Looking for expertise or support on Data Integrity? Contact us today. Recently, the pharmaceutical industry has been challenged with the regulatory requirements to provide complete, consistent and accurate data, throughout all GMP regulated processes. Moreover, during audits the regulatory bodies have observed a level of inconsistency in the application of the predicate rules in GMP processes. This has become a growing concern and has led to a set of new (draft) guidances from different market authorities. Index: Data Integrity – Why / What Data life cycle Core Data Integrity concepts & building blocks Short & mid-term actions enabling a focused road to compliance

1. Data Integrity – Essentials & Solutions
Fréderique Backaert – November 8th 2016

2. What you will learn
• Data Integrity – Why / What
• Data life cycle
• Core Data Integrity concepts & building blocks
• Short & mid-term actions enabling a focused road to compliance
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3. Data Integrity – Why
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Scope & Application
21 CFR Part 11
FDA
2003
“generics scandal”
late 1980s
Data Integrity
Pilot Program
FDA
2010
2007
FDA
computerised systems
in GCP
1997
FDA
21 CFR Part 11
Final Rule
Quality
Impact on Patient
Business
Oct, 2016
CFDA
guidance
(draft)
(draft)
guidance PIC/S
Aug, 2016
Jul, 2016
MHRA
guidance
(draft)
guideline
WHO
Jun, 2016
Apr, 2016
FDA
Data Integrity
guidance (draft)
guidance
MHRA
Mar, 2015
2011
EudraLex
Volume 4
Annex 11
# 483 citations on
Data Integrity topics
Insufficient data security
Poor data storage and archives
No adequate data review processes
Poor knowledge of data streams
Data Integrity part of routine GMP inspectionsFocus on
raw data
Focus on
computerised systems
Focus on
“ALCOA”

4. “The extent to which all data are accurate, complete and consistent throughout
the data life cycle” (MHRA, March 2015)
Applies for both electronic & paper-based documentation streams in GMP
regulated environments.
Data Integrity – What
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validated
reviewed
data/metadata good documentation practices
training
method management
guaranteed throughout
the legal hold

5. “all phases in the life of data” (MHRA, March 2015)
Data life cycle
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Review
Creation Record
Processing
Calculations
Report
Reprocessing
Recalculations
(Meta)Data
Modifications
Retirement Archive Retention Backup
Data owner
User - reviewer
User - author
Administrator
Core concepts:
ALCOA
Audit trail review

6. ALCOA model
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ALCOA
• Acronym created by the FDA as a guide to the expectations concerning source data
A – Attributable
Who performed the action that gathered the data?
Paper Electronic
initials of author no generic login accounts
actions are documented &
dated
metadata unambiguously
linked to data
good documentation
practices
validation for intended use

7. ALCOA model
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• Acronym created by the FDA as a guide to the expectations concerning source data
L – Legible
The data remains available & accessible throughout the
life cycle
Paper Electronic
permanent ink,
single-line cross-outs
maintain human
readability
reason of change old and new values
good documentation
practices
validation for intended use
ALCOA

8. ALCOA model
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• Acronym created by the FDA as a guide to the expectations concerning source data
C – Contemporaneous
The data is recorded as the action takes place
Paper Electronic
chronological
batch record design
automatic saving
controlled printing
date & time stamps cannot
be altered
good documentation
practices
software design
validation for intended use
ALCOA

9. ALCOA model
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• Acronym created by the FDA as a guide to the expectations concerning source data
O – Original
Documentation should be performed on original records
Paper Electronic
no scratches
no copies of electronic
records can be made
good documentation
practices
software design
validation for intended use
ALCOA
Original

10. ALCOA model
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• Acronym created by the FDA as a guide to the expectations concerning source data
A – Accurate
Records should be honest and thorough
Paper Electronic
witness checks
technical controls on
input fields
reason of change all changes are reviewed
good documentation
practices
audit trail review
validation for intended use
ALCOA
Original

11. automatic
or
systematic
Audit trail review
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“metadata which represents a log of performed GMP-critical actions which facilitates a
reconstruction of the performed GMP activities”
forensic
Analysis 1 Analysis 2 Analysis 3
Analysis performance
Method management
User management
Data management
System configuration
frequency
Review 1 Review 3Review 2

12. Audit trail review
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“metadata which represents a log of performed GMP-critical actions which facilitates a
reconstruction of the performed GMP activities”
Analysis performance
Method management
User management
Data management
System configuration
frequency
Data review – Analysis audit trail
Part of batch release process
• actions in line with procedures and/or
with pre-validated conditions
• accuracy, completeness & consistency of (meta)data
• focus on data creation, modification and deletion
Review 1 Review 2 Review 3

13. Audit trail review
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“metadata which represents a log of performed GMP-critical actions which facilitates a
reconstruction of the performed GMP activities”
Analysis performance
Method management
User management
Data management
System configuration
frequency
Analysis 1 Review 1 Analysis 2 Review 2Analysis 3 Review 3 Periodic Review
Periodic review – System audit trail review
Frequency depends on modern QRM
• method creation or method parameter changes
• system configuration changes
• data backup and/or archival
• login of business admin or system admin
• focus on holistic changes to instrument / software

14. The road to compliance
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Data Integrity
Compliance
Quality System
Instrument
Software
Qualification status
Data management
CSV approach
Data Integrity policy
Corporate security
procedure
Measurement
Trending
CAPA handling
Deviation management
Culture
Company tolerance
for common mistakes
Open communication
Management responsibility
Materials
Procedures
Audit trail review
Data review tools
Ability & Motivation
Human
Day-to-day focus on
ALCOA principles
Separated roles
& responsibilities
Deviation
management

15. The road to compliance
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Short-term actions
• Identify business processes with data streams
mapping of all business processes
• DI evaluations with pi’s proprietary DI Quick Scan
assessment of both electronic and paper-based streams in
business processes towards current DI requirements
quick matrix-based, system-by-system
efficient 1:1 relation with regulatory requirements
prioritised according to GMP criticality
Outcome
reveals
non-conformities
&
identifies
globalised CAPA plan
detailed data flows
&
GMP criticality of
all data streams

16. The road to compliance
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Mid-term actions
• Data Integrity remediation
full project support including assessment program, driving
the CAPA plan and deployment of remediation.
• Data Integrity training
setting-up training programs tailored to different focus areas:
production, internal audit, quality assurance, quality control, …
• Data Integrity strategy
implementing a risk-based, lean and effective data integrity
strategy, on par with the latest GMP requirements and
embedded within your corporate quality system.
Outcome
corporate-driven
culture towards DI
Data Integrity
compliance
secured base

17. Conclusions
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• Data Integrity remains to have a direct impact on patient
business and
quality of product & processes.
• Data Integrity, as a KPI, is an indicator for LEAN data flows.
• DI Quick Scan is a swift assessment towards Data Integrity compliance for both
paper & electronic based data streams.
• DI Quick Scan sets the standard for a roadmap towards Data Integrity compliance.

18. About the speaker
Fréderique Backaert
Business Developer
pi
Fréderique Backaert holds a PhD in Organic Chemistry and has been working for pi life sciences consultancy
for more than two years. In the meanwhile, he has been challenged with specific data integrity improvement
programs in the pharmaceutical industry.
These experiences were the fundamentals of pi's risk-based solutions towards arising data integrity questions.
18 | © 2016 pi pi | contact@3-14.com | www.3-14.com

19. Our Data Integrity services include:
Data Integrity services
DI Quick Scan
• Two step process, on both paper and electronic records, including a
quick scan using our proprietary method and tools and in-depth audit
of QC and manufacturing records.
• Thorough audit of your software, analysing whether or not they meet
current data integrity requirements.
• An efficient yet detailed analysis, saving you time and assuring a
minimal disruption of operations.
Data Integrity Remediation
• Full project support, including assessment, driving the CAPA plan
and deployment and implementation of remediation
Data Integrity Strategy
• Design and roll-out of a lean and effective data integrity strategy, on
par with the latest GMP requirements and embedded within your
QMS
Data Integrity Training
• On-site training on Data Integrity for management, operations, QC
and QA staff .
• Focus on data integrity methodology and compliance program, the
importance of data integrity and on creating the right culture to
maintain data integrity.
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20. References
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• 21 CFR Part 11 Electronic records; Electronic Signatures
• Guidance for Industry, Part 11 Electronic records; Electronic Signatures – Scope & Application
• Guidance for Industry Computerized Systems Used in Clinical Investigations
• EudraLex Volume 4 Annex 11 - Computerised systems
• MHRA GMP Data Integrity Definitions and Guidance for Industry March 2015
• Data Integrity and Compliance With cGMP Guidance for Industry
• Guidance on good data and record management practices
• MHRA GxP Data Integrity Definitions and Guidance for Industry
• Good Practices for Data Management and Integrity in Regulated GMP/GDP Environments

21. Our value proposition
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pi is the strategic partner of choice to some of the world’s leading life science companies.
We offer our clients unique expertise and strategic consultancy of the highest quality.
We dedicate ourselves to bringing excellence to the life sciences industry.
We’ve grown by learning how to be better.
Better resourced to focus our faculty of consultants’ singular experience and knowledge.
Better managed to share their depth of understanding.
Better prepared to challenge orthodox thinking.
And better able to redefine accepted best practice.
To accept no other standard than excellence.
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