Process Capability: Practical Challenges to Implementation

Transcription

1 Process Capability: Practical Challenges to Implementation [in pharmaceutical manufacturing] 33rd Quality & Productivity Research Conference Tempe, AZ June 16, 2016 Julia O Neill julia.oneill@tunnellconsulting.com

2 Key Points Motivation modernize quality control for medicines» FDA drive» Customer and business need Challenges making metrics meaningful» Specifications versus true requirements» Control limits for real manufacturing processes Future do something!» Get started and improve as we go» Maintain some flexibility in these early days 2

3 Modernize quality control for manufacturing medicines MOTIVATION 3

4 Why Apply Statistics to Pharmaceutical Manufacturing? Antibiotic Meningitis vaccine Statin Beta blocker Anticoagulant 4

5 Disruptions in Drug Manufacturing In 2012, 66% of disruptions in drug manufacturing were the result of» efforts to address product-specific quality failures, or» broader efforts to remediate or improve an unsafe manufacturing facility. From FDA s Strategic Plan for Preventing and Mitigating Drug Shortages, 5

6 CDC Vaccine Shortages and Delays 6

7 Process Capability Impact of State of Control Continued Process Verification (CPV):» Ongoing assurance is gained during routine production that the process remains in a state of control. shortages of drugs and biologics pose a significant public health threat, delaying, and in some cases even denying, critically needed care for patients. Taking action to reduce drug shortages remains a top priority for FDA. The Agency has found that the majority of drug shortages stem from quality concerns FDA Draft Guidance for Industry: Request for Quality Metrics,

8 Reliability and Quality Metrics Benefits of FDA Quality Metrics initiative» Common language to gauge progress» Potential to reduce shortages» Path to achieve regulatory flexibility and reduced postmarket change control burden» Risk-based inspection required under FDASIA FDA Vision for the 21 st Century A maximally efficient, agile, flexible pharmaceutical manufacturing sector that reliably produces high quality drugs without extensive regulatory oversight FDASIA Title VII:» 705 requires FDA to do risk based inspection» 706 allows FDA to collect information that would have been available on inspection in advance or in lieu of an inspection Credit: Russell Wesdyk, CDER/OSP, Quality Metrics update, January

9 FDA Public Meeting August 24, 2015 Janet Woodcock s Opening Remarks (excerpts): ~13,000 sites globally surveillance of the inventories Goal: Quality Revolution -Reproducible production at multiple sites -Commitment of employees and leadership to consistent quality -Drive toward improving quality management» Manufacturers have a heavy responsibility for public health» FDA has responsibility to ensure medicines are fit for purpose They are what they are supposed to be They are not contaminated» Majority of FDA effort has been inspections Field/district offices were responsible for inspections Today medicines are made all over the world Dosage forms are increasingly complex» FDA is engaged in very broad effort to modernize 9

10 PLCM: 3 stages of validation Intent of Process Validation (PV) The collection and evaluation of data, from the process design stage through commercial production, which establishes scientific evidence that a process is capable of consistently delivering quality product. Capable process Quality product 10

11 PLCM: 3 stages of validation Excerpts from the January 2011 PV Guidance Basic principle of quality assurance» a drug should be produced that is fit for its intended use Continued Process Verification:» Ongoing assurance is gained during routine production that the process remains in a state of control. 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. FDA (2011) Process Validation: General Principles and Practices guidance for industry. 11

12 PLCM: 3 stages of validation Emphasis on Statistics 2011 guidance mentions statistic 15 times in 22 pages. FDA recognizes the importance of statistical process control as a tool in understanding and managing variability in both product and processing We recommend that a statistician or person with adequate training in statistical process control techniques develop the data collection plan and statistical methods and procedures used in measuring and evaluation process stability and process capability. Procedures should guard against overreaction to individual events as well as against failure to detect unintended process variability. FDA (2015), Request for Quality Metrics draft guidance for industry. FDA (2011) Process Validation: General Principles and Practices guidance for industry. 12

13 Data versus Metrics The FDA is requesting DATA, NOT METRICS The FDA plans to calculate the metrics for each product and establishment, where applicable:» Lot acceptance rate = 1-x (x=the number of specification-related rejected lots in a timeframe divided by the number of lots attempted by the same establishment in the same timeframe)» Product Quality Complaint Rate = the number of product quality complaints received for the product divided by the total number of lots of the product released in the same timeframe» Invalidated Out-of-Specification (OOS) rate = the number of OOS test results for the finished product invalidated by the establishment divided by the total number of OOS test results divided by the total number of tests performed by the establishment in the same timeframe» Annual Product Review (APR) or Product Quality Review (PQR) on time rate = the number of APRs or PQRs completed within 30 days of annual due date at the establishment divided by the number of products produced by the establishment 13

14 Optional Metrics Related to Quality Culture and Process Capability / Performance Senior Management Engagement:» Review and approval of APR or PQR by (1) head of Quality Unit, (2) head of operations, (3) both, (4) neither? CAPA Effectiveness:» What percentage of your corrective actions involved re-training of personnel (i.e. root cause of the deviation was lack of adequate training) Process Capability/ Performance:» Did the establishment calculate a process capability or performance index for each critical quality attribute (CQA) as part of that product s APR or PQR?» Does the establishment s management have a policy of requiring a corrective action or preventive action (CAPA) at some lower process capability or performance index?» If yes to the above question what is the process capability or performance index that triggers a CAPA? 14

15 Making metrics meaningful CHALLENGES 15

16 A notable comment from the audience at FDA Public Meeting August 24, 2015 Rejected lots don t impact the patient. 16

17 Cpk and Ppk Compare Process Variation to Specifications Process Capability Demands that we set meaningful specifications and meaningful estimates of variation! 17

18 ICH Q6B: Patient Needs Product Specifications Specifications Excerpts from ICH Q6B on Specifications:» List of tests, references to analytical procedures, and acceptance criteria.» Conform to be considered acceptable for its intended use.» One part of a total control strategy.» Focus on characteristics useful in ensuring safety and efficacy.» Should be based on data for lots used in pre-clinical and clinical studies. Acceptable for its intended use ICH Q6B: Specifications: Test Procedures and Acceptance Criteria for Biotechnological/Biological Products,

19 Specifications Specifications in Other Industries American Society for Quality (ASQ) Chemical and Process Industries Division (CPID) (1999):» Agreed upon, documented requirements between customer and supplier.» Customer requirements should be the primary basis for specifications.» Specifications based entirely on customer requirements should be changed only when customer requirements change. American Society for Quality Chemical and Process Industries Division Chemical Interest Committee (ASQ CPID CIC), Quality Assurance for the Chemical and Process Industries: A Manual of Good Practices, 2nd edition, Milwaukee (WI): ASQ Quality Press;

20 Specifications ASQ CPID: Product Specifications In preferred order: Required property values if measured without measurement variability Ideal Customer requirements, determined by technically sound procedures Agreed to by the supplier and customer [regulator] Supplier s demonstrated process performance Pharma If product has not been satisfactory, what the customer is willing to accept Temporary specifications for new products or when insufficient data available American Society for Quality Chemical and Process Industries Division Chemical Interest Committee (1999), Quality Assurance for the Chemical and Process Industries: A Manual of Good Practices, 2nd edition. 20

21 Specifications Challenge 1: Determining True Requirements 1 st challenge: testing safe efficacious range in human patients Efficacy limit Acceptable for its intended use Safety limit Dose 21

22 Specifications Specifications (ideally) ~ Safety & Efficacy Limits on CQAs would insure product is acceptable for intended use Product Attribute Upper Spec Limit Lower Spec Limit Safety limit Efficacy limit Dose 22

23 Specifications Challenge 2: Determining True Requirements 2 nd challenge (for large molecules): variability in testing Product Attribute Safety limit Efficacy limit Dose 23

24 Specifications Challenge 3: Determining True Requirements 3rd challenge (for large molecules): characterizing product 24

25 Specifications Challenge 3: Determining True Requirements HPV Virus-Like Particle Method-Structure Link Abraham D, Establishing Comparability with Process and Manufacturing Changes for Recombinant Vaccine: A case study of HPV Vaccine. WCBP,

26 What We Can Do: Specifications = Consistency with Clinical Lots Specifications For biologics, "the product is the process." Manufacturers ensure product consistency, quality, and purity by ensuring that the manufacturing process remains substantially the same over time. Specifications set limits on the range of CQA results to be consistent with clinical lots. 26

27 Specifications Tolerance Intervals Support conclusions about the performance of a relatively large number of future lots, based on the data from a random sample from the distribution of interest. The exact number of future lots of interest is unknown. The interval will contain a specified proportion of future lots. 95/99 tolerance interval:» 95% = Confidence» 99% = Coverage; contains 99% of future lots Meeker W, Hahn G, Escobar L. Statistical Intervals: A Guide for Practitioners, 2 nd edition. New York(NY): Wiley;

28 Specifications Example Residual Host Cell DNA Clinical lots = asterisks» Consistent with other lots With 40 results, control limits and tolerance interval limits are close 28

29 Specifications for Accelerated Approvals and Orphan Drugs Specifications Major challenge: very limited data available» Very few clinical lots and full-scale development or commercial lots» Future production (especially for orphan drugs) may be very low volume perhaps only one lot per year in routine production. Some strategies that may work:» Develop provisional specifications and control strategy» Establish Continued Process Verification as a seamless continuation of Process Performance Qualification» Validation stages 1, 2, and 3 may run in parallel when the drivers for acceleration are high enough. 29

30 Specifications Orphan Drug Residual Host Cell DNA Two clinical lots = asterisks» Consistent with other lots Two manufacturing suites» Very consistent results 30

31 Specifications Orphan Drug Provisional Specifications Clinical lots = asterisks» Consistent with other lots With only 11 results, tolerance interval is broader than control limits, but will approach the control limits if updated with ongoing results» LSL = Lower Spec Limit = 0 (negative values not meaningful)» USL = Upper Spec Limit = 95/99 Upper Tolerance Interval limit Control risk by actively monitoring through product lifecycle 31

32 Specifications Specifications for Pharmaceuticals In preferred order: Required property values if measured without measurement variability Ideal Customer requirements, determined by technically sound procedures Agreed to by the supplier and customer [regulator] Supplier s demonstrated process performance Pharma If product has not been satisfactory, what the customer is willing to accept Temporary specifications for new products or when insufficient data available The challenges of determining true patient requirements, and translating those to CQAs, limit our options for setting specifications. American Society for Quality Chemical and Process Industries Division Chemical Interest Committee (1999), Quality Assurance for the Chemical and Process Industries: A Manual of Good Practices, 2nd edition. 32

33 Process Capability Control Limits and Specification Limits State of Control Acceptable for Intended Use Specification Limits = What the Patient Needs Control Limits = What the Process Produces 33

34 Process Capability SPC for Ceramics Manufacturing Campaign effect engineer despairingly declared that the statistical process control methods he was taught as part of a Six Sigma program were worthless. Steady process» exhibiting only a small and completely tolerable amount of variation» pronounced out of control all the time Bisgaard S, Kulahci M Quality Quandaries*: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering, 2005;17:

35 Process Capability Process Design Autocorrelation lot 1 lot 1 lot 1 lot 1 lot 1 lot 1 lot 1 lot 1 lot 1 critical raw material lot a critical raw material lot b chromatography resin lot x chromatography resin lot y chromatography resin lot z The common-cause model explains the variation and can include:» Stable systems that vary about a fixed mean» In which successive deviations are dependent» Can be represented by a fixed common-cause model 35

36 Process Capability Monitoring autocorrelated results Stationary autocorrelated process data Autoregressive Moving Average (ARMA) time series model. Predictable variation Appropriately inflate the control limits: 36 Bisgaard S, Kulahci M Quality Quandaries*: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering, 2005;17:

37 Process Capability Monitoring autocorrelated results Control limits:» Based on process variation from the time-series model.» ~ 2.4 times residual variability.» Close to the overall long-term sigma limits. Bisgaard S, Kulahci M Quality Quandaries*: The Effect of Autocorrelation on Statistical Process Control Procedures. Quality Engineering, 2005;17:

38 Do something! FUTURE 38

39 Process Capability Ppk Assessment One Product Ppk provides a dimensionless index to compare across products, attributes, manufacturing sites, manufacturers, etc. CQA Ppk upper 95 % total outside of spec Ppk Ppk lower 95 Lower Spec Upper Spec Mean Stdev N

40 Process Capability Cpk or Ppk can Forecast OOS Rates P pk USL X X LSL Min, 3 3 Expected % P pk of population OOS >1.33 < <0.33 >

41 Process Capability Practices for Interpreting Capability Cpk or Ppk Interpretation CPV action > 1.33 Meets specifications with a very high level of consistency Routinely meets specifications but less consistently Cannot be expected to routinely meet specifications. Minimal monitoring needed; focus on higherrisk attributes or parameters. Monitoring may be worthwhile to identify shifts and trends early. Monitor frequently and invest in variability reduction. Process Capability for attributes with specifications based on consistency likely to 1.0 at best. 41

42 Summary of Key Points Motivation modernize quality control for medicines» FDA drive» Customer and business need Challenges making metrics meaningful» Specifications versus true requirements» Control limits for real manufacturing processes Future do something!» Get started and improve as we go» Maintain some flexibility in these early days 42

43 Contact information Julia O Neill Tunnell Consulting 900 East Eighth Avenue, Suite 106 King of Prussia, PA julia.oneill@tunnellconsulting.com (215) linkedin.com/in/juliaconeill 43