1. FDA/Industry Statistics Workshop Washington D.C. September 27-29, 2006
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. 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. 21st Century Initiatives
Pharmaceutical CGMPs for the 21st Century – a risk-based approach (9/04)
ONDQA White Paper on Pharmaceutical Quality Assessment System (PQAS)

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. 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

6. Product Knowledge Continuous Improvement Quality by Design
Process Understanding
Product Knowledge
Product Specifications
Product Quality Attributes
Process Controls
Process Parameters
Desired Product
Performance
Process Design
Unit operations, control
strategy, etc.
Product Design
Dosage form, stability,
formulation, etc.
Cpk, robustness, etc.
Process Performance
Quality by
Design
FDA’s view on QbD, Moheb Nasr, 2006

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. 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. Pharmaceutical Development & Product Lifecycle
Product Design & Development
Process Design & Development
Manufacturing Development
Continuous Improvement
Product Approval
Candidate Selection

10. Statistical Process Control
Pharmaceutical Development & Product Lifecycle
Statistical Tool
Product Design & Development:
Initial Scoping
Product Characterization
Product Optimization
Design of
Experiments
(DOE)
Process Design & Development: Initial Scoping
Process Characterization
Process Optimization
Process Robustness
Model Building
And Evaluation
Statistical Process Control
Manufacturing Development and Continuous Improvement:
Develop Control Systems
Scale-up Prediction
Tracking and trending

11. Process Terminology Critical Quality Attributes Process Step
Design Space
Process Step
Input Materials
Output Materials
(Product or Intermediate)
Measured
Parameters
or Attributes
Control Model
Process Measurements
and Controls
Input
Process Parameters

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. 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. DOE Methodology (1) Choose experimental design
(e.g., full factorial, d-optimal)
(2) Conduct randomized experiments
Experiment
Factor A
Factor B
Factor C 1 + - 2 3 4 A B C
(3) Analyze data
(4) Create multidimensional
surface model
(for optimization or control)

15. Model Building & Evaluation - Examples
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

16. Model Building & Evaluation - Chemometrics
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

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 Specification Limit
Upper Control Limit 3s
Target
Lower Control Limit
Lower Specification Limit
Special cause variation?

18. Process Capability Index (Cpk)

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. 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. 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. 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