Data Integrity : A Basic Concept of data recording and analysis

Quick understanding regarding integrity of data compiling in pharmaceutical industry.

1. Md. Mosaruf Hossan
Deputy Production Manager

2. •Data
•Types of Data
•Data Integrity
•Why Data Integrity (DI)?
•Common Data Integrity Issues
•Consequence of DI Failure
•Factors Minimize the Risk of DI
•ALCOA & ALCOA Plus
•Why ALCOA & ALCOA Plus are necessary?

3. Data
Facts,FigureandStatisticscollectedtogetherforreferenceandanalysis
Anexampleofdataisanemail.(computing)
Arepresentationoffactsorideasinaformalizedmannercapableofbeing
communicatedormanipulatedbysomeprocess
TypeofData
Rawdata
Metadata
information,especiallyfactsornumbers,collected
to beexaminedandconsideredand used
tohelpdecision-making

4. Raw Data
Raw data, also known as primary data, are data
collected from a source. In the context of
examinations, the raw data might be described as a
raw score.
Wikipedia
Raw data is the data that is collected from a source, but
in its initial state. It has not yet been processed —or
cleaned, organized, and visually presented.
Raw data can be manually written down or typed,
recorded, or automatically input by a machine.

5. Example :

6. Metadata isdata that describes data, but it isn't
the data itself.
Meta Data
Metadata is defined as the data providing
information about one or more aspects of the data;
it is used to summarize basic information about
data which can make tracking and working with
specific data easier

7. Example :
Adigital image
the size of the
image,
its color depth,
resolution,
when it was created,
the shutter speed,
and other data
Data
Meta Data

8. Meta Data Vs Data

9. Data Integrity

11. Why Data Integrity?
•Assures Quality, Safety & Efficacy of Drug
•Record of activity
•Reliability of Data
•Questioning Data Integrity is loss of Trust
•False Data is a Criminal Violation to FDA

13. Data Integrity
Data integrity refers to the
Completeness
Consistency and
Accuracy of Data throughout the
Lifecycle.
Data should be complete, consistent, accurate, legible, reliable, prompt and
data maintained throughout the Lifecycle.

14. Data Integrity Errors

16. 1. Personnel
2. Task preparation and execution
3. Materials
4. Procedures
5. Data collection (Capture/Interpretation/Review)
6. Handling issues
7. Data management and archiving
Common data integrity issues

17. 1. Personnel
•Personnel qualification
•Unqualified persons performing critical tasks
•Inadequate training
•No demonstration of competency
•Using improper techniques
•Inexperienced reviewers
•Differences between the site contract labs personnel and systems
•Not enough qualified personnel
•Active fraud/falsification [12]

18. •Unapproved suppliers
•System/equipment
•Not calibrated/validated-accuracy and reliability issues
•Lack of appropriate access controls
•Overwrite/delete information [13]
3. Materials
2. Task preparation and execution
• Unlabeled samples
• Processing equipment
• Unapproved/unverified materials used [14]
• Not thorough enough, leading to variability in performance
• Missing equipment for data recording
• Missing data capture [15]
4. Procedures

19. 5. Data collection (Capture/Interpretation/Review)
• Lack of handling with the deviations tends to fail to record, report,
investigate or covering up out of specifications (including discarding/not
documenting failing results)
• No proper documentation and compilation
• Inadequate investigations, including a failure to identify root cause
• Use of un-validated analytical methods
• Inaccuracies between data systems and specification documents [16]
• Personnel overloaded and cutting corners
• Documenting changes to approved records without re-approval
• Failure to follow procedures
• Lack of verification
• Mislabeling/not labeling samples
• Not completing documentation
• Running trial samples [17]
6. Handling issues

20. 7. Data management and archiving
• Ability to edit data/delete methods
• Lack of backups/protection of records from loss
• Failure to retain raw data/complete data as generated
• Incomplete files/records of data acquired
• No backup or backups that overwritten earlier data
• Hybrid systems of both paper/electronic record [18]

21. Consequences of data integrity noncompliance
• Loss of trust
• Recalls
• Warning letter/483 observations
• Import alert/injunction
• Seizure
• Non-compliance report
• Loss of job/loss of business [19]

22. •In recent years, significant data integrity lapses have occurred
at global pharmaceutical, biotechnology, and medical device
firms.
•Repercussions have included new products not being
approved, rescinded approval of products on the market,
closure of manufacturing plants, massive product recalls, FDA
import alerts, and drug shortages.
At theXavier Artificial Intelligence (AI) Summitheld
virtually in August 2020, a session addressed data integrity
and the challenges for artificial intelligence. Industry
veteranSteve Niedelmandiscussed the importance and
criticality of data, the consequences of data integrity
failures, and how to find and eliminate data integrity
issues.

23. •Niedelman is the Lead Quality System and Compliance Consultant in the
FDA and Life Science Practice at Washington, D.C. office of law firmKing
and Spalding. He completed a 34-year distinguished career with FDA and
serves as a consultant to medical device, drug, biologics, tobacco, and
other FDA-regulated industries. Although not an attorney, Niedelman is an
FDA drug, biologics, and medical device expert.
“Solid data integrity provides a foundation for the quality and safety
of pharmaceuticals and medical devices in the product development
and manufacturing phases of the product lifecycle,” Niedelman
maintained. “
It plays a fundamental role in ensuring that the decisions being made
by a company have the supportive documentation behind them and
that they can be defended.”

24. Info graphics :

25. Warning Letter

26. Data
Backup
Trained
Personnel
GDP
ALCOA &
ALCOA +
Audit
trial
QMS
Audit
Aware-
ness
Validated
System
Limited
Computer
Access

27. Data Integrity Lifeline
FDA
WHO
MHRA
EMA
Application Integrity Policy
Data Integrity & Compliance with cGMP
21 CFR Part:11 Electronic Record, Electronic Signature
Guideline on Data Integrity
GXP Data Integrity Guidance & Definitions
Data Integrity (Q & A)

28. ALCOA & ALCOA Plus
ALCOAis an acronym name which stand for_
Attributable
Legible
Contemporaneous
Original
Accurate
ALCOA is expanded to ALOCA Plus
by addition of few more concept.
They are_
Complete
Consistent
Enduring
Available

29. ALCOA & ALCOA Plus
Attributable
Clearly indicates who recorded the data / performed the activity with
sign data (manually/electronically).
Record who wrote it and when.
FDA requirement is data should be trace or link with its source like
study, analytical run, test system, etc.
Legible (Readable)
Data should be readable after it is recorded.
Data recorded permanently in long-lasting (durable) a medium like a
pen, non-removable ink.
Legibility is applicable for both printed and handwritten documents.

30. Attributable :

31. Attributable :

32. Attributable :

33. Legible :

34. Legible :

35. Contemporaneous :

36. Contemporaneous :

37. Contemporaneous (Online Record)
Record the data at the time it was generated i.e. contemporaneously.
It is well known online recording of data. If more promptly (no delay) data recorded,
better the quality. The date of data entry should be required.Never pre-date or
backdate Never pre-date or backdate
ALCOA & ALCOA Plus
Original

38. Original :

39. Original :

40. Original :

41. ALCOA & ALCOA Plus
Datareflectits
actualvalue/
trueness,freefrom
error.
Accuracyindicates
quality.

42. Accurate :

43. ALCOA & ALCOA Plus
Complete
Datareflectno
deletionthathas
beentakeplace
fromthedateof
documentation
Thisincludeany
changesthathave
beenmadeduring
thelifeofthe
Data
This is mainly applicable to manual documents. Here the
data includes all information from the study. For
example, suppose you are recording data related to a
given activity.
In that case, it should be recorded on a document
approved and issued by the quality unit rather than
recording it on a piece of paper or any loose sheet.

44. ALCOA & ALCOA Plus
Consistent
Data should be
chronologically
arranged with
time stamp
included for any
addition to the
original Data.
Consistency
should be ensure
by applying
various audits
over the life of
the Data
A sequence of events about an activity should
align with the expected series of events.

45. Records need to be consistent, both internally, within its
immediate set, and with regards to the larger body of
information.
For example, records should be consistent with respect to
order of generation, units, signing, procedures used, etc.
Data should be gathered using a system that enforces the
use of approved data acquisition and analysis methods,
reporting templates, and laboratory workflows.

46. Example of Consistency :
The date column
shows not only
inconsistencies in
format, but also
inconsistencies in date
order.
What happened at
the end of August?
Was this someone
going on holiday, or
was the refrigerator
out of action?
The
inconsistencies
in the
temperature
column data
are also very
interesting.
Some Tem.
Recorded with
two extra digit
after decimal
place, some
only one.

47. Enduring
Making sure
records exist
for the entire
period and
readable
condition.
ALCOA & ALCOA Plus
this principle emphasizes to ensure details available
for longer durations, even decades, in some
situations

48. Available
Data should be
available for review
at any time until the
defined storage of
the document.
Available at the
time of audit and
whenever required
for review.
ALCOA & ALCOA Plus
Paper and electronic data are required to be readily
available for review, audits, or inspections for the
required lifetime of the record. Paper and electronic
data should be indexed and appropriately labelled to
facilitate retrieval.

49. ALCOA Plus