Lab Data Integrity Master Class - Live Online Training Case Studies, Best Practices, Practical Solutions for GMP Laboratories

19-21 September 2023

Course No 21215

This course is part of the GMP Certification Programme "ECA Certified Data Integrity Manager". Learn more.

All times mentioned are CEST.

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Tel.: +49 (0)6221 / 84 44 0 E-Mail: info@gmp-compliance.org

Speakers

Dr Christopher Burgess, Chairman of the ECA Analytical Quality Control Working Group
Dr Markus Dathe, F. Hoffmann-La Roche AG, Switzerland
Dr Bob McDowall, Member of the ECA IT Compliance Interest Group

Objectives

The objectives of this Laboratory Data Integrity Master Class are:

Identify areas where Data Integrity can be compromised
Provide a methodology to assess your current processes and identify Data Integrity risks that attendees can use in their own laboratories
To take attendees on a journey through the analytical process to identify area of Data Integrity risk and to develop both short-term remediation and, more importantly, long-term Solutions
Move from principles and requirements into Data Integrity best practice scenarios

Note, this Live Online Training assumes that attendees already understand the principles for Laboratory Data Integrity and the contents of regulatory guidance documents as these aspects will not be covered.

Background

Data Integrity has become the major regulatory concern with Regulatory Agencies who have issued several guidances that focus on computerised systems, data governance and Data Integrity audits and investigations. Manual processes are discussed but focus on uncontrolled blank forms and the control measures. The analytical process is not covered in any Data Integrity guidance. This is surprising as sample management and sample preparation are primarily manual, paper based and error prone.

The WHO regulatory guidance document notes:
The use of hybrid systems is discouraged
Replacement of hybrid systems should be a priority

PIC/S Guidance PI-041, section 5.3.2 states:
Manufacturers and analytical laboratories should design and operate a [quality management] system which provides an
acceptable state of control based on the data integrity risk, and which is fully documented with supporting rationale.

These guidance documents push laboratories and pharmaceutical companies towards implementing automated systems as technical controls are superior to procedural controls for ensuring Data Integrity.

This Live Online Training will provide attendees with tools for:

Understanding your process
Identifying process risks
Assessing Data Integrity criticality
Developing practical mitigation strategies

Target Group

This Laboratory Data Integrity Master Class is directed at

Managers and scientists from QC and Analytical Development Laboratories
Quality Assurance personnel responsible for Data Integrity
CRO and CMO laboratory and QA personnel
Auditors responsible for laboratory quality and Data Integrity

Technical Requirements

We use WebEx for our live online training courses and webinars. At https://www.gmp-compliance.org/training/online-training-technical-information you will find all the information you need to participate in our trainings and you can check if your system meets the necessary requirements to participate. If the installation of browser extensions is not possible due to your rights in the IT system, please contact your IT department. WebEx is a standard nowadays and the necessary installation is fast and easy.

Programme

Introduction to the Master Class

Course objectives
Introduction to the teaching team
Structure of the course: presentation / Workshop combinations

Workshop 1:
Regulatory Citations – What Would You Do?

Inspection findings can hit everybody – be prepared for Data Integrity
What do the agencies expect: Case Studies from recent citations and appropriate reaction?
What helps, what not?
How to prevent that specific finding? How to fix the issue?

How Poor is your Process? Data Process Mapping and Analysis

More than Data Integrity: the benefits of mapping
Process flow and data flow: strengths and weaknesses
Tools for mapping and the best time to use them
Parts of a complete flowchart (sample process)
Shortcuts to get to the finish more quickly
Priority of risks - create your “quick wins”

Analytical Process 1: Sampling

Importance of sampling in the analytical process
Regulatory requirements for sampling
Sample management procedures and flows to ensure traceability
Is the laboratory sample representative of the batch or lot?
Tools and techniques for sampling

Workshop 2:
Sampling and Data Integrity

Case study scenarios for a variety of sampling and sample management activities will be presented for evaluation and critique by groups
The objective of the evaluation is to ensure that scientifically sound methodologies are employed and Data Integrity maintained throughout the sample management procedure

Analytical Process 2: Sample Preparation

Scope of sample preparation in the analytical process
How is sample preparation typically documented?
Data Integrity issues with sample preparation
Ways of improving sample preparation data integrity

Workshop 3:
Sample Preparation

Attendees are presented with a sample preparation scenario where Data Integrity issues have been identified during an internal audit
Identify what, if any, short term remediation is required and what would be options for long term solution
How would the long term solutions be justified?

Analytical Process 3: Instrumental Analysis

Scope of instrumental analysis in the analytical process
Risk based strategies for classification for Data Integrity
Criticality and lifecycle of instruments
Role of Audit Trail Review (ATR) in the Instrumental Analysis
What do we expect from the suppliers?

Workshop 4:
Instrumental Analysis

Develop risk-based strategies on case studies
Participants are encouraged to present their own examples
Risk identification on examples and ATR implementation

Analytical Process 4: Data Interpretation

Scope of data evaluation in the analytical process
‘Fitness for purpose’ of analytical data
Acceptance criteria and procedure mapping
Statistical tools for the detection of imprecision and bias

Quality Metrics for Laboratory Analysis

Regulatory expectation for Data Integrity metrics
Benefits and limitations in Data Integrity metrics
Example metrics for governance
Example metrics for operations
Finding ideas for new metrics

Forensic Auditing of Laboratory Data

Uncovering file deletions - what is forensic auditing?
When do you use forensic auditing?
Tools for forensic auditing
Pros and cons of forensic auditing

Workshop 5:
Data Interpretation

Case study scenarios
The objective of the evaluation is to determine if scientifically sound methodologies are employed and Data Integrity maintained throughout the data evaluation procedure

Analytical Process 5: Calculation of the Reportable Result

Scope of calculating the reportable result in the analytical process
Automation vs manual intervention in calculations
Benefits of converting to auto-integration
Importing factors from other systems: benefits and risks

Workshop 6:
Calculation of the Reportable Result

Procedural controls for calculations (chromatography SOP)
Testing into compliance and other practices to avoid
Use of metrics to provide quality oversight of calculations

Analytical Process 6: Second Person Review

Scope of a second person review in the analytical process
Electronic, hybrid and paper-based processes
Options for Second Person Review
What do the inspectors expect?
Role of data and records definition

Workshop 7:
Second Person Review

Data are not records - find the difference. Explain it! Defend it!
Falsification detection and prevention
Where and how is Audit Trail Review performed
Short term remediation and long term solutions for
paper-based and hybrid systems

Developing and Maintaining an Open Culture in a Regulated Laboratory

Defining “culture” in a practical way
Opportunity, means and motive in the lab
Getting People to cheat is easy!
What you said versus what they heard
Changing your Organization’s Perspective

Workshop 8:
Pulling it all Together

Attendees will be presented with a laboratory scenario containing Data Integrity issues.
Using the principles learnt in the course, participants must identify the Data Integrity issues and propose long term solutions
What would be the order of implementing your options and why?

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