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

1. An Introduction to QbD (Quality by Design) and Implications for Technical Professionals Timothy D. Blackburn, P.E. (Pfizer, GWU) GWU Advisors: Dr. Thomas Mazzuchi Dr. Shahram Sarkani, P.E. ISPE CASA Annual Technology Show Tuesday, April 5, 2011; RBC Center; Raleigh, North Carolina

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

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.

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

5. The Migliaccio Conjecture . . . The pharmaceutical industry produces six-sigma products with three-sigma 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 . . . 1) The conjecture is true in principle +/- 6s Product Supply 4) Moving to higher S-Curve(s) will require QbD toresolve system contradictions 2) Without designing for quality (DFSS or QbD), production systems hit a sigma limit +/- 3s Production System 3) Prod. Systems follow S-Curve Technological Evolution

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.

9. System Comparison: Traditional vs. QbD

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)

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. Better Quality . . .

12. The flow of QbD

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:

16. A Design Space [11b] 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. A Multidimensional Design Space A design space can be a series of inequality equations that must be satisfied (multiple dimensions difficult to illustrate without mathematics) The following is an example from the A-Mab case study: [22] Here, the CQA (aFuc) is satisfied (i.e. <13%) if the inequality is satisfied

18. 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]

19. 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

20. 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]

21. 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

22. Envisioning the future . . .

23. 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.)

24. 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 True DPMO

25. 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

26. 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

27. 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

28. 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)

29. 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

30. 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

31. Discussion/Questions © Timothy D. Blackburn, P.E. January, 2011 Contact the author for permission

32. References [1] ICH, "Pharmaceutical development Q8," ICHHT, vol. 10, 2005. [2] J. Berridge, "Overview: Quality by Design (QbD)," Engineering Pharmaceutical Innovation, vol. KB-0001, 2005. [3] T. Tyson, "Pharmaceutical Manufacturing Innovation," Journal of Pharmaceutical Innovation, vol. 2, pp. 37-37, 2007. [4] D. Dean, et al., "The Inherent Limitations of Quality by Inspection: Proving the Migliaccio Conjecture," (Unpublished), 2005. [5] D. Dean and F. Bruttin, "A Quantitative Challenge to Quality by Inspection: The Business Case for Change," 2004. [6] S. Arlington, et al., "The metamorphosis of manufacturing," ed: IBM Business Consulting Services, May 2005. [7] R. Benson and J. McCabe, "From good manufacturing practice to good manufacturing performance," Pharmaceutical Engineering, vol. 24, pp. 26-35, 2004. [8] J. Peterson, et al., "Statistics in Pharmaceutical Development and Manufacturing," Journal of Quality Technology, vol. 41, pp. 111–147, 2009. [9] PhRMA, "Pharmaceutical Industry Profile," ed. Washington, DC: Pharmaceutical Research and Manufacturers of America, 2010. [10] V. McCurdy, et al., "Quality by Design using an Integrated Active Pharmaceutical Ingredient - Drug Product Approach to Development," Pharmaceutical Engineering, vol. 30, pp. 12-32, July/August 2010. [11] A. Shanley. (June 2010) Toyota's Meltdown and Lessons for Pharma. Pharmaceutical Manufacturing. 23-28. [11b] J. Coffman, et al., "Controversy in QbD: Implementing design space and control strategy," in DIA, 46th Annual Meeting, Washington, DC, 2010 [11c] D. Mader, "Design for six sigma," Quality progress, vol. 35, pp. 82-86, 2002. [12] K. Kucharavy and R. De Guio, "Application of S-Shaped Curves," in TRIZ-Future Conference 2007: Current Scientific and Industrial Reality, Frankfurt, Germany, 2007 [13] D. Mann and E. Domb, "The 4.5 Sigma Wall," ed: Systematic Innovation, 2007. [14] R. Stratton and D. Mann, "Systematic innovation and the underlying principles behind TRIZ and TOC," Journal of Materials Processing Technology, vol. 139, pp. 120-126, 2003. [15] J. Drennen, "Quality by Design—What Does it Really Mean?," Journal of Pharmaceutical Innovation, vol. 2, pp. 65-66, 2007. [16] G. Migliaccio, "The Impact of Quality by Design on Manufacturing and Quality," in DIA, 46th Annual Meeting, Washington, DC, 2010. [17] G. Gianacakes, "Breaking the 5-Sigma Barrier with Systems Engineering," in INCOSE 13th Annual International Symposium; Engineering Tomorrow's World Today!, 2003, pp. 313-323. [18] N. Lorenzi, et al., "Crossing the implementation chasm: a proposal for bold action," Journal of the American Medical Informatics Association, vol. 15, p. 290, 2008. [19]E. M. Rogers, Diffusion of Innovations, Fifth Edition ed. New York: The Free Press, 2003. • [20] J. K. Towns, "Current Consensus Thinking on QbD," in Quality by Design: From Theory to Practice, 2010. • [21] A. Sood and G. Tellis, "Technological evolution and radical innovation," Journal of Marketing, vol. 69, p. 152, 2005. • [22] G. Tennant, "TRIZ - Table of Contradictions," ed: Mulbury Six Sigma, 2003. • [23] M. Adams, "Quality by Design & Design for Six Sigma: Allies in Pharmaceutical Development," in ISPE Breakfast Seminar, Toronto and Montreal, 2009.

33. References (continued) [24] R. Nosal and T. Schultz, "PQLI definition of criticality," Journal of Pharmaceutical Innovation, vol. 3, pp. 69-78, 2008. [25] J. ReVelle, Manufacturing handbook of best practices: an innovation, productivity, and quality focus: CRC, 2002. [26] CMC, "A-Mab: A Case Study in Bioprocess Development," CMC Biotech Working Group October 30, 2009 2009. [27] Y. Kwak and F. Anbari, "Benefits, obstacles, and future of six sigma approach," Technovation, vol. 26, pp. 708-715, 2006. [28] S. Chowdhury, "Design for Six Sigma," ActionLINE, pp. 16-20, 2003. [29] J. Jiang, et al., "DFX and DFSS: how QFD integrates them," Quality progress, vol. 40, p. 45, 2007. [30] M. L. Berryman. (2002) DFSS and Big Payoffs. Six Sigma Forum Magazine. 23-28. [31] C. Huber and R. Launsby, "Straight talk on DFSS," 2002, pp. 20-25. [32] FDA, "Drug Administration. Guidance for industry PAT: A framework for innovative pharmaceutical development, manufacturing, and quality assurance," FDA, Silver Spring, 2004. [33] T. Garcia, et al., "PQLI key topics-criticality, design space, and control strategy," Journal of Pharmaceutical Innovation, vol. 3, pp. 60-68, 2008. [34] M. M. Nasr, "Pharmaceutical Quality for the 21st Century," in The 2nd Annual QbD Conference in Israel, Jerusalem, 2010. [35] T. Garcia, et al., "PQLI key topics-criticality, design space, and control strategy," Journal of Pharmaceutical Innovation, vol. 3, pp. 60-68, 2008. [36] ICH, "Pharmaceutical Quality System Q10," ICHHT, vol. 4, 2007. [37] P. Wechselberger, et al., "PAT-Method to gather bioprocess parameters in real-time using simple input variables and first principle relationships," Chemical Engineering Science, pp. 1-13, 2010. [38] FDA, "Drug Administration. Guidance for industry PAT: A framework for innovative pharmaceutical development, manufacturing, and quality assurance," FDA, Silver Spring, 2004. [39] A. Rathore and H. Winkle, "Quality by design for biopharmaceuticals," Nat Biotechnol, vol. 27, pp. 26-34, 2009.

34. References (continued) [40] F. Bruttin, et al., "The metamorphosis of manufacturing - From art to science," S. Arlington, et al., Eds., ed: IBM Business Consulting Services, May 2005. [41] J. Rodriguez, "How To Prepare For A Systems-Based Inspection-Understanding FDA's Risk-Based Inspections Approach," Journal of GXP Compliance, vol. 9, p. 14, 2005.

35. Additional material

36. Early QbD Promising Results [16] • 2007 study • Traditional development and manufacturing resulted in 81% of 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 • Based on % of batches right first time • 15 yr + Traditionally Developed Product: 3.33 sigma • First-year QbD Developed Product: 3.96 sigma 36

37. QbD Enablers to Overcome System Contradictions (super-system level) (Continuous-like processing) 37

38. 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 38

39. 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)