Advancing Paediatric and
Orphan Drug Development
through Innovative
Modelling and Simulation
About the Project
The overall objective of ERAMET is to 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).
What is ERAMET?
ERAMET stands for “Ecosystem for Rapid Adoption of Modelling and Simulation Methods to Address Regulatory Needs in the Development of Orphan and Paediatric Medicines.” The project is EU-funded and managed by a multidisciplinary consortium of 17 partners located in Belgium, Norway, United Kingdom, Italy, Spain, France and the Netherlands.
Consortium Members
Countries
Papers Published
Our Objective
The primary goal of ERAMET is to create a comprehensive system that aids in the development and regulatory approval of drugs for children and rare diseases.
This system will help both drug developers and regulators make better decisions using advanced modelling and simulation (M&S) methods and real-world data, such as medical records and registries.
The Three Pillars of ERAMET
Repository
A central hub that connects important questions, data, and methods, making it easier to find and use the right information.
Standards Development
Establishing and validating high-quality standards for data and analytical methods. This includes cutting-edge approaches like digital twins, artificial intelligence (AI), and hybrid methods combining different types of data and analyses.
AI Based Development
An advanced platform that automates data collection, formatting, and analysis using M&S. This platform will also assess the credibility of the data and methods used, ensuring reliability and accuracy.
Latest News & Information
What are modelling and simulation methods?
Modelling and simulation (M&S) methods in medical and drug implementation research use computational and mathematical techniques to replicate biological systems, disease progressions, and drug interactions, helping predict outcomes, optimize treatments, and reduce reliance on clinical trials. For example, pharmacokinetic and pharmacodynamic models simulate drug absorption and effects, agent-based models study disease spread and patient behaviour, and machine learning techniques predict treatment responses for personalized medicine…
