Imagine trying to find the right key for a lock, but every lock is slightly different and you have 19 different keys to try. This project builds a smart digital assistant that analyzes a patient's unique health data to predict which medicine will actually work first. It replaces the guessing game of trial-and-error with a data-backed map of the patient's future health.
Doctors currently use a trial-and-error method to treat Multiple Sclerosis because the disease varies wildly between patients. This leads to poor treatment responses and higher healthcare costs.
A companion diagnostic platform that visualizes biomarkers and predicts disease trajectories. It includes a common data model for harmonizing clinical and MRI data.
If you are a drug developer dealing with variable treatment responses in real-world settings — this project developed a companion diagnostic platform that predicts disease trajectories under different treatment options. This allows for better targeting of the 19 available disease modifying treatments to the right patients.
If you are a software vendor dealing with fragmented clinical data — this project developed a common data model (CDM) that harmonized data from over 5,000 real-world patients. This enables the creation of scalable AI tools for biomarker extraction and visualization.
If you are a clinic manager dealing with high costs of care and unpredictable patient relapses — this project developed an AI-assisted care platform that uses subclinical biomarkers to optimize treatment. This aims to improve patient outcomes while lowering the total cost of care.
Based on available project data, specific pricing or cost details for the platform are not provided.
Yes, the project uses state-of-the-art technologies designed for reliable and scalable implementation across the world.
Based on available project data, the specific licensing terms are not mentioned, but the project aims to submit the platform for regulatory approval.
The project is developing a companion diagnostic platform and intends to submit it for regulatory approval to be used in clinical settings.
The project runs from 2023-06-01 to 2027-05-31, with an initial platform version already intended for installation with clinical partners for field tests.
The consortium is highly commercially oriented, with a 50% industry ratio consisting of 8 industrial partners, including 6 SMEs. This balance, combined with 5 universities and 2 research institutes across 9 countries, suggests a strong pipeline from academic research to market-ready medical devices.
Contact the project lead at Charité - Universitätsmedizin Berlin
Contact SciTransfer for a detailed partnership analysis of the 8 industrial partners in the CLAIMS consortium.
