Introduction to the Master Class
- Course objectives
- Introduction to the teaching team
- Structure of the course: presentation / Workshop combinations
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
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
- 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?
- 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
- 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
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
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
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?