1. MBSW 2012 Midwest Biopharmaceutical Statistics Workshop May 21-23, 2012 Presenter: Krista Witkowski Co-author: Julia O’Neill Merck & Co., Inc. Capability Assessments and Process Validation Stage 3 Implementation: 1.33 and Beyond

2. 2 Abstract This talk will discuss considerations for practitioners in pharmaceutical manufacturing as they implement the new FDA guidance for process validation. We will focus on Stage 3 - ongoing monitoring, or continued process verification - and how process capability is established, evaluated, and monitored. Examples on overcoming obstacles to implementation will be discussed, and the use of statistical thinking in our implementation strategy is highlighted.

3. 3 Requirements of FDA Validation Guidance FDA Guidance for Industry: Process Validation: General Principles and Practices, published January 2011 distinguishes three stages of validation: –Stage 1 – Process Design: The commercial manufacturing process is defined during this stage based on knowledge gained through development and scale-up activities. –Stage 2 – Process Qualification: During this stage, the process design is evaluated to determine if the process is capable of reproducible commercial manufacturing. –Stage 3 – Continued Process Verification: Ongoing assurance is gained during routine production that the process remains in a state of control. Further states that manufacturers should understand the sources of variation –Detect the presence and degree of variation –Understand the impact of variation on the process and ultimately on product attributes –Control the variation in a manner commensurate with the risk it represents to the process and product

4. 4 Stage 3: Continued Process Verification Stage 1 Stage 2 Stage 3 Process Validation

5. 5 Stage 3: Continued Process Verification Develop Monitoring Reports Assessing the data on a frequent basis (e.g., monthly, quarterly) Make any adjustments to continually assure the process remains in a state of control. Update Control Strategy document if needed Develop Monitoring Plan from Control Strategy Document. Continually monitor critical areas of the process Goal=To continually assure that the process remains in a state of control (the validated state) during commercial manufacture.

6. 6 Understanding Variation for Pharmaceutical Processes Pharmaceutical Processes –Autocorrelation –Specifications based on process history –Non-normal distributions common (e.g., lognormal) SPC Assumptions –Independent results –Specifications based on customer needs –Normally distributed results Issue: Statistical Process Control (SPC) procedures are generally designed based on assumptions not typically met by pharmaceutical processes:

7. 7 Issue 1: Autocorrelation Independent results Result vs. Previous Result – correlation not significant Autocorrelated results Result vs. Previous Result – significant correlation =.35

8. 8 One Cause for Autocorrelation Introduction of New raw material lot Production lots Growth PropagationPurification Production (Weeks) Introduction of New raw material lot A new raw material lot introduced late in the production cycle has little opportunity to impact a product lot; however, a new raw material lot introduced early in the production cycle has a much greater opportunity to impact a product lot. This creates gradual trends (autocorrelation), rather than abrupt shifts, in product properties.

9. 9 One Solution: Use long-term sigma Independent results: short-term and long-term limits are nearly equal. Autocorrelated results: short-term limits are narrower than long-term limits. Long-term limits are more representative of process capability. Long-term Short-term

10. 10 Example 2: Inherent mean shifts Mean shifts may be inherent – due to campaign effects, raw material changes, slight changes in processing conditions (e.g., seasonal effects). Results with mean shifts: short-term limits are narrower than long-term limits. Long-term limits are more representative of process capability. Short-term Long-term

11. 11 Distribution of variable A reflecting initial sources of variability, µ=20, σ=2 Distribution of Variable A, with additional source of variability, µ=21.5, σ=2 Distribution of Variable A, with additional source of variability, µ=19, σ=2.5 Distribution reflecting all sources of variability Understanding sources of variability Do not set limits too early, before all sources of variability are captured. Early limits (n=30) Final limits (n=90)

12. 12 Statistical Thinking Strategy: for Autocorrelation Standard Statistical Process Control (SPC) chart assumptions: –Observations are statistically independent – very important! –Observations are Normally distributed – much less important. –Limits are representative of expected performance. Autocorrelation can have profound effects on the performance of SPC charts. Considerations for control chart design: –Quickly signal real changes in results. –Reduce false alarms. –Make the chart easy to interpret – present results in original scale, and limits with a physical meaning. Recommendation; –Set limits using the overall standard deviation based on a “long” stable period. –Bisgaard and Kulahci provide an elegant justification.

13. 13 Issue 2: Establishing Process Capability Two challenges: –Fundamental questions for pharmaceutical processes: Are long-term shifts (for example, from raw material trends) “extraneous” sources of instability? Or are they known and predictable special causes inherent to pharmaceutical process behavior? –Specifications may be set based on process consistency, not customer requirements.

14. 14 Three Approaches to Capability Strategy higher is better Business Requirements 6 * long-term Sigma “Business” = Specification Spread 6 * long-term Sigma “Quality” = Specification Spread 6 * short-term Sigma Short-Term = Often underestimates total process variation

15. 15 Basics of Capability Calculations Well Off-target / Too Much Variation Relatively Close to Target / Moderate Variation Very Little Deviation From Target LSLUSL Cpk < 1 LSLUSL Cpk = 1 LSLUSL Cpk > 1 The mean and standard deviation are estimated from the centerline and control limits of the control charts, where three sigma is half the width of (UCL-LCL).

16. 16 Short term vs Long term Grp 1 Grp 2 Grp 3 Grp 4 Grp 5 Long Term Study Short Term Studies

17. 17 Example 2: Short term variability < Long term C indices underestimate total process variation when autocorrelation is present (when “within subgroup” variation is low compared to overall). Use the P-indices to provide a realistic assessment of long-term performance. For independent (not autocorrelated) processes, the P-indices and C-indices will be nearly equal. Short-term Long-term Cpk = 1.28 Ppk = 0.81

18. 18 Example 2: Short term variability < Long term C indices underestimate total process variation when autocorrelation is present (when “within subgroup” variation is low compared to overall). Use the P-indices to provide a realistic assessment of long-term performance. For independent (not autocorrelated) processes, the P-indices and C-indices will be nearly equal. Short-term Long-term Cpk = 1.28 Ppk = 0.81 Long-term Short-term One-sided: USL = 1

19. 19 Risk Strategy: Ppk Comparison of CQA’s Process Robustness & Simplification Opportunities (<1.33) Capable & Stable Process (≥1.33) Ppk for 27 Critical Quality Attributes of a family of pharmaceutical products. Each bar represents the estimated Ppk for a single CQA. The bars are ordered from lowest Ppk (greatest risk) to highest. Note: Ppk is long-term capability, but takes into account centering of the process within specifications. In cases when there is a very large range of values for Ppk, a log scale can make this more read-able, while still maintaining the “red, yellow, green” risk categories Frequency of monitoring report guided by risk strategy

20. 20 Other Choices in Capability Indicators Gather Data Process Characterization Summary Statistics X bar (Mean)  (Std. Dev.) Indicators C p C pk P p P pk Z Score Z UPPER Z LOWER Convert to DPM DPM(Upper) + DPM(Lower) = DPM (Total) Calculate using specifications and process data Calculate Proportion Defects OR Calculate Process Z Score Use Z Table or Minitab

21. 21 Translating Pass/Fail to Ppk - type Index Non-normal or pass/fail data: Use a "z-score" approach Calculate the z-score using normal distribution theory –Proportion good  z-score –Translate z-score to a “Ppk-type" scale: divide by 3. Does not account for sample size, so results should be viewed in light of the amount of information you have Example: –If 99% is "good“ (“within spec”): z-score is 2.33, Ppk = 2.33/3 = 0.78 3*Ppk = z-score Ppk = z-score / 3 USL- LSL 6  C p for a “6 sigma process”: 12  6  = 2 C p ==

22. 22 Statistical Background on Capability Capability index assesses whether a process is capable of meeting customer requirements. Capability: “the natural or undisturbed performance after extraneous influences are eliminated” –from the Western Electric Company Statistical Quality Control Handbook (1956) “Cpk can be calculated when the process is stable. Otherwise, for processes with known and predictable special causes and output meeting specifications Ppk should be used.” –from the AIAG PPAP Manual (2006) Most important: PLOT THE DATA ON A CONTROL CHART. –Exact value of capability index is secondary.

23. 23 Issue 3: LogNormally Distributed Results Normal ResultsLogNormal Results Error is proportional to measurement. Characterized by constant Relative Standard Deviation (RSD) Results are not symmetric within limits. Error does not depend on measurement. Characterized by constant Standard Deviation. Results are symmetric within limits. Has little impact if range of results is less than 10X. Easily corrected by analyzing results on the log scale.

24. 24 Solution: Log Transform Results LogNormal Results Log (LogNormal Results) Log transform makes error constant and results symmetric within limits. Same data on different scale

25. 25 References Bisgaard, S., Kulahci, M.. (2005) Quality Quandaries: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering 17: 481-489. AIAG. “Definition of Process Measures.” Statistical Process Control. AIAG, 1995. pp 80-81. 2 nd Printing. The Black Belt Memory Jogger TM. (2002) GOAL/QPC Six Sigma Academy. First edition. p. 96.