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E-grāmata: Preparative Chromatography for Separation of Proteins [Wiley Online]

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Preparative Chromatography for Separation of ProteinsPalielināt
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The book is divided into three parts: modeling, industrial separations, and case studies. The modeling section will describe the recent developments in chromatographic theory and general approaches to research to obtain increased understanding of the fundamentals behind chromatographic separation and behavior of proteins in that environment. Topics covered include thermodynamic approaches to mechanistic modeling and how it is used to e.g. obtain improved adsorption isotherms, and fundamental descriptions of e.g. protein aggregation and how it may be applied to model the separation behavior. The industrial separations section presents new and existing chromatographic unit operations and how mechanistic and empirical modeling approaches are used to optimize equipment and methodologies. Equipment include column hardware, scale-down equipment, continuous operation mode etc. as well as tools for monitoring and control e.g. on-, in- and at-line equipment for improved process development and manufacturing methods. Improved methodologies comprise scaling approaches, use of models for validation, uncertainty and robustness evaluations, and process design. A mix of industrial, equipment vendor and academic authors contribute to this section. The last section contains case stories from industry on implementation and application of modeling approaches, such as multivariate data analysis, mechanistic modeling for establishment of design space, PAT methods for monitoring and control of chromatographic processes, and on-column refolding.
List of Contributors xiv
Series Preface xvii
Preface xviii
1 Model-Based Preparative Chromatography Process Development in the QbD Paradigm 1(10)
Arne Staby
Satinder Ahuja
Anurag S. Rathore
1.1 Motivation
1(1)
1.2 Regulatory Context of Preparative Chromatography and Process Understanding
1(5)
1.3 Application of Mathematical Modeling to Preparative Chromatography
6(2)
Acknowledgements
s8
References
8(3)
2 Adsorption Isotherms: Fundamentals and Modeling Aspects 11(70)
Jorgen M. Mollerup
2.1 Introduction
11(1)
2.2 Definitions
12(2)
2.3 The Solute Velocity Model
14(3)
2.4 Introduction to the Theory of Equilibrium
17(4)
2.4.1 Phase Equilibria
17(1)
2.4.2 Reversible Chemical Reaction
18(1)
2.4.3 Adsorption of a Single Component
18(3)
2.5 Association Equilibria
21(3)
2.5.1 The Asymmetric Reference Potential
22(2)
2.6 The Classical Adsorption Isotherm
24(2)
2.6.1 Protein Association to Immobilized Ligands
24(2)
2.7 The Classical Ion Exchange Adsorption Isotherm
26(12)
2.7.1 The Adsorption Isotherm of a GLP-1 Derivative
28(10)
2.7.1.1 The Adsorption Isotherm and the Wave Velocities
28(3)
2.7.1.2 Simulations
31(2)
2.7.1.3 How the Wave Velocities Shape the Elution Profiles
33(3)
2.7.1.4 Modeling the Trailing Edge of a Peak at High Load
36(2)
2.8 Hydrophobic Adsorbents, HIC and RPC
38(9)
2.8.1 The Adsorption of Lysozyme
40(3)
2.8.2 The Retention of Three Insulin Components on Two HIC Adsorbents
43(4)
2.8.3 Concluding Remarks
47(1)
2.9 Protein-Protein Association and Adsorption Isotherms
47(4)
2.9.1 Protein-Protein Association in the Fluid Phase
48(2)
2.9.2 Protein Association to Immobilized Protein
50(1)
2.9.3 The Equivalence Between the Models in 2.9.1 and 2.9.2
51(1)
2.10 The Adsorption Isotherm of a GLP-1 Analogue
51(8)
2.10.1 The Adsorption Isotherm and the Wave Velocities
51(3)
2.10.2 Simulations
54(2)
2.10.3 How the Wave Velocities Shape the Elution Profiles
56(2)
2.10.4 Calculation of Second Derivatives from Simulated Elution Profiles
58(1)
2.11 Concluding Remarks
59(1)
Appendix 2.A Classical Thermodynamics
60(17)
References
77(4)
3 Simulation of Process Chromatography 81(30)
Bernt Nilsson
Niklas Andersson
3.1 Introduction
81(1)
3.2 Simulation-Based Prediction of Chromatographic Processes
82(12)
3.2.1 Size Exclusion Chromatography
83(1)
3.2.2 Ion Exchange Chromatography
84(5)
3.2.3 Hydrophobicity-Based Chromatography
89(1)
3.2.4 Affinity-Based Chromatography
90(4)
3.3 Numerical Methods for Chromatography Simulation
94(2)
3.4 Simulation-Based Model Calibration and Parameter Estimation
96(1)
3.5 Simulation-Based Parametric Analysis of Chromatography
97(4)
3.6 Simulation-Based Optimization of Process Chromatography
101(6)
3.7 Summary 106
Acknowledgement
107(1)
References
108(3)
4 Simplified Methods Based on Mechanistic Models for Understanding and Designing Chromatography Processes for Proteins and Other Biological Products-Yamamoto Models and Yamamoto Approach 111(48)
Noriko Yoshimoto
Shuichi Yamamoto
4.1 Introduction
111(3)
4.1.1 Operation Mode of Chromatography and Zone Movement in the Column
112(2)
4.2 HETP and Related Variables in Isocratic Elution
114(6)
4.2.1 Resolution R, in Isocratic Elution
119(1)
4.3 Linear Gradient Elution (LGE)
120(10)
4.3.1 Retention in Linear Gradient Elution (LGE)
121(3)
4.3.2 Peak Width, HETP, and R, in Linear Gradient Elution
124(2)
4.3.3 Iso-Resolution Curve in Linear Gradient Elution (LGE)
126(4)
4.4 Applications of the Model
130(15)
4.4.1 Stepwise Elution (SE) Process Design Based on Linear Gradient Elution (LGE) Data
130(5)
4.4.2 Flow-Through Chromatography
135(1)
4.4.3 Process Understanding and Analysis
136(3)
4.4.4 High-Throughput Data Acquisition Method
139(2)
4.4.5 Characterization of Chromatography Stationary Properties and Binding of (Modified) Proteins or DNAs onto the Stationary Phase
141(4)
4.5 Summary
145(4)
Appendix 4.A Mechanistic Models for Chromatography
149(1)
Appendix 4.B Distribution Coefficient and Binding Sites [ 20]
149(3)
References
152(7)
5 Development of Continuous Capture Steps in Bioprocess Applications 159(18)
Frank Riske
Introduction
159(1)
5.2 Economic Rationale for Continuous Processing
160(2)
5.3 Developing a Continuous Capture Step
162(3)
5.4 The Operation of MCC Systems
165(2)
5.5 Modeling MCC Operation
167(2)
5.6 Processing Bioreactor Feeds on a Capture MCC
169(2)
5.7 The Future of MCC
171(1)
References
172(5)
6 Computational Modeling in Bioprocess Development 177(50)
Francis Insaidoo
Suvrajit Banerjee
David Roush
Steven Cramer
6.1 Linkage of Chromatographic Thermodynamics (Affinity, Kinetics, and Capacity)
177(3)
6.2 Binding Maps and Coarse-Grained Modeling
180(8)
6.2.1 Protein-Surface Interaction Maps
182(2)
6.2.1.1 Binding Maps and Preferred Binding Orientations
182(1)
6.2.1.2 Comparison with Chromatography Experiments
182(2)
6.2.1.3 Effects of Salt and Inclusion of the Hydrophobic Effect
184(1)
6.2.2 Characterization of Chemical Heterogeneities on Protein Surfaces
184(4)
6.2.2.1 Electrostatic Patches
185(1)
6.2.2.2 Hydrophobic Patches
185(1)
6.2.2.3 Using Protein Surface Characterization Techniques to Explain Protein-Ligand Binding in NMR Spectroscopy
186(2)
6.3 QSPR for Either Classification or Quantification Prediction
188(4)
6.3.1 QSPR Models for Ion Exchange Chromatography
189(1)
6.3.2 QSPR Models for Hydrophobic Interaction Chromatography (HIC)
190(1)
6.3.3 QSPR Models for Hydroxyapatite Chromatography
191(1)
6.3.4 QSPR Models for Multimodal Chromatography
191(1)
6.4 All Atoms MD Simulations for Free Solution Studies and Surfaces
192(12)
6.4.1 Fundamentals about Molecular Dynamics Simulation
193(1)
6.4.2 Protein Dynamics and Time Scale of Molecular Motion
194(2)
6.4.3 Representations of Proteins and Ligands
196(1)
6.4.4 Effect of Protein Amino Acid Mutation and Dynamics on Affinity Ligand Binding
196(1)
6.4.5 Protein-Ligand Docking and Molecular Dynamics Simulation
197(1)
6.4.6 Free Ligand Simulations
198(2)
6.4.7 Analysis Techniques for Free Ligand Simulations
200(3)
6.4.7.1 Cutoff-Based Probability of Binding
200(1)
6.4.7.2 Spherical Harmonics Expansion Approach to Quantify Distribution of Ligands
200(3)
6.4.8 Comparisons of Free Ligand Simulations with Experiments
203(1)
6.4.9 Surface Simulations
204(1)
6.5 Ensemble Average and Comparison of Binding of Different Proteins in Chromatographic Systems
204(1)
6.6 Antibody Homology Modeling and Bioprocess Development
205(4)
6.6.1 Molecular Modeling of Antibody Structures
207(2)
6.6.2 Antibody Modeling and Bioprocess Development
209(1)
6.7 Summary of Gaps and Future State
209(3)
Acknowledgment
212(1)
References
212(15)
7 Chromatographic Scale-Up on a Volume Basis 227(20)
Ernst B. Hansen
7.1 Introduction
227(2)
7.1.1 The Rigidity of Linear Scale-Up
227(1)
7.1.2 Increasing the Flexibility
228(1)
7.2 Theoretical Background
229(4)
7.2.1 Separation Performance: The Lower Limit
229(1)
7.2.2 Pressure Restriction: The Upper Limit
230(1)
7.2.3 Design Window
231(1)
7.2.4 General Theory
231(2)
7.3 Proof of Concept Examples
233(4)
7.4 Design Applications: How to Scale up from Development Data
237(4)
7.4.1 Industrial Cases
237(1)
7.4.2 Process Design: Multiple Steps
237(4)
7.5 Discussion
241(2)
7.6 Recommendations
243(2)
7.6.1 How to Scale up from Development Data
243(1)
7.6.2 The Real Challenges of Scale-Up
244(1)
References
245(2)
8 Scaling Up Industrial Protein Chromatography: Where Modeling Can Help 247(22)
Chris Antoniou
Justin McCue
Venkatesh Natarajan
Jorg Thommes
Qing Sarah Yuan
8.1 Introduction
247(1)
8.2 Packing Quality: Why and How to Ensure Column Packing Quality Across Scales
248(9)
8.2.1 Impact of Packing Quality on Separations
248(2)
8.2.2 Predicting Packing Quality Across Scales
250(7)
8.3 Process Equipment: Using CFD to Describe Effects of Equipment Design on Column Performance
257(7)
8.3.1 Model Verification
258(6)
8.4 Long-Term Column Operation at Scale: Impact of Resin Lot-to-Lot Variability
264(1)
8.5 Closing Remarks
265(1)
References
265(4)
9 High-Throughput Process Development 269(24)
Silvia M. Pirrung
Marcel Ottens
9.1 Introduction to High-Throughput Process Development in Chromatography
269(2)
9.2 Process Development Approaches
271(7)
9.2.1 Trial and Error Approach
271(1)
9.2.1.1 One Factor at a Time (OFAT)
272(1)
9.2.1.2 DoE
272(1)
9.2.2 Expert Knowledge-Based Process Development
272(1)
9.2.3 High-Throughput Experimentation
273(1)
9.2.4 Model-Based Approaches
273(4)
9.2.4.1 Modeling of a Chromatography Column
274(1)
9.2.4.2 Parameter Estimation
275(1)
9.2.4.3 Modeling of a Chromatographic Process
276(1)
9.2.5 Hybrid Methods
277(1)
9.2.5.1 Parameter Estimation
277(1)
9.2.5.2 Process Optimization
278(1)
9.3 Case Descriptions
278(7)
9.3.1 Optimization of a Single Chromatographic Purification Step
278(3)
9.3.2 Multiple-Column Process Design
281(4)
9.4 Future Directions
285(1)
References
286(7)
10 High-Throughput Column Chromatography Performed on Liquid Handling Stations 293(40)
Patrick Diederich
Jurgen Hubbuch
10.1 Introduction
293(6)
10.1.1 High-Throughput Column Chromatography: Method Review
294(3)
10.1.2 High-Throughput Column Chromatography: Error Sources
297(2)
10.2 Chromatographic Methods
299(1)
10.2.1 HTCC Experiments
299(1)
10.2.1.1 Isocratic Elution
299(1)
10.2.1.2 Gradient Elution
299(1)
10.2.2 Lab-Scale Experiments
300(1)
10.3 Results and Discussion
300(28)
10.3.1 Pipetting Accuracy
300(1)
10.3.2 Absorption Measurement in Micro-Titer Plates
301(3)
10.3.2.1 Determination of Volume Based on Absorption Difference
302(1)
10.3.2.2 Determination of Protein Concentration Based on UV 280 nm
303(1)
10.3.3 Effect of Fractionation and Number of Fractions
304(9)
10.3.3.1 In Silico Fractionation Method
304(3)
10.3.3.2 Effect of Peak Fitting
307(3)
10.3.3.3 Effect of Fraction Number: General Trends
310(1)
10.3.3.4 Accuracy of Retention Times
310(1)
10.3.3.5 Effect of Volume Errors
311(1)
10.3.3.6 Effect of Concentration Errors
312(1)
10.3.3.7 Effect of Dilution Errors
312(1)
10.3.4 Influence of Flow Regime
313(3)
10.3.5 Gradient Elution Experiments
316(20)
10.3.5.1 Salt Step Height
317(1)
10.3.5.2 Salt Steps and Flow Interruptions
318(7)
10.3.5.3 Comparability of Simulation, HTCC, and Laboratory LC Results
325(3)
10.4 Summary and Conclusion
328(1)
Acknowledgements
329(1)
References
329(4)
11 Lab-Scale Development of Chromatography Processes 333(48)
Hong Li
Jennifer Pollard
Nihal Tugcu
11.1 Introduction
333(3)
11.2 Methodology and Proposed Workflow
336(41)
11.2.1 High-Throughput Process Development
339(21)
11.2.1.1 Case 1: Utilizing HTPD for Early Developability Assessment
340(1)
11.2.1.2 Case 2: Polishing Resin Screening with Hydrophobic Interaction Chromatography Using Miniature Columns
341(4)
11.2.1.3 Case 3: Flow-through Chromatography Step Optimization Using Resin Slurry Plates and Miniature Columns
345(8)
11.2.1.4 Case 4: Bind and Elute CEX Polishing Chromatography Step Optimization Using Resin Slurry Plates and Miniature Columns
353(2)
11.2.1.5 Case 5: AEX Chromatography Optimization Utilizing Resin Slurry Plates
355(5)
11.2.2 Column Verification and Final Process Definition
360(12)
11.2.2.1 Verification of Dynamic Binding Capacity
360(1)
11.2.2.2 Verification of Operating Conditions and Ranges
360(12)
11.2.3 Additional Considerations
372(10)
11.2.3.1 Intermediate Stability
372(3)
11.2.3.2 Viral Clearance Studies
375(2)
11.3 Conclusions 377
Acknowledgments
377(1)
References
377(4)
12 Problem Solving by Using Modeling 381(18)
Martin P. Breil
Soren S. Frederiksen
Steffen Kidal
Thomas B. Hansen
12.1 Introduction
381(1)
12.2 Theory
382(3)
12.2.1 Column Model
382(1)
12.2.2 Gradient Mixer
383(2)
12.3 Materials and Methods
385(1)
12.4 Determination of Model Parameters
385(3)
12.5 Optimization In Silico
388(9)
12.6 Extra-Column Effects 390
Abbreviations
397(1)
References
398(1)
13 Modeling Preparative Cation Exchange Chromatography of Monoclonal Antibodies 399(30)
Stephen Hunt
Trent Larsen
Robert J. Todd
13.1 Introduction
399(2)
13.2 Theory
401(2)
13.2.1 General Rate Model
401(2)
13.2.2 Steric Mass Action Binding Isotherm
403(1)
13.3 Model Development
403(10)
13.3.1 Model Solution
403(1)
13.3.2 Determination of Transport Parameters
404(3)
13.3.3 Determination of SMA Parameters
407(5)
13.3.4 Model Qualification
412(1)
13.4 Model Application
413(11)
13.4.1 Resin Selection and Process Optimization
414(5)
13.4.2 Process Robustness and Control Strategy
419(3)
13.4.3 Raw Material Variability
422(2)
13.5 Conclusions
424(1)
13.6 Acknowledgments 425
Nomenclature 425
Greek letters
425(1)
References
426(3)
14 Model-Based Process Development in the Biopharmaceutical Industry 429(28)
Lars Sejergaard
Haleh Ahmadian
Thomas B. Hansen
Arne Staby
Ernst B. Hansen
14.1 Introduction
429(1)
14.2 Molecule-FVIII
430(1)
14.3 Overall Process Design
431(1)
14.4 Use of Mathematical Models to Ensure Process Robustness
432(3)
14.5 Experimental Design of Verification Experiments
435(3)
14.6 Discussion
438(1)
14.7 Conclusion
439(1)
Acknowledgements
439(1)
Appendix 14.A Practical MATLAB Guideline to SEC
439(10)
Appendix 14.B Derivation of Models Used for Column Simulations
449(6)
References
455(2)
15 Dynamic Simulations as a Predictive Model for a Multicolumn Chromatography Separation 457(22)
Marc Bisschops
Mark Brower
15.1 Introduction
457(2)
15.2 BioSMB Technology
459(1)
15.3 Protein A Model Description
460(3)
15.4 Fitting the Model Parameters
463(1)
15.5 Case Studies
464(5)
15.6 Results for Continuous Chromatography
469(6)
15.7 Conclusions
475(1)
References
476(3)
16 Chemometrics Applications in Process Chromatography 479(22)
Anurag S. Rathore
Sumit K. Singh
16.1 Introduction
479(1)
16.2 Data Types
480(1)
16.2.1 Basic Structure of Chromatographic Data
481(1)
16.3 Data Preprocessing
481(4)
16.3.1 Scaling
482(1)
16.3.2 Mean Centering
483(1)
16.3.3 Transformation
483(1)
16.3.4 Trimming and Winsorizing
484(1)
16.3.5 Data Preprocessing of Chromatographic Data
484(1)
16.4 Modeling Approaches
485(5)
16.4.1 Principal Component Analysis
486(1)
16.4.2 Partial Least Squares Regression
487(3)
16.4.3 PLS-Discriminant Analysis (PLSDA)
490(1)
16.5 Case Studies of Use of Chemometrics in Process Chromatography
490(5)
16.6 Guidance on Performing MVDA
495(2)
References
497(4)
17 Mid-UV Protein Absorption Spectra and Partial Least Squares Regression as Screening and PAT Tool 501(36)
Sigrid Hansen
Nina Brestrich
Arne Staby
Jurgen Hubbuch
17.1 Introduction
501(2)
17.2 Mid-UV Protein Absorption Spectra and Partial Least Squares Regression
503(8)
17.2.1 Intrinsic Protein Mid-UV Absorption
503(4)
17.2.2 Partial Least Squares Regression (PLS)
507(1)
17.2.3 Application of PLS and Mid-UV Protein Absorption Spectra for Selective Protein Quantification
508(3)
17.2.3.1 PLS Model Calibration
509(1)
17.2.3.2 PLS Model Validation
510(1)
17.2.3.3 Prediction of Unknown Samples
511(1)
17.3 Spectral Similarity and Prediction Precision
511(5)
17.3.1 Overview of Protein Spectra
511(3)
17.3.2 Spectral Similarity and Prediction Precision
514(2)
17.4 Application as a Screening Tool: Analytics for High-Throughput Experiments
516(2)
17.5 Application as a PAT Tool: Selective In-line Quantification and Real-Time Pooling
518(5)
17.5.1 PAT Tool Setup
520(1)
17.5.2 Selective In-line Protein Quantification
521(1)
17.5.3 Real-Time Pooling Decisions
521(2)
17.6 Case Studies
523(9)
17.6.1 mAb Monomer, Aggregates, and Fragments
525(3)
17.6.2 Serum Proteins
528(2)
17.6.3 Selective Quantification of Deamidated Insulin Aspart
530(2)
17.7 Conclusion and Outlook
532(1)
References
532(5)
18 Recent Progress Toward More Sustainable Biomanufacturing: Practical Considerations for Use in the Downstream Processing of Protein Products 537(46)
Milton T.W. Hearn
18.1 Introduction
537(6)
18.2 The Impact of Individualized Unit Operations versus Integrated Platform Technologies on Sustainable Manufacturing
543(6)
18.3 Implications of Recycling and Reuse in Downstream Processing of Protein Products Generated by Biotechnological Processes: General Considerations
549(4)
18.4 Metrics and Valorization Methods to Assess Process Sustainability
553(20)
18.5 Conclusions and Perspectives 573
Acknowledgment
573(1)
References
574(9)
Index 583
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).
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