Development, optimisation and implementation of artificial intelligence methods for real world data analyses in regulatory decision-making and health technology assessment along the product lifecycle

Real-world evidence derived from real-world data (RWD) has a promising role to inform regulatory decision-making. Based on highly relevant use cases from regulatory practice and across the product lifecycle Real4Reg develops AI-based data-driven methods and tools for the assessment of medicinal products. Findings will inform training activities on good practice examples and will be implemented in existing and emerging guidelines for both health regulatory authorities and health technology assessment (HTA) bodies across Europe. There is urgent need to enable the use and establish the value of the application of RWD across the spectrum of regulatory use cases. The use of RWD is established in regulatory processes such as safety monitoring, but evidentiary value for further use cases, especially in the pre-authorisation and evaluation phase of medicinal products, is rudimentary. The use of RWD in post-authorisation steps is constrained by data variability and by challenges in analysing data from different settings and sources. Thus, the development of new and optimised methods for RWD analyses is essential. Real4Reg addresses the challenges and opportunities of RWD analyses across different health care systems by involving multiple stakeholders to work together in a collaborative approach, also outreaching to already established European initiatives. Our consortium assembles partners with outstanding excellence in the field of RWD analyses, including experts from regulatory agencies/ HTA (BfArM, DKMA, Infarmed), academia (Fraunhofer, UEF, CSC, AU, DZNE) and patient organisations (EUpALS, EIWH). In an advisory board stakeholders provide input and guidance to the project, including patients, industry, payers, HTA bodies and healthcare professionals. The structure and approach of our project facilitates the successful implementation of the effective use of RWD in regulatory decision-making and HTA, and ultimately supports the application of better medicines for patients.