein Production and Bioprocesses 854.3.5 Case Studies 864.4 Conclusion 86Acknowledgments 88References 885 Genome Variation, the Epigenome and Cellular Phenotypes 97Martina Baumann, Gerald Klanert, Sabine Vcelar,Marcus Weinguny, Nicolas Marx, and Nicole Borth5.1 Phenotypic Instability in the Context of Mammalian Production Cell Lines 975.2 Genomic Instability 995.3 Epigenetics 1015.3.1 DNA Methylation 1025.3.2 Histone Modifications 1025.3.3 Downstream Effectors 1045.3.4 Noncoding RNAs 1045.4 Control of CHO Cell Phenotype by the Epigenome 1055.5 Manipulating the Epigenome 1075.5.1 Global Epigenetic Modification 1075.5.1.1 Manipulating Global DNA Methylation 1075.5.1.2 Manipulating Global Histone Acetylation 1085.5.2 Targeted Epigenetic Modification 1095.5.2.1 Targeted Histone Modification 1105.5.2.2 Targeted DNA Methylation 1125.6 Conclusion and Outlook 113References 1146 Adaption of Generic Metabolic Models to Specific Cell Lines for Improved Modeling of Biopharmaceutical Production and Prediction of Processes 127Calmels Cyrielle, Chintan Joshi, Nathan E. Lewis, Malphettes Laetitia, and Mikael R. Andersen6.1 Introduction 1276.1.1 Constraint-Based Models 1276.1.2 Limitations of Flux Balance Analysis 1316.1.2.1 Thermodynamically Infeasible Cycles 1316.1.2.2 Genetic Regulation 1316.1.2.3 Limitation of Intracellular Space 1326.1.2.4 Multiple States in the Solution 1326.1.2.5 Biological Objective Function 1336.1.2.6 Kinetics and Metabolite Concentrations 1336.2 Main Source of Optimization Issues with Large Genome-Scale Models: Thermodynamically Infeasible Cycles 1346.2.1 Definition of Thermodynamically Infeasible Fluxes 1346.2.2 Loops Involving External Exchange Reactions 1346.2.2.1 Reversible Passive Transporters from Major Facilitator Superfamily (MFS) 1356.2.2.2 Reversible Passive Antiporters from Amino Acid-Polyamine-organoCation (APC) Superfamily 1366.2.2.3 Na+-linked Transporters 1366.2.2.4 Transport via Proton Symport 1376.2.3 Tools to Identify Thermodynamically Infeasible Cycles 1386.2.3.1 Visualizing Fluxes on a Network Map 1386.2.3.2 Algorithms Developed 1386.2.4 Methods Available to Remove Thermodynamically Infeasible Cycles 1396.2.4.1 Manual Curation 1396.2.4.2 Software and Algorithms Developed for the Removal of Thermodynamically Infeasible Loops from Flux Distributions 1406.3 Consideration of Additional Biological Cellular Constraints 1446.3.1 Genetic Regulation 1446.3.1.1 Advantages of Considering Gene Regulation in Genome-Scale Modeling 1446.3.1.2 Methods Developed to Take into Account a Feedback of FBA on the Regulatory Network 1456.3.2 Context Specificity 1466.3.2.1 What Are Context-Specific Models (CSMs)? 1466.3.2.2 Methods and Algorithms Developed to Reconstruct Context-Specific Models (CSMs) 1466.3.2.3 Performance of CSMs 1486.3.2.4 Cautions About CSMs 1496.3.3 Molecular Crowding 1506.3.3.1 Consequences on the Predictions 1506.3.3.2 Methods Developed to Account for a Total Enzymatic Capacity into the FBA Framework 1516.4 Conclusion 152References 1537 Toward Integrated Multi-omics Analysis for Improving CHO Cell Bioprocessing 163Kok Siong Ang, Jongkwang Hong, Meiyappan Lakshmanan, and Dong-Yup Lee7.1 Introduction 1637.2 High-Throughput Omics Technologies 1657.2.1 Sequencing-Based Omics Technologies 1657.2.1.1 Historical Developments of Nucleotide Sequencing Techniques 1657.2.1.2 Genome Sequencing of CHO Cells 1667.2.1.3 Transcriptomics of CHO Cells 1677.2.1.4 Epigenomics of CHO Cells 1687.2.2 Mass Spectrometry-Based Omics Technologies 1687.2.2.1 Mass Spectrometry Techniques 1687.2.2.2 Proteomics of CHO Cells 1707.2.2.3 Metabolomics/Lipidomics of CHO Cells 1717.2.2.4 Glycomics of CHO Cells 1727.3 Current CHO Multi-omics Applications 1727.3.1 Bioprocess Optimization 1747.3.2 Cell Line Characterization 1747.3.3 Engineering Target Identification 1767.4 Future Prospects 177References 1788 CRISPR Toolbox for Mammalian Cell Engineering 185Daria Sergeeva, Karen Julie la Cour Karottki, Jae Seong Lee, and Helene Faustrup Kildegaard8.1 Introduction 1858.2 Mechanism of CRISPR/Cas9 Genome Editing 1868.3 Variants of CRISPR-RNA-guided Endonucleases 1878.3.1 Diversity of CRISPR/Cas Systems 1878.3.2 Engineered Cas9 Variants 1888.4 Experimental Design for CRISPR-mediated Genome Editing 1888.4.1 Target Site Selection and Design of gRNAs 1898.4.2 Delivery of CRISPR/Cas9 Components 1918.5 Development of CRISPR/Cas9 Tools 1928.5.1 CRISPR/Cas9-mediated Gene Editing 1928.5.1.1 Gene Knockout 1928.5.1.2 Site-Specific Gene Integration 1948.5.2 CRISPR/Cas9-mediated Genome Modification 1958.5.2.1 Transcriptional Regulation 1958.5.2.2 Epigenetic Modification 1968.5.3 RNA Targeting 1968.6 Genome-Scale CRISPR Screening 1978.7 Applications of CRISPR/Cas9 for CHO Cell Engineering 1978.8 Conclusion 199Acknowledgment 200References 2009 CHO Cell Engineering for Improved Process Performance and Product Quality 207Simon Fischer and Kerstin Otte9.1 CHO Cell Engineering 2079.2 Methods in Cell Line Engineering 2089.2.1 Overexpression of Engineering Genes 2089.2.2 Gene Knockout 2099.2.3 Noncoding RNA-mediated Gene Silencing 2099.3 Applications of Cell Line Engineering Approaches in CHO Cells 2119.3.1 Enhancing Recombinant Protein Production 2119.3.2 Repression of Cell Death and Acceleration of Growth 2219.3.3 Modulation of Posttranslational Modifications to Improve Protein Quality 2279.4 Conclusions 233References 23410 Metabolite Profiling of Mammalian Cells 251Claire E. Gaffney, Alan J. Dickson, and Mark Elvin10.1 Value of Metabolic Data for the Enhancement of Recombinant Protein Production 25110.2 Technologies Used in the Generation of Metabolic Data Sets 25210.2.1 Targeted and Untargeted Metabolic Analysis 25310.2.2 Analytical Technologies Used in the Generation of Metabolite Profiles 25310.2.2.1 Nuclear Magnetic Resonance 25410.2.2.2 Mass Spectrometry 25510.2.3 Metabolite Sample Preparation 25610.2.3.1 Extracellular Sample Preparation 25710.2.3.2 Quenching of Intracellular Metabolite Samples 25710.2.3.3 Metabolite Extraction from Quenched Cells 25710.2.3.4 Metabolic Flux Analysis 25710.3 Approaches for Metabolic Data Analysis 25710.3.1 Data Processing 25810.3.2 Data Analysis 25810.3.3 Data Interpretation and Integration 26010.4 Implementation of Metabolic Data in Bioprocessing 26110.4.1 Relationship Between Growth Phase and Metabolism 26110.4.2 Identification of Metabolic Indicators Associated with High Cell-Specific Productivity 26310.4.3 Utilizing Metabolic Data to Improve Biomass and Recombinant Protein Yield 26310.4.4 Utilizing Metabolic Understanding to Improve Product Quality 26510.4.5 Cell Line Engineering to Redirect Metabolic Pathways 26510.5 Future Perspectives 266Acknowledgments 267References 26711 Current Considerations and Future Advances in Chemically Defined Medium Development for the Production of Protein Therapeutics in CHO Cells 279Wai Lam W. Ling11.1 Introduction 27911.2 Traditional Approach to Medium Development 27911.2.1 Cell Line Selection 27911.2.2 Design and Optimization 28011.2.3 Process Consideration 28211.2.4 Additional Considerations in Medium Development 28411.3 Future Perspectives for Medium Development 28411.3.1 Systems Biology and Synthetic Biology 284Acknowledgment 288Conflict of Interest 288References 28812 Host Cell Proteins During Biomanufacturing 295Jong Youn Baik, Jing Guo, and Kelvin H. Lee12.1 Introduction 29512.2 Removal of HCP Impurities 29512.2.1 Antibody Product 29612.2.2 Non-antibody Protein Product 29712.2.3 Difficult-to-Remove HCPs 29812.3 Impacts of Residual HCPs 29812.3.1 Drug Efficacy, Quality, and Shelf Life 29812.3.2 Immunogenicity 29912.3.3 Biological Activity 29912.4 HCP Detection and Monitoring Methods 30012.4.1 Anti-HCP Antiserum and Enzyme-Linked Immunosorbent Assay (ELISA) 30012.4.2 Proteomics Approaches as Orthogonal Methods 30212.5 Efforts for HCP Control 30212.5.1 Upstream Efforts 30312.5.2 Downstream Efforts 30412.5.3 HCP Risk Assessment in CHO Cells 30512.6 Future Directions 305Acknowledgments 306References 30613 Mammalian Fed-batch Cell Culture for Biopharmaceuticals 313William C. Yang13.1 Introduction 31313.2 Objectives of Cell Culture Process Development 31413.2.1 Yield and Product Quality 31413.2.2 Glycosylation 31413.2.3 Charge Heterogeneity 31513.2.4 Aggregation 31613.3 Cells and Cell Culture Formats 31613.3.1 Adherent Cells 31613.3.2 Suspended Cells 31613.3.3 Batch Cultures 31713.4 Fed-batch Cultures 31713.5 Cell Culture Media 31913.5.1 Basal Media 31913.5.2 Feed Media 32013.6 Feeding Strategies 32113.6.1 Metabolite Based 32113.6.2 Respiration Based 32313.7 Feed Media Design 32313.8 Process Variable Design 32513.8.1 Temperature 32513.8.2 pH and pCO2 32513.8.3 Dissolved Oxygen 32613.8.4 Culture Duration 32713.9 Cell Culture Supplements 32713.9.1 Yield 32813.9.2 Glycosylation 32813.10 New and Emerging Technologies 32913.10.1 Analytical Technologies 32913.10.2 Bioreactor Technologies 33113.11 Future Directions 332References 33314 Continuous Biomanufacturing 347Sadettin S. Ozturk14.1 Introduction 34714.2 Continuous Upstream (Cell Culture) Processes 34714.2.1 Continuous Culture without Cell Retention (Chemostat) 34814.2.2 Continuous Culture with Cell Retention (Perfusion) 34814.2.2.1 Cell Retention by Immobilization or Entrapment 34914.2.2.2 Cell Retention by Cell Retention Device 35014.2.3 Semicontinuous Culture 35114.3 Advantages of Continuous Perfusion 35114.3.1 Higher Volumetric Productivities 35114.3.2 Better Utilization of Biomanufacturing Facilities 35214.3.3 Better Product Quality and Consistency 35214.3.4 Scale-up and Commercial Production 35314.4 Cell Retention Systems for Continuous Perfusion 35414.4.1 Cell Retention Devices 35414.4.1.1 Filtration-Based Devices 35414.4.1.2 Spin Filters 35514.4.1.3 Continuous Centrifugation 35614.4.1.4 Settler 35614.4.1.5 BioSep Device 35714.4.1.6 Hydrocyclones 35814.5 Operation and Control of Continuous Perfusion Bioreactors 35814.5.1 Feed and Harvest Flow and Volume Control 35814.5.2 Circulation or Return Pump 35914.5.3 Control of Perfusion Rate and Cell Density 35914.5.3.1 Cell Build-up Phase 35914.5.3.2 Production Phase 36014.5.3.3 Cell Bleed or Purge 36014.6 Current Status of Continuous Perfusion 36014.7 Conclusions 362Acknowledgment 362References 36315 Process Analytical Technology and Quality by Design for Animal Cell Culture 365Hae-Woo Lee, Hemlata Bhatia, Seo-Young Park, Mark-Henry Kamga, Thomas Reimonn, Sha Sha, Zhuangrong Huang, Shaun Galbraith, Huolong Liu, and Seongkyu Yoon15.1 PAT and QbD – US FDA’s Regulatory Initiatives 36515.2 PAT and QbD – Challenges 36515.3 PAT and QbD Implementations 36615.3.1 NIR Spectroscopy 36615.3.2 Mid-Infrared (MIR) Spectroscopy 36715.3.3 Raman Spectroscopy 36715.3.4 Fluorescence Spectroscopy 36815.3.5 Chromatographic Techniques 36815.3.6 Other Useful Techniques 36915.3.7 Data Analysis and Modeling Tools 36915.4 Case Studies 37015.4.1 Estimation of Raw Material Performance in Mammalian Cell Culture Using Near-Infrared Spectra Combined with Chemometrics Approaches 37015.4.2 Design Space Exploration for Control of Critical Quality Attributes of mAb 37215.4.3 Quantification of Protein Mixture in Chromatographic Separation Using Multiwavelength UV Spectra 37215.4.4 Characterization of Mammalian Cell Culture Raw Materials by Combining Spectroscopy and Chemometrics 37415.4.5 Effect of Amino Acid Supplementation on Titer and Glycosylation Distribution in Hybridoma Cell Cultures 37515.4.6 Metabolic Responses and Pathway Changes of Mammalian Cells Under Different Culture Conditions with Media Supplementations 37715.4.7 Estimation and Control of N-Linked Glycoform Profiles of Monoclonal Antibody with Extracellular Metabolites and Two-Step Intracellular Models 37815.4.8 Quantitative Intracellular Flux Modeling and Applications in Biotherapeutic Development and Production Using CHO Cell Cultures 38115.5 Conclusion 383References 38316 Development and Qualification of a Cell Culture Scale-Down Model 391Sarwat Khattak and Valerie Pferdeort16.1 Purpose of the Scale-Down Model 39116.1.1 Development Challenges 39116.2 Types of Scale-Down Models 39216.2.1 Power/Volume (P/V) and Air velocity 39216.2.2 Oxygen Transfer Coefficient (kLa) 39216.2.3 Gas Entrance Velocity (GEV) 39316.2.4 Oxygen Transfer Rate (OTR) 39316.2.5 Model Refinement Workflow 39516.3 Evaluation of a Scale-Down Model 39516.3.1 Univariate Analysis 39516.3.2 Multivariate Analysis 39616.3.2.1 Statistical Background 39616.3.2.2 Qualification Data Set 39616.3.2.3 Observation Level Analysis 39716.3.2.4 Batch-Level Analysis 39716.3.2.5 Scores Contribution Plots 39816.3.3 Equivalence Testing 39916.3.3.1 Statistical Background 39916.3.3.2 Considerations for Evaluation and Test Data Sets 39916.3.3.3 Types of Analysis Outcomes 40016.4 Conclusions and Perspectives 401References 402Index 407