The Data Quality Framework for EU Medicines Regulation: Setting the standard

On December 12, 2023, the European Medicines Agency (EMA) and EU Heads of Medicines Agencies (HMA) achieved a significant milestone with the public release of their ‘Data Quality Framework for EU medicines regulation’. This collaborative effort builds upon public feedback collected since October 2022 and is a part of the Big Data Workplan 2022-2025 established by HMA-EMA Joint Big Data Steering Group (BDSG). This group aims to guide the utilization of Big Data in the development, authorization, and supervision of medicines in Europe. [1] 

Background: Big Data Workplan 2022-2025

With the increasing volume and complexity of data within healthcare systems, both HMA and EMA recognized the imperative necessity for establishing a Data Quality Framework (DQF) that provides a standardized approach to accessing particularly the quality and representativeness of data used for decision-making purposes. 

Figure 1: The HMA-EMA Joint Big Data Steering Group Workplan, Data Quality & Representativeness Timeline  

This framework follows the key recommendations of the Big Data Task Force and primarily targets the EU medicine regulatory network, its relevance extends to a broader spectrum of stakeholders, including pharmaceutical companies. It is a powerful guide for supporting the selection of trustworthy data sources from different regulatory activities, e.g., clinical trial data, preclinical data, chemistry, manufacturing, and controls (CMC) data, that are “fit for purpose” for regulatory decision-making. [2] 

The DQF´s initial part includes a comprehensive approach to Data Quality (DQ) framework, focusing on the definition DQ dimensions. The second serves as the foundation for developing implementable guidelines that adapts to changes in data and technology. [3] 

Implications for Stakeholders: A Roadmap for Data Quality Determinants and successful implementation of the DQF

We strongly encourage stakeholders, particularly pharmaceutical companies, to establish a robust data governance program to effectively support the successful implementation of the Data Quality Framework (DQF):   

• Master Data Management

The DQF emphasizes the critical need to address data quality aspects early in the data lifecycle, especially during data collection and generation processes. Stakeholders, including pharmaceutical companies, are strongly encouraged to adopt robust practices. Shared Master Data Management (MDM) and reference data utilization emerge as essential strategies, ensuring data consistency and reliability from the outset. [4] 

• The Role of Quality Management Systems and ISO Standards  

For regulatory compliance, stakeholders are urged to embrace Quality Management Systems (QMS) and adhere to industry-specific standards, such as ISO guidelines for clinical trials and medical devices. More detail about data governance in clinical trials can be found here [5]. 

ISO standards take centre stage in governing data quality and data models. The DQF underscores their significance in providing a robust framework for stakeholders in the pharmaceutical industry. ISO 9000 for quality management systems and ISO 8000 for Master Data quality are specifically mentioned. 

ALCOA+ principles provide a practical framework for maintaining data integrity, they align closely with data quality dimensions outlined in the DQF. By adhering to these principles, stakeholders gain practical insights into enhancing data reliability, extensiveness, coherence, and timeliness. [6] 

• Foundations for Data Quality in the Digital Era 

The DQF’s emphasis on foundational determinants impacted by computerized systems and highlights the necessity for stakeholders to integrate, within their digital advancement efforts, data quality processes and respective validation in a system life cycle to ensure “fit for purpose”. [7] 

The future

In the upcoming years, there are plans to update the DQF regularly, incorporating insights from deep dives into specific regulatory use cases. These deep dives involve in-depth examinations of how the framework applies to scenarios or challenges within the regulatory landscape. 

Conclusion  

Overall, the framework signifies the commitment of regulatory authorities like the EMA to ensure the integrity and reliability of data used in healthcare decision-making. Its role in the Big Data landscape marks it not only as a current necessity but also as a proactive guide for future developments in data quality regulations.  

With our evolving understanding of the latest frameworks, GxP-CC specializes in assisting pharmaceutical stakeholders in achieving data integrity and quality standards and establishing data governance policies. This not only ensures compliance but also optimises the use of data in the market and accelerates regulatory decision-making. Don’t hesitate to reach out to us for tailored support and expert guidance. 

References: 

1. https://www.ema.europa.eu/en/documents/report/big-data-steering-group-bdsg-2023-report_en.pdf 
2. https://www.ema.europa.eu/en/documents/work-programme/workplan-2022-2025-hma-ema-joint-big-data-steering-group_en.pdf 
3. https://www.ema.europa.eu/system/files/documents/regulatory-procedural-guideline/data-quality-framework-eu-medicines-regulation_en_1.pdf 
4.  https://www.ema.europa.eu/system/files/documents/regulatory-procedural-guideline/data-quality-framework-eu-medicines-regulation_en_1.pdf 
5. updates to look for in the latest ICH GCP E6 guideline (R3) by Dr. Thaleia Papadopoulou: https://www.gxp-cc.com/insights/blog/5-updates-to-look-for-in-the-latest-ich-gcp-e6-guideline-r3/ 
6.  https://www.ema.europa.eu/system/files/documents/regulatory-procedural-guideline/data-quality-framework-eu-medicines-regulation_en_1.pdf 
7.  https://www.ema.europa.eu/system/files/documents/regulatory-procedural-guideline/data-quality-framework-eu-medicines-regulation_en_1.pdf