Imagine trying to test a new medicine but having very few patients to try it on because the disease is so rare. Instead of guessing, this project builds a high-tech digital rehearsal space using AI and math to predict how a drug will work. It creates a gold-standard rulebook so regulators trust these digital results as much as real-world trials.
Developing drugs for children and rare diseases is slow and expensive due to a lack of patients for clinical trials. This makes it difficult to provide the evidence regulators require for approval.
An AI-powered platform and data repository that automates simulation analysis and establishes standards for digital twins and pharmacometrics.
If you are a drug developer dealing with tiny patient pools for rare diseases — this project developed an AI-based platform that automates data collection and simulation. This allows you to submit validated tools through the EMA qualification procedure to get medicines to market faster.
If you are a software company dealing with inconsistent data standards for clinical simulations — this project developed rigorous standards for digital twins and hybrid AI approaches. This ensures your tools meet regulatory needs for pediatric and orphan medicine assessment.
If you are a CRO dealing with the difficulty of pediatric extrapolation in clinical trials — this project developed a repository connecting questions, data, and methods. This helps you use real-world data and registries to provide stronger evidence for regulatory approval.
Based on available project data, there is no information regarding the pricing or cost of the platform and repository.
The project specifically focuses on 5 use cases for pediatric and rare diseases, including ataxia and neuromuscular disorders. Based on available project data, it is not specified if it will scale to general medicine.
Based on available project data, the IP and licensing terms are not disclosed; however, the goal is to achieve regulatory approval via the EMA qualification procedure.
The project aims for the rapid adoption of simulation methods to address regulatory needs, specifically targeting the submission and approval of at least one validated tool per use case.
The AI-based platform is designed to automate and optimize the collection and formatting of data from sources like registries, eHealth data, and historical regulatory submissions.
The consortium is well-balanced for commercialization, featuring 19 partners across 8 countries. With a 26% industry ratio (5 companies, including 2 SMEs), there is a clear link between academic research (6 universities, 4 research centers) and market application, ensuring the AI tools are built for actual regulatory submission rather than just theoretical study.
Contact Universite de Namur in Belgium for partnership inquiries.
Contact us to identify which of the 5 rare disease use cases fits your drug pipeline.
