Preparative Chromatography for Separation of Proteins

ARNE STABY is a Fellow and Senior Principal Scientist at Novo Nordisk A/S, Denmark, and the author of numerous papers and presentations in the field.ANURAG S. RATHORE is a Professor in the Department of Chemical Engineering at the Indian Institute of Technology, New Delhi, India. He has published several books that include Quality by Design for Biopharmaceuticals: Principles and Case Studies (Wiley, 2009). SATINDER AHUJA is President of Ahuja Consulting, USA, and the author/editor of numerous books including Chiral Separation Methods for Pharmaceutical and Biotechnological Products (Wiley, 2010), Trace and Ultratrace Analysis by HPLC (Wiley, 1992), and Selectivity and Detectability Optimizations in HPLC (Wiley, 1989).

• List of Contributors xvSeries Preface xixPreface xxi1 Model-Based Preparative Chromatography Process Development in the QbD Paradigm 1Arne Staby, Satinder Ahuja, and Anurag S. Rathore1.1 Motivation 11.2 Regulatory Context of Preparative Chromatography and Process Understanding 11.3 Application of Mathematical Modeling to Preparative Chromatography 6Acknowledgements 8References 82 Adsorption Isotherms: Fundamentals and Modeling Aspects 11Jørgen M. Mollerup2.1 Introduction 112.2 Definitions 122.3 The Solute Velocity Model 142.4 Introduction to the Theory of Equilibrium 172.5 Association Equilibria 212.6 The Classical Adsorption Isotherm 242.7 The Classical Ion Exchange Adsorption Isotherm 262.8 Hydrophobic Adsorbents, HIC and RPC 382.9 Protein–Protein Association and Adsorption Isotherms 472.10 The Adsorption Isotherm of a GLP-1 Analogue 512.11 Concluding Remarks 59Appendix 2.A Classical Thermodynamics 60References 773 Simulation of Process Chromatography 81Bernt Nilsson and Niklas Andersson3.1 Introduction 813.2 Simulation-Based Prediction of Chromatographic Processes 823.3 Numerical Methods for Chromatography Simulation 943.4 Simulation-Based Model Calibration and Parameter Estimation 963.5 Simulation-Based Parametric Analysis of Chromatography 973.6 Simulation-Based Optimization of Process Chromatography 1013.7 Summary 106Acknowledgement 107References 1084 Simplified Methods Based on Mechanistic Models for Understanding and Designing Chromatography Processes for Proteins and Other Biological Products 111Noriko Yoshimoto and Shuichi Yamamoto4.1 Introduction 1114.2 HETP and Related Variables in Isocratic Elution 1144.3 Linear Gradient Elution (LGE) 1204.4 Applications of the Model 1304.5 Summary 145Appendix 4.A Mechanistic Models for Chromatography 149Appendix 4.B Distribution Coefficient and Binding Sites [20- 149References 1525 Development of Continuous Capture Steps in Bioprocess Applications 159Frank Riske and Tom Ransohoff5.1 Introduction 1595.2 Economic Rationale for Continuous Processing 1605.3 Developing a Continuous Capture Step 1625.4 The Operation of MCC Systems 1655.5 Modeling MCC Operation 1675.6 Processing Bioreactor Feeds on a Capture MCC 1695.7 The Future of MCC 171References 1726 Computational Modeling in Bioprocess Development 177Francis Insaidoo, Suvrajit Banerjee, David Roush, and Steven Cramer6.1 Linkage of Chromatographic Thermodynamics (Affinity, Kinetics, and Capacity) 1776.2 Binding Maps and Coarse-Grained Modeling 1806.3 QSPR for Either Classification or Quantification Prediction 1886.4 All Atoms MD Simulations for Free Solution Studies and Surfaces 1926.5 Ensemble Average and Comparison of Binding of Different Proteins in Chromatographic Systems 2046.6 Antibody Homology Modeling and Bioprocess Development 2056.7 Summary of Gaps and Future State 209Acknowledgment 212References 2127 Chromatographic Scale-Up on a Volume Basis 227Ernst B. Hansen7.1 Introduction 2277.2 Theoretical Background 2297.3 Proof of Concept Examples 2327.4 Design Applications: How to Scale up from Development Data 2337.5 Discussion 2407.6 Recommendations 242References 2458 Scaling Up Industrial Protein Chromatography: Where Modeling Can Help 247Chris Antoniou, Justin McCue, Venkatesh Natarajan, Jörg Thömmes, and Qing Sarah Yuan8.1 Introduction 2478.2 Packing Quality: Why and How to Ensure Column Packing Quality Across Scales 2488.3 Process Equipment: Using CFD to Describe Effects of Equipment Design on Column Performance 2578.4 Long-Term Column Operation at Scale: Impact of Resin Lot-to-Lot Variability 2648.5 Closing Remarks 265References 2659 High-Throughput Process Development 269Silvia M. Pirrung and Marcel Ottens9.1 Introduction to High-Throughput Process Development in Chromatography 2699.2 Process Development Approaches 2719.3 Case Descriptions 2799.4 Future Directions 286References 28610 High-Throughput Column Chromatography Performed on Liquid Handling Stations 293Patrick Diederich and Jürgen Hubbuch10.1 Introduction 29310.2 Chromatographic Methods 29910.3 Results and Discussion 30010.4 Summary and Conclusion 328Acknowledgements 329References 33011 Lab-Scale Development of Chromatography Processes 333Hong Li, Jennifer Pollard, and Nihal Tugcu11.1 Introduction 33311.2 Methodology and Proposed Workflow 33611.3 Conclusions 377Acknowledgments 377References 37712 Problem Solving by Using Modeling 381Martin P. Breil, Søren S. Frederiksen, Steffen Kidal, and Thomas B. Hansen12.1 Introduction 38112.2 Theory 38212.3 Materials and Methods 38512.4 Determination of Model Parameters 38512.5 Optimization In Silico 38812.6 Extra-Column Effects 390Abbreviations 397References 39813 Modeling Preparative Cation Exchange Chromatography of Monoclonal Antibodies 399Stephen Hunt, Trent Larsen, and Robert J. Todd13.1 Introduction 39913.2 Theory 40113.3 Model Development 40313.4 Model Application 41313.5 Conclusions 424Nomenclature 425Greek letters 425References 42614 Model-Based Process Development in the Biopharmaceutical Industry 429Lars Sejergaard, Haleh Ahmadian, Thomas B. Hansen, Arne Staby, and Ernst B. Hansen14.1 Introduction 42914.2 Molecule—FVIII 43014.3 Overall Process Design 43114.4 Use of Mathematical Models to Ensure Process Robustness 43214.5 Experimental Design of Verification Experiments 43514.6 Discussion 43814.7 Conclusion 439Acknowledgements 439Appendix 14.A Practical MATLAB Guideline to SEC 439Appendix 14.B Derivation of Models Used for Column Simulations 449References 45515 Dynamic Simulations as a Predictive Model for a Multicolumn Chromatography Separation 457Marc Bisschops and Mark Brower15.1 Introduction 45715.2 BioSMB Technology 45915.3 Protein A Model Description 46015.4 Fitting the Model Parameters 46315.5 Case Studies 46415.6 Results for Continuous Chromatography 46915.7 Conclusions 475References 47616 Chemometrics Applications in Process Chromatography 479Anurag S. Rathore and Sumit K. Singh16.1 Introduction 47916.2 Data Types 48016.3 Data Preprocessing 48116.4 Modeling Approaches 48516.5 Case Studies of Use of Chemometrics in Process Chromatography 49016.6 Guidance on Performing MVDA 495References 49717 Mid-UV Protein Absorption Spectra and Partial Least Squares Regression as Screening and PAT Tool 501Sigrid Hansen, Nina Brestrich, Arne Staby, and Jürgen Hubbuch17.1 Introduction 50117.2 Mid-UV Protein Absorption Spectra and Partial Least Squares Regression 50317.3 Spectral Similarity and Prediction Precision 51117.4 Application as a Screening Tool: Analytics for High-Throughput Experiments 51617.5 Application as a PAT Tool: Selective In-line Quantification and Real-Time Pooling 51817.6 Case Studies 52317.7 Conclusion and Outlook 532References 53218 Recent Progress Toward More Sustainable Biomanufacturing: Practical Considerations for Use in the Downstream Processing of Protein Products 537Milton T. W. Hearn18.1 Introduction 53718.2 The Impact of Individualized Unit Operations versus Integrated Platform Technologies on Sustainable Manufacturing 54318.3 Implications of Recycling and Reuse in Downstream Processing of Protein Products Generated by Biotechnological Processes: General Considerations 54918.4 Metrics and Valorization Methods to Assess Process Sustainability 55318.5 Conclusions and Perspectives 573Acknowledgment 573References 574Index 583