EVOLVING TRENDS IN THE USE OF STATISTICS FOR PROCESS VALIDATION IVT 3 RD ANNUAL STATISTICS IN VALIDATION JUNE 20-22, 2017

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1 EVOLVING TRENDS IN THE USE OF STATISTICS FOR PROCESS VALIDATION IVT 3 RD ANNUAL STATISTICS IN VALIDATION JUNE 20-22, 2017 KATHERINE GIACOLETTI PARTNER, SYNOLOSTATS LLC

2 OUTLINE Background How we got to where we are now Stage 1 Statistics in process design Stage 2 Number of batches Statistical analyses of PPQ data Stage 3 What to monitor & how Responding to control chart signals Where are we headed? 2

3 BACKGROUND HOW DID WE GET HERE? 3

4 WHERE ARE WE NOW & HOW DID WE GET HERE? 2011 FDA Guidance Process Validation: General Principles and Practices Expectation for ongoing process understanding throughout the process lifecycle Emphasis on statistics to facilitate understanding ( statistics or statistical appears 15 times in the guidance) Gaps & confusion Process engineers/management: little experience using statistics Statisticians: limited experience in processes & manufacturing part of the business 4

5 WHERE ARE WE NOW & HOW DID WE GET HERE? Since 2011 Intense discussion within companies and at industry meetings regarding how to implement statistics in PPQ & CPV Many questions How does the 2011 PV Guidance tie to QbD, & design space, if at all? How many batches for PPQ? Does PPQ fail if statistical criteria not met? How long to continue enhanced sampling? What to monitor & how often? How to reduce false positives on control charts? etc. 5

6 EVOLVING TRENDS STAGE 1 6

7 STAGE 1: STATISTICS IN PROCESS DESIGN The goal of this stage is to design a process suitable for routine commercial manufacturing that can consistently deliver a product that meets its quality attributes. All attributes and parameters should be evaluated in terms of their roles in the process and impact on the product Emphasis in 2011 PV Guidance is on developing a robust control strategy through process understanding and risk management tools Designing an efficient process with an effective process control approach is dependent on the process knowledge and understanding obtained. Lots of flexibility in Stage 1, emphasis is on a process understanding and risk-management 7

8 STAGE 1: STATISTICS IN PROCESS DESIGN Design of Experiment (DOE) studies can help develop process knowledge by revealing relationships, including multivariate interactions, between the variable inputs (e.g., component characteristics 13 or process parameters) and the resulting outputs (e.g., in-process material, intermediates, or the final product) DOE studies are not required, but many companies see advantages, being used more provide clear evidence for control strategy justification Answer ready when asked How did you establish your operating ranges? can support justification for fewer PPQ batches Shift from strictly compliance-based thinking to focus on patient and business risk SYNOLOSTATS LLC 5/11/2017 8

9 EVOLVING TRENDS STAGE 2 9

10 STAGE 2: HOW MANY BATCHES? Following publication of the 2011 Guidance, many, many papers & talks on statistically based # of PPQ batches 2012, 2014, & 2016 ISPE Discussion Papers (many co-authors) 2013 Pharmaceutical Sicence & Technology Paper (H. Yang) 2016 AAPS PharmSciTech Paper (A. Pazhayattil, et al.) etc. 10

11 STAGE 2: HOW MANY BATCHES? Many different statistical approaches, but similar shortcomings: Infeasible # of batches or very relaxed statistical criteria to keep N small Confusing to plan PPQ campaign and interpret results Statistical approach for some CQAs may not work for others Arbitrary statistical criteria -> high risk to the PPQ campaign Easily misunderstood & misused any statistical calculations across batches must be done carefully (and require >3 batches to be feasible) e.g. Cannot simply combine results across multiple batches to calculate a statistical interval 11

12 What is the purpose of PPQ in the context of the PV lifecycle? A successful PPQ will confirm the process design and demonstrate that the commercial manufacturing process performs as expected. In the lifecycle context, PPQ is not the end of process validation Process variability understanding will increase over the lifecycle of the process it s not meant to be fully understood within PPQ SYNOLOSTATS LLC 5/11/

13 STAGE 2: HOW MANY BATCHES? Remember, PPQ is not meant to be the final estimate of variability & capability, so why so much focus on # of batches? Large N-> are you not ready for PPQ? Small N -> between batch variability estimate unreliable Between-batch variability is affected by factors acting over longer periods than reasonable for a PPQ campaign Seasonal effects, raw material changes, etc. 13

14 STAGE 2: HOW MANY BATCHES? Rationale for # of batches should be consistent with the lifecycle approach, and the goals of the 2011 guidance Risk-based rather than statistical based approach but the # of batches still must be justified (and approach stated in SOPs) Many companies use 2014 ISPE Discussion Paper residual risk approach Other considerations (enough PPQ batches to cover RM lots, different product formats, equipment trains, etc.) Focus should be on risk (to patient, to business), not statistics! SYNOLOSTATS LLC 5/11/

15 STAGE 2: STATISTICAL ANALYSES OF PPQ DATA How will between-batch variability be addressed in PPQ then? Still must assess between-batch variability in PPQ, but trend is toward using Statistical intervals for intra-batch assessment of process performance Risk-based assessment of between batch consistency (graphics, variance components) Assessment at end of PPQ: how confident are you that the next batch will produce quality material? Based on totality of evidence: statistical analyses/metrics, risk-assessments, process knowledge Remember: A successful PPQ will confirm the process design and demonstrate that the commercial manufacturing process performs as expected. A statistical calculation alone should determine neither the success or failure of PPQ 15

16 EVOLVING TRENDS STAGE 3 16

17 STAGE 3: WHAT TO MONITOR & HOW Too many charts!! Concern from many companies about having hundreds (or thousands) of control charts to review Multiple products, multiple sites, etc. Don t monitor every quality attribute, raw material, process parameter, and in-process measurement! 17

18 STAGE 3: WHAT TO MONITOR & HOW How to choose what to monitor? 2011 Guidance: The data collected should include relevant process trends and quality of incoming materials or components, in-process material, and finished products. relevant is the key word Use process knowledge derived from Stages 1 & 2 to determine what to monitor in order to ensure unusual process variability which could increase patient risk will be detected Monitoring plan does not have to be the same for all products or for all measurements from a given product Focus is on risk, not statistics! 18

19 STAGE 3: WHAT TO MONITOR & HOW Designing monitoring program: what types of charts, how often to review, by whom? Guiding principle: Goal = process understanding & continuous improvement, not simply detection of out-ofcontrol signals Monitoring & trending in CPV is not simply a compliance exercise Value of the program decreases as frequency of review decreases Balance time & effort (cost) of more frequent review against risk/benefit of less frequent review 19

20 STAGE 3: WHAT TO MONITOR & HOW Pay attention to business process, not just statistics Must a report be written every time charts are reviewed? Different types of reports (every period less detailed & less frequent fully detailed, e.g.) Who monitors? Statistician should help design the program (choice of chart type, etc.), process expert should review Flexibile, risk-based CPV plan may change over time build in change criteria & process into quality system How to respond to signals SYNOLOSTATS LLC 5/11/

21 STAGE 3: CONTROL CHART SIGNALS From the 2011 FDA Guidance on Process Validation 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 SYNOLOSTATS LLC 5/11/

22 STAGE 3: CONTROL CHART SIGNALS Increasing awareness in the industry that (bio)pharmaceutical process data routinely do not meet assumptions of Shewhart control charts: Independent (successive observations are not related to each other) From a single distribution (single mean and variance) leading to Overly narrow control limits More frequent signals than theory indicates for an in-control process Thus, not all signals indicate a process that is out of control SYNOLOSTATS LLC 5/11/

23 STAGE 3: CONTROL CHART SIGNALS What to do? Transform the data? Turn off all the signals? Manipulating the charts and/or the data make the charts less useful for gaining process knowledge Likewise, using more rules & more sensitive charts (CUSUM, EWMA) to detect smaller shifts ignores the way charts are used in pharmaceutical processes (retrospective review) Many companies are choosing instead to use simpler, easy to interpret charts, but react to signals based on risk SOPs must define the risk-based approach..not all signals are created equally. Magnitude of reaction depends on the severity of the signal (1) (1) Alex Viehmann, FDA/CDER/OPQ, ISPE PV Statistician Forum April 2015 SYNOLOSTATS LLC 5/11/

24 STAGE 3: CONTROL CHART SIGNALS Process Established Short term Operator Adjustment Raw Material Change Different Machine Recognize factors that affect the process Understand whether they affect it randomly Interpretation of charts and signals on charts must recognize the effect of non-randomness that is inherent to the process Long term 24

25 STAGE 3: CONTROL CHART SIGNALS Effect of non-independence, multiple populations Expect to see shift signals due to non random use of process factors Short term limits (used in default Shewhart charts) will reflect within group variability, and will therefore typically be more narrow than long term limits SYNOLOSTATS LLC 5/11/

26 STAGE 3: CONTROL CHART SIGNALS Some special cause variation is expected. That is the state of control. The process is not out of control. When special cause variation triggers an appropriate risk-based response, the resulting visible patterns can help achieve CPV goals. [Scherder, Pharmaceutical Engineering (37) 2017] SYNOLOSTATS LLC 5/11/

27 WHERE ARE WE NOW & WHERE ARE WE HEADED Focus on risk, not just statistics! Use statistics as an integral part of a risk-based approach to the PV lifecycle Use statistics to understand, assess, and monitor/react to variability Mindset shift from compliance to patient & business risk Increased sampling and statistical analysis, more process understanding, more reliable processes Don t think I have to, but I get to use statistical tools to continually improve and provide quality to the patient SYNOLOSTATS LLC 5/11/

28 REFERENCES Determining the Number of Process Performance Qualification Batches Using Statistical Tools Supplement to Topic 1 Stage 2 Process Validation Discussion Paper ISPE Discussion Paper (2016) M. Bryder, et al., Topic 1 Stage 2 Process Validation: Determining and Justifying the Number of Process Performance Qualification Batches (Version 2), ISPE Discussion Paper (2012, updated 2014) ISPE Discussion Paper (2016): H. Yang, How Many Batches Are Needed for Process Validation under the New FDA Guidance, PDA J Pharm Sci Technol January/February :53-62 A. Pazhayattil, et al., Stage 2 Process Performance Qualification (PPQ): a Scientific Approach to Determine the Number of PPQ Batches, AAPS PharmSciTech 2016 Aug; 17(4): T. Scherder, Embrace Special Cause Variation During CPV, Pharmaceutical Engineering, May-June 2017 Vol 37, No 3 Continued Process Verification: An Industry Position Paper with Example Plan, BPOG (2014) US FDA Guidance for Industry, Process Validation: General Principles and Practices (2011) EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use, Annex 15: Qualification and Validation (2015) 28