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)