Lab Data Integrity Master Class

Lab Data Integrity Master Class

Berlin, Germany

Course No 17005


Costs

Non ECA Member: EUR 2390,--
ECA-Member: EUR 2190,--
EU/GMP Inspectorates: EUR 1195,--
APIC Member Discount: EUR 2290,--

(All prices excl. VAT)

If you have any questions, please contact us:
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 ComplianceInterest Group
Mark Newton,  Principal, Heartland QA, USA (ex Eli Lilly)

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 course 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 course 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

Programme

Introduction to the Master Class
  •   Course objectives
  •   Introduction to the teaching team
  •   Structure of the course: presentation / workshop combinations
Workshop1: 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 (Group work session)
  •  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
  •  Working in teams, 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 working in teams
  • Participants are encouraged to discuss their own examples
  • Risk identification on examples and ATR implementation
Analytical Process 4: Data Evaluation
  • 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
  • Working in teams, attendees will be presented with a laboratory scenario containing Data Integrity issues. 
  • Using the principles learnt in the course, each team 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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