Imagine trying to fix a tiny part of a machine, but you can't see the part until the machine is already running. This project creates a way to identify and grow the right stem cells in a lab first, so doctors can fix them precisely before putting them back into a patient. It's like having a high-resolution map and a test-run area to ensure the treatment works perfectly without needing slow animal tests.
Current stem cell therapies rely on surrogate cells and slow animal testing because true stem cells cannot be identified before transplantation. This leads to unpredictable clinical outcomes and long development cycles.
A system for ex vivo stem cell expansion combined with single-cell multiomics profiling and a machine-learning tool for ATMP characterization.
If you are a developer dealing with slow and unpredictable stem cell quality control — this project developed a multiomics profiling system that allows for the prospective identification of functional stem cells. This reduces the reliance on time-consuming mouse xenotransplantation experiments.
If you are a firm dealing with the difficulty of turning blood cells into therapeutic vehicles for cancer — this project developed expansion-based engineering protocols. This enables the creation of more efficient and safer genetically modified cells for clinical application.
If you are a biotech dealing with inherited bone marrow failure syndromes — this project developed a toolkit using base and prime editing with lipid nanoparticles. This improves the efficiency of correcting genetic defects in stem cells.
Based on available project data, specific pricing or implementation costs are not provided.
The project focuses on improving throughput and turnaround time for testing engineering conditions, which supports future industrial scaling of ATMPs.
Based on available project data, the project aims to create an open source tool for the characterization of genetically-engineered ATMPs.
It integrates by replacing or reducing the need for in vivo xenograft experiments with in vitro multiomic readouts to predict cell function.
The project period runs from 2022-10-01 to 2026-09-30, with the final goal of enabling clinical development of new HSC gene therapies.
The consortium consists of 7 partners across 4 countries, showing a strong academic and research lean with 3 universities and 3 research institutions. Only 1 industry partner is involved (14% ratio), suggesting the project is currently in a high-tech validation phase rather than a commercial rollout phase, led by a large Italian healthcare entity (Ospedale San Raffaele).
Contact Ospedale San Raffaele SRL in Italy
Contact us to explore licensing opportunities for the open-source ATMP characterization tool.
