Process Control, Intensification, and Digitalisation in Continuous Biomanufacturing

Ganapathy Subramanian, PhD, is a biotechnology consultant with more than 30 years of experience in industry and academia. His professional focus is on the application and development of processing and purification methodologies and chromatographic systems for large-scale use in environmental science.

• Preface xiiiPart I Continuous Biomanufacturing 11 Strategies for Continuous Processing in Microbial Systems 3Julian Kopp, Christoph Slouka, Frank Delvigne, and Christoph Herwig1.1 Introduction 31.1.1 Microbial Hosts and Their Applications in Biotechnology 31.1.2 Regulatory Demands for Their Applied Cultivation Mode 51.2 Overview of Applied Cultivation Methods in Industrial Biotechnology 61.2.1 Batch and Fed-Batch Cultivations 71.2.1.1 Conventional Approaches and Their Technical Limitations 71.2.1.2 Feeding and Control Strategies Using E. coli as a Model Organism 81.2.2 Introduction into Microbial Continuous Biomanufacturing (CBM) 91.2.2.1 General Considerations 91.2.2.2 Mass Balancing and the Macroscopic Effects in Chemostat Cultures 111.2.3 Microbial CBM vs. Mammalian CBM 131.2.3.1 Differences in Upstream of Microbial CBM Compared with Cell Culture 131.2.3.2 Downstream in Microbial CBM 141.3 Monitoring and Control Strategies to Enable CBM with Microbials 161.3.1 Subpopulation Monitoring and Possible PAT Tools Applicable for Microbial CBM 161.3.2 Modeling and Control Strategies to Enable CBM with Microbials 191.4 Chances and Drawbacks in Continuous Biomanufacturing with E. coli 211.4.1 Optimization of Plant Usage Using CBM with E. coli 211.4.2 Reasons Why CBM with E. coli Is Not State of the Art (Yet) 231.4.2.1 Formation of Subpopulation Following Genotypic Diversification 231.4.2.2 Formation of Subpopulation Following Phenotypic Diversification 251.4.2.3 Is Genomic Integration of the Target Protein an Enabler for CBM with E. coli? 261.4.3 Solutions to Overcome the Formation of Subpopulations and How to Realize CBM with E. coli in the Future 271.5 Conclusion and Outlook 29References 302 Control of Continuous Manufacturing Processes for Production of Monoclonal Antibodies 39Anurag S. Rathore, Garima Thakur, Saxena Nikita, and Shantanu Banerjee2.1 Introduction 392.2 Control of Upstream Mammalian Bioreactor for Continuous Production of mAbs 402.3 Integration Between Upstream and Downstream in Continuous Production of mAbs 462.3.1 Continuous Clarification as a Bridge Between Continuous Upstream and Downstream 462.3.2 Considerations for Process Integration 482.4 Control of Continuous Downstream Unit Operations in mAb Manufacturing 492.4.1 Control of Continuous Dead-End Filtration 492.4.2 Control of Continuous Chromatography 502.4.3 Control of Continuous Viral Inactivation 532.4.4 Control of Continuous Precipitation 542.4.5 Control of Continuous Formulation 562.5 Integration Between Adjacent Unit Operations Using Surge Tanks 572.6 Emerging Approaches for High-Level Monitoring and Control of Continuous Bioprocesses 592.6.1 Artificial Intelligence (AI) and Machine Learning (ML) Control 602.6.2 Statistical Process Control 612.6.3 Process Digitalization 622.7 Conclusions 63References 633 Artificial Intelligence and the Control of Continuous Manufacturing 75Steven S. Kuwahara3.1 Introduction 753.2 Continuous Monitoring and Validation 843.3 Choosing Other Control Charts 843.4 Information Awareness 853.5 Management and Personnel 86References 90Part II Intensified Biomanufacturing 934 Bioprocess Intensification: Technologies and Goals 95William G. Whitford4.1 Introduction 954.2 Bioprocess Intensification 984.2.1 Definition 984.2.2 New Directions 1004.2.3 Sustainability Synergy 1024.3 Intensification Techniques 1034.3.1 Enterprise Resource Management 1034.3.2 Synthetic Biology and Genetic Engineering 1044.3.3 New Expression Systems 1054.3.4 Bioprocess Optimization 1064.3.5 Bioprocess Simplification 1074.3.6 Continuous Bioprocessing 1084.4 Materials 1094.4.1 Media Optimization 1094.4.2 Variability 1104.5 Digital Biomanufacturing 1104.5.1 Data 1114.5.2 Bioprocess Control 1124.5.3 Digital Twins 1134.5.4 Artificial Intelligence 1144.5.5 Cloud/Edge Computing 1144.6 Bioprocess Modeling 1144.7 Automation and Autonomation 1154.8 Bioprocess Monitoring 1174.9 Improved Process and Product Development 1184.9.1 Design of Experiments 1184.9.2 QbD and PAT 1194.9.3 High-Throughput Systems 1194.9.4 Methods 1204.9.5 Commercialized Systems 1204.10 Advanced Process Control 1214.11 Bioreactor Design 1214.12 Single-Use Systems 1224.13 Facilities 1234.14 Conclusion 126Abbreviations and Acronyms 126Acknowledgment 129References 1295 Process Intensification Based on Disposable Solutions as First Step Toward Continuous Processing 137Stefan R. Schmidt5.1 Introduction 1375.1.1 Theory and Practice of Process Intensification 1375.1.2 Current Bioprocessing 1405.1.3 General Aspects of Disposables 1405.2 Technical Solutions 1415.2.1 Process Development 1415.2.2 Upstream Processing Unit Operations 1425.2.2.1 High-Density, Large-Volume Cell Banking in Bags 1435.2.2.2 Seed Train Intensification 1445.2.2.3 Cell Retention and Harvest 1455.2.3 Downstream Processing Unit Operations 1495.2.3.1 Depth Filtration 1495.2.3.2 In-line Virus Inactivation 1515.2.3.3 In-line Buffer Blending and Dilution 1525.2.3.4 Chromatography 1535.2.3.5 Tangential Flow Filtration 1595.2.3.6 Drug Substance Freezing 1615.3 Process Analytical Technology and Sensors 1625.3.1 Sensors for USP Applications 1635.3.2 Sensors for DSP Applications 1645.4 Conclusions 1655.4.1 Transition from Traditional to Intensified Processes 1655.4.2 Impact on Cost 1695.4.3 Influence on Time 170References 1716 Single-Use Continuous Manufacturing and Process Intensification for Production of Affordable Biological Drugs 179Ashish K. Joshi and Sanjeev K. Gupta6.1 Background 1796.2 State of Upstream and Downstream Processes 1806.2.1 Sizing Upstream Process 1816.2.2 Sizing Downstream Process 1826.2.3 Continuous Process Retrofit into the Existing Facility 1846.2.3.1 Upstream Process 1846.2.3.2 Downstream Process 1846.2.4 Learning from Chemical Industry 1856.3 Cell Line Development and Manufacturing Role 1866.3.1 Speeding Up Upstream and Downstream Development 1886.3.2 The State of Manufacturing 1896.4 Process Integration and Intensification 1906.4.1 Intensification of a Multiproduct Perfusion Platform 1906.4.2 Upstream Process Intensification Using Perfusion Process 1926.5 Process Intensification and Integration in Continuous Manufacturing 1926.6 Single-Use Manufacturing to Maximize Efficiency 1946.6.1 The Benefits of SUT in the New Era of Biomanufacturing 1956.6.2 Managing an SUT Cost Profile 1956.6.3 In-Line Conditioning (ILC) 1966.6.4 Impact of Single-Use Strategy on Manufacturing Cost of Goods 1976.6.5 Limitations of SUT 1986.7 Process Economy 1996.7.1 Biopharma Market Dynamics 2006.7.2 Management of the Key Risks of a Budding Market 2016.8 Future Perspective 202References 203Part III Digital Biomanufacturing 2097 Process Intensification and Industry 4.0: Mutually Enabling Trends 211Marc Bisschops and Loe Cameron7.1 Introduction 2117.2 Enabling Technologies for Process Intensification 2137.2.1 Process Intensification in Biomanufacturing 2137.2.2 Process Intensification in Cell Culture 2147.2.3 Process Intensification in Downstream Processing 2147.2.4 Process Integration: Manufacturing Platforms 2167.2.5 The Two Elephants in the (Clean) Room 2177.3 Digital Opportunities in Process Development 2207.4 Digital Opportunities in Manufacturing 2227.5 Digital Opportunities in Quality Assurance 2237.6 Considerations 2247.6.1 Challenges 2247.6.2 Gene Therapy 2267.7 Conclusions 227References 2278 Consistent Value Creation from Bioprocess Data with Customized Algorithms: Opportunities Beyond Multivariate Analysis 231Harini Narayanan, Moritz von Stosch, Martin F. Luna, M.N. Cruz Bournazou, Alessandro Buttè, and Michael Sokolov8.1 Motivation 2318.2 Modeling of Process Dynamics 2328.2.1 Hybrid Models 2348.2.2 Conclusion 2388.3 Predictive Models for Critical Quality Attributes 2388.3.1 Historical Product Quality Prediction 2388.3.2 Synergistic Prediction of Process and Product Quality 2428.4 Extrapolation and Process Optimization 2428.5 Bioprocess Monitoring Using Soft Sensors 2478.5.1 Static Soft Sensor 2488.5.2 Dynamic Soft Sensors 2508.5.3 Concluding Remarks 2518.6 Scale-Up and Scale-Down 2518.6.1 Differences Between Lab and Manufacturing Scales 2528.6.2 Scale-Up 2538.6.3 Scale-Down 2548.6.4 Conclusions 2558.7 Digitalization as an Enabler for Continuous Manufacturing 255References 2579 Digital Twins for Continuous Biologics Manufacturing 265Axel Schmidt, Steffen Zobel-Roos, Heribert Helgers, Lara Lohmann, Florian Vetter, Christoph Jensch, Alex Juckers, and Jochen Strube9.1 Introduction 2659.2 Digital Twins in Continuous Biomanufacturing 2699.2.1 USP Fed Batch and Perfusion 2739.2.2 Capture, LLE, Cell Separation, and Clarification 2739.2.2.1 Fluid Dynamics (Red) 2779.2.2.2 Phase Equilibrium (Blue) 2779.2.2.3 Kinetics (Green) 2779.2.3 UF/DF, SPTFF for Concentration, and Buffer Exchange 2789.2.4 Precipitation/Crystallization 2829.2.5 Chromatography and Membrane Adsorption 2829.2.5.1 General Rate Model Chromatography 2829.2.5.2 SEC 2849.2.5.3 Adsorption Mechanism 2849.2.5.4 IEX-SMA 2849.2.5.5 HIC-SMA 2859.2.5.6 Modified Mixed-Mode SMA 2859.2.5.7 Modified HIC-SMA Process Model Exemplification by mab Purification 2879.2.5.8 Model Parameter Determination 2899.2.5.9 Phase Equilibrium Isotherms 2909.2.5.10 Mass Transfer Kinetics 2929.2.6 Lyophilization 2939.2.6.1 Thermal Conductivity of the Vial 2939.2.6.2 Product Resistance 2939.2.6.3 Product Temperature 2959.2.6.4 Water Properties 2959.3 Process Integration and Demonstration 2959.3.1 USP Fed Batch and Perfusion 3019.3.2 Capture, LLE, Cell Separation, and Clarification 3069.3.3 UF/DF, SPTFF for Concentration, and Buffer Exchange 3099.3.4 Precipitation/Crystallization 3119.3.5 Chromatography and Membrane Adsorption 3149.3.6 Lyophilization 3149.3.7 Comparison Between Conceptual Process Design and Experimental Data 3199.4 PAT in Continuous Biomanufacturing 3209.4.1 State-of-the-Art PAT 3219.4.2 QbD-based PAT Control Strategy 3229.4.3 Process Simulation Toward APC-Based Autonomous Operation 3239.4.4 Applicability of Spectroscopic Methods in Continuous Biomanufacturing 3289.4.5 Proposed Control Strategy Including PAT 3329.4.6 Evaluation and Summary of PAT 3379.5 Conclusion 338Acknowledgments 339References 33910 Regulatory and Quality Considerations of Continuous Bioprocessing 351Britta Manser and Martin Glenz10.1 Introduction 35110.2 Integrated Processing 35210.3 Process Traceability 35310.3.1 Batch and Lot Definition 35310.3.2 Lot Traceability and Deviation Management 35410.4 Process Consistency 35510.4.1 Process Control 35610.4.1.1 Automation 35610.4.1.2 Process Analytical Technologies (PAT) 35710.4.1.3 Data Analysis 35910.4.1.4 Real-Time Release Testing 36010.4.2 Quality by Design 36010.4.2.1 Multicolumn Protein A Chromatography 36110.4.2.2 Continuous Virus Inactivation 36210.4.2.3 Bind/Elute Cation Exchange Chromatography 36210.4.2.4 Flow-Through Anion Exchange Chromatography 36310.4.2.5 Ultrafiltration and Diafiltration 36310.4.2.6 Sterile Filtration 36310.4.2.7 Virus Reduction Filtration 36310.4.2.8 Connection of Unit Operations 36410.5 Patient Safety 36510.5.1 Contamination Control 36510.5.2 Virus Safety 36610.5.2.1 Virus Reduction in Chromatography 36710.5.2.2 Low-pH Virus Inactivation 36710.5.2.3 Virus Reduction Filtration 36810.6 Equipment Design 36910.7 Conclusion 370References 371Index 377