GMP Data Governance - Principles for Data Integrity Assurance

GMP Data Governance - Principles for Data Integrity Assurance

Copenhagen, Denmark

Course No 15844


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

Dr Christopher Burgess, Chairman of the ECA Analytical Quality Control Working Group

Klaus Eichmüller, Wolnzach c/o Regional Council Darmstadt, GMP Inspectorate, Germany

Dr Bob McDowall, Member of the ECA IT Compliance Interest Group

Roland Miksche, Shire, Vienna, Austria

Margarita Sabater, QC Scientist, ALK-Abelló A/S, Denmark

Philip Vaering, QA Chemist, ALK-Abelló A/S, Denmark

Objectives

The objectives of this ECA educational course are to provide:

  • An understanding of the scope of data governance within a pharmaceutical quality system
  • The roles and responsibilities of senior management for data governance
  • Roles and responsibilities of data owners and data stewards in ensuring data integrity of specific systems and processes
  • Ensuring the correct culture for data integrity

Background

Data integrity is the hottest regulatory topic today in GMP as a result of inspectors finding poor data management practices and data falsification. As a result, guidance documents have been issued by MHRA and WHO with other guidance due from EMA and PIC/S. In these documents, there is the phrase “Data Governance” used. The same definition of the term is used by MHRA and WHO:

The sum total of arrangements to ensure that data, irrespective of the format in which it is generated, is recorded, processed, retained and used to ensure a complete, consistent and accurate record throughout the data lifecycle.

As guidance, the “sum total of arrangements” is not very illuminating or informative. In fact, the definition of data governance from the draft WHO guidance starts with the phrase “Management leadership …” which is much more informative and focussed.

This course is designed to help GMP organisations understand the term and implications of data governance. Data governance and data integrity is not just about correct numbers, it is much more than that and involves management leadership, influencing others, change of culture, effective training and personal honesty.

Target Group

  • Managers and staff from Manufacturing, QC/QA and Analytical Development Laboratories of pharmaceutical companies
  • Contract Research Organisation and Contract Manufacturing Organisation manufacturing, laboratory and QA personnel
  • Auditors (internal and external) responsible for performing self-inspections or external audits and needing to understand and assess data integrity

Programme

Setting the Scene: Introduction to the Course and Scope of Data Governance

  • Data Integrity Model with focus on Data Governance
  • Definitions of Data Governance
  • Scope of Data Governance in ensuring Data Integrity: Who is involved?
Regulations and Regulatory Guidance for Data Handling - Expectations of an EU GMP Inspector
  • GMP Regulations – EU
  • ICH Q10
  • Data integrity guidance documents – WHO, MHRA and others
Case Study: Corporate Data Integrity Policy
  • Scope and content
  • Authorship and Approval
Data Governance Roles and Responsibilities
  • Who is involved – in the whole organisation
  • What do they have to do?
Workshop 1: Content of the Data Integrity Policy
  • Work on specific sections of the policy in groups
Case Study: Corporate Culture and Organisation for Data Governance and Data Integrity
  • Role of Senior Management
  • Interdependencies of function
  • How to implement the change process
Data Owners and Data Stewards
  • What do they have to do?
  • Roles and responsibilities
  • Identifying and training the individuals
Workshop 2: Defining Roles and Responsibilities for Data Owners, Stewards, Staff & IT
  • Laboratory
  • Production
  • Quality Assurance Systems
Changing the Culture of an Organisation
  • What is required?
  • Behaviours: no blame culture & whistleblower line
  • Defining expectations: expected and prohibited actions with consequences
  • Delivering change
Data Integrity Training
  • Learning from outside the pharmaceutical industry
  • Linking the data integrity policy and local procedures for processes and systems
  • Assessing and measuring understanding
Data Integrity Audits of Processes and Systems
  • Data Life cycle for manual and automated processes
  • What data integrity controls are required throughout the life cycle?
  • Security and confidentiality considerations
Investigating Data Integrity Violations
  • Reporting the issue
  • Sequestering the data and investigating the issue
  • Resolving the problem
Workshop 3: Auditing a System and Identifying Data Integrity
  • Problems
  • Laboratory
  • Production
  • Quality Assurance Systems

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