Label-Free Assays for High-Throughput Monoclonal Antibody Characterization

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1 Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Broadcast Date: Thursday, December 13, 2012 Time: 2 pm ET, 11 am PT Sponsored by

2 Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Your Moderator Tamlyn Oliver Managing Editor Genetic Engineering & Biotechnology News

3 Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Sriram Kumaraswamy, Ph.D. Director, Applications Fortebio - A Division of Pall Life Sciences

4 Bio-layer Interferometry (BLI): Label-Free Technology for Rapid Protein Quantitation and Binding Kinetics Sriram Kumaraswamy, Ph.D., Global Director of Applications ForteBio, A Division of Pall Life Sciences

5 BLI Instruments for Rapid Protein Analysis Label-free protein analysis platform for easy, rapid quantitation, binding affinity and kinetics Octet RED384 Octet QK384 Octet RED96 Octet QKe BLItz

6 BLI: One Platform, Many Capabilities Direct Sandwich ELISA mg/ml to sub-ng/ml Quantitation Label-free k a, k d, K D Proteins Peptides, Oligos Small molecules Kinetics

7 Shift (nm) Bio-layer Interferometry (BLI) Technology Time(s)

8 BLI Detects Broad Range of Analytes

9 ForteBio Biosensors and Assay Kits Biosensor AHQ AMQ FAB ProA ProG ProL SA HIS NTA GST AMC AHC AR2G APS SSA Kits/Methods Biosensor Functionality Anti-Human IgG Fc Anti-Murine IgG Fv Anti-Human Fab CH1 Protein A Protein G Protein L Streptavidin Anti-Penta-HIS Ni-Tris NTA Anti-GST Anti-Murine IgG Fc Capture Anti-Human IgG Fc Capture Amine Reactive 2 nd Gen Aminopropylsilane Super Streptavidin Dip and Read Immunogenicity Dip and Read Residual Protein A Dip and Read HCP Custom Assay Custom biosensors can be made to specification by Fortebio

10 Sensors Move to Samples No Microfluidics

11 Kinetic Characterization of Protein:Protein Interactions Kinetic information often reveals details of interactions missed in a purely affinity-based screen that are important in ranking and binning clones. Example: 1) Tightest binding clones may not have the slowest off-rates Sample K D = 51 nm K D = 411 nm K D = 48 nm K D = 473 nm 7 slower off-rate 8 tighter affinity K D = 9 nm K D = 170 nm K D = 9 nm K D = 129 nm

12 BLI Instruments Benefits For Everyone Label-free measurement on native proteins and other biomolecules Specific detection of protein of interest in crude samples Simple assay technique (Dip and Read) Higher throughput, microplate format Multiple measurement capabilities in one system

13 Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Vishal Kamat, Ph.D. Scientist Therapeutic Proteins Regeneron Pharmaceuticals

14 Label-Free Epitope Binning Strategies Used in the Characterization of Fully Human Therapeutic Monoclonal Antibodies Isolated from VelocImmune Mice Vishal Kamat, Ph.D. Scientist, Therapeutic Proteins Regeneron Pharmaceuticals, Inc. Tarrytown, NY 13 th December, 2012 Confidential VISHAL KAMAT

15 Presentation Overview Cross-Competition Assay Formats Label-Free Technology Platforms Advantages & Challenges of Different Platforms Data Analysis Interpretation of Results Confidential VISHAL KAMAT

16 Regeneron is a fully integrated biopharmaceutical company that discovers, invents, develops, manufactures, and commercializes medicines for the treatment of serious medical conditions Marketed Products: ARCALYST, EYLEA & ZALTRAP Product Pipeline: 11 product candidates in various stages of clinical development In 2012, Regeneron was Rated #1 among all global biopharmaceutical companies by Science magazine Technology Platforms - VelociGene, VelociMouse VelocImmune Mice Produces monoclonal antibodies with human variable region but preserving the mouse constant regions Confidential VISHAL KAMAT

17 Why is Epitope Binning Important? Primary Screening: Survey different properties of therapeutic antibodies such as binding affinity, binding specificity & species cross-reactivity, functional properties, etc. Epitope Binning: Potential to provide additional information to the Classical primary screening paradigm Mode of Receptor Antagonism: Direct vs Steric Crystal Structure of EGFR ectodomain bound to Fab fragments of C225 (Cetuximab) and Matuzumab Fab-C225 Fab-Matuzumab Fab-Matuzumab Confidential Kamat, V. (2010) Combined Use of Multiple Monoclonal Antibodies Targeting Epidermal Growth Factor Receptor for Improved Cancer Therapeutics, Ph.D. Drexel University VISHAL KAMAT

18 Typical Cross-Competition Assay Formats - (ELISA, Biacore, Octet, etc.) Classical Sandwich Assay Premix Assay In Tandem Assay Need good quality homogeneous reagents Antigen dissociation from mab-1 Use anti-hfc or anti-mfc or anti-fab surface to capture mab-1. Need to quench unsaturated capture surface. Use new sensor surface or optimize regeneration condition Works for heterogeneous & dimeric antigens Need lot of reagents mab-1 (Saturating mab) Use anti-hfc or anti-mfc or anti-fab surface to capture mab-1. Need to quench unbound capture surface. mab-2 (Competing mab) Use new sensor surface or optimize regeneration condition Target / Antigen Works for heterogeneous reagents Tagged antigen or directly couple to the surface Influenced by the size of antigen Capturing the antigen might block the epitope for mab binding Use new sensor surface or optimize regeneration condition Abdiche, Y. N. et al. (2012) J. Immunol. Methods 382(1-2), pp Confidential VISHAL KAMAT

19 Different Interaction Analysis Platforms in Regeneron Biacore 2000 Biacore 3000 Biacore T200 Biacore 4000 Octet RED96 Octet QK96 KinExA 3200 Luminex Octet QK384 Confidential VISHAL KAMAT

20 SPR High Throughput Technology Biacore Flow Cell 1 Flow Cell 2 Flow Cell 3 Flow Cell 4 Confidential VISHAL KAMAT

21 Cross-Competition Assay On Biacore 4000 First mab (mab-1) is immobilized on chip surface (16 mabs immobilized) Antigen was allowed to first bind to mab-1 Second mab (mab-2) was later injected and binding response was measured in real time Surface regenerated at the end of each cycle, repeated 32 times Confidential VISHAL KAMAT

22 Challenges: Failure to Completely Regenerate mab-1 Surface a c a Complete Regeneration of Surface Antigen bound to mab-1 was completely dissociated from mab-1 surface all the way to the baseline c b c a b Partial Regeneration of Surface Antigen bound to mab-1 was partially dissociated from mab-1 surface c Poor Regeneration of Surface Antigen bound to mab-1 was minimally dissociated from mab-1 surface Confidential VISHAL KAMAT

23 Cross-Competition Cross-Competition on Octet Optimization Octet of Regeneration Condition No Regeneration: 32x32 mab cross-competition 1024 anti-his Octet biosensors Cost ~$7000 With Regeneration (16 regenerations per biosensor) : 32x32 mab cross-competition 64 anti-his Octet biosensors Cost ~$440 3sec pulse of Gly ph2.0, 2x (16 Regeneration Cycles) Confidential VISHAL KAMAT

24 Challenges: Antigen Capture Using anti-his Sensors Depends on Protein All the antigen samples were prepared at 20ug/ml concentration Confidential VISHAL KAMAT

25 Schematic of Cross-Competition Assay On Octet D D Work Flow R R R R B B B B L L L L Regenerate Surface (3sec pulse of Gly ph2.0, 2x) 2. Establish Stable Baseline 3. Load Antigen Anti-Penta- His sensors R B L Saturate Antigen Surface with mab-1 5. Check Binding of mab-2 Confidential VISHAL KAMAT

26 32x32 Cross-Competition Experimental Set-up on QK384 Set-up two 384 well plates for a typical 32x32 cross-competition 16x32 MATRIX Need to include a nonspecific mab as a negative control Can set-up at the most 31x31 cross-competition Confidential VISHAL KAMAT

27 Raw Data for a Typical 384 Well Plate 16x32 Cross-Competition ForteBio Data Analysis: Raw Data for a 16x32 Cross-Competition Assay Confidential VISHAL KAMAT

28 Baseline Drift Observed Following Sensor Regeneration Confidential VISHAL KAMAT

29 Development of Data Analysis Tool Sensorgrams from 64 anti-his Octet Sensors Open the result in Octet Data Analysis Software & Save Raw Data Microsoft Excel Macro: Chops the long binding cycle with 16 regenerations into individual cycles & saves data as text file Overlay of 1024 Sensorgrams Sensor Regeneration Scrubber 2.0c: Open the saved text file Antigen Capture mab-1 Binding mab-2 Binding Measure Binding Responses: Antigen captured mab-1 binding mab-2 binding Confidential VISHAL KAMAT

30 Data Analysis Raw Data for 26x26 Cross-Competition Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) (-) Confidential VISHAL KAMAT

31 Data Analysis Highlight Self-Self Competition Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) (-) Self-Competition Confidential VISHAL KAMAT

32 Data Analysis Highlight Negative Control mab Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) (-) Self-Competition Confidential VISHAL KAMAT Negative Control

33 Data Analysis Highlight Cells with Low Binding Response Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Confidential mab # ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) Self-Competition Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

34 Data Analysis Highlight Cells with Poor mab-1 Binding Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Confidential mab # ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) Self-Competition Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) No to Low mab Binding VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

35 Data Analysis Move mab-1 Non-Binders to the Bottom Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Self-Competition Confidential mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

36 Amount of Antigen Capture (nm) Self-Competition Confidential Data Analysis Simultaneously Move Rows & Columns 50 ug/ml mab1 Bound (nm) mab# 20 & 21 were moved horizontally & vertically so that self-self competition is unchanged mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

37 Data Analysis Bin mabs Based on Binding Response Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Self-Competition Confidential mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

38 Data Analysis Bin mabs Based on Binding Response Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Self-Competition Confidential mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Low mab Binding Response (<0.3 nm) 27 (-) Negative Control

39 Data Analysis Bin mabs Based on Binding Response Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Self-Competition Confidential mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Competing mabs 27 (-) Negative Control

40 Data Analysis Re-order the mab# for Simplistic Representation Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) Self-Competition Confidential mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) No to Low mab Binding VISHAL KAMAT Competing mabs 27 (-) Negative Control

41 Data Analysis Representation of Cross-Competition Data Amount of Antigen Capture (nm) 50 ug/ml mab1 Bound (nm) mab # Binding Respone of mab-2 Binding to Antigen Pre-bound to mab-1 (nm) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) (-) Confidential VISHAL KAMAT

42 Tutorial: Drawing Circles to Represent Cross-Competition Data ~50kD Antigen 18x18 Amount of Antigen Captured ± Std Dev (nm) Amount of mab-1 Bound ± Std Dev (nm) mab # (-) 0.21 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) mab-1, mab-2, mab-11, mab-12, mab-13 mab-16 mab-3, mab-4, mab-7, mab-8, mab-5, mab-6 mab-9, mab-10 mab-14, mab-17 mab-15 Confidential VISHAL KAMAT

43 Tutorial: Drawing Circles to Represent Cross-Competition Data ~60kD Antigen 14x14 Amount of Antigen Captured ± Std Dev (nm) mab1 Bound ± Std Dev (nm) mab # ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± mab-1, mab-2, mab-3, mab-4 mab-5, mab-6 mab-7 mab-8, mab-9, mab-10, mab-11 mab-12 Confidential VISHAL KAMAT

44 We Have Our MOMENTS!!!!!!!! Confidential VISHAL KAMAT

45 Challenges: Cross-Competition Data is Not Always Interpretable Uni-directional Competition Amount of Antigen Captured ± Std Dev (nm) Amount of mab-1 Bound ± Std Dev (nm) mab # (-) 0.27 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) Confidential VISHAL KAMAT

46 Challenges: Cross-Competition Data is Not Always Interpretable Uni-directional Competition Amount of Antigen Captured ± Std Dev (nm) Amount of mab-1 Bound ± Std Dev (nm) mab # ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± (-) (-) Confidential VISHAL KAMAT

47 Biacore 3000/T200 vs Biacore 4000 vs Octet QK384 Factors Number of mabs tested for cross-competition Biacore 3000 / Biacore T200 Biacore 4000 Octet QK Assay Format Classical Sandwich Format Classical Sandwich Format In Tandem Format Amount of sensors required Measurement Times 8 Biacore sensor chips (4 mabs per chip) Cost: ~$ sec Antigen loading, 180sec mab-2 binding 2 Biacore sensor chips (16 mabs per chip) Cost: ~$375 90sec Antigen loading, 180sec mab-2 binding 64 anti-his Octet Biosensors Cost: ~$ sec Antigen Capture, 300sec mab-1 Binding, 120sec mab-2 Binding Experiment run time ~ 64 hours ~ 14 hours ~ 17 hours Actual run time (with chip changes & preparing plates) Amount of mab-2 required Amount of Antigen required Other Considerations Confidential 8 days (assuming 1 chip/day) VISHAL KAMAT 2 days (assuming 1 chip/day) 1 day (assuming 2 plates/day) ~1.4 50nM ~0.6 50nM 300nM 50nM 50nM 100nM Need experienced users Requires optimization of surface regeneration Need experienced users Requires optimization of surface regeneration Minimal user training required Easy experimental set-up

48 Questions Vishal Kamat, Ph.D. Scientist, Therapeutic Proteins Office: Confidential VISHAL KAMAT

49 Label-Free Assays for High-Throughput Monoclonal Antibody Characterization Thilo Riedl, Ph.D. Senior Scientist Assay & Bio-Analytical Science Genmab

50 Rapid characterization of antibody antigen binding by label-free cross-competition binning and hybrid library mapping Thilo Riedl, PhD Sr.Scientist Assay & BioAnalytical Science December 13, 2012

51 Genmab is a leading international biotechnology company developing Fully Human Antibody Therapeutics Founded in 1999 Headquarters in Copenhagen, Denmark R&D facilities in Utrecht, The Netherlands UltiMAb transgenic mouse technology platform Various product formats including Genmab NL Research, Drug Discovery & Development 110 employees, 4000m 2 lab and office space ADCs, DuoBody format, and UniBody format Marketed product in collaboration with GSK: Arzerra Genmab partners include Novartis, Janssen Biotech, GSK, Lundbeck, Roche, Seattle Genetics,

52 Approved therapeutic antibodies 52

53 Current and future antibody product innovation Future product formats will be developed from building blocks with diverse characteristics and functions 53

54 Conventional antibody screening cascade Immunisations Library generation 10,000s Primary screening 100s Primary Screening 170,000 assay points per run w format Secondary Screening Secondary screening 10s In vitro screening 10 In vivo screening Cell based functional Later Stage Panel Diversity Characterization Biochemical 1 54

55 State-of-the-Art antibody discovery Obtain antibody libraries with large diversity Binding assays CDR Sequences Region mapping Early Stage Panel Diversity (Pre)Characterization Affinity (I) Functional in vitro Characterization Assays on selected cell lines and models (II) (IV) (III) In vivo characterization Tumor models in mice 55

56 Panel diversity & characterization Octet BLI contributions Binding region assessment Cross-competition binning Hybrid library mapping Species cross reactivity Affinity Functional assays (e.g. Ligand binding inhibition assays) 56

57 Octet RED384 integration to Tecan Freedom EVO Off-line Biosensor handling Integrated Data Managment ActivityBase XE Enabling HTS BLI 57

58 BLI cross-competition binning 58

59 BLI cross-competition designs Sandwich Premix In Tandem Y AG Y AG AG + Affinity information - k off AB 1 limiting Y Y AB 1 AB 2 - AB 2 in excess - K D AB 2 limiting - AG-AB 2 premixes (no re-use) + Avidity (/Affinity) information - Putative epitope masking - k off AB 1 limiting - AB 1 in saturation - Buffer control mandatory 59

60 BLI cross-competition - Sandwich format NO cross-competition Y AG Y AG Y DISTINCT cross-competition bins cross-competition Y Y AG + Y ONE cross-competition bin Amine Reactive biosensors allow covalent antibody coupling Kinetic data on antigen binding retrieved from buffer control 60

61 HTS cross-competition process design Automation by docking Octet 384RED to Tecan Freedom EVO Coupled or Uncoupled Bi-modular process Module A: Bulk production of Antibody Biosensor Arrays Module B: Automated Cross-Competition Matrix 61

62 Quality Control of Antibody Biosensor Arrays AmineReactive biosensors preferred format Generic ph value for coupling Storage of Antibody Biosensor Arrays Automation on Tecan Freedom EVO Antigen binding capacity Antigen binding kinetics (Average of 16 biosensors) (Average of 16 biosensors) 62

63 Regeneration of Antibody Biosensor Arrays Eight antibody A biosensors (bulk AR coupling) Acidic regeneration each cross-competition round Stable assay quality over regeneration cycles Regeneration allows efficient HTS cross-competition binning 63

64 HTS BLI versus ELISA cross-competition binning HTS BLI ELISA Antibody pairs analyzed (excluding Controls) Non-bidirectional cross-competition 27 / 5.1% (14 / 3.5%)* 18 / 4.5% Non-bidirectional cross-competition overlap 2 / 0.5%* *: 397 antibody pairs analyzed by ELISA 64

65 Cross-competition binning: HTS BLI versus ELISA Comparable data sets obtained Real-Time Quality Control (antigen binding) Additional kinetic information Reduced hands-on time and material consumption A matrix analysis of 34x34 antibodies (1,156 reactions) can be accomplished in 6h run time in an automated setting 65

66 BLI hybrid library mapping 66

67 BLI hybrid library mapping Principle: Loss of binding shuffle assay Ortholog and/or paralog shuffling Tox species cross-reactivity Plus factor: Maintenance of antigen structural integrity Example: 8x linear, duo species shuffle (human - mouse): 67

68 BLI hybrid library mapping design Reducing the risk of epitope masking: Terminal tagged antigens Streptavidin biosensors preferred: High K D Biotin-Streptavidin Optional regeneration of antigen coated biosensors Introducing BAP technology: Need of antigen purification eliminated Strep AG-Bio human shuffle species hybrid constructs tox species others 68

69 Combining Technologies: Biotin Acceptor Peptide (BAP)-tag Biotin, Vitamin B 7 (H) BAP-tag: G L N D I F E A Q K I E W H E Co-Expression Biotin ligase: BirA (E. Coli) Site-specific in vivo biotinylation Use of crude supernatants in combination with Streptavidin biosensors 69

70 BLI hybrid library mapping process Repeated cycles Regeneration Regeneration Buffer Generate hybrid library biosensores (up to 16) Analyse samples sequentially Regeneration optional and antigen dependent Real time QC Automated runs Octet sensorgram: Association of Antibody X to 16 hybrid biosensors 70

71 BLI hybrid library mapping example 265 antibodies tested in 4688 dips (16x hybrid library) 43 samples excluded by QC (low or aberrant binding) 152 samples yielded high quality data (binding > 0.20 nm) 22h unattented run time with 2% Biosensor drop-off Benchmark Negative Control 01B03 01C03 X reactive D1 D3 D4/11 D5/6 D7 D8 D9 D9/10 D10 D11 others 71

72 Alternative HTS hybrid library mapping format Biotinylated hybrid library proteins immobilized on streptavidin beads HTS homogeneous binding assay: 1536 well format Fluorescence scanning Up to 16x hybrid library (16 quadrants) Shuffle 1 Shuffle 4 Shuffle 2 Shuffle 5 Shuffle 3 Shuffle 6 Human Mouse 72

73 Bead based HTS versus BLI hybrid library mapping 265 antibodies case study with 16x hybrid library: 87% result match HTS hybrid library mapping BLI hybrid library mapping Fluorescence based assay + Tens of thousands samples + ~ 0.02 US$/ assay point + ~ 10,000 samples/ 24h run time - No re-use of samples - No real time QC control Label-free method - Hundreds of samples - ~ 0.25 US$/ assay point samples/ 24h run time + Re-use of samples + Real time QC control * Sensor integrity * Binding rates + Additional kinetic information 73

74 Added value of binding region assesment to antibody library generation 74

75 Correlating degrees of diversity Hybrid library mapping CDR3HC sequence analysis # Antibodies # Antibodies # Antibodies # different CDR3HC # different sequences CDR3HC sequences # distinct CDR3HC sequences D9mm/D10mm D10mm D11mm D9mm D4mm/ D11mm D5mm/ D6mm D7mm D8mm Mouse X reactive D1mm D2mm D3mm D9mm/D10mm D10mm D11mm D9mm D4mm/ D11mm D5mm/ D6mm D7mm D8mm Mouse X reactive D1mm D2mm D3mm D9mm/D10mm D10mm D11mm D4mm/ D11mm D5mm/ D6mm D7mm D9mm D8mm Mouse X reactive D1mm D2mm D3mm D9mm/D10mm D10mm D11mm D4mm/ D11mm D5mm/ D6mm D7mm D9mm D8mm Mouse X reactive D1mm D2mm D3mm Zoo hybrid Zoo mapping hybrid mapping CDR3HC CDR3HC sequence sequence analysis analysis 75

76 Internalization [RFU] Correlating cross-competition bins with function Antibody internalization assay BLI cross-competition Group 1 Group 2 Group 3 Group 4 Cross-competition bins might correlate with specific biological functions 76

77 BLI cross-competition binning versus BLI hybrid library mapping 77

78 BLI hybrid library mapping compared to BLI cross-competition binning + Binning to defined target regions + Applicable also to small antigens + Tox species cross-reactivity can be easily integrated + Higher Throughput capability - Limitated by cross-reactivity with shuffle species - Generation of shuffle constructs required - Possible limitation to avidity data (AB format dependent) - Antigen biosensor versus Antibody biosensor regeneration Both methods show results comparable to classical approaches Combination: Cross-competition with mapped antibodies 78

79 Summary 79

80 Summary Important role of binding region assessment in antibody panel generation and characterization Octet BLI technology offers various formats for assessing antibody binding regions and kinetics BLI cross-competition binning and BLI hybrid library mapping provide comparable binning results to classical methods BLI approaches provide additional kinetic data and quality control Integration of the 16-channel Octet 384RED to automated liquid handling significantly increases throughput and opportunities of the Octet platform 80

81 Acknowledgements Ferdi van der Horst Arnout Gerritsen Dennis Verzijl Rob de Jong Bart de Goeij Cell & Molecular Sciences Protein Separations Biochemical Analysis 81

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