European Medicines Agency and EU Heads of Medicines Agencies publish Data Quality Framework for EU medicines regulation
European Medicines Agency and EU Heads of Medicines Agencies jointly issue the first version of their Data Quality Framework for EU medicines regulation, incorporating feedback following the October 2022 draft publication.
On December 12, 2023, the European Medicines Agency (EMA) and EU Heads of Medicines Agencies (HMA) jointly published the first full release of their ‘Data Quality Framework for EU medicines regulation’. The 42-page publication, which has been updated with feedback from the October 2022 draft version, provides a set of definitions, principles and guidelines that can be applied to current and novel data sets for the purpose of characterizing, assessing, and assuring data quality (DQ) for regulatory decision-making.
Background to the Data Quality Framework
The progress in healthcare digitalization and information technology is creating new opportunities through real-world data (RWD) capture and artificial intelligence enhanced curation, but is also contributing to an increasingly complex landscape for regulatory decision-making. New types of data are becoming available and guidelines and methods to demonstrate whether such data are adequate for decision-making are still scarce.
The recommendations of the HMA–EMA Joint Big Data Task Force and their Big Data Steering Group workplan both voiced the need to establish an EU framework for quality and representativeness of data in the context of medicines regulation, naming this: “a critical element for realising the full potential of big data and driving regulatory decisions.”
HMA–EMA viewed development of a Data Quality Framework (DQF) necessary to guide coherent and consistent quality assessment procedures, enabling regulators to make reliable assessments of whether data are fit for the purpose of decision-making.
Scope of the Data Quality Framework
The new publication aims to provide a set of terminology, definitions, principles and guidelines that can coherently be applied to any data source for the purpose of characterizing, assessing, and assuring DQ for regulatory decision-making. It seeks to provide a coherent basis to identify, define, and further develop DQ assessment procedures and recommendations for current and novel data types.
The objectives of the framework are to achieve consistency in DQ related processes, foster the development of horizontal systems for DQ and eventually enable a more adequate and automated use of data for regulatory decision-making. The framework provides general considerations on DQ that are relevant for regulatory decision-making, definitions for DQ dimensions and sub-dimensions, as well as their characterization and related metrics. It provides an analysis of what DQ actions and metrics should be considered in different use cases and introduces a maturity model to guide the evolution of automation to support data-driven regulatory decision-making.
The framework builds on the definitions and recommendations proposed in several existing DQ frameworks, particularly the recommendations of TEHDAS (Towards the European Data Space), extending them with a classification of quality dimensions and assessment criteria and guidelines for their application.
Learn more about TEHDAS in this interview:
Many examples provided in the framework relate to RWD, including within clinical trials to supplement trial-specific data collection. However, the scope of the framework extends to a broad range of regulatory activities and their respective data types, including bioanalytical omics data, animal health data, cell- and animal-based laboratory preclinical data, spontaneous adverse event reporting data, chemical and manufacturing control data, and more.
The publication is intended to be a general resource from which more focused recommendations can be derived for specific regulatory domains with specified metrics and checks.
Structure of the Data Quality Framework
The DQF for EU medicines regulation is composed of two parts:
- A general framework designed to provide a coherent approach to DQ, encompassing a broad range of data types and extendible to novel use cases. It provides a common ground on different DQ aspects that apply to different data types and scenarios, including definitions, DQ dimensions, and examples of metrics covering such dimensions. It identifies general patterns for the applicability of DQ processes and articulates a set of maturity models designed to drive increased automation of data-driven medicines regulatory decision-making.
- A framework for specialization that extends the generic recommendations to cater for specific data types or regulatory questions. It is this part of the framework that poses the basis for the derivation of actual implementable guidelines, and these will need to evolve as data and technologies change over time.
The future
EMA–HMA plan to regularly update the DQF over the coming years, with deep dives into regulatory use cases of particular interest. They will keep the document in line with developments in TEHDAS to further strengthen the EMA data qualification process and the collaboration with the European Health Data Space.
Whilst the document is targeted primarily at the EU medicine regulatory network, the relevance of its content will hold value to a wider range of stakeholders, such as marketing authorization holders, data source holders, researchers, and patient associations.
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