Data Integrity Concepts - Without Logo.pptx

Data Integrity Concepts based on the GMP regulations for the pharmaceutical industry

1. Data Integrity Concepts

2. Agenda Introduction to data, types and its lifecycle Data Integrity – definition, objectives and types ALCOA+ principle Data Governance Data Integrity vs Data Quality Ensuring Data Integrity Key Steps to ensure Data Integrity Advantages of Data Integrity Data Integrity Violations Causes of Data Integrity Violations Prevention Factors Consequences of Data Integrity Breach Warning Letters Responding to DI breaches

3. Data - Definition Data is a factual information (such as facts, values or statistics) collected together and used for reference and analysis.  (MHRA, 2018)

4. Types of Data

5. Raw Data Raw data is the data originally generated by a system, device or operation, that can be captured either electronically or recorded on paper manually. (MHRA, 2015) Example : Laboratory worksheets, records, memoranda, notes, or exact copies of original observations

6. Source Data Source data is also raw data that is captured/recorded and has not been processed/converted into a meaningful information. Example: Manually written values such as air temperature measurements that are needed to converted into an excel file.

7. Meta Data Meta data can be used to describe information such as file type, format, author, user rights, etc. and is usually attached to files, but invisible to the user. (ISPE, GAMP 5 ) Examples: A uthor name, date created, date modified, and file size.

8. Data Lifecycle

9. Understanding about Data Integrity Data Integrity refers to the accuracy, consistency and completeness of the data stored with a database, throughout its entire lifecycle.   FDA have a “ ZERO TOLERANCE ” policy for data integrity

10. Objectives of Data Integrity

11. Types of Data Integrity

12. ALCOA+ Principle A ttributable Record who performed an action and when. L egible Readable throughout the entire life cycle of the record. C ontemporaneous Documented at the time of the activity. O riginal Data in the originally generated format without any changes. A ccurate No errors or editing without documented amendments. + Complete The data should be complete. + Consistent The data should be self-consistent. + Enduring Durable; lasting throughout the data lifecycle. + Available Readily available for review or inspection purposes.

13. Data Governance Data governance is the practice of creating, updating and consistently enforcing the processes, rules and standards that prevent errors, data loss, data corruption, mishandling of sensitive or regulated data, and data breaches.

14. Data Integrity vs Data Quality

15. Ensuring Data Integrity

16. Key steps to ensure data integrity ?

17. Key steps to ensure data integrity ?

18. Key steps to ensure data integrity ?

19. Key steps to ensure data integrity ?

20. Advantages of Data Integrity

21. Data Integrity Violations Improper data access and security control Legacy systems and outdated procedures Incomplete/Inaccurate data recording Erroneous, false and edited data/reports Record deletion Human Errors Lack of audit trails and their review Orphan and unreported data Inadequate third-party management Improper environmental monitoring measures

22. Causes of Data Integrity Violations Lack of employee technical knowledge Reliance on legacy systems and outdated procedures Poor quality culture, organizational or individual behaviour , leadership, processes, or technology Internal pressure to achieve key performance indicators (KPIs) Shortcuts through an overly bureaucratic process Confusion in a fragmented system Professional ignorance Lack of awareness of SOPs and compliance-related requirements

23. Prevention Factors

24. Consequences of Data Integrity Breach Productivity and revenue loss Warning Letters from regulatory bodies Prosecution ( including indictments and temporary or permanent debarment) Post-marketing issues Frequent product recalls Seizure Consent decree of permanent injunction Civil money penalties Import alerts Withheld product approvals Cancellation of government contracts Loss of brand reputation in the market Loss of customer’s trust Suspension or revocation of licenses Closing or take-over company

25. FY 2023 FDA DI Warning Letter Statistics

26. FY 2023 Warning Letter and Violations

27. 2024 FDA Warning Letters In a letter issued to China-based Sichuan Deebio Pharmaceutical Co. Ltd on 5 February, FDA stated the company failed to ensure the integrity of data generated by the QC microbiology laboratory . A separate letter issued to Amman Pharmaceutical Industries of Jordan on 14 February detailed issues with environmental monitoring at their facility . The FDA also wrote to S & J International Enterprises Public Company Limited in January, stating that the company’s quality system “does not adequately ensure the accuracy and integrity of data to support the safety, effectiveness, and quality of the drugs.

28. Sun Pharma – FDA Warning In 2014, Sun Pharma faced scrutiny and legal issues related to quality control and compliance with regulatory standards at its manufacturing facilities. The United States Food and Drug Administration (FDA) had issued warning letters to Sun Pharma for violations of good manufacturing practices (GMP) at its facilities in Gujarat, India. These violations included issues related to data integrity, quality control procedures, and manufacturing practices.

29. Wockhardt – FDA Warning In 2013, Wockhardt faced similar challenges when the FDA issued warning letters to its manufacturing facilities in Waluj and Chikalthana , Maharashtra, India. The warning letters cited violations of GMP regulations, including inadequate control over manufacturing processes, quality assurance systems, and documentation practices.

30. Responding to DI breaches Develop Data Integrity Policy and Procedures to address data ownership throughout the lifecycle Consider the design, operation and monitoring of processes / including control over intentional and unintentional changes to information Investigate, correct & prevent deviations and abnormalities If warranted, conduct an in-depth documented investigation of any alleged instance of falsification, fabrication, or other misconduct involving data integrity issues