Control Strategy for Glycosylation
Using a QbD Approach:
Monoclonal Antibody with Effector
Function from the A-Mab Case Study
CMC Forum Washington, DC
Workshop I - CQAs
July , 2010
Presented by Victor Vinci, Eli Lilly
CMC BWG – A-Mab Case Study
Working Group Members
•
•
•
Amgen Team: Joseph Phillips (Lead), Bob Kuhn
Abbott Team: Ed Lundell (Lead), Hans-Juergen Krause, Christine Rinn, Michael
Siedler, and Carsten Weber
Eli Lilly Team: Victor Vinci (Lead), Michael DeFelippis, John R Dobbins, Matthew
Hilton, Bruce Meiklejohn, and Guillermo Miroquesada
Genentech Team: Lynne Krummen (Lead), Sherry Martin-Moe, and Ron Taticek
GSK Team: Ilse Blumentals (Lead), John Erickson, Alan Gardner, Dave Paolella,
Prem Patel, Joseph Rinella, Mary Stawicki, Greg Stockdale
MedImmune Team: Mark Schenerman (Lead), Sanjeev Ahuja, Laurie Kelliher ,
Cindy Oliver , Kripa Ram, Orit Scharf, and Gail Wasserman
Pfizer Team: Leslie Bloom (Lead) and Amit Banerjee, Carol Kirchhoff, Wendy
Lambert, Satish Singh
Facilitator Team: John Berridge, Ken Seamon, and Sam Venugopal
•
Plus help from many others
•
•
•
•
•
Vinci/Defelippis - CMC BWG
QbD Case Study
Lilly - Company Confidential 2010
2
Creating a Biotech Case Study:
“A-Mab”
• Based on a monoclonal
antibody drug substance and
drug product
–
–
–
–
–
–
“A-Mab”
Humanized IgG1 (w/ effector function)
IV Administered Drug (liquid)
Expressed in CHO Cells
Treatment of NHL
Molecule designed to maximize
clinical outcomes and minimize
impact on quality attributes (TPP)
• Publically and freely available
as a teaching tool for industry
and agencies at CASSS or ISPE
Vinci/Defelippis - CMC BWG
QbD Case Study

Why Monoclonal Antibody?
 Represents a significant number
of products in development
 Good product and process exp.
in dev. & manufacture
 Reasonable level of complexity
Lilly - Company Confidential 2010
3
QbD Development Paradigm
Creation of a Control Strategy
Animal In-Vitro
Studies Studies
Input Material Controls
High Criticality
Attributes
Product Quality
Attributes
Procedural Controls
1.Quality attributes to be
considered and/or controlled
by manufacturing process
Criticality
Assessment
2. Acceptable ranges for
quality attributes to ensure
drug safety and efficacy
Process Targets
for Quality
Attributes
Process
Development and
Characterization
Design
Space
Control Strategy Elements
Safety and
Efficacy Data
Process Controls
Continuous Process Verification
Prior
Clinical
Knowledge Studies
Process Parameter
Controls
Testing
In-Process Testing
Specifications
Characterization &
Comparability Testing
Attributes that do not need to
be considered or controlled
by manufacturing process
Process Monitoring
Low Criticality
Attributes
Product Understanding
Vinci/Defelippis - CMC BWG QbD Case
Study
Process Understanding
Lilly - Company Confidential 2010
4
CQA Risk Ranking & Filtering Tool
A Continuum of Criticality (Tool #1 Ex.)
• Assess relative safety and efficacy risks using two factors:
– Impact and Uncertainty used to rank risks
• Impact = impact on safety or efficacy, i.e. consequences
– Determined by available knowledge for attribute in question (prior, clinical, etc)
– More severe impact = higher score
• Impact on biological activity, PK/PD, immunogenicity, adverse effects
• Uncertainty = uncertainty that attribute has expected impact
– Determined by relevance of knowledge for each attribute
– High uncertainty = high score (no information with variant or published lit. only)
– Low uncertainty = low score (data from material used in clinical trials)
Severity = Impact x Uncertainty
• Severity = risk that attribute impacts safety or efficacy
Vinci/Defelippis - CMC BWG
QbD Case Study
Lilly - Company Confidential 2010
5
Criticality Ratings for Glycosylation
Attribute
Criticality
Aggregation
60
aFucosylation
60
Galactosylation
48
Deamidation
4
Oxidation
12
HCP
36
DNA
6
Protein A
16
C-terminal lysine
variants (charge
variants)
4
Glycoslyation - High Criticality
• Example is for afucosylation
and galactosylation; other
glycan structures require
individual consideration
• Primarily impacted by
production BioRx
• No clearance or modification in
DS
• Not impacted by DP process or
stability
Note: Assessment at beginning of development
Vinci/Defelippis - CMC BWG QbD
Case Study
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6
Platform and Product Specific Experience
Attribute
Galactose
Content
aFucosylation
Prior
Knowledge
Clinical
experience of 1040% G0 for YMab, another
antibody with
CDC activity as
part of MOA; no
negative impact
on clinical
outcome
In-vitro
Studies
0-100% gal
content has
statistical
correlation w/
CDC activity w/
A-Mab
Studies show that
100% G0 or
100%G2 have
comparable
ADCC
1-11%; Clinical
A-Mab with 2experience with
13% afucosylation
X-Mab and Ytested in ADCC
Mab; both X-Mab assay; linear
and Y-Mab have correlation; 70ADCC as part of 130%
MOA
Vinci/Defelippis - CMC BWG QbD Case
Study
Non-clinical
Studies
Clinical
Experience
Claimed
Acceptable
Range
No animal studies 10-30%
10-40%
Animal model
available;
modeled material
(15%) shows no
significant
difference from
5%
2-13%
Lilly - Company Confidential 2010
5-10%;
Phase II and
Phase III
7
CQA Linkage to Process Knowledge
afucosylation and galactosylation are assigned as CQAs due to linkage to
ADCC and CDC activity and proposed NHL therapeutic need
Analytical characterization method for afucosylation and galactosylation is
CE-LIF:
Bioassay development led to a robust assay with a linear correlation between
aFuc (2-13%) and ADCC activity (bioassay range of 70 – 130%)
Bioassay for CDC showed no impact over the range of galactosylation (10 – 40%)
produced in clinical material
Ranges of afucosylation and galactosylation can be ensured by control of
bioreactor process parameters found to have influence on these structures.
Release testing with Biopotency assay for drug product (acceptance
criterion 70 – 130%) confirms appropriate product quality
Vinci/Defelippis - CMC BWG QbD
Case Study
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8
Influence of Glycosylation on ADCC and
CDC Effector Functions
Vinci/Defelippis - CMC BWG QbD
Case Study
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9
Experimental Design
Progression of Studies for Production Bioreactor
Prior knowledge and risk assessments inform designed experiments:
• Risk analysis tools guide informed assessments
• Risk assessment links product attributes with parameters
• DOE’s allow understanding of the impact of process parameters and
attributes
• Risk assessments are iterative and continue through the lifecycle of
product
Vinci/Defelippis - CMC BWG QbD
Case Study
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10
Risk Assessment Approach
Multiple Assessments Throughout the
A-Mab Development Lifecycle for Entire Process
Process 2
Quality
Attributes
Process 1 2
Life Cycle
Management
Design Space
Prior Knowledge
Process Understanding
Process
Development
Process
Characterization
Product Understanding
Draft Control
Strategy
Process
Performance
Verification
Final Control
Strategy
Process
Parameters
Risk
Assessment
Risk
Assessment
Risk
Assessment
Risk
Assessment
You Are Here
Vinci/Defelippis - CMC BWG QbD
Case Study
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11
Example of Risk Assessment Tool
Approach to Process Characterization
Step 1. Use a Fish-bone (Ishikawa) diagram to identify parameters and attributes that
might affect product quality and process performance
Agitation
Production
Bioreactor
N-1 Bioreactor
In Vitro Cell
Age
Seed
Seed Density
Viability
Temperature
Shear/
Mixing
Working
Volume
Harvest
# of
Impellers
CO2
DO
Control
Parameters
Scale
Effects
pH
Nominal
Vessel Volumne
Design Impeller
Design
Duration
Baffles
Procedures
Gas
Transfer
Temperature
pH
Airflow
Sparger Design
Aggredates
Fucosylation
Galactosylation
CEX AV
HCP
DNA
Antifoam
Time of Feeding
Filtration
Volume of
Feed
Operations
Amount Delivered
Storage
Temperature
Concentration
Preparation
pH
Pre-filtration
hold time
Procedures
Age
Number of
Feeds
Age
Operations
Procedures
Storage
Temperature
Pre-filtration hold
time
[Antifoam]
Age
[NaHCO3]
Timing
Preparation
Osmolality
Filtration
Feed
Vinci/Defelippis - CMC BWG QbD Case
Study
[Glucose]
Glucose Feed
Lilly - Company Confidential 2010
Medium
Concentration
12
A-Mab: Mid-Development Risk Assessment Approach
Rank parameters and attributes from Step 1 based on severity of impact and control capability.
Identify interactions to include in DOE studies
Inoculum Viable Cell Concentr
Inoculum Viability
Inoculum In Vitro Cell Age
N-1 Bioreactor pH
N-1 Bioreactor Temperature
Osmolality
Antifoam Concentration
Nutrient Concentration in
medium
Medium storage temperature
Medium hold time before
filtration
Medium Filtration
Medium Age
Timing of Feed addition
Volume of Feed addition
Component Concentration in
Feed
Timing of glucose feed
addition
Amount of Glucose fed
Dissolved Oxygen
Dissolved Carbon Dioxide
Temperature
pH
Culture Duration (days)
Remnant Glucose
Concentration
Vinci/Defelippis - CMC BWG
QbD Case Study
Risk Mitigation
Viability at
Harvest
Turbidity at
harvest
Product Yield
DNA
Process Attributes
HCP
Deamidation
Galactosylation
aFucosylation
Process Parameter in
Production Bioreactor
Aggregate
Quality Attributes
DOE
Linkage Studies
EOPC Study
Linkage Studies
Linkage Studies
DOE
Not Required
Potential impact to
significantly affect a
process attribute
such as yield or
viability
DOE
Medium Hold Studies
Medium Hold Studies
Medium Hold Studies
Medium Hold Studies
Not Required
DOE
Potential impact to QA
with effective control of
parameter or less
robust control
DOE
DOE-Indirect
DOE-Indirect
DOE
DOE
DOE
DOE
DOE
DOE-Indirect
Lilly - Company Confidential 2010
Note: pH is red or
critical at this stage due
to linkage to
glycosylation
13
MCC Bioreactor
Control Strategy Elements by System - pH
Raw Materials (Reg/QMS) – vendor qualification; media (or buffer) make-up based on
instructions, weight based; pH check post make-up
Equipment (QMS) – bioreactor design (probe type/placement), probe vendor
qualification, receipt verification, linked to IQ/OQ and PV for bioreactor
Automation (QMS) – control loop qualified (CSV) and controlled via DCS, alert/action
alarms aligned with process, data monitored continuously and archived
DOE and Models (QMS/Reg) – small-scale models use parameter ranges intended for
large-scale; confirm during pivotal and commercial tech transfer
In Process/Operations (QMS) – pH probe calibration (pre-run), batch record
instructions on how to do daily check and adjustment, data trended
Specification Limits/Tests (Reg/QMS) – Control Strategy in place, validated methods
reflecting QbD analytical development
Process Verification/Continuous Monitoring (QMS/Reg) – MVA (PLS) or SPC
monitoring of performance over manufacturing lifecycle
Vinci/Defelippis - CMC BWG
QbD Case Study
Lilly - Company Confidential 2010
14
Continuity of Ranges
Attributes and Parameters in Study
Levels of CQAs:
CQA
Afucosylation (%)
Galactosylation (%)
Lower Limit
2
10
Higher Limit
13
40
Parameter Ranges:
Platform (2 liters and at-scale FHD)
Screening Study (Central Composite)
Design Space Proposal
Batch Record (Pivotal and Comm.)
Automation Alarms
pH 6.6 – 7.1 (initial set pt*)
pH 6.6 – 7.1 (2 liter)
pH 6.6 -7.1 (commercial)
pH 6.95
(initial ref pt)
pH 6.85 lo/pH 6.95 hi alert-control space
pH 6.7 lo/pH 7.1 action-design space
*Note that pH variable is set at initial as ref pt and moves through low (base) and high (acid or
CO2) control
Vinci/Defelippis - CMC BWG QbD
Case Study
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15
DOE Studies to Define Design Space
Bringing Together Process and Product Attributes
Example of DOE Results from Screening Study (Process 2). N=20.
Prediction Profiler
Titer (g/L)
3.743131
±0.076052
5
4
HCP (ppm) Galactosylation aFucosylation
695538
(%)
6.439933
±16518.3
29.28939
±0.226948
±0.674582
3
8
6
4
32
28
24
2500
2000
1500
32
28
24
3.0
2.6
2.2
35
Temperature
(C)
50
DO (%)
Vinci/Defelippis - CMC BWG QbD
Case Study
100
CO2 (%)
6.85
pH
1.2
[Medium]
(X)
400
Osmo (mOsm)
Lilly - Company Confidential 2010
12
Feed (X)
1
IVCC (e6
cells/mL)
19
-0.1
.1
.3
.5
.7
18
17
16
440
9
10
11
12
13
14
15
.7
.8
.9
1
1.1
1.2
1.3
15
420
400
380
1.6
360
1.4
1.2
1
7
7.1
.8
6.9
6.8
6.7
70
40
60
80
100
120
140
160
6.6
60
50
40
36
30
35
35.5
34.5
1.8
34
Aggregates
(%)
2.515119
±0.03524
CEX % Acidic
Variants
27.66898
±0.480814
DNA (ppm)
1935.343
±89.55908
1e+6
8e+5
6e+5
4e+5
17
Duration
(d)
0.21
Curvature
16
Moving Toward Design Space
Follow-up Studies and Analysis
Augment the screening design to enable estimation of a full response surface:
all main effects
two-way interactions
quadratic effects
Additional runs form Central Composite Design (when comb. w/ previous runs):
8 additional runs form full factorial on important parameters.
8 axial points allow to estimate non-linear relationships
4 parameters and 6 QA’s (responses)
N=40 total bioreactor runs
(4 blocks of 10, ~12 weeks)
8 center points total
Response surface model captures all input – output
relationships and is suitable to define the design space
Vinci/Defelippis - CMC BWG QbD
Case Study
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17
Contour Profiler
Contour Profiler
Horiz Vert Factor
Temperature (C)
DO (%)
CO2 (%)
pH
[Medium] (X)
Osmolality (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
Current X
35
50
100
6.85
1.2
360
12
1
15
Horiz Vert Factor
Temperature (C)
DO (%)
CO2 (%)
pH
[Medium] (X)
Osmolality (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
Current X
34.999293
50
100
6.85
1.2
440
12
1
15
Develop Multivariate Models to define Design Space
Response
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Aggregates (%)
Contour
Current
Y
Lo Limit
Hi Limit
Contour
Profiler
0
4.8
.
.
Horiz Vert Factor
11
8.3
2
11
Temperature (C)
40
33.4
20
40
DO (%)
15000000
513494.5
.
15000000
CO2 (%)
19000
1471.7
.
.
pH
40
28.2
20
40
[Medium] (X)
3
1.3
.
3
Osmolality (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
Response
Titer
Current
X (g/L)
aFucosylation
35
Galactosylation
(%)
50
HCP
70 (ppm)
DNA (ppm)
6.85
CEX
1.2 % Acidic Variants
Aggregates
(%)
400
Design Space for Culture Duration 15 Days
Osmolality
7
Response
Titer (g/L)
6.9 Factor
Horiz Vert
360mOsm
400mOsm
Current
X
aFucosylation
Temperature (C)
35
Galactosylation (%)
50
6.8 DO (%)
HCP (ppm)
CO2 (%)
40
DNA (ppm)
pH
6.85
6.7
CEX % Acidic Variants
[Medium] (X)
1.2
Aggregates (%)
Osmolality (mOsm)
360
aFucosylation
6.6
Feed (X)
12
7.1
IVCC (e6 cells/mL)
1
34 Duration
34.5 (days)
35
35.5
36
Culture
15
7
Temperature (C)
Response
Contour
Current Y
Lo Limit
Hi Limit
Titer (g/L)
0
5.2
.
.
6.9
aFucosylation
11
9.8
2
11
160 mmHg
Galactosylation (%)
40
36.8
20
40
100 mmHg
6.8
HCP (ppm)
15000000
469303.1
.
15000000
DNA (ppm)
19000
1465.4
.
.
40 mmHg
CEX % Acidic Variants
40
20
40
6.7 33.1
Aggregates (%)
3
1.3
.
3
pH
>11%
12
7.1
1
15
Galactosylation (%)
<20%
pH
>40%
36
Contour
0
2
40
15000000
19000
40
3
Current Y
4.5
6.6
34.4
458789.8
1479.0
31.0
1.3
One model for each CQA: describes
relationships with CPPs
Lo Limit
.
2
20
.
.
20
.
6.6
7.1
Contour
Profiler
Galactosylation (%)
36
>40%
Osmolality
6.6
36
34
34.5
Contour
Current Y
Lo Limit
0
5.4
.
Contour
Profiler
2
4.3
2
20
24.8
20
Horiz
Vert Factor
15000000
891294.0
Temperature (C) .
19000
2459.8
.
DO (%)
40
28.6 (%)
20
CO2
3
1.8
.
pH
pH
<20%
Galactosylation (%)
Hi Limit
.
11
40
15000000
.
40
3
36
mmHg
Contour
0
2
20
15000000
19000
40
3
CO2
Horiz Vert Factor
Temperature (C)
7
DO (%)
CO2 (%)
6.9
pH
[Medium] (X)
6.8
Osmolality (mOsm)
Feed (X)
6.7
IVCC (e6 cells/mL)
Culture Duration (days)
6.6
Response
Titer (g/L)
34
34.5
35
35.5
aFucosylation
Galactosylation (%)Temperature (C)
Current X
HCP (ppm)
35
DNA (ppm)
50
CEX % Acidic Variants
70
Aggregates (%)
6.85
1.2
7.1
400
12
71
19 Profiler
Contour
6.9
Current
Y
Horiz Vert
Factor
440mOsm Contour
Current
X
Response
360mOsm
400mOsm
35 (g/L)
Titer
50
aFucosylation
40
Galactosylation
(%)
6.85
HCP (ppm)
1.2 (ppm)
DNA
360
CEX % Acidic Variants
12
Aggregates
(%)
1
19
7.1
pH
35.5
Temperature (C)
Response
Titer (g/L)
aFucosylation
160
Galactosylation
(%)mmHg
100
HCP (ppm)
DNA (ppm)
40 mmHg
CEX % Acidic Variants
Aggregates (%)
Contour
0
2
20
15000000
19000
40
3
Current Y
4.9
5.9
30.9
674274.3
1961.3
30.7
1.6
Current X
35
50
100
6.85
1.2
440
12
1
19
<20%
Osmolality
< 2%
Horiz Vert
6.9 Factor
Temperature (C)
DO (%)
6.8
CO2 (%)
pH
6.7 [Medium] (X)
Osmolality (mOsm)
6.6 Feed (X)
IVCC (e6 cells/mL)
Culture
Duration
34
34.5 (days)
35
36
Current Y
6.1
7
5.3
30.2
6.9
870128.1
2482.5
6.8
32.8
1.8
6.7
Lo Limit
.
2
20
.
.
20
.
pH
7
35.5
[Medium] (X)
Osmolality (mOsm)
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
aFucosylation
7.1
35
Temperature (C)
Design Space for Culture Duration 19 Days
Contour Profiler
36
pH
pH
Current X
35
50
100
6.85
1.2
360
12
1
19
>40%
Current X
35
50
40
6.85
1.2
440
12
1
17
Galactosylation (%)
Contour
Profiler
7.1
Contour
7.1 Profiler
Horiz Vert Factor
7 Temperature (C)
DO (%)
6.9 CO2 (%)
pH
6.8 [Medium] (X)
Osmolality (mOsm)
Feed (X)
6.7
IVCC (e6 cells/mL)
Culture Duration (days)
6.6
Response
Galactosylation (%)
Titer (g/L)
34
34.5
35
35.5
aFucosylation
Galactosylation (%) Temperature (C)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Aggregates (%)
0
2
20
15000000
19000
40
3
Lo Limit
5.4
Temperature
(C) .
6.8 DO 5.1
2
(%)
25.9
20
CO2
(%)
900138.3
.
pH
6.7
2528.5
.
[Medium] (X)
30.1
20
6.6 Osmolality (mOsm)
1.8
.
Feed
(X)
<20%
7.1
36
36
7
34.5
35
35.5
Temperature (C)
6.9
pH
pH
6.9
<20%
36
6.8
6.8
6.7
6.7
6.6
Current Y
5.4
5.2
27.3
889758.9
2443.6
30.5
1.9
Lo Limit
.
2
20
.
.
20
.
For the production bioreactor the limits of
Design Space are defined by a subset of
CQAs:
Galactosylation
aFucosylation
Hi Limit
.
11
40
15000000
.
40
3
Lo Limit
.
2
20
.
.
20
.
Hi Limit
.
11
40
15000000
.
40
3
All other CQAs did not exceed Quality Limits
when process operated within Knowledge
Space & Design Space
Lo Limit
.
2
20
.
.
20
.
Hi Limit
.
11
40
15000000
.
40
3
*Note that DO and Feed Conc from earlier study
are controlled in same range
Galactosylation (%)
7.1
34
Current Y
4.9
4.7
21.1
898827.7
2434.0
27.0
1.9
Current X
35
50
40
6.85
1.2
440
12
1
19
Contour
0
2
20
15000000
19000
40
3
6.6
7
Contour
0
2
20
15000000
19000
40
3
Hi Limit
.
11
40
15000000
.
40
3Galactosylation (%)
IVCC (e6 cells/mL)
34 Duration
34.5 (days)
35
35.5
Culture
Temperature (C)
Response
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Aggregates (%)
Hi Limit
.
11
40
15000000
.
40
3
Hi Limit
.
11
40
15000000
.
40
3
aFucosylation
Temperature (C)
Response
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Aggregates (%)
CO2
Lo Limit
.
2
20
.
.
20
.
< 2%
<20%
pH
pH
12
Galactosylation (%)
7.1
1
17
7
Contour
Current
Y
Lo Limit
Hi Limit
Contour
Profiler
0
5.0
.
.
Horiz Vert
6.9 Factor
2
6.5
2
11
440mOsm
Temperature (C)
40
29.8
20
40
DO (%)
15000000
697946.1
.
15000000
6.8
CO2 (%)
19000
2040.3
.
.
pH
40
30.2
20
40
6.7 [Medium]
(X)
3
1.5
.
3
Osmolality (mOsm)
6.6 Feed (X)
IVCC (e6 cells/mL)
Culture
34 Duration
34.5 (days)
35
35.5
pH
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
Response
Titer (g/L)
Horiz Vert Factor
Current X
aFucosylation
6.9
360mOsm
400mOsm
Temperature (C)
35
Galactosylation (%)
DO (%)
50
HCP (ppm)
6.8
CO2 (%)
40
DNA (ppm)
pH
6.85
CEX % Acidic Variants
6.7
[Medium] (X)
1.2
Aggregates (%)
Osmolality (mOsm)
360
Feed (X)
12
6.6
7.1
IVCC (e6 cells/mL)
1
Culture Duration (days)
17
34
34.5
35
35.5
36
7
Response
Contour
Current Y
Lo Limit
Hi Limit
Temperature (C)
Titer (g/L)
0
5.7
.
.
6.9
aFucosylation
2
7.5
2
11
160
mmHg
Galactosylation
(%)
40
33.5
20
40
100 mmHg
HCP (ppm)
15000000 6.8
669715.6
.
15000000
DNA (ppm)
19000
1973.9
.
.
mmHg
CEX % Acidic 40
Variants
40 6.7
32.9
20
40
Aggregates (%)
3
1.5
.
3
Intersection of all CQA models define the
Design Space
>40%
Design Space for Culture Duration 17 Days
7.1
Contour
Profiler
7.1
Horiz Vert Factor
Current X
7 Temperature (C)
35
DO (%)
50
100
6.9 CO2 (%)
pH
6.85
1.2
Galactosylation (%)
6.8 [Medium] (X)
Osmolality (mOsm)
440
Feed (X)
12
6.7
IVCC (e6 cells/mL)
1
Culture Duration (days)
17
6.6
Response
Contour
Current Y
Titer
0
4.6
Current
X (g/L)
34
34.5
35
35.5
36
aFucosylation
2
5.2
35
Galactosylation
(%) Temperature (C)
20
25.7
50
HCP
15000000
694855.9
70 (ppm)
DNA (ppm)
19000
1966.2
6.85
CEX
40
26.9
1.2 % Acidic Variants
Aggregates (%)
3
1.6
400
pH
>11%
7
Contour
Profiler
Hi Limit
.
11
40
15000000
.
40
3
<20%
pH
34
34.5
35
35.5
Horiz Vert Factor
Current X
7
Temperature (C)
Temperature (C)
35
DO (%)
50
6.9
CO2 (%)aFucosylation
100
pH
6.85
[Medium] (X)
1.2
6.8
Osmolality (mOsm)
360
Galactosylation (%)
Feed (X)
12
6.7
IVCC (e6 cells/mL)
1
Culture Duration (days)
17
6.6
Contour
ProfilerLo Limit
Response
Contour
Current Y
Hi Limit
Titer (g/L)
0
5.1
.
.
Horiz
Vert Factor
34
34.5
35
35.5
36
aFucosylation
2
6.3
11
Temperature
(C) 2
Temperature (C)
Galactosylation (%)
20
29.1
20
40
DO
(%)
HCP (ppm)
15000000
702394.3
.
15000000
CO2 (%)
DNA (ppm)
19000
1965.8
.
.
pH
CEX % Acidic Variants
40
28.4
20
40
[Medium]
(X)
Aggregates (%)
3
1.6
.
3
Osmolality
(mOsm)
Hi Limit
.
11
40
15000000
.
40
3
A better way to look at the data:
Current X
35
50
40
6.85
1.2
440
12
1
15
6.6
CO2
Lo Limit
.
2
20
.
.
20
.
< 2%
Hi Limit
.
11
40
15000000
.
40
3
Feed (X)
IVCC (e6 cells/mL)
Culture Duration (days)
34
34.5
35
35.5
Response
Temperature (C)
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Aggregates (%)
>11%
Current Y
4.2
5.7
30.3
490873.2
1498.3
26.7
1.3
aFucosylation
7
Contour
Current
Y
Lo Limit
Contour
Profiler
0
4.7
.
Horiz Vert Factor
440mOsm
2 6.9
7.8
2
Temperature (C)
40
33.7
20
DO (%)
15000000 6.8
495754.0
.
CO2 (%)
19000
1552.2
.
pH
40
30.2
20
6.7
[Medium] (X)
3
1.2
.
Osmolality (mOsm)
pH
7.1
Contour Profiler
Contour
0
2
20
15000000
19000
40
3
6.6
34
34.5
35
35.5
36
Temperature (C)
Vinci/Defelippis - CMC BWG QbD
34
34.5
35
35.5
36
Temperature (C)
Lilly - Company Confidential 2010
18
Contour Profiler
Design Space Based on Process Capability
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 Contour Current Y Lo Lim it
Response
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
Temperature (C)
Temperature (C)
Vinci/Defelippis - CMC BWG QbD
Case Study
34.2
34.4
34.6
34.8
35
35.2
35.4
35.6
35.8
36
Temperature (C)
Lilly - Company Confidential 2010
19
Risk Assessment Approach
Multiple Assessments Throughout the
A-Mab Development Lifecycle for Entire Process
Process 2
Quality
Attributes
Process 1 2
Life Cycle
Management
Design Space
Prior Knowledge
Process Understanding
Process
Development
Process
Characterization
Product Understanding
Draft Control
Strategy
Process
Performance
Verification
Final Control
Strategy
Process
Parameters
Risk
Assessment
Risk
Assessment
Risk
Assessment
Risk
Assessment
You Are Here
Vinci/Defelippis - CMC BWG QbD
Case Study
Lilly - Company Confidential 2010
20
Control Strategy for Upstream Production
Quality-linked
Process Parameters
(WC-CPPs)
Key Process
Parameters
(KPPs)
Temperature
Time
Temperature
pH
Dissolved CO2
Culture Duration
Osmolality
Remnant Glucose
Vinci/Defelippis - CMC BWG QbD
Case Study
Working Cell Bank
Step 1
Seed Culture Expansion
in Disposable Shake
Flasks and/or bags
Viable Cell Concentration
Viability
Temperature
pH
Dissolved Oxygen
Culture Duration
Initial VCC/Split Ratio
Step 2
Seed Culture Expansion
in Fixed Stirred Tank
Bioreactors
Viable Cell Concentration
Viability
Antifoam Concentration
Time of Nutrient Feed
Volume of Nutrient Feed
Time of Glucose Feed
Volume of Glucose Feed
Dissolved Oxygen
Step 3
Production Culture
Step 4
Centrifugation and Depth
Filtration
Clarified Bulk
In-Process
Quality Attributes
Viable Cell Concentration
Viability
Temperature
Culture Duration
Initial VCC/Split Ratio
Flow Rate
Pressure
Controlled within the
Design Space to
ensure consistent
product quality and
process performance
Key Process
Attributes
Product Yield
Viability at Harvest
Turbity at Harvest
Bioburden
MMV
Mycoplama
Adventitious Virus
Product Yield
Turbidity
Controlled within acceptable
limits to ensure consistent
process performance
Lilly - Company Confidential 2010
Assay results part
of batch release
specifications
Slide 21
Example of Control Strategy for Selected CQAs
CQA
Criticality
Process
Capability
Testing
Criteria
Other Control
Elements
Aggregate
High (48)
High Risk
DS and DP
release
Yes
Parametric Control of
DS/DP steps
aFucosylation
High (60)
Low Risk
DS Process
Monitoring
Yes
Parametric Control of
Production BioRx
Galactosylation
High (48)
Low Risk
DS Process
Monitoring
Yes
Parametric Control of
Production BioRx
High (24)
Very Low
Risk
Charact.
Comparability
Yes
Parametric 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
Host Cell
Protein
Vinci/Defelippis - CMC BWG QbD
Case Study
Lilly - Company Confidential 2010
22
Lifecycle Management of Design Space
Dynamic Modeling
Challenge:
• Data from a limited number of batches is required for process validation
ex: n=5 or more for 3 bioreactors ; costly and often critical path
• Limited replicates are not statistically significant – at best test the “system”
including facility, equipment, process, operators, etc
Alternative Lifecycle Approach or Continuous Process Verification:
• Quality Mgt System assures site’s readiness and compliance
• Use 1 or 2 batches to confirm or demonstrate validity of design space
• Utilize a multivariate statistical partial least squares (PLS) model for continuous
process verification as commercial experience grows in number of runs
• Scheduled reviews of product quality data trends and design space validity during
the product lifecycle
Vinci/Defelippis - CMC BWG QbD
Case Study
Lilly - Company Confidential 2010
23
Design Space Linkage to Critical Attributes
Successful acceptance or utilization of our evolving view of design space relies
on linking the multiple elements of documented knowledge and systems:
Facilitated formal attribute rankings and parameter risk assessments to
guide DOEs
Linkage of critical attributes and parameter ranges used
Delineation of how lifecycle oversight (control strategy) of critical and noncritical parameters and specification/limit testing occurs
Movement to best practices for engineering first principles/mechanistic
models and statistical modeling as they apply to QbD paradigm
Vinci/Defelippis - CMC BWG QbD Case
Study
Lilly - Company Confidential 2010
24
Upstream Development Team
Ilse Blumentals
Guillermo Miroquesada
Kripa Ram
Ron Taticek
Victor Vinci
GSK
MedImmune
MedImmune
Genentech
Lilly
Special thanks to Mike DeFelippis
*Help from many others – CMC BWG member company reps and internal
resources at each company
Vinci/Defelippis - CMC BWG
QbD Case Study
Lilly - Company Confidential 2010
25