Leveraging the power of data for public and animal health

The workplan translates the objectives of the European medicines agencies network strategy to 2028 into concrete deliverables. These include strengthening the network’s data analytics capabilities to generate high-quality evidence using both established and novel methods. The clinical study data pilot by EMA’s human medicines committee (CHMP) will continue to clarify the benefits and practicalities of accessing individual patient data from clinical trials. The Data Analysis and Real World Interrogation Network, DARWIN EU®, will further expand and deliver evidence that helps fill knowledge gaps and understand the use, safety and benefits of medicines.

A review of methodologies, including biostatistics, modelling and simulation, AI and pharmacoepidemiology and lesser-used data types, including genomic data, synthetic data, digital twins data and patient experience data, will help the network establish shared understanding and position the future use of such methods and data types.

The workplan aims to enable efficient discovery, access, and use of the network’s data assets through cataloguing and strengthening data quality, starting with real-world data, adverse drug reaction data and medicinal product master data. Master data, the core data needed for the operations of the network, is essential for increasing the interoperability of data assets and systems. The workplan will advance and harmonise the implementation of the Product Management Service (PMS), recognised as the network’s source of product master data for all EU medicinal products, supporting EU-wide use cases.

AI offers clear opportunities across the medicines lifecycle. Key initiatives of the workplan include supporting EMA’s scientific committees and the pharmaceutical industry in evaluating AI through the medicines lifecycle, developing guidance on AI in clinical development and in pharmacovigilance, fostering EU-wide and international collaboration, and providing the network with training on AI and a framework for sharing and collaborating on AI tools. The aim is to facilitate safe and responsible use of AI that benefits public and animal health.