Biophysical Methods for Biotherapeutics E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 756

Veröffentlichungsjahr: 2014

Beschreibung

With a focus on practical applications of biophysical techniques, this book links fundamental biophysics to the process of biopharmaceutical development. * Helps formulation and analytical scientists in pharma and biotech better understand and use biophysical methods * Chapters organized according to the sequential nature of the drug development process * Helps formulation, analytical, and bioanalytical scientists in pharma and biotech better understand and usestrengths and limitations of biophysical methods * Explains how to use biophysical methods, the information obtained, and what needs to be presented in a regulatory filing, assess impact on quality and immunogenicity * With a focus on practical applications of biophysical techniques, this book links fundamental biophysics to the process of biopharmaceutical development.

Leseprobe

BIOPHYSICAL METHODS FOR BIOTHERAPEUTICS

Discovery and Development Applications

Edited by

Tapan K. Das

Center of Excellence for Mass Spectrometry and Biophysics Bristol-Myers Squibb Hopewell, NJ

Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data:

Biophysical methods for biotherapeutics: discovery and development applications / edited by Dr. Tapan K. Das.         p. ; cm.     Includes bibliographical references and index.     ISBN 978-0-470-93843-0 (cloth)     I. Das, Tapan K., editor of compilation.

    [DNLM: 1. Biopharmaceutics–methods.    2. Drug Discovery–methods.    3. Technology, Pharmaceutical–methods. QV 35]     RM301.5     615.7–dc23

2013038071

CONTENTS

PREFACE

ABOUT THE EDITOR

LIST OF CONTRIBUTORS

SECTION 1 EARLY DISCOVERY STAGES AND BIOTHERAPEUTIC CANDIDATE SELECTION

CHAPTER 1 BIOPHYSICAL METHODS APPLIED IN EARLY DISCOVERY OF A BIOTHERAPEUTIC: CASE STUDY OF AN EGFR-IGF1R BISPECIFIC ADNECTIN

1.1 Introduction

1.2 Target Identification

1.3 Target Generation

1.4 Hit Evaluation

1.5 Lead Selection

1.6 Lead Optimization

1.7 Lead Formatting

1.8 Final Development Candidate Selection

1.9 Concluding Remarks

Acknowledgment

References

CHAPTER 2 X-RAY CRYSTALLOGRAPHY FOR BIOTHERAPEUTICS

2.1 Introduction to X-ray Crystallography

2.2 Modern X-ray Crystallography

2.3 X-ray Data Collection

2.4 Solving the Structure of the Crystal

2.5 Understanding the Target Through Structure

2.6 Applications of X-ray Crystallography to Biotherapeutics

2.7 Future Applications of Crystal Structures in Biotherapeutics

2.8 Conclusion

Acknowledgments

References

CHAPTER 3 SOLUBILITY AND EARLY ASSESSMENT OF STABILITY FOR PROTEIN THERAPEUTICS

3.1 Introduction

3.2 Measuring Protein Solubility

3.3 Assessment of Protein Stability

3.4 Computational Predictions

3.5 Enhance the Solubility of Biotherapeutics

3.6 Development of Rapid Methods to Identify Soluble and Stable Biotherapeutics

3.7 Concluding Remarks

References

SECTION 2 FIRST-IN-HUMAN AND UP TO PROOF-OF-CONCEPT CLINICAL TRIALS

CHAPTER 4 BIOPHYSICAL AND STRUCTURAL CHARACTERIZATION NEEDED PRIOR TO PROOF OF CONCEPT

4.1 Introduction

4.2 Biophysical Methods for Elucidation of Protein Structure and Physiochemical Properties

4.3 Biophysical and Structural Characterization Data

4.4 Case Study—Characterization of Higher Order Structure of a Fusion Protein with Biophysical Methods

4.5 Biophysical and Structural Characterization Data in Analytical Comparability Assessments

4.6 Summary and Future Perspectives

Acknowledgments

References

CHAPTER 5 NUCLEATION, AGGREGATION, AND CONFORMATIONAL DISTORTION

5.1 Introduction

5.2 Nonnative Aggregation Involves Multiple Competing Processes

5.3 Importance of Conformational Changes in Forming/Nucleating Aggregates

5.4 Conformational Changes During Aggregate Growth

5.5 Surface-Mediated Unfolding and Assembly

5.6 Summary

References

CHAPTER 6 UTILIZATION OF CHEMICAL LABELING AND MASS SPECTROMETRY FOR THE BIOPHYSICAL CHARACTERIZATION OF BIOPHARMACEUTICALS

6.1 Mass Spectrometry of Biopharmaceuticals

6.2 Introduction to Hydrogen/Deuterium Exchange

6.3 Applications of Hydrogen/Deuterium Exchange and Mass Spectrometry to Proteins

6.4 Introduction to Covalent Labeling Techniques

6.5 Overview and Applications of Hydroxyl Radical Footprinting to Mass Spectrometry

6.6 Overview and Applications of Chemical Cross-Linking to Mass Spectrometry

6.7 Overview and Applications of Specific Amino Acid Labeling to Mass Spectrometry

6.8 Conclusions

References

CHAPTER 7 APPLICATION OF BIOPHYSICAL AND HIGH-THROUGHPUT METHODS IN THE PREFORMULATION OF THERAPEUTIC PROTEINS —FACTS AND FICTIONS

7.1 Introduction

7.2 Considerations for a Successful Protein Drug Product

7.3 Protein Preformulation Strategies

7.4 Conclusions

References

CHAPTER 8 BIOANALYTICAL METHODS AND IMMUNOGENICITY ASSAYS

8.1 Introduction

8.2 Assays to Assess PK

8.3 Biomarker Assays

8.4 Assays for Detection and Prediction of Anti-drug Antibodies

8.5 New Trends: Biosimilars, Biobetters, Antibody–Drug Conjugates

8.6 Conclusions

References

CHAPTER 9 STRUCTURES AND DYNAMICS OF PROTEINS PROBED BY UV RESONANCE RAMAN SPECTROSCOPY

9.1 Introduction

9.2 Experimental

9.3 Applications of UVRR Spectroscopy to Membrane-Associated Peptides

9.4 Protein Conformational Changes

9.5 Challenges and Beneffiits of UVRR Spectroscopy

9.6 Conclusion

Acknowledgments

References

CHAPTER 10 FREEZING- AND DRYING-INDUCED MICRO- AND NANO-HETEROGENEITY IN BIOLOGICAL SOLUTIONS

10.1 Introduction

10.2 Freezing-Induced Heterogeneity

10.3 Drying-Induced Heterogeneity

10.4 Methods of Detection

10.5 Summary

Acknowledgments

References

SECTION 3 PHASE III AND COMMERCIAL DEVELOPMENT

CHAPTER 11 LATE-STAGE PRODUCT CHARACTERIZATION: APPLICATIONS IN FORMULATION, PROCESS, AND MANUFACTURING DEVELOPMENT

11.1 Introduction

11.2 Strategies in Using Biophysical Methods in Late-Stage Development

11.3 Analytical Methods Applications Considerations

11.4 Concluding Remarks

References

CHAPTER 12 BIOPHYSICAL ANALYSES SUITABLE FOR CHEMISTRY, MANUFACTURING, AND CONTROL SECTIONS OF THE BIOLOGIC LICENSE APPLICATION (BLA)

12.1 Introduction

12.2 The Biophysical Tool Box

12.3 Common Biophysical Methods for Assessing Folded Structure

12.4 Common Biophysical Methods for Assessing Size Heterogeneity, Association State, Aggregation

12.5 Methods for Assessing Subvisible Particulates

12.6 Evolving Biophysical Technologies

12.7 Case Study for the Use of Biophysical Methods in the Elucidation of Structure Section of the License Application

12.8 Case Study for the use of Biophysical Methods in the Comparability Section of the License Application

12.9 Case Study for the use of Biophysical Methods in the Pharmaceutical Development Section of the License Application

12.10 Biophysical Methods for Evaluating Protein—Surface/Device Interaction

12.11 Implication for Biosimilars

12.12 Concluding Remarks

Acknowledgments

References

INDEX

List of Tables

Chapter 1

Table 1.1

Chapter 2

Table 2.1

Scheme 2.1

Chapter 3

Table 3.1

Table 3.2

Table 3.3

Chapter 5

Table 5.1

Table 5.2

Table 5.3

Chapter 7

Table 7.1

Table 7.2

Chapter 8

Table 8.1

Table 8.2

Table 8.3

Table 8.4

Chapter 9

Table 9.1

Chapter 11

Table 11.1

Table 11.2

Table 11.3

Chapter 12

Table 12.1

Table 12.2

Table 12.3

Table 12.4

Table 12.5

List of Illustrations

Chapter 1

Figure 1.1

Scheme showing the different classes of protein reagents and drug candidates produced and characterized by biophysical methods from the initiation of a drug discovery program through selection of a final molecule for subsequent progression into development. Initially the protein production and biophysical characterization efforts are focused on the target(s) and reagents. As the program progresses, the amount of protein chemistry increases and shifts toward the production and characterization of protein therapeutic candidate molecules. The type and extent of biophysical characterization done for each class of protein and for each stage of discovery is different as described in Table 1.1.

Figure 1.2

Analytical size-exclusion chromatography data showing examples of the elution profiles of early-stage Adnectin drug candidates. The top panel shows a homogeneous, monomeric drug candidate, and the bottom panel shows a candidate that has high molecular weight (HMW). Data of this type is used to select the most promising drug candidates for advancement.

Figure 1.3

Example of thermal stability of a biotherapeutic candidate molecule as measured by thermal stability fluorescence. (a) Fluorescence of an Adnectin candidate in the presence of the extrinsic fluorophore anilinonaphthalene sulfonic acid (ANS) as a function of temperature. As the protein unfolds, hydrophobic regions are exposed to solvent, bind ANS, and cause an increase in fluorescence. The midpoint of the transition (Tm) is obtained by curve fitting and is used as a qualitative measure of thermal stability. The Tm for the curve shown is 70.2°C. (b) Tm values measured in high-throughput mode for the same Adnectin in many different buffer pH conditions. The experiment was done in 384-well format and demonstrates the ability to rapidly screen buffer conditions that may influence the thermal stability of the drug candidate.

Figure 1.4

Self-association analysis of an Adnectin candidate as determined by size-exclusion chromatography combined with multiple angle light scattering shown in (a) and (b) and analytical ultracentrifugation shown in (c). The Adnectin eluted from a size-exclusion column (a) with a major peak (99% of 280 nm signal) at 21.6 min and a minor peak (1%) at 20.5 min. From light-scattering data collected during the run, the MW versus elution time plot (b) shows that the main peak eluted with a MW consistent with monomeric protein, and the shoulder likely contained dimer. (c) shows the sedimentation equilibrium analysis of the absolute mass of the Adnectin as measured by analytical ultracentrifugation. The best-fit curve shown is the one representative from a set of multiple centrifugation speeds fit globally to a single mass species. The best-fit from the global analysis yielded a mass of 10.3 kDa. This agrees well with the theoretical mass of 10.9 kDa.

Figure 1.5

Differential scanning calorimetry (DSC) of two different Adnectin drug candidate molecules. The top two traces are indicative of a thermally irreversible protein system. The Adnectin in the top portion of the panel denatured in the first thermal scan (a) shows no evidence of regaining structural integrity in the time frame allotted for the second thermal scan (b). The lower half of the panel displays a different Adnectin molecule that displays essentially complete thermal reversibility under the conditions tested. The first thermal scan (c) and second scan (d) are observed to overlay, indicating the protein melted in the first scan has recovered structural integrity and behaves identically when thermally scanned a second time.

Figure 1.6

Biosensor data, measured on a Biacore T100 surface plasmon resonance instrument, showing an EGFR Adnectin does not compete with clinically approved mAbs for binding to EGFR. EGFR was immobilized by amine coupling and experiments were conducted as described elsewhere [2]. Briefly, the EGFR Adnectin was flowed over the EGFR surface alone at 450 nM, and then either alone at 450 nM or together with 450 nM mAb as shown. Reproduced with permission from Reference 2.

Figure 1.7

Biosensor data for anti-IGF1R Adnectin binding to IGF1R-Fc from different species: human (a), monkey (b), mouse (c and d) and rat (e and f) IGF1R. In each panel, the IGF1R-Fc target was captured on the surface by protein A and the Adnectin was flowed across the surface at multiple concentrations from 1 to 500 nM. The data for human and monkey IGF1R were well described by a simple Langmuir fitting model. In contrast, the kinetic data for mouse and rat (c, e) could not be well described with a simple model. Instead, the affinities for mouse and rat IGF1R were obtained from a steady-state equilibrium analysis (d, f) by fitting to a simple equilibrium model [20] based upon the amount of Adnectin bound versus total concentration of Adnectin in solution. Binding equilibrium dissociation constants for the four interactions were thus determined as 0.3 nM (a, human), 0.2 nM (b, monkey), 84 nM (d, mouse), and 99 nM (e, rat).

Figure 1.8

Determination of relative solubility values for seven EGFR mono-Adnectins and an anti-IGF1R Adnectin (a), and the same seven when formatted as a bispecific Adnectin with the single anti-IGF1R Adnectin (b). The IGF1R Adnectin in (a) is shown as stars and is the furthest curve to the right. Relative solubility values were measured by an adaptation of the ammonium sulfate precipitation method reported by Trevino et al. [24]. The data in this figure show the concentration of drug candidate remaining soluble as a function of molarity of ammonium sulfate. The higher the solubility of a protein, the more ammonium sulfate required to cause precipitation. Thus, the more left-shifted the curve, the less soluble the protein therapeutic candidate. The results provide a way to rank-order the solubilities of the drug candidate molecules.

Chapter 2

Figure 2.1

Flowchart of the construct and crystallization process. PTM, posttranslational modifications; HDXMS, high-resolution deuterium exchange mass spectroscopy

Figure 2.2

Schematic representations of the most common macromolecular crystallization techniques: (a) sitting drop vapor diffusion, (b) hanging drop vapor diffusion, (c) sandwich drop vapor diffusion, (d) free interface diffusion, and (e) batch crystallization under oil

Figure 2.3

Examples of current high-throughput crystallization platforms: (a) nano-volume dispenser apparatus used to set up 96 well sitting drop vapor diffusion plates at submicroliter volumes (Art Robbins Instruments Sunnyvale CA, USA, Phoenix); (b) a microfluidic system used to prepare liquid/liquid diffusion plates (left), a plugmaker liquid/liquid interface plate capable of setting up screen with as little as 10 μL of protein (right) (Emerald Bio Seattle WA, USA)

Figure 2.4

Automated imaging systems for recording crystallization images: (a) custom-built automated imaging systems for the storage and recording of crystallization images (GNF Systems, San Diego, CA); (b) Rigaku's Automation Carlsbad CA, USA minstrel automated imaging system incorporating UV imaging for the detection of crystals

Figure 2.5

A collection of protein crystal images demonstrating the diversity of shape and sizes of crystals that have produced viable X-ray data sets

Figure 2.6

Common protein folds encountered in biotherapeutics. Ribbon diagrams of structures of common folds found in biotherapeutics: (a) structure of a typical immunoglobulin domain taken from the heavy chain of a Fab fragment (PDB ID 1MJU [168]), this two-layer β-sandwich consisting of between 6 and 9 β-strands is the most common domain in extracellular proteins. (b) A four-helix bundle (interleukin-2, PDB ID 1M47 [169]); the four helices in this domain are arranged in an antiparallel fashion with this motive occurring in many cytokines. (c) A β-trefoil fold is a 12-stranded β-strand structure with pseudo threefold symmetry represented in the diagram by interleukin 1 receptor antagonist (PDB ID 1IRA [170]). (d) The small cysteine bridge-mediated insulin-like fold is characterized by insulin-like growth factor (PDB ID 1IMX [171])

Figure 2.7

Antibody structure. (a) Schematic of IgG showing domain nomenclature and regions of interest in the IgG; complementarity determining regions highlighted as bold lines. (b) Ribbon representations of full-length IgG structure, heavy chains shown as black, light chains as light gray. Left: PDB ID 1IGY [172] showing the expected symmetric Y-shaped nature. Right: a more asymmetric full-length antibody (PDB ID 1HZH [12]) demonstrating the inherent flexibility in the quaternary structure

Figure 2.8

Structures of antibody, Fab, and fragment crystallizable (Fc) domains. (a) Ribbon representation of structure of Fab in complex with lysozyme; the antigen lysozyme is shaded black (PDB ID 3HFM [173]). (b) Structure of a typical Fc domain; conserved glycosylation is shown as stick representation (PDB ID 1FC1 [174]). (c) The C

H

3 interface used in the design of asymmetric knob–hole interactions to generate heterodimeric monoclonal antibodies

Figure 2.9

Alternative scaffolds for biotherapeutics. Ribbon representation of alternative scaffolds. (a) An engineering single-chain Fv antibody (PDB ID 2A9N [175]). (b) Camelid single domain antibody. Extended CDR3 shaded black (PDB ID 1MEL [131]). (c) Shark VNAR heavy chain variable domain (PDB ID 2I24 [134]). (d) Non-immunoglobulin-like DARPIN scaffold binding loops are formed from repeated loop/helix structure and are shaded black (PDB ID 2J8S [176])

Figure 2.10

Ribbon diagrams of immunoglobulin domain receptors and their complex interactions with endogenous ligands. (a) Structure of the four-helix bundle erythropoietin (EPO), shaded black, dimerizing the erythropoietin receptor, shaded white (PDB ID 1CN4 [148]). (b) Structure of platelet-derived growth factor-β (PDGF-β), shaded black, dimerizing the PDGF-β receptor, shaded white (PDB ID 3MJG [177]). (c) Structure of interleukin-1 receptor antagonist (IL1R-A), shaded black, with its monomeric receptor shaded white (PDB 1IRA [170])

Figure 2.11

Modifying proteins with unnatural chemical entities. (a) Crystal structure of a protein incorporating a chemically functional zinc chelating unnatural amino acid (quinolin-8-01) (PDB ID 3FCA [160]). (b) Model of using structure to optimally position half-life extending polyethylene glycol (represented as filled blobs) to the surface of an interferon-α molecule

Chapter 3

Figure 3.1

A plot of the B

22

value versus antibody solubility of CNTO607 and its solubility-enhanced variants. Wild-type CNTO607 antibody and variants are all in PBS buffer, pH 7.4.

Figure 3.2

Schematic representation of the approximate range of detectable protein sizes (diameter) of various analytical methods. Reproduced with permission from Reference

37

.

Figure 3.3

Antigen recognition surface of CNTO607 and its interaction with IL-13. The hydrophobic ridge in the CNTO607 antigen-binding site. VL and VH are colored cyan and magenta. The hydrophobic ridge is colored green. N, S, and O atoms in the ridge are colored blue, yellow, and red. Adapted from Reference

13

.

Figure 3.4

Retention of selected therapeutic antibody candidates on polyclonal human IgG column. Reproduced from Reference

12

.

Chapter 4

Figure 4.1

Second-derivative UV spectra of human serum albumin and interferon alfa-2b (top panel); second-derivative UV spectra of albinterferon alfa-2b, equimolar mixture of human serum albumin and interferon alfa-2b, and mathematical summation of human serum albumin and interferon alfa-2b (bottom panel).

Figure 4.2

Intrinsic fluorescence spectra of human serum albumin and interferon alfa-2b (top panel); intrinsic fluorescence spectra of albinterferon alfa-2b, equimolar mixture of human serum albumin and interferon alfa-2b, and mathematical summation of human serum albumin and interferon alfa-2b (bottom panel).

Figure 4.3

CD spectra of human serum albumin and interferon alfa-2b (top panel); CD spectra of albinterferon alfa-2b, equimolar mixture of human serum albumin and interferon alfa-2b, and mathematical summation of human serum albumin and interferon alfa-2b (bottom panel).

Figure 4.4

DSC thermograms of human serum albumin and interferon alfa-2b (top panel); DSC thermograms of albinterferon alfa-2b, equimolar mixture of human serum albumin and interferon alfa-2b, and mathematical summation of human serum albumin and interferon alfa-2b (bottom panel).

Figure 4.5

Second-derivative FTIR spectra of product in previous and new formulation buffers at intended product concentrations.

Figure 4.6

Second-derivative FTIR spectra of product in previous and new formulation buffers at pre-change product concentration.

Figure 4.7

DSC thermogram of product in previous and new formulation buffers.

Figure 4.8

IE-HPLC profiles of “Protein C.”

Figure 4.9

N-linked glycan profiles of “Protein C” by CE-LIF.

Chapter 5

Figure 5.1

Schematic overview of the multistage nature of aggregate nucleation, including multiple steps that potentially involve conformational changes.

Figure 5.2

Equilibrium unfolding monitored by spectral center of mass for intrinsic fluorescence (panels a and c) and dynamics of unfolding/refolding monitored by the intensity of intrinsic fluorescence (panels b and d) for aCgn at pH 3.5, 10 mM sodium citrate (a and b) and the bhx domain of P22 tailspike at pH 7, 100 mM sodium phosphate (c and d). Panels (a) and (b) are from Reference

41

, while panels (c) and (d) are from Reference

55

). The temperature values for (a) and (b) are 26°C (closed diamonds), 30°C (open diamonds), 34°C (closed squares), 38°C (filled squares). Spectral center of mass (COM) is defined as and is a measure of the emission peak position that is weighted proportional to higher energy (lower wavelength) emission values.

Figure 5.3

Comparison of time course of the fractional extent of monomer loss on a mass basis (1–

m

) to the change in a variety of spectroscopic signals and laser light scattering (see figure legend and

y

-axis labels) during aggregate growth versus time (

t

), under solution conditions where aggregate growth occurs primarily via monomer addition. From Reference

51

.

Chapter 6

Figure 6.1

Schematic of the two continuous labeling H/D exchange approaches for proteins. The protein of interest is diluted in deuterated buffer with the appropriate formulation components and allowed to exchange for discrete periods of time. Amide hydrogens (represented as gray dots) take up deuterium at varying exchange times. The reaction is quenched by dropping the pH to around 2 and the temperature to near 0°C. One can chose to analyze the intact protein or to localize differences using an acidic protease such as pepsin. The total mass change (Δ

D

) of the protein is representative of the total exchangeable amide sites for that particular condition, whereas the mass change of a component peptide (also Δ

D

) is representative of the total exchangeable amide sites in that particular region of the protein. Low Δ

D

values indicate decreased H/D exchange rates from strong hydrogen bonds and high Δ

D

values indicate increased H/D exchange rates from weaker hydrogen bonds.

Chapter 7

Figure 7.1

Pillars for successful formulation development.

Figure 7.2

Life cycle of a drug product.

Figure 7.3

Glass fogging phenomenon observed post lyophilization.

Figure 7.4

The complex loop of different preformulation/biophysical approaches preceding formulation development.

Figure 7.5

Correlation of physical stability of different mAbs (numbered 1–7 in the figure) after storage for 6 months at 40°C to ThT binding constants (

K

) from equilibrium ThT fluorescence binding assays and SAP scores (SAP scoring was performed only on mAbs of the same class). Figure modified from Reference

66

.

Figure 7.6

As described by Kamerzell et al., the general flow diagram illustrates HTS steps for protein formulation development. DLS, dynamic light scattering, DSC, differential scanning calorimetry; ITC, isothermal titration calorimetry; DSF, differential scanning fluorescence; CD, circular dichroism; UV/Vis, ultraviolet/visible; EPD, empirical phase diagram; PCA/SVD, principle component analysis/singular value decomposition. Initially, a large number of prospective experiments are designed. Robotics can then be used for liquid sample preparation prior to the use of high-throughput analytical methods for physicochemical characterization. The high-throughput methods are equipped with 96–364 micro-well plates. The large amounts of data are rapidly analyzed by advanced numerical methods using pre-programmed criteria. Figure modified from Reference

10

.

Figure 7.7

Empirical phase diagram of a recombinant protein. The diagram was prepared from CD at 217 nm, intrinsic fluorescence (peak position), static light scattering, and ANS fluorescence data as a function of pH (3–8) and temperature (10–87.5°C). Unpublished data, figure used with kind permission and courtesy of Sangeeta Joshi and Russ Middaugh.

Figure 7.8

Screening different protein formulations based on thermal transition (melting) temperatures at selected pH values. (a) Transition temperature as a function of pH (4–9) and buffer concentration (10–150 mM) from a pH and buffer screening study. (b) Protein transition temperature during a formulation screening study. Solid bars are formulations at pH 6.2, striped bars are formulations at pH 7.5. Figures modified from Reference

78

.

Figure 7.9

A comparison of Tm(FTIR) values at high and low protein concentrations and Tm(DSC) values at low protein concentrations. Figure reconstructed from Reference

79

.

Figure 7.10

Correlation between physical aggregation rate constants (assuming first-order kinetics) to Tm(FTIR) values in buffer, amino acid, and stabilizer (sugar) screening studies. Figure modified from Reference

81

.

Chapter 8

Figure 8.1

Tiered approach to anti-drug antibody testing.

Figure 8.2

Anti-drug antibody titration curve and cutpoint.

Chapter 9

Figure 9.1

Schematic of Raman process for a single, harmonic normal mode. Left: Off-resonance Stokes Raman spectroscopy in which

E

exc

does not overlap with an absorption band. The difference in energy between

E

exc

and

E

scatt

, Δ

E

, is the Raman shift and reflects the vibrational energy of a specific normal mode. When the excitation wavelength overlaps with an allowed absorption band

E

abs

, Stokes (middle) or anti-Stokes (right) resonance Raman scattering is observed. The efficiency of RR scattering is several orders of magnitude greater than off-resonance Raman scattering, enabling RR spectroscopy to be a selective analytical tool.

Figure 9.2

Absorption (main graph) and UVRR (insets) spectra of ∼40 μM melittin in phosphate buffer where the peptide is a random coil. UVRR spectra with 210 nm excitation primarily reflects backbone vibrations; peaks in the region ∼1200–1700 cm

−1

can be assigned to amide I, II, III, and S modes while peaks at ∼750–1020 cm

−1

primarily reflect tryptophan peaks. UVRR spectra with 230 nm enhances signal from the single tryptophan residue in tryptophan; all strong peaks in this spectrum can be assigned to tryptophan normal modes. Spectra were acquired using apparatus shown in Figure 9.3. Sample was flowed in 100 μm (230 nm) or 250 μm (210 nm) quartz capillaries with 5 mW (230 nm) or 3 mW (210 nm) of UV power at the sample.

Figure 9.3

Schematic of a UVRR apparatus used in the Department of Chemistry and Biochemistry at UC San Diego. The output of a 1 kHz, nanosecond Ti:sapphire laser is telescoped and passed through a lithium triborate (LBO) crystal for second-harmonic generation. The doubled blue light is separated from the fundamental infrared beam by a dichroic mirror. The blue light is then doubled in a β-barium borate (BBO) crystal to generate UV light; these beams are separated by a fused silica Pellin-Broca prism. The UV excitation beam is passed through a pair of cylindrical lenses to a spot size of ∼300 μm (height) × 75 μm (width) and focused on a fused silica capillary through which sample is pumped vertically via a syringe pump. The Raman scattered light is collected and focused onto the entrance slit of a prism-based prefilter, dispersed in a spectrograph, and imaged onto a CCD detector.

Figure 9.4

UVRR spectra (210 nm excitation) of ∼40 μM melittin in phosphate buffer and in membrane bilayers of vesicles comprised of 2:1 neutral:anionic lipids. Structure of folded melittin is from PDB ID: 2MLT. Backbone peaks are labeled. Spectral decomposition indicates that melittin is random coil in buffer and α-helical in membranes. The α-helical structure is evident by the lack of amide S intensity. Spectra were acquired using apparatus shown in Figure 9.3. Sample was flowed in a 250 μm quartz capillary with 3 mW of power at the sample.

Chapter 10

Figure 10.1

Transition of a “simple” solution to a multi-phase mixture during freezing. Note that the actual freezing-induced heterogeneity is much more complicated than what is shown here. In many cases, there are no clear boundaries among different phases but there are heterogeneous regions composed of different constituents (e.g., proteins entrapped in ice; see Reference

3

). The amount of remaining water at temperatures below the eutectic temperature is a complex function of the thermal history of the specimen as well as its specific constituents.

Figure 10.2

Frozen aqueous dimethyl sulfoxide (DMSO) solutions (w/w). Note that the eutectic concentration of aqueous DMSO is ∼52% w/w. In solutions less than 50% DMSO w/w, ice crystals form whereas at concentrations higher than 70% DMSO w/w, DMSO crystals form. Large bright rectangles are 200 μm × 200 μm.

Figure 10.3

Distribution of (a) trehalose and (b) lysozyme along the centerline of a desiccated sessile droplet. Data collected at the InfraRed ENvironmental Imaging (IRENI) beamline in Synchrotron Resource Center, Stoughton WI. Pixel size: 0.54 μm.

Guide

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PREFACE

The biotechnology industry, emerging since 1970s, is enjoying robust growth with estimated equity investments in the tune of $400 billion today. The growth is fueled by opportunities of biologic drugs in unmet medical-need areas including cancer and immunology. Currently there are over 270 approved biotech drugs for a wide range of therapies, and hundreds of candidates are in clinical development. The biotherapeutic class of drugs encompasses a range of biologic-based compounds including monoclonal antibodies, enzymes, antibody fragments, glycosylated proteins, other recombinant proteins, peptides, conjugated or fused peptides, antibody drug conjugates (ADC), protein-based vaccines, oligonucleotides, protein–lipid complexes, and carbohydrates. Protein- and peptide-based biologics dominate the list of approved as well as clinical development candidates.

In contrast to small molecule-based therapeutic candidates, biotherapeutic molecules present a much higher level of complexity arising from several degrees of structural elements that are required for appropriate biological function. For protein-based biologics, in addition to the amino acid sequence (referred to as primary structure), there are various forms of secondary structures (helical, β-sheet, β-turn, unordered, etc.) that often coexist in a protein. When the higher order structural builds (tertiary, quaternary) are added to the secondary structure, a composite and complete structure is formed that is often referred to as protein conformation. Maintenance of the integrity of protein conformation requires a great deal of attention in all stages of development—from early discovery through clinical phases to commercial development. A good understanding of protein structure and function and its sensitivity to a variety of solution, interface, and environmental conditions is critical to ensure an active and nondegraded form is preserved through the development stages. Additionally, protein drug substance is most often a heterogeneous mixture of closely related species that add complexity to efforts toward maintaining original conformation(s) and ensuring drug stability.

It is well recognized that in addition to using appropriate analytical techniques to monitor stability and integrity of a biologic candidate, employing a wide range of biophysical methods is paramount in the development process. To deal with the numerous degradation issues known to occur with biologic molecules, biotech researchers have innovatively adapted physical and chemical technologies from across diverse fields in addition to using the classical biophysical/biochemical methods. However, there is no comprehensive textbook available that discusses application diversity of biophysical methods and the type of information sought as a function of the phases in biotherapeutic development.

This book focuses on systematic applications of biophysical technologies and methods in stages of biotherapeutics development. Four areas are emphasized in this book: (1) novel applications of traditional biophysical techniques, (2) emerging technologies and their applications, (3) biophysical applications relevant to stage-wise development of a clinical biotherapeutic candidate—from discovery through clinical phases to commercial, and (4) focused discussion of some of the thermodynamic, conformational, and stabilization concepts aided by biophysical research.

The chapters in this book are laid out in a theme (Sections 1–3) based on clinical phases of development (Section 1: Early discovery stages and biotherapeutic candidate selection; Section 2: First-in-human and up to proof-of-concept; Section 3: Phase III and commercial development). This gives a comprehensive view to a biotherapeutics development scientist of what biophysical studies are needed for which phase and what purpose, how to apply some of the biophysical techniques for what type of information sought, what type of orthogonal biophysical characterization may be expected by regulatory agencies, and very importantly the limitations of each technique and its applications—“myth versus truth.” The editor believes that this book will be a good guide to biophysics experts as well as beginners to help them with the big picture of biotherapeutics development and for developing an organization's short- and long-term strategies for resource investment in biophysical research.

The contributing authors in this book are prominent researchers with proven track records. The authors added excellent CASE STUDIES and discussed results with literature data and concepts. My deepest gratitude to all authors for making outstanding contributions to make this book possible. My sincere thanks to many individuals for guidance, help with reviewing, and the publishing process. In particular, I thank Drs. Kevin King and Sandeep Nema of Pfizer; Dr. Andy Vick of Wilresearch; Dr. Michael Hageman of Bristol-Myers Squibb; and Jonathan Rose and Amanda Amanullah of Wiley.

This book is dedicated to my parents who unconditionally nurtured and supported my passion for science and technology and my wife Paramita for inspiring and supporting me to complete the book.

Tapan K. Das Hopewell, NJ, USA

ABOUT THE EDITOR

Dr. Tapan K. Das is Director of Center of Excellence, Mass Spectrometry and Biophysics in the Biologics Development group, Bristol-Myers Squibb. Prior to this position, he was an Associate Research Fellow and Head of Biophysical Center of Excellence in Pharmaceutical R&D, Biotherapeutics Pharmaceutical Sciences, Pfizer.

His key interest areas include structural characterization of biotherapeutic candidates progressing through clinical development toward commercialization, pharmaceutics, and development of novel biophysical and biochemical applications. He is actively engaged in fostering collaborations and sharing learning through internal and external networks.

Dr. Das began his career at Albert Einstein College of Medicine, New York. He currently serves as the Chair of the Biotechnology Section of American Association of Pharmaceutical Scientists (AAPS), USA. He has published 61 articles in professional scientific journals, books, and patents and presented numerous talks.

LIST OF CONTRIBUTORS

Ahmad M. Abdul-Fattah

Pharmaceutical Development and Supplies, Pharma Technical Development Biologics EU, F. Hoffmann-La Roche Ltd., Basel, Switzerland

Alptekin Aksan

Biostabilization Laboratory, Mechanical Engineering Department and BioTechnology Institute, University of Minnesota, Minneapolis, MN

Angela W. Blake-Haskins

BioPharmaceutical Development, Human Genome Sciences, Inc., Rockville, MD

James W. Bryson

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Christine P. Chan

Biologics R&D, Genzyme—A Sanofi Company, Boston, MA

Michael L. Doyle

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Yiqing Feng

Lilly Research Laboratory, Indianapolis, IN, USA

Gary L. Gilliland

Janssen R&D, LLC, Spring House, PA

Boris Gorovits

Department of Pharmacokinetics, Pharmacodynamics, and Meta- bolism, Pfizer, Andover, MA

Carol Hirschmugl

Department of Physics, University of Wisconsin, Milwaukee, WI, and Synchrotron Resource Center, University of Wisconsin, Stoughton, WI

Lisa M. Jones

Department of Chemistry, Washington University in St. Louis, St. Louis, MO, and Department of Chemistry and Chemical Biology, Indiana University-Purdue University, Indianapolis, IN

Judy E. Kim

Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA

Corinna Krinos-Fiorotti

Department of Pharmacokinetics, Pharmacodynamics, and Metabolism, Pfizer, Andover, MA

Virginie Lafont

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Brian S. Leigh

Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA

Yen-Huei Lin

BioPharmaceutical Development, Human Genome Sciences, Inc., Rockville, MD

Zheng Lin

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Hanns-Christian Mahler

Pharmaceutical Development and Supplies, Pharma Technical Development Biologics EU, F. Hoffmann-La Roche Ltd., Basel, Switzerland

Paul E. Morin

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Hendrik Neubert

Department of Pharmacokinetics, Pharmacodynamics, and Metabolism, Pfizer, Andover, MA

Melissa D. Perkins

BioPharmaceutical Development, Human Genome Sciences, Inc., Rockville, MD

Vishard Ragoonanan

Department of Pharmaceutics, University of Minnesota, Minneapolis, MN

Christopher J. Roberts

Department of Chemical Engineering, University of Delaware, Newark, DE

Bonita Rup

Department of Pharmacokinetics, Pharmacodynamics, and Metabolism, Pfizer, Andover, MA

Nazila Salamat-Miller

Rare Disease Business Unit, Shire, Department of Pharmaceutical and Analytical Development, Lexington, MA

Diana E. Schlamadinger

Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA

Lumelle A. Schneeweis

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Zahra Shahrokh

Rare Disease Business Unit, Shire, Department of Pharmaceutical and Analytical Development, Lexington, MA

Li Shi

Shanghai Zerun Biotechnology Co., Ltd., Member of Wison Group, Shanghai, China

Justin B. Sperry

Analytical Research and Development, Biotherapeutics Pharmaceutical Sciences, Pfizer, Inc., Chesterfield, MO

Thomas M. Spitznagel

BioPharmaceutical Development, Human Genome Sciences, Inc., Rockville, MD

Glen Spraggon

Genomics Institute of the Novartis Research Foundation, San Diego, CA

John J. Thomas

Rare Disease Business Unit, Shire, Department of Pharmaceutical and Analytical Development, Lexington, MA

Sheng-Jiun Wu

Janssen R&D, LLC, Spring House, PA

Zhuchun Wu

BioPharmaceutical Development, Human Genome Sciences, Inc., Rockville, MD

Aaron P. Yamniuk

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

Joseph Yanchunas, Jr.

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ

SECTION 1EARLY DISCOVERY STAGES AND BIOTHERAPEUTIC CANDIDATE SELECTION

CHAPTER 1BIOPHYSICAL METHODS APPLIED IN EARLY DISCOVERY OF A BIOTHERAPEUTIC: CASE STUDY OF AN EGFR-IGF1R BISPECIFIC ADNECTIN

Michael L. Doyle, James W. Bryson, Virginie Lafont, Zheng Lin, Paul E. Morin, Lumelle A. Schneeweis, Aaron P. Yamniuk, and Joseph Yanchunas, Jr.

Protein Science and Structure Department, Bristol-Myers Squibb Research and Development, Princeton, NJ, USA

1.1 Introduction

1.2 Target Identification

1.3 Target Generation

1.3.1 Multiple Constructs Strategy

1.4 Hit Evaluation

1.4.1 Qualitative and Rapid Self-Association Check

1.4.2 Qualitative and Rapid Thermal Stability Check

1.4.3 Confirmation of Binding

1.5 Lead Selection

1.5.1 Self-Association

1.5.2 Thermal Stability

1.5.3 Binding Affinity, Kinetics, and Epitope

1.6 Lead Optimization

1.7 Lead Formatting

1.7.1 Solubility

1.7.2 Thermal Unfolding Behavior

1.8 Final Development Candidate Selection

1.9 Concluding Remarks

Acknowledgment

References

1.1 INTRODUCTION

Biophysical characterization of protein therapeutics and associated reagents in drug discovery is critical to selection and optimization of molecules that have the desired biological activity and to selection of drug candidates that can be efficiently developed and manufactured. Protein therapeutic molecules are larger and more complex than small-molecule drugs. Consequently, analytical strategies for determining whether a protein therapeutic is pure, stable, and homogeneous require that a larger number of physical properties be investigated, including characterization of tertiary and quaternary structures. Furthermore, several physical properties of protein therapeutics, for example, aggregation state, require multiple, orthogonal methods to confidently define them (Table 1.1).

Table 1.1 Biophysical and biochemical methods used to characterize targets, reagents, and drug candidates for protein therapeutic discovery programs in terms of identity, purity, stability, oligomeric status, binding activity, and molecular binding mechanism

In addition to production and characterization of hundreds or thousands of drug candidates during drug discovery, a large number and diversity of protein reagents must also be produced and characterized. To begin with, the biological target must be produced in a form that is well behaved and representative of the functional form to be targeted in vivo. There are a multitude of other protein reagents needed to run the program as well (e.g., multiple affinity-tagged forms of the target for use in a variety of assays, truncated forms of the target for structural studies, counter-targets, co-targets, and nonhuman species ortholog variants of the target; Figure 1.1; see also Kim and Doyle [1] for a detailed listing). Target reagents that are aggregated or misfolded confound the drug discovery process during hit identification and downstream assays. The famous admonition “garbage in, garbage out” is often cited as a reminder that biophysically well-behaved reagents generally lead to higher success rates during lead identification and optimization of protein therapeutics. Biophysical methods thus play a wide variety of roles in the characterization of biotherapeutic candidates and protein reagents during the early discovery stages of biotherapeutics.

Figure 1.1 Scheme showing the different classes of protein reagents and drug candidates produced and characterized by biophysical methods from the initiation of a drug discovery program through selection of a final molecule for subsequent progression into development. Initially the protein production and biophysical characterization efforts are focused on the target(s) and reagents. As the program progresses, the amount of protein chemistry increases and shifts toward the production and characterization of protein therapeutic candidate molecules. The type and extent of biophysical characterization done for each class of protein and for each stage of discovery is different as described in Table 1.1.

Biophysical characterization is a central part of the selection and optimization process. But how much biophysical characterization is optimal for each type of reagent or biotherapeutic candidate molecule, and how does the extent of biophysical characterization change during each stage of the discovery process? The goals of this chapter are to describe the types of biophysical methods that are used in a stage-dependent manner throughout discovery for reagent and drug candidate production of protein therapeutics and to discuss how the application of these methods in discovery help to de-risk the potential costly challenges later in the development and manufacturing phases.

The discovery process is described in this chapter by several stages: target generation, hit evaluation, lead selection, lead optimization, lead formatting, and final lead candidate selection of a molecule to progress into development. We note that the types and extent of biophysical characterization will depend to some degree on the molecular class of the protein therapeutic (monoclonal antibody, Adnectin, antibody fragments, non-antibody fragments, etc.) and the technology used for selecting lead candidates (immunizations, phage display, RNA display, etc.). The purpose here is to present a case study of biophysical applications during the discovery of a bispecific Adnectin against epidermal growth factor receptor (EGFR) and insulin-like growth factor-1 receptor (IGF1R). Many of the details for this system have been reported elsewhere [2].

1.2 TARGET IDENTIFICATION

Identification of a drug's biological target is a critically important part of a biotherapeutic discovery program. One of the expanding areas in biotherapeutics research is the design of bispecific biotherapeutics that bind to two different, already validated biological targets. The proposed benefits for the bispecific-targeting approach include improved efficacy and lower cost of goods than developing two drugs independently.

Drug targets may also be identified from genetic validation studies (correlation between mutation of target and disease state) or pharmacological validation studies (utilizing a surrogate molecule such as a natural ligand to demonstrate efficacy in a non-clinical setting). The Holy Grail for identification of completely novel targets is to utilize the growing information from genomic, proteomic, and interactomic studies to draw correlations between specific drug targets, or sets of drug targets, and treatment of disease.

This chapter describes a case study for discovery of an Adnectin [3] bispecific biotherapeutic that targets inhibition of both EGFR and IGF1R (Emanuel et al. [2]). EGFR is a clinically validated target for cancer therapy, and there are both small-molecule kinase inhibitors and biotherapeutic inhibitors of the extracellular domains presently available as marketed drugs. IGF1R is also an attractive target for cancer therapy and there are several small-molecule and biotherapeutic inhibitors in preclinical and clinical studies [4].

1.3 TARGET GENERATION

Once a target has been identified, it is usually produced recombinantly to provide sufficient material to enable selection of biotherapeutic candidate “hits” through a screening or selection process. There are several technologies commonly used for generating biotherapeutics hits, including in vivo immunization, phage display, mRNA display, and yeast display [5,6]. All of these technologies rely on the production of biophysically well-behaved target molecule. Biophysical methods thus play a critical role as “gate-keeper” at this phase of discovery, to ensure the quality of the target being used for screening or selections is suitable for generating the best candidates.

The first step in producing the target reagent is to engineer a form of the target molecule that will be expressed well and has acceptable biophysical behavior when purified. Sometimes the design is fairly straightforward. For instance, the construct design, expression, and purification for some targets may be well described in the literature. Construct design may also be straightforward if the protein target itself is structurally small and simple. An example would be a soluble target such as a cytokine. The construct design of a simple small protein could be as straightforward as expressing the entire native protein. On the other hand, construct design of large membrane-spanning protein targets can be much more challenging since the membrane-spanning and intracellular regions usually need to be deleted in order to make well-behaved soluble extracellular fragment(s) of the target. Whether or not some or all of the extracellular domains extracted from the full-length protein can be expressed, purified, and well behaved biophysically is often not known in advance.

1.3.1 Multiple Constructs Strategy

Given significant uncertainties and risk surrounding the production of critical target molecules, it is prudent to approach the problem with the design of multiple constructs in parallel, at least through DNA expression vector or small-scale expression screening stages. There are several reasons for designing multiple constructs up front for a target molecule. First, most target molecules need to be produced as fusions with a variety of affinity tags (e.g., His tags, Flag tag) to facilitate purification and development of different types of downstream assays. These non-native sequences may in turn alter the native functional or biophysical behavior of the target. Thus, different types of tags, each having different linker sequences joining them to the target molecule, may need to be made and tested for suitable functional and biophysical behaviors by trial and error. Second, different domain regions, or fragments, of a target protein will have different intrinsic expression and biophysical properties, some of which will have acceptable biophysical and functional behaviors and others will not. As a general rule, the more novel is the target, the less is known about its expression and biophysical and functional properties and the greater the risk is of making it in useable form. Novel targets thus deserve more upfront engineering of multiple constructs. Finally, different forms of a target protein may generate different types of epitope families of lead drug candidates from the high-throughput screening or selection process, for reasons that may not be obvious. In order to obtain a sufficient diversity of initial drug candidates to evaluate during discovery, it is therefore useful to screen against multiple forms of the target molecule. For all these reasons, it is prudent to carefully plan out the target design strategy and backup strategies at the beginning of the target generation process, since the cycle time from construct engineering through biophysical and functional assessments is measured in weeks to months.

In the cases of EGFR and IGF1R, there are extensive precedences in the literature for making a variety of extracellular fragments. Moreover, there are three-dimensional crystal structures for some of these fragments, showing where the self-contained domain regions are at atomic resolution. We designed multiple variants of the extracellular regions of EGFR and IGF1R target proteins. The variants included different purification tags, different expression hosts, and different length variants of the extracellular regions. A subset of the constructs designed were expressed, purified, and characterized with biochemical and biophysical methods as described in Table 1.1.

Production of the target molecule, and multiple variants thereof, is only a subset of the total number of reagents needed to support a protein therapeutics drug discovery program. The scheme in Figure 1.1describes the various classes of additional reagents needed, as well as protein therapeutic drug candidates, that must be produced and characterized during the discovery phase. Ideally one would like to have all the variants of the target, co-targets, counter-targets, and species ortholog targets upfront in the early phase of a discovery program in order to facilitate selection of leads with the optimal diversity and cross-reactivity profiles. However, producing all these reagents upfront is very time consuming and it is not uncommon for a program to move forward as soon as an adequate amount of the human target protein is available, and then to produce the other reagents for optimizing cross-reactivity and potency later in the program.

1.4 HIT EVALUATION

In the earliest stage of drug candidate biophysical assessment, many potential lead candidate molecules need to be evaluated in high-throughput mode (typically on the order of hundreds or thousands, or more, depending on the hit identification technology being used). The purification methods used at this stage are high throughput and must be robust and simple enough to generate large numbers of candidates within a reasonable period of time, but do not need to yield proteins that are as high in purity or quantity as will be needed in the later stages of discovery. The biophysical assessment at this stage must also be rapid and simple and be able to distinguish the higher-quality lead candidates from the lower-quality leads. Some of the key biophysical methods used for hit identification include analytical size-exclusion chromatography (SEC), biosensor analysis, and thermal stability fluorescence (TSF; Table 1.1). These methods provide information about the self-association, binding affinity, and conformational stability properties of the hit molecules, respectively, and can be conducted in high-throughput mode using small quantities (sub-milligram) of protein sample.

1.4.1 Qualitative and Rapid Self-Association Check

Figure 1.2 shows example analytical SEC data [7,8] for a well-behaved homogeneous candidate protein therapeutic in comparison to one that is heterogeneous and contains high molecular weight (HMW) species. Here we assume the homogeneous profile reflects a monomeric drug candidate. This assumption will be more rigorously tested at later stages of discovery using the more rigorous methods in Table 1.1. The presence of aggregates or HMW species suggests that production and storability of the molecule will likely involve more challenges during discovery than the molecule that exhibits homogeneous, monomeric behavior. Furthermore, the heterogeneity observed at the hit stage signals a risk that the poorer behavior might be retained during the later stages of discovery and during development. Barring any other exceptionally redeeming properties of the candidate having the HMW species present (such as being one of the very few hits having unique cellular activity or potency), one would normally select the homogeneous molecule to progress into the subsequent stages of discovery.

Figure 1.2 Analytical size-exclusion chromatography data showing examples of the elution profiles of early-stage Adnectin drug candidates. The top panel shows a homogeneous, monomeric drug candidate, and the bottom panel shows a candidate that has high molecular weight (HMW). Data of this type is used to select the most promising drug candidates for advancement.

1.4.2 Qualitative and Rapid Thermal Stability Check

The conformational (or folding) stability of a protein is broadly used as a general measure of stability. This is because the partially or fully unfolded species of proteins are usually more prone to physical and chemical mechanisms of degradation (e.g., aggregation, proteolytic clipping, deamidation) than are the natively folded species. Thermal denaturation of proteins can be measured by many different technologies. One commonly used method that is rapid and requires only microgram amounts of protein is TSF [9]. This method goes by several different names such as thermofluor, thermal stability perturbation, and thermal shift assay. Here we refer to it as thermal stability fluorescence or TSF. Figure 1.3 shows an example of thermal stability for an Adnectin as measured by TSF. In this experiment, the temperature of the protein sample in the presence of an extrinsic fluorophore is increased, while the fluorescence of the sample is monitored. When the protein unfolds, there is an increase in exposed hydrophobic surface area which then binds to the extrinsic fluorophore and causes an increase in fluorescence. In principle, one can monitor the extent of unfolding from the extent of the change in fluorescence shown in the figure. A convenient measure of the thermal stability that can be used to rank-order the relative thermal stabilities of a series of closely related protein drug candidates is the temperature at which half the protein is unfolded, also called the midpoint temperature and denoted by Tm [10]. Generally speaking, a higher Tm is preferred, as it implies the conformational stability is higher. All other parameters being equal, one would prefer to progress drug candidates that have higher thermal stability, with the anticipation that they may be easier to produce, handle, and store. However, it is also important to recognize that the Tm by itself does not always predict shelf life or manufacturability of a protein therapeutic. In some cases, aggregation can be initiated by the solubility limit of the natively folded protein or a chemically modified folded form of the protein [11].

Figure 1.3 Example of thermal stability of a biotherapeutic candidate molecule as measured by thermal stability fluorescence. (a) Fluorescence of an Adnectin candidate in the presence of the extrinsic fluorophore anilinonaphthalene sulfonic acid (ANS) as a function of temperature. As the protein unfolds, hydrophobic regions are exposed to solvent, bind ANS, and cause an increase in fluorescence. The midpoint of the transition (Tm) is obtained by curve fitting and is used as a qualitative measure of thermal stability. The Tm for the curve shown is 70.2°C. (b) Tm values measured in high-throughput mode for the same Adnectin in many different buffer pH conditions. The experiment was done in 384-well format and demonstrates the ability to rapidly screen buffer conditions that may influence the thermal stability of the drug candidate.

1.4.3 Confirmation of Binding

One of the most important factors used to evaluate hit candidates is to determine whether or not they bind the target molecule, and if so, how tight the interaction is. Biosensor is a biophysical method often used at the hit evaluation stage because they can be run in higher throughput mode, while consuming very little of the hit molecules [12,13,14]. Biosensor is a workhorse technology for all phases of protein therapeutics drug discovery and more will be described about this technology later in this chapter and throughout the book. Because the purity values of the hit molecules may not be accurately understood, analysis of the association kinetics is difficult to interpret quantitatively (the association kinetics are dependent on an accurate knowledge of the active concentration of reactant in solution phase which is usually the drug candidate). Instead, the main goal for biosensor work at the hit evaluation stage is to confirm the hit molecules bind to the target. This would normally be done at concentrations of reactants high enough to allow detection of binders that have an acceptable affinity, but low enough to reduce potential nonspecific interactions with the surface. For example, the hits could be tested at a single concentration of 1 μM to discern if they bind with equilibrium dissociation constants of at least approximately 1 μM. If binding is not detected at 1 μM, then the hit molecule either does not bind the target or its affinity is much weaker than 1 μM and perhaps of little interest as a lead molecule. The rate of a hit dissociating from the target may also provide useful information for comparing between hits. Hits having unusually long dissociation rates likely indicate they are binding either with higher affinity or by distinct binding modes compared to hits with much faster dissociation rates.

1.5 LEAD SELECTION

The next stage of discovery is the selection of lead families of candidates for optimization and progression into the later stages of discovery. The decisions about which candidate molecules to advance have long-term consequences for the success and challenges that will be encountered by the program, including whether the binding epitopes are able to elicit suitable biological efficacy from the target and whether there are any chemical or physical liabilities associated with the lead candidate or family. Ideally one would like to select multiple lead families that bind to a diversity of epitopes, to maximize likelihood of favorable biological activity, and have favorable biophysical properties, to increase the chances of ultimately producing candidates that have superior stability and manufacturability attributes.

The biophysical properties that are used as part of the selection criteria include self-association, conformational stability, binding affinity, and binding epitope. In order to measure these biophysical properties rigorously, it is necessary to produce the potential lead molecules at the milligram scale and to purify them to a higher purity standard (e.g., sample is at least 95% molecule of interest). The biophysical methods themselves are also more rigorous at this stage. Prior to biophysical analysis the candidates usually undergo an evaluation of purity and identity by SDS PAGE and LC/MS. SDS PAGE and the LC part of LC/MS provide information about purity, and the mass spectrometry data provide mass information of sufficient accuracy to confirm the identity of the protein candidate to its expected amino acid sequence.

1.5.1 Self-Association