Implementing a QbD program to make Process Validation a Lifestyle

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The FDA now defines process validation as “the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality products.” On-going process validation is therefore the most important practical outcome of any QbD program. This sessions helps make the connection between process validation and QbD and why QbD starts in process development and doesn’t end in manufacturing.

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Implementing a QbD program to make Process Validation a Lifestyle

  1. 1. Implementing a QbD Program To Make Process Validation A Lifestyle Rather than an EventJustin O. Neway, Ph.D.Chief Science OfficerAegis Analytical Corporationjneway@aegiscorp.comwww.aegiscorp.com
  2. 2. Overview• The best way to achieve the goals of QbD and process validation is to begin a team collaboration during Process Development and continue for the full product life cycle• Todays manual data access and disconnected analytics (aka spreadsheet madness) are inconsistent with achieving the goals of QbD• Self-service data access and descriptive and investigational analytics are required for successful collaboration between Process Development (PD) Manufacturing (MFG) and Quality (QA)• Significant business, quality assurance and compliance benefits can be achieved from this collaboration by using technology available today 2
  3. 3. FDAs Ideas on Quality by Design (QbD) • The Goals of QbD: – Product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from the performance of test batches –QbD results from: • A combination of prior knowledge and experimental investigation • A cause-and-effect model that links CPPs and CQAs 3
  4. 4. Process Validation and Continuous QualityVerification (CQV) Process validation is defined as the collection and evaluation of data, from the process design stage throughout production, which establishes scientific evidence that a process is capable of consistently delivering quality productsProcess validation and CQV are a life-style, not an event 4
  5. 5. Requirements for Collaboration• Provide self-service, on-demand data access by end users to all the data (discrete, continuous, replicate, event, keyword, text) in a meaningful context to complete investigations in minutes not months• Deliver practical analytics that include descriptive (what happened?) as well as investigational (why did it happen?) capabilities to the PD, MFG and QA team• Include all types of electronic data, as well as paper-based data, to make meaningful analysis possible• Allow non-programmers and non-statisticians to complete tasks quickly and effectively as a collaborative team 5
  6. 6. Context is King• Context is the organization of related elements that enables interpretation• Simple example of information enabled through context – The temperature is 36.5ºC - data – The temperature is rising – some context – The specification limit is 37.5ºC – sufficient context• Other examples of meaningful contexts for data analysis: – Data type context: enables specific types of data analyses – Batch context: enables batch-to-batch comparisons – Process context: enables process-to-process comparisons – Site context: enables site-to-site comparisons – Genealogy context: enables upstream / downstream correlations 6
  7. 7. Genealogy Context: CorrelatingDownstream with Upstream Process Process Process Process Step 1 Step 2 Step 3 Outcome S1 B1 ID S3 B1 ID S3 B1 ID S2 B1 ID S1 B2 ID S3 B2 ID S3 B2 ID S4 B1 ID S1 B3 ID S3 B3 ID S2 B2 ID S4 B2 ID S1 B2 ID S3 B4 ID S1 B4 ID S3 B5 ID S4 B3 ID S2 B3 ID S1 B2 ID S3 B6 ID 7
  8. 8. Paper Record Data Entry Paper Paper Records Paper Records Records 8
  9. 9. Paper Record Data Entry Paper Paper Records Paper Records PRIMR Records 9
  10. 10. Data Access and Contextualization Dynamic Mapping Engine PRIMR 10
  11. 11. Data Access and Contextualization Dynamic Mapping Engine LIMS PRIMR HIST PRIMR 11
  12. 12. Data Access and Contextualization Dynamic Mapping Engine HIST Eng ERP LIMS PRIMR CAPA MES Maint 12
  13. 13. Data Access and Contextualization Analysis Group A small subset of the data universe needed now for specific work. Automatically organized by data type and batch genealogy Dynamic Mapping Engine HIST Eng ERP LIMS PRIMR CAPA MES Maint 13
  14. 14. Data Access and Contextualization Dynamic Mapping Engine HIST Eng ERP LIMS PRIMR CAPA MES Maint 14
  15. 15. Data Access and Contextualization Analysis Group A small subset of the data universe needed now for specific work. Automatically organized by data type and batch genealogy Dynamic Mapping EngineUse with Discoverant analytics or accessexternally via ODBCfor Mobile, Business Objects, Minitab, Crystal, Cognos, Umetrics, JMP, etc. HIST Eng ERP LIMS PRIMR CAPA MES Maint 15
  16. 16. Process Development Analytics Dynamic Mapping Engine Eng ERP LIMS PRIMR HIST CAPA MES Maint 16
  17. 17. Process Development Analytics Dynamic Mapping Engine Eng ERP LIMS PRIMR HIST CAPA MES Maint 17
  18. 18. Process Development Analytics Dynamic Mapping Engine Eng ERP LIMS PRIMR HIST CAPA MES Maint 18
  19. 19. Process Development Analytics First derivative of Dynamic transition Salt Mapping Transition Engine Derived values for trending and specifications Eng ERP LIMS PRIMR HIST CAPA MES Maint 19
  20. 20. Manufacturing Analytics Out of control based on Rule 5 Customize theWestern Electric rules for yourspecific process Dynamic Mapping Engine Key for rules and symbols Eng ERP LIMS PRIMR HIST CAPA MES Maint 20
  21. 21. Manufacturing Analytics CUSUM plot shows when the process changed Dynamic Mapping Engine Profile line to line up the data Drill down for Display several kinds of more detail events by batch Eng ERP LIMS PRIMR HIST CAPA MES Maint 21
  22. 22. Manufacturing Analytics Variability calculated for a Dynamic specific batch Mapping Engine Erroneous data is excluded Region selected Region selected for variability for variabilityfeature extraction determination Eng ERP LIMS PRIMR HIST CAPA MES Maint 22
  23. 23. Practical Aspects of PD Data Access,Aggregation, Analysis and Visibility Raw Materials Process Step 1 Process Step 2 Process Step 3 Process Steps… Final Product PRIMR HIST 23
  24. 24. Practical Aspects of PD Data Access,Aggregation, Analysis and Visibility Raw Materials Process Step 1 Process Step 2 Process Step 3 Process Steps… Final Product PRIMR HIST 24
  25. 25. Practical Aspects of PD Data Access,Aggregation, Analysis and Visibility Raw Materials Process Step 1 Process Step 2 Process Step 3 Process Steps… Final Product PRIMR HIST 25
  26. 26. Practical Aspects of PD Data Access,Aggregation, Analysis and Visibility Raw Materials Process Step 1 Process Step 2 Process Step 3 Process Steps… Final Product PRIMR HIST 26
  27. 27. Practical Aspects of PD Data Access,Aggregation, Analysis and Visibility Chemistry, Manufacturing & Controls Submission Raw Materials Process Step 1 Process Step 2 Process Step 3 Process Steps… Final Product LIMS PRIMR HIST 27
  28. 28. Enabling Platform for Collaboration 28
  29. 29. Enabling Platform for Collaboration 29
  30. 30. Multi-site Collaboration AG Sharing AG AG 30
  31. 31. SummaryA process validation lifestyle is enabled by: – Self-service access to data from multiple disparate sources – Collaboration across different disciplines scales & sites – Retrieval of data from current and previous runs at any scale – Reduction in the time required to CPP / CQA relationships – Working with continuous (online) and discrete data together – Automatically accounting for process splits and recombinations – Sharing of data analysis results and reports in widespread teams – Efficient reporting (e.g. Batch Reports, CMC, APR, PQR) 31
  32. 32. Information From Practical Experience Practical Aspects of On-Demand Access to Critical Process Data for Monitoring and Control of a Biopharmaceutical Manufacturing Process Monika Jungen, Merck Serono, Vevey, CH BioProcess International Conference and Exhibition, RTP, NC, October 15, 2009 The Global Approach to Manufacturing Intelligence at Merck Serono: Action Plans, Challenges Overcome and Benefits Achieved Damien Voisard & Yves Berthouzuz, Merck Serono, Vevey & Geneva, CH Discoverant Annual User Conference, Boulder, CO, October 6, 2009 Links to the videos of these presentation are available on request 32
  33. 33. Thank you Justin O. Neway, Ph.D.Vice President and Chief Science Officer Aegis Analytical Corporation jneway@aegiscorp.com http://www.aegiscorp.com 33

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