Control of Fc Glycosylation of mAbs targeting soluble antigens

1. CQA Assessment of Fcglycosylation for Mabs targeting soluble antigensBhavin Parekh, Ph.D.Group Leader-Bioassay DevelopmentEli Lilly and CompanyIndianapolis, IN 46221

2. Control of FcGlycosylation of mAbs targeting soluble antigens Case study 3: Targeting soluble antigen (eg., IL-1beta, IL-23, IL-x) Key questions: How is ‘potential’ of Fc-functionality assessed for soluble antigens. What type of data to collect and when? How do we use the data to develop an appropriate glycosylation control strategy?

3. Mechanisms of therapeutic antibodies Nature Reviews Immunology10, 301-316 (May 2010)

4. Mechanism of action (target biology) • In principle, risk of Fc-functionality is deemed to be ‘low’ because of lack of cellular target to kill • Claim of ‘soluble’ target should be substantiated • Demonstration that mAb ‘neutralizes’ or completely blocks antigen binding to target cellular receptor

5. Is the target antigen truly soluble? Is the antigen secreted as soluble protein? AAAA Protease cleavage Is the antigen also exist as membrane anchored or cell-associated? AAAA Extracellular matrix

6. Demonstrating mAb ‘neutralization’ or ‘blocking’ • Is the mAb-Antigen and Antigen-Receptor epitope shared? • Epitope mapping • Competitive binding studies Antigen epitope receptor

7. IgG biology (subclass and engineering) • Potential of Fc-mediated effector function is also dependent on IgG subclass and molecule specific engineering • IgG1 and IgG3 have higher potential than IgG4 and IgG2 because of inherent higher binding affinities to Fc Receptors and complement protein (C1q) • Further engineering of IgG1, IgG4 (Ala-Ala mutation in the Fc, glycoengineering) further reduce binding affinity to Fc receptors and C1q.

8. Types of data that could be collected • Binding assays (ELISA, SPR, etc) based on IgG-FcR and IgG-C1q binding • Cell-based assays are not possible since target is not membrane bound/associated • Glycoform analysis (eg., CE-LIF, HPLC, MS) as part of characterization of the molecule • Binding data can be correlated with glycoform data

9. Examples of IgG1 and IgG4 binding to FcRIIIAa (CD16a) and C1q • IgG1 Mabs may show capacity to bind FcR such as CD16. • Engineered IgG1 (Fc mutations or glycoengineering) IgG2, IgG4 have lower binding capability

10. Assessing lot-to-to variability: CD16a and C1q binding RSD=26% • Process consistency assessed based on glycoform profiles and CD16a and C1q binding data. • EC50 determination is not possible with IgG4, IgG1 (Ala-Ala), IgG2 due to the inability to generate full-dose response curves

11. 0.6 0.6 0.5 0.5 0.4 0.4 Gal/Glycan Gal/Glycan 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0.84 0.84 0.86 0.86 0.88 0.88 0.90 0.90 0.92 0.92 0.94 0.94 0.96 0.96 Fuc/Glycan Fuc/Glycan Lot-to-lot variability in glycoforms for a IgG1 and IgG4 targeting soluble antigen Glycoform analysis for IgG1 Glycoform analysis for IgG4

12. Criticality Ratings for Glycosylation Glycoslyation – Low Criticality Note: Assessment at beginning of development

13. Design Space Based on Process Capability Understanding Variability • Example: Day 15, Osmo=360 mOsm and pCO2=40 mmHg >99% confidence of satisfying all CQAs 50% contour approximates “white” region” in contour plot aFucos >11% pH pH Galact >40% Temperature (C) Temperature (C) Lilly - Company Confidential 2010

14. Example of Control Strategy for Selected CQAs

15. Fc Effector Function Potential of MAbs MODERATE HIGH LOW • Initial thorough evaluation and demonstration of effector functions • Effector function monitoring during development and manufacturing (routine monitoring and/or characterization assays) • Identification and monitoring of Critical Quality Attributes including carbohydrates (CQA) impacting effector function potential (routine monitoring and/or characterization assays) • Initial thorough evaluation of effector functions • Effector function characterization for comparability and manufacturing consistency • Identification and characterization of CQAs including carbohydrates impacting effector function potential (characterization assays for comparability and manufacturing consistency) • Initial demonstration of reduced or ablated effector function • No need to monitor Fc effector function unless new data changing the Fc potential Control strategy for mAbs based on the ‘potential’ for Fc functionality

16. Key questions…. • In principle, risk of Fc-functionality is deemed to be ‘low’ because of lack of cellular target to kill • Monitor Fc-glycosylation via analytical methods as part of characterization to assess process consistency • Is glycoform analysis sufficient? • Is demonstration of correlation between glycoform analysis and binding data necessary? What is the relevance of the binding data when targeting a soluble antigen • Is data from a subset of Mabs sufficient for the platform? How much data is needed? • Potential of Fc-mediated safety risk based on preclinical and clinical information • T-cell/NK cell activation markers?

17. Acknowledgements Michael DeFelippis (Lilly) UmaKuchibhotla (Lilly) John Dougherty (Lilly) Bruce Meiklejohn (Lilly) Andrew Glasebrook (Lilly) Robert Benschop (Lilly) Xu-Rong Jiang (MedImmune) An Song (Genentech) Svetlana Bergelson (Biogen Idec) Thomas Arroll (Amgen) Shan Chung (Genentech) Kimberly May (Merck) Robert Strouse (MedImmune) Anthony Mire-Sluis (Amgen) Mark Schenerman (MedImmune)