CQA Assessment of Fc glycosylation for Mabs targeting soluble antigens
Bhavin Parekh, Ph.D.Group Leader-Bioassay DevelopmentEli Lilly and CompanyIndianapolis, IN 46221
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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?
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Mechanisms of therapeutic antibodies
Nature Reviews Immunology 10, 301-316 (May 2010)
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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
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Is the target antigen truly soluble?
AAAA
Is the antigen secreted as soluble protein?
Is the antigen also exist as membrane anchoredor cell-associated?
AAAA
Protease cleavage
Extracellular matrix
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Demonstrating mAb ‘neutralization’ or ‘blocking’
epitope
Is the mAb-Antigen and Antigen-Receptor epitope shared?Epitope mappingCompetitive binding studies
receptor
Antigen
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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.
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Types of data that could be collected
Binding assays (ELISA, SPR, etc) based on IgG-FcR and IgG-C1q bindingCell-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
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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
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Assessing lot-to-to variability: CD16a and C1q binding
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
0 2 4 6 8 10 120
0.51
1.52
2.53
3.54
4.55 C1q binding to IgG1
Lots
EC50
(ug/
ml)
RSD=26%
0 2 4 6 8 10 120
10
20
30
40
50
60
70
CD16a binding to IgG1
Lots
EC50
(ug/
ml)
RSD=30%
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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 0.96Fuc/Glycan
Ga
l/G
lyc
an
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 0.96Fuc/Glycan
Ga
l/G
lyc
an
Glycoform analysis for IgG1 Glycoform analysis for IgG4
Criticality Ratings for Glycosylation
Attribute Criticality
Aggregation 60
aFucosylation 10
Galactosylation 10
Deamidation 4
Oxidation 12
HCP 36
DNA 6
Protein A 16
C-terminal lysine variants (charge
variants)
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Glycoslyation – Low Criticality
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Note: Assessment at beginning of development
Horiz Vert
Temperature (C)
DO (%)
CO2 (mmHg)
pH
[Medium] (X)
Osmo (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Duration (d)
Factor
35
50
40
6.85
1.2
360
12
1
15
Current X
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Response
3
11
40
675000
2250
40
Contour
5.3408326
9.1879682
38.227972
466955.66
1382.1644
34.420095
Current Y
3
.
.
.
.
.
Lo Limit
.
11
40
.
.
.
Hi Limit
6.6
6.7
6.8
6.9
7
7.1
pH
aFucosylation
Galactosylation (%)
34 34.5 35 35.5 36
Temperature (C)
Contour ProfilerDesign Space Based on Process Capability Understanding Variability
Vinci/Defelippis - CMC BWG QbD Case Study Lilly - Company Confidential 2010 13
Galact >40%
aFucos >11%
34 34.2 34.4 34.6 34.8 35 35.2 35.4 35.6 35.8 366.6
6.65
6.7
6.75
6.8
6.85
6.9
6.95
7
7.05
0.5
Example: Day 15, Osmo=360 mOsm and pCO2=40 mmHg >99% confidence
of satisfying all CQAs50% contour
approximates “white” region” in contour plot
pH pH
Temperature (C) Temperature (C)
Example of Control Strategy for Selected CQAs
CQA 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 Low (10) Low Risk Comparability No Parametric Control of Production BioRx
Host Cell Protein High (24) Very Low
RiskCharact.
Comparability YesParametric Control of Prod BioRx, ProA, pH inact, CEX , AEX steps
DNA High (24) Very Low Risk
Charact.Comparability Yes
Parametric Control of Prod Biox and AEX
Steps
Deamidated Isoforms Low (12) Low Risk Charact.
Comparability No Parametric Control of Production BioRx
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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
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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?
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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)