CQA Assessment of Fc glycosylation for Mabs targeting soluble antigens Bhavin Parekh, Ph.D. Group Leader-Bioassay Development Eli Lilly and Company Indianapolis, IN 46221

Control of Fc Glycosylation 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?

Mechanisms of therapeutic antibodies Nature Reviews Immunology 10, 301-316 (May 2010)

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

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

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

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.

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

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

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

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

Criticality Ratings for Glycosylation Attribute Criticality Aggregation 60 aFucosylation 10 Galactosylation Deamidation 4 Oxidation 12 HCP 36 DNA 6 Protein A 16 C-terminal lysine variants (charge variants) Glycoslyation – Low Criticality Note: Assessment at beginning of development

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 Before showing the design space plots let me give an example of how the Bayesian reliability approach works. To the left we have one of the panels from the figure I showed a couple slides earlier. As I mentioned, this shows the regions where the mean afucosylation and galactosylation are predicted to be outside of acceptable limits. This is the plot for pH and temperature when osmo=360 mOsm and pCO2=40 mmHg To the right is the corresponding plot showing the region where the predicted reliability of the process is equal or higher than 99% (darker-red or maroon colored). You can see how the white space in the left plot roughly approximates the 50% contour in the right plot (the 0.5 label). Therefore, if we were to use the left plot to define the design space the process will have a reliability as low as 50% if we decide to operate close to the limits in the left plot. We strongly advise that when dealing with a process with inherent variability like cell culture we need to use an approach that considers this variability when defining the design space. Also, data from GMP runs, scale-up runs and other sources should also be used in this analysis so that we incorporate our best quantitative estimate of this variability. Galact >40% Temperature (C) Temperature (C) Vinci/Defelippis - CMC BWG QbD Case Study Lilly - Company Confidential 2010

Example of Control Strategy for Selected CQAs Criticality Process Capability Testing Criteria Other Control Elements Aggregate High (60) High Risk DS and DP release Yes Parametric Control of DS/DP steps aFucosylation Low (10) Low Risk Comparability No Parametric Control of Production BioRx Galactosylation Host Cell Protein High (24) Very Low Risk Charact. Parametric Control of Prod BioRx, ProA, pH inact, CEX , AEX steps DNA Parametric Control of Prod Biox and AEX Steps Deamidated Isoforms Low (12)

Fc Effector Function Potential of MAbs Control strategy for mAbs based on the ‘potential’ for Fc functionality Initial demonstration of reduced or ablated effector function No need to monitor Fc effector function unless new data changing the Fc potential HIGH MODERATE 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) Fc Effector Function Potential of MAbs

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?

Acknowledgements Michael DeFelippis (Lilly) Uma Kuchibhotla (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)