Lack of specific relevant know-how in regulatory science delays the development of new treatment strategies or limits the chances that promising innovations will reach patients. STARS aims to improve the direct regulatory impact of results obtained in medical research. Seventeen European countries are represented in the consortium through their national competent authorities, alongside academic and industry representatives, and associations with relevant experience. The work plan includes the development of a Comprehensive Inventory of existing support activities based on a detailed analysis of the currently established programmes. This analysis is also the basis for development of a Common Strategy to strengthen regulatory sciences and two curricula, the Core Curriculum specifying essential knowledge for the professional training of clinical scientists and the Comprehensive Curriculum defining relevant knowledge for specific post-graduate programmes. Three pilot projects aim (i) to transfer an identified bes

ERAMET will provide an integrated approach for developers and regulators’ decision-making for paediatric and orphan drugs, centred on the drug development questions. This will constitute a transparent ecosystem for drug development and assessment, that will facilitate the adoption of modelling and simulation (M&S) methods and related data types (including real word data such as registries and electronic healthcare data). The overall objective of ERAMET is to provide and implement a framework for establishing the credibility of M&S methods and related results as sources of evidence within regulatory procedures. The ecosystem proposed by ERAMET will be based on three pillars: (1) A repository connecting questions, data and methods. (2) The development and validation of high-quality standards for data and analytical methods (including M&S and hybrid approaches). These will cover computational M&S, digital twins, AI, hybrid approaches, standard statistics and pharmacometrics, as analytical methods and alternative data types and sources such as RWD, eHealth data, registries, historical regulatory submissions, scientific and (non)clinical trials). (3) An AI-based platform that will automate and optimise the data collection, formatting and modelling and simulation analysis and implement the credibility assessment. As part of ERAMET, the ecosystem will be applied to five use-cases including paediatric extrapolation and characterisation of drug benefit/risk in 4 groups of rare diseases, namely ataxia, transfusion dependent haemoglobinopathies, bronchopulmonary dysplasia, and degenerative neuromuscular. Each of the use-case is planned to lead to submission and regulatory approval of at least one validated M&S tool via the EMA qualification procedure. Training will be proposed to familiarise regulatory assessors, drug developers and clinical researchers with this new approach.