Data is one of the greatest assets of a company. The integrity of the data is a critical aspect to the design, implementation and usage of any system which stores, processes, or retrieves data. The development and maintenance of a robust data integrity system is therefore imperative.

1. Datahandling in QC
environment
Dimitris Papamatthaiakis
Pharma Life-cycle Consultancy

2. Outline
 Definitions come first..
 Advance of data integrity
 What is GDPR
 Inspection
 Mock inspection
 CSV
 Best CSV practices
 Inspection checklists
 Regulatory reference

3. Datahandling in QC
environment
Definitions come first..

4. Data handlingin
QC
environment
Definitionscomefirst..
Data
Raw Data
Meta Data
Transient
Data
Data
Capture
Recording
Data

5. Data handlingin
QC
environment
Definitionscomefirst..
Data
Integrity
Data
Integrity
Principles
Data
Lifecycle
Data
Transfer
Data
Processing
Data
Governance

6. Data handlingin
QC
environment
Definitionscomefirst..
 Root Cause of Data Integrity*
– Lack of process control
– Poorly developed process
– Inadequate resources and infrastructure
– Concerns of falsification
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0
200
400
600
800
1000
1200
Lack of essential
documentation
Lack of document
control process
Lack of security
controls
Data storage not
contemporaneous
Unclear data
management
process
Failure to assess
SOP against
requirements
Lack of adequate
training
Inadequate system
for data back-up
User roles don't
match job
description
Criteria for
compliance
Data Integrity Violations
*Source: EMA 2nd International Awereness
Session March 2018

7. Data handlingin
QC
environment
Definitionscomefirst..
 Examples of electronic record violations
 Deletion of raw data files which contain failed
results of a batch
 Reprocessing of chromatograms in HPLC/GC
analysis (eg manual integration)
 Intentional modification of the data to release the
batch
 A partial backup of electronic data
 Backup procedures are not validated
 Data Backup policy is not developed by the
company
 Unsecured electronic signatures
 Backdated data

8. Data handlingin
QC
environment
Definitionscomefirst..
 Static & Dynamic Record
– Static is a fixed data document such as a paper
record (eg printed chromatograms).
– Dynamic allows interaction between user or
reviewer and record content
– Printouts include metadata and the static or
dynamic nature of original record. Once printed
the data lose the capability of being reprocessed

9. Data handlingin
QC
environment
Definitionscomefirst..
 Data Review and Approval
Time limit for
data review
Review
against SOP,
specs, ATP
Verify correct
reporting
Ensure
traceability
Verify
electronic
data
Review audit
trail
Review
justifications
Ensure status
of events
Discuss
discrepancies
with analyst
Data-reviewer cannot review the SOP itself
Data-reviewer cannot review the data system

10. This is a free preview copy of the original training “Analytical Quality By Design”
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