Raw Data & Data Integrity Master Class
Im Auftrag der ECA Academy

Programm

Raw Data - Understanding, Defining and Managing

Cutting Through the Confusion and Fog of Regulatory Terms
Currently there are many terms used in GMP regulations and data integrity guidance documents. What do they mean? How are they relevant to debate? Definition and interpretation of

• Original record / record
• Raw data – MHRA GMP and US GLP definitions
• Data and metadata
• True copy
• Complete data
• Initial data
• Translating raw data for a GMP environment: should we treat manufacturing and laboratory the same when it comes to raw data?

Interpretation of Raw Data for Production Systems
Using a manufacturing process that is automated by standalone PLCs, PLCs linked to a SCADA system and an automated Manufacturing Execution System, what constitutes raw data will be outlined.

Raw Data for PLCs

PLCs linked to a SCADA system

SCADA linked to a Manufacturing Execution System

Interpretation of Raw Data for Laboratory Systems
Using a process involving a chromatography data system and a LIMS, what constitutes raw data will be outlined in two scenarios

• Hybrid CDS and manual input to the LIMS
• Electronic CDS with automatic transfer to the LIMS
• Managing sample management and preparation records

Can a True Copy be Raw Data?
This brief presentation will start from the definitions of raw data and true copy and explore if and how a true copy can be considered raw data.

Workshop: Defining Raw Data for Production, QA and Laboratory Systems
This workshop is intended to reinforce the

Data Integrity Master Class

Regulatory Update

• EU GMP Requirements: Chapter 4, Annex 11
• Guidance Documents Overview (state of the art): GMDP Inspectors WG, Data Integrity Q&A, (PIC/S Good Practices for Data Management and Integrity in regulated GMP/GDP Environments), WHO, Annex 5 Guidance on Good Data and Record Management Practices, MHRA GxP Data Integrity Definitions and Guidance for Industry
• FDA, Data Integrity and Compliance with cGMP
• “These Guides are not intended to impose additional regulatory burden upon regulated entities” Is This correct? Data Governance, Dynamic Data

Data Integrity in the pharmaceutical quality system / Data Governance

• Which PQS elements need to be added or updated?
• The Data Integrity Program: Priorities (immediate/short/mid-term), Capacity, Timing
• Governance responsibilities
• Data governance vs. IT governance
• Elements of a data governance
• Embedding data governance into the PQS

Data flow analysis

• Objective and purpose
• Electronic data flow
• Complete data flow
• Identification of possible weaknesses

Metrics for Data Integrity

• Metrics in the context of a corporate data integrity programme
• Suggested metrics in the assessment phase
• Suggested metrics in the operational phase

Control of Master Templates and Blank Forms

• Why is control of master templates and blank forms important?
• Regulatory requirements from FDA, MHRA, WHO, EMA and PIC/S
• Devising and controlling the master template
• Operational use of the blank forms
• Do you really want to work this way?

DI Inspections

• Basis for Inspections: “PIC/S Good Practices for Data Management and Integrity in regulated GMP/GDP Environments”
• Data Integrity Assessment during Inspection: Quality Control | Manufacturing
• Inspection Findings

Preparing your company for an Data Integrity inspection

• How to present the DI status and future approach?
• Gap analysis
• Training program coverage
• Experience from FDA inspections – Hot Buttons

Second Person Review

• Regulatory and guidance document requirements for the second person review
• Role of the second person review
• Scope of the second person review
• Documenting the review for paper, hybrid and electronic systems
• Facilitated discussion on Second Person review

Facilitated discussion on Second Persons Review

Quality Culture for Data Integrity

• Regulatory expectations for a data integrity quality culture
• Role senior management in creating the culture
• Components of a quality culture
• Reinforcement of the culture

QA oversight for data integrity

• Data integrity training
• Enforce data flows
• Reviews
• Internal inspection
• Audit of external organisations

Vulnerability of Records

• What is record vulnerability?
• Protection and security of electronic records requirements
• What can go wrong? Scope of misfortunes that can impact records
• Assessment of record vulnerability and implementation of control measures

Audit trail review

• Regulatory Overview
• Essential Audit Trails in QA/QC/Manufacturing. Risk-based approach.
• What about legacy systems w/o Audit Trail?
• Who shall review Audit Trails? Documentation
• What process and documentation is appropriate in case of deviations/discrepancies?

Options for Long Term Data Retention of Laboratory Data

• Proprietary v open standards for laboratory data
• Options for long term retention: Keep original system, Virtualisation, Data migration

Case study Data Migration

• Principles of data migration
• Design of the migration process
• Risk-based elaboration of the verification strategy – case study examples

Cybersecurity / Cloud Computing / Time synchronisation

• Cybersecurity securing data integrity
• Robust IT infrastructure
• Time synchronisation
• Qualification of time dissemination

Results of a Data integrity audit from a contract laboratory

• Audit context
• Audit scope
• Findings
• Root causes

Data Integrity Investigations

• What are data integrity investigations?
• Human and technical triggers for DI investigations
• Who should investigate the problem?
• Process description and how to document a DI investigation
• Should we inform regulatory authorities?

Workshop on Data Integrity Investigations
Based on a case study, attendees will be presented with key facts and determine what an organisation should do to investigate a data integrity issue. At the end of the workshop, during the discussion of the team outputs there will be a comparison with the work performed in the case study.

Key Learning Points and Final Discussion

Workshop on Data Flow Analysis
Identification of data flow weaknesses and non-compliances

Workshop on QA Oversight for Data Integrity
Identification of QA oversight weaknesses

Workshop on Vulnerability of Records
Working in teams, the attendees will be presented with a scenario of a computerised system that generates electronic records. They will assess the record vulnerability and determine the controls to put in place to protect the records and ensure data integrity. Team outputs will be discussed with all participants.