Lab Data Integrity Conference

Lab Data Integrity Conference

Vienna, Austria

Course No 15488


Costs

This conference already took place.

If you have any questions, please contact us:
Tel.: +49 (0)6221 / 84 44 0 E-Mail: info@gmp-compliance.org

Speakers

Monica Cahilly, Green Mountain Quality Assurance LLC, USA

Rob De Proost, Janssen Pharmaceutica, Belgium

Dr Bob McDowall, R D McDowall Ltd., UK

Karl-Heinz Menges, Inspector, Regional Authority Darmstadt, Germany

Roland Miksche, Shire, Austria

Helga Peters, F. Hoffmann-La Roche Ltd., Switzerland

Margarita Sabater, ALK-Abelló A/S, Denmark

Paul Smith, Agilent Technologies, UK

Dr Markus Zeitz, Novartis Pharma, Switzerland

Objectives

Data integrity in the GMP analytical laboratory is the major global topic amongst both regulatory agencies and pharmaceutical companies. The headline news is seen as cases of falsification and fraud from a minority of organisations and typically feature citations of data falsified manually as well as results manipulated to pass within computerised systems used in the laboratory.

However, the majority of data integrity cases seen in warning letters and 483 observations are due to poor data management practices where records created are not adequately documented, electronic records that are poorly protected or not protected at all and / or a reliance on paper as raw data. Concerning the definition of paper as raw data for all laboratory computerised systems, a discussion refuting this has been available on the FDA web site for over six years but often companies ignore this or are not aware that the advice exists.

However, if a company understands that data integrity is a potential problem, they want advice to begin a programme of work or to check what they are doing is consistent with other companies approaches.

The Laboratory Data Integrity conference is designed to present, from a practical perspective, the following areas:

  • Understanding the scope of data integrity
  • How to structure a data integrity programme from the boardroom to the laboratory bench
  • Roles and responsibilities of management
  • Metrics and key performance indicators for laboratory data integrity
  • Training for data integrity in the analytical laboratory
  • Assessment and remediation of analytical systems
  • Options for audit trails and their review
  • Role of the supplier in data integrity
  • Can I use a spreadsheet in the new data integrity world?
In addition, there will be a Discussion Forum at the end of day 1 where all delegates will have the opportunity to ask specific questions in order to benefit from the speakers’ experiences in this field.

Target Group

The conference is intended for the technical and managerial personnel who deal with data integrity issues including analytical development and quality control analytical laboratories in pharmaceutical companies contract research and manufacturing organisations. QA personnel responsible for quality oversight of data integrity work will also find the conference invaluable.

Programme

Introduction – Ensuring Laboratory Data Integrity: Understanding the Layers of Control

  • Data integrity is more than numbers
  • To help understanding a four later data integrity model is presented
  • The model begins at the pharmaceutical quality system to sample analysis
  • Interactions between the layers are key to success
Regulations and Regulatory Guidance for Data Handling - Expectations of an EU GMP Inspector
  • EU GMPs data requirements
  • WHO Guidance on good data and record management
  • PIC/S Good practices for data management and integrity
  • GMP / GDP inspectors working group Q&A document
  • Examples from inspections
Metrics for Data Governance / Data Integrity

Case Study - Data Governance from Corporation to the Laboratory – How to put a Data
Governance System in Place

The cloud around laboratory records: corporate systems supporting the quality of laboratory records:
  • Management of data (Backup, archival) and their access
  • Business Continuity Plan and Disaster Recovery Process
  • Management of documentation (DQ, Test docs, SOPs, Forms) and training
  • Calibration management (schedules, verification)
  • Supplier management (Supplier audits, support)
  • Change management (Impact, mitigation) and CAPA
  • LIMS (results; reports)
Case Study – From Guidance to Action - Implementing Data Integrity in the
  • Analytical Lab – Assessment and Remediation of Systems how to do it
  • Assessment and prioritisation of the inventory of systems and instruments
  • The assessment check list: key focus areas
  • Example of an assessment of a system
  • Common remediation approaches to save time and effort
Importance of Audit Trails: Design and Regular Reviews
  • Risk based approach to audit trails - are there other options?
  • Key requirements for design of audit trail entries
  • Frequency of audit trail review
  • What to review when looking at audit trails?
Data Integrity and System Suitability
  • SST – What to consider before the SST
  • SST - What can go wrong (Parameter selection, Calculation..)
  • Inspectional Findings related to SST and DI
Data Integrity and Spreadsheets – Are They Compatible?
  • What are the data integrity issues with spreadheets?
  • Ways to ensure data integrity of spreadsheets
  • Should I save the spreadsheet electronic file?
  • Record signature linking
Training for Laboratory Data Integrity
  • Why does Data Integrity matter?
  • Mistake or Falsification: Mindset
  • Warning Signs
  • Inspection Management
  • Dos and Don‘ts
  • Training for D.I. Inspections
Data Integrity: An Instrument Suppliers’ Perspective
  • Examples of “Real” Data Integrity questions – asked of Agilent
  • Poor deision – Data Integrity focus – getting the balance right
  • Data Integrity - non compliance (trends from FDA Warning letters to Eudra GMDP –
  • latest statistics)
  • Supplier Quality Agreement – Re-Visit for Data Integrity
  • Best Practice Advice - what you should do – if you have data integrity concern
Lab Data Integrity – Role and Responsibilities of Management
  • Roles and Responsibility according ICH Q10
  • Management Commitment
  • Data Integrity an integral part of the QMS
  • Importance of a harmonized implementation
ECA Lab Data Integrity Guidance Document

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