1. An Introduction to QbD (Quality by
Design) and Implications for
Technical Professionals

2. Biography and Disclaimer
 Director, Training and Continuous Improvement, Pfizer
Global Manufacturing, Richmond, VA, previously
Associate Director, Engineering, Wyeth Pharmaceuticals
 Ph.D. Candidate, Systems Engineering, the George
Washington University (dissertation topic: “QbD and
Pharmaceutical Production System Fundamental Sigma
Limits”). GWU Ph.D. Committee Advisors:
 Dr. Thomas Mazzuchi
 Dr. Shahram Sarkani, P.E.
 This is the outcome of the author’s research and
does not necessarily represent the views of Pfizer or
GWU
2

3. Session Abstract
Currently, most pharmaceutical development and
manufacturing systems rely on a Quality by Testing (QbT)
model to ensure product quality. Industry at large and
regulators recognize many of the limitations of the current
QbT approach. As a response, Quality by Design (QbD)
is emerging to enhance the assurance of safe, effective
drug supply to the consumer, and also offers promise to
significantly improve manufacturing quality performance.
This session provides an overview of QbD, and
implications for pharmaceutical technical professionals.
3

4. Learning Objectives
Upon completion of this session, the learner should
be able to
 Provide a rationale for moving to QbD based on the
inherent inability of a system to achieve desired
performance based on quality by testing
 Describe the key elements of QbD
 Describe implications of QbD on technical professionals
4

5. The Migliaccio Conjecture . . .
The pharmaceutical industry produces
six-sigma products with three-sigma
5
processes
Adapted from Migliaccio, G., SVP Network Performance, Pfizer Global Manufacturing, in: FDA will seek
Consultant Help in Implementing Quality Initiative, The Goldsheet, vol. 36, no. 9, September 2002 and a
presentation by Doug Dean, Ph.D. and Francis Bruttin, 2004
The problem with this:
 High quality shipped product, but . . .
 Gap between shipped quality and production sigma
 High cost of quality, inherent risks
 Plus, low return on investment for quality improvement initiatives

6. Why the Gap? The Hypotheses . . .
6
1) The conjecture
is true in principle
2) Without designing
for quality (DFSS or
QbD), production
systems hit a sigma
limit
+/- 6s Product
Supply
+/- 3s Production
System
3) Prod. Systems
follow S-Curve
Technological
Evolution
4) Moving to
higher S-Curve(s)
will require QbD to
resolve system
contradictions

7. Results of the Research (summarized)
1. There is a gap between pharmaceutical production
systems and supply sigma
2. Systems essentially reach a fundamental limit
3. Production systems follow an S-Curve technological
evolution profile
4. Rising to higher S-Curves requires elimination of
system conflicts (contradictions, trade-offs)
5. QbD offers significant opportunities over QbT to
eliminate conflicts, but additional opportunity for
development within the QbD scope exists (see
“Additional Material” at the end)

8. An intro to QbD – what is it?
 ICH: “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.” [1,2]
 Berridge: A “holistic, systems-based approach to the
design, development, and delivery of any product or
service to a consumer.” [2]
 My view : An approach to designing product and
processes to supply product to meet patient needs at
desired quality levels without reliance on release testing.
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9. System Comparison: Traditional vs. QbD
Production Patient
System
Product
Distribution
Product
Quarantine
Fixed
Packaging
Process
Fixed, Batch
Manufacturing
Process
Product
Development
As-Is: Traditional Pharmaceutical Product Supply System
Production Variable Pkg
Patient
System
Variable Batch
or Continuous
Mfg Process
Maintain in Design Space
(PAT, etc.)
Design Space,
Variable
Parameters
Release Testing,
Document
Integrity
In-process
Testing,
Documentation
In-process
Testing,
Documentation
Fixed
Parameters,
Ranges
Quality
System
As-Is: QbT Pharmaceutical Product Supply System
Product
Distribution
Responsive Pkg
Process
Responsive
Batch or
Continuous
Mfg Process
Product
Development
Real-time
Release
Control Strategy: Maintain
in Design Space (PAT, etc.)
Design Space
Quality
System
To-Be: QbD Pharmaceutical Product Supply System
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10. QbD Key Concepts
 Build in quality versus test in quality
 Scientific-based knowledge of the products and
processes
 Identify, understand, and control CQA’s (Critical Quality
Attributes) and CPP’s (Critical Process Parameters)
 QrM (Quality Risk Management) approach (risk
assessment, risk control, and risk review) [10]
 Design Space (DS) to identify acceptable limits of
operation via DOE (Design of Experiments)
 Control Strategy to ensure production is maintained
within the DS
 Use advanced statistical tools and technology such as
PAT (Process Analytical Technology). This can extend to
real-time release (RTR)
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11. Advantages of QbD [2,10]
 Better innovation due to the ability to improve processes
without resubmission to the FDA when remaining in the
Design Space
 More efficient technology transfer to manufacturing
 Less batch failures
 Greater regulator confidence of robust products
 Risk-based approach and identification
 Innovative process validation approaches
 Less intense regulatory oversight and less post-approval
submissions
 For the consumer, greater drug consistency
 More drug availability and less recall
 Improved yields, lower cost, less investigations, reduced
testing, etc.
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Better Quality . . .

12. The flow of QbD
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13. TPP (Target Product Profile)
 The TPP “identifies the desired performance
characteristics of the product” related to the patient’s
needs. [35]
 Linkage from patient to product to process is
described as follows: [34]
 Patient: Clinical outcome
 Product: CQAs (Critical Quality Attributes)
 Process: Material attributes and process parameters

14. Attributes/Parameters
 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.” [26]
 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.” [26]
 Material Attributes: Raw material or component
factors that impact CQAs

15. DOE (Design of Experiment)
 DOE is defined as “a structured analysis wherein
inputs are changed and differences or variations in
outputs are measured to determine the magnitude of
the effect of each of the inputs or combination of
inputs.” [25]
 Full factorial example:
Dependent
Variable
(Response)
Independent Variable
(Controlling Factors)
Run Factor X1 Factor X2 Factor Y1
1 High High Output1
2 Low High Output2
3 High Low Output3
4 Low Low Output4

16. A Design Space [11b]
Knowledge Space
16
Design Space
NOR
Design Space: “Multidimensional
combination and interaction of input
variables (e.g., material attributes) and
process parameters that have been
demonstrated to provide assurance of
quality.” [1]
CQA
Knowledge Space: “A
summary of all process
knowledge obtained
during product
development.” [33]

17. Control Strategy
 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.” [36]
 When the Control Strategy demonstrates CPPs (Critical
Process Parameters) are controlled within the DS (Design
Space), product can be released in real-time. [1]

18. Control Strategy Elements
 Input material attribute control
 Product specifications
 Unit operation controls that have a downstream
impact
 In-process testing or real time release in lieu of end-product
testing
 Monitoring program that verifies multivariate
prediction models

19. PAT (Process Analytical Technology)
 “A system for designing, analyzing, and controlling
manufacturing through timely measurements (i.e.,
during processing) of critical and performance
attributes of raw and in-process materials and
processes with the goal of ensuring final product
quality.” [38]
 PAT is considered a subset or enabling aspect of
QbD [8] and is needed to ensure a process remains
inside the DS (Design Space). [39]

20. PAT Example
 Liquid product, used to determine mix time
 CQA related to mix uniformity
 CPP’s (Critical Process Parameters) included agitator speed, time
after addition of one ingredient until the addition of another, solution
temperature, and recirculation flow rate.
 Process analyzer used was a refractometer
 Resulted in cost savings and quality enhancement
Mix
Tank
Pump
RI
Sensor
SCADA,
User
Interface
Control
System
Data
Historian

21. Envisioning the future . . .

22. Implications for R&D/Development
 Adapt to emerging QbD
 Better mechanistic understanding of factor interactions
 Emphasis on statistics
 Must involve manufacturing professionals early
 Better understanding of raw materials and components (e.g.
leachables and extractables)
 QbD-based filings
 Need to develop cost effective and timely methods to
compensate for additional QbD activities (e.g. DoE)
 Technology acumen (continuous processing, PAT, etc.)

23. Implications for Manufacturing
 More predictable quality
 Flexible processes
 QbD concepts (Design Space)
 Higher technologies (PAT, dynamic systems, data-centric)
 Skillsets (QbD, DOE, multivariate statistics, etc.)
 Early alignment with development needed
 Continuous-like manufacturing and continuous
quality verification
 Revitalized continuous improvement opportunities

24. Implications for Quality
 More predictable quality
 Less post-production testing
 Real-time-release (RTR)
 Shift focus to remaining in Design Space and
ensuring robust Control Strategy
 Multivariate statistics – skillsets
 Different technologies and systems
 Flexible processes
 Reduced laboratory work
 Data historian emphasis for investigations

25. Implications for Validation
 The costly approach to produce three batches is
expected to be replaced by “demonstrated scientific
process and product knowledge.” [40]
 Continued Process Verification (CPV) defined as “an
alternative approach to process validation in which
manufacturing process performance is continuously
monitored and evaluated.” [41]
 DS (Design Space) limits provide the basis for
validation acceptance criteria. [39]
 Skillsets (similar to previous)
 Intimate knowledge of QbD

26. Validation Implications (continued)
 Also see FDA’s recent “Guidance for Industry:
Process Validation: General Principles and
Practices”
 Must understand implications of variations on quality
 Control of in-process material
 Evaluation of all attributes and controls
 Goal is homogeneity in a batch and consistency between
batches
 PAT may warrant a different approach (e.g. “focus on the
measurement system and control loop for the measured
attribute”)
 Emphasis on the use of quantitative and statistical
methods

27. Implications for Engineering
 Expect more robust requirements from Development
 Design flexible processes
 Smaller facilities (move from stainless steel focus to
disposables, continuous improvement, flexible factories, etc.)
 Knowledge of quantitative methods (e.g. multivariate statistics)
 IT integration, data acquisition and control
 PAT
 Better mechanistic understanding of product (VOC)
 Embed contradiction-eliminating design features
 Team approach more essential (e.g. will need to add analytical
chemistry expertise)

28. Implications for Vendors and Suppliers
 Knowledge of QbD
 Embed QbD principles
 Standardization, platforms
 Incorporate QbD elements early (e.g. DS, PAT, etc.)
 Robust software
 Continuous processes
 Disposables
 Further development of PAT needed
 Improve sensor technologies or prove for pharma application
 Standardized platforms would be useful
 Fully integrate in continuous processing

29. Summary
 QbD offers significant opportunities over traditional
approaches to improve performance
 QbD will enable moving from fixed
processes/variable product to variable
process/consistent product
 Technology is needed to enable and facilitate QbD
 New skillsets and knowledge is needed for technical
professionals
 If QbD continues to emerge, we all will have to
change
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30. Early QbD Promising Results [16]
 2007 study
 Traditional development and manufacturing resulted in 81% of
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the measured PpK (Process Capability) > 1
 QbD developed products resulted in 92% of the measured
PpK >1
 Represents a 14% improvement in PpK (Process Capability)
 Assay PpK
 At launch 1.2 (3-4 sigma)
 Six months after launch PpK = 1.8 (5-6 sigma)
 Tablet production
푃푝푘 = min 푃푝푢 , 푃푝푙
푃푝푢 =
 Based on % of batches right first time
 15 yr + Traditionally Developed Product: 3.33 sigma
 First-year QbD Developed Product: 3.96 sigma
푈푆퐿 − 휇
3휎

31. QbD Enablers to Overcome System
Contradictions (super-system level)
Contradiction QbD Enabler
QbD
Current
State
Quality by Documentation
Focus on Control Strategy,
Design Space
?
Batch-centric Processing Continuous quality control +
Static Processes
Flexible, responsive
manufacturing (e.g. PAT)
++
Batch-based Quality Continuous quality control ++
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(Continuous-like processing)

32. QbD Elements Effective to Eliminate System
Contradictions
 Move to continuous quality monitoring and control
 Design Space (DS) including CQAs (Critical Quality Attributes), Raw
Material Attributes, and CPPs (Critical Process Parameters)
 Ability to divide DS into parts for various steps in the production
process
 Focus on what is critical to quality through a risk-based approach
 Variable processes to react to changing inputs while ensuring
consistent outputs
 Process Analytical Technology to monitor and provide feedback to
support a Control Strategy
 Multivariate statistical approach
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33. Areas of QbD opportunity
 Emphasis on less quality by documentation reliance
 Consider optimal state during development to minimize
system conflicts during production (e.g. phase state, etc.)
 Emphasize QbD extension to the supply chain
 Continuous verification of raw materials and components prior
to entering production stream
 Disposables
 More emphasis on continuous-like processing (or parallel
processing, quasi-continuous, mini-batch)
 PAT standardization, plug-and-play
 TRIZ principles in engineering design
 More robust KM
 Human factoring (training, development, skill-sets, change
management)