1. Role of Statistics in
Pharmaceutical Development
Using Quality-by-Design
Approach – an FDA Perspective
Chi-wan Chen, Ph.D.
Christine Moore, Ph.D.
Office of New Drug Quality Assessment
CDER/FDA
FDA/Industry Statistics Workshop
Washington D.C.
September 27-29, 2006
2. 2
Outline
FDA initiatives for quality
Pharmaceutical CGMPs for the 21st Century
ONDQA’s PQAS
The desired state
Quality by design (QbD) and design space (ICH Q8)
Application of statistical tools in QbD
Design of experiments
Model building & evaluation
Statistical process control
FDA CMC Pilot Program
Concluding remarks
3. 3
21st Century Initiatives
Pharmaceutical CGMPs for the 21st
Century – a risk-based approach (9/04)
http://www.fda.gov/cder/gmp/gmp2004/GMP_
finalreport2004.htm
ONDQA White Paper on Pharmaceutical
Quality Assessment System (PQAS)
http://www.fda.gov/cder/gmp/gmp2004/ondc_
reorg.htm
4. 4
The Desired State
(Janet Woodcock, October 2005)
A maximally efficient, agile,
flexible pharmaceutical
manufacturing sector that
reliably produces high-quality
drug products without
extensive regulatory oversight
A mutual goal of industry,
society, and regulator
5. 5
FDA’s Initiative on Quality by Design
In a Quality-by-Design system:
The product is designed to meet patient
requirements
The process is designed to consistently meet product
critical quality attributes
The impact of formulation components and process
parameters on product quality is understood
Critical sources of process variability are identified
and controlled
The process is continually monitored and updated to
assure consistent quality over time
7. 7
Design Space (ICH Q8)
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
Working within the design space is not considered
as a change. Movement out of the design space is
considered to be a change and would normally
initiate a regulatory post-approval change process.
Design space is proposed by the applicant and is
subject to regulatory assessment and approval
8. 8
Current vs. QbD Approach to
Pharmaceutical Development
Current Approach QbD Approach
Quality assured by testing and
inspection
Quality built into product & process
by design, based on scientific
understanding
Data intensive submission – disjointed
information without “big picture”
Knowledge rich submission – showing
product knowledge & process
understanding
Specifications based on batch history Specifications based on product
performance requirements
“Frozen process,” discouraging
changes
Flexible process within design space,
allowing continuous improvement
Focus on reproducibility – often
avoiding or ignoring variation
Focus on robustness – understanding
and controlling variation
9. 9
Pharmaceutical Development &
Product Lifecycle
Candidate
Selection
Product Design & Development
Process Design & Development
Manufacturing Development
Product
Approval
Continuous Improvement
10. Design of
Experiments
(DOE)
Model Building
And Evaluation
Process Design & Development:
Initial Scoping
Process Characterization
Process Optimization
Process Robustness
Statistical Tool
Product Design & Development:
Initial Scoping
Product Characterization
Product Optimization
Manufacturing Development
and Continuous Improvement:
Develop Control Systems
Scale-up Prediction
Tracking and trending
Statistical
Process Control
Pharmaceutical Development
& Product Lifecycle
11. 11
Process Terminology
Process Step
Input Materials Output Materials
(Product or Intermediate)
Input
Process
Parameters
Measured
Parameters
or Attributes
Control Model
Design
Space
Critical Quality Attributes
Process
Measurements
and Controls
12. 12
Design Space Determination
First-principles approach
combination of experimental data and
mechanistic knowledge of chemistry, physics, and
engineering to model and predict performance
Statistically designed experiments (DOEs)
efficient method for determining impact of
multiple parameters and their interactions
Scale-up correlation
a semi-empirical approach to translate operating
conditions between different scales or pieces of
equipment
13. 13
Design of Experiments (DOE)
Structured, organized method for determining
the relationship between factors affecting a
process and the response of that process
Application of DOEs:
Scope out initial formulation or process design
Optimize product or process
Determine design space, including multivariate
relationships
14. 14
DOE Methodology
(1) Choose experimental design
(e.g., full factorial, d-optimal)
(2) Conduct randomized
experiments
(4) Create multidimensional
surface model
(for optimization or control)
(3) Analyze data
Experiment Factor A Factor B Factor C
1 + - -
2 - + -
3 + + +
4 + - +
A
B
C
www.minitab.com
15. 15
Models for process development
Kinetic models – rates of reaction or degradation
Transport models – movement and mixing of mass or heat
Models for manufacturing development
Computational fluid dynamics
Scale-up correlations
Models for process monitoring or control
Chemometric models
Control models
All models require verification through statistical
analysis
Model Building & Evaluation -
Examples
16. 16
Chemometrics is the science of relating
measurements made on a chemical system or
process to the state of the system via application
of mathematical or statistical methods (ICS definition)
Aspects of chemometric analysis:
Empirical method
Relates multivariate data to single or multiple responses
Utilizes multiple linear regressions
Applicable to any multivariate data:
Spectroscopic data
Manufacturing data
Model Building & Evaluation -
Chemometrics
17. 17
Statistical Process Control -
Definitions
Statistical process control (SPC) is the application
of statistical methods to identify and control the
special cause of variation in a process.
Common cause variation – random fluctuation of response
caused by unknown factors
Special cause variation – non-random variation caused by
a specific factor
Upper Control Limit
Lower Control Limit
Target
Upper Specification Limit
Lower Specification Limit
Special cause variation?
3s
18. *Percent out of specification beyond the high risk specification limit.
σ
3
)
SL
X
(
min
Cpk
2.28%
2s
0.7
15.9%
1s
0.33
0.135%
3s
1
0.003%
4s
1.33
0
5s
1.7
0
6s
2
Expected Avg. OOS%*
|X - SL|
Cpk
2.28%
2s
0.7
15.9%
1s
0.33
0.135%
3s
1
0.003%
4s
1.33
0
5s
1.7
0
6s
2
Expected Avg. OOS%*
|X - SL|
Cpk
Industry Practice is to
consider processes with
Cpk below 1.33 as “not
capable” of meeting
specifications.
Cpk = 1.33 Cpk = 0.33
Process Capability Index (Cpk)
19. 19
Quality by Design & Statistics
Statistical analysis has multiple roles in
the Quality by Design approach
Statistically designed experiments (DOEs)
Model building & evaluation
Statistical process control
Sampling plans (not discussed here)
20. 20
CMC Pilot Program
Objectives: to provide an opportunity for
participating firms to submit CMC information based on QbD
FDA to implement Q8, Q9, PAT, PQAS
Timeframe: began in fall 2005; to end in spring 2008
Goal: 12 original or supplemental NDAs
Status: 1 approved; 3 under review; 7 to be submitted
Submission criteria
More relevant scientific information demonstrating use of QbD
approach, product knowledge and process understanding, risk
assessment, control strategy
21. 21
CMC Pilot - Application of QbD
All pilot NDAs to date contained some elements of
QbD, including use of appropriate statistical tools
DOEs for formulation or process optimization (i.e.,
determining target conditions)
DOEs for determining ranges of design space
Multivariate chemometric analysis for in-line/at-line
measurement using such technology as near-infrared
Statistical data presentation and usefulness
Concise summary data acceptable for submission and
review
Generally used by reviewers to understand how
optimization or design space was determined
22. 22
Concluding Remarks
Successful implementation of QbD will require
multi-disciplinary and multi-functional teams
Development, manufacturing, quality personnel
Engineers, analysts, chemists, industrial
pharmacists & statisticians working together
FDA’s CMC Pilot Program provides an
opportunity for applicants to share their QbD
approaches and associated statistical tools
FDA looks forward to working with industry to
facilitate the implementation of QbD