The project Real4Reg will conduct a retrospective observational cohort study based on national healthcare registers and claims data. The project will develop methods for the effective analyses of real-world data (RWD) in regulatory decision-making and health technology assessment (HTA), by using emerging methods (artificial intelligence and machine learning (AI/ ML)).

The key steps of Real4Reg methodological work will be:

  • The description and understanding of the heterogeneity of RWD sources and patient characteristics. The datasets from all partners will be harmonised and standardised by developing a common data model (CDM) and analytical workflows, which can be employed in future projects. As a result, Real4Reg will enable the use of different RWD in a more standardised way and enable data FAIRification (Findable, Accessible, Interoperable, Reusable) via a metadata catalogue.
  • The assessment of current analytical needs and the optimisation of the potential of the available methods will help to increase the evidentiary value of RWD analyses. In addition, Real4Reg will focus on the emerging opportunities of AI/ ML approaches to address current challenges in RWD analyses.
  • In a final step, Real4Reg will derive recommendations and develop guidance and training for the scientific and regulatory audiences. The results will be disseminated to patients and the general public to impact RWD use along the product lifecycle.

ENCePP Study Seal

The Real4Reg study was registered in the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) database.

As the Real4Reg study meets the rigorous criteria set by ENCePP’s Code of Conduct, it has received an ENCePP Seal.

ENCePP database

From a methodological point of view, the project is aligned with the decision-making process in regulatory agencies, which can be broadly broken down into the pre-authorisation, evaluation, and post-authorisation phases of the product lifecycle.

Four use cases and corresponding suitable phenotypes were selected to develop and evaluate standards and data analytical approaches:

Use Case 1 (description of the study population) and Use Case 2 (historical controls and synthetic data) will provide hands-on data experiences for the application of RWD in pre-authorisation and evaluation with phenotypes with high current regulatory and HTA interest: Breast cancer and Amyotrophic lateral sclerosis (ALS).

Real4Reg will also provide hands-on data experiences for phenotypes with current regulatory interest from the post-authorisation perspective. These phenotypes are complementary as they address treatments for both acute and chronic conditions: Fluoroquinolones (FQs), a class of broad-spectrum antibiotics, were chosen to evaluate safety as Use Case 3. Use Case 4 will evaluate the effectiveness and drug repurposing of SGLT2 inhibitors, combining two major public health concerns: diabetes and heart failure.

The following image shows an overview of the methodology:

Description of Study PopulationBreast Cancer, Amyotrophic Lateral Sclerosis Use Case 1 Historical controls & synthetic dataBreast Cancer, Amyotrophic Lateral Sclerosis Use Case 2 SafetyFluoroquinolones Use Case 3 Effectiveness & drug repurposingSGLT2 Inhibitors Use Case 4 Analytical solutions developed in and applied on European national register data & statutory health insurance data Pre-Authorisation & Evaluation Post-Authorisation