Quality by Design
Questions to Consider
Seed
Seed Density
Viability
Temperature
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
Contour Profiler
Horiz Vert Factor
Temperature
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
Filtration
Current
Horiz
Vert X
Factor
35
Temperature
50
DO
70
Dissolved CO2
3.7
Split Ratio
100
Basal Strength (Dilution)
100
Feed Strength (Dilution)
90
Feed Neutralization
440
Osmo
17.5
Duration
Response
Productivity
aFucosylation
Galactosylation
Contour
3
11
25
Current
Y
Lo Limit
Response
8.644125
3
Productivity
6.1354189
3
aFucosylation
32.452376
Galactosylation 25
100
Current X
37
50
70
3.7
100
100
90
440
17.5
Hi Limit
.
11
55
Contour
3
11
25
Current Y
8.951625
7.5034189
32.837639
Volume of
Feed
Operations
Amount Delivered
Storage
Temperature
Concentration
Preparation
pH
Age
Lo Limit
3
3
25
Hi Limit
.
Feed
11
55
Number of
Feeds
Pre-filtration
hold time
Procedures
Age
Operations
Procedures
Storage
Temperature
Pre-filtration hold
time
[Antifoam]
[NaHCO3]
Age
Timing
Preparation
[Glucose]
Osmolality
Filtration
Glucose Feed
Medium
Concentration
100
Contour Profiler
Contour Profiler
Horiz Vert Factor
Temperature
80
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
60
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
40
Response
Productivity
400
420
440
aFucosylation
Osmo
Galactosylation
Current
Horiz
Vert XFactor
35Temperature
80 50
DO
70Dissolved CO2
4.9Split Ratio
100Basal Strength (Dilution)
60
100Feed Strength (Dilution)
90Feed Neutralization
440Osmo
17.5Duration
40
Contour
Current
Y
Lo Limit
Hi Limit
Response
3
6.896625
. 460
Productivity
480
400
420 3
440
11 6.1244052
3
aFucosylation
Osmo 11
25 30.595296
55
Galactosylation 25
Galactosylation
460
Galactosylation
100
Time of Feeding
Contour Profiler
Dissolved CO2
•
Agitation
In Vitro Cell
Age
Shear/
Mixing
Production
Bioreactor
N-1 Bioreactor
80
60
Galactosylation
Contour
3
480
11
25
Current X
37
50
70
4.9
100
100
90
440
17.5
Current Y
5.974125
9.5011447
30.980559
Lo Limit
3
3
25
Hi Limit
.
11
55
Galactosylation
100
Dissolved CO2
•
Dissolved CO2
•
How can we maximize the benefits to
the industry and other stakeholders?
How can we ensure that this will
speed up development and reduce
the investment for process and
product development?
QbD may be implemented in parts or
as part of a development philosophy.
How can this be implemented during
early development?
What is the best way to ensure that
smaller enterprises can benefit from
the work going on with QbD and
facilitate innovation?
Dissolved CO2
•
80
Productivity
aFucosylation
60
40
40
400
420
440
Osmo
460
480
400
420
440
460
480
Osmo
Slide 1
A-Mab: a Case Study in
Bioprocess Development
CMC Biotech Working
Group
Background and Goal
• To create a publicly available case study that helps translate
the ‘what’ of ICH guidelines into practical ‘how’ for a
biological molecule with emphasis on Quality by Design
• Started in August 2008
• 7 companies divided across the various sections into teams
– GlaxoSmithKline, Abbott, Lilly, Pfizer, Genentech, MedImmune,
Amgen
– John Berridge, Sam Venugopal, and Ken Seamon, co-facilitators
• Combination of regular telecon and in- person meetings
• Relentless focus on science and risk-based approaches, not
traditional ways
• Colleagues from regulatory authorities provided unique
insights to help stimulate our case study
Slide 3
Creating a Biotech Case Study:
“A-Mab”
• Based on a monoclonal
antibody drug substance
and drug product
–
–
–
–
–
“A-Mab”
Humanized IgG1
IV Administered Drug (liquid)
Expressed in Cho Cells
Treatment of NHL
• Publicly and freely available
as a teaching tool for
industry and agencies

Why Monoclonal Antibody?
 Represents a significant number
of products in development
 Good product and process
experience in development and
manufacture
Slide 4
Outline and Intent of Case Study
Content
• Structure
• Introduction
• Quality Attributes
• Upstream
• Downstream
• Drug Product
• Control Strategy
• Regulatory
Intent
• Contains pieces/ sections that
appear realistic and represent
selected QbD principles
• Illustrates the benefits of a
QbD development approach
• Information represents real
data or appropriate fictitious
data
• Not a mock CTD-Q
• Not a Gold Standard
Slide 5
A-Mab is a Public Document
• Publication and Sponsorship
– CASSS
– ISPE
•
•
•
•
http://www.casss.org
http://www.ispe.org
Maintain CMC Working Group interactions
Coordinate workshops
Develop training
Facilitate regulatory interactions
Slide 6
Background and Linkage to ICH
CMC Biotech Working Group
The New Qs underwrite the Quality Paradigm
Product and
Process
Understanding
Q8 (R1)
Q9, Q10 Q11
Quality Risk
Management
Pharmaceutical
Quality System
Q9
Q10
21st Century Quality Paradigm
Lower Risk Operations
Innovation and Continual Improvement
Optimized Change Management Process
Enhanced Regulatory Approaches
Slide 8
Historical Perspective
Historical Perspective
• Companies have always used science and risk based processes to develop
new products and gain process understanding
– But they often did not submit knowledge or information to regulators
• Focus on minimum controversy registration, launch and then compliance
– Processes became fixed
Future Goal
• Knowledge management and risk management processes more
extensively used, documented and submitted
– Intention of clearer communication of product and process understanding
• Opportunities for flexibility and post-approval process optimisation
– A challenge to do this well
– Leads to opportunities
Slide 9
Overall Goals of the A-mAb Case Study
To illustrate options to achieve enhanced product and process
understanding
Demonstrate Industry’s vision for QbD as applied to biotech product
realisation
•
Identification of CQAs
– Examples of CQA risk ranking tools
•
•
•
Use of prior knowledge and platform technologies
Risk based approaches
Use of DoEs and statistical approaches
– To identify CPPs and their linkage to CQAs
•
•
•
•
Approaches to define and describe Design Spaces
Upstream , Downstream and Drug Product
Rational approach to defining a Control Strategy that reflects product & process
understanding and risk
Risk-based, lifecycle approach to managing continual improvement
Slide 10
Our Focus is on the key differentiators
of QbD (from ICH Q8R1)
• An enhanced, quality by design approach to product
development would additionally include the following
elements:
• A systematic evaluation, understanding and refining of the
formulation and manufacturing process, including;
– Identifying, through e.g., prior knowledge, experimentation, and
risk assessment, the material attributes and process parameters
that can have an effect on product CQAs;
– Determining the functional relationships that link material
attributes and process parameters to product CQAs;
• Using the enhanced product and process understanding in
combination with quality risk management to establish an
appropriate control strategy that includes proposals for a
design space(s) and/or real-time release testing
Slide 11
Linking Product and Process
Understanding
Animal In-Vitro
Studies Studies
Input Material Controls
High Criticality
Attributes
Product Quality
Attributes
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
Procedural Controls
Process Targets
for Quality
Attributes
Process
Development and
Characterization
Design
Space
Control Strategy Elements
Safety and
Efficacy Data
Process Controls
Process Parameter
Controls
Testing
In-Process Testing
Specifications
Characterization &
Comparability Testing
Attributes that do not need to
be considered or controlled
by manufacturing process
Continuous Process Verification
Prior
Clinical
Knowledge Studies
Process Monitoring
Low Criticality
Attributes
Product Understanding
Process Understanding
Slide 12
“Systematic Evaluation”
Agitation
In Vitro Cell
Age
Seed
Working
Volume
DO
Viability
Harvest
# of
Impellers
CO2
Seed Density
Temperature
Shear/
Mixing
Production
Bioreactor
N-1 Bioreactor
Control
Parameters
Scale
Effects
pH
Nominal
Vessel Volumne
Design Impeller
Design
Duration
Baffles
Procedures
Operations
Amount Delivered
Storage
Temperature
Pre-filtration
hold time
Preparation
pH
Age
Pre-filtration hold
time
Operations
Procedures
[Antifoam]
[NaHCO3]
Age
Timing
Age
Procedures
Storage
Temperature
Number of
Feeds
Concentration
Preparation
[Glucose]
Osmolality
Filtration
Feed
Concentration
Medium
Glucose Feed
Platform
Knowledge
Tox
500L
Optimization
DOE I - 2L
PhI/PhII
1,000L
PhIII
5,000L
Optimization
DOE II - 2L
Prediction Profiler
40
35
Contour Profiler
36
Temperature
70
Dissolved
CO2
50
DO
Response
Productivity
aFucosylation
Galactosylation
Contour
3
11
25
Hi Limit
.
11
55
Contour
3
11
25
Current Y
8.951625
7.5034189
32.837639
440
Osmo
Lo Limit
3
3
25
100
Feed Strength
(Dilution)
18
17.6
94
17.2
92
90
Feed
Neutralization
16.8
90
88
86
110
95
105
100
480
90
460
440
420
110
400
95
105
90
100
Current X
37
50
70
3.7
100
100
90
440
17.5
100
Basal Strength
(Dilution)
4.3
Split Ratio
Current
Y
Lo Limit
Response
8.644125
3
Productivity
6.1354189
3
aFucosylation
32.452376
Galactosylation 25
100
17.3778
Duration
Hi Limit
.
11
55
100
Contour Profiler
Contour Profiler
Horiz Vert Factor
Temperature
80
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
60
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
40
Response
Productivity
400
420
440
aFucosylation
Osmo
Galactosylation
Current
Horiz
Vert XFactor
35Temperature
80 50
DO
70Dissolved CO2
4.9Split Ratio
100Basal Strength (Dilution)
60
100Feed Strength (Dilution)
90Feed Neutralization
440Osmo
17.5Duration
40
Contour
Current
Y
Lo Limit
Hi Limit
Response
3
6.896625
. 460
Productivity
480
400
420 3
440
11 6.1244052
3
aFucosylation
Osmo 11
25 30.595296
55
Galactosylation 25
Galactosylation
460
Galactosylation
100
3.8
4
4.2
4.4
4.6
4.8
Current
Horiz
Vert X
Factor
35
Temperature
50
DO
70
Dissolved CO2
3.7
Split Ratio
100
Basal Strength (Dilution)
100
Feed Strength (Dilution)
90
Feed Neutralization
440
Osmo
17.5
Duration
70
40
50
60
70
80
90
100
60
50
40
37
30
36
36.5
35
Contour Profiler
Horiz Vert Factor
Temperature
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
35.5
25
Dissolved CO2
30
80
60
Galactosylation
Contour
3
11
25
480
Current X
37
50
70
4.9
100
100
90
440
17.5
Current Y
5.974125
9.5011447
30.980559
Lo Limit
3
3
25
Hi Limit
.
11
55
Galactosylation
100
Dissolved CO2
5.
Filtration
Volume of
Feed
Dissolved CO2
3.
4.
Aggredates
Fucosylation
Galactosylation
CEX AV
HCP
DNA
Time of Feeding
Dissolved CO2
2.
Use of prior platform knowledge
and process risk assessments to
identify CQAs and those steps
that need additional
experimentation.
Demonstration that laboratory
scale models are representative
of the full-scale operations.
DOE to determine CPPs & KPPs
Linkage of process parameters to
product Quality Attributes to
create a Design Spaces.
Final risk assessment and
categorization of process
parameters to develop control
strategy.
Airflow
Sparger Design
Antifoam
Galactosylation
32.02279
±0.930555
1.
Gas
Transfer
Temperature
pH
80
Productivity
aFucosylation
60
40
40
400
420
440
Osmo
460
480
400
420
440
460
480
Osmo
Slide 13
“Prior knowledge”
• Extensive use of prior knowledge and platform
technologies
– Previous Mabs extensively leveraged to assist in risk
assessments
• Seed Expansion from frozen WCB to N-1 Bioreactor not
critical and not dependent on process format
– Use engineering and process characterization to
define design space for production bioreactor
• Demonstrate that Design Space is valid at multiple scales of
operation
• Parametric control of selected critical quality attributes
Slide 14
Critical Quality Attributes (CQAs)
• One of the greatest challenges is identifying CQAs
• In the case study, we focus on severity, not process capability
– Risk assessment is based on:
• prior knowledge (encompasses laboratory to clinic)
• nonclinical studies and biological characterization throughout clinical
development
• clinical experience
– Key Decisions:
• Assign a Criticality Level (continuum) instead of critical/non-critical
• Criticality based on potential impact to safety and efficacy
– Key Issues that were discussed:
• Is there a cutoff for critical?
• What would make critical into non-critical?
• Linkage of QA ranking to Control Strategy
Slide 15
Risk Assessment Approach used through
A-MAb development lifecycle
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
Slide 16
CQA Risk Ranking & Filtering Approach
Severity = Impact x Uncertainty
• Severity = risk that attribute impacts safety or efficacy
• Assess relative safety and efficacy risks using two factors:
– Impact and Uncertainty
• Impact = impact on safety or efficacy, i.e. consequences
– Determined by available knowledge for attribute in question
– More severe impact = higher score
• Uncertainty = uncertainty that attribute has expected impact
– Determined by relevance of knowledge for each attribute
– High uncertainty = high score
– Low uncertainty = low score
Slide 17
Impact Definition & Scale
Impact
(Scor
e)
Biological Activity or
PK/PDa
Immunogenicity
Safety
Very significant change
Significant change
on PK
ATA detected and confers
limits on safety
Irreversible AEs
High
(16)
Significant change
Moderate change
with impact on
PD
ATA detected and confers
limits on efficacy
Reversible AEs
Moderate
(12)
Moderate change
Moderate change
with no impact
on PD
ATA detected with in vivo
effect that can be
managed
Manageable AEs
Low
(4)
Acceptable change
Acceptable change
with no impact
on PD
ATA detected with minimal in
vivo effect
Minor, transient AEs
None
(2)
No change
No impact on PK or
PD
ATA not detected or ATA
detected with no
relevant in vivo effect
No AEs
Very High
(20)
Efficacya
AE = adverse event; ATA = anti-therapeutic antibody
aQuantitative criteria should be established for biological activity/efficacy and PK/PD. Significance of the change is
assessed relative to assay variability.
Uncertainty Definition & Scale
Uncertainty
(Score)
Description
(Variants and Host Related Impurities)
Description
(Process Raw
Material)
a
7
(Very High)
No information (new variant)
No information (new
impurity)
5
(High)
Published external literature for variant in related
molecule.
---
3
(Moderate)
Nonclinical or in vitro data with this molecule. Data
(nonclinical, in vitro or clinical) from a similar class of
molecule.
Component used in
previous processes
2
(Low)
Variant has been present in material used in clinical
trials.
---
1
(Very Low)
Impact of specific variant established in Clinical Studies
with this molecule.
GRAS or studied in clinical
trials
GRAS = generally regarded as safe
a Assesses the impact of a raw material as an impurity. Impact of the raw material on the product
during manufacturing is assessed during process development.
Only a Subset of Quality Attributes is
Evaluated in the Case Study
Attribute
Criticality
Aggregation
48
Glycosylation
48
Deamidation
4
Oxidation
12
HCP
24
DNA
12
Protein A
12
C-terminal lysine
variants (charge
variants)
4
High Criticality
Impacted by multiple steps in the process
Exemplify linkage across multiple unit ops
through Design Space and Control Strategy
High Criticality
Primarily impacted by production BioRx ; no
clearance or modification in DS or DP
Provide example of Parametric Control
Low Criticality
Impacted by multiple steps in the process
Exemplify linkage to Control Strategy
Medium Criticality
Impacted by multiple steps in DS but not
affected by DP
Exemplify linkage to Control Strategy
Slide 20
A-Mab Case Study
Upstream Process Development
CMC Biotech Working Group
Upstream Process
Thaw
Working Cell Bank
STEP 1
Seed Culture Expansion
in disposable shake flasks and/
or bags
Seed Maintenance
STEP 2
Seed Culture Expansion in fixed
stirred tank reactors
Seed Maintenance
N-1 Seed Culture Bioreactor
3,000L WV
STEP 3
Nutrient Feed
Leverage Prior Knowledge
with platform process
Risk-based approach to
demonstrate no impact to
product quality
Engineering and process
characterization to define
Design Space and Control
Strategy
Production Bioreactor
15,000L WV
Glucose Feeds
STEP 4
Harvest
Centrifugation & Depth Filtration
Demonstrate that Design
Space is applicable to
multiple scales of operation
Clarified Bulk
Lifecycle validation approach that includes
continued process verification
Slide 22
A-Mab Batch History
Process
Scale
Batches
Process 1
500 L
2
Pre-clinical studies
3
Phase 1 & 2
Product/process
understanding.
5
Phase 3
Confirm end-to-end process
performance.
2
Commercial launch supplies
Confirm Design Space and
Control Strategy at commercial
scale
Process 1
Process 2
Process 2
1,000 L
5,000 L
15,000 L
Disposition
Clinical
Exposure
Slide 23
Upstream Process Steps 1 & 2: Seed expansion
Non-Critical based on Risk Assessment
1.
2.
3.
4.
No product is accumulated during seed expansion steps.
Prior knowledge with platform process (X-Mab, Y-Mab, and Z-Mab) shows
that process performance is consistent and robust
Prior knowledge also demonstrates that process is flexible: successful use of
multiple formats and scales (shake flasks, cell bags, spinners, bioreactors)
Risk Assessments of seed steps up to N-2 stage shows no impact on product
quality
Seed Culture Steps
Product Accumulation
Risk of Impact to Product
Quality
Seed Expansion in Spinner or Shake
Flasks
Negligible
Very Low
Seed Expansion in Wave Bag Bioreactor
Negligible
Very Low
Seed Expansion in Fixed Bioreactor
Negligible
Very Low
Seed expansion process is not part of the Design Space
and is not included in the registered detail
Slide 24
N-1 Seed Impacts Process Performance but
NOT Product Quality
P-Values
Process Parameters
N-1 Seed Bioreactor
Performance Parameters
Production
Bioreactor
Performance
Production Bioreacotr
Product Quality
Variables
Peak
VCC
% Viab
Culture
Duration
Titer
aFucos.
Galact.
HCP
Aggreg.
pH
0.03
0.24
0.04
0.001
0.27
0.53
0.63
0.64
0.31
0.25
0.19
0.35
0.77
0.73
0.31
0.49
Temperature
0.02
0.05
0.03
0.005
0.43
0.22
0.23
0.60
pH × Dissolved
Oxygen
0.04
0.78
0.65
0.37
0.17
0.78
0.59
0.85
pH × Temperature
0.32
0.26
0.32
0.02
0.98
0.36
0.80
0.36
Dissolved Oxygen ×
Temperature
0.42
0.86
0.74
0.37
0.80
0.38
0.61
0.26
Dissolved oxygen
Seed expansion process is not part of the Design
Space and is not included in the registered detail
Slide 25
Upstream Process: Production Bioreactor
Approach to Define a Design Space
Leverage Prior Knowledge and A-Mab Development Experience
Data from
other MAbs
Platform
Knowledge
A-Mab Data
Process 1
Process 1
Tox
500L
Ph 1/Ph 2
1,000L
Process
Development (2L)
Ph 3
5,000L
Process 2
Slide 26
Example of Risk Assessment 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]
[NaHCO3]
Age
Timing
Preparation
Osmolality
Filtration
Feed
[Glucose]
Glucose Feed
Medium
Concentration
Slide 27
Example of Risk Assessment Approach
Step 2: 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
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
DOE
Potential impact to QA
with effective control of
parameter or less
robust control
DOE-Indirect
DOE-Indirect
DOE
DOE
DOE
DOE
DOE
DOE-Indirect
Slide 28
35
Temperature
(C)
50
DO (%)
100
CO2 (%)
6.85
pH
1.2
[Medium]
(X)
400
Osmo (mOsm)
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
7.1
.8
7
6.9
6.8
6.7
70
40
60
80
100
120
140
160
6.6
60
50
40
36
30
35.5
35
34.5
34
Aggregates
(%)
2.515119
±0.03524
CEX % Acidic
Variants
27.66898
±0.480814
DNA (ppm)
1935.343
±89.55908
HCP (ppm) Galactosylation aFucosylation
695538
(%)
6.439933
±16518.3
29.28939
±0.226948
±0.674582
Titer (g/L)
3.743131
±0.076052
DOE Studies to Define Design Space: Identify CPPs and Interactions
Example of DOE Results
Prediction Profiler
5
4
3
8
6
4
32
28
24
1e+6
8e+5
6e+5
4e+5
2500
2000
1500
32
28
3.0
24
2.6
2.2
1.8
17
Duration
(d)
0.21
Curvature
29
Classification of Process Parameters based on Risk
Assessment
Process
Parameter
Within Design Space
Regulatory-Sensitive
Yes
High
Risk
Risk
Assessment
Severity of Impact,
ability to control
within Design
Space
Not in Design Space
Managed through QMS
Risk
Assessment
Does variability in
parameter significantly
impact CQAs
No
Risk
Assessment
Low
Risk
No
Does variability in
parameter impact process
performance or
consistency?
Yes
Critical Process
Parameter
(CPP)
Well Controlled Critical
Process Parameter
(WC-CPP)
High
Risk
Key Process
Parameter
(KPP)
Risk
Assessment
Severity of Impact,
ability to control within
acceptable
ranges
Low
Risk
General Process
Parameter
(GPP)
Slide 30
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
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
Assay results part
of batch release
specifications
Slide 31
Define Engineering Design Space for Production
Bioreactor
Analogous to the design space defined by scaleindependent parameters, the engineering design
space is a multidimensional combination of
bioreactor design characteristics and engineering
parameters that provide assurance that the
production bioreactor performance will be robust
and consistent and will meet product quality
targets
Slide 32
Engineering Design Space
Design Space applicability to multiple operation scales
demonstrated using PCA/MVA models
500 L – 25,000 L
Randal All en
Design Space for scaleindependent parameters was
developed using qualified scaledown models
2L Scale
Engineering Design Space includes bioreactors of multiple scales and designs (2L -25K L)



Based on keeping microenvironment experienced by cells equivalent between scales
Characterization of bioreactor design, operation parameters, control capabilities,
product quality and cell culture process performance provide basis for scientific
understanding of the impact of scale/design
Includes bioreactor design considerations and scale-dependent process parameters
linked to fluid dynamics and mass transfer
Slide 33
Lifecycle Approach to Validation
1. Multivariate model based on process characterization (e.g. DOE) - Model 1
A comprehensive Design Space based on 2-L characterization studies as well as 500L, and 5000L experience for A-Mab. Includes scale-independent operational
parameters: iVCC, temp, pH, pCO2 etc
2. Design Space for Scale-up, based on BioRx engineering parameters- Model 2
Based on engineering characterization and DOE studies. Establish 2L as a reliable
model system by: a) Establishing hydrodynamic similarity and ensuring appropriate
equipment design and operation; b) Establishing scalability through demonstration of
overlapping performance of either scale in a MVA model that includes process inputs,
outputs and product quality – for previous aMAb product (Model 3)
3. Demonstration of scalability and Design Space for A-Mab by execution of 2
batches at the intended commercial scale (15K)
4. Use process monitoring during routine manufacturing for continuous
verification that process is in state of control
Build MVA model for A-Mab; define acceptance criteria
Slide 34
Case Study
Downstream Process and Drug Product
CMC Biotech Working Group
Downstream Process
Clarified Bulk
Leverages Prior Knowledge
with platform process to
define Design Space
Leverages prior knowledge
and A-Mab results to justify a
modular approach to viral
clearance
Justification of two process
changes post-launch :
1. Change resin for Protein
A 2. Change from resin
to membrane format for
AEX
Step 5
Protein A Affinity
Chromatography
Step 6
Low pH
Incubation
Step 7
Cation Exchange
Chromatography
Step 8
Anion Exchange
Chromatography
Step 9
Small Virus
Retentive
Filtration
Step 10
Formulation:
Ultrafiltration and
Diafiltraion
Step 11
Final Filtration,
Fill and Freeze
Design Space based on
worst case scenario for AMab stability and worst case
for viral inactivation
Design Space based on
multivariate model that links
all three purifications steps
(Protein A, AEX and CEX)
A-mAb
Slide 37
Multi-step Design Space for
Chromatography Columns
• Design Space is defined based on model that links
performance of the 3 purification steps
– HCP clearance example
• Model based on results of individual DOE studies
• No extrapolation of parameters outside ranges tested
allowed in design space
• No interaction of parameters from different steps assumed.
– Assumption was experimentally verified.
• 99.5% prediction interval added to mean predicted HCP
levels
– To reflect high level of assurance specifications will be met if
process operated in design space.
Slide 38
Protein A
CEX
6
5
4
3
10
20
30
40
50
10
Protein A
30
20
30
1.6
7.2
40
5
4
50
10
15
20
25
3.6
2.6
1.6
30
7.2
Equil Wash Conductivity
5
4
Protein Load
20
7.6
7.8
AEX
6
15
7.4
Load pH
7
10
7.8
4.6
Protein Load
50
7.6
5.6
CEX
40
7.4
AEX
3
10
2.6
Load pH
6
Protein A
3.9
3.8
3.7
3.6
3.5
3.4
3.3
3.2
3.6
30
3
20
4.6
CEX
Wash Conductivity
Elution pH
25
7
Protein Load
Full range on axis is range explored in DOE
20
Equil Wash Conductivity
3.9
3.8
3.7
3.6
3.5
3.4
3.3
3.2
10
Case 3: If full range allowed in Protein A
and AEX, CEX is constrained
15
5.6
Protein Load
Wash Conductivity
Elution pH
Protein Load
Case 2: Constraining Protein A and CEX
ranges allows full ranges for AEX
AEX
7
Equil Wash Conductivity
3.9
3.8
3.7
3.6
3.5
3.4
3.3
3.2
Wash Conductivity
Case 1: If full range allowed in Protein A
and CEX, AEX is constrained
Elution pH
Acceptable range for each step depends on
acceptable ranges for other two steps
25
Protein Load
30
5.6
4.6
3.6
2.6
1.6
7.2
7.4
7.6
Load pH
Acceptable Range
Slide 39
7.8
Drug product process steps exemplifying QbD
supported by optimized formulation design
A-Mab Drug
Substance
Step 1
Step 2
Drug substance
preparation/handling
Compounding
Step 3
Sterile filtration
Step 4
Filling, stoppering and
Capping
Packaged A-Mab Drug
Product
Design spaces
Multiple or single
lots/container
Frozen or unfrozen
Unclassified or class
100,000
50-1500 L
Stir time
Hold time
Tank configuration
50-1500 L
Hold time
Filter configuration
Risk Assessment
Design Space
Control Strategy
Reservoir pressure
Pumping configuration
Capper spring pressure
40
A- Mab Case Study
Control Strategy
CMC Biotech Working Group
Control Strategy: Linking Product and
Process Understanding
Animal In-Vitro
Studies Studies
Input Material Controls
High Criticality
Attributes
Product Quality
Attributes
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
Procedural Controls
Process Targets
for Quality
Attributes
Process
Development and
Characterization
Design
Space
Control Strategy Elements
Safety and
Efficacy Data
Process Controls
Process Parameter
Controls
Testing
In-Process Testing
Specifications
Characterization &
Comparability Testing
Attributes that do not need to
be considered or controlled
by manufacturing process
Continuous Process Verification
Prior
Clinical
Knowledge Studies
Process Monitoring
Low Criticality
Attributes
Product Understanding
Process Understanding
Slide 42
Control Strategy is based on a final Risk
Assessment for each CQA
Overall CQA Risk
Assessment
RPN
=
CQA Criticality
Assessment
X
Process
Capability
X
Testing
Strategy
Severity
Occurrence
Detectability
Risk Assessment
Risk
Assessment
In-Process
Controls
RPN = SxOxD
Specifications
Severity of Impact
x Certainty
Design Space
Categorization
of Process
Parameters
Slide 43
Example of Control Strategy for selected CQAs
CQA
Criticality
Process
Capability
Testing
Spec
Limits
Other Control
Elements
Aggregate
High (48)
High Risk
DS and DP
release
Yes
Parametric Control of
DS/DP steps
aFucosylation
High (48)
Low Risk
DS Process
Monitoring
Yes
Parametric Control of
Production BioRx
Very Low Risk
Charact.
Comparability
Yes
Parametric Control of
Prod BioRx, ProA, pH
inact, CEX , AEX steps
Yes
Parametric Control of
Prod Biox and AEX
Steps
No
Parametric Control of
Production BioRx
Host Cell
Protein
High (24)
DNA
High (24)
Very Low Risk
Charact.
Comparability
Deamidated
Isoforms
Low (12)
Low Risk
Charact.
Comparability
From A-Mab Case Study
www.casss.org
Drug Substance & Product Release Testing is
Only one Element of Control Strategy
Example: Drug Substance Release Testing
Attribute
Test
Acceptance Criteria
Release
Stability
Identity
CEX
Consistent with Ref Std
and No New Peaks
Yes
No
Monomer
HPSEC
NLT 97%
Yes
Yes
Aggregates
HPSEC
NMT 3%
Yes
Yes
Endotoxin (LAL)
USP <85>
NMT 12.5 EU/mL
Yes
No
Reduced testing in comparison with
traditional approaches
Slide 45
A-Mab Case Study
Regulatory Considerations
CMC Biotech Working Group
Regulatory Aspects of the Case Study
• Objectives of the Regulatory section of the case study:
– Describe information that is provided in the filing to convey process &
product understanding -vs- license commitments
– Describe how elements not covered by license commitments will be
addressed in the Quality System
– Describe how development and monitoring of process knowledge
throughout the product’s lifecycle will differ from traditional process
validation activities and lead to continued improvement
– Propose a general risk-based approach for managing post-approval
changes within and outside the design space and provide specific
examples
Slide 47
Linking Product and Process Understanding to
Regulatory Commitments & Process Lifecycle
Quality
Attributes
Life Cycle
Managemen t
Design Space
Prior Knowledge
Process Understanding
Process
Development
Process
Characterization
Product Understanding
Draft Control
Strategy
Process
Performance
Verification
Final Control
Strategy
BLA/MAA
Process
Parameters
Risk
Assessment
Risk
Assessment
Risk
Assessment
Risk
Assessment
 The regulatory filing presents a summary of the risk
assessment methodology and accumulated
process & product knowledge
 Regulatory commitments are the critical elements
of the overall control strategy developed based on
the outcomes of the overall risk assessments
 The overall approach to risk-based process
management becomes the basis for lifecycle and
change management
Design space
controls
In-process tests
Lot release tests
Stability
commitments
Slide 48
Justification of the Design Space
• The overall knowledge that justifies the Design Space is based on
– Product and process specific knowledge
– Historical and platform data
• Summary of the knowledge that justifies the outcomes of the risk
assessment and the limits for design space will be presented in the
Process Development History section
– Conclusions will be supported by process characterization reports available
upon request or inspection
• The design space may be applied across many scales, or pieces of
equipment (different bioreactors, columns of different widths), provided
data sufficient justification is provided in the application
• The design space is not “validated” at manufacturing scale in the
traditional sense
Slide 49
Lifecycle Approach to Process
Validation
• Begins during development and continues post-launch
• Builds on knowledge from multiple scales
• Departure from the traditional 3-batch validation
approach prior to submission
– Process validation encompasses cumulative knowledge
– Includes continued process verification
• To demonstrate validity of Design Space
• To maintain validity of models
Slide 50
Lifecycle Management of Process
Improvements & Changes
• Movements within the design space are managed without
regulatory notification
• Changes outside the design space will involve a regulatory action
– From notification to pre-approval depending on risk assessment
• Specific examples addressed in case study
–
–
–
–
Scale-up of production culture
Replace new chromatography resin with similar from same vendor
Replace new chromatography resin with new technology (membrane)
Manufacturing Site Changes for DS and DP
Slide 51
Assessing Change: Scope of Change is Initially Assessed
at the Unit Operation Level
Movement
w/in
approved
DS
Output from
previous step
Unchanged
MATERIAL INPUTS
(Vendor, Scale,
Technology)
Changed
DS
Parameters
Unchanged
Outputs from
previous step &
other material inputs
Same
Minor
Change
Major
change
Major
change
Design Space
Parameters
Same
Same,
Data not in
original
filing
New
New
Step Outputs
Same
Same
Same
New
DS
Parameters
Changed
Output
Output
Changes outside approved DS
Output
Degree to which outputs overlap denotes
risk associated with change
Risk
Changes which represent more risk drive more extensive data collection
Slide 52
Quality by Design
Questions to Consider
Seed
Seed Density
Viability
Temperature
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
Contour Profiler
Horiz Vert Factor
Temperature
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
Filtration
Current
Horiz
Vert X
Factor
35
Temperature
50
DO
70
Dissolved CO2
3.7
Split Ratio
100
Basal Strength (Dilution)
100
Feed Strength (Dilution)
90
Feed Neutralization
440
Osmo
17.5
Duration
Response
Productivity
aFucosylation
Galactosylation
Contour
3
11
25
Current
Y
Lo Limit
Response
8.644125
3
Productivity
6.1354189
3
aFucosylation
32.452376
Galactosylation 25
100
Current X
37
50
70
3.7
100
100
90
440
17.5
Hi Limit
.
11
55
Contour
3
11
25
Current Y
8.951625
7.5034189
32.837639
Volume of
Feed
Operations
Amount Delivered
Storage
Temperature
Concentration
Preparation
pH
Age
Lo Limit
3
3
25
Hi Limit
.
Feed
11
55
Number of
Feeds
Pre-filtration
hold time
Procedures
Age
Operations
Procedures
Storage
Temperature
Pre-filtration hold
time
[Antifoam]
[NaHCO3]
Age
Timing
Preparation
[Glucose]
Osmolality
Filtration
Glucose Feed
Medium
Concentration
100
Contour Profiler
Contour Profiler
Horiz Vert Factor
Temperature
80
DO
Dissolved CO2
Split Ratio
Basal Strength (Dilution)
60
Feed Strength (Dilution)
Feed Neutralization
Osmo
Duration
40
Response
Productivity
400
420
440
aFucosylation
Osmo
Galactosylation
Current
Horiz
Vert XFactor
35Temperature
80 50
DO
70Dissolved CO2
4.9Split Ratio
100Basal Strength (Dilution)
60
100Feed Strength (Dilution)
90Feed Neutralization
440Osmo
17.5Duration
40
Contour
Current
Y
Lo Limit
Hi Limit
Response
3
6.896625
. 460
Productivity
480
400
420 3
440
11 6.1244052
3
aFucosylation
Osmo 11
25 30.595296
55
Galactosylation 25
Galactosylation
460
Galactosylation
100
Time of Feeding
Contour Profiler
Dissolved CO2
•
Agitation
In Vitro Cell
Age
Shear/
Mixing
Production
Bioreactor
N-1 Bioreactor
80
60
Galactosylation
Contour
3
480
11
25
Current X
37
50
70
4.9
100
100
90
440
17.5
Current Y
5.974125
9.5011447
30.980559
Lo Limit
3
3
25
Hi Limit
.
11
55
Galactosylation
100
Dissolved CO2
•
Dissolved CO2
•
How can we maximize the benefits to
the industry and other stakeholders?
How can we ensure that this will
speed up development and reduce
the investment for process and
product development?
QbD may be implemented in parts or
as part of a development philosophy.
How can this be implemented during
early development?
What is the best way to ensure that
smaller enterprises can benefit from
the work going on with QbD and
facilitate innovation?
Dissolved CO2
•
80
Productivity
aFucosylation
60
40
40
400
420
440
Osmo
460
480
400
420
440
460
480
Osmo
Slide 53
What are Biosimilars?
• Biosimilars
– Are biological products that claim to be similar to
an innovator biological product
– The innovator’s product is off-patent and no
regulatory data protection remains
– Are manufactured by a second manufacturer with
new cell line, new process and new analytical
methods
– Require original data for approval
EMEA Approach for Biosimilar Medicines:
Guideline on Similar Biological Medicinal Products
(CHMP/437/04)
• Overall Approach
– Similar biological medicinal products are not generic medicinal
products
– Comparability studies need to demonstrate the similar nature in
terms of quality, safety, and efficacy
• Biosimilars will be different from the reference
– It is not expected that the quality attributes in the biosimilar
and reference product will be identical
– The biosimilar product may exhibit a different safety profile (in
terms of nature, seriousness, or incidence of adverse reactions)
US Definition of Biosimilarity
• Biosimilarity
– The biological product is highly similar to the
reference product not withstanding minor
differences in clinically inactive components
– There are no clinically meaningful differences
between the biological product and the reference
product in terms of the safety, purity, and potency
of the product.
Criteria for Biosimilar
EU
US – BPCA
• Similar nature to reference
product based on:
•
– Quality
– Safety
– Efficacy
• Should be similar in molecular
and biological terms
• Pharmaceutical form, strength,
and route should be the same
or if different additional data
should be provided
• Class specific guidelines are
referenced
Highly similar to reference product
based on:
– Analytical studies
– Animal studies
– Clinical study or studies
•
•
•
•
•
Utilizes same mechanism of action
Conditions of use have been
approved
Route of administration, dosage
form, and strength are the same
Not all data elements may be
necessary
Allows for a determination of
interchangeability
US Definition of Interchangeability
• The biological product may be substituted for the
reference product without the intervention of the
health care provider
• Determination of Interchangeability
– Finding of biosimilarity and expectation to produce
the same clinical result in any patient
– For a product that is administered more than once
• The risk in terms of safety or diminished efficacy of
alternating or switching between use of the biological
product and the reference product is not greater than using
the reference product alone
Specification Limits Vs. Control Limits
Differentiate Specification Limits
from
Control Limits
Based on process capability
to provide assurance of
process consistency
Regulatory Commitment
Managed through QMS
Design Space enabled
Process Improvements enabled
Process Monitoring
Continued Process Verification
Product Understanding
Process Understanding
Design Space
Control Limits
Control Space
Specification Limits
Based on clinical relevance
to provide assurance of
safety and efficacy
CQA 1
CQA 2
CQA 3
Specifications are linked to clinical relevance not process capability
Changes in specifications during product lifecycle reflect improved understanding of
relationship between product and clinical relevance
From Ilse Blumentals, GSK
Step 2: Consider Impact to Other Unit Operations and Requirements
for Extended Characterization
Movement in
approved DS
Change outside approved DS
Outputs from previous
step & other material
inputs
Same
Minor Change
Major change
Major change
Major change
Design Space Parameters
Same
Same,
Data not in
original filing
New
New
New
Step Outputs
Other Unit Operations
Affected
Meets IP & Lot Release
Criteria
Same
Minor Changes
New
New
ReportingSame
requirements
are based
on the reassessment
by Multiple
the
Single
Single
Singleof risk posed
Multiple
change
including
results
of new
Yes
Yes
Yes
Lot release met, Lot release
some IPCs
met, some
design and testing if necessary
changed
IPCs changed
Comparability required
__________________
Results Observed
no
no
Yes,
__________
No changes
Yes
__________
minor changes
Yes
__________
new peaks
Supportive non-clin/clin
data
no
no
no
maybe
Yes
Reporting Requirement
No Reporting
Notification
Pre-approval
Slide 66