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.

1. Implementing a QbD Program
To Make Process Validation
A Lifestyle Rather than an Event
Justin O. Neway, Ph.D.
Chief Science Officer
Aegis Analytical Corporation
jneway@aegiscorp.com
www.aegiscorp.com

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
• Today's 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
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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. Process Validation and Continuous Quality
Verification (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 products
Process validation and CQV are a life-style, not an event
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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
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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
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7. Genealogy Context: Correlating
Downstream 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
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8. Paper Record Data Entry
Paper
Paper
Records
Paper
Records
Records
8

9. Paper Record Data Entry
Paper
Paper
Records
Paper
Records
PRIMR
Records
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10. Data Access and Contextualization
Dynamic
Mapping
Engine
PRIMR
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11. Data Access and Contextualization
Dynamic
Mapping
Engine
LIMS PRIMR HIST
PRIMR
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12. Data Access and Contextualization
Dynamic
Mapping
Engine
HIST Eng
ERP LIMS PRIMR CAPA MES
Maint
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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
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14. Data Access and Contextualization
Dynamic
Mapping
Engine
HIST Eng
ERP LIMS PRIMR CAPA MES
Maint
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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
Engine
Use with Discoverant
analytics or access
externally via ODBC
for Mobile, Business
Objects, Minitab,
Crystal, Cognos,
Umetrics, JMP, etc.
HIST Eng
ERP LIMS PRIMR CAPA MES
Maint
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16. Process Development Analytics
Dynamic
Mapping
Engine
Eng
ERP LIMS PRIMR HIST CAPA MES
Maint
16

17. Process Development Analytics
Dynamic
Mapping
Engine
Eng
ERP LIMS PRIMR HIST CAPA MES
Maint
17

18. Process Development Analytics
Dynamic
Mapping
Engine
Eng
ERP LIMS PRIMR HIST CAPA MES
Maint
18

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
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20. Manufacturing Analytics
Out of control
based on Rule 5
Customize the
Western Electric
rules for your
specific process Dynamic
Mapping
Engine
Key for rules
and symbols
Eng
ERP LIMS PRIMR HIST CAPA MES
Maint
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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
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22. Manufacturing Analytics
Variability
calculated for a Dynamic
specific batch
Mapping
Engine Erroneous data
is excluded
Region selected
Region selected
for variability
for variability
feature extraction
determination
Eng
ERP LIMS PRIMR HIST CAPA MES
Maint
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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
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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. 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. 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. 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
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28. Enabling Platform for Collaboration
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29. Enabling Platform for Collaboration
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30. Multi-site Collaboration
AG
Sharing
AG AG
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31. Summary
A 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)
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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
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33. Thank you
Justin O. Neway, Ph.D.
Vice President and Chief Science Officer
Aegis Analytical Corporation
jneway@aegiscorp.com
http://www.aegiscorp.com
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