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?
2
Mechanisms of therapeutic antibodies
Nature Reviews Immunology 10, 301-316 (May 2010)
3
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
4
Is the target antigen truly soluble?
AAAA
Is the antigen secreted as soluble protein?
Protease cleavage
AAAA
Is the antigen also exist as membrane anchored
or cell-associated?
Extracellular matrix
5
Demonstrating mAb ‘neutralization’ or
‘blocking’
Antigen
epitope
receptor
Is the mAb-Antigen and
Antigen-Receptor epitope
shared?
Epitope mapping
Competitive binding
studies
6
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.
7
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
8
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
9
Assessing lot-to-to variability: CD16a and C1q binding
CD16a binding to IgG1
70
RSD=30%
50
EC50 (ug/ml)
EC50 (ug/ml)
60
40
30
20
10
0
0
2
4
6
8
10
12
C1q binding to IgG1
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
RSD=26%
0
2
4
6
8
10
12
Lots
Lots
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
10
Lot-to-lot variability in glycoforms for a IgG1 and IgG4
targeting soluble antigen
0.6
Gal/Glycan
0.6
Gal/Glycan
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0.0
0.0
0.84 0.86 0.88 0.90 0.92 0.94 0.96
Fuc/Glycan
Glycoform analysis for IgG1
0.84 0.86 0.88 0.90 0.92 0.94 0.96
Fuc/Glycan
Glycoform analysis for IgG4
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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)
4
Glycoslyation – Low Criticality
Note: Assessment at beginning of development
12
Design Space Based on Process Capability
Contour Profiler
Horiz Vert Factor
Temperature (C)
DO (%)
CO2 (mmHg)
pH
[Medium] (X)
Osmo (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Duration (d)
Current X
35
50
40
6.85
1.2
360
12
1
15
Understanding Variability
Example: Day 15, Osmo=360 mOsm and
pCO2=40
mmHg
Response
Contour Current Y
Lo Lim it
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
3
11
40
675000
2250
40
5.3408326
9.1879682
38.227972
466955.66
1382.1644
34.420095
3
.
.
.
.
.
>99% confidence
of satisfying all
CQAs
Hi Lim it
.
11
40
.
.
.
50% contour
approximates “white”
region” in contour plot
0. 99
7.1
7.05
7
7
9
0. 0.80.7 0.5
aFucos >11%
0.
95
6.95
pH
pH
6.8
Galactosylation (%)
0.99
95
0.
pH
5
0.2
6.9
6.9
0.
99
0.
9
0.
8
0.7
6.85
0.99
0.5
6.8
0.25
aFucosylation
Galact >40%
0.8
0.7
0.5
0.25
6.75
6.7
0.95
0.9
0.95
0.9
0.8
0.7
0.5
0.25
6.7
6.65
6.6
34
34.5
35
35.5
36
6.6
34
34.2
Temperature (C)
Temperature
(C)
Vinci/Defelippis - CMC BWG QbD
Case Study
34.4
34.6
34.8
35
35.2
35.4
35.6
35.8
36
Temperature (C)
Lilly - Company Confidential 2010
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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)
Comparability
No
Parametric Control of
Production BioRx
Yes
Parametric Control of
Prod BioRx, ProA, pH
inact, CEX , AEX steps
Host Cell
Protein
Low Risk
High (24)
Very Low
Risk
Charact.
Comparability
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
Fc Effector Function
Potential of MAbs
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)
•Initial demonstration of
reduced or ablated
effector function
•No need to monitor Fc
effector function unless
new data changing the Fc
potential
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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
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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)
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