Ultrafiltration of Bacterial Polysaccharides and Protein

The Pennsylvania State University The Graduate School College of Engineering

ULTRAFILTRATION OF BACTERIAL POLYSACCHARIDES AND

PROTEIN-POLYSACCHARIDE CONJUGATES USED IN VACCINES

A Dissertation in Chemical Engineering by Mahsa Hadidi

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctorate of Philosophy

December 2016

The dissertation of Mahsa Hadidi was reviewed and approved* by the following:

Andrew L. Zydney Distinguished professor of Chemical Engineering Dissertation Advisor Chair of Committee

Ali Borhan Professor of Chemical Engineering

Craig Cameron Eberly Chair in Biochemistry and Molecular Biology

Themis Matsoukas Professor of Chemical Engineering

Janna Maranas Professor of Chemical Engineering Chair of Graduate Program

*Signatures are on file in the Graduate School.

ABSTRACT

Polysaccharide-based vaccines can protect against various diseases such as pneumonia and meningitis. In order to increase the effectiveness of the vaccine, bacterial capsular polysaccharides are covalently attached to an immunogenic protein, resulting in a protein- polysaccharide conjugate vaccine. Purification of these very large biomolecules can be a significant challenge in commercialization of these vaccines. The overall objective of this dissertation was to develop a fundamental understanding of the potential of using membrane ultrafiltration for the separation / purification of these polysaccharides and their corresponding conjugates. Experiments were performed using several pneumococcus polysaccharide serotypes provided by Pfizer. Ultrafiltration data were obtained in a stirred cell using cellulose and polyethersulfone membranes with different molecular weight cutoffs. Polysaccharides were characterized using dynamic light scattering and size exclusion chromatography. Polysaccharide transmission in dilute solutions was a strong function of filtrate flux due to concentration polarization effects, with the data in good agreement with available hydrodynamic models.

Polysaccharide fouling became significant at high filtrate flux using more concentrated solutions, consistent with the presence of a critical wall concentration for fouling that was dependent on the specific serotype. The flux and polysaccharide transmission were both strong functions of solution ionic strength due to a combination of inter- and intra-molecular electrostatic interactions. The polysaccharide sieving coefficients were well-correlated with the effective polysaccharide size as determined by size exclusion chromatography, with results in good agreement with available hydrodynamic models for membrane transport. The increase in effective size at low salt concentrations could be explained using the worm-like chain model accounting for the increase in persistence length and excluded volume at low ionic strength.

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These results provide important insights into the factors controlling the ultrafiltration behavior of bacterial polysaccharides as well as an initial framework for the design of membrane processes for purification of polysaccharide-based vaccines.

TABLE OF CONTENTS

List of Figures ...... ix List of Tables ...... xix Acknowledgements ...... xxi Chapter 1 ...... 1 1.1 Background ...... 1 1.2 Membrane Technology ...... 3 1.3 Previous Work ...... 4 1.4 Dissertation Program ...... 9 Chapter 2 ...... 12 2.1 Introduction ...... 12 2.2 Bulk Mass Transport ...... 13 2.2.1 Stagnant Film Model...... 14 2.2.2 Bulk Mass Transfer Coefficient ...... 17 2.3 Membrane Transport ...... 18 2.3.1 Solvent Transport ...... 19 2.3.2 Solute Transport ...... 20 2.4 Log-Normal Pore Size Distribution ...... 30 2.5 Worm-like Chain Model ...... 32 2.6 Dynamic Light Scattering (DLS) ...... 35 2.7 Size Exclusion Chromatography (SEC)...... 36 Chapter 3 ...... 38 3.1 Introduction ...... 38 3.2 Membranes ...... 38 3.3 Solution Preparation...... 41 3.3.1 Buffer Solutions ...... 41 3.3.2 Polysaccharide Solutions ...... 42 3.3.3 Dextran Solutions...... 45 3.4 Ultrafiltration ...... 47 3.4.1 Apparatus ...... 47

3.4.2 Membrane Hydraulic Permeability ...... 48 3.4.3 Sieving Experiments ...... 49 3.4.4 Fouling Experiments ...... 50 3.5 Size Exclusion Chromatography...... 51 3.5.1 Concentration Assay ...... 51 3.5.2 Hydrodynamic Size Evaluation ...... 55 3.6 Membrane Characteristics ...... 56 3.7 Dynamic Light Scattering ...... 57 Chapter 4 ...... 59 4.1 Introduction ...... 59 4.2 Material and Methods ...... 60 4.3 Results and Discussions ...... 61 4.3.1 Size Exclusion Chromatography...... 61 4.3.2 Theoretical Analysis ...... 72 4.3.3 Dynamic Light Scattering ...... 76 4.4 Conclusions ...... 79 Chapter 5 ...... 81 5.1 Introduction ...... 81 5.2 Materials and Methods ...... 81 5.3 Results and Discussions ...... 84 5.3.1 Effect of Membrane Pore Size ...... 84 5.3.2 Effect of Filtrate Flux...... 87 5.3.3 Concentration Polarization...... 90 5.3.4 Effect of Stirring ...... 95 5.3.5 Hydrodynamic Analysis...... 98 5.4 Conclusions ...... 102 Chapter 6 ...... 104 6.1 Introduction ...... 104 6.2 Materials and Methods ...... 105 6.3 Results and Discussions ...... 106 6.3.1 Filtrate Flux ...... 106

6.3.2 Polysaccharide Ultrafiltration ...... 107 6.3.3 Model Analysis ...... 113 6.4 Conclusions ...... 119 Chapter 7 ...... 121 7.1 Introduction ...... 121 7.2 Materials and Methods ...... 121 7.3 Results and Discussions ...... 123 7.3.1 Effect of Membrane Material and Pore Size...... 123 7.3.2 Concentration Polarization Analysis...... 125 7.3.3 Effect of Stirring ...... 127 7.3.4 Effect of Membrane Orientation ...... 128 7.3.5 Effect of Ionic Strength ...... 129 7.3.6 Structural Characteristics ...... 131 7.3.7 Conjugate Ultrafiltration in Series ...... 137 7.3.8 Hydrodynamic Model ...... 139 7.4 Conclusions ...... 142 Chapter 8 ...... 144 8.1 Introduction ...... 144 8.2 Materials and Methods ...... 144 8.3 Results and Discussions ...... 147 8.3.1 Sieving Experiments ...... 147 8.3.2 Fouling Experiments ...... 154 8.4 Conclusions ...... 160 Chapter 9 ...... 162 9.1 Introduction ...... 162 9.2 Materials and Methods ...... 162 9.3 Results and Discussions ...... 166 9.3.1 Effect of Membrane Pore Size ...... 171 9.3.2 Effect of Ionic Strength ...... 173 9.3.3 Diafiltration Experiments...... 174 9.4 Conclusions ...... 178

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Chapter 10 ...... 180 10.1 Introduction ...... 180 10.2 Conclusions ...... 181 10.2.1 Polysaccharide / Conjugate Characterization ...... 181 10.2.2 Polysaccharide / Conjugate Ultrafiltration...... 182 10.2.3 Effect of Solution Conditions on Ultrafiltration ...... 184 10.2.4 Conjugate Purification by Ultrafiltration ...... 185 10.3 Recommendations for Future Work...... 186 References ...... 189

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List of Figures

Figure 2.1 Schematic of concentration polarization in the solution above a membrane. 퐶푏, 퐶푤, and 퐶푓 represent the concentrations of the solute in the bulk solution, at the membrane surface

(wall), and in the filtrate solution, respectively...... 14

Figure 2.2 Schematic representation of concentration polarization of a solute near the membrane surface with concentration polarization boundary layer thickness 훿...... 16

Figure 2.3 Actual sieving coefficient as a function of membrane Peclet number...... 22

Figure 2.4 Hindrance factors as a function of the ratio of the solute to pore radii...... 25

Figure 3.1 Molecular structure of (A) polyethersulfone and (B) regenerated cellulose...... 39

Figure 3.2 Scanning electron micrographs showing the cross sections of (A) the Biomax™ polyethersulfone and (B) the Ultracel™ composite regenerated cellulose membrane as provided by the manufacturer...... 40

Figure 3.3 Bis-Tris molecular structure (CAS Registry Number 6976-37-0)...... 42

Figure 3.4 Molecular structure of capsular polysaccharide serotypes A, B, and C...... 44

Figure 3.5 Schematic of ultrafiltration stirred cell apparatus...... 48

Figure 3.6 Schematic of the size exclusion chromatography set-up...... 53

Figure 3.7 Concentration calibration curves for native Serotype A and B, and activated

Serotype C polysaccharides...... 54

Figure 3.8 Concentration calibration curve for Conjugate C...... 55

Figure 3.9 Dextran molecular weight calibration for PL Aquagel-OH 60 column...... 56

Figure 3.10 Schematic of the dynamic light scattering instrument...... 58

Figure 4.1 Size exclusion chromatogram of Conjugate C in 250 mM buffered KCl at pH 7. . 62

Figure 4.2 Elution volume in size exclusion chromatography as a function of solution ionic strength for Serotypes A, B, and C...... 63

Figure 4.3 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for Serotypes A, B, and C and thyroglobulin as a function of Debye length. . 66

Figure 4.4 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for Serotypes A and B and the protein thyroglobulin as a function of Debye length for various salts...... 67

Figure 4.5 Effective hydrodynamic radius determined by size exclusion chromatography as a function of pH at solution ionic strengths of 5 and 100 mM for Serotypes A, B, and C...... 69

Figure 4.6 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for Conjugates A, B, and C as a function of Debye length...... 70

Figure 4.7 Effective hydrodynamic radius determined by size exclusion chromatography as a function of pH at solution ionic strength of 5 and 100 mM for Conjugates A, B, and C...... 71

Figure 4.8 Total persistence length vs the product of the linear charge density and the Debye length for Serotypes A, B, and C...... 74

Figure 4.9 Experimental radius of gyration vs the theoretical radius of gyration for polysaccharide Serotypes A, B, and C...... 76

Figure 4.10 Typical chromatogram for polysaccharide Serotype A using the Malvern

OMNISEC system...... 78

Figure 5.1 Observed sieving coefficient of 1 g/L solutions of Serotype A polysaccharide in

150 mM buffered KCl at pH 7 during ultrafiltration through various membranes at a transmembrane pressure of 1-5 psi (30 psi for DV50)...... 85

Figure 5.2 Observed sieving coefficient as a function of filtrate flux for 0.1 g/L solutions of the activated Serotype B through different Ultracel™ membranes. Data obtained in 250 mM ionic strength solutions at pH 7. Solid lines simply connect the data points at different filtrate flux...... 86

Figure 5.3 Observed sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of native Serotype B and an equivalent size dextran through Biomax™ 300 kDa membranes. Data were obtained using 250 mM ionic strength solutions at pH 7. Solid curves are model calculations based on the classical concentration polarization model as described in the text...... 89

Figure 5.4 Observed sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of native and activated Serotype B through Biomax™ 300 kDa membranes. Data were obtained using 250 mM buffered KCl at pH 7. Solid curves are model calculations based on the classical concentration polarization model as described in the text...... 90

Figure 5.5 Linearized concentration polarization analysis of the sieving coefficient data for native Serotype B, activated Serotype B, and a 2600 kDa dextran during ultrafiltration through the Biomax™ 300 kDa membrane. Solid lines are linear regression fits...... 91

Figure 5.6 Observed sieving coefficients as a function of filtrate flux for 0.1 g/L solutions of

Serotype B through the Biomax™ 300 and 500 kDa membranes. Data obtained in 250 mM ionic strength solutions at pH 7. Solid curves are model calculations based on the classical concentration polarization model as described in the text...... 93

Figure 5.7 Linearized concentration polarization analysis of the sieving coefficient data for native Serotype B during ultrafiltration through the Biomax™ 300 and 500 kDa membranes at pH 7 in a 250 mM KCl solution. Solid lines are linear regression fits...... 94

Figure 5.8 Sieving coefficient and filtrate flux as a function of time for ultrafiltration of a 0.1 g/L solution of Serotype B polysaccharide through a Biomax™ 300 kDa membrane...... 96

Figure 5.9 Observed sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of Serotype B through the Biomax™ 300 kDa membrane in 250 mM buffered KCl at pH 7 using a stirring speed of 0 and 1200 rpm...... 97

Figure 5.10 Sieving coefficient as a function of stirring rate for ultrafiltration of Serotype B polysaccharide through a Biomax™ 300 kDa membrane in the normal and reverse orientation.

Data obtained with a 0.5 g/L solution in 250 mM buffered KCl solution at pH 7...... 98

Figure 5.11 Actual sieving coefficients as a function of the ratio of solute to pore radii for ultrafiltration of 0.1 g/L solutions of the different polysaccharides through Ultracel™ and

Biomax™ membranes. The solid and dashed lines are given by Equation 5.7...... 101

Figure 6.1 Filtrate flux versus transmembrane pressure for ultrafiltration of 0.1 g/L solutions of polysaccharide Serotype A through the Biomax™ 30, 100 and 300 kDa membranes in 10 and

250 mM ionic strength solutions...... 107

Figure 6.2 Observed sieving coefficients versus filtrate flux for ultrafiltration of 0.1 g/L solutions of polysaccharide Serotype A through a Biomax™ 300 kDa membrane in Bis-Tris buffer at pH 7 with 5, 20, 50, and 250 mM ionic strength. The filled and shaded squares show results for repeat measurements in the 250 mM solution. The solid curves are calculations developed using the classical concentration polarization model...... 109

Figure 6.3 Schematic of the polysaccharide size in low and high ionic strength solutions. ... 109

Figure 6.4 Observed sieving coefficients for ultrafiltration of 0.1 g/L solutions of Serotype B through a Biomax™ 300 kDa membrane in Bis-Tris buffer at pH 7 with 5, 50, and 250 mM ionic

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strength. The solid curves are determined using the classical concentration polarization model.

...... 110

Figure 6.5 Observed sieving coefficients for ultrafiltration of 0.1 g/L solutions of Serotype C through a Biomax™ 300 kDa membrane in Bis-Tris buffer at pH 7 with 50 and 250 mM ionic strength. The solid curves are determined using the classical concentration polarization model.

...... 112

Figure 6.6 Observed sieving coefficients versus solution ionic strength for the three polysaccharides (Serotypes A, B, and C) and thyroglobulin (the filled and shaded circles are for repeat measurements). Data obtained with Biomax™ 300 kDa membranes at pH 7 and a filtrate flux of 10 µm/s using 0.1 g/L solutions of the polysaccharides / protein...... 113

Figure 6.7 Sieving coefficient data plotted according to the linearized form of the concentration polarization model (Equation 6.2). Results are shown for Serotype A in 5, 20, 50, and 250 mM KCl solutions at pH 7 during ultrafiltration through the Biomax™ 300 kDa membrane...... 115

Figure 6.8 Sieving coefficient data plotted according to the linearized form of the concentration polarization model (Equation 6.2). Results are shown for Serotype B in 5, 50, and

250 mM KCl solutions at pH 7 during ultrafiltration through the Biomax™ 300 kDa membrane.

...... 116

Figure 6.9 Actual sieving coefficients versus the effective solute radius for polysaccharide

Serotypes A, B, and C and thyroglobulin using Biomax™ 300 kDa membranes...... 119

Figure 7.1 Observed sieving coefficient as a function of filtrate flux for 0.1 g/L solutions of

Conjugate B through various Ultracel™ and Biomax™ ultrafiltration membranes. Data obtained

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in 250 mM ionic strength solution at pH 7. Solid curves simply connect the data points at different filtrate flux...... 124

Figure 7.2 Linearized concentration polarization analysis of the sieving coefficient data for 0.1 g/L solutions of Conjugates A, B, and C in 250 mM KCl during ultrafiltration through Biomax™

300 kDa membranes. Solid lines are linear regression fits...... 126

Figure 7.3 Sieving coefficient and filtrate flux as a function of time for ultrafiltration of a 0.1 g/L solution of Conjugate B in 250 mM KCl through a Biomax™ 300 kDa membrane...... 128

Figure 7.4 Effect of membrane orientation on the observed sieving coefficient during ultrafiltration of 0.1 g/L solutions of Conjugate B in 250 mM KCl through the Biomax™ 300 kDa membranes...... 129

Figure 7.5 Observed sieving coefficients as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of Conjugate B through Biomax™ 300 kDa membranes in 5 and 250 mM buffered

KCl at pH 7...... 130

Figure 7.6 Observed sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of Conjugate C through Biomax™ 300 kDa membranes in 5 and 250 mM buffered

KCl at pH 7...... 131

Figure 7.7 Size exclusion chromatograms for Conjugate C in 5, 50, and 250 mM buffered KCl solutions at pH 7...... 132

Figure 7.8 Intensity as a function of size for dynamic light scattering of 0.1 g/l solutions of

Conjugate C in 250 mM buffered KCl at pH 7 using a Malvern OMNISEC system (discussed in

Chapter 4)...... 133

Figure 7.9 Size exclusion chromatograms for Conjugate B in 5, 50, and 250 mM buffered KCl solutions at pH 7...... 134

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Figure 7.10 Size exclusion chromatogram for Conjugate A in 5, 50, and 250 mM buffered KCl solution at pH 7...... 135

Figure 7.11 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for both components of Conjugates A, B, and C as a function of Debye length...... 136

Figure 7.12 Size exclusion chromatograms of the feed, retentate, and filtrate solutions obtained after ultrafiltration of Conjugate C in 250 mM buffered KCl solution at pH 7 through a

Biomax™ 300 kDa membrane...... 138

Figure 7.13 Sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of the original and pre-filtered Conjugate C in 250 mM buffered KCl through

Biomax™ 300 kDa membranes...... 139

Figure 7.14 Actual sieving coefficients versus the effective solute radius for Conjugates B, C, and thyroglobulin using Biomax™ 300 kDa membranes...... 141

Figure 8.1 Observed sieving coefficients as a function of filtrate flux during ultrafiltration of

Serotype B polysaccharide with different feed concentrations through Biomax™ 300 kDa membranes. Solid curve is model calculation based on stagnant film model using best fit values of 푆푎= 0.06 and 푘푚= 3.1×10-6 m/s...... 148

Figure 8.2 Observed sieving coefficients as a function of filtrate flux during ultrafiltration of

Serotype A polysaccharide with different feed concentrations through Biomax™ 300 kDa membranes. Solid curve is model calculation based on stagnant film model using best fit values of 푆푎= 0.03 and 푘푚= 3.6×10-6 m/s...... 150

Figure 8.3 Critical wall concentration determined during ultrafiltration of Serotype B through the Biomax™ 300 kDa membranes as a function of bulk polysaccharide concentration...... 151

Figure 8.4 Observed sieving coefficients versus filtrate flux for 0.1 and 0.5 g/L solutions of

Serotype A during ultrafiltration through Biomax™ 300 kDa membranes in 10 and 250 mM ionic strength solutions at pH 7...... 153

Figure 8.5 Observed sieving coefficients versus filtrate flux for ultrafiltration of 0.1 and 0.5 g/L solutions of polysaccharide Serotype B through Biomax™ 300 kDa membranes at 10 and

250 mM ionic strength...... 154

Figure 8.6 Filtrate flux as a function of time during ultrafiltration of a 0.05 g/L solutions of the native and conjugated Serotype C in 250 mM buffered KCl at pH 7. Data were obtained at a constant pressure of 7 kPa (1 psi) using Biomax™ 300 kDa membranes...... 155

Figure 8.7 Filtrate flux as a function of time during ultrafiltration of a 0.05 g/L solution of

Conjugate C in 250 mM buffered KCl at pH 7. Data were obtained at a constant pressure of 35 kPa (5 psi) using Biomax™ and Ultracel™ 300 kDa membranes...... 158

Figure 8.8 Filtrate flux as a function of time during ultrafiltration of a 0.1 g/L solutions of

Conjugate A in 5 and 250 mM buffered KCl at pH 7. Data were obtained at a constant pressure of 7 kPa (1 psi) using Ultracel™ 300 kDa membranes...... 160

Figure 9.1 Concentration calibration curve for Activated Serotype C polysaccharides using the refractive index detector...... 163

Figure 9.2 Concentration calibration curve for Conjugate C polysaccharides using the refractive index detector...... 164

Figure 9.3 Concentration calibration curve for Conjugate C polysaccharides using absorbance at 280 nm...... 165

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Figure 9.4 Sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L mixtures of the conjugated and activated Serotype C through a Biomax™ 500 kDa membrane in

250 mM buffered KCl at pH 7...... 167

Figure 9.5 Sieving coefficient as a function of filtrate flux for filtration of 0.1 g/L solutions of the pure activated polysaccharide and pure conjugated Serotype C through Biomax™ 500 kDa membranes in 250 mM buffered KCl at pH 7. The solid curves are the calculated values based on concentration polarization model for the pure components. The data in the binary mixture

(Figure 9.4) are shown for comparison...... 168

Figure 9.6 Predicted selectivity as a function of filtrate flux for the free and conjugated

Serotype C during ultrafiltration through Biomax™ 500 kDa in 250 mM buffered KCl at pH 7.

Model calculations based on the concentration polarization equations using the best fit values of the actual sieving coefficients and mass transfer coefficients determined from data for the pure solutions of the two components...... 171

Figure 9.7 Sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L mixtures of the conjugated and activated Serotype C through Biomax™ 500 and 300 kDa membranes in 250 mM buffered KCl at pH 7...... 172

Figure 9.8 Sieving coefficient as a function of filtrate flux for ultrafiltration of a 0.1 g/L mixture of conjugated and activated Serotype C through Biomax™ 500 kDa membranes in 5 and

250 mM buffered KCl at pH 7...... 174

Figure 9.9 Normalized solute concentrations as a function of number of diavolumes for a diafiltration performed using Biomax™ 500 kDa for a mixture of the activated Serotype C and

Conjugate C. Data were obtained at a filtrate flux of 15 µm/s in 250 mM buffered KCl solution at pH 7. Solid curves are model calculations based on Equation 9.3...... 176

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Figure 9.10 Yield for Conjugate C as a function of purification factor. Diamonds are the experimental data for removal of the activated Serotype C from Figure 9.9. Solid curves are model calculations based on Equation 9.5 for different values of the selectivity (훹)...... 178

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List of Tables

Table 2.1 Expansion coefficients for 퐾푡 and 퐾푠 functions in Equation 2.14 and Equation 2.15.

...... 24

Table 3.1 Catalog number for the membranes used in this work...... 40

Table 3.2 Molecular weight of the polysaccharide serotypes as received from Pfizer Inc...... 44

Table 3.3 Molecular weight and catalog number of dextran samples...... 46

Table 3.4 Molecular weight and catalog number of dextran standards...... 46

Table 4.1 Properties of polysaccharide serotypes...... 64

Table 4.2 Effective size of polysaccharides Serotype A and B in 250 mM buffered KCl solution at pH 7 and 4.2 as determined by dynamic light scattering at 173˚...... 77

Table 4.3 The number average molecular weight, polydispersity, and hydrodynamic radius of polysaccharide Serotypes A and B and Conjugate B at pH 7 in 250 mM buffered KCl solution determined using SEC-MALS...... 79

Table 5.1 Hydrodynamic radius of various polysaccharides as measured using SEC (discussed in Chapter 4) in 250 mM buffered KCl at pH 7...... 82

Table 5.2 Hydraulic permeability and mean pore radius of the Ultracel™ and Biomax™ membranes...... 87

Table 6.1 Mass transfer coefficient and actual sieving coefficient for polysaccharides Serotype

A, B, and C at various ionic strengths as evaluated from Equation 6.2...... 117

Table 7.1 Mean hydrodynamic size of Conjugates A, B, and C determined from SEC measurements in both 5 and 250 mM buffered KCl solution at pH 7...... 122

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Table 7.2 Mass transfer coefficients and actual sieving coefficients for Conjugates A, B, and C during ultrafiltration through Biomax™ 300 kDa membranes in 250 mM buffered KCl solution at pH 7 as determined from the linearized concentration polarization model...... 127

Table 8.1 Flux recovery ratio for ultrafiltration of Conjugates A and C through Biomax™ and

Ultracel™ 300 kDa membranes in 250 mM buffered KCl at pH 7...... 159

Table 9.1 Known and calculated concentrations of the free and conjugated Serotype C in three binary mixtures...... 166

Table 9.2 Selectivity for separation of the activated and conjugated Serotype C determined from experiments with the pure components and a binary mixture. Data obtained using 0.1 g/L solutions in 250 mM buffered KCl at pH 7 through Biomax™ 500 kDa membranes...... 170

Acknowledgements

This dissertation would not have been possible without the help and support from many people that I have met along the way. I am most grateful to have the pleasure to work with Dr.

Andrew Zydney as my Ph.D. advisor; this was an honor and an incredible experience. Dr.

Zydney’s mentorship has immeasurably influenced my personal and professional development.

He always supported me with his deep knowledge and valuable guidance in each stage of my research. In addition to all the invaluable technical and scientific knowledge that I learnt from him, he had a strong influence on my problem-solving, mentorship and team working skills, which surely will help me in my future career. Thanks for always being so understanding, I eagerly look forward to having an opportunity to collaborate with you in the future.

I would like to acknowledge my committee members, Dr. Themis Matsoukas, Dr. Craig

Cameron, and Dr. Ali Borhan for their encouraging advice and feedback on my research and generously spending their time reviewing this dissertation and helping me improve this work. I would like to sincerely thank Dr. Borhan for all of his advice and support in my personal life.

I had the great opportunity to work closely with members of Dr. Zydney’s research group including Mahsa Rohani, Ehsan Espah Borujeni, Elaheh Binabaji, Achyuta Teella, Krisada

Ruanjaikaen, Shudipto Dishari, Melissa Woods, and Ying Li. I would like to specifically thank

Ehsan, Ela, Achyuta, and Kris for helping me learn lab basics when I first joined the group, and

Mahsa for being wonderfully patient with my endless questions. I also would like to thank former PhD graduates Dave Latulippe, Jessica Molek, and Meisam Bakhshayeshi who I have enjoyed their advice and experience.

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Funding for this work was provided by Pfizer Inc. and I have had the great opportunity of working with and benefiting from the guidance of our amazing collaborators at Pfizer Inc., including John Buckley, which I am extremely grateful for. Also I would like to thank Pfizer Inc. for the generous donation of the polysaccharide and conjugate stock solutions and Millipore for donation of the membranes used in this work.

I am truly blessed to have a wonderful family and group of friends who have always been there for me. Most importantly, I must thank my parents, Azar Khosravi and Nader Hadidi for all their love, encouragement, and support along the way, my caring grandfather, Siavash Khosravi, who has always believed in me, and my wonderful brother, Kasra. A special thanks to my fantastic friends Pouria and Ali whose priceless assistance and cares eased this being-far-away- from-family time. Thanks for being there for me and supporting me.

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Chapter 1

Introduction

1.1 Background

Streptococcus pneumoniae (pneumococcus) is an important human pathogen causing respiratory-track infections such as pneumonia, meningitis, otitis media, and sepsis [1-5]. The number of fatal pneumococcal infections in the United States is reported to be more than 40,000 each year [3]. Risk groups for pneumococcal diseases are mainly young children and the elderly because their immune systems are unable to respond effectively to pneumococcus infections [3,

6]. The emergence of antibiotic-resistance pneumococcal strains has placed even greater importance on the development and widespread use of vaccines for the prevention of pneumococcal disease [5, 7].

Traditional Pneumoniae vaccines are composed of a selection of capsular polysaccharides from Streptococcus pneumoniae. Currently, a vaccine containing 23 of the most prevalent pneumococcus serotypes is used, which provides high protection against the most frequent pathogens [5, 8]. This vaccine is T-independent and thus provides minimal immunogenic response in children under 2 years of age [9-11]. On the other hand, the vaccine is reported to be only 50-80% effective for older people [7], with the vaccine induced protection declining within several years of vaccination [12]. Therefore, the second generation of pneumococcal vaccines, used pneumococcal capsular polysaccharides that were first covalently

conjugated to a highly immunogenic carrier protein like CRM197, a non-toxic cross-reacting mutant of diphtheria toxin [4, 13]. Unlike the original capsular polysaccharide vaccines, these conjugated vaccines are T-dependent, providing significant vaccination for children with long- term maintenance of the immunological response [9, 14]. The current 13-valent polysaccharide- protein conjugated vaccine, sold as Prevnar 13 [15-17], provides almost 100% protection from

Streptococcus pneumoniae diseases [3].

The production of high value polysaccharide-based vaccines requires the development of robust, cost-effective, and high-resolution purification methods that can provide high yield and purification of the desired product. Although there can be considerable variability in the economics for different biotherapeutics, several studies have reported that up to 80% of the total manufacturing cost is associated with the downstream purification process [18]. This is particularly important in the production of vaccines [4] since they need to be taken prophylactically at a price that can accommodate broad distribution. The current cost of conjugated vaccines typically exceeds $50 per dose [8], making the desired three-dose regimen infeasible much of the world [4]. Therefore, there is tremendous interest in the development of improved, lower cost, separation technologies that would allow these life-saving vaccines to be distributed more widely.

Although there are many steps in the downstream purification process for vaccine products, one of the critical challenges in the production of conjugated vaccines is meeting the current FDA standard that more than 99% of the polysaccharide be present in the form of the conjugated product [9]. In developing such multivalent vaccines, each protein-polysaccharide conjugate is prepared by covalently coupling a purified capsular polysaccharide to the purified carrier protein [1, 9]. To maximize the formation of the conjugate, the conjugation reaction is

carried in the presence of excess polysaccharide [1, 10, 19], requiring the removal of significant amounts of unreacted (free) polysaccharide from the final product [1, 9, 10, 14]. Several different methods have been explored for this polysaccharide – conjugate separation including gel filtration (or size exclusion) chromatography [9, 14], hydrophobic interaction chromatography

[9, 14], reverse phase chromatography [20], ultracentrifugation [9], liquid-liquid extraction [9], and ammonium sulfate precipitation/fractionation [9]. Although chromatographic methods have been widely used in bioprocessing for protein purification, they have very low dynamic binding capacity for large polysaccharides / conjugates, with much of the internal pore space in available chromatographic resins remaining inaccessible. In addition, these chromatographic processes tend to be costly, time consuming, and difficult to scale up. Precipitation methods typically require multiple (repetitive) precipitations, and it can be challenging to resolubilize the precipitated conjugates. Extraction can provide a high throughput purification method, but it often involves the use of potentially toxic solvents, and it is difficult to achieve the required resolution between the free and conjugated polysaccharides. Hence, there is a critical need to develop alternative methods for purification of these conjugated vaccine products.

1.2 Membrane Technology

Ultrafiltration (UF) is used extensively in downstream processing of biopharmaceuticals, such as monoclonal antibodies, hormones / cytokines, and clotting factors. These membranes have pores between 1 and 10 nm, providing the capability for molecular-level separations.

Ultrafiltration is the dominant method used for product concentration, desalting, and final formulation, with the desired therapeutic product retained by the membrane while smaller buffer components and water are removed in the permeate. UF has also been explored for the

production of many vaccine products, both for concentration of various intermediates and for removal of unreacted components in the production of vaccine conjugates. The main advantages of UF processes are: (1) operation under mild conditions that avoid degradation or denaturation of the biological product, (2) high-throughput, (3) easy scale-up, (4) environmental friendly operation (no need for toxic chemicals), and (5) relatively low capital and operating costs [2, 21].

It was originally believed that ultrafiltration membranes separate species entirely on the basis of differences in size, although recent experimental and theoretical investigations have demonstrated that significant improvements in separation quality can be achieved by exploiting electrostatic interactions between charged solutes and charged membranes [22].

1.3 Previous Work

Several studies have demonstrated that the efficiency and immunogenicity of both polysaccharide and protein-polysaccharide conjugate vaccines significantly depends on the molecular weight / size [23-25]. For example, Lee et al. [26] evaluated the effect of size on the immunogenicity of meningococcal polysaccharide-protein conjugates, and clearly demonstrated that the larger conjugates were more immunogenic.

Although there are a number of methods for evaluating the effective size of polysaccharides, size exclusion chromatography (SEC) has been most commonly used for characterization of carbohydrate polymers, including capsular bacterial polysaccharides and their conjugates [26-31]. For example, Lee et al. [26] used size exclusion chromatography for the purification and analysis of a meningococcal conjugated vaccine. Hunolstein and colleagues [30,

32, 33] used the PL Aquagel-OH 60 column to determine the molecular size distribution of high- molecular-weight Haemophilus influenzae type b-tetanus toxoid conjugate vaccines. Jumel et al.

[34] evaluated the mean molecular mass of three different protein-meningococcal C conjugates and their constituent proteins using a Superose 6 size exclusion chromatography column.

Harding et al. [13] comprehensively characterized a wide range of native capsular polysaccharides from Streptococcus pneumoniae using size exclusion chromatography coupled with multi-angle laser light scattering (MALLS). Thiebaud et al. [35] recently discussed the development and validation of a high performance SEC method to characterize Haemophilus influenzae type b polysaccharides and their corresponding conjugates, while Ho et al. [36] used

SEC to track the thermal stability of tetanus toxoid conjugate vaccines, showing an increase in the relative amount of a low molecular weight component after storage at elevated temperatures.

As discussed in previous section, there has been significant interest in the application of ultrafiltration (UF), both for the concentration of various intermediates and for removal of unreacted polysaccharides from vaccine conjugates. Ultrafiltration is not limited by the large size of the polysaccharides since the separation is driven by convection (with minimal diffusional limitations), but resolution between species with similar size (e.g., the unreacted polysaccharides and the conjugate) can be challenging. Goncalves et al. [2] described the use of tangential flow microfiltration and ultrafiltration for purification of polysaccharide serotype 23F, although no data were reported for either the filtrate flux or polysaccharide retention. Meacle et al. [1] examined the filtration of several polysaccharides and conjugates through 0.1 and 0.2 µm microfiltration membranes. Significant polysaccharide retention was seen with both membranes, despite the large pore size. Polysaccharide transmission was largely independent of the transmembrane pressure, which the authors attributed to the combined effects of membrane fouling and concentration polarization, although no detailed analysis of this behavior was presented. Wen et al. [10] found relatively high retention (greater than 90%) during

ultrafiltration of a polysaccharide through a 0.05 µm polysulfone membrane, with the retention increasing with increasing transmembrane pressure. Greater transmission (although still much less than 20%) was obtained in the presence of backpulsing. In contrast, Brou et al. [37] obtained essentially complete transmission of a bacterial exopolysaccharide through a 0.2 µm pore size membrane in a rotating disk filter with no evidence of fouling. Takagi et al. [38] used a 100 kDa ultrafiltration membrane to remove small proteins from a capsular polysaccharide produced by

Haemophilus influenzae type b. The polysaccharide recovery was only 75%, suggesting significant loss of polysaccharide due to transmission through this relatively small pore size ultrafiltration membrane. Although these studies clearly demonstrate the potential of using ultrafiltration for purification of polysaccharide-based vaccines, there is considerable discrepancy over the retention characteristics and the key factors controlling the ultrafiltration behavior of these polysaccharides through different pore size membranes.

Charged (non-bacterial) polysaccharides are also extensively used in industrial and medical applications. This includes the use of xanthan gum as a gelling / thickening agent in many food products [39, 40], hyaluronic acid as an additive in cosmetics (primarily as a moisturizer) [40-43], alginates for use in waterproofing fabrics [44] and immobilizing cells [45,

46], chitosan as a biopesticide and seed treatment [40, 47], and heparin as an anticoagulant

[40].The thermodynamic and hydrodynamic properties of these charged polysaccharides are quite complex, with significant changes in molecular conformation, degree of entanglement, and rigidity in different ionic strength solutions [48]. For example, Florian-Algarin and Acevedo [49] showed that aqueous solutions of sodium alginate undergo thermally induced reversible gelation at concentrations between 1 and 2.5 weight percent, with a large hysteresis between gel formation and melting. The solutions were highly shear-thinning and showed significant

viscoelastic behavior. Kim et al. [50] used field flow fractionation and multi-angle light scattering to investigate the structure of hyaluronic acid, with the results showing both aggregation and charge-induced molecular expansion depending upon the solution ionic strength and the molecular weight of the polysaccharide. Choppe et al. [51] showed that the relaxation time for xanthan gum increased by more than two orders of magnitude when 100 mM NaCl was added at low temperatures. They also found that the lifetime of the cross-linked bonds in xanthan gels was significantly dependent on the solution ionic strength and polysaccharide concentration.

The viscosity of these polysaccharide solutions is also a strong function of solution conditions. Freitas et al. [52] examined the viscosity of an anionic extracellular polysaccharide produced by a Pseudomonas strain and found an abrupt shear thinning regime at low shear rates

(around 0.01 s-1) associated with the break-up of a weak gel that was not observed in neutral polysaccharides like guar gum. Median–Torres et al. [53] found that the steady-shear viscosity of a mucilage gum decreased with increasing salt concentration, with this effect being more pronounced for solutions containing divalent ions. Gravanis et al. [54] studied the rheological behavior of a succinoglycan polysaccharide and attributed the decrease in viscosity with increasing ionic strength to a stretched chain conformation at low salt concentrations and a worm-like chain behavior at higher salt concentrations. Similar behavior was observed for chitosan solutions; Cho et al. [55] showed that the intrinsic viscosity of chitosan is related to the effective macromolecular volume in solution, with the polymer chains becoming more flexible and compact at high salt concentrations due to a reduction in the intra-molecular electrostatic repulsive potential. Morris et al. [56] explained the stabilization of a more ordered conformation of xanthan gum with increasing ionic strength by a reduction in electrostatic repulsion between the charged side chains.

Delcroix et al. [57] studied the effects of solution conditions on the retention behavior of a sulfated (charged) pentasaccharide using various tubular ceramic ultrafiltration membranes with molecular weight cutoffs ranging from 1 to 150 kDa. Polysaccharide retention by the 10 kDa membrane increased from 50% to more than 95% as the NaCl concentration was reduced from 0.5 to 0 M (pure water). Delcroix et al. [57] attributed this behavior to a reduction in charge screening at low NaCl concentrations leading to greater inter-molecular electrostatic repulsion between the charged polysaccharide and the charged membrane. Zhou et al. [58] used a combined microfiltration (MF) and ultrafiltration (UF) process to purify hyaluronic acid directly from a fermentation broth. Transmission of the polysaccharide in both the MF and UF steps increased significantly at high ionic strength (at pH 7) and at pH 4. This behavior was explained by a reduction in the intra- and inter-molecular electrostatic interactions under these conditions, although no information was provided on the relative contribution of these interactions. Oueslati et al. [59] used a diafiltration process to remove small peptides and proteins in the purification of hyaluronic acid. The filtrate flux actually increased during the diafiltration process, which the authors attributed to the reduction in solution ionic strength and its effects on both the mass transfer coefficient and the wall concentration. However, there was no fundamental quantitative study of the effect of solution conditions on the characteristics and ultrafiltration behavior of bacterial polysaccharides.

Besides the factors mentioned above, the decrease in membrane efficiency due to fouling has always been a major obstacle in using ultrafiltration for biomolecule processing. Fouling is caused by adsorption or deposition of molecules on/within the membrane pores, leading to a decline in flux during constant pressure operation. Polysaccharide fouling is typically more severe and rapid than the fouling observed with proteins [19, 60]. Fouling causes a dramatic

reduction in polysaccharide transmission during ultrafiltration due to pore blockage/constriction

[19]. Susanto et al. [60] and Ye et al. [61] showed that alginate fouled UF membranes by the rapid formation of a cake/gel on the membrane surface, with the flux data well-described by the classical cake formation model. Susanto et al. [60] demonstrated that the flux reduction decreased with increasing pH, which was attributed to the increase in electrostatic repulsion between the more highly charged alginate and the membrane. The extent of alginate fouling was also influenced by solution ionic strength due to the shielding of electrostatic interactions at high salt concentration. Previous studies on protein fouling in ultrafiltration have shown that repulsive electrostatic interactions between the charged membrane and protein reduce the degree of fouling when the protein and membrane have the same polarity [62].

1.4 Dissertation Program

Although limited studies have demonstrated the feasibility of using ultrafiltration for the concentration / purification of polysaccharide vaccines, there is little quantitative information on the key factors controlling the behavior of polysaccharides and polysaccharide-protein conjugates during ultrafiltration. The overall objective of this dissertation was to develop a more fundamental understanding of the performance characteristics of bacterial polysaccharides and protein-polysaccharide conjugates during ultrafiltration processes. This included: (1) characterization of the polysaccharides and conjugates over a wide range of solution conditions,

(2) studying the ultrafiltration behavior of pure polysaccharides and conjugates as a function of both solution properties, such as ionic strength and pH, and operating conditions, such as filtrate flux and pressure, (3) investigating membrane fouling and inter-molecular interactions during ultrafiltration of binary mixtures of polysaccharides and their corresponding conjugates, and (4)

determining the potential of using ultrafiltration for separation of free and conjugated vaccine polysaccharides.

Chapter 2 describes the general theoretical background used to analyze the ultrafiltration results. This includes a brief review of available models for solute and solvent transport through membranes with relatively small pores. There is also a review of the worm-like chain model used to evaluate the size of the charged polysaccharides and a brief overview of the theoretical basis for the main characterization techniques used in this dissertation.

Chapter 3 describes the experimental set-up, materials, and methods used in the experimental studies described in this dissertation. Specific details on some of the more specialized experimental procedures are provided in the appropriate Chapters.

Chapter 4 summarizes the molecular properties of the polysaccharides and conjugates as determined using size exclusion chromatography and dynamic light scattering.

Chapter 5 presents data for the ultrafiltration behavior of polysaccharide solutions over a wide range of operating conditions and solution properties, including the effects of concentration polarization.

Chapter 6 discusses the effects of electrostatic interactions on the ultrafiltration behavior of bacterial polysaccharides and conjugates. The data are also discussed in terms of available hydrodynamic models for solute transport through semipermeable membranes.

Chapter 7 investigates the ultrafiltration behavior of protein-polysaccharide conjugates with an emphasis on the relationship between the biophysical characteristics of the conjugates and their transmission through ultrafiltration membranes.

Chapter 8 highlights the effects of membrane fouling on the ultrafiltration behavior of polysaccharides and conjugates.

Chapter 9 presents data for the ultrafiltration of binary mixtures of the free and conjugated polysaccharide. The data are analyzed in terms of the combination of inter-molecular interactions and fouling effects on the separation characteristics, and the results are used to examine the potential of ultrafiltration for polysaccharide – conjugate separations.

Chapter 10 summarizes the major contributions of this dissertation and makes several recommendations for future studies of high performance ultrafiltration systems for vaccine purification.

Chapter 2

Theoretical Background

2.1 Introduction

This Chapter provides a brief review of the theoretical models developed to describe the basic mass transport and separation phenomena governing the behavior of ultrafiltration systems.

The Chapter also provides a discussion on the mathematical models developed to estimate the size of polyelectrolytes in solution. Previous reviews of the membrane transport models have been presented by Zeman and Zydney [63] and in several dissertations published under the direction of Professor Andrew Zydney. The discussion below draws extensively from these prior reviews. A brief overview on the theoretical background of the experimental methods / instruments used throughout the dissertation is provided at the end of the Chapter.

The overall rate of solute transport in ultrafiltration is governed by both the rate of transport from the bulk solution to the membrane surface and the rate of transport through the membrane pores. These phenomena are discussed separately in the following sections, including both the rate of solvent and solute transport through the membrane. Transport in the bulk solution is controlled by the system hydrodynamics while transport through the membrane has contributions from hydrodynamics and thermodynamics including the electrostatic interactions between any charge groups on the solute and membrane surfaces.

2.2 Bulk Mass Transport

Ultrafiltration is a pressure-driven process where solvent and solute are convectively transported towards and through a semipermeable membrane. When the membrane is completely or partially retentive to the solute, there is an accumulation of the retained solute near the upstream surface of the membrane. This phenomenon is generally referred to as concentration polarization and is shown schematically in Figure 2.1, including the expected concentration profile. Concentration polarization causes the solute concentration to vary from a value of 퐶푏 in the bulk solution to a much higher value of 퐶푤 adjacent to the membrane surface or “wall”; this variation occurs over the distance defined as the concentration polarization boundary layer thickness, 훿. The mathematical description of the concentration profile is discussed subsequently. The increase in solute concentration at the surface of the membrane can reduce the filtration rate by three distinct mechanisms: (1) the accumulated solute can give rise to a large osmotic pressure on the feed side of the membrane, reducing the effective pressure driving force for filtration, (2) the solute can form a dense cake or gel layer that provides an additional hydraulic resistance to the solvent flow, and (3) the high solute concentration at the surface of the membrane can lead to irreversible fouling both on and within the membrane pores.

2.2.1 Stagnant Film Model

The transport of solute molecules to the membrane surface reflects the balance between the convective flow towards the membrane and diffusion away from the region of high concentration of retained solutes that develops near the membrane surface. The most common approach used to describe the effects of concentration polarization is the stagnant film model, which treats the mass transfer problem as a one-dimensional (stagnant) boundary layer, neglecting the complexities associated with the detailed fluid flow within the membrane device and the coupling between mass and momentum transport. The model assumes that solute transport occurs in a stagnant film above the membrane surface. In the classical stagnant film model, particle-particle interactions are neglected and the diffusivity and viscosity are both assumed constant (independent of solute concentration). At steady state, the solute flux through

the membrane and into the filtrate solution is equivalent to the net solute flux towards the membrane (Figure 2.2):

푑퐶 −퐽 퐶 = −퐽 퐶 − 퐷 푣 푓 푣 ∞ 푑푍 Equation 2.1 where 퐽푣 is the filtrate flux through the membrane (equal to the volumetric filtrate flow rate per unit membrane area), 퐶푓 is the concentration of solute in the filtrate solution, 퐶 is the local solute concentration at a position 푧 above the membrane surface, and 퐷∞ is the free solution diffusion coefficient of the solute. The first term on the right hand side represents convective solute transport to the membrane surface while the second term represents diffusion away from the concentrated region near the membrane surface (Fick’s law). Equation 2.1 is integrated across the concentration polarization boundary layer (from 퐶 = 퐶푤 @ 푧 = 0 to 퐶 = 퐶푏 @ 푧 = 훿) giving the following expression for the filtrate flux:

퐷∞ 퐶푤 − 퐶푓 퐽푣 = ln ( ) 훿 퐶푏 − 퐶푓 Equation 2.2

A more detailed analysis of this model, including the validity of the one-dimensional transport analysis, is provided by Zydney [64].

Figure 2.2 Schematic representation of concentration polarization of a solute near the membrane surface with concentration polarization boundary layer thickness 훿.

In addition to altering the filtrate flux, concentration polarization also affects the rate of solute transport through the membrane, which is governed by the solute concentration at the membrane surface. This is typically described in terms of the observed sieving coefficient, which is defined as the ratio of the solute concentration in the filtrate solution to that in the bulk solution well above the membrane (푆0 = 퐶푓/퐶푏). The observed sieving coefficient can be related to the actual sieving coefficient (푆푎), which is defined as the ratio of the solute concentration in the filtrate to that at the membrane surface (푆푎 = 퐶푓/퐶푤) by rearranging Equation 2.2 to give:

퐽푣 푆푎푒푥푝⁡( )⁡ 퐷∞/훿 푆표 = 퐽푣 1 − 푆푎 + 푆푎푒푥푝⁡( )⁡ 퐷∞/훿 Equation 2.3

Equation 2.3 has been used to successfully analyze experimental data for a variety of macromolecules including protein, DNA, dextran, etc. However, at very high degrees of polarization, the observed sieving coefficient is found to be a weaker function of the filtrate flux than given by Equation 2.3. This is typically attributed to the effects of solute-solute interactions on bulk transport [65]. In particular, the solute-solute interactions in the bulk tend to enhance the diffusive flux away from the membrane surface, reducing 퐶푤 relative to the value given by the

퐽 훿 classical concentration polarization model. At low filtrate flux ( 푣 ≪ 1), the observed sieving 퐷∞ coefficient is equal to the actual sieving coefficient since concentration polarization is minimal. 푆표 increases with increasing filtrate flux, approaching a value of one as 퐽푣훿/퐷∞ → ∞.

2.2.2 Bulk Mass Transfer Coefficient

The ratio of the solution diffusion coefficient (퐷∞) to the boundary layer thickness (훿) in

Equation 2.2 and Equation 2.3 is typically defined as the solute mass transfer coefficient, 푘. The mass transfer coefficient is a function of the solute diffusivity and the hydrodynamics of the particular membrane device. Although in principle it is possible to evaluate the mass transfer coefficient theoretically for relatively simple module configurations, the analysis of mass transfer in the stirred cells used in this research is difficult due to the complex flow profiles in this system. Therefore, semi-empirical correlations are typically developed based on a combination of experimental data and simplified theoretical analyses [63]. The mass transfer coefficient in the stirred cell geometry used in this dissertation was evaluated from the empirical correlation provided by Smith et al. [66]:

푆ℎ = 휒⁡푅푒푑푆푐0.33 Equation 2.4

푘푏 휔⁡푏2 휐 where 푆ℎ = ⁡⁡is the Sherwood number, 푅푒 = is the Reynolds number, 푆푐 = ⁡is the 퐷∞ 휐 퐷∞

Schmidt number, 푏 is the radius of the stirred cell, 휔 is the stirring speed, and 휐 is the kinematic viscosity. The value of 휒 has been determined experimentally and is a weak function of the stirred cell geometry. Colton [67] calculated the value of 휒 to be 0.285 based on the dissolution of benzoic acid. Opong and Zydney [68] evaluated 휒 as 0.23 for laminar flow in a 25 mm diameter Amicon stirred cell. The exponent d varies with 푅푒 from a value of 0.567 in the laminar flow region (푅푒 < 10,000) to a value of 0.746 in the turbulent region (푅푒 > 60,000). Reynolds numbers between 10,000 and 60,000 are in the transition region. The appropriate value of d in this region is typically determined by linear interpolation between the values for laminar and turbulent flow.

The solute diffusion coefficient in an infinitely dilute solution can be evaluated using the

Stokes-Einstein equation:

푘퐵⁡푇 푘퐵⁡푇 퐷∞ = = 푓 6⁡휋⁡휂⁡푅ℎ Equation 2.5

-23 where 푘퐵 is Boltzmann’s constant (1.38×10 J/K), 푇 is the absolute temperature, 푓 is the frictional coefficient, 휂 is the solution viscosity, and 푅ℎ is the hydrodynamic radius of the solute.

2.3 Membrane Transport

The rate of solute and solvent transport through porous membranes is typically described using hydrodynamic theories in which the membranes are modeled as an array of well-defined cylindrical pores [69, 70]. The advantage of the hydrodynamic models is that the key transport parameters can be calculated directly in terms of the physical properties of the solute and the

pores. Hydrodynamic theories can be easily extended to incorporate the effects of a pore size distribution (by numerical integration over the distribution) as well the effects of electrostatic interactions [71]. The available models used for solute transport through cylindrical pores are discussed in more detail in the following sections.

2.3.1 Solvent Transport

2.3.1.1 Permeability

The rate of solvent transport through a membrane is experimentally described in terms of the hydraulic permeability (퐿푝):

휂⁡퐽 퐿 = 푣 푝 ∆푃 Equation 2.6 where 훥푃 is the transmembrane pressure, 휂 is the solution viscosity, and 퐽푣 is the volumetric filtrate flux (volumetric flow rate per total membrane area). For a membrane with a uniform array of cylindrical pores, the filtrate flux can be evaluated using the Hagen-Poiseuille equation:

2 휀⁡푟푝 ⁡Δ푃푇푀 퐽푣 = 8⁡휂⁡훿푚 Equation 2.7

where 푟푝 is the pore radius, 훿푚 is the membrane thickness, and 휀 is the membrane porosity.

Equation 2.7 assumes that end effects are negligible, i.e. 훿푚 >> 푟푝, which is valid for typical ultrafiltration membranes since the membrane thickness is typically on the order of 100 times greater than the pore radius. In some cases a tortuosity factor is added to Equation 2.7 to account for

the irregular flow path through the membrane. Equation 2.7 can also be used to estimate the effective membrane pore size based on measured values of the membrane permeability.

2.3.1.2 Electrokinetic Effects

The fluid flow characteristics through a charged membrane are considerably more complicated. The solvent flux through a charged pore is reduced compared to that through a neutral pore due to the interactions between the fluid flow and the ions adjacent to the wall boundary. The presence of a net charge on the pore wall causes an accumulation of counterions in the region adjacent to the pore wall (the region typically referred to as the electrical double layer). The convective flux through the membrane due to the applied transmembrane pressure will create a net convective flux of counterions through the pore. As a result, an induced electrical (streaming) potential is developed that generates a back ion transport that balances the convective ion flux, resulting in a steady state where there is no electrical current flow through the pore. This induced streaming potential causes a net electrophoretic flux of ions that exerts stresses on the fluid, a phenomenon known as counter electro-osmosis. More details are given by

Newman et al. [72] but are beyond the scope of this dissertation. The influence of counterelectroosmosis on the measured filtrate flux is less than 10% for conditions typical of the ultrafiltration membranes used in this dissertation.

2.3.2 Solute Transport

The average solute flux (〈푁푠〉) through the membrane has contributions from diffusive and convective transport [69, 73] as described by Equation 2.8:

푑〈퐶 〉 〈푁 〉 = 퐾 ⁡〈푉〉⁡〈퐶 〉 − 퐾 ⁡퐷 ⁡ 푠 푠 푐 푠 푑 ∞ 푑푧 Equation 2.8 where 〈푉〉 and 〈퐶푠〉 are the radially averaged velocity of the fluid (solvent flux) and solute concentration inside the pore. The coefficients 퐾푐 and 퐾푑 are hindrance factors associated with convection and diffusion, respectively.

Equation 2.8 is expressed in terms of the radially averaged solute concentration within the pore 〈퐶푠〉. The values of 〈퐶푠〉 at the pore entrance and exit can be related to the concentrations in the solutions immediately outside the membrane pore (퐶푓 and 퐶푤) using the equilibrium partition coefficient (휙):

1−휆 〈퐶 〉 〈퐶푠〉푧=훿 Ψ 휙 = 푠 푧=0 = 푚 = 2 ∫ 푒푥푝 (− ) 훼⁡푑훼 퐶푤 퐶푓 푘퐵⁡푇 0 Equation 2.9

The upper limit of the integral represents the exclusion of the spherical solute molecule from the region within one solute radius of the pore wall.

The convective contribution to the solute flux is described by the asymptotic sieving coefficient (푆∞).

푆∞ = 휙⁡퐾푐 Equation 2.10

The solute flux through the membrane can be set equal to the solute flux into the filtrate solution, with the resulting expression rearranged to evaluate the actual sieving coefficient, 푆푎, in terms of

푆∞ as:

푆∞⁡exp⁡(푃푒푚) 푆푎 = 푆∞ + exp(푃푒푚) − 1 Equation 2.11 where 푃푒푚 is the membrane Peclet number, and 푆푎 (the actual sieving coefficient) is defined as the ratio of the solute concentration in the filtrate solution to that at the upstream surface of the membrane. The Peclet number describes the relative contribution of the convective and diffusive fluxes. Thus, at very large filtrate fluxes (large Peclet numbers), 푆푎 = 푆∞. This effect is demonstrated in Figure 2.3, which shows a plot of the actual sieving coefficient calculated using

Equation 2.11 versus the membrane Peclet number for different values of 푆∞. The actual sieving coefficient (푆푎) decreases from a value of one at very low 푃푒푚 to a constant asymptotic sieving coefficient (푆∞) at high filtration velocities, in good agreement with experimental observations

[63].

Figure 2.3 Actual sieving coefficient as a function of membrane Peclet number.

In order to determine the actual rate of solute transport through the membrane, it is necessary to evaluate the equilibrium partition coefficient (휙) and the parmeters 퐾푐 and 퐾푑 as a function of the solute and membrane properties as discussed in the following sections.

2.3.2.1 Hydrodynamic Analysis

The hindrance factors for convection (퐾푐) and diffusion (퐾푑) are related to properties of the membrane and the solute. These terms are typically evaluated using the centerline approximation which assumes that the solute is located at the axis of the pore (i.e., at the pore centerline) [69, 74]. The hindrance factors have been evaluated as a function of 휆 using matched asymptotic expansions for both small and large pores [69, 74, 75] with the final results expressed as:

퐾푠 2 퐾푐 = [2 − (1 − 휆) ] 2⁡퐾푡 Equation 2.12

6⁡휋 퐾푑 = (휆, 0) 퐾푡 Equation 2.13 where 퐾푠 and 퐾푡 are given by Equation 2.14 and Equation 2.15 [74] using the expansion coefficients provided in Table 2.1.

2 7 9 −5 퐾 = 휋2⁡√2(1 − 휆) 2 [1 + ∑ 푎 (1 − 휆)푛] + ∑ 푎 휆푛−3 푡 4 푛 푛 푛=1 푛=3 Equation 2.14

2 7 9 −5 퐾 = 휋2√2(1 − 휆) 2 [1 + ∑ 푏 (1 − 휆)푛] + ∑ 푏 휆푛−3 푠 4 푛 푛 푛=1 푛=3 Equation 2.15

Table 2.1 Expansion coefficients for 퐾푡 and 퐾푠 functions in Equation 2.14 and Equation 2.15.

Subscript, n 풂풏 풃풏

1 -73/60 7/60

2 77,293/50,400 -2,227/50,400

3 -22.5083 4.0180

4 -5.6117 -3.9788

5 -0.3363 -1.9215

6 -1.216 4.392

7 1.647 5.006

It is important to note that Equation 2.12 to Equation 2.15 were developed by neglecting electrostatic interactions between the pore and the solute. More detailed analyses for 퐾푐 and 퐾푑, including the effects of electrostatic interactions on these coefficients, are provided by

Dechadilok and Deen [76]. The results suggest that electrostatic interactions are of secondary importance for the hindrance factors, although they can have a large effect on the equilibrium partition coefficient.

Figure 2.4 shows the predicted values of the convective (퐾푐) and diffusive (퐾푑) hindrance factors as a function of the solute to pore radius. The value of the convective hindrance factor varies between 1 and 1.47 depending on the value of 휆. 퐾푐 is greater than one because the solute flux is expressed in terms of the area averaged velocity while the velocity at the pore centerline is twice the average velocity in the pore. The hindrance factor for diffusion is much smaller than

퐾푐, particularly for large value of 휆, due to the greater hydrodynamic interactions associated with solute diffusion.

Figure 2.4 Hindrance factors as a function of the ratio of the solute to pore radii.

2.3.2.2 Thermodynamic Analysis

The equilibrium partition coefficient (휙) for a solute in a cylindrical pore can be expressed in terms of the energy of interaction between the solute and the pore boundary as described by Equation 2.9. The total interaction potential is the sum of the contributions from steric, electrostatic, and inter-molecular (solute-solute) forces:

Ψ푡표푡푎푙 = Ψ푠푡푒푟푖푐 + Ψ푒푙푒푐푡푟표푠푡푎푡푖푐 + Ψ푖푛푡푒푟푚표푙푒푐푢푙푎푟 Equation 2.16

2.3.2.2.1 Steric Interactions

The equilibrium partition coefficient for a spherical solute in a cylindrical pore in the presence of purely hard-sphere (steric) interactions can be evaluated directly from Equation 2.9

using 훹푠푡푒푟푖푐 → ⁡∞ when the solute overlaps the pore wall and 훹푠푡푒푟푖푐 = 0 in the pore interior, giving:

1−휆 휙 = 2 ∫ 훽⁡푑훽 = (1 − 휆)2 0 Equation 2.17

Equation 2.17 describes the steric exclusion of the solute from the region within one solute radius of the pore wall.

Equation 2.17 is only valid for a membrane with uniform cylindrical pores of a given pore radius. Giddings et al. [77] analyzed the partitioning behavior of solutes in porous media defined by an array of intersecting planes. The partition coefficient was governed primarily by the dimensionless quantity:

푅∗ 휆∗ = 2⁡푠 Equation 2.18

∗ ∗ where 푅 is the mean projected solute radius (e.g., 푅 = 푟푠 for s spherical solute) and s is the specific area of the pore (the pore volume divided by the pore surface area). The partition coefficient was given approximately as:

휙 = 푒푥푝(−2⁡휆∗) Equation 2.19 which asymptotically goes to one as the pore radius approaches infinity due to the presence of a small number of very large pores in the pore size distribution.

2.3.2.2.2 Electrostatic Interactions

The presence of electrostatic interactions influences both the equilibrium partition coefficient and the hindrance factors for convection and diffusion. Smith and Deen [78, 79] evaluated the partition coefficient by solving the linearized Poisson-Boltzmann equation for a charged sphere in a charged cylindrical pore:

2 훻 훹 = (휅⁡푟푝)훹 Equation 2.20

Equation 2.20 is valid at low electrical potentials (<25 mV) due to the linearization of the exponential in the Boltzmann distribution (i.e., the Debye-Huckel approximation). Smith and

Deen [79] solved Equation 2.20 using matched asymptotic expansions in spherical and cylindrical coordinates. The interaction energy at constant surface charge density was determined using the procedure described by Verwey and Overbeek [80] as:

2 2 훹(0) (퐴푠⁡휎푠 + 퐴푠푝⁡휎푠⁡휎푝 + 퐴푝⁡휎푝 ) 2 = 푘퐵⁡푇 퐴푑 휀0⁡휀푟⁡푟푝 ⁡( 푒 ) Equation 2.21

where the coefficients 퐴푠, 퐴푠푝, 퐴푝, and 퐴푑 are all positive functions of the solution ionic strength, solute radius, and pore radius. For a solute located at the pore axis (centerline approximation) the coefficients can be evaluated as:

4⁡휋⁡휏⁡휆4⁡푒휏휆푀 퐴 = 0 푠 1 + 휏휆 Equation 2.22

휋2ℎ(휏휆) 퐴푝 = 2 2 휏 퐼1 (휏) Equation 2.23

4⁡휋2휆2 퐴푠푝 = 퐼1(휏) Equation 2.24

−휏휆 퐴푑푒푛 = 휋휏(1 + 휏휆)푒 − 푀0ℎ(휏휆) Equation 2.25

ℎ(휏) = (1 + 휏휆)푒−휏휆 − (1 − 휏휆)푒휏휆 Equation 2.26

∞ 1 2 2 퐾1[(휏 + 휃 )2] 푀0 = ∫ 1 ⁡푑휃 2 2 0 퐼1[(휏 + 휃 )2] Equation 2.27

where 퐼1 and 퐾1 are modified Bessel functions, 휏 = 휅⁡푟푝 is the dimensionless pore radius, and 휅 is the inverse Debye length:

퐹2 ∑ 푧 2퐶 휅 = √ 푖 푖 푖 휀0⁡휀푟⁡푅⁡푇 Equation 2.28 where 푅 is the ideal gas constant (= 푘퐵푁퐴, 8.314 J/mol.K).

The dimensionless surface charge densities of the solute, 휎푠, and pore, 휎푝, are defined as:

퐹⁡푟푝⁡푞푠 휎푠 = 휀0⁡휀푟⁡푅⁡푇 Equation 2.29

퐹⁡푟푝⁡푞푝 휎푝 = 휀0⁡휀푟⁡푅⁡푇 Equation 2.30

-12 with 휀0 the permittivity of vacuum (8.854×10 F/m), 휀푟 the dielectric constant of the solution, 퐹

4 the Faraday constant (= 푒⁡푁퐴, 9.65×10 C/mol), and 푅 the ideal gas constant (= 푘퐵푁퐴, 8.314

J/mol.K). The dimensional surface charge densities of the solute and pore (푞푠 and 푞푝) can be

evaluated in terms of the net electronic charge on the solute (푍) and the apparent zeta potential of the membrane pore (휁푎푝푝), respectively, as:

푒푍 푞푠 = 2 4휋푟푠 Equation 2.31

퐹휁푎푝푝 푞 = 4퐶 퐹휅−1 sinh ( ) 푝 0 2푅푇 Equation 2.32

-19 where 푒 is the electron charge (1.61×10 C) and 퐶0 is the bulk electrolyte concentration.

The potential energy of interaction in Equation 2.21 is represented as the sum of three separate contributions: the energy associated with the deformation of the electrical double layer surrounded by the solute, the direct charge-charge interactions between the pore and the solute, and the deformation of the double layer within the pore. The change in energy due to the deformation of the electrical double layer surrounding the solute or the pore always increases the potential energy, leading to a reduction in the magnitude of the partition coefficient, while the direct interaction term is positive if the membrane pore and solute have like charges and is negative if they are oppositely charged.

The analysis of the potential energy of interaction has been extended to account for off- center positions within the pore [78], constant surface potential boundary conditions [79], and the effects of charge regulation [81].

2.3.2.2.3 Solute-Solute Inter-molecular Interactions

The effects of solute-solute interactions on the sieving coefficient are not generally observed during protein ultrafiltration unless the feed concentration approaches approximately

50 g/L [63]. However, these effects can be significant at lower solute concentrations for long polymer chains and in multi-component mixtures with non-ideal solution behavior.

The effects of solute-solute interactions on the partition coefficient can be evaluated theoretically using Equation 2.9 by accounting for the change of the solute chemical potential between the external solution adjacent to the membrane and the solution space inside the membrane pores, i.e. by treating the solution and pore space as two adjacent phases:

∆퐺 휙 = 푒푥푝 (− ) 푘퐵⁡푇 Equation 2.33 where ∆퐺 is the change in the solute chemical potential. Equation 2.33 indicates that the partition coefficient associated with solute-solute interactions could be greater than or less than one depending upon whether the free energy for the solute in the pore is less than or greater than that in the external solution.

2.4 Log-Normal Pore Size Distribution

The previous sections describe the transport of solvent and solute through a single pore, with the final results limited to membranes with a uniform pore radius. In order to apply these equations to real membranes it is necessary to account for the distribution of pore sizes within the membrane. Large numbers of previous studies have employed a log-normal pore size distribution [71, 82], which is conveniently expressed as:

2 푟푝 푏 (푙푛 + ) 2 푛 푟푝̅ 2 휎 푛(푟 ) = 0 ⁡푒푥푝 − ,⁡⁡⁡푏 = 푙푛 [1 + ( ) ] 푝 2⁡푏 푟̅ 푟푝⁡√2⁡휋⁡푏 푝 [ ] Equation 2.34

where 푟푝 is the pore radius, 푟푝̅ is the mean pore radius, 푛0 is the number of pores at the maximum of the distribution function, and 휎 is the standard deviation. The log-normal pore size distribution is convenient for this analysis because it is only defined for positive values of the pore radius (in contrast to the Gaussian distribution which is defined for −∞ < 푟푝 < ∞).

The average solute flux through the membrane is evaluated by integrating the solute flux for each pore over the pore size distribution.

∞ 〈푁 〉⁡푛(푟 )⁡휋⁡푟2⁡푑푟 ∫0 푠 푝 푝 푝 푁̅푠 = ∞ 푛(푟 )⁡휋⁡푟2⁡푑푟 ∫0 푝 푝 푝 Equation 2.35

where 푁̅푠 is the average solute flux through the membrane, 〈푁푠〉 is the average flux through a single pore with radius 푟푝, and 푛 is the number of pores with radius 푟푝. The weighting of the

2 integral by 휋⁡푟푝 accounts for the cross-sectional area of the pore.

The hydraulic permeability for a membrane with a pore size distribution (assuming no electrostatic interactions) is given by Equation 2.36.

∞ 푛(푟 )⁡푟4⁡푑푟 휀 ∫0 푝 푝 푝 퐿푝 = ∞ 8⁡훿푚 푛(푟 )⁡푟2⁡푑푟 ∫0 푝 푝 푝 Equation 2.36

4 where the 푟푝 dependence in the numerator comes from the Hagen-Poiseuille equation (Equation

2 2.7) while the 푟푝 dependence in the denominator comes from the circular cross sectional area of the cylindrical pore.

2.5 Worm-like Chain Model

The radius of gyration of uncharged polymers can be evaluated using the worm-like chain model in terms of the persistence length (퐿푝), which provides a measure of the conformational flexibility of the polymer and has a limiting value of 0 for a random coil and ∞ for a perfectly stiff rod. The final expression is given in terms of the total contour length (퐿) (the maximum length of a fully stretched polymer chain) as [83]:

4 퐿 3 2⁡퐿푝 [1 − exp (− )] 퐿⁡퐿푝 2퐿푝 퐿푝 푅2 = − 퐿2 +⁡ − 푔 3 푝 퐿 퐿2 Equation 2.37

For long polymer chains, i.e., for 퐿 ≫ 퐿푝, Equation 2.37 reduces to:

퐿⁡퐿푝 푅2 = 푔 3 Equation 2.38

Electrostatic interactions in charged polymers (polyelectrolytes) have two primary effects on the structure / size: (1) there is an increase in the effective stiffness of the polymer chain, reflected in an increase in the persistence length at low ionic strength, and (2) there is an increase in the excluded volume of the polymer chain. A large number of theoretical models have been developed to describe both of these phenomena [84-86], although there is still considerable debate over the most appropriate theoretical framework. The simplest approach is to define an effective persistence length, 퐿푝 as:

퐿푝 = 퐿표 + 퐿퐸 Equation 2.39

where 퐿퐸 accounts for the increase in chain stiffness due to intra-molecular electrostatic interactions and 퐿표 is the intrinsic persistence length for the uncharged polymer, i.e., the persistence length in the limit of very high solution ionic strength where electrostatic interactions are negligible.

Several authors have shown that the electrostatic contribution to the apparent persistence length scales linearly with the Debye length and the linear charge density of the polymer [83, 87,

88]:

퐿퐸 = 훾휉휆퐷 Equation 2.40 where 휉 is the number of charges per Bjerrum length (the distance at which the electrostatic interactions between two elementary charges equals 푘퐵푇):

푒2 휆퐵 = 4휋휀표휀푟푘퐵푇 Equation 2.41

(휆퐵 = 0.71 nm in aqueous solutions at 25 ˚C) and 훾 is a proportionality constant.

Although Equation 2.39 and Equation 2.40 provide a convenient approach for evaluating the apparent (total) persistence length, and in turn the radius of gyration using Equation 2.38, the value of 훾 needs to be determined experimentally. An alternative approach for evaluating the effective radius of a charged polysaccharide is presented by de Nooy et al. [83]. In this case, the excluded volume contribution is evaluated explicitly as:

2 2 2 푅푔 = 푎푠 ⁡푅푔0 Equation 2.42

where 푅푔표 is given by Equation 2.38 and 푎푠 is an expansion parameter given by the Yamakawa-

Tanaka equation [89]:

2 0.46 푎푠 = 0.541 + 0.459⁡(1 + 6.04⁡퐸) Equation 2.43

At moderate ionic strength, the excluded volume contribution is dominated by electrostatic interactions, with

33/2 퐸 = ⁡훽 ⁡퐿1/2⁡퐿 −7/2 32⁡휋3/2 푒푙 푝 Equation 2.44 where 훽푒푙 is the effective volume excluded from one Kuhn segment (each rigid segment in a chain consisting of N segments where each can move freely) in the worm-like chain model:

2 훽푒푙 = 2⁡휋⁡퐿푝푑푒푓푓 Equation 2.45 with 푑푒푓푓the effective diameter of the polymer chain [90]:

휆퐵 푑푒푓푓 = 휆퐷[−퐿푛 ( ) + 2 ln(휉) + 2.61] 휆퐷 Equation 2.46

Equation 2.46 gives small (or negative) values of 푑푒푓푓at high ionic strength (휆퐷 ≪ 1); thus, the minimum value of 푑푒푓푓is typically set at a fixed value (as discussed by de Nooy et al. [83]).

The total persistence length 퐿푝 in Equation 2.44 and Equation 2.45 is given by Equation

2.39 with the electrostatic contribution evaluated explicitly as [85, 86]:

2 2 휉 ⁡휆퐷 퐿퐸 = 4⁡휆퐵⁡ Equation 2.47

Note that 퐿퐸 given by Equation 2.47 scales with the square of the charge parameter.

2.6 Dynamic Light Scattering (DLS)

Dynamic light scattering (DLS) is one of the common methods used to determine the diffusion coefficient (or size) of particles / proteins / polymers in solutions. The random

(Brownian) motion of the particles leads to scattering of the incident light (Rayleigh scattering) in all directions. For monochromatic light sources (such as a laser), the scattering intensity and the distance between the scattering centers in the solution is constantly fluctuating over time due to particle movements. The dynamics of the system are described in terms of the autocorrelation of the intensity trace recorded during the analysis. The fluctuations are quantified via a second order correlation function given by 푔2(휏):

〈퐼(푡)퐼(푡 + 휏)〉 푔2(휏) = 〈퐼(푡)〉2 Equation 2.48 where 퐼(푡) is the light intensity at time 푡 and 휏 is the delay time. Note that at short delay times, the correlation is high because particles are very close to their initial state. At long times, the correlation function decays to zero because there is no more correlation between the initial and the final state of the particles as they move randomly in the solution due to diffusion. The decay function for a monodisperse sample is related to the motion of the particles, which can be expressed in terms of the diffusion coefficient (퐷) as:

푔2(휏) = 퐴 + 훼⁡푒푥푝(−2⁡Γ⁡휏) Equation 2.49 where 퐴 is the baseline at infinite delay (typically zero), 훼 is the correction function amplitude at zero delay, and 훤 is the decay rate which is directly proportional to the diffusion coefficient:

훤 = 푞2퐷 Equation 2.50 where 푞 is the scattering vector given by:

4⁡휋⁡푛 휃 푞 = 0 sin( ) 휆0 2 Equation 2.51 and 푛0 is the solvent index of reflection, 휆0 is the wavelength of the incident light, and 휃 is the scattering angle. Equation 2.49 to Equation 2.51 are discussed in more detail by Pecora [91]. The hydrodynamic radius, 푅ℎ, of the particles can then be evaluated using the Stokes-Einstein equation (Equation 2.5).

2.7 Size Exclusion Chromatography (SEC)

In size exclusion chromatography (SEC), molecules move through the column at a rate determined by the extent to which they partition into the pores of the stationary resin. Most analyses assume that diffusion is very rapid, with the solute concentration in the stationary and mobile phases assumed to be in equilibrium at any point in the column. Larger solutes pass quickly through the column as they are excluded from most of the pore space, while small solutes have access to the entire pore volume. The equilibrium partition coefficient (휑) between the pore volume and the external fluid volume is evaluated as:

(푉 − 푉 ) 휑 = 푟 푖 (푉푣 − 푉푖) Equation 2.52 where 푉푟 is the retention volume of the species of interest, 푉푖 is the interstitial (excluded) volume within the pores, and 푉푣 is the total void volume (sum of the interstitial and pore volumes). The

partition coefficient ranges from zero to one with a value of zero for the largest molecules that do not enter into the pore structure of the resin and a value of one for the smallest molecules that can freely penetrate all of the pores within the resin.

Chapter 3

Materials and Methods

3.1 Introduction

This chapter describes the materials, apparatus, and experimental methods commonly employed for the experimental studies throughout this dissertation. Additional details on specific materials, procedures, and methods are provided in individual chapters as appropriate.

3.2 Membranes

Asymmetric membranes are used in almost all commercial applications of ultrafiltration.

These membranes are anisotropic and have a thin skin, which provides the membrane its functionality, and a much thicker and more porous support that provides the membrane its structural integrity (see Figure 3.2). The small thickness of the skin allows much higher fluxes to be obtained compared to symmetric membranes with comparable selectivity.

Although a variety of polymers can be used to make asymmetric ultrafiltration membranes, polyethersulfone (PES) and regenerated cellulose (RC) are two of the most attractive membrane materials. Polyethersulfone membranes have been widely adopted in bioprocessing due to the excellent mechanical strength, thermal tolerance, and chemical resistance. In cellulosic membranes, the free hydroxyl groups on the glucose rings within the cellulose polymer renders the membrane highly hydrophilic, significantly reducing protein binding

and fouling during use. These hydroxyl groups are also available for chemical modification if needed. Figure 3.1 shows a schematic of the chemical structure of polyethersulfone and cellulose.

Figure 3.1 Molecular structure of (A) polyethersulfone and (B) regenerated cellulose.

Ultrafiltration experiments were performed using Biomax™ and Ultracel™ membranes with nominal molecular weight cut-off (MWCO) of 100, 300, 500, and 1000 kDa provided by

MilliporeSigma (Bedford, MA).

Table 3.1 provides the catalog and lot numbers for the membranes used in this work. The nominal molecular weight cut-off refers to the molecular weight of a solute which has approximately 90% rejection as determined by the manufacturer. It is generally used as a rating of membrane pore size since macromolecule molar mass is usually (but not always) proportional to the molecular size. Scanning electron micrographs (SEM) of the cross section of a polyethersulfone and composite regenerated cellulose membrane are shown in Figure 3.2. The skin thickness is approximately 0.5 - 1 μm while the total membrane thickness is around 100 µm.

The effective pore size for the membranes is discussed in more detail in Chapter 5.

Table 3.1 Catalog number for the membranes used in this work.

Membrane Catalog Number Lot Number

Ultracel™ 30 kDa PLCTK M072712ACT-10

Ultracel™ 100 kDa PLCHK M070512BCH-7A

Ultracel™ 300 kDa PLCMK M110813ACM-8

Ultracel™ 1000 kDa PLCXK 020402BCX

Biomax™ 300 kDa PBMK M082013ABM-14

Biomax™ 500 kDa PBVK M072512ABV-17

Biomax™ 1000 kDa PBXK M101112ABX-12

The surface charge density of the Ultracel™ and Biomax™ membranes are slightly negative at solution pH greater than 3 [92, 93]. This small negative charge is due primarily to the preferential adsorption/association of negative anions from the bulk electrolyte solution onto the membrane [22].

Membrane disks with 25 mm diameter were cut from large flat sheets using a stainless- steel cutting device fabricated in our laboratory. The membranes were treated with care in order to prevent any damage to the skin. All membranes were soaked in 90% (V/V) isopropanol for 45 min to remove any storage agents and to insure complete wetting of the pore structure. The membranes were then thoroughly rinsed with at least 45 mL of deionized (DI) water before any measurements. The membranes were soaked in buffer solution and stored in closed cap containers at 4˚C between use to prevent collapse of the membrane skin due to drying and to minimize bacterial contamination.

3.3 Solution Preparation

3.3.1 Buffer Solutions

Appropriate buffers were used to maintain the desired solution pH and ionic strength.

Different buffer solutions were prepared by dissolving pre-weighed amounts of the appropriate salts in deionized distilled water obtained from a NANOpure® DIamond™ water purification system (Barnstead Thermolyne Corporation, Dubuque, IA) with a resistivity greater than 18

MΩ-cm. KCl powder (BDH Chemicals, BDH0258) and Bis-Tris (MPBiomedicals, 101038) were used for pH 6 and 7. KCl buffer at pH 5 was prepared using C2H3NaO2 (sodium acetate) (Sigma,

S7670) instead of Bis-Tris. Other saline buffer solutions were made using CaCl2 (EMD

Chemicals, CX0156-1) or MgSO4 (BDH Chemicals, BDH9246) buffered with Bis-Tris. All salts

were certified ACS analytical reagent grade. The 푝퐾푎value of Bis-Tris is 6.5 (see Figure 3.3) at

25˚C; this buffer provides satisfactory buffering capacity within the pH range 5.8 to 7.2. The solution pH was measured using a Model 402 Thermo Orion pH meter (Beverly, MA) and adjusted within ±0.05 pH unit of the desired value by addition of small amounts of 0.1 M sodium hydroxide (NaOH) or hydrochloric acid (HCl) (EM Science, HX0603-3) as required. A 105 A plus conductivity meter (Thermo Orion, Beverly, MA) was used to measure the solution conductivity. All buffer solutions were filtered through a 0.2 μm pore size Supor® 200 membrane

(Pall Corporation, Ann Arbor, MI) prior to use to remove any particles or un-dissolved salt. The ionic strength of the buffer solution was evaluated as:

1 퐼 = ∑ 푧2퐶2 2 푖 푖 푖 Equation 3.1 where 푧푖 and 퐶푖 are the net charge and concentration of each ion, respectively.

Figure 3.3 Bis-Tris molecular structure (CAS Registry Number 6976-37-0).

3.3.2 Polysaccharide Solutions

Purified capsular polysaccharides of the S. pneumoniae bacteria and the corresponding activated and conjugated versions were provided by Pfizer Inc. (Chesterfield, MO).

Polysaccharides are made up of oligosaccharide repeating units consisting of 2-8 ionizable and non-ionizable monosaccharides (e.g., glucose and glucuronic acid). 93 serologically distinct capsular polysaccharides (serotypes) have been identified to date [7]; however, a much smaller number of them are associated with different disease causing strains [4, 94].

Ultrafiltration experiments were performed using capsular polysaccharide serotypes A, B, and C and conjugated polysaccharides serotypes B and C as shown in Figure 3.4. The native polysaccharides are extracted from the bacterial capsule. The capped activated versions are produced by partial hydrolysis using periodate thus reducing the effective size. The conjugates were formed by coupling an activated form of the capsular polysaccharide to CRM197, a non- toxic cross-reacting mutant of the diphtheria toxin protein [95]. Note that there is no measurable unreacted protein in the conjugate solutions.

Figure 3.4 Molecular structure of capsular polysaccharide serotypes A, B, and C.

The polysaccharides were diluted to the desired concentration using a KCl solution (at the desired ionic strength) buffered with Bis-Tris at pH 7. Polysaccharide solutions were stored at 4˚C between experiments and were filtered through 0.2 μm Acrodisc® syringe filters (Pall

Corporation, Ann Arbor, MI) immediately prior to use to remove any aggregates. The molecular weights of the native and capped activated polysaccharides are listed in Table 3.2.

Table 3.2 Molecular weight of the polysaccharide serotypes as received from Pfizer Inc.

Polysaccharide Monomers Molecular Weight (kDa)

Serotype A Glc, GlcA 1936

Serotype B Glc, GlcA, Gal, ManNAc 709

Activated Serotype B Glc, GlcA, Gal, ManNAc 414

Activated Serotype C Glc, Gal, GlcNAc 794

3.3.3 Dextran Solutions

Dextrans are branched polymers made of glucose, joined by α-1,6 linkages in the main chain with a small number of branches attached to the main chain by α-1,3 links. They are synthesized naturally by the bacterium Leuconostoc mesenteroides. Dextrans have been used extensively in the past for membrane characterization [71, 96] since they are readily available in a range of sizes, they tend to cause minimal fouling, and they do not have any ionizable side groups (thus providing a purely size-based measure of the membrane sieving characteristics).

The dextran diffusion coefficient is a function of the molecular weight as evaluated by Granath

[97]:

−9 −0.47752 퐷 = 7.667 × 10 × 푀푤 Equation 3.2

2 where 퐷 is the diffusion coefficient (in m /s) and 푀푤 is the molecular weight in Da. The Stokes radii can be evaluated from the diffusivity using the Stokes-Einstein equation to give [98]:

0.47752 푅푆퐸 = 0.31 × 푀푤 Equation 3.3

with 푅푆퐸⁡given in⁡Å.

A polydisperse dextran solution was prepared using equal amounts of the different molecular weight dextrans in order to cover a wide range of dextran hydrodynamic radius (from

10 to 90 nm). Dextran samples were obtained from Sigma Aldrich. Table 3.3 summarizes the average molecular weight and the catalog number for the dextrans used in this work.

Table 3.3 Molecular weight and catalog number of dextran samples.

Dextran Samples Average Molecular Weight, Mw (kDa) Catalog Number

Sample 1 10 D-9260

Sample 2 40 D-1662

Sample 3 75 D-1390

Sample 4 150 D-4876

Sample 5 2000 D-5376

Narrow molecular weight dextran standards, obtained from American Polymer Standards

(Mentor, OH), were used for calibration of the SEC column. Table 3.4 summarizes the average molecular weight, peak molecular weight, and catalog number of the dextran standards.

Table 3.4 Molecular weight and catalog number of dextran standards.

Dextran Average Molecular Peak Molecular Catalog Mw/Mn Standards Weight, Mw (kDa) Weight, Mp (kDa) Number

Standard 1 165.6 150 1.49 DXT165K

Standard 2 326.6 245 1.59 DXT325K

Standard 3 548.3 350 1.58 DXT550K

Standard 4 749.5 560 1.5 DXT750K

Standard 5 1360 1199 1.55 DXT1300K

Standard 6 2800 2655 1.42 DXT2800K

3.4 Ultrafiltration

3.4.1 Apparatus

Ultrafiltration experiments were performed in an Amicon 8010 stirred cell (Millipore

Corp., Bedford, MA). A membrane disc with effective area of 4.1 cm2 was placed in the bottom of the stirred cell directly on top of a porous Tyvek® support that provides mechanical support and minimizes deformation of the membrane at high pressure. The stirred cell was equipped with a stir bar, which was suspended from the top of the cell so that it was located directly above the membrane. The stirred cell was placed on a magnetic stir plate and connected to an acrylic feed reservoir that was pressurized with compressed air at 7-70 kPa (corresponding to 1-10 psi) as determined using a digital differential pressure gauge (Ashcroft Inc., Stratford, CT). The fluid flow was normal to the membrane surface, with good bulk mass transfer achieved by stirring the solution with the stirring speed set using a 461830 digital stroboscope (Extech Instruments,

Nashua, NH). The permeate flux was controlled using a Dynamax peristaltic pump (Model RP-1,

Rainin Instruments). All filtration experiments were performed at room temperature (23 ± 2ºC) with samples stored at 4 ºC until analysis. A schematic of the ultrafiltration apparatus is shown in

Figure 3.5.

Figure 3.5 Schematic of ultrafiltration stirred cell apparatus.

3.4.2 Membrane Hydraulic Permeability

The membrane hydraulic permeability (퐿푝) is an important indicator of the membrane functionality. It was evaluated by measuring the filtrate flux as a function of transmembrane pressure using a 250 mM buffered KCl solution at pH 7. The high salt concentration was employed in order to minimize the contribution of counter electro-osmosis [99, 100]. The permeability was evaluated from the slope of the data using at least 4 transmembrane pressures:

휇퐽 퐿 = 푣 푝 ∆푃 Equation 3.4 where 휇 is the solution viscosity, 퐽푣 is the filtrate flux (volumetric flow rate divided by the total membrane area), and ∆푃 is the transmembrane pressure. Data were obtained at transmembrane pressures between 14 and 34 kPa (2 and 5 psi) as determined by the differential pressure gauge.

The filtrate flow rate (volumetric flow rate) was evaluated by timed collection using a digital balance (Model AG104, Mettler Toledo, Columbus, OH) with an accuracy of 100 μg.

Ultrafiltration experiments were only performed if the measured hydraulic permeability was within ± 10% of the mean value for that membrane lot.

3.4.3 Sieving Experiments

Ultrafiltration experiments were performed in an Amicon stirred cell after thoroughly flushing the membrane with buffer. The stirred cell was filled with the polysaccharide solution and connected to a pressurized feed reservoir containing additional feed solution. The filtrate flux was controlled using a Dynamax peristaltic pump connected to the outlet tubing from the stirred cell. The actual filtrate flux was evaluated by timed collection. Sieving data were obtained over a range of transmembrane pressure from 0 to 70 kPa (corresponding to 0 to 10 psi) as measured using the digital differential pressure gauge. Samples were obtained periodically after collecting at least 1 mL of filtrate to ensure stable operation, including wash-out of the dead volume beneath the membrane and elimination of any transients associated with the change in pressure. A small sample of the bulk solution was taken directly from the stirred cell right before and after the experiment for subsequent analysis. All experiments were performed at room temperature (22 ± 3 °C).

The extent of solute transmission through the membrane was quantified by the observed sieving coefficient as:

퐶푓 푆푂 = 퐶푏 Equation 3.5

where 퐶푓 and 퐶푏 are the solute concentrations in the filtrate and bulk solutions, respectively. The evaluation of 퐶푏 needs to account for the slight increase in solute concentration in the stirred cell over the course of each sieving experiment. The bulk solution concentration at each experimental point was thus evaluated by interpolation between the measured pre-sieving and post-sieving bulk solution concentrations. Membranes were discarded after each experiment to avoid complications from low levels of fouling.

The concentrations of the free and conjugated polysaccharides were evaluated by SEC with the actual concentrations determined by integration of the SEC peak using a serotype- specific calibration curve prepared from analysis of a series of known standards as described in

more detail subsequently. The uncertainty in 푆표 (훿푆표) for most experiments was determined from the estimated error in the absorbance, or the standard derivation calculated from repeat measurements of the filtrate and bulk concentrations, using standard propagation of error analysis as:

2 2 훿퐶 퐶 ⁡훿 √ 푓 푓 퐶푏 훿푆표 = ( ) + ( 2 ) 퐶푏 퐶푏 Equation 3.6

3.4.4 Fouling Experiments

Fouling experiments were performed using a series of Ultracel™ and Biomax™ membranes with different MWCO. Data were obtained with relatively concentrated solutions of the free and conjugated polysaccharides. The membrane permeability was initially evaluated for the clean membrane. The membrane was then soaked overnight in the polysaccharide / conjugate solution, returned to the stirred cell, rinsed with DI water, and the permeability re-evaluated. The

membrane was then flushed with at least 25 L/m2 of buffer at a pressure of 69 kPa (10 psi). The stirred cell and feed reservoir were then filled with the polysaccharide / conjugate solution, the system was re-pressurized to 69 kPa, and the filtrate flux was measured as a function of time for

60 min. Filtrate samples were collected periodically for subsequent analysis, with bulk samples collected directly from the stirred cell immediately before and after the experiment. After completion of the ultrafiltration experiment, the stirred cell was carefully rinsed with DI water and the buffer flux re-evaluated at 69 kPa. All experiments were performed at room temperature

(22 ± 3 °C).

The amount of polysaccharide (or conjugate) adsorbed by the membrane in a static fouling system was evaluated using the solution depletion method. A single membrane was placed in 10 mL of a concentrated polysaccharide solution and allowed to soak overnight. The change in the polysaccharide concentration in the bulk solution was used to calculate the amount adsorbed on the membrane by a simple mass balance.

3.5 Size Exclusion Chromatography

3.5.1 Concentration Assay

Size exclusion chromatography (SEC), also known as gel permeation chromatography

(GPC), was used for both concentration measurements and for evaluation of the effective hydrodynamic size of the polysaccharides / conjugates. The apparatus is shown schematically in

Figure 3.6. An Agilent 1200 Series high performance liquid chromatography system (Agilent

Technologies, Palo Alto, CA) was used with a PL Aquagel-OH 30 (8 µm particle size, 300×7.5 mm, 0.1-60 kDa molecular weight range) analytical column from Agilent Technologies

following the basic procedures described by Xu et al. [101]. Data collection was performed using

ChemStation software version C.01.05 (Agilent Technologies, Palo Alto, CA). The running buffer (mobile phase) was a 250 mM KCl solution buffered with 10 mM Bis-Tris at pH 7 at a flow rate of 1 ml/min. The buffer was degassed before entering the system to avoid cavitation.

The column was initially equilibrated with a minimum of 2 column volumes of the mobile phase.

75 μL samples solution were then injected by an autosampler immediately upstream of the column. Sample detection was performed using a refractive index detector (RID) for polysaccharides and a combination of RID and variable wavelength UV absorbance detector

(VWD) at 280 nm for polysaccharide-protein conjugates, with the two detectors operated in series. The polysaccharide and conjugate concentrations could be measured to within ± 0.001 g/L (with baseline resolution of the peaks). Actual concentrations were determined by comparison of the signal with that of known protein and polysaccharide standards. Free polysaccharides are effectively invisible to the UV detector, providing much more accurate measurement of the conjugated polysaccharide concentrations. Details on the concentration assay for mixed polysaccharide and protein-polysaccharide solutions are discussed in Chapter 9.

Figure 3.6 Schematic of the size exclusion chromatography set-up.

Calibration curves were constructed using samples of known concentration, with the peak areas determined by numerical integration. Figure 3.7 shows typical calibration curves for

Serotypes A, B, and C polysaccharides using refractive index detection. The slope of the linear regression fit was used to evaluate the concentration of unknown samples.

Figure 3.7 Concentration calibration curves for native Serotype A and B, and activated Serotype C polysaccharides.

Figure 3.8 shows a typical calibration curve for conjugate Serotype C using the UV absorbance. The best fit linear regression to the data was used to evaluate the concentration of unknown conjugate samples.

Figure 3.8 Concentration calibration curve for Conjugate C.

3.5.2 Hydrodynamic Size Evaluation

The effective size of the polysaccharides and conjugates were also evaluated using SEC with the PL Aquagel-OH 60 analytical column as described above. The composition of the running buffer was matched to that of the polysaccharide (or conjugate) sample using a flow rate of 0.8 ml/min. Narrow molecular weight dextran standards (Table 3.4) were used for column calibration. The effective hydrodynamic size was evaluated from the measured sample retention time based on results with the dextran standards. A typical calibration curve for the PL Aquagel-

OH 60 column is shown in Figure 3.9 for data obtained using 250 mM KCl solution buffered with 10 mM Bis-Tris at pH 7.

Figure 3.9 Dextran molecular weight calibration for PL Aquagel-OH 60 column.

3.6 Membrane Characteristics

Additional insights into the sieving characteristics of the ultrafiltration membranes were obtained from sieving experiments performed using 0.5 g/L solutions with a mixture of neutral dextrans in 250 mM KCl buffered with 10 mM Bis-Tris at pH 7. The ultrafiltration was performed at high ionic strength (250 mM) to minimize electrostatic interactions. Dextran samples from the filtrate and feed were analyzed by size exclusion chromatography, with the sieving coefficients calculated as the ratio of the peak areas for narrow slices of the chromatogram corresponding to a specific dextran molecular weight [102]. This method is discussed in detail in chapter 5.

3.7 Dynamic Light Scattering

The effective size of the polysaccharide products were evaluated using dynamic light scattering (DLS) data obtained with a Zetasizer ZS (Malvern Instruments, UK) at a scattering angle of 173o, a wavelength of 633 nm, and a temperature of 25 ºC. The instrument is schematically shown in Figure 3.10. The solvent was assumed to have the same characteristics as water (absorbance=0.01) with a refractive index of 0.13 [103]. A 1 mL sample with polysaccharide (or conjugate) concentration of 0.5 g/L was filtered through a 0.2 μm pore size syringe filter immediately before loading in a cuvette (to ensure the removal of any possible aggregates or large particles). The cuvette was inserted into the instrument and equilibrated for

120 s prior to any measurements. Each sample was analyzed at least 5 times with 3 measurements at each time. The average of all 15 measurements was reported as the final value with the experimental error determined from the standard deviation evaluated from the repeat measurements. Data were analyzed using Zetasizer Nano ZS software provided by Malvern

Instruments (UK). A schematic of the DLS instrument is shown in Figure 3.10.

Limited data were also obtained with a Malvern OMNISEC system (Westborough, MA) with a multi-angle light scattering detector using the OmniSEC V 10 software. Details are provided in Chapter 4.

Figure 3.10 Schematic of the dynamic light scattering instrument.

Chapter 4

Size Exclusion Chromatography and Dynamic Light Scattering of Bacterial Polysaccharides and Conjugate Vaccines

Note: Most of the material presented in this chapter was previously published [104].

4.1 Introduction

As discussed in Chapter 1, the molecular properties of bacterial polysaccharides and protein-polysaccharide conjugates play an important role in determining the efficacy and immunogenicity of the final vaccine product. Size exclusion chromatography (SEC) is commonly used to analyze and characterize biopolymers, including capsular polysaccharides.

However, previous SEC studies examined the behavior of the polysaccharides and their conjugates at relatively high ionic strength (greater than 0.1 M) and near neutral pH [94]. The objective of the work described in this Chapter was to obtain quantitative data for the effects of solution ionic strength and pH on the SEC retention of several capsular polysaccharides from S. pneumoniae bacteria in their native and conjugated forms. Experiments were performed using an

Agilent 1200 series high performance liquid chromatography (HPLC) system. SEC retention data were compared with that of narrow molecular weight dextran standards, with the results analyzed in terms of available theoretical models for the radius of gyration of charged polymers.

Limited data were also gathered using Malvern dynamic light scattering (DLS) systems. The results provide important insights into the effects of solution ionic strength on the physical properties and SEC behavior of capsular polysaccharides and their corresponding conjugates.

4.2 Material and Methods

Size exclusion chromatography was performed using an Agilent 1200 series HPLC system equipped with a PL Aquagel-OH 60 size exclusion resin (particle size of approximately 8

μm). Previous studies have shown that this column can provide effective resolution of high molecular weight polysaccharides and vaccine conjugates [33]. The flow rate of the running buffer was maintained at 0.8 mL/min and the sample injection volume was 80 μL. Sample detection was performed using an Agilent 1200 series refractive index detector (RID) at 35 °C

(maintained using a column oven). Data were analyzed using ChemStation software.

Dynamic light scattering was performed using Malvern Zetasizer ZS and OMNISEC systems. The data were gathered using both 173˚ and multi-angle light scattering detectors. The polysaccharides samples were filtered through a 0.2 μm Acrodisc® syringe filter immediately before loading in a cuvette to ensure the removal of any possible aggregates or large particles.

Data were analyzed using Zetasizer Nano ZS and OminiSEC V 10 software.

Buffer solutions were prepared by dissolving pre-weighed amounts of appropriate salts in deionized water. KCl, CaCl2, MgSO4, Bis-Tris, C2H3NaO2, and HCl were used to adjust the ionic strength and pH. The mobile phase was prefiltered through a 0.2 μm pore size Supor® 200 membrane prior to use to remove any particles or undissolved salts.

Solutions of native and conjugated polysaccharides from S. pneumoniae bacteria were provided by Pfizer, Inc. The conjugates were formed by coupling an activated (partially hydrolyzed) form of the capsular polysaccharide, to the small protein CRM197 [95]. SEC samples were prepared by diluting with the desired mobile phase to obtain a concentration of approximately 0.1 g/L. The resulting solutions were filtered through 0.22 μm Acrodisc® syringe

filters immediately prior to use. The polysaccharide solutions were stored at 4˚C and gradually brought to room temperature (22 ± 3˚C) prior to each experiment. Data were also obtained with dextran standards obtained from American Polymer Standards Corp. as discussed in Chapter 3.

Limited SEC data were obtained using the protein thyroglobulin (Sigma, T1001) with a molecular weight of 푀푤 = 660 kDa, which is comparable to that of the polysaccharides examined in this work. Total void volume (푉푡) of the column was determined using ᴅ-glucose

(Sigma, G5767) with 푀푤=180 Da as a completely accessible small molecule while the interparticle void volume (푉표) was evaluated using lambda DNA (New England Biolabs,

7 N3011S) with 푀푤=3.2×10 Da, which is 3.2 times larger than the reported size exclusion limit for the PL Aquagel-OH 60 resin.

4.3 Results and Discussions

4.3.1 Size Exclusion Chromatography

The effective size of the polysaccharides and conjugates were determined using the SEC retention time. The column was calibrated using narrow molecular weight dextran standards as discussed in detail in Chapter 3. The total void volume of the SEC column was estimated using glucose as 9.91 ± 0.08 mL while the interparticle volume was evaluated as 6.40 ± 0.12 mL using results for lambda DNA. Data obtained with different polysaccharide concentrations (from

0.1 to 1 g/L) gave identical results for both the retention volumes / times and peak shape. Typical chromatograms for 0.1 and 0.3 g/L solutions of Conjugate C in 250 mM buffered KCl at pH 7 are shown in Figure 4.1. The breadth of the peak reflects the inherent polydispersity of the conjugates in combination with the peak spreading that occurs in the SEC column. The retention

volume for each species was evaluated at the location of the peak maximum, with values of

11.02 min at both concentrations.

Figure 4.1 Size exclusion chromatogram of Conjugate C in 250 mM buffered KCl at pH 7.

4.3.1.1.1 Effect of Ionic Strength

Figure 4.2 shows data for the elution (or retention) volume of three polysaccharide serotypes (properties in Table 4.1) using the PL Aquagel-OH 60 size exclusion column with 10 mM Bis-Tris plus added KCl at pH 7 as the running buffer. In each case the elution volume was calculated based on the location of the peak maximum. Data were plotted as a function of the buffer ionic strength:

1 퐼 = ∑ 푧2퐶 2 푖 푖 푖 Equation 4.1 where 푧푖 and 퐶푖 are the net charge and concentration of each ion, respectively. The elution volume for Serotype C is independent of the ionic strength, consistent with the absence of any charged groups in its structure. In contrast, Serotypes A and B both show a sharp increase in elution volume with increasing ionic strength, corresponding to a reduction in their effective hydrodynamic volume. The net result is that Serotypes B and C have fairly similar retention volumes at high ionic strength but have dramatically different elution behavior at low ionic strength. This is discussed in more detail subsequently.

Figure 4.2 Elution volume in size exclusion chromatography as a function of solution ionic strength for Serotypes A, B, and C.

Table 4.1 Properties of polysaccharide serotypes.

Repeating Molecular Charge Contour Charge Unit Serotype Monomers weight Parameter, Length, Density Length (kDa) ξ L (nm) (nm)

Serotype A Glc, GlcA 1940 1 / 2 0.894 0.8 4302

Glc, GlcA, Gal, Serotype B 710 1 / 5 0.358 2 1520 ManNAc

Glc, Gal, Serotype C _ 0 _ _ _ GlcNAc

The data in Figure 4.2 were used to evaluate the effective hydrodynamic radius of the

polysaccharides based on a calibration curve constructed with a series of relatively monodisperse

dextran standards. The dextrans were run at each ionic strength, immediately before / after the

polysaccharides; the dextran elution volume was nearly independent of ionic strength (and pH,

discussed in next section), with variations less than ±1%. The radius of each dextran was

calculated from the empirical correlation given by [97]:

−11 0.47752 푅푆퐸퐶 = 3.1 × 10 ⁡(푀푤) Equation 4.2

where the radius is in meters and the molecular weight is in Da. Results are plotted in Figure 4.3

as a function of the Debye length:

휀⁡푘퐵⁡푇 휆퐷 = √ 2 2⁡푁퐴⁡푒 ⁡퐼 Equation 4.3

where 휀 is the electrical permittivity of the solution, 푘퐵 is the Boltzmann constant, T is the absolute temperature, 푁퐴 is Avogadro’s number, e is the electron charge, and I is the solution ionic strength. Note that Figure 4.3 is equivalent to plotting the data versus I-1/2 due to the dependence of the Debye length on the ionic strength as given in Equation 4.3.

The effective radius of Serotype A in the 250 mM solution was 52 nm, with 푅0= 43 nm nm by extrapolation of the data in Figure 4.3 to zero Debye length (infinite ionic strength). The hydrodynamic radii of the polysaccharide Serotypes A and B increase by 80 nm and 42 nm, respectively, as the Debye length increases from 0.61 to 4.3 nm (corresponding to a change in ionic strength from 250 to 5 mM). This large increase in polysaccharide radius is due primarily to the expansion of the polysaccharide associated with intra-molecular electrostatic repulsion between the negatively charged carboxylic acid groups. The greater effect of ionic strength on the hydrodynamic radius of Serotype A is consistent with its higher charge density (as summarized in Table 4.1). The SEC partitioning behavior can also be affected by inter-molecular electrostatic interactions associated with the distortion of the external electrical double layer surrounding the polysaccharide. However, previous studies have shown that this latter effect is relatively small [105]; this behavior was confirmed using SEC data for the globular protein thyroglobulin, which showed less than a 12 nm increase in size over the same range of ionic strength (data shown in Figure 4.3). Note that the hydrodynamic radius of thyroglobulin was evaluated as 푅ℎ ≈ 11⁡푛푚 using dynamic light scattering independent of the solution ionic strength. Bednar and Hennessey [94] reported a radius of gyration of 66 nm for Serotype B and

59 nm for Serotype C at high ionic strength solution. The larger values from that study are consistent with the larger molecular weight of their polysaccharides (between 1100 and 1400

kDa as measured using SEC with multi-angle laser light scattering or from the specific viscosity).

Figure 4.3 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for Serotypes A, B, and C and thyroglobulin as a function of Debye length.

4.3.1.1.2 Effect of Salt

Additional insights into the effects of intra-molecular electrostatic interactions on the effective hydrodynamic radius were obtained by performing experiments in which the ionic strength was adjusted using CaCl2 or MgSO4 instead of KCl. Figure 4.4 compares the data in

KCl solutions to that in solutions of the divalent salts. The data obtained using CaCl2 (divalent- monovalent salt) and MgSO4 (divalent-divalent salt) were nearly indistinguishable from each other at the same ionic strength (i.e., at the same Debye length). In addition, the results at very

high ionic strength (small Debye length) were very similar for any given polysaccharide. The hydrodynamic radius increased with increasing Debye length (decreasing ionic strength) for solutions containing the divalent cations, but the dependence was much weaker than that seen in the KCl solutions. For example, the hydrodynamic radius of Serotype A at a Debye length of 4.3 nm, corresponding to an ionic strength of 5 mM, is only 96 nm in CaCl2 and 94 nm in MgSO4 compared to 132 nm in KCl. This is likely due to the ability of the divalent Ca2+ or Mg2+ to form a salt bridge between the negatively charged carboxylic acid groups, reducing the expansion of the polysaccharide in the low ionic strength solutions [53, 106]. This behavior was not seen with thyroglobulin (Figure 4.4). The hydrodynamic size of thyroglobulin in KCl buffer was the same as the size in CaCl2 (as expected) since intra-molecular electrostatic interactions are unimportant in determining the size of the protein molecule.

4.3.1.1.3 Effect of pH

The effect of solution pH on the effective hydrodynamic radius of the polysaccharides is shown in Figure 4.5. The effective radius of neutral Serotype C was independent of pH (푅푆퐸퐶=

24 ± 1.2 nm) as expected. In contrast, the hydrodynamic radius of Serotypes A and B decreased with decreasing solution pH, particularly in the low ionic strength solution, consistent with a reduction in the degree of ionization of the carboxylic acid groups (푝푘푎 ≈ 4.2). This effect was most pronounced for the more highly charged Serotype A, with the radius decreasing from 132 to 53 nm as the pH was reduced from pH 7 to 5 in the 5 mM ionic strength solution. The net result is that Serotype A has a smaller effective radius than Serotype B at pH 5 and low ionic strength (53 vs 69 nm), with the reverse behavior seen at both higher pH and higher ionic strength.

The large change in effective size of Serotype A with solution pH is likely due to the effects of charge regulation. The carboxylic acid groups in both Serotypes A and B should have similar 푝퐾푎 values. However, the high linear charge density of Serotype A will lead to a strong partitioning of positive H+ ions into the void space within the polysaccharide chain. This will lead to a decrease in local pH (= − log[퐻+]) and in turn an increase in the protonation of the negatively-charged carboxylic acids, reducing the intra-molecular electrostatic repulsion and the effective size of the polysaccharide. This type of charge regulation will be less pronounced with

Serotype B due to the lower linear charge density and thus the weaker partitioning of H+.

Figure 4.5 Effective hydrodynamic radius determined by size exclusion chromatography as a function of pH at solution ionic strengths of 5 and 100 mM for Serotypes A, B, and C.

4.3.1.2 Conjugates

4.3.1.2.1 Effect of Ionic Strength

The effective hydrodynamic radius of the conjugates for Serotypes A, B, and C, again evaluated directly from the measured SEC retention volumes using the dextran standards for calibration, are shown in Figure 4.6. The conjugates were produced by reacting an activated version of the native polysaccharides with the protein CRM197. The effective radii of the conjugates increase with increasing Debye length (decreasing ionic strength), similar to the behavior seen with the free polysaccharides. For example the hydrodynamic radius of Conjugate

A increases from 12 to 38 nm as the ionic strength is reduced from 250 to 5 mM; this compares 69

to an increase from 52 to 132 nm for the native Serotype A. The effective size of Conjugate C increases slightly with decreasing ionic strength (from 14 to 17 nm), even though Serotype C is electrically neutral; this is likely due to the small net negative charge on the CRM197 at pH 7

(isoelectric point of the CRM197 is around pH 5.5). The net result is that Conjugate C has the largest size at high ionic strength but is much smaller than Serotypes A and B at low ionic strength (large Debye length).

Figure 4.6 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for Conjugates A, B, and C as a function of Debye length.

4.3.1.2.2 Effect of pH

The effect of solution pH on the effective hydrodynamic radius of the conjugates is shown in Figure 4.7. Experiments were only performed at pH 6 and 7 due to the instability of the

conjugates at lower pH. In all cases, the hydrodynamic radius of the conjugate was smaller at pH

6 than at pH 7, with this difference being greater in the low ionic strength solution. This behavior is consistent with a reduction in the intra-molecular electrostatic interactions associated with a decrease in the net charge of the polysaccharide and the CRM197 as one moves towards the isoelectric point. However, the effect of solution pH on the effective radius was relatively small.

For example, the radius of Conjugate A only decreased from 38 to 35 nm as the pH was reduced from pH 7 to 6 in the 5 mM ionic strength solution.

Figure 4.7 Effective hydrodynamic radius determined by size exclusion chromatography as a function of pH at solution ionic strength of 5 and 100 mM for Conjugates A, B, and C.

4.3.2 Theoretical Analysis

The size of the semi-flexible polysaccharides can be evaluated using the worm-like chain model as discussed in detail in Chapter 2. The radius of gyration is expressed in terms of the persistence length (퐿푝) and the contour length (퐿) as [83]:

퐿⁡퐿푝 푅2 = 푔0 3 Equation 4.4 with the effective persistence length for the charged polysaccharides defined as [107]:

퐿푝 = 퐿표 + 퐿퐸 Equation 4.5

where LE accounts for the increase in chain stiffness due to intra-molecular electrostatic interactions and Lo is the intrinsic persistence length for the uncharged polymer, i.e., the persistence length in the limit of very high solution ionic strength where electrostatic interactions are negligible.

Several authors have shown that the electrostatic contribution to the apparent persistence length scales linearly with the Debye length and the linear charge density [83, 87, 88]:

퐿퐸 = 훾⁡휉⁡휆퐷 Equation 4.6 where 휉 is the number of charge groups per Bjerrum length (the distance at which the electrostatic interaction energy between two elementary charges is approximately equal to the thermal energy, 푘퐵푇):

푒2 휆퐵 = 4휋휀표휀푟푘퐵푇 Equation 4.7

where 휆퐵= 0.71 nm in aqueous solution and 훾 is a proportionality constant .

The calculated values of the apparent (total) persistence length for Serotypes A, B, and C, calculated directly from Equation 4.4 using the contour lengths given in Table 4.1, are plotted in

Figure 4.8 as a function of the charge parameter, 휉휆퐷. In each case, the contour length was evaluated assuming that the length of each saccharide monomer was 0.4 nm [108, 109]. The Lp data for Serotype A show a highly linear dependence on Debye length (R2 >0.99); the results for

Serotype B show somewhat greater scatter although the R2 value is still greater than 0.97. The different slopes for the two serotypes could be due to errors in the calculated values of the contour length arising from uncertainties in the molecular weight of the polysaccharides

(including the presence of a molecular weight distribution). The best fit value of the slope for

Serotype A gives 훾 = 3.2 while that for Serotype B is 훾 = 7.2; these values are in fairly good agreement with correlations presented previously for charged pullulans (훾 = 3.7) [83] and for charged polyacrylamide-polyacrylate polymers (훾 = 3.6) [107]. Note that the intrinsic persistence length can be evaluated from extrapolation of 퐿푝 to 휉휆퐷= 0, yielding a value between 0.25 and 1 nm. This is much smaller than the value of 퐿푝= 6 nm reported by Harding et al. [13] based on data for the intrinsic viscosity and sedimentation coefficient of a range of polysaccharides from

S. pneumoniae at pH 6.8 and 0.1 M ionic strength.

Figure 4.8 Total persistence length vs the product of the linear charge density and the Debye length for Serotypes A, B, and C.

Although Equation 4.5 and Equation 4.6 provide a convenient approach for evaluating the apparent (total) persistence length, and in turn the radius of gyration using Equation 4.4, the value of 훾 needs to be determined experimentally. An alternative approach for evaluating the effective radius of a charged polysaccharide considering the increase in the excluded volume by introducing the electrostatic interactions is presented by [83]:

2 2 2 푅푔 = 푎푠 ⁡푅푔0 Equation 4.8 where 푅푔표 is given by Equation 4.4 and 푎푠 is an expansion parameter given by the Yamakawa-

Tanaka equation [89]:

2 0.46 푎푠 = 0.541 + 0.459⁡(1 + 6.04⁡퐸) Equation 4.9

At moderate ionic strength, the excluded volume contribution is dominated by electrostatic interactions, with

33/2 퐸 = ⁡푑 ⁡퐿1/2⁡퐿 −3/2 16⁡휋1/2 푒푓푓 푝 Equation 4.10

with deff the effective diameter of the polymer chain [90]:

휆퐵 푑푒푓푓 = 휆퐷[−퐿푛 ( ) + 2 ln(휉) + 2.61] 휆퐷 Equation 4.11

Equation 4.11 gives small (or negative) values of 푑푒푓푓 at high ionic strength; thus, the minimum value of 푑푒푓푓 was taken as 0.6 nm as discussed by [83].

The total persistence length (퐿푝) in Equation 4.10 is given by Equation 4.5 with the electrostatic contribution evaluated explicitly as [85, 86]:

2 2 휉 ⁡휆퐷 퐿퐸 = 4⁡휆퐵⁡ Equation 4.12

Note that LE given by Equation 4.12 scales with the square of the charge parameter.

The model calculations are compared with the experimental data in Figure 4.9. The model predictions are in good agreement with the data for Serotypes B and C, but the model clearly over-predicts the radius of gyration for Serotype A. The reason for this discrepancy is unclear.

Figure 4.9 Experimental radius of gyration vs the theoretical radius of gyration for polysaccharide Serotypes A, B, and C.

4.3.3 Dynamic Light Scattering

The effective size of the polysaccharides was also determined from the intensity autocorrelation function as discussed in Chapter 2. The autocorrelation function (푔2(휏)) was fit to a decaying exponential in terms of the diffusion coefficient 퐷 as:

푔2(휏) = 퐴 + 훼⁡푒푥푝(−2⁡퐷⁡푞2⁡휏) Equation 4.13 where A is the baseline at infinite delay, 훼 is the correction function amplitude at zero delay, and q is the scattering vector. The hydrodynamic radius, 푅ℎ, was evaluated from the Stokes-Einstein equation as:

푘 ⁡푇 푅 = 퐵 ℎ 6⁡휋⁡휇⁡퐷 Equation 4.14 where 푘퐵 is the Boltzmann constant and 푇 is the absolute temperature.

4.3.3.1 Single Angle Detector

Table 4.2 shows the calculated radii of the different polysaccharide serotypes as determined by dynamic light scattering at pH 7 and 4.2 (isoelectric point of polysaccharide). The

DLS radius was determined from the dynamic fluctuations of the scattered light intensity at a scattering angle of 173˚ using Equation 4.13 and Equation 4.14. The results at pH 7 from both

DLS and SEC are in excellent agreement for Serotypes A and B. Corresponding data at pH 4.2 were indistinguishable from the data at neutral pH as expected. Note that this method could not be used for polysaccharide size measurements at low ionic strength due to the low scattering intensity under these conditions as a result of the very open polysaccharide confirmation.

Table 4.2 Effective size of polysaccharides Serotype A and B in 250 mM buffered KCl solution at pH 7 and 4.2 as determined by dynamic light scattering at 173˚.

DLS Radius (nm)

pH 4.2 pH 7

Serotype A 51 ± 4 53 ± 4

Serotype B 32 ± 1 32 ± 1

4.3.3.2 Multi Angle Detector

Limited data were obtained using a Malvern OMNISEC system, which is equipped with refractive index, viscosity, and multi-angle light scattering detectors. Figure 4.10 shows typical data for Serotype A with the different peaks associated with each of the three detectors. The refractive index detector is used to provide a measure of the sample concentration. The light scattering detector provides absolute molecular weight measurements, while the viscometer provides information on molecular size.

Figure 4.10 Typical chromatogram for polysaccharide Serotype A using the Malvern OMNISEC system.

The molecular weight and hydrodynamic radius of polysaccharide Serotypes A and B along with Conjugate B are summarized in Table 4.3; the error estimates were evaluated from the standard deviation of repeat measurements. The effective radius of Serotypes A and B are 44 nm and 29 nm, respectively, as determined by the MALS results. These values are in excellent agreement with the extrapolated values given by the SEC data in Figure 4.3 in the limit of zero

Debye length (infinite ionic strength), which gives 푅푆퐸퐶≈ 43 and 26 nm for Serotypes A and B, respectively. Conjugate B shows the largest molecular weight but the smallest hydrodynamic

radius. This is likely due to the more compact structure of the conjugate arising from internal cross-linking by the CRM197, although the results could also be influenced by the high degree of polydispersity in the conjugate (푀푤/푀푛 = 2.33 compared to values of 1.26 and 1.74 for

Serotypes A and B).

Mn (kDa) Mw/Mn Rh (nm)

Serotype A 482 ± 8 1.26 44.2 ± 0.5

Serotype B 389 ± 3 1.74 28.7 ± 0.2

Conjugate B 527 ± 4 2.33 21.4 ± 0.1

4.4 Conclusions

The experimental results presented in this Chapter provide some of the first quantitative data for the effects of solution ionic strength and pH on the effective radius of capsular polysaccharides and polysaccharide-protein conjugates, both of which are of interest in the production of multivalent vaccines against bacterial infections. The effective size increased significantly at low ionic strength due to the expansion of the polysaccharide due to intra- molecular electrostatic interactions. Similar effects were seen with the conjugates, including the conjugate formed from the neutral Serotype C, with the electrostatic interactions now due to the presence of the charge groups on the protein. Limited experiments performed using dynamic

light scattering showed that the hydrodynamic radius evaluated from DLS was in very good agreement with the corresponding values measured using SEC in the limit of very high ionic strength (zero Debye length).

The experimental data for the native polysaccharides were analyzed using available models for the radius of gyration of charged polymers accounting for both the change in persistence length and the excluded volume associated with the intra-molecular electrostatic interactions. The model calculations were in good qualitative agreement with the experimental results, providing a framework for analysis of the SEC behavior of the charged polysaccharides in different solution conditions. Interestingly, the coefficient describing the electrostatic persistence length in Equation 4.6 was very similar to that evaluated previously for charged pullulans and polyacrylamide-polyacrylate polymers, suggesting that this parameter may be largely independent of the detailed structure of the polysaccharide. It was not possible to apply this theoretical framework to the analysis of the polysaccharide-protein conjugates since the worm-like chain model does not provide an appropriate physical model for the structure of the conjugates. Additional work will be required to fully elucidate the structure and charge characteristics of these complex polysaccharide-protein conjugates.

Chapter 5

Ultrafiltration Behavior of Bacterial Polysaccharides Used in Vaccines

Note: Most of the material presented in this chapter was previously published [110].

5.1 Introduction

As discussed in Chapter 1, ultrafiltration is widely used for the purification of polysaccharide-based vaccines against pneumococci and meningococci, but there is currently no fundamental understanding of the factors controlling the ultrafiltration behavior of these polysaccharides. The objective of this Chapter was to obtain quantitative data for the transmission of both native and activated forms of Pneumococcus polysaccharide serotypes A, B, and C through a series of ultrafiltration membranes with different pore size. Ultrafiltration data were obtained in a stirred cell as a function of filtrate flux. Data were analyzed using the classical concentration polarization model along with available hydrodynamic models, with the effective size of the different polysaccharides determined from size exclusion chromatography and dynamic light scattering as discussed in Chapter 4. The results provide important insights into the factors controlling the ultrafiltration behavior of bacterial polysaccharides of interest in bioprocessing applications.

5.2 Materials and Methods

Buffer solutions were prepared by dissolving 150 or 250 mM KCl with 10 mM Bis-Tris in deionized water. The solution pH was set to 7 using small amounts of 1 M HCl as required.

Buffer solutions were prefiltered through 0.2 μm pore size membranes prior to use. Purified capsular polysaccharides of the S. pneumoniae bacteria were provided by Pfizer Inc., including both the native and activated versions of Serotypes A, B, and C. The activated polysaccharides are produced by partial oxidation of the native polysaccharides with periodate followed by reaction with sodium borohydride [111, 112]. This process causes partial hydrolysis of the native polysaccharide thus reducing the effective size (as shown in

Table 5.1). The polysaccharides were diluted to the desired concentration using a buffered KCl solution before filtering through 0.2 μm Acrodisc® syringe filters to remove any un-dissolved material. The polysaccharides were stored at 4 oC and slowly brought to room temperature (23 ± 2 oC) before use in the experiments.

Table 5.1 Hydrodynamic radius of various polysaccharides as measured using SEC (discussed in Chapter 4) in 250 mM buffered KCl at pH 7.

Polysaccharide Effective Hydrodynamic Radius, Rh (nm)

Native Serotype A 52

Native Serotype B 32

Activated Serotype B 20

Activated Serotype C 24

Ultrafiltration experiments were performed using Ultracel™ and Biomax™ membranes with nominal molecular weight cut-offs (MWCO) between 30 and 1000 kDa. Small 25 mm diameter disks (cut from large flat sheets) were soaked in isopropyl alcohol for 45 min before flushing with at least 100 L/m2 of deionized (DI) water. Limited experiments were performed

using Durapore® 0.1 and 0.2 μm microfiltration membranes (Millipore Corp., MA) and Ultipor® grade DV50 (Pall Corp, MI) virus filtration membranes with an effective pore size of approximately 50 nm.

Ultrafiltration data were obtained in a 10 mL stirred cell with the membrane placed on top of a porous support to provide mechanical stability. The stirring speed was adjusted using a digital stroboscope. The stirred cell was connected to a feed reservoir that was pressurized with compressed air at 7-70 kPa (corresponding to 1-10 psi) as determined using a digital differential pressure gauge. All filtration experiments were performed at room temperature (23 ± 2ºC) with samples stored at 4 ºC until analysis.

The membrane hydraulic permeability (Lp) was evaluated by measuring the filtrate flux as a function of the transmembrane pressure using a 250 mM buffered KCl solution at pH 7:

휇퐽 퐿 = 푣 푝 ∆푃 Equation 5.1 where 휇 is the solution viscosity, 퐽푣 is the filtrate flux, and ∆푃 is the transmembrane pressure. In each case, the permeability was evaluated from the slope of data for 퐽푣 as a function of ∆푃 using simple linear regression fits.

The stirred cell was then emptied, refilled with the polysaccharide solution, and connected to a pressurized feed reservoir. The filtrate flux was controlled using a peristaltic pump connected to the outlet tubing from the stirred cell. Samples were obtained periodically with the observed sieving coefficient calculated as:

퐶푓 푆푂 = 퐶푏 Equation 5.2

where 퐶푓 and 퐶푏 are the polysaccharide concentrations in the filtrate and bulk solutions, respectively. The polysaccharide concentrations were evaluated by SEC as described in Chapter

5.3 Results and Discussions

5.3.1 Effect of Membrane Pore Size

Figure 5.1 shows typical results for ultrafiltration of polysaccharide Serotype A in 150 mM buffered KCl at pH 7 through various ultrafiltration and microfiltration membranes. Data were obtained at a transmembrane pressure of 1-5 psi (except 30 psi for the very low permeability DV50 membrane) with membranes having a range of pore size, including ultrafiltration (Ultracel™ 1000 kDa and Omega 300 kDa), virus filtration (DV50), and microfiltration (Durapore®) membranes. These membranes are made of a variety of polymers

(cellulose, polyethersulfone, and polyvinylidene fluoride); the effects of membrane polymer and pore size are discussed in more detail subsequently. In each case, the observed sieving coefficient was evaluated as the ratio of the polysaccharide concentration in the filtrate solution to that in the feed as determined from the peak areas in size exclusion chromatography; this eliminated possible artifacts associated with the presence of any low molecular weight impurities in the polysaccharide solutions. The polysaccharide transmission increases with increasing membrane pore size as expected. The sieving coefficient of Serotype A is less than 5% for membranes with pore radius smaller than 15 nm, consistent with the 52 nm radius of the polysaccharide. The larger pore size microfiltration membranes are essentially non-retentive to

Serotype A, consistent with the >100 nm pore size of these membranes.

Figure 5.1 Observed sieving coefficient of 1 g/L solutions of Serotype A polysaccharide in 150 mM buffered KCl at pH 7 during ultrafiltration through various membranes at a transmembrane pressure of 1-5 psi (30 psi for DV50).

Figure 5.2 shows results for transmission of the activated Serotype B through a series of

Ultracel™ membranes with different nominal molecular weight cut-offs (permeabilities and pore size summarized in Table 5.2). Transmission of the activated Serotype B increased with increasing membrane pore size (similar to the behavior seen in Figure 5.1). There is almost complete retention of the activated Serotype B by the small pore size Ultracel™ 30 kDa membrane, while transmission through the Ultracel™ 1000 kDa membrane approached 90% at high filtrate flux. The dependence of the transmission on the filtrate flux is discussed in more detail in the following section.

The nearly complete transmission of Serotype B through the Ultracel™ 1000 kDa membrane at high filtrate flux seen in Figure 5.2 is surprising given the large effective size of the polysaccharide (

Table 5.1). The mean pore size of the Ultracel™ 1000 kDa membrane can be estimated from the measured value of the hydraulic permeability assuming that the membrane is composed of a uniform array of cylindrical pores:

2 휀⁡푟푝 퐿 = 푝 8⁡훿 Equation 5.3

where 휀 is the membrane porosity and 훿푚 is the thickness of the skin layer of the asymmetric membrane. This gives 푟푝= 9.4 nm using 휀 = 0.5 and 훿푚 = 1 µm. Thus, activated Serotype B polysaccharide is more than 2 times as large as the mean pore size of the Ultracel™ 1000 kDa membrane. This behavior is discussed subsequently.

Table 5.2 Hydraulic permeability and mean pore radius of the Ultracel™ and Biomax™ membranes.

Membrane MWCO Permeability (m) Average pore radius (nm)

Ultracel™ 30 kDa 0.74 ×⁡10−12 3.4 ± 0.2

Ultracel™ 100 kDa 3.2 ×⁡10−12 7.1 ± 0.1

Ultracel™ 300 kDa 4.4 ×⁡10−12 8.4 ± 0.1

Ultracel™ 1000 kDa 5.5 ×⁡10−12 9.4 ± 0.1

Biomax™ 300 kDa 5.4 ×⁡10−12 9.3 ± 0.1

Biomax™ 500 kDa 17 ×⁡10−12 16 ± 0.8

Biomax™ 1000 kDa 32 ×⁡10−12 22 ± 1

5.3.2 Effect of Filtrate Flux

Typical data for the observed sieving coefficient (푆표) as a function of filtrate flux for the ultrafiltration of a 0.1 g/L solution of native Serotype B through the Biomax™ 300 kDa membrane are shown in Figure 5.3. Figure 5.3 also shows corresponding data for an equivalent size dextran (dextran with the same hydrodynamic size as Serotype B as determined by SEC).

The much larger molecular weight of the dextran (2600 kDa compared to 710 kDa for Serotype

B) indicates that Serotype B has a more open (less dense) configuration in solution than a dextran with the same molecular weight. This is likely due to the presence of the charged glucuronic acids, which will cause intra-molecular electrostatic repulsion even in the 250 mM ionic strength solutions. The sieving coefficients for both the polysaccharide and the dextran were a strong function of filtrate flux. For example, the sieving coefficient for Serotype B increased from 푆표= 0.05 to 푆표 = 0.95 as the flux increased from 1 to 20 µm/s (corresponding to

3.6 to 72 L/m2/h). There was no evidence of any fouling in these experiments; the permeability of the membrane evaluated after the ultrafiltration experiment was statistically identical to that

-12 for the clean membrane (퐿푝= 5.3 ± 0.1 × 10 m after the ultrafiltration compared to 5.4 ± 0.1 ×

10-12 m). The sieving data were also highly reproducible; repeat experiments performed with the

Biomax™ 300 kDa membrane gave sieving coefficients that were within ±5% of the values seen in Figure 5.3.

Figure 5.4 compares the ultrafiltration behavior of native Serotype B with that of the activated version of Serotype B. The observed sieving coefficient for the activated polysaccharide increases with increasing filtrate flux, similar to the behavior seen with the native

Serotype B and the equivalent dextran. However, the transmission of the activated Serotype B is uniformly larger than that for the native serotype, consistent with the smaller hydrodynamic size

(radius of 20 nm for the activated serotype compared to 32 nm for the native Serotype B).

5.3.3 Concentration Polarization

In order to obtain a better understanding of the dependence of the polysaccharide sieving coefficient on the filtrate flux seen in the previous sections, the data were analyzed using the classic stagnant film model [64]:

푆푎 푆표 = 퐽푣 (1 − 푆푎)⁡푒푥푝 (− ) + 푆푎 푘푚 Equation 5.4 where 푆푎 is the actual sieving coefficient, equal to the ratio of the solute concentration in the filtrate solution to that in the solution immediately upstream of the membrane surface (typically

referred to as the wall concentration), 퐽푣 is the filtrate flux, and 푘푚 is the bulk mass transfer coefficient in the membrane device. Equation 5.4 can be re-arranged to provide a linearized relationship of the form:

1 1 퐽 푙푛 [ − 1] = 푙푛 [ − 1] − 푣 푆표 푆푎 푘푚 Equation 5.5

The data in Figure 5.3 and Figure 5.4 have been re-plotted in Figure 5.5 using the form given by Equation 5.5. The results for both the native and activated polysaccharides, as well as the dextran, are highly linear when plotted in the form given by Equation 5.5, demonstrating that the flux dependence for the observed sieving coefficient is due to concentration polarization effects in the stirred ultrafiltration cell.

The actual sieving coefficients and mass transfer coefficients for the different polysaccharides can be determined directly from the best fit values of the slope and intercept obtained by linear regression fits to the data as shown in Figure 5.5. This gives 푆푎= 0.06 and

-6 -6 푘푚= 3.1×10 m/s for the native Serotype B and 푆푎= 0.39 and 푘푚= 4.1×10 m/s for the activated version of Serotype B. The actual sieving coefficient and mass transfer coefficient for the activated polysaccharide are large than the values for the native Serotype B, consistent with the smaller size (and thus larger diffusion coefficient) of the activated polysaccharide [63].

Corresponding data for ultrafiltration of 0.1 g/L Serotype B polysaccharide through the

300 and 500 kDa ultrafiltration membranes in 250 mM buffered KCl at pH 7 are shown in Figure

5.6 and Figure 5.7. The polysaccharide transmission increases with increasing filtrate flux in both membranes, with the data obtained with the Biomax™ 500 kDa membrane lying uniformly above the results for the Biomax™ 300 kDa. The sieving data are highly linear when plotted in the form given by the polarization model (Figure 5.7), with identical slopes of 320,000 s/m for

-6 both membranes, corresponding to a mass transfer coefficient of 푘푚= 3.1×10 m/s. The intercept for the Biomax™ 500 kDa membrane gives an actual sieving coefficient of 푆푎= 0.21 compared to 푆푎= 0.06 for the Biomax™ 300 kDa, consistent with the larger pore size for the Biomax™ 500 kDa membrane (16 vs 9.3 nm).

Figure 5.6 Observed sieving coefficients as a function of filtrate flux for 0.1 g/L solutions of Serotype B through the Biomax™ 300 and 500 kDa membranes. Data obtained in 250 mM ionic strength solutions at pH 7. Solid curves are model calculations based on the classical concentration polarization model as described in the text.

The solid curves in Figure 5.3, Figure 5.4, and Figure 5.6 were developed directly from

Equation 5.4 using the best fit values of 푆푎 and 푘푚. The model calculations are in very good agreement with the experimental data over the full range of filtrate flux, properly describing the increase in transmission to nearly 100% at the highest values of the flux. This good agreement strongly suggests that the flux dependence is due to concentration polarization effects in the stirred cell, with no evidence of any flow-induced elongation under the conditions of these experiments.

Concentration polarization can be affected by other factors such as stirring speed and membrane orientation, with these discussed in the following sections.

5.3.4 Effect of Stirring

Further confirmation of the concentration polarization effects were obtained by evaluating the effects of stirring speed on the polysaccharide transmission. Figure 5.8 shows results from an experiment in which the stirring speed was changed in a stepwise fashion. The first 5 min shows the buffer flux before the polysaccharide ultrafiltration. The feed was then switched to a solution of Serotype B polysaccharide and an ultrafiltration was performed in the presence of stirring for the first 15 min, at which point the stirring was turned off and the ultrafiltration was then continued for an additional 15 min. The final 5 min shows the buffer flux after the ultrafiltration; this final buffer flux was about 35% smaller than the initial flux, which is due to irreversible fouling after the 30 min ultrafiltration experiment. The effects of membrane fouling on the flux and polysaccharide transmission are discussed in Chapter 8. The filtrate flux declines throughout the ultrafiltration, with a slightly greater rate of flux decline after cessation of the stirring. The initial transmission of Serotype B was very high, consistent with the relatively high flux in this experiment. Serotype B transmission decreases from about 푆표= 0.94 to

0.62 during the stirred ultrafiltration but then increases rapidly to essentially 푆표= 1 when the stirring was stopped. This increase in transmission cannot be due to a flow-induced elongation of the polysaccharide since the filtrate flux in the latter half of the experiment is smaller than that in the first half. Instead, these data provide strong evidence for the effects of concentration polarization on Serotype B transmission. The removal of the stirring causes a significant reduction in the rate of bulk mass transfer, leading to an increase in concentration polarization and a corresponding increase in polysaccharide transmission.

Figure 5.8 Sieving coefficient and filtrate flux as a function of time for ultrafiltration of a 0.1 g/L solution of Serotype B polysaccharide through a Biomax™ 300 kDa membrane.

Figure 5.9 shows results from two experiments performed in the Amicon stirred cell, one at a stirring speed of 1200 rpm and one with the stirrer turned off. The observed sieving coefficient of Serotype B at a filtrate flux of 5 μm/s was greater than 0.99 in the absence of any stirring compared to a value of only 푆표 = 0.2 at a stirring speed of 1200 rpm. The much greater transmission in the absence of stirring is consistent with the much greater degree of concentration polarization; stirring increases back mass transport in the stirred cell, reducing the polysaccharide concentration at the membrane surface and in turn the observed sieving coefficient.

Figure 5.10 shows the effects of different stirring speeds on the sieving coefficient of

Serotype B polysaccharide. Data were obtained for two different Biomax 300 kDa membranes, one used in the normal orientation and one used with the reverse orientation. Stirring rates of “2” and “4” correspond to stirring speeds of 300 and 600 rpm, respectively. The sieving coefficient in the normal orientation decreases significantly with increasing stirring speed, which reflects the reduction in concentration polarization in the stirred solution above the membrane. In contrast, the sieving coefficient in the reverse orientation was nearly independent of the stirring rate since the bulk fluid flow has no significant effect on the degree of internal polarization within the highly asymmetric membrane substructure. The sieving coefficient evaluated in the absence of

any stirring was essentially independent of the membrane orientation; this reflects the very similar degree of polarization within the membrane substructure and in the unstirred bulk liquid above the membrane.

Figure 5.10 Sieving coefficient as a function of stirring rate for ultrafiltration of Serotype B polysaccharide through a Biomax™ 300 kDa membrane in the normal and reverse orientation. Data obtained with a 0.5 g/L solution in 250 mM buffered KCl solution at pH 7.

5.3.5 Hydrodynamic Analysis

The actual sieving coefficient is determined by both thermodynamic and hydrodynamic interactions between the solute and the pore as discussed in Chapter 2. This is commonly expressed in the form [69]:

푆푎 = 휙퐾푐 Equation 5.6 where 휙 is the equilibrium partition coefficient between the solution in the pore and that in the membrane (describing the thermodynamics) and 퐾푐 is the hindrance factor for convection.

Equation 5.6 is valid at high membrane Peclet numbers, i.e., under conditions where solute transport is dominated by convection. Giddings et al. [77] evaluated the partition coefficient for a solute in an isotropic porous network formed by the intersection of a random array of parallel planes as:

푅 휙 = 푒푥푝 (−⁡ ℎ) 푠 Equation 5.7 where 푅ℎ is the solute radius and 푠 is the specific area of the pore. Equation 5.7 has been used to analyze protein [68] and dextran [102] transport through a variety of ultrafiltration membranes.

Figure 5.11 shows results for the actual sieving coefficients for serotypes A, B, and C obtained with both the Ultracel™ and Biomax™ membranes. The polysaccharide radius was evaluated using SEC (discussed in Chapter 4 and listed in

Table 5.1), while the specific area of the pore was determined directly from the measured values of the membrane permeability using the expression given by Mochizuki and Zydney

[102]:

푘⁡훿푚⁡퐿푝 푠 = √ 휀 Equation 5.8 where 푘 is the Kozeny constant, which is typically about 5 for random porous media [113].

The solid line in Figure 5.11 is the model calculation given by Equation 5.6 to Equation

5.8 neglecting the hindrance factor for convection (i.e., assuming that 퐾푐= 1). More detailed analyses of the hydrodynamic interactions in cylindrical pores have shown that 퐾푐 only varies between 1 and 1.47 over the entire range of solute to pore size ratios as discussed in Chapter 2.

The specific area of the pore was evaluated using 푘 = 5 and 휀 = 0.5 [63], with the thickness of the membrane skin taken as 1 µm. Although there is considerable scatter in the data, the results do tend to collapse to a single line when plotted in the form given by Equation 5.7. However, the measured values of 푆푎 fall well above the model prediction (solid line). This could be due to errors in the values of 푘, 휀, and / or 훿푚 used to evaluate the specific area of the pore using

Equation 5.8. Alternatively, the highly flexible polysaccharides may have larger partition coefficients due to the ability of the polysaccharides to deform and enter even small pores in the membrane. The dashed line in Figure 5.11 is a best fit determined by minimizing the sum of the squared residuals between the model and data, with the best fit slope consistent with a 2-fold increase in 푠.

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Figure 5.11 Actual sieving coefficients as a function of the ratio of solute to pore radii for ultrafiltration of 0.1 g/L solutions of the different polysaccharides through Ultracel™ and Biomax™ membranes. The solid and dashed lines are given by Equation 5.7.

The actual sieving coefficient data for the activated form of Serotype C lie well above the best fit line in Figure 5.11. This could be related to the lack of any net electrical charge on

Serotype C; the 3 saccharides in the structure of Serotype C are all neutral, in contrast to the presence of negatively-charged glucuronic acids in Serotypes A and B polysaccharides. These charge groups would be expected to cause an expansion of the polysaccharide due to intra- molecular interactions, leading to a reduction in the actual sieving coefficient. The effect of polysaccharide charge on the ultrafiltration behavior is discussed in Chapter 6.

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5.4 Conclusions

Ultrafiltration is of considerable interest in the purification of capsular polysaccharides in the preparation of important conjugated vaccines. The data presented in this Chapter provide a quantitative analysis of the ultrafiltration behavior of these polysaccharides, including results with several different purified pneumococcus polysaccharide serotypes. Polysaccharide transmission in dilute solutions was a strong function of filtrate flux due to concentration polarization effects. For example the transmission of Serotype B polysaccharide increased by more than a factor of 4 as the filtrate flux was increased from 5 to 13 µm/s. The flux dependence of the transmission was well described using the classical stagnant film (or concentration polarization) model, with no evidence of any flow-induced elongation under these experimental conditions. The actual sieving coefficients for the different polysaccharides / membranes were in good qualitative agreement with predictions of the partitioning model originally presented by

Giddings et al. [77], providing a simple method for estimating polysaccharide transmission based on independent data for the polysaccharide size (e.g., by SEC), the membrane permeability, and the filtrate flux (which determines the extent of concentration polarization). This model could also be used to provide a priori predictions of polysaccharide transmission using the worm-like chain model to estimate the polysaccharide size in different ionic strength solutions as discussed in Chapter 4.

These results clearly demonstrate that it is possible to obtain high transmission of the bacterial pneumoniae polysaccharides through commercially available ultrafiltration membranes.

The Biomax™ polyethersulfone membranes provide significantly greater transmission than the

Ultracel™ composite regenerated cellulose membranes for the same nominal molecular weight cutoff. This is consistent with the difference in pore size for the Ultracel™ and Biomax™

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membranes. For example, the Biomax™ 1000 kDa membrane has an average pore size of 22 nm compared to only 9.4 nm for the Ultracel™ 1000 kDa (Table 5.2). In addition, polysaccharide transmission was significantly greater when the membranes were used in the reverse (skin-side down) orientation due to the high degree of concentration polarization that occurs within the membrane substructure in this orientation. Data obtained in the presence / absence of stirring also show the effects of concentration polarization on polysaccharide transmission (when the membrane is used in the normal orientation). Increasing the stirring speed reduces the extent of concentration polarization in the bulk solution, leading to a reduction in the polysaccharide concentration at the membrane surface and in turn a reduction in the observed sieving coefficient.

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Chapter 6

Effect of Electrostatic Interactions on the Ultrafiltration Behavior of Bacterial Polysaccharides

6.1 Introduction

As discussed in Chapters 1 and 4, the viscosity, thermodynamics, and hydrodynamic properties of charged polysaccharides are known to be strongly dependent on the solution ionic strength due to both inter- and intra-molecular electrostatic interactions. The objective of this work was to quantitatively investigate the effects of electrostatic interactions on the ultrafiltration behavior of several charged capsular polysaccharides used in vaccines.

Ultrafiltration data were obtained with various polysaccharide serotypes, all of which are of interest in the production of Pneumococcus vaccines, using Biomax™ ultrafiltration membranes having different nominal molecular weight cutoffs. The effective size of the polysaccharides in the different ionic environments was evaluated by size exclusion chromatography as discussed in Chapter 4. Results for the polysaccharides were compared with data obtained using a large globular protein (thyroglobulin) to obtain further understanding of the effects of polysaccharide conformation as well as inter- and intra-molecular electrostatic interactions on the ultrafiltration behavior. The results provide important fundamental insights and practical guidelines for exploiting the effects of electrostatic interactions during the ultrafiltration of charged polysaccharides.

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6.2 Materials and Methods

Buffer solutions were made by dissolving appropriate amounts of Bis-Tris powder in DI water. KCl was used to adjust the ionic strength of the buffer, with the ionic strength evaluated as:

1 퐼 = ∑ 푧2 퐶 2 푖 푖 푖 Equation 6.1 where 푧푖 is the valence and 퐶푖 is the concentration of the different ionic species. HCl was used to adjust the pH to 7. The buffer solutions were filtered through 0.2 μm pore size membranes to remove any particles or undissolved material prior to use.

Concentrated solutions of purified capsular polysaccharides were provided by Pfizer Inc.

The stock solutions were diluted to 0.1 g/L concentration using the appropriate buffer. The resulting solutions were prefiltered through 0.2 µm syringe filters (Acrodisc®) right after dilution. Data were also obtained with the globular protein thyroglobulin, which has a molecular weight of 660 kDa and an isoelectric pH of 4.6.

Ultrafiltration experiments were performed in a 10 mL stirred cell using Biomax™ membranes with nominal molecular weight cut-offs (MWCO) of 30, 100, and 300 kDa. A small membrane disk was cut from large membrane sheets and soaked in isopropyl alcohol solution before use. The membrane was then installed in the stirred cell and flushed with deionized water using at least 100 L/m2 membrane area. The stirred cell and feed reservoir were then filled with the polysaccharide solution of interest. Data were obtained at multiple values of the permeate flow rate, which was set and controlled by a pump placed on the outlet tubing from the stirred cell. Small samples from the bulk solution were taken before and after the experiment for

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subsequent analysis. The polysaccharide concentrations were then determined using size exclusion chromatography as discussed in Chapter 3. All filtration experiments were carried out at room temperature (23 ± 2ºC); samples were refrigerated (4 ºC) until they could be analyzed.

6.3 Results and Discussions

6.3.1 Filtrate Flux

Figure 6.1 summarizes a typical data set for the filtrate flux versus transmembrane pressure during ultrafiltration of 0.1 g/L solutions of Serotype A polysaccharide through the 30,

100, and 300 kDa Biomax™ membranes at both low (10 mM) and high (250 mM) ionic strength.

The 30 and 100 kDa membranes were both fully retentive to Serotype A (filtrate concentrations less than 0.001 g/L), while the 300 kDa membrane was partially permeable with the polysaccharide transmission increasing at higher filtrate flux. This is discussed in more detail subsequently. The filtrate flux through the 30 and 100 kDa membranes attains a nearly pressure- independent value at high pressures, consistent with the effects of concentration polarization.

The filtrate flux values in the two ionic strength solutions are similar at very low pressures, where the flux is dominated by the membrane resistance, but the flux obtained in the 10 mM ionic strength solutions are much larger than those in the 250 mM solutions at high transmembrane pressures (for both fully and partially retentive membranes). The filtrate flux was very similar for the fully retentive 30 and 100 kDa membranes (at any given ionic strength), but the flux was significantly larger for the partially permeable 300 kDa membrane. For example, the filtrate flux at a transmembrane pressure of 35 kPa (5 psi) was only 17 µm/s (60 L/m2/h) for the

30 and 100 kDa membranes at an ionic strength of 250 mM, but this increased to 28 µm/s (100

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L/m2/h) in the 10 mM solution and to 88 µm/s (320 L/m2/h) for the 300 kDa membrane in the low ionic strength solution. This behavior is discussed in more detail subsequently.

Figure 6.1 Filtrate flux versus transmembrane pressure for ultrafiltration of 0.1 g/L solutions of polysaccharide Serotype A through the Biomax™ 30, 100 and 300 kDa membranes in 10 and 250 mM ionic strength solutions.

6.3.2 Polysaccharide Ultrafiltration

The behavior of the observed sieving coefficient (푆표) during ultrafiltration of a 0.1 g/L solution of Serotype A at pH 7 through the partially permeable Biomax™ 300 kDa membrane over a range of KCl concentrations is shown in Figure 6.2. The observed sieving coefficient was evaluated as the ratio of the polysaccharide concentration in the permeate solution (퐶푓) to that in the bulk (퐶푏). The extent of fouling was minimal during the experiments; the permeabilities of

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the membrane evaluated before and after the ultrafiltration experiment were statistically indistinguishable from each other. The sieving coefficient data were highly reproducible; repeat experiments performed with the Biomax™ 300 kDa membrane gave very similar values of the sieving coefficients (shaded and the filled squares for the 250 mM solution in Figure 6.2).

The observed sieving coefficients increased at high flux due to concentration polarization effects as described by Hadidi et al. [110]. For example, the transmission of Serotype A in the

250 mM solution increased from 푆표= 0.05 to 푆표= 0.95 as the flux increased from 3 to 33 µm/s

(11 to 120 L/m2/h). At any given flux, transmission of the polysaccharide decreased with decreasing ionic strength. This effect was quite pronounced; the sieving coefficient at 퐽푣= 10

μm/s decreased from 푆표= 0.45 in the 250 mM solution to 푆표= 0.01 in the 5 mM ionic strength solution. This reduction in transmission at low salt concentrations is consistent with the significant increase in the effective radius seen in Figure 6.3, although there may also be repulsive electrostatic interactions between the negatively charged polysaccharides and the membrane. The solid curves in Figure 6.2 are model calculations developed using the concentration polarization model described in Chapter 5 and discussed in more detail subsequently.

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Figure 6.3 Schematic of the polysaccharide size in low and high ionic strength solutions.

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Corresponding data for transmission of Serotype B through a Biomax™ 300 kDa membrane are shown in Figure 6.4. The transmission increases with increasing filtrate flux and increasing ionic strength, similar to the results for Serotype A. However, the transmission of

Serotype B is uniformly larger than that seen for Serotype A, consistent with the much smaller values for the hydrodynamic radius of Serotype B as determined by SEC (52 nm for Serotype A vs 32 nm for Serotype B as discussed in Chapter 4). These differences are most pronounced at low salt concentrations due to the smaller charge density for Serotype B relative to that of

Serotype A.

Figure 6.4 Observed sieving coefficients for ultrafiltration of 0.1 g/L solutions of Serotype B through a Biomax™ 300 kDa membrane in Bis-Tris buffer at pH 7 with 5, 50, and 250 mM ionic strength. The solid curves are determined using the classical concentration polarization model.

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Corresponding data for ultrafiltration of Serotype C in 50 and 250 mM ionic strength solutions are shown in Figure 6.5. In this case, the transmission is independent of the solution ionic strength. For example, the observed sieving coefficient at 퐽푣= 10 μm/s is 푆표= 0.88 for both the 50 and 250 mM solutions. This behavior is consistent with the neutral structure of Serotype

C. The sieving coefficient for Serotype C is larger than the corresponding values for Serotype A and B at any given filtrate flux. For example at 50 mM ionic strength and 퐽푣= 10 μm/s, the sieving coefficient of Serotype C is 푆표= 0.88 compared to 푆표= 0.05 and 푆표= 0.66 for Serotypes A and B, respectively. This behavior is consistent with smaller effective size of Serotype C (푅푒푓푓=

23 nm) compared to the other serotypes (푅푒푓푓= 76 nm for Serotype A and 푅푒푓푓= 41 nm for

Serotype B in the 50 mM ionic strength solutions) combined with any possible effect of electrostatic interactions between the polysaccharides and the membrane.

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The effect of solution ionic strength on the sieving coefficients through the Biomax™

300 kDa membrane is shown explicitly in Figure 6.6 for all 3 polysaccharides along with the protein thyroglobulin (the filled and shaded circles show results for repeat measurements). The data were obtained at a low feed concentration (0.1 g/L) and low filtrate flux (10 µm/s = 36

L/m2/h) to minimize membrane fouling. The transmission of the neutral Serotype C was relatively constant over this range of ionic strength, with the values in the 10 mM ionic strength solution being slightly larger than the corresponding values at higher ionic strength. The data for thyroglobulin show a small increase in transmission with increasing ionic strength; this is likely due to shielding of the inter-molecular electrostatic repulsion from the charged membrane since

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the size / structure of thyroglobulin should not be significantly affected by the salt concentration over this range of conditions. In contrast, the sieving coefficients for Serotypes A and B increase significantly with increasing ionic strength due to the large reduction in the effective size of these charged polysaccharides.

6.3.3 Model Analysis

The actual sieving coefficients (푆푎), which provide a true measure of the membrane retention characteristics, were evaluated from the observed transmission by accounting for the effects of concentration polarization [110]:

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1 퐽 1 푙푛 [ − 1] = − 푣 + 푙푛 [ − 1] 푆표 푘푚 푆푎 Equation 6.2 where 푘푚 is the bulk mass transfer coefficient and 푆푎 = 퐶푓/퐶푤 where 퐶푓 is the polysaccharide concentration in the filtrate solution and 퐶푤 is the polysaccharide concentration in the solution immediately upstream of the membrane surface. Results for Serotype A are shown in Figure 6.7 at different KCl concentrations (corresponding to the observed sieving coefficient data in Figure

6.2). The data are highly linear when plotted in the form given by Equation 6.2, with the reciprocal of the slope equal to the mass transfer coefficient. The data in the 5 mM solution yield

푘푚= 11 μm/s compared to 푘푚= 5.3 μm/s in the 250 mM solution (based on simple linear regression fits), consistent with the higher values of the filtrate flux seen in Figure 6.1. The solid curves in Figure 6.2 and Figure 6.7 were evaluated using Equation 6.2 with the best fit values of

푆푎 and 푘푚 determined from linear regression fits to the observed sieving coefficient data. In each case, the model fits are in very good agreement with the experimental data, properly capturing the increase in the observed sieving coefficient with increasing filtrate flux.

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The corresponding linearized data for Serotype B are shown in Figure 6.8 (based on the data presented in Figure 6.4). The data for Serotype B are also highly linear when plotted in this fashion. The mass transfer coefficient in 5 mM solution is 푘푚= 13 μm/s compared to 푘푚= 5

μm/s in the 250 mM solution; these values are very similar to the mass transfer coefficients determined for Serotype A (data in Figure 7). The larger 푘푚 value in the low ionic strength solution is also consistent with the higher filtrate flux seen in Figure 6.1. The solid curves in

Figure 6.4 and Figure 6.8 were evaluated using Equation 6.2 with the best fit values of 푆푎 and

푘푚 determined from the linear regression fits to the observed sieving coefficient data.

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Figure 6.8 Sieving coefficient data plotted according to the linearized form of the concentration polarization model (Equation 6.2). Results are shown for Serotype B in 5, 50, and 250 mM KCl solutions at pH 7 during ultrafiltration through the Biomax™ 300 kDa membrane.

The best fit values of the actual sieving coefficients and mas transfer coefficients for each serotype at each ionic strength were evaluated from the intercept and slope of the linearized plots using Equation 6.2. Table 6.1 summarizes the data for Serotypes A, B, and C at various ionic strength conditions. The mass transfer coefficients increase with decreasing ionic strength due to the increase in the diffusion coefficient associated with the intermolecular electrostatic interactions. Note that Tivant et al. [114] reported a 4-fold increase in the diffusion coefficient of chondroitin sulfate (determined by dynamic light scattering) with decreasing ionic strength from

5.8 x 10-11 m2/s in a 1 M NaCl solution to 24.6 x 10-11 m2/s in a 10 mM NaCl solution.

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Table 6.1 Mass transfer coefficient and actual sieving coefficient for polysaccharides Serotype A, B, and C at various ionic strengths as evaluated from Equation 6.2.

Mass Transfer Actual Sieving Ionic Strength (mM) Coefficient, km (μm/s) Coefficient

5 11 ± 2 0.005 ± 0.001

20 8 ± 1 0.012 ± 0.002 Serotype A 50 7.1 ± 0.6 0.018 ± 0.003

250 5.3 ± 0.8 0.055 ± 0.028

5 12 ± 1 0.044 ± 0.009

Serotype B 50 6 ± 1 0.16 ± 0.09

250 5.0 ± 0.6 0.28 ± 0.15

Serotype C 250 2.3 ± 0.1 0.08 ± 0.02

The calculated values of 푆푎 are shown in Figure 6.9 as a function of the effective size of the polysaccharide (or protein) as determined from SEC (data in Chapter 4) at the same ionic strength as used in the ultrafiltration experiment. The results for polysaccharide Serotypes A, B, and C, along with the protein thyroglobulin, all collapse to a single line when plotted as the logarithm of 푆푎versus 푅푒푓푓. This behavior is in excellent agreement with the partitioning model presented by Giddings et al. [77] and discussed in the context of membrane sieving by Zydney and co-workers [68, 102]:

푆푎 = 휙⁡퐾푐 Equation 6.3

Equation 6.3 is valid at high membrane Peclet numbers where solute transport is determined primarily by convection. Hydrodynamic models for the hindrance factor for spherical solutes in

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cylindrical pores indicate that Kc varies from 1 to 1.47 over the entire range of pore size; thus, the actual sieving coefficient can be approximated as being equal to the partition coefficient.

More details of this analysis can be found in Chapter 2. The equilibrium partition coefficient for a rigid solute for a membrane with a pore size distribution can be approximated as [68, 102]:

푅푒푓푓 휙 = 푒푥푝 (− ) 푠 Equation 6.4 where 푅푒푓푓 is the effective hydrodynamic radius of the solute and s is the specific pore area. The slope of the best fit line in Figure 6.9 is equal to 1/푠 where 푠 = 27 nm using linear regression analysis. This value for 푠 is somewhat larger than the effective membrane pore size, evaluated as

푟푝= 9 nm using the Hagen-Poiseuille equation, similar to the behavior discussed previously in

Chapter 5; this is likely due to the deformability of the large polysaccharides.

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Figure 6.9 Actual sieving coefficients versus the effective solute radius for polysaccharide Serotypes A, B, and C and thyroglobulin using Biomax™ 300 kDa membranes.

6.4 Conclusions

Polysaccharides are used as the main component of several current vaccine products. The solution ionic strength has a significant impact on the biophysical properties of these charged polysaccharides due to a combination of intra- and inter-molecular electrostatic interactions. The data presented in this Chapter provide the first quantitative analysis of the effects of electrostatic interactions on the ultrafiltration behavior of several charged polysaccharide serotypes obtained from Streptococcus pneumoniae and used in the production of Pneumococcus vaccines.

Polysaccharide transmission in dilute solutions decreased with decreasing ionic strength. For example, the sieving coefficient of Serotype A at 퐽푣= 10 μm/s decreased from 푆표= 0.45 in a 250

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mM solution to 푆표= 0.01 in a 5 mM ionic strength solution. This effect was less pronounced for

Serotype B, consistent with its smaller charge density compared to Serotype A. Transmission of

Serotype C was almost independent of the ionic strength, which was consistent with its neutral structure. These changes in polysaccharide transmission were consistent with the observed changes in the effective size of the individual polysaccharides as determined by size exclusion chromatography.

The data for the actual sieving coefficients for the different serotypes, determined using the linearized form of the concentration polarization model, collapsed to a single curve when plotted as a function of the effective hydrodynamic radius (given by SEC). This behavior was in excellent agreement with available theoretical models for solute partitioning in porous membranes [68, 77, 102]. The relatively small effect of solution ionic strength on the transmission of the charged protein thyroglobulin provides further demonstration that the dependence of the increase in the sieving coefficients of the charged polysaccharides with increasing ionic strength is due primarily to the shielding of the intra-molecular electrostatic interactions governing the expansion of the polysaccharide at low salt concentrations.

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Chapter 7

Ultrafiltration Behavior and Structural Characteristics of Conjugated Bacterial Polysaccharides

7.1 Introduction

As discussed in Chapter 1, one of the critical steps in the purification of conjugated vaccine products is the removal of any unreacted (residual) polysaccharide. In principle, this can be done by ultrafiltration if one can identify a membrane / operating conditions that provide significant transmission of the free polysaccharide through the membrane while the conjugate is highly retained. The objective of this Chapter was to obtain quantitative data on the ultrafiltration behavior of polysaccharide conjugates produced from Serotypes A, B, and C. Ultrafiltration experiments were performed with a series of Biomax™ and Ultracel™ membranes in a small- scale stirred cell. Data were analyzed using available theoretical models to obtain a more quantitative description of the ultrafiltration behavior.

7.2 Materials and Methods

Buffer solutions were prepared by dissolving appropriate amounts of KCl and Bis-Tris in deionized water. The solution pH was set to 7 by addition of small amounts of 1 M HCl as required. Buffer solutions were prefiltered through 0.2 μm pore size membranes prior to use.

Purified conjugated capsular polysaccharides of the S. pneumoniae bacteria were provided by

Pfizer Inc. The conjugates were formed by coupling an activated (partially hydrolyzed) form of

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the capsular polysaccharide to the small protein CRM197. The conjugates were diluted to 0.1 g/L concentration using buffered KCl solution before filtering through 0.2 μm Acrodisc® syringe filters to remove any un-dissolved material. Table 7.1 summarizes the mean hydrodynamic size of the conjugates as determined using SEC (details provided in Chapter 4). All solutions were stored at 4 oC and slowly brought to room temperature (23 ± 2 oC) before use in the experiments.

Table 7.1 Mean hydrodynamic size of Conjugates A, B, and C determined from SEC measurements in both 5 and 250 mM buffered KCl solution at pH 7.

Hydrodynamic Radius, nm

Buffer 5 mM Buffer 250 mM

Conjugate A 35 ± 2 11 ± 1

Conjugate B 25 ± 1 11 ± 1

Conjugate C 14 ± 1 13 ± 1

Ultrafiltration experiments were performed using Ultracel™ and Biomax™ membranes with nominal molecular weight cut-offs (MWCO) between 30 and 500 kDa. Small 25 mm diameter disks were soaked in isopropyl alcohol for 45 min before flushing with at least 100

L/m2 of deionized (DI) water.

Ultrafiltration data were obtained in a 10 mL stirred cell with the membrane placed on top of a porous support to provide mechanical stability. The stirring speed was adjusted using a digital stroboscope. The stirred cell was filled with the conjugate solution and connected to a feed reservoir that was pressurized with compressed air at 7-70 kPa (corresponding to 1-10 psi) as determined using a digital differential pressure gauge. The filtrate flux was controlled using a

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peristaltic pump connected to the outlet tubing from the stirred cell. Samples were obtained periodically from the stirred cell and the filtrate solution, with the observed sieving coefficient calculated as:

퐶푓 푆푂 = 퐶푏 Equation 7.1 where 퐶푓 and 퐶푏 are the concentrations of the conjugate in the filtrate and bulk solutions, respectively. All filtration experiments were performed at room temperature (23 ± 2ºC) with samples stored at 4 ºC until analysis. The conjugate concentrations were evaluated by SEC as described in Chapter 3.

7.3 Results and Discussions

7.3.1 Effect of Membrane Material and Pore Size

Figure 7.1 shows typical results for ultrafiltration of Conjugate B in 250 mM buffered

KCl solution at pH 7 through various Ultracel™ and Biomax™ ultrafiltration membranes. The data were obtained over a wide range of filtrate flux. In each case, the observed sieving coefficient was evaluated as the ratio of the conjugate concentration in the filtrate solution to that in the feed as determined from the peak areas in size exclusion chromatography; this eliminated any artifacts associated with the presence of any low molecular weight impurities in the conjugate solutions. The SEC was performed using a UV detector, so there was no interference from any residual free polysaccharide (the polysaccharide itself had negligible absorbance at 280 nm). The conjugate transmission increases with increasing membrane pore size as expected, with the data for the Biomax™ membranes lying uniformly above the corresponding data for the

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Ultracel™ membranes with equal molecular weight cut-off. For example, the sieving coefficient of Conjugate B at a filtrate flux of 퐽푣= 40 μm/s is 푆표= 0.5 through the Biomax™ 300 kDa but only 푆표 = 0.2 through the Ultracel™ 300 kDa. This is consistent with larger pore size for the

Biomax™ membranes [115, 116] as discussed in Chapter 5. The observed sieving coefficient of the conjugate increases with increasing filtrate flux, similar to the behavior seen with the free polysaccharides. This increase is due to concentration polarization effects associated with the accumulation of retained conjugate at the upstream surface of the membrane.

Figure 7.1 Observed sieving coefficient as a function of filtrate flux for 0.1 g/L solutions of Conjugate B through various Ultracel™ and Biomax™ ultrafiltration membranes. Data obtained in 250 mM ionic strength solution at pH 7. Solid curves simply connect the data points at different filtrate flux.

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7.3.2 Concentration Polarization Analysis

To obtain a better understanding of the dependence of the conjugate sieving coefficient on the filtrate flux seen in Figure 7.1, the data were analyzed in terms of the linearized form of the stagnant film model [64] as discussed previously in Chapters 5 and 6:

1 1 퐽 푙푛 [ − 1] = 푙푛 [ − 1] − 푣 푆표 푆푎 푘푚 Equation 7.2 where 푆푎 is the actual sieving coefficient, equal to the ratio of the solute concentration in the filtrate solution to that in the solution immediately upstream of the membrane surface (typically referred to as the wall concentration), 퐽푣 is the filtrate flux, and 푘푚 is the bulk mass transfer coefficient in the membrane device.

The sieving coefficient data for ultrafiltration of 0.1 g/L solutions of Conjugates A, B, and C through the Biomax™ 300 kDa membrane are plotted in terms of the linearized form of the stagnant film model (Equation 7.2) in Figure 7.2. The data are highly linear when plotted in this form, highlighting the effect of concentration polarization during ultrafiltration of the conjugates.

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Figure 7.2 Linearized concentration polarization analysis of the sieving coefficient data for 0.1 g/L solutions of Conjugates A, B, and C in 250 mM KCl during ultrafiltration through Biomax™ 300 kDa membranes. Solid lines are linear regression fits.

The best fit values of the actual sieving coefficients and mass transfer coefficients for each conjugate can be determined directly from the slope and intercept of the linear regression fits to the data in Figure 7.2 as summarized in Table 7.2. The smaller value of the actual sieving coefficient for Conjugate A could be due to the high charge density of the base polysaccharide as discussed in Chapter 6. However, the origin of the high mass transfer coefficient for Conjugate C is unclear considering that it has the lowest charge density due to the neutral structure of the base polysaccharide. More studies would be required to understand this behavior.

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Mass Transfer Coefficient, km (μm/s) Actual Sieving Coefficient, Sa

Conjugate A 7.5 0.008

Conjugate B 14 0.084

Conjugate C 19 0.049

7.3.3 Effect of Stirring

The extent of concentration polarization is also a function of stirring. Figure 7.3 summarizes data for a stepwise stirring experiment where the stirrer was turned off in the middle of the ultrafiltration. The data were obtained using 0.1 g/L solutions of Conjugate B in 250 mM buffered KCl at pH 7 through a Biomax™ 300 kDa membrane. The ultrafiltration was performed for 15 min in the presence of 600 rpm stirring, at which point the stirrer was turned off and the ultrafiltration was then continued for an additional 15 min. The filtrate flux declines slightly throughout the ultrafiltration due to low levels of membrane fouling. The initial transmission of

Conjugate B is about 10%, but this increases rapidly to 30% when the stirring was stopped. This increase in transmission is a direct result of the increase in concentration polarization associated with the large reduction in bulk mass transfer in the absence of stirring, which more than compensates for the reduction in flux.

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Figure 7.3 Sieving coefficient and filtrate flux as a function of time for ultrafiltration of a 0.1 g/L solution of Conjugate B in 250 mM KCl through a Biomax™ 300 kDa membrane.

7.3.4 Effect of Membrane Orientation

Concentration polarization can also significantly be affected by membrane orientation when using asymmetric membranes due to the poor mass transfer in the unstirred fluid within the support layer of the membrane [117]. Figure 7.4 shows data for ultrafiltration of 0.1 g/L solutions of Conjugate B in 250 mM buffered KCl solution at pH 7 through two separate

Biomax™ 300 kDa membranes, one used in the normal (skin-side up) orientation and one used in the reverse (skin-side down) orientation. The observed sieving coefficient in the reverse orientation is significantly larger than that in the normal orientation. This is most pronounced at the higher filtrate flux values where concentration polarization is most significant. For example,

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Conjugate B has a sieving coefficient of 푆표= 0.2 at 퐽푣= 12 μm/s in the normal orientation compared to 푆표= 0.55 in the reverse orientation.

Figure 7.4 Effect of membrane orientation on the observed sieving coefficient during ultrafiltration of 0.1 g/L solutions of Conjugate B in 250 mM KCl through the Biomax™ 300 kDa membranes.

7.3.5 Effect of Ionic Strength

The net charge on the conjugate is determined by the combination of the ionizable groups in the base polysaccharide structure and those associated with the CRM197 protein. As discussed in Chapter 4 and shown in Table 7.1, the hydrodynamic size of the conjugates increases with decreasing solution ionic strength due to the expansion associated with intra-molecular electrostatic interactions. In order to obtain a better understanding of the effect of ionic strength

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on the ultrafiltration behavior of the conjugated polysaccharides, a series of experiments were performed at both low and high salt concentrations. Figure 7.5 shows data for ultrafiltration of

0.1 g/L solutions of Conjugate B in both 5 and 250 mM buffered KCl at pH 7 with the Biomax™

300 kDa membranes. The transmission increases with increasing filtrate flux at both ionic strengths due to concentration polarization. However, the sieving coefficient of Conjugate B is somewhat larger in the 250 mM solution, consistent with the smaller effective size (11 nm at 250 mM versus 25 nm at 5 mM).

Figure 7.5 Observed sieving coefficients as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of Conjugate B through Biomax™ 300 kDa membranes in 5 and 250 mM buffered KCl at pH 7.

Corresponding data for ultrafiltration of 0.1 g/L solutions of conjugate C in 5 and 250 mM buffered KCl at pH 7 through the Biomax™ 300 kDa membranes are shown in Figure 7.6.

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In this case, the observed sieving coefficient values in the 5 and 250 mM solution ionic strength solutions are nearly identical. This is consistent with the very low surface charge, which leads to a similar hydrodynamic size of Conjugate C in the two ionic strength solutions (14 nm at 5 mM vs 13 nm at 250 mM).

Figure 7.6 Observed sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of Conjugate C through Biomax™ 300 kDa membranes in 5 and 250 mM buffered KCl at pH 7.

7.3.6 Structural Characteristics

The chemical coupling of the polysaccharide to CRM197 occurs randomly, with multiple sites for conjugation present on both the polysaccharide and the protein. Additional information on the structural characteristics of the conjugate was obtained using size exclusion

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chromatography following the procedures described in Chapter 4. Figure 7.7 shows typical chromatograms for Conjugate C in the 5, 50, and 250 mM ionic strength solutions. The chromatograms were obtained using the UV detector (absorbance at 280 nm), which is essentially invisible to any free polysaccharide that might be present in the solutions. The SEC results show two distinct peaks, with the relative concentration (area) of the first eluted peak

(corresponding to the larger component) increasing with decreasing ionic strength. In addition, the locations of both peaks tend to shift to shorter times at low ionic strength, corresponding to an increase in effective size. The origin of the double peak for the conjugate is unclear, although it is known that these polysaccharide conjugates can self-associate in solution [34, 118]

Figure 7.7 Size exclusion chromatograms for Conjugate C in 5, 50, and 250 mM buffered KCl solutions at pH 7.

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Figure 7.8 shows the DLS data obtained with a Malvern OMNISEC system for an 0.1 g/L solution of Conjugate C in 250 mM buffered KCl at pH 7. The presence of two components is also visible in the DLS data with hydrodynamic radii of 15 and 45 nm for the smaller and larger components, respectively. These values are in good qualitative agreement with the radii determined by SEC (13 and 36 nm for the two components of Conjugate C).

Figure 7.8 Intensity as a function of size for dynamic light scattering of 0.1 g/l solutions of Conjugate C in 250 mM buffered KCl at pH 7 using a Malvern OMNISEC system (discussed in Chapter 4).

Corresponding chromatograms for Conjugate B are shown in Figure 7.9. The results are qualitatively similar to the data obtained for Conjugate C, with the relative concentration of the larger component increasing with decreasing ionic strength. The shift in peak location to shorter times at low ionic strengths is more pronounced for Conjugate B, which is consistent with the different charge on the base polysaccharides; Serotype B polysaccharide has 20% charged monomers while Serotype C is composed entirely of neutral sugars.

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Figure 7.9 Size exclusion chromatograms for Conjugate B in 5, 50, and 250 mM buffered KCl solutions at pH 7.

The SEC data for Conjugate A are shown in Figure 7.10. In contrast to the other two serotypes, there is no evidence of a split peak for Conjugate A, suggesting that this sample only contains a single component. The large increase in effective size of Conjugate A at low salt concentrations is due to the expansion of the highly charged conjugate, similar to the behavior seen with the native polysaccharide A.

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Figure 7.10 Size exclusion chromatogram for Conjugate A in 5, 50, and 250 mM buffered KCl solution at pH 7.

In order to obtain a better understanding of the effect of ionic strength on the effective size of the two components in the conjugate solutions, the results from Figure 7.7 to Figure 7.10 are plotted in Figure 7.11 as a function of the Debye length:

휀⁡푘퐵⁡푇 휆퐷 = √ 2 2⁡푁퐴⁡푒 ⁡퐼 Equation 7.3 where 휀 is the electrical permittivity of the solution, 푘퐵 is the Boltzmann constant, T is the absolute temperature, 푁퐴 is Avogadro’s number, e is the electron charge, and I is the solution ionic strength. Note that Figure 7.11 is equivalent to plotting the data versus I-1/2 due to the dependence of the Debye length on the ionic strength as given in Equation 7.3. 135

The effective radii of both components of Conjugate B increase with increasing Debye length. For example, the effective size of the larger component increases from 24 to 59 nm as the

Debye length increases from 0.61 to 4.3 nm. The size of the smaller component increases with increasing Debye length as well but with a slightly smaller slope. Similar behavior is observed for Serotype C but with a considerably smaller size dependence on Debye length, consistent with the smaller charge density of this serotype. However, the hydrodynamic radius of the larger component in Conjugate C increases 11 nm as the Debye length increases from 0.61 to 4.3 nm while the radius of the smaller component only increases 1 nm over the same range of Debye length. More detailed studies of this behavior might provide some insights into the nature of these components.

Figure 7.11 Effective hydrodynamic radius determined by size exclusion chromatography using dextran standards for both components of Conjugates A, B, and C as a function of Debye length.

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7.3.7 Conjugate Ultrafiltration in Series

The large difference in hydrodynamic size of the two components seen in the conjugate solutions is likely to have a significant effect on the ultrafiltration behavior. In order to obtain a better understanding of the ultrafiltration behavior of these conjugate solutions, the conjugate was pre-filtered to significantly reduce the amount of the larger component. In this case, an initial ultrafiltration was performed using a Biomax™ 300 kDa membrane with a 0.1 g/L solution of Conjugate C in the 250 mM ionic strength buffer; the use of a dilute solution minimized the extent of fouling. The permeate collected from this ultrafiltration was then used as the feed for a subsequent ultrafiltration.

Figure 7.12 shows SEC chromatograms of the feed solution, the retentate, and the filtrate obtained after ultrafiltration through the Biomax™ 300 kDa membrane. The filtrate solution is highly enriched in the smaller component, consistent with the nearly complete retention of the larger component by the Biomax™ 300 kDa membrane.

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Figure 7.12 Size exclusion chromatograms of the feed, retentate, and filtrate solutions obtained after ultrafiltration of Conjugate C in 250 mM buffered KCl solution at pH 7 through a Biomax™ 300 kDa membrane.

Since the filtrate solution collected after ultrafiltration through the Biomax™ 300 kDa was relatively dilute (퐶푓푖푙푡푟푎푡푒≈ 0.01 g/L), this solution was concentrated by ultrafiltration through an Ultracel™ 30 kDa membrane that was fully retentive to the conjugate (푆표< 0.01). The

30 kDa ultrafiltration was performed at low temperature and in a larger stirred cell (effective membrane area = 28.7 cm2) to minimize the risk of bacterial growth or conjugate degradation.

The volume was reduced by approximately 10-fold, giving a final conjugate concentration of approximately 0.1 g/L. This material was then used as the feed for a separate ultrafiltration experiment through a clean Biomax™ 300 kDa membrane

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Results are shown in Figure 7.13 along with corresponding data for ultrafiltration of a fresh solution of Conjugate C (without any pre-filtration). The overall transmission of the pre- filtered Conjugate C was 3-fold greater than the original feed, consistent with the smaller size of the single species in the pre-filtered solution.

Figure 7.13 Sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L solutions of the original and pre-filtered Conjugate C in 250 mM buffered KCl through Biomax™ 300 kDa membranes.

7.3.8 Hydrodynamic Model

The results in Figure 7.13 show that the low transmission of the conjugates is mainly due to the presence of the larger components in the feed solution that are almost completely retained by the membrane. The observed sieving coefficients for the smaller components in the solutions

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of Conjugates B and C were determined by assuming that the permeate was composed entirely of the smaller component, with the concentration of that component in the feed determined from the peak area in SEC (simply splitting the two peaks at the location of the minimum). The actual sieving coefficient (푆푎) for these components was then evaluated by extrapolation of the observed sieving coefficient data to 퐽푣= 0 since there was insufficient data to evaluate 푆푎 based on the linearized form of the polarization model. Figure 7.14 shows the results for 푆푎 as a function of the effective radius of the smaller component determined from the retention time of the appropriate peak in the SEC (Figure 7.11). The solid line is the calculation given by the hydrodynamic model as:

−⁡푅푒푓푓 푆 = exp ( ) 푎 푠 Equation 7.4 where 푅푒푓푓 is the effective hydrodynamic radius of the solute and 푠 is the specific pore area determined directly from the membrane permeability using the expression given by Mochizuki and Zydney [102]:

푘⁡훿푚⁡퐿푝 푠 = √ 휀 Equation 7.5 where 푘 is the Kozeny constant, which is typically about 5 for random porous media [113]. The average permeability of the Biomax™ 300 kDa membranes used in these experiments was 5.4

×10-12 m, giving 푠 = 7.4 nm based on a membrane thickness of 1 μm and a porosity of 0.5. This value is only slightly smaller than the effective membrane pore size evaluated using the Hagen-

Poiseuille equation (푟푝= 9 nm). The experimental data are in excellent agreement with the hydrodynamic model. This is consistent with the behavior observed with the free

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polysaccharides (Chapters 5 and 6) except that the value of 푠 required to fit the polysaccharide data was considerably larger for the free polysaccharides (푠 ≈ 27 nm for the Biomax 300 kDa membrane). Alternatively, one could use the same value of 푠 for both the conjugates and the free polysaccharides, but with the effective size of the polysaccharides (as determined by SEC) reduced by about a factor of three due to the very highly deformable structure of the polysaccharides. Also for comparison is the data for thyroglobulin protein which is in excellent agreement with the hydrodynamic model suggesting that the conjugates behave similar to hard proteins rather than deformable polysaccharides.

Figure 7.14 Actual sieving coefficients versus the effective solute radius for Conjugates B, C, and thyroglobulin using Biomax™ 300 kDa membranes.

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7.4 Conclusions

Ultrafiltration is of significant interest for the final purification of conjugated vaccines to remove both unreacted (free) polysaccharides and any residual protein (CRM197). The data presented in this Chapter provide one of the first quantitative studies of the ultrafiltration behavior of these protein-polysaccharide conjugates, including results with several different conjugate serotypes. Conjugates showed qualitatively similar ultrafiltration behavior to the unreacted polysaccharides, with the transmission in dilute solutions increasing with increasing filtrate flux (due to concentration polarization effects) and decreasing at low ionic strength (due to expansion of the conjugates caused by intra-molecular electrostatic interactions). For example, the transmission of Conjugate B through the Biomax™ 300 kDa membrane increased by more than a factor of 2.5 as the filtrate flux was increased from 3 to 40 µm/s. The flux dependence was well described using the classical stagnant film (concentration polarization) model, with no evidence of flow-induced elongation. The ultrafiltration behavior of various conjugated serotypes at different salt concentrations was in excellent agreement with available hydrodynamic models, with the effective size of the conjugates evaluated from the SEC measurements.

A detailed analysis of the conjugates by size exclusion chromatography demonstrated that these solutions contain at least two distinct components, with the relative concentration of the larger component increasing with decreasing salt concentration. Similar results were obtained using dynamic light scattering. The larger component was almost fully retained by the Biomax™

300 kDa membrane, with the overall transmission of a pre-filtered solution of Conjugate B being a factor of 3 greater than that for the fresh solution. Although the origin of the large component in the conjugate solutions is currently unclear, it is likely related to the reversible association of

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the conjugates into soluble aggregates. The actual sieving coefficients of the smaller component in the conjugate solutions are smaller than those of the polysaccharides with similar effective size (as determined by SEC retention), which is likely due to the differences in flexibility of the conjugates versus that of the free polysaccharide. The data indicate that the free polysaccharides are able to pass more easily through the small pores of the ultrafiltration membranes due to their greater flexibility / deformability. Further studies will be required to fully explore the impact of this association on the ultrafiltration / separation characteristics of the polysaccharide conjugates.

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Chapter 8

Fouling Behavior of Bacterial Polysaccharides and Polysaccharide- Protein Conjugates

8.1 Introduction

As discussed in previous chapters, membrane-based separations are potentially attractive for use in polysaccharide and protein-polysaccharide conjugate purification. However, one of the challenges in using membrane systems for this type of purification is membrane fouling.

Membrane fouling can cause a large reduction in filtrate flux, solute transmission, and selectivity during ultrafiltration processes. The results presented in Chapters 5 through 7 specifically employed very dilute solutions to minimize membrane fouling; however, these dilute solutions are often impractical in commercial bioprocessing. This chapter presents results for fouling during ultrafiltration of various polysaccharide serotypes and the corresponding conjugates through both the Biomax™ and Ultracel™ membranes. The results suggest the presence of a critical wall concentration associated with membrane fouling.

8.2 Materials and Methods

Buffer solutions were prepared by dissolving pre-weighed amounts of KCl and Bis-Tris in deionized water. The solution pH was set to 7 using small amounts of 1 M HCl as required.

Buffer solutions were pre-filtered through 0.2 μm pore size membranes prior to use. The ionic strength of the buffer was evaluated as:

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1 퐼 = ∑ 푧2퐶 2 푖 푖 푖 Equation 8.1 where 푧푖 is the valence and 퐶푖 is the concentration of the different ionic species. Purified capsular polysaccharides Serotypes A, B, and C of the S. pneumoniae bacteria and their corresponding conjugates were provided by Pfizer Inc. The polysaccharides and conjugates were diluted to the desired concentration using buffered KCl before filtering through 0.2 μm Acrodisc® syringe filters to remove any un-dissolved material. The samples were stored at 4 oC and slowly brought to room temperature (23 ± 2 oC) before use in the experiments.

Ultrafiltration experiments were performed using Ultracel™ and Biomax™ membranes with 300 kDa nominal molecular weight cut-offs (MWCO). Small 25 mm diameter disks (cut from large flat sheets) were soaked in isopropyl alcohol for 45 min before flushing with at least

100 L/m2 of deionized (DI) water. Ultrafiltration data were obtained in a 10 mL stirred cell with the membrane placed on top of a porous support to provide mechanical stability. The stirring speed was set to 1200 rpm using a digital stroboscope. Two sets of experiments were performed.

In the fouling experiments, the membrane was flushed with buffer solution for 10 min, the stirred cell and feed reservoir were then filled with the polysaccharide / conjugate solution, and the ultrafiltration continued for an additional 60 min. Finally, the buffer flux was re-evaluated. In the ultrafiltration experiments, the stirred cell was connected to a feed reservoir containing the polysaccharide / conjugate solution and pressurized with compressed air to 7-70 kPa

(corresponding to 1-10 psi) as determined using a digital differential pressure gauge. The filtrate flux was controlled by a peristaltic pump at the outlet of the stirred cell, with data obtained over a range of flux (typically by increasing the flux in step increments). Samples were obtained

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periodically from the permeate and stirred cell, with the observed sieving coefficient calculated as:

퐶푓 푆푂 = 퐶푏 Equation 8.2 where 퐶푓 and 퐶푏 are the polysaccharide concentrations in the filtrate and bulk solutions, respectively. The polysaccharide concentrations were evaluated by SEC as described in Chapter

3. All filtration experiments were performed at room temperature (23 ± 2ºC) with samples stored at 4 ºC until analysis.

Static fouling (the amount of solute adsorbed by the membrane) was evaluated using the solution depletion method by soaking the membrane in a concentrated polysaccharide / conjugate solution. The change in the solute concentration in the soaking solution was used to calculate the amount adsorbed on the membrane by a simple mass balance.

The membrane hydraulic permeability (Lp) was evaluated by measuring the filtrate flux as a function of the transmembrane pressure using a 250 mM buffered KCl solution at pH 7:

휇퐽 퐿 = 푣 푝 ∆푃 Equation 8.3 where 휇 is the solution viscosity, 퐽푣 is the filtrate flux, and ∆푃 is the transmembrane pressure. In each case, the permeability of the membrane was evaluated before and after the ultrafiltration.

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8.3 Results and Discussions

8.3.1 Sieving Experiments

The data presented in the earlier chapters of this dissertation were all obtained at low solute concentrations where membrane fouling is negligible. However, the ultrafiltration behavior is considerably different for more concentrated polysaccharide / conjugate solutions as discussed in the following sections.

8.3.1.1 Effect of Polysaccharide Concentration

Figure 8.1 shows typical results for the observed sieving coefficient for the ultrafiltration of Serotype B polysaccharide through a Biomax™ 300 kDa membrane at different feed concentrations. The data at low filtrate flux appear to collapse to a single curve, consistent with the calculated values of the observed sieving coefficient given by the concentration polarization model using the values of Sa and km determined from the linearized plot (Chapter 5). However, the data in the more concentrated solutions show a significant reduction in the observed sieving coefficient when the filtrate flux exceeds some critical value. The net result is that the maximum sieving coefficient obtained with the 0.5 g/L solution is 푆표≈ 0.45, and this drops to 푆표≈ 0.30 for the 1 g/L solution. The value of the filtrate flux at which the sieving coefficient begins to deviate from the polarization model decreases with increasing feed concentration, going from 13 µm/s for the 0.25 g/L solution to 7 µm/s for the 1 g/L solution.

The reduction in sieving coefficient when 퐽푣 exceeds some critical flux is a direct result of membrane fouling. In contrast to the data obtained at low flux, where the sieving coefficient

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remained stable during the ultrafiltration experiment, the 푆표 values at high filtrate flux decreased with time; the values shown in Figure 8.1 were obtained after collection of approximately 1 mL of filtrate (after setting the filtrate flux), with much smaller values obtained after longer times

(larger filtrate volumes). In addition, the membrane permeability evaluated after these ultrafiltration experiments was slightly below that for the clean membrane. For example, the

-12 permeability evaluated at the end of the experiment using the 0.5 g/L solution was 퐿푝= 5.3×10 m compared to 5.7×10-12 m for the clean membrane. An even greater decline in the permeability was seen with Serotype A.

Figure 8.1 Observed sieving coefficients as a function of filtrate flux during ultrafiltration of Serotype B polysaccharide with different feed concentrations through Biomax™ 300 kDa membranes. Solid curve is model calculation based on stagnant film model using best fit values -6 of 푆푎= 0.06 and 푘푚= 3.1×10 m/s.

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Figure 8.2 shows corresponding data for Serotype A polysaccharide. The solid curve is the model calculation based on the 푆푎 and 푘푚 values determined from data for a dextran with equivalent size as Serotype A (푀푤= 16,400 kDa) since there was significant fouling even with the 0.1 g/L solution. Note that the 푀푤 of the equivalent dextran (16,400 kDa) was more than 8 times as large as the 푀푤 of Serotype A (1,940 kDa), consistent with a more expanded structure of the polysaccharide due to the high charge density (large number of glucuronic acids) of this serotype. The results with Serotype A are similar to those seen with Serotype B; the observed sieving coefficient data at low filtrate flux collapse to a single curve as described by the concentration polarization model. However, the maximum value of the observed sieving coefficient for Serotype A was only 푆표= 0.36 in the 0.1 g/L solution, even though the polarization model predicts a sieving coefficient well above 0.8 at the highest filtrate flux examined in Figure 8.2. In addition, the values of the critical flux for Serotype A are much smaller than those seen with Serotype B. For example, the critical flux for the 0.25 g/L solution of Serotype A is around 7 µm/s compared to 13 µm/s for the same concentration of Serotype B, with data for the 0.1 g/L solution of Serotype B showing no fouling (critical flux above 22 µm/s) compared to a critical flux around 9.6 µm/s for Serotype A.

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Figure 8.2 Observed sieving coefficients as a function of filtrate flux during ultrafiltration of Serotype A polysaccharide with different feed concentrations through Biomax™ 300 kDa membranes. Solid curve is model calculation based on stagnant film model using best fit values -6 of 푆푎= 0.03 and 푘푚= 3.6×10 m/s.

The critical flux data were used to calculate the wall concentration at which fouling occurs based on the concentration polarization model:

푆표 퐶푤 = 퐶푏 ( ) 푆푎 Equation 8.4 where 푆푎 was determined from the linear regression analysis of the data using the stagnant film model. The calculated values of the critical wall concentration for Serotype B based on the data in Figure 8.1 are plotted as a function of the bulk polysaccharide concentration in Figure 8.3. The critical wall concentration ranges from 3.5 to 5.3 g/L, suggesting that fouling may occur when

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the wall concentration exceeds some critical value for the polysaccharide. The calculated values of the critical wall concentration appear to increase slightly with increasing bulk polysaccharide concentration; the origin of this behavior is unclear, although it may simply be related to the limited data (and relatively large error bars) in Figure 8.3. Similar results were obtained with

Serotype A, with a much smaller value of the critical wall concentration (between 1.2 and 1.6 g/L).

Figure 8.3 Critical wall concentration determined during ultrafiltration of Serotype B through the Biomax™ 300 kDa membranes as a function of bulk polysaccharide concentration.

Although there are no detailed data in the literature on the thermodynamic properties of the capsular polysaccharides examined in this study, there is extensive evidence that charged polysaccharides undergo a gel transition in relatively dilute solutions. For example, Clark and

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Farrer [119] reported data for the rheological behavior and gelation of pectin, a linear polysaccharide containing high concentrations of galacturonic acid, with a critical gel concentration of 0.56 weight percent, i.e., 5.6 g/L, which is very similar to the critical wall concentration seen in Figure 8.3. Santiago et al. [120] showed that a 0.5% (5 g/L) solution of κ-

Carrageenan in 40 mM KCl forms a dense three dimensional gel. Morris et al. [121] reported a critical gel concentration of 0.127 weight percent for gellan, a polysaccharide produced by

Pseudomonas bacterium and containing one glucuronic acid sugar per tetra-saccharide repeat unit. Note that studies with neutral dextrans show a sol-gel transition of more than 25% (w/w) in solutions with high salt concentrations [122]; solutions with even higher dextran concentrations

(>700 g/L) remain stable in water (in the absence of salt) [123]. There was no evidence of any fouling during ultrafiltration of the dextran solutions examined in this work (bulk concentrations all less than 2 g/L).

8.3.1.2 Solution Ionic Strength

As discussed in Chapters 6 and 7, the ultrafiltration behavior of charged polysaccharides and conjugates is considerably affected by the solution ionic strength. Figure 8.4 shows typical results for the observed sieving coefficient during ultrafiltration of Serotype A through a

Biomax™ 300 kDa membrane at concentrations of 0.1 and 0.5 g/L in 250 and 10 mM ionic strength solutions. The sieving coefficient data at low filtrate flux are similar to those seen with the 0.1 g/L solution, but there is a significant decline in polysaccharide transmission for the 250 mM solution at high values of the filtrate flux due to membrane fouling. The net result is that the transmission of Serotype A in the 10 mM solution is greater than that in the 250 mM ionic strength solution at a filtrate flux above 11 µm/s, with the reverse behavior seen at low flux. The

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reduced fouling in the low ionic strength solution is consistent with previous studies showing less fouling when there are repulsive electrostatic interactions between the charged solutes and membrane [62].

Figure 8.4 Observed sieving coefficients versus filtrate flux for 0.1 and 0.5 g/L solutions of Serotype A during ultrafiltration through Biomax™ 300 kDa membranes in 10 and 250 mM ionic strength solutions at pH 7.

Corresponding data for ultrafiltration of 0.5 g/L solutions of Serotype B through

Biomax™ 300 kDa membranes are shown in Figure 8.5. There is no measurable fouling in the

10 mM solution, with the data in the 0.5 g/L solution nearly identical to that for the 0.1 g/L solution. In contrast, the data in the 250 mM solution show a sharp decline in sieving coefficient above a flux of about 9 µm/s, with the transmission dropping well below that obtained with the

10 mM ionic strength solution. Interestingly, the permeability of the membrane evaluated after

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the ultrafiltration was only about 10% smaller than that for the clean membrane, suggesting that the fouling layer was easily removed simply by rinsing with buffer.

Figure 8.5 Observed sieving coefficients versus filtrate flux for ultrafiltration of 0.1 and 0.5 g/L solutions of polysaccharide Serotype B through Biomax™ 300 kDa membranes at 10 and 250 mM ionic strength.

8.3.2 Fouling Experiments

Typical fouling data for ultrafiltration of a 0.05 g/L solution of the native and conjugated

Serotype C through Biomax™ 300 kDa membranes in 250 mM buffered KCl at pH 7 at a transmembrane pressure of 1 psi are shown in Figure 8.6. Data are shown for the initial buffer flux, the flux during ultrafiltration of the polysaccharide / conjugate solution, and finally the flux with buffer immediately after the sample ultrafiltration. The filtrate flux for native Serotype C is

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significantly larger than that during ultrafiltration of the corresponding conjugate, with values at the end of the filtration of 26 and 12 μm/s, respectively. This difference is mainly due to differences in concentration polarization, with the sieving coefficient for the native polysaccharide being much larger than that of the conjugate (So ≈ 0.98 compared to So < 0.1 for the conjugate). The net result is that the concentration of the retained conjugates right at the membrane wall will be significantly larger than that of the native polysaccharide, leading to a much larger reduction in the filtrate flux.

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In order to obtain a better understanding of the extent of fouling in the ultrafiltration systems, the buffer and sample fluxes in Figure 8.6 were used to evaluate the flux recovery ratio as:

퐽퐵푢푓푓푒푟,푓 − 퐽푆푎푚푝푙푒,푓 퐹푅푅 = 퐽퐵푢푓푓푒푟,푖 − 퐽푆푎푚푝푙푒,푓 Equation 8.5 where 퐽퐵푢푓푓푒푟,푖 and 퐽퐵푢푓푓푒푟,푓 are the flux of the buffer before and after the ultrafiltration and

퐽푆푎푚푝푙푒,푓 is the sample (polysaccharide / conjugate) flux at the end of the ultrafiltration. The data in Figure 8.6 give RFF = 0.085 for the native Serotype C and RFF = 0 for the Conjugate, demonstrating that the fouling for both the polysaccharide and conjugate was largely irreversible under the conditions of these experiments.

Static adsorption tests were performed using the solution depletion method. Data were obtained with the Biomax™ 300 kDa membranes using 0.05 g/L solutions of the native and conjugated Serotype C in 250 mM buffered KCl at pH 7. Membranes were soaked in the sample solution for 30 min, with the permeability measured before and after exposure to the polysaccharide / conjugate. The concentration difference in the solution was used to determine the amount adsorbed to the membrane using a simple mass balance. For both solutions, there was negligible adsorption on the membrane; the polysaccharide / conjugate concentrations at the end of the adsorption were statistically indistinguishable from those measured at the start of the experiment. The membrane soaked in conjugate C solution did show about a 30 % reduction in permeability after exposure to the conjugate. In contrast, the experiment with the native polysaccharide showed no significant change in permeability after soaking in the solution of

Serotype C.

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8.3.2.1 Effect of Membrane Material

Figure 8.7 shows typical experimental data for ultrafiltration of Conjugate C through

Ultracel™ and Biomax™ 300 kDa membranes. The concentration of Conjugate C in the filtrate was less than 0.01 for both membranes. The sharp decline in filtrate flux that occurs at the start of the ultrafiltration is due to a combination of concentration polarization effects and any initial fouling due to adsorption of the conjugate on or within the membrane pores. The filtrate flux with the conjugate solution remains nearly constant over the course of the 45 min ultrafiltration, indicating that there is very little ongoing fouling during the experiment. The buffer flux after the conjugate ultrafiltration was approximately 47% smaller than the initial buffer flux for the

Ultracel™ membrane. In contrast, the final buffer flux for the Biomax™ membrane was only slightly greater than that during the ultrafiltration, reflecting a greater amount of irreversible fouling for the Biomax™ membrane compared to that for the Ultracel™ membrane.

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Figure 8.7 Filtrate flux as a function of time during ultrafiltration of a 0.05 g/L solution of Conjugate C in 250 mM buffered KCl at pH 7. Data were obtained at a constant pressure of 35 kPa (5 psi) using Biomax™ and Ultracel™ 300 kDa membranes.

The FRR values for ultrafiltration experiments performed with Conjugates A and C are summarized in Table 8.1. Conjugate A was filtered at a pressure of 1 psi at a concentration of 0.1 g/L, giving 푆표≈ 0.1 for ultrafiltration through the Ultracel™ membrane compared to 푆표≈ 0 for the Biomax™ membrane. The Biomax™ membrane showed minimal flux recovery compared to that for the Ultracel™. Similar behavior was seen for Conjugate C after an ultrafiltration experiment performed at 5 psi using 0.05 g/L solutions. The higher degree of fouling seen with the Biomax™ membrane is likely due to the more hydrophobic surface of the polyethersulfone compared to the cellulosic material of the Ultracel™ membrane.

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Table 8.1 Flux recovery ratio for ultrafiltration of Conjugates A and C through Biomax™ and Ultracel™ 300 kDa membranes in 250 mM buffered KCl at pH 7.

Operating Concentration FRR in Biomax™ FRR in Ultracel™

Pressure (psi) (g/L) 300 kDa 300 kDa

Conjugate A 1 0.1 1.4 % 63.1 %

Conjugate C 5 0.05 3.2 % 46.8 %

8.3.2.2 Effect of Ionic Strength

Figure 8.8 shows data for fouling experiments obtained with 0.1 g/L solutions of

Conjugate A through the Ultracel™ 300 kDa membrane in 5 and 250 mM buffered KCl at pH 7 using a pressure of 1 psi. The sharp decrease in the flux right after switching from buffer to the conjugate solution is due to a combination of concentration polarization plus any rapid fouling.

The much smaller decrease in filtrate flux over the course of the ultrafiltration is due to gradual fouling by the conjugate. Much of the fouling with the Ultracel™ membranes is reversible, with

FRR = 0.90 for the 5 mM solution and 0.63 for the 250 mM solution. The greater irreversible fouling in the high ionic strength solution is consistent with a reduction in the inter-molecular repulsive electrostatic interactions between the negatively-charged conjugates and the negatively-charged membrane under these operating conditions. Note that the sieving coefficients of Conjugate A in the 250 mM solution were also larger than the corresponding values in the 5 mM solution, although this is primarily due to the expansion of the conjugate caused by intra-molecular electrostatic interactions.

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Figure 8.8 Filtrate flux as a function of time during ultrafiltration of a 0.1 g/L solutions of Conjugate A in 5 and 250 mM buffered KCl at pH 7. Data were obtained at a constant pressure of 7 kPa (1 psi) using Ultracel™ 300 kDa membranes.

8.4 Conclusions

In contrast to the data obtained with dilute solutions, membrane fouling occurred in more concentrated solutions of the polysaccharides and conjugates, particularly at high filtrate flux, leading to a maximum in the observed sieving coefficient with increasing flux. The increase in transmission at low flux is due to concentration polarization effects, while the decrease in transmission at high flux is due to fouling, possibly due to the formation of a gel layer on the membrane surface. Fouling was less significant at low ionic strength, probably due to inter- molecular electrostatic repulsion between polysaccharides and / or between the polysaccharides

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and the membrane. Dynamic ultrafiltration experiments showed that the fouling of the Biomax™ membranes was largely irreversible, while the flux recovery for the more hydrophilic Ultracel™ membranes was around 50%. These data suggest that hydrophobic interactions may have an important effect on the nature of the membrane fouling observed with the polysaccharides and conjugates.

Fouling for the polysaccharides / conjugates occurred above a given value of the filtrate flux, with the flux associated with the onset of fouling decreasing with increasing bulk polysaccharide concentration. This behavior is qualitatively consistent with the presence of a serotype-specific critical value for the wall concentration, which may be related to the gelation point for these polysaccharides. The critical wall concentration for the more highly charged

Serotype A was significantly smaller than that for Serotype B; additional experimental studies conducted with a wider range of polysaccharides would be needed to quantify the relationship between the critical wall concentration for fouling and the saccharide composition / properties of the polysaccharides. These results also suggest that inter-molecular interactions could have a significant effect on the ultrafiltration behavior of mixtures of the vaccine conjugate and the free

(unreacted) polysaccharide due to fouling at the membrane surface.

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Chapter 9

Ultrafiltration for the Separation of Polysaccharide and Conjugate Mixtures

9.1 Introduction

The results in Chapters 5 to 8 provide significant information on the ultrafiltration behavior of pure solutions of the different pneumococcal polysaccharides and their corresponding conjugates. However, these data do not provide any insights into the presence or magnitude of any inter-molecular interactions between these species in the binary mixtures that would be encountered in actual vaccine manufacturing. The objective of the work described in this Chapter was to obtain quantitative data on the ultrafiltration behavior of binary mixtures of polysaccharides and their corresponding conjugates, with a focus on identifying the role of inter- molecular interactions in these systems.

9.2 Materials and Methods

The concentration of the polysaccharides and conjugates in the binary mixture were evaluated using SEC as described in Chapter 3. This involves the use of dual detectors on the

SEC system: the UV absorbance provides a measure of the conjugate concentration (with negligible contribution from the free polysaccharides) while the RI signal has significant contributions from both the conjugate and free polysaccharide. The combination of RI and UV detectors allows the concentrations of both the free and conjugated polysaccharides to be

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evaluated in the binary mixture. Calibration curves were constructed using solutions of the pure free and conjugated polysaccharide of each serotype based on the following equations:

푅퐼⁡푆𝑖푔푛푎푙 = 푅퐼푝푠 + 푅퐼푐표푛푗 = (퐴⁡퐶푝푠 + 퐵) + (퐶⁡퐶푐표푛푗 + 퐷)

푈푉⁡푠𝑖푔푛푎푙 = 푈푉푐표푛푗 = ⁡퐸⁡퐶푐표푛푗 + 퐹 Equation 9.1 where 퐶푝푠 and 퐶푐표푛푗 are the unknown concentrations of the free and conjugated polysaccharide, respectively. Figure 9.1 to Figure 9.3 show the calibration curves for pure activated Serotype C and Conjugate C using both the UV and RI detectors. These calibration curves are highly linear with R2 > 0.99. The slope and intercept of the linear regression fit to the calibration data were used to evaluate coefficients A through F in Equation 9.1.

Figure 9.1 Concentration calibration curve for Activated Serotype C polysaccharides using the refractive index detector.

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Figure 9.2 Concentration calibration curve for Conjugate C polysaccharides using the refractive index detector.

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Figure 9.3 Concentration calibration curve for Conjugate C polysaccharides using absorbance at 280 nm.

Table 9.1 examines the accuracy of the assay using data for multiple samples with different ratios of free to conjugated polysaccharide. The left column provides the known concentrations based on the mass of the free and conjugated polysaccharide added to make the samples. The middle column provides the concentrations of each species calculated from

Equation 9.1 using the coefficients determined from the calibration curves in Figure 9.1 to Figure

9.3. The final column shows the absolute error in the concentration measurements. The use of the combined RI and UV detectors allows the concentrations of the free and conjugated polysaccharides in the binary mixture to be determined with accuracy of better than ±4% over a wide range of polysaccharide / conjugate concentrations.

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Table 9.1 Known and calculated concentrations of the free and conjugated Serotype C in three binary mixtures.

Real Concentration (g/L) Calculated Concentration (g/L) Error (%)

Activated Serotype C 0.136 0.14 3

Conjugate C 0.359 0.365 2

Activated Serotype C 0.212 0.219 4

Conjugate C 0.294 0.294 1

Activated Serotype C 0.326 0.338 4

Conjugate C 0.196 0.203 4

9.3 Results and Discussions

Figure 9.4 shows typical sieving data for ultrafiltration of a mixture of 0.1 g/L Serotype C polysaccharide with 0.1 g/L Conjugate C in 250 mM buffered KCl solution at pH 7 through a

Biomax™ 500 kDa membrane. The concentration of the polysaccharide and the conjugate in the feed and permeate solutions were evaluated using the method described in the previous section.

The transmission of the polysaccharide and its corresponding conjugate both increase with increasing filtrate flux due to concentration polarization effects. However, the transmission of

Conjugate C is uniformly lower than that for the activated polysaccharide. For example, at 퐽푣=

30 μm/s the sieving coefficient for the activated polysaccharide is 0.81 compared to 0.32 for the conjugate. Thus, the permeate solution is significantly enriched in the free polysaccharide, providing the opportunity for developing a membrane-based separation process for the purification of the conjugate. This is discussed in more detail subsequently.

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Figure 9.4 Sieving coefficient as a function of filtrate flux for ultrafiltration of 0.1 g/L mixtures of the conjugated and activated Serotype C through a Biomax™ 500 kDa membrane in 250 mM buffered KCl at pH 7.

In order to obtain a better understanding of the effects of inter-molecular interactions on the ultrafiltration behavior with the binary mixture, separate ultrafiltration experiments were performed using the pure components under otherwise identical conditions. Figure 9.5 shows the sieving coefficient data for the binary solution (Figure 9.4) along with the corresponding data for two different experiments with the Biomax™ 500 kDa membranes, one performed using the pure conjugate and one using pure activated Serotype C. All 3 experiments used 0.1 g/L concentrations in 250 mM KCl at pH 7. The sieving coefficient data for the pure polysaccharide and conjugate look qualitatively similar to the results obtained with the binary mixture, with the transmission increasing with increasing flux and with 푆표 for the activated Serotype C being

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significantly larger than that for the conjugate. However, the data obtained with the pure components show a significantly larger transmission than that in the binary mixture. For example, the sieving coefficient of the activated polysaccharide through the Biomax™ 500 kDa membrane at 퐽푣=17 μm/s is 0.98 in the pure solution but only 푆표 = 0.55 in the binary mixture.

The corresponding results for Conjugate C give 푆표 = 0.3 in the pure solution compared to 푆표 =

0.2 in the mixture. The reduction in the observed sieving coefficients in the binary mixture are likely due to some type of inter-molecular interaction, either in the bulk solution or on the membrane, with the latter related to a higher degree of fouling with the binary mixture.

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The data in Figure 9.5 were used to evaluate the selectivity for the separation as:

푆 Ψ = 푃푠 푆퐶표푛푗 Equation 9.2 where 푆푃푠 and 푆퐶표푛푗 are the sieving coefficient of the polysaccharide and the conjugate, respectively.Table 9.2 summarizes the selectivity results obtained with the Biomax™ 500 kDa membrane for the actual binary mixture and from extrapolation of data for the pure components

(i.e., neglecting any inter-molecular or fouling interactions between the polysaccharide and conjugate). The selectivity values based on the pure component data are uniformly larger than those obtained with the binary mixture, particularly at low filtrate flux where the data with the binary mixture actually show a “reverse” selectivity (corresponding to greater transmission of the conjugate). However, this reverse selectivity may simply be due to small errors in the measured values of the sieving coefficient at low filtrate flux due to the very dilute filtrate solution under these conditions. In both cases the selectivity reached a maximum value at an intermediate filtrate flux of around 퐽푣= 16 μm/s for the binary mixture and 퐽푣= 5 – 10 µm/s for the pure components. The reduction in the selectivity at very high flux is due to concentration polarization effects, with the selectivity expected to approach a value of one at very high flux when 푆표 → 1 for both the free polysaccharide and the conjugate.

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Filtrate Flux (μm/s) Pure Components Binary Mixture

2.5 3.0 0.7

5 4.0 0.6

10 4.0 1.8

16 3.4 2.8

32 2.6 2.5

The observed sieving coefficient values for the pure components were calculated using the concentration polarization model as a function of filtrate flux as shown by the solid curves in

Figure 9.5. The predicted selectivity is shown as a function of filtrate flux in Figure 9.6, with the maximum selectivity obtained at 퐽푣= 9 µm/s (consistent with the data in Table 9.2). The predicted maximum is simply due to concentration polarization effects (since the model ignores membrane fouling). In this case, the sieving coefficient of the free polysaccharide increases with increasing filtrate flux more rapidly than that of the conjugate at low flux due to the smaller value of the mass transfer coefficient (푘푚= 2.3 µm/s for the polysaccharide versus 19 µm/s for the conjugate), with the reverse behavior seen at high filtrate flux as the sieving coefficient of the free polysaccharide approaches an asymptotic value of 푆표 = 1.

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Figure 9.6 Predicted selectivity as a function of filtrate flux for the free and conjugated Serotype C during ultrafiltration through Biomax™ 500 kDa in 250 mM buffered KCl at pH 7. Model calculations based on the concentration polarization equations using the best fit values of the actual sieving coefficients and mass transfer coefficients determined from data for the pure solutions of the two components.

9.3.1 Effect of Membrane Pore Size

Figure 9.7 shows typical experimental data for ultrafiltration of a 0.1 g/L mixture containing both the conjugated and activated Serotype C (0.1 g/L of each) through both a

Biomax™ 300 kDa and Biomax™ 500 kDa membrane. Experiments were performed in 250 mM buffered KCl at pH 7 with the concentration of the components determined using both the UV and RI detectors as described previously. The data obtained with the 300 kDa membrane are uniformly below the corresponding data for the Biomax™ 500 kDa as expected. The

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transmission of the activated Serotype C through the Biomax™ 300 kDa increases with increasing filtrate flux at low flux which is consistent with the concentration polarization model.

However, there is a sharp decline in transmission above 15 μm/s due to fouling. Note that there was no fouling observed during ultrafiltration of the pure activated Serotype C under comparable conditions, although data with the pure Conjugate did show some fouling. The high degree of fouling seen with the binary mixture may reflect some type of interaction between the conjugate and free polysaccharide, leading to large aggregates that more readily foul the membrane.

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9.3.2 Effect of Ionic Strength

Figure 9.8 shows data for ultrafiltration of a 0.1 g/L mixture containing both the conjugated and activated serotype C through a Biomax™ 500 kDa membrane in 5 mM buffered

KCl at pH 7. The polysaccharide and conjugate sieving coefficients in the 5 mM solution are uniformly larger than those at 250 mM ionic strength (Figure 9.4). Solution ionic strength has two competing effects on polysaccharide / conjugate transmission: (1) Decreasing ionic strength reduces the extent of fouling, leading to an increase in the sieving coefficients, and (2)

Decreasing ionic strength increases the effective size of the polysaccharide and conjugate due to intra-molecular electrostatic interactions, thereby decreasing the sieving coefficient. The second effect is relatively small for Conjugate C since that serotype is uncharged; the effective size of the activated Serotype C is essentially independent of solution ionic strength as shown previously in Chapter 4 although there is a small increase in the size of the Conjugate due to the charge groups on the protein. The net result is that the decreased fouling dominates the behavior at low ionic strength, leading to the observed increase in the sieving coefficients under these conditions. This behavior is in sharp contrast to the results with the charged polysaccharides (in pure solution) where the transmission decreased significantly with decreasing ionic strength due to the intra-molecular electrostatic interactions.

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Figure 9.8 Sieving coefficient as a function of filtrate flux for ultrafiltration of a 0.1 g/L mixture of conjugated and activated Serotype C through Biomax™ 500 kDa membranes in 5 and 250 mM buffered KCl at pH 7.

9.3.3 Diafiltration Experiments

Based on the experimental data obtained in the previous sections, a diafiltration process was developed to separate the activated Serotype C from Conjugate C using a Biomax™ 500 kDa membrane. The diafiltration process was performed in 250 mM buffered KCl solution at pH

7 using a filtrate flux of 15 µm/s to achieve the maximum selectivity (Table 9.2). Experimental results are shown in Figure 9.9 for the scaled concentration of Conjugate C and activated

Serotype C in the retentate solution, where 퐶푖 is the initial concentration of the given species in the feed. The results are plotted as a function of the number of diavolumes (푁), which is simply

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equal to the total collected filtrate volume divided by the constant retentate volume (diafiltration buffer was added to the stirred cell at the same rate as which the permeate was removed so that the feed volume remained constant). The concentrations of both the Conjugate and the activated polysaccharide decrease with increasing number of diavolumes as the species are slowly washed through the membrane. The rate of removal of the Conjugate is less than that of the free polysaccharide, with the net result that the normalized concentration of Conjugate C remains

>80% after 푁=1.5 while the concentration of the activated Serotype C drops to below 60% at this point in the diafiltration.

The solid curves in Figure 9.9 are model calculations developed from solution of the overall mass balance for a diafiltration process [124]:

퐶 = 푒푥푝⁡(−푁푆표) 퐶푖 Equation 9.3 where the observed sieving coefficient (푆표) was determined at the beginning of the experiment and assumed to remain constant throughout the diafiltration. The model calculations are in good agreement with the experimental data, although model does tend to underpredict the concentrations at larger number of diavolumes, particularly for the activated Serotype C. This is likely due to low levels of membrane fouling, which cause a reduction in the transmission of the free polysaccharide with time (i.e., a decrease in 푆표 with increasing 푁).

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Figure 9.9 Normalized solute concentrations as a function of number of diavolumes for a diafiltration performed using Biomax™ 500 kDa for a mixture of the activated Serotype C and Conjugate C. Data were obtained at a filtrate flux of 15 µm/s in 250 mM buffered KCl solution at pH 7. Solid curves are model calculations based on Equation 9.3.

The separation performance of the diafiltration process can be quantified in terms of the tradeoff between the yield (푌) and purification factor (푃) for the desired Conjugate C. The yield of the Conjugate is simply equal to the normalized Conjugate concentration since the feed volume remains constant during the diafiltration process. The purification factor is defined as the ratio of the product concentration in the retentate to that of the impurity (in this case the free polysaccharide) divided by the initial concentration ratio at the start of the diafiltration:

퐶푐표푛푗 퐶푐표푛푗,푖 푃 = ( )⁄( ) 퐶푝푠 퐶푝푠,푖 Equation 9.4

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The data in Figure 9.9 have been replotted in Figure 9.10 with the product yield as an explicit function of the purification factor. The diafiltration begins in the upper left-hand corner with a product yield of 푌 = 1 and a purification factor of 푃 = 1 since the conjugate is fully contained in the retentate at the start of the process. The purification factor increases over the course of the diafiltration as the free polysaccharide is preferentially removed from the retentate, with the reduction in yield reflecting the leakage of product into the permeate. The data from

Figure 9.4 correspond to a maximum purification factor of 1.5 with 푌= 0.75.

The solid curves in Figure 9.10 are model calculations developed from solution of the corresponding mass balances for the product and impurity. The results are conveniently expressed in terms of the selectivity as defined by [124]:

푃 = 푌1−훹 Equation 9.5

The model calculations using 훹 = 2.5 are in excellent agreement with the experimental data, properly capturing the trade-off between the yield and purification factor over the course of the diafiltration process. Model simulations indicate that a membrane with 훹 = 10 would yield a purification factor of more than 13 with 75% product yield, while a system with a selectivity of

50 could provide more than 100-fold purification with more than 90% yield of the Conjugate.

These results clearly demonstrate the potential of using membrane ultrafiltration for purification of the polysaccharide-protein conjugate, although membranes / operating conditions with much higher selectivity would be needed to achieve these high performance separations.

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9.4 Conclusions

Ultrafiltration has been proposed as an attractive method for the purification of vaccine conjugates, with any unreacted (activated) polysaccharide removed in the collected permeate while the purified conjugate is obtained in the retentate. The data presented in this chapter show that it is possible to obtain at least some selectivity between the activated polysaccharide and the conjugate by proper selection of the ultrafiltration conditions. However, the selectivity obtained with the binary mixture was lower than that seen in experiments using pure solutions of the conjugate and free polysaccharide, due primarily to an increase in membrane fouling in the binary mixture. The selectivity attains its maximum value at an intermediate filtrate flux due to

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concentration polarization effects, with the detailed dependence on the flux controlled by the extent of both polarization and fouling.

The selectivity was also a function of the salt concentration, which changes both the intrinsic sieving coefficients and the mass transfer coefficients of the two components, while also altering the fouling behavior. Experimental results obtained with a 500 kDa Biomax™ membrane at 250 mM ionic strength solution showed a conjugate yield of 90% at a purification factor of 1.2. Model calculations showed that the purification factor could be increased to more than 100 with greater than 90% product yield if the selectivity of the ultrafiltration process could be increased to 훹 = 50. Additional experimental studies will be required to determine how to obtain this increased selectivity for the separation of the conjugate and activated polysaccharide.

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Chapter 10

Conclusions and Future Work

10.1 Introduction

Streptococcus pneumoniae is an important human pathogen causing pneumonia, meningitis, and sepsis. Traditional vaccines made up of native Streptococcus capsular polysaccharides are not effective in children under 2 years of age and the elderly, two of the primary target groups for vaccination. Recently, there have been significant developments in the production of second generation pneumococcal vaccines, which are able to generate strong immunological responses in a much broader fraction of the population, including infants. The key to these new vaccines is the conjugation of the capsular polysaccharide to a highly immunogenic carrier protein like CRM197, a non-toxic cross-reacting mutant of the diphtheria toxin protein. The conjugation process is carried out in the presence of excess polysaccharide which must then be removed prior to final formulation of the vaccine. One of the critical issues in the successful production of these conjugated vaccines is the high cost associated with the purification of the conjugated polysaccharide. Ultrafiltration is used extensively for the concentration and formulation of high value biological products causing little damage even to highly labile molecules, while providing high throughput operation at lower cost than chromatographic methods. Although there are some limited previous studies showing the potential of using ultrafiltration for the purification of conjugated vaccines, there is a lack of quantitative information regarding the ultrafiltration behavior of both the native polysaccharides

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and the conjugates, nor is there any information on the key factors governing the development of effective membrane-based separation systems for this important application.

The results obtained in this dissertation provide critical insights into the factors controlling the behavior of ultrafiltration processes for pneumococcal conjugated vaccines. Data were obtained for several serotypes of the native (unreacted) and conjugated capsular polysaccharides from Streptococcus pneumoniae. A number of efforts were made to evaluate the physical characteristics of the polysaccharides, with the goal of using this information to develop a better understanding of the ultrafiltration behavior in different solution conditions. The following subsections summarize the key experimental and theoretical results from this dissertation. Recommendations for future work are also discussed.

10.2 Conclusions

10.2.1 Polysaccharide / Conjugate Characterization

The experimental data presented in this work provide some of the first quantitative data for the effects of solution ionic strength and pH on the effective radius of polysaccharides and protein-polysaccharide conjugates. The polysaccharides and conjugates were characterized using both size exclusion chromatography (SEC) and dynamic light scattering (DLS). The effective size increased significantly at low ionic strength due to the expansion of the polysaccharide caused by strong intra-molecular electrostatic interactions; similar effects were observed with the conjugates. This increase in size was more pronounced for the more heavily charged serotypes.

Solution pH also altered the extent of intra-molecular interactions due to changes in the extent of ionization of the various charged monomers. A detailed analysis of the conjugates by size

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exclusion chromatography demonstrated that in many cases these solutions contain at least two distinct components, most probably associated with the reversible association of the conjugates into small clusters, with the relative concentration of the larger component increasing with decreasing salt concentration.

Limited experiments performed using dynamic light scattering showed that the hydrodynamic radius evaluated from DLS was in very good agreement with the corresponding values measured using SEC in the limit of very high ionic strength condition (zero Debye length). The DLS data also showed the presence of two components in the conjugate solutions, with the radii of the two components in good agreement with the SEC results.

The effective size data for the native polysaccharides were analyzed using available models for the radius of gyration of charged polymers accounting for both the change in persistence length and the excluded volume associated with the intra-molecular electrostatic interactions. The model calculations were in good qualitative agreement with the experimental results, providing a framework for estimating the effective size of the different polysaccharides and for analyzing the SEC behavior in different solution conditions.

10.2.2 Polysaccharide / Conjugate Ultrafiltration

The data presented in this dissertation provide the most extensive analysis of the ultrafiltration behavior of several different native and conjugated pneumococcus polysaccharide serotypes currently available. Polysaccharide transmission in dilute solutions was a strong function of filtrate flux due to concentration polarization effects. The flux dependence of the transmission was well described using the classical stagnant film (or concentration polarization) model, with no evidence of any flow-induced elongation under these experimental conditions.

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Increasing the stirring speed reduces the extent of concentration polarization in the bulk solution, leading to a reduction in the polysaccharide concentration at the membrane surface and in turn a reduction in the observed sieving coefficient.

The actual sieving coefficients for the different polysaccharides / membranes were in good qualitative agreement with predictions of the partitioning model originally presented by

Giddings et al. [77], providing a simple method for estimating polysaccharide transmission based on independent data for the polysaccharide size (e.g., by SEC), the membrane permeability, and the filtrate flux (which determines the extent of concentration polarization). Conjugates showed qualitatively similar ultrafiltration behavior to the native polysaccharides, although the transmission of the polysaccharides was greater than that of the conjugates (with similar size) due to the greater flexibility / deformability of the free polysaccharides.

These results clearly demonstrate that it is possible to obtain high transmission of the bacterial polysaccharides through commercially available ultrafiltration membranes by operating at sufficiently high filtrate flux. The Biomax™ polyethersulfone membranes provide significantly greater transmission than the Ultracel™ composite regenerated cellulose membranes for the same nominal molecular weight cutoff, which is consistent with the slightly greater pore size of the BiomaxTM membranes.

In contrast to the data in dilute solutions, membrane fouling occurred in more concentrated solutions of the polysaccharides and conjugates, particularly at high filtrate flux, leading to a maximum in the observed sieving coefficient with increasing flux. The increase in transmission at low flux is due to concentration polarization effects, while the decrease in transmission at high flux is due to membrane fouling, possibly due to the formation of a gel layer on the membrane surface. Fouling for the polysaccharides / conjugates occurred above a given

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value of the filtrate flux, with the flux associated with the onset of fouling decreasing with increasing bulk polysaccharide concentration. This behavior is qualitatively consistent with the presence of a serotype-specific critical value for the wall concentration, which may be related to the gelation point for these polysaccharides.

10.2.3 Effect of Solution Conditions on Ultrafiltration

The solution ionic strength was shown to have a significant impact on the ultrafiltration of the charged polysaccharides due to a combination of intra- and inter-molecular electrostatic interactions. The polysaccharide transmission decreased with decreasing ionic strength in dilute solutions, which this effect being more pronounced for serotypes with greater charge density.

These changes in polysaccharide transmission were consistent with the observed changes in the effective size of the individual polysaccharides as determined by size exclusion chromatography

(Chapter 4). Conjugates showed qualitatively similar ultrafiltration behavior to the unreacted polysaccharides, with the transmission in dilute solutions decreasing at low ionic strength due to expansion of the conjugates caused by intra-molecular electrostatic interactions.

The data for the actual sieving coefficients for the different serotypes, determined using the linearized form of the concentration polarization model, collapsed to a single curve when plotted as a function of the effective hydrodynamic radius as determined by SEC. This behavior was in excellent agreement with available theoretical models for solute partitioning in porous membranes. The ultrafiltration behavior of the different conjugates followed a similar trend, although the conjugates were more highly retained than the polysaccharides due to differences in deformability.

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Membrane fouling in concentrated solutions was less significant at low ionic strength, probably due to inter-molecular electrostatic repulsion between the charged polysaccharides and

/ or between the polysaccharides and the membrane.

10.2.4 Conjugate Purification by Ultrafiltration

Ultrafiltration has been proposed as an attractive method for the purification of vaccine conjugates, with any unreacted (activated) polysaccharide removed in the collected permeate while the purified conjugate is obtained in the retentate. The data presented in this work showed that it is possible to obtain at least some selectivity between the activated polysaccharide and the conjugate by proper selection of the ultrafiltration conditions. However, the selectivity obtained with the binary mixture was lower than that seen in experiments using pure solutions of the conjugate and free polysaccharide, due primarily to an increase in membrane fouling in the binary mixture. The selectivity attains its maximum value at an intermediate filtrate flux due to a combination of concentration polarization and fouling.

The selectivity was also a function of the salt concentration, which changes both the intrinsic sieving coefficients and the mass transfer coefficients of the two components, while also altering the fouling behavior. Model simulations were used to explore the trade-off between the yield and purification factor, with the results demonstrating that membranes with higher selectivity would be very effective for the purification of the conjugates from any residual activated polysaccharide present after the conjugation reaction.

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10.3 Recommendations for Future Work

The results presented in this work provide important insights into the ultrafiltration behavior of bacterial polysaccharides and protein-polysaccharide conjugates. However, there are a number of important areas that would benefit from additional experimental and theoretical investigations.

The experimental studies presented in this dissertation were performed using a small laboratory-scale stirred cell. Commercial separations of these polysaccharides / conjugates would be done using modules with much greater membrane area, typically employing tangential flow filtration (TFF) in which the feed flow is parallel to the membrane surface and perpendicular to the filtrate flow. Future experimental studies are needed to explore the ultrafiltration behavior in these

TFF modules, including both concentration polarization and fouling phenomena. Note that TFF modules tend to have much better mass transfer characteristics than the stirred cell, which could lead to reduced fouling and thus a greater selectivity for the polysaccharide / conjugate separation.

The size exclusion chromatography and dynamic light scattering data showed that there are two components present in several of the conjugate solutions. Although the origin of the large component in the conjugate solutions is currently unclear, it is likely related to the reversible association of the conjugates into soluble aggregates. Further studies will be required to fully explore the impact of this association on the ultrafiltration / separation characteristics of the polysaccharide conjugates. These studies should also consider the possibility of using specific excipients to control the relative concentration of these two components, which could potentially be exploited to increase the selectivity of the conjugate – polysaccharide separation

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(e.g., by shifting the equilibrium so that more of the conjugate is in the form of the very highly retained larger component).

Chapter 4 discussed the use of the worm-like chain model to describe the radius of gyration for the charged polysaccharides accounting for the effects of solution ionic strength on the persistence length and the excluded volume of the polymer chain. It was not possible to apply this theoretical framework to the analysis of the polysaccharide-protein conjugates since the worm-like chain model does not provide an appropriate physical model for the structure of the conjugates. Additional work will be required to fully elucidate the structure and charge characteristics of these complex polysaccharide-protein conjugates, including the development of appropriate modeling frameworks that are appropriate for description of the behavior of the conjugates.

Diafiltration experiments suggest that inter-molecular interactions could have a significant effect on the ultrafiltration behavior of mixtures of the vaccine conjugate and the free

(unreacted) polysaccharide due to fouling at the membrane surface. Additional experimental studies will be required to determine how to minimize this fouling to obtain higher selectivities for the separation of the conjugate and activated polysaccharide. This might include experiments in systems with greater bulk mass transfer coefficients (e.g., TFF modules) or the use of backpulsing to disrupt the formation of a gel layer on the membrane surface. It may also be possible to change the gelation conditions by addition of specific excipients that alter the thermodynamics of these polysaccharides / conjugates.

It would also be very desirable to expand the characterization techniques to look at the rheological behavior of these biopolymers under both steady and oscillatory flow. These rheological measurements could help develop a deeper understanding of the relative contributions

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of intra- and inter-molecular interactions on the structure and dynamics of the different polysaccharides and conjugates, both as pure components and in mixtures. These results might also provide additional insights into the gelation behavior of the polysaccharides, which may be directly related to membrane fouling phenomena.

Previous studies of membrane ultrafiltration have demonstrated that the use of charged membranes can enhance the separation selectivity, and in some cases reduce membrane fouling, by exploiting repulsive electrostatic interactions between the charged polysaccharides and the charged membrane. It would also be interesting to look at the use of novel zwitterionic membranes for ultrafiltration of polysaccharides and conjugates; these zwitterionic membranes have been shown to have very low fouling characteristics in a wide variety of applications, but they have never been used for conjugate separations.

The experimental studies performed in this work examined the the ultrafiltration and SEC behavior of three different polysaccharides with very different characteristics, including both molecular weight and charge density. It would be highly desirable to extend these studies to a wider range of polysaccharide serotypes, each with a unique size, charge, and monomer composition. These studies would potentially provide a framework for predicting the ultrafiltration behavior of different polysaccharides / conjugates based directly on their saccharide composition and number of monomers. Such information would be invaluable in performing initial calculations for the design of ultrafiltration systems for the separation of vaccine conjugates from residual polysaccharides.

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VITA Mahsa Hadidi

EDUCATION

Ph.D., Chemical Engineering Jun 2013- Dec 2016 The Pennsylvania State University, University Park, PA GPA: 4.0/4.0

M.S., Chemical Engineering Sep 2011- June 2013 The Pennsylvania State University, University Park, PA GPA: 4.0/4.0

B.S., Chemical Engineering Sep 2007- May 2011 Sharif University of Technology, Tehran, Iran GPA: 16.9/20.0

PROFESSIONAL EXPERIENCE

Graduate Research Assistant Dept. of Chemical Engineering, The Pennsylvania State University Sep 2011- July 2016

Teaching Assistant Dept. of Chemical Engineering, The Pennsylvania State University Sep 2013- Dec 2014 Dept. of Chemical Engineering, Sharif University of Technology Sep 2009- May 2010

Chem-E-Car Design Team Leader Dept. of Chemical Engineering, Sharif University of Technology Sep 2009- Dec 2009

JOURNAL PUBLICATIONS

 M. Hadidi, J. J. Buckley, and A. L. Zydney, Carbohydrate Polymers, 152:12-18 (2016).  M. Hadidi, J. J. Buckley, and A. L. Zydney, J. Membrane Science, 490: 294-300 (2015).  M. Hadidi and A. L. Zydney, J. Applied Polymer Science, 132: 41540-41548 (2015).  M. Hadidi and A. L. Zydney, J. Membrane Science, 452: 97-103 (2014).

PROFESSIONAL ACTIVITIES & HONORS

. Best Presentation and Paper Award, College of Engineering Research Symposium, The Pennsylvania State University, 2014 . North American Membrane Society, Elias Klein Award, Boise, ID, 2013 . Reviewer for the Journal of Membrane Science