Implementation Activities for QbD:
FDA Office of Biotechnology
Products
WCBP CMC Strategy Forum
QbD for Biologics
Bethesda, MD
7/19/2010
Steven Kozlowski, Director
Office of Biotechnology Products
OPS, CDER
Quality by Design
• A systematic approach to development
that begins with predefined objectives and
emphasizes product and process
understanding and process control, based
on sound science and quality risk
management (ICH Q8R)
• QbD concepts apply to complex products
• Implementation strategy may differ
– Product heterogeneity may complicate the
definition of the CQAs
– Processes are often also more complex,
with multiple interactions
Potential Benefits for Embracing QbD
• Smoother transitions from IND to Licensure
• Increase productively/efficiency
• Less lot rejections, recalls, and investigations for
manufacturing deviations
• Expedited implementation of process changes
• Manufacturing processes that are adaptable
• Reliable supply of high quality products
• Fewer inspections
• Fewer submissions to the Agency
QbD Implementation
• Link to small-molecule learnings
– ONDQA pilot and application experience
• ICH IWG, Q8R, Q9, Q10
• ICH Q11
• OBP staff participating in conferences,
forums and training on QbD
– Design of Experiments Training
• Mock Case Studies
• OBP Pilot
Biotech QbD Mock Case Studies
• EFPIA drafting a case study
• Industry CMC Biotech Working Group
Publishes a QbD Case study (10/31/09)
– A-Mab: a Case Study in Bioprocess Development
Abbott, Amgen, Eli Lilly & Company, Genentech,
GlaxoSmithKline, MedImmune, and Pfizer
– Available on the ISPE Website Nov 2009
Workshops on case study planned (ISPE &
CASSS)
– 278 pages
not….
A-Mab is …
but it can be….
• A template for a
QbD submission
• A definitive
source of
regulatory
definitions &
terminology
• The final ideal
scientific
approach to
biotech QbD
• A source of challenging, well
thought out examples
• The basis for discussions and
forums likely to contribute to
QbD implementation
• A tremendous effort by Ken
Seamon, John Berridge and
the top scientific talent of
multiple companies
– Thanks to Anjali Katarina &
Conformia
ICH Q8R: Design Space
• Definition
– The multidimensional combination and
interaction of input variables (e.g., material
attributes) and process parameters that have
been demonstrated to provide assurance of
quality
• Regulatory Flexibility
– Working within the design space is not
considered a change
• Important to Notice
– Design space is proposed by the applicant and
is subject to regulatory assessment and
approval
Many Attributes
pyro-E
O
pyro-E
O
D
D
D
D
O
O
G
G
D
D
G
G
• Biological Studies
– In vitro
– Animal
– Clinical
• Prior Knowledge
– Platform
K K
• (9600)2≈ 108
Risk Assessment & Ranking Approaches
Good Science
From Attributes to Spaces
• Assign relative risk
for each factor
= [severity]
x [occurrence]
x [detectability]
Risk assessment
includes process develop.,
manufact., QC staff, etc.
& trained facilitator
Occurrence
FMEA
Severity
•
•
•
•
Risk Ranking
Screening DOE
Optimization
Process
Characterization
A-Mab Design Space Based on
Process Capability Bayesian Reliability
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
Example: Day 15, Osmo=360 mOsm and pCO2=40
mmHg
Response
Titer (g/L)
aFucosylation
Galactosylation (%)
HCP (ppm)
DNA (ppm)
CEX % Acidic Variants
Contour Current Y
3 5.3408326
11 9.1879682
40 38.227972
675000 466955.66
2250 1382.1644
40 34.420095
Lo Lim it
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
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
Galactosylation (%)
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
Temperature (C)
Temperature
(C)
35.5
36
6.6
34
34.2
34.4
34.6
34.8
35
35.2
35.4
35.6
35.8
36
Temperature (C)
10
pH
Load Conductivity
Critical Process
Parameters (CPP)
• Critical Process Parameter (CPP): A
process parameter whose variability has
an impact on a critical quality attribute
and therefore should be monitored or
controlled to ensure the process
produces the desired quality. (ICH
Q8(R2))
What else is in the Design Space?
• Are only CQAs used to defining Design Space?
Acidic variants used in A-Mab Production Bioreactor
• Are only CPPs used in a design space?
• A CPP is a function of the range evaluated
(either experimentally or through prior knowledge)
5oC
40oC
CPP
15oC
25oC
KPP
18oC
GPP
22oC
Can a Design Space be larger
than the Knowledge Space?
5oC
Fundamental Models
15oC
25oC
40o
C
Prior Knowledge
22oC
18oC
Experiments
No failures seen; Models, Prior Knowledge &
Experiments show interactions are unlikely
If not included in the Design Space (not a CPP),
is the Design Space infinite for that parameter?
Design
Space
Temperature
pH
Cycle #
Load
Conductivity
Equilibration pH
– The
multidimensional
combination and
interaction of input
variables (e.g.,
material attributes)
and process
parameters that
have been
demonstrated to
provide assurance
of quality
Initial Design Space Weaknesses
• Based on model (DOE)
– Predictions are extrapolations
• inside as well as outside explored space
• Missed factors
• Missed interactions at screening
– Each factor alone has little impact
– Larger risk with complex processes
– A-Mab interaction risk score is of value
• Missed important responses
– Larger risk with complex products
– Interactions between responses
• Experiments done at lab scale
Lifecycle Approach
• Managing uncertainty
pH
– Complex products
– Complex processes
– 1st Prin. Models rare
Adapted from
T. Kourti
• Multivariate SPC
– Facilitates moving
across scales
Dimensionless
Variables
Load
Engineering Approaches
To Modeling
A-Mab 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
Includes bioreactors of multiple scales and designs (2L -25K L)
 Based on keeping microenvironment experienced by cells equivalent
between scales
 Includes bioreactor design considerations and scale-dependent
process parameters linked to fluid dynamics and mass transfer
Can a Design Space
Specify Evaluation Methods & Criteria ?
Designing Spaces
Fixed equipment,
scale, etc.
Load
pH
Conductivity
Fixed resin
characteristics
&
methodology;
Scalable
Moving within the
Design Space
Control Strategy (CS)
& Quality
System (QS):
Evaluation beyond
CS not filed
Evaluation and
criteria beyond
routine CS filed
pH
Load
Adaptive Control
Strategy
Control Strategy definition from Q10
• Control Strategy: A planned set of controls,
derived from current product and process
understanding, that assures process
performance and product quality.
• The controls can include parameters and
attributes related to drug substance and drug
product materials and components, facility and
equipment operating conditions, in-process
controls, finished product specifications, and the
associated methods and frequency of
monitoring and control.
Designing Spaces
Flexible methodology & Scale
Moving within the
Design Space
Charge
Separation
QS: evaluation and
criteria beyond
SC filed;
Change reported
(e.g. AR)
Design SpaceWorking within the
design space is not
considered a change
Use of Expanded
Change Protocol
or
Post-market
Management Plan
Non-ionic
Interactions
OBP Pilot Program
FR Notice July 2, 2008
• To define clinically relevant attributes for protein
products (regulated by OBP) and link them to
manufacturing processes
• To consider quality-by-design (QbD) approaches to
unit operations in supplements (10) as well as
original applications (5)
• To explore the use of protocols submitted under (21 CF 314.70(e) and 601.12(e))
• Notice of Extension of Deadlines and Increase in
the Number of Original Applications to 8
– Federal Register/Vol. 74, No. 179 /Sep 17, 2009
Biotechnology QbD Pilot Status
• Applications Accepted in QbD Pilot
– 5 Original Applications
• 4 Monoclonal Antibodies and 1 Fc Fusion Protein
– 4 Post-approval Supplements
• 2 Monoclonal Antibodies, 1 Therapeutic Protein, 1 multi-product
– 2 with site transfers; Working closely with
Compliance
• MAPP 4730.3 OBP & DMPQ Interactions on BLAs
• OBP QbD Pilot Meetings
– 6 meetings held with Pilot sponsors in 2009
QbD Pilot 2009 Meeting Questions
• 29 questions
– Questions with subparts were counted separately if
they covered a different set of QbD categories or topics
• 25 associated only with Mabs
– 4 included other therapeutic proteins
• Question General categories
–
–
–
–
–
Design Space
Risk Assessment Methods
Control Strategy
Expanded Change Protocols
Small-scale Models
13
6
4
4
3
QbD Pilot Meetings
• Typical Question
– Here is our plan and some example data
– Is this acceptable?
• Typical Answer
– In principle YES…
• but we will need to see that actual submission
– Comments on clarifying ranges, scoring,
definitions
• Occasionally closer to NO (“Only” or “All”)
– Only small scale stability on significant changes
– ECP covering all changes
Design Space Comments
• Factor choices (e.g. raw materials)
• Prior knowledge base
• Appropriateness of the experimental design and
statistical analysis
• Impact of assay variability on design space
• Viral clearance
• Linkage to other steps
• Claims for scale in design space
• Protocols as part of a design space
• CPPs alone do not define a design space-assurance
of quality does
• Limits for parameters that may not be critical
parameters; Relationship to Design Space
2010 (Very) Preliminary Update
• 10 meetings total so far
• 47 questions used for initial analysis
– within a meeting some questions counted
separately; some fused based on topics
– monoclonal antibodies with some
multiproduct
– 4 companies for 5 pilot applications
• 3 original: 2 supplements
– Typical question; Typical Answer -- still true
• Comments a bit more granular
Questions Per Meeting (Avg)
General Categories
Categories
Risk Assessment Methods
Design Space
Scaled down models
Expanded Change Protocols
Control Strategy
0.00
2009 Meetings
1.00
2.00
2010 Meetings
Questions Per Meeting (Avg)
Neutral/Decrease
Categories
Process characterization
Model qualification
Attribute criticality
Viral Clearance
0.00
2009 Meetings
0.40
0.80
2010 Meetings
1.20
Questions Per Meeting (Avg)
Increase
Categories
Parameter Criticality
Scope
Change Evaluation Strategy
Regulatory Filings
Inspection/Facility
Experimental Design & Statistics
0.00
2009 Meetings
0.40
0.80
2010 Meetings
1.20
Topics
• Design space & CPPs
– Review issue
• Details on criteria for ECP
– Statistics; trend analysis
• Complex risk tools for process
– Thresholds
– Linking product and process risks
– Factors & details
• Definitions
– Cannot be company specific
– Even ICH definitions need a shared commentary
• CPPs
ACE Case Roller Compaction
 r  fcn1  D, , s, p0 , E ,  y , ,  0 , 0 ,  pr ,  pp 
s

p0
r
E y
 fcn2  ,
,
,
,

,

,

,

0
pr
pp 
2 2
0
D


D
p
E
0
0


Johansen (1965)
NIR surrogate
for ribbon density
This process
understanding
may establish the
independence of
site, scale, and
equipment.
Looking Forward
•
•
•
•
Continue to learn from small-molecule QbD
ICH Q11
Clarity on definitions
Reviewer Training
– Risk Assessment
– DOE
• Mock Case Studies
– Conferences
• OBP Pilot
• Get into the details
Credits
•
•
•
•
•
•
Barry Cherney
Patrick Swann
Keith Webber
Susan Kirshner
Ken Seamon
John Berridge
•
•
•
•
•
•
Janet Woodcock
Helen Winkle
Moheb Nasr
Christine Moore
Jon Clark
Joe Kutza