New from PIC/S: Draft Guidance on Data Integrity
By Deb Bartel,
This post was recently updated on September 19, 2018
Earlier this year, the FDA published draft guidance on Data Integrity and Compliance for GMP. And now PIC/S has published a new, draft document Good Practices for Data Management and Integrity to guide manufacturers of medicinal products in complying with GMP and GDP requirements.
As a member of PIC/S, the US FDA participated in the development of this guidance. The draft PIC/S guidance contains the participating inspecting agencies’ expectations regarding subjects such as record retention, protection, and archival; ensuring data integrity for out-sourced functions; system validation and periodic validation review; data migrations; the use of electronic signatures; best practices for audit trail reviews; and, how to address data integrity issues.
What’s in the draft guidance:
Clarifications and Expectations
- Data Governance
- Data Life Cycle
- Data Risk Management
- Management Review and Resource Allocation
- Data Integrity Issues
- Record Retention, Protection, Archival
- Data Integrity for Out-Sourced Functions
PIC/S Recommendations
The draft guidance also provides recommendations regarding:
- Qualification and Validation of Computerized Systems
- Validation Master Plans
- Periodic Validation Review of Computerized Systems
- Data Transfers and Migrations
- System Security
- Electronic Signatures
- Audit Trail Reviews
Download the PIC/S PI 041-1 Draft Guidance here: Good Practices for Data Management and Integrity
Do you need more help implementing these recommendations in your organization? Talk to our CSV experts about how you can update your practices for compliance.
About the Author:
Deb Bartel is a long-time leader of software quality and information system initiatives. Her expertise is built on 25 years of work in pharmaceuticals, medical device, chemicals, and textile manufacturing industries. She is a Principal at Praxis Life Sciences, where she is responsible for the Computer System Validation (CSV) and Software Quality Assurance (SQA) practice.
Deb has trained hundreds of Life Sciences professionals in risk-based computer system validation.
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