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TGA presentation given at the PDA conference, July 2015

Data integrity: TGA expectations

6 August 2015

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Presentation

  • Presented by: Stephen Hart, Senior Inspector, Manufacturing Quality Branch, TGA
  • Presented at: PDA conference July 2015
  • Presentation summary: This presentation provides an overview of data integrity and the expectations of the TGA

Transcript

Data integrity: TGA expectations

Stephen Hart
Senior Inspector
Manufacturing Quality Branch
TGA

PDA conference July 2015

Slide 1 - Presentation overview

  • What is data integrity?
  • Global/Australian/US FDA environments
  • Data integrity general examples
  • Basic data integrity expectations
  • ALCOA principles
  • TGA licensed manufacturers expectations
  • Conclusions

Slide 2 - What is data integrity?

  • The extent to which all data are complete, consistent and accurate throughout the data lifecycle
  • From initial data generation and recording through processing (including transformation or migration), use, retention, archiving, retrieval and destruction.

(MHRA Guidance March 2015)

Slide 3 - Why so much interest now? - Global environment

Manufacturer 1

  • Overwriting of electronic raw data until acceptable results were achieved
  • OOS not initiated
  • Falsification of data to support regulatory filings
  • Stand alone GC systems without adequate controls

Manufacturer 2

  • Falsification of batch records (re-writing clean records)
  • Non-contemporaneous recording of lab data
  • Recording of sample weights on scraps of paper
  • Missing raw data

Manufacturer 3

  • Unofficial testing of samples (trial samples)
  • OOS results not investigated
  • Retesting completed but not justified
  • No restriction/protection of electronic data

Manufacturer 4

  • Chromatographic software was not validated to ensure re- writing, deletion of data prohibited

Manufacturer 5

  • IPQC performed without batch record present
  • Unexplained 'trial' samples run before analysis
  • Deletion of HPLC data -lack of data security
  • Missing stability samples

Manufacturer 6

  • Lack of records demonstrating who performed analysis
  • Raw data not recorded contemporaneously nor by the performing analyst
  • Failed injections of QC standards (SS) deleted, repeated and inserted into the analytical sequence without explanation.

Slide 4 - US FDA

PAI objectives (Sections 3.3-3.4 CPGM)

  • Objective 1: Readiness for commercial manufacturing
    • 1a: Investigations/Trends
    • 1b: Material handling
    • 1c: Contamination
    • 1d: Procedures
    • 1e: Process feasibility
  • Objective 2: Conformance to application
  • Objective 3: Data integrity

PAI objectives (Sections 3.3 - 3.4 CPGM)

www.fda.gov.downloads/drugs/developmentapprovalprocess/smallbusinessassistance/UCM407991.pdf

Slide 5 - Australian environment: Inspection report

Definitions

  • Critical Deficiency
  • A deficiency in a practice or process that has produced, or may result in, a significant risk of producing a product that is harmful to the user. Also occurs when it is observed that the manufacturer has engaged in fraud, misrepresentation or falsification of products or data.

Slide 6 - Data integrity: General examples

  • Human errors - Need to know the difference between falsification and poor/bad GMP/practice
    • data entered by mistake
    • ignorance (not being aware of regulatory requirements or poor training)
    • Wilfully (falsification or fraud with the intent to deceive)
  • Selection of good or passing results to the exclusion or poor or failing results
  • Unauthorised changes to data post acquisition

Ref: "Data Integrity" pharmauptoday@gmail.com

Slide 7 - Data integrity: General examples

  • Errors during transmission from one computer to another
  • Changes due to software bugs or malware of which the user is unaware
  • Hardware malfunctions
  • Technology changes making an older item obsolete - old records may become unreadable or inaccessible

Ref: "Data Integrity" pharmauptoday@gmail.com

Slide 8 - Basic data integrity expectations - Manufacturing principles

  • PIC/S Guide PE009-8:
    • Chapter 4
    • Annex 11
  • Australian Code GMP human blood, blood components, human tissues and human cellular therapy products
    • Sections 400 - 415
  • ISO 13485
    • Sections 4.2.3, 4.2.4

Slide 9 - Basic data integrity expectations

  • Regulator responses
    • MHRA notifications to industry: December 2013 & March 2015
    • FDA
    • Health Canada
  • Influencing factors:
    • Organisational culture, risk awareness and leadership
    • QMS design of systems to comply with DI principles
      • "ALCOA" principles
    • Company processes for data review and system monitoring

Slide 10 - ALCOA principles

Attributable

  • Clearly indicates who recorded the data or performed the activity
  • Signed / dated
  • Who wrote it / when

Legible

  • It must be possible to read or interpret the data after it is recorded
  • Permanent
  • No unexplained hieroglyphics
  • Properly corrected if necessary

Contemporaneous

  • Data must be recorded at the time it was generated
  • Close proximity to occurrence

Original

  • Data must be preserved in its unaltered state
  • If not, why not
  • Certified copies

Accurate

  • Data must correctly reflect the action / observation made
  • Data checked where necessary
  • Modifications explained if not self- evident

Slide 11 - TGA licensed manufacturers expectations

  • Manufacturers should:
    • Understand their vulnerabilities to DI issues
      • Not just about your site
        • Contractors (outsourced activities)
    • Assess risks relating to data integrity- QRM Approach

Slide 12 - TGA licensed manufacturers expectations

  • Manufacturers should:
    • Design systems to prevent DI issues
      • Ensure the data is authentic and retrievable
    • Train staff and encourage correct behaviours and practices
      • Open communication
      • Encourage feedback
    • System for ongoing DI review

Slide 13 - Conclusions

  • GMP requirements already include provisions for DI- inspection report definitions, PIC/S Guide to GMP for medicinal products
  • Existing systems should be able to ensure data integrity, traceability and reliability-Understand your vulnerabilities to DI issues
    • The inability of a manufacturer to detect and prevent poor data integrity practices = lack of quality system effectiveness
  • QRM approach to prevent, detect and control potential risks

Slide 14 - Conclusions continued

  • Where data is generated and used to make manufacturing and quality decisions, ensure it is trustworthy and reliable
  • Increased regulator focus on DI
  • Remember it's the responsibility of the manufacturer to prevent and detect data integrity vulnerabilities

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