The document discusses the definition and types of data, emphasizing data integrity as completeness, consistency, and accuracy, as per the ALCOA+ principles. It categorizes raw data, source data, and metadata, detailing examples and the importance of maintaining data integrity within the pharmaceutical industry. Common issues related to data integrity, such as manipulation and incomplete records, are highlighted, along with strategies to minimize risks and ensure compliance with FDA regulations.

1. Data Integrity

2. What is Data?
• Facts, figures and statistics collected together for
reference or analysis. All original records and true copies
of original records, including source data and metadata
and all subsequent transformations and reports of these
data, that are generated or recorded at the time of the
GXP activity and allow full and complete reconstruction
and evaluation of the GXP activity (MHRA, Mar-2018)

3. Types of Data
• Raw data
• Source data
• Meta data

4. Raw Data
• Raw data is the original record which captured the first time either
electronicallly or recorded on paper (manually). In the case of balance,
pH meter instrument data do not store electronially they provide printed
data output that time printed data consider as raw data. (MHRA, Mar-
2018)
• Complete record in the form of laboratory worksheet, records, notes,
memoranda, microfilms, photographs, computer printouts, magnetic
media, dictated observation, recorded data from automated instruments
also consider raw data. (USFDA, Apr-2016)

5. Source Data
• This terminology is used for clinical investigation purposes. Source data
is the same as raw data (laboratory investigation purpose). Source data
include original records of clinical observation and investigation. Source
data review by both sponsor and FDA for safety, quality and integrity
(ALCOA+) (USFDA,Nov-2012).

6. Meta Data
• Data which indicates attributes (Specialty) of other data and gives
reference and meaning of that other data. Contextual information required
to understand data. Metadata described as data about data. The audit trail
is considered as metadata. Data that automatically generated by the
original data source also considered as metadata. (MHRA,Mar-2018)

7. Meta Data
Example: Audit Trail
• Audit trail is form of metadata having information about creation,
alteration, or deletion of GXP record. This secure recording of product
details during the manufacturing life cycle. This is the medium includes
who, what, when, why chronologically the action performed. The
computerized system responsible for the generation of raw data always
links with an audit trail to identify the alteration, deletion, or any changes in
data by retaining both altered data and original data.

8. List of Data Generated in Pharma Industry but not
be limited
• Training Record
• Laboratory test reports (Inprocess/Finished product)
• Out of Specification (OOS)
• Out of Trend (OOT)
• Deviation
• Change control
• Validation (Analytical/Process/Equipment/Cleaning)
• Logbooks
• Job description of the employee
• Health checkup
• Preventive maintenace
• Internal / External Audit
• Audit Compliance
• Approved vendor documents

9. What is Data Integrity?
• Completeness, consistency and accuracy of data is data
integrity. Data must follow the ALCOA+ principle i.e,
Attributable, Legible, Contemporaneous, Original, Accurate,
Available, Enduring, Complete, Consistent. (USFDA)
• Data are complete, consistent, accurate, legible, reliable,
promt and data maintained throughout the lifecycle. The
ALCOA+ principle i.e, Attributable, Legible,
Contemporaneous, Original, Accurate, Available, Enduring,
Complete, Consistent. (MHRA)

10. ALCOA+
• ALCOA acronym was introduced by Stan W. Woollen. ALCOA
represents the terms
• Attributable This “+” represents
• Legible - Available
• Contemporaneous - Enduring
• Original - Complete
• Accurate - Consistent

11. Attributable
• Clearly indicates who recorded the data / performed the activity with
sign data (manually/electroically). Record who wrote it and when. FDA
requirement is data should be trace or link with its soure like study,
analytical run, test system, etc.

12. 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.

13. Contemporaneous (Online Record)
• Record the data at the time it was generated i.e, contemporaneously.
Itis well known online recording of data. If more promptly (no delay) data
recorded, better the quality. The data of data entry should be required.

14. Original
• Prevent data in its unaltered state like raw data, source data. This is the
first data generated electronically or manually.

15. Accurate
• Data reflect its actual value / trueness, free from error. Accuracy
indicates quality.

16. 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.

17. Enduring
• Making sure records exist for the entire period and readable condition.

18. Common Data Integrity Issues
• Data Manipulation
• Share Username & Password
• No Computer system control
• Backdated documentation
• Incomplete data
• Improper data backup system
• Cyber
• Lack of knowledge
• Destruction without redcording

19. Minimize the risk of Data Integrity
• Training / Personnel Qualification
• Follow Good Documentation Practices
• Follow ALCOA+
• Consultant Guidance
• Audit trail
• Quality Management System
• Annual Product Quality Review (APQR)
• Comprehensive evaluation
• Internal Audits / Self Inspection
• Data Backup
• Management Awareness
• Validate System
• Limited Computer Access

20. Why necessary to minimize data integrity / FDA basic
requirement?
• Reconstruct the manufacturing processs -
o Regulator and industry to be able to reconstruct the
manufacturing process to record.
• To avoid falsification –
o There is no false, omission, hiding and substitution of
data. Regulatory bodies take serious action on such an
issue.
• To avoid regulatory action -
o Regulatory bodies take serious action if they are not satisfies
with your data handling procedure, product mix up, cross-
contamination, cleaning the procedure, manufacturing
procedure, etc. They consider its serious cGMP violation.

21. THANK YOU