Introduction

1
Introduction Regulatory Perspective on
Quality by Design
QbD Session Recovery of Biologic Products 13
Québec City, Canada

• Steven Kozlowski, M.D., Director
• Office of Biotechnology Products OPS/CDER

6/26/2008
2
Overview

• Definitions
• Product Attributes Design Space
• Lifecycle Implementation

3
ICH Q8 Design Space
Product Understanding
Quality by Design
Process Understanding
Moheb Nasr
4
ICH Q8 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

• Process Importance
• The product is the processa biologics mantra

5
Manufacturing Process
Variability
Raw Material
Product
Endpoint Response
Input Response
Jon Clark
6
Overview

• Definitions
• Product Attributes Design Space
• Future Directions/Implementation

7
Structure of complex molecules

• 1? structure
• higher order structure

• post-translational modifications
• heterogeneity

• (9600)2 108

8
Functional Event Sequence Diagram
Oxidation Site 1
9
Biological Activity Matrix
10
Monoclonal Antibodies
Fc binding Effector Function
Nature of target interaction
11
From Attributes to Spaces
Critical Quality Attribute (CQA) A physical,
chemical, biological or microbiological property
or characteristic that should be within an
appropriate limit, range, or distribution to
ensure the desired product quality Q8R1
12
Biotechnology Manufacturing Process
Chromatography Columns
Harvest
Fermentor

• Removes
• Sub-potent charge variants
• Impurities

13
Risk Assessment

• Assign relative risk for each factor

Risk assignment includes process development,
manufacturing, QC staff, etc.
14
Design of Experiments (DOE)

• Efficient approach
• Uses only 2 to 3 factor levels (high, low
  center)
• Can evaluate interactions
• Screening Designs
• Large range (1.5-3X) many factors
• Confounds some terms of model
• usually interactions
• Many Approaches
• Fractional Factorial

• Optimization
• Fewer factors
• Data to support more sophisticated modeling of
  responses
• Modeling used for a contour map of factors and
  responses

15
Surface Responses of Important Product Attributes
Impurity lt50 ng/mg
Acidic Variants 10-25
Basic Variants 10-20
Protein Load
pH
16
Overlay of Response Surfacesinto an Initial
Design Space
Protein Load
pH
17
DOE Considerations

• Experimental Data
• Low replicate variability versus exper. ranges
• May need transformation to normalize data
• Modeling
• MLR or PLS
• Model Linear, Interaction, Quadratic
• Need replicates to assess model variability
• Model goodness of fit R2 Q2
• ANOVA
• Model regression fit test (significant p value)
• Lack of fit test (insignificant p value)

18
Thoughts on DOE Submission

• DOEs that are important for quality
• Narrative for FMEA
• prior knowledge and rationales of experts shared
• support for factors not further studied
• Surrogate for HETP and thus column packing
  (detectability)
• Rationale for quality related responses
• Validity of scale-down model
• Narrative story of DOE
• Screening to optimization
• DOE often done as iterations
• Adding experimental points Changing model
• Worksheets, Results Graphics

19
Overview

• Definitions
• Product Attributes Design Space
• Lifecycle Implementation

20
Initial Design Space Weaknesses

• Based on model (DOE)
• Predictions are extrapolations
• inside as well as outside explored space
• Experiments done at lab scale
• Missed factors
• Missed interactions at screening
• Each factor alone has little impact
• e.g cycle and regeneration buffer salt
• Larger risk with complex processes
• Missed important responses
• Larger risk with complex products
• Interactions between responses

21
Lifecycle Approach

• Multivariate SPA

• Fill gap between process experience and DOE
• Link to AEs new MOA knowledge

Adapted from T. Kourti
22
Complex API Pilot

• Discussed at ACPS and AAPS/ISPE meeting
• Further consideration recommended
• Possible strategies include
• Full Applications
• QbD applied to multiple unit operations
• Interaction at earlier points in development
• Supplements
• Expanded Change Protocols
• Use of current protocol regulations
• 601.12 314.70
• Expansion of comparability protocols

23
Expanded Change Protocols (ECPs)

• More than one change for one product
• Making changes across a platform of products
• Making changes at multiple sites
• Multiple applications of protocol for similar
  changes
• Leverage product process knowledge
• Not bundled separate protocols
• Greater change space with downgraded reporting
  categories
• Role of quality systems
• Inspectional issues

24
Moving Forward

• ECPs would not require new regulations
• Approaches need to be developed
• Pilot vs non-pilot ECPs
• Resources
• Management involvement
• Knowledge in submissions
• Data needed to support knowledge
• Platform Strategies
• Link to small-molecule learnings
• Mock Cases Studies
• ISPE PQLI, EFPIA, Conformia

25
Credits

• Barry Cherney
• Patrick Swann
• Moheb Nasr
• Keith Webber
• Ajaz Hussain
• Brian Kelley

• Jon Clark
• Christine Moore
• Chris Watts
• Ali Afnan
• Chris Joneckis
• James Seely