Role of Statistics in Pharmaceutical Development Using Quality-by-Design Approach – an FDA Perspective

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 AssessmentCDER/FDA FDA/Industry Statistics WorkshopWashington 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)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. 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. Process Understanding Continuous Improvement Product Knowledge Product Quality Attributes Process Controls Process Parameters Product Specifications Product Design Unit operations, control strategy, etc. Process Design Desired Product Performance Dosage form, stability, formulation, etc. Process Performance Cpk, robustness, etc. 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

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

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

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

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

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