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40 Pharmaceutical Technology Regulatory Sourcebook October 2020 P h a r mTe c h . c o m Data Integrity • Balance printouts are assumed to be legible and therefore this is not recorded in the table. • "Consistent and enduring" are applicable throughout the process, so only one entry is made for the whole analysis. It might appear that this approach is burden- some, but the majority of such procedures are ge- neric and, once a particular procedure is mapped, it is reusable many times across monographs, with minor modifications. Summary and recommendation Pharmacopoeias are evolutionary documents with long timescales and legacy requirements. This slow evolution is based upon regulatory and consumer- driven needs as well as alignment with best ana- lytical and scientific practices. Pharmacopoeias are large, complex compendia and are amended and updated by expert committee and panel structures that are not always coordinated. The blend of classical and non-classical testing requirements to meet a standard make it difficult for the practitioner to comply with modern reg- ulatory expectations regarding data integrity. A formal requirement is necessary in the pharmaco- peia as to the necessity for detailed interpretation of testing procedures required to assure compli- ance with the monograph standard and current regulatory best practice. For example, USP should have either an explicit statement in the General Notices or General Chapter below 1000 explicitly requiring a that a documented, detailed analytical test interpretation is needed for each monograph test, with cross-references to the informational General Chapter <1029>. References 1. MHRA, GMP Data Integrity Definitions and Guidance for In- dustry, 2nd Edition (2015). 2. MHRA, GXP Data Integrity Guidance and Definitions (2018). 3. WHO, Technical Report Series No.996, Annex 5 Guidance on Good Data and Records Management Practices (2016). 4. FDA, Guidance for Industry Data Integrity and Compliance With Drug CGMP Questions and Answers (2018). 5. PIC/S, PI-041-3 Good Practices for Data Management and In- tegrity in Regulated GMP/GDP Environments Draft (2018). 6. ISPE, GAMP Guide Records and Data integrity (2017). 7. ISPE, GAMP Good Practice Guide: Data Integrity–Key Con- cepts (2018). 8. PDA, Technical Report 80: Data Integrity Management System for Pharmaceutical Laboratories (2018). 9. APIC Data Integrity Task Force, Practical Risk-Based Guide for Managing Data Integrity, Version 1 (2019). 10. C Burgess, Pharm. Tech., 41 (10) 82-83, 92 (2017) 11. USP, USP General Notices and Requirements, (Rockville, MD, 2020). 12. USP, USP General Chapter <1225> "Validation of Compendial Procedures," USP 42-NF 37 (Rockville, MD, 2020). 13. ICH, Q2(R1) Validation of Analytical Procedures: Text and Methodology (2005). 14. USP, USP General Chapter <1029> "Good Documentation Guidelines," USP 42-NF 37 (Rockville, MD, 2020). 15. CFR Title 21, Part 211 Current Good Manufacturing Practice for Finished Pharmaceutical Products (2008). 16. USP, USP General Chapter <731> "Loss on Drying" USP 42-NF 37 (Rockville, MD, 2020). 17. USP, USP General Chapter <1226> "Verification of Compen- dial Procedures" USP 42-NF 37 (Rockville, MD, 2020). 18. USP, USP General Chapter <1058> "Analytical Instrument Qualification," USP 42-NF 37 (Rockville, MD, 2020). 19. C. Burgess and R.D. McDowall, LC-GC Europe 29 (9) 498-504 (2016). 20. R.D.McDowall, Data Integrity and Data Governance: Practical Implementation in Regulated Laboratories (Cambridge: Royal Society of Chemistry, 2019). 21. EMA, Ref lection paper on expectations for electronic source data and data transcribed to electronic data collection tools in clinical trials (London, 2010). 22. USP, USP General Chapter <41> "Balances," USP 42-NF 37 (Rockville, MD, 2020). 23. S.W.Wollen, "Data Quality and the Origin of ALCOA" in The Compass, Newsletter of the Southern Regional Chapter, Society of Quality Assurance (Summer 2010). 24. USP, USP General Chapter <1251> "Weighing on an Analyti- cal Balance," USP 42-NF 37 (Rockville, MD, 2020). PT The blend of classical and non-classical testing requirements to meet a standard make it difficult for the practitioner to comply with modern regulatory expectations regarding data integrity.