TranSYS · Project

AI-Powered Patient Sorting Tools That Match Treatments to Individual Biology

healthPrototypeTRL 4Thin data (2/5)

h2020transys.eu

Imagine going to the doctor and instead of getting the same pill everyone else gets, your treatment is picked based on your unique biology — like a tailor-made suit instead of off-the-rack. TranSYS trained 15 young researchers across 13 organizations to build software tools that analyze massive patient datasets and sort people into groups that respond differently to treatments. They built everything from apps that show you your personal drug-response profile to AI tools that find hidden patterns in biological data. The goal is to stop the expensive trial-and-error of medicine and get the right treatment to the right patient the first time.

By the numbers

Early-stage researchers trained in precision medicine

Partner organizations in the consortium

Countries represented in the network

Industry partners involved

Total deliverables produced

The business problem

What needed solving

Drug development and clinical treatment still follow a one-size-fits-all approach, leading to expensive trial failures and patients receiving treatments that don't work for their specific biology. Companies need better tools to sort patients into meaningful subgroups before committing resources to large trials or treatment protocols. The gap between raw biological data and actionable clinical decisions remains a major bottleneck for pharma, diagnostics, and digital health companies.

The solution

What was built

The project delivered 18 deliverables including: a multi-omics patient stratification tool using advanced pattern recognition, a multi-level data integration tool for patient subtyping, a user-friendly pharmacogenetic passport app for the LifeLines-Deep cohort, a pipeline for individual-specific biological network construction, and an assessment of blockchain technology for clinical trial data management.

Audience

Who needs this

Pharma companies with high clinical trial failure rates seeking better patient selectionHealth IT vendors building clinical decision support platformsDiagnostic companies developing companion diagnostics or biomarker panelsBiotech startups working on precision oncology or rare diseasesContract research organizations (CROs) offering advanced trial design services

Business applications

Who can put this to work

Pharmaceutical R&D

enterprise

Target: Mid-to-large pharma companies running clinical trials

If you are a pharma company spending millions on clinical trials with high failure rates — this project developed a multi-omics patient stratification tool that sorts trial participants into biologically distinct subgroups. This means you can identify which patients will actually respond to your drug before the trial runs its course, potentially reducing the cost of failed Phase III studies. The tool was built and validated across a consortium of 13 partners including 3 industry players.

Health IT & Digital Health

mid-size

Target: Companies building clinical decision support or health analytics platforms

If you are a health IT company looking to add precision medicine capabilities to your platform — this project built a pipeline for constructing individual-specific biological networks and a user-friendly pharmacogenetic passport app. These ready-made components can be integrated into existing clinical software to offer doctors personalized drug-response insights for their patients. The tools were developed across 8 countries with input from both academic and industry partners.

Clinical Diagnostics

mid-size

Target: Diagnostic companies developing companion diagnostics or biomarker panels

If you are a diagnostics company trying to develop better biomarker panels for patient selection — this project delivered a multi-level data integrative tool specifically designed for biomarker discovery and patient subtyping validation. Instead of building your own analytics from scratch, you could license or adapt these validated approaches. The consortium included 5 research organizations with deep expertise in molecular medicine and data science.

Frequently asked

Quick answers

What would it cost to access or license these tools?

The project data does not include licensing terms or pricing. Since TranSYS was an MSCA training network coordinated by KU Leuven (a public university), IP arrangements would need to be negotiated individually with the relevant partner institutions. Contact the coordinator for licensing discussions.

Are these tools ready for industrial-scale deployment?

Based on available project data, the tools are at research-prototype stage. The multi-omics stratification tool and pharmacogenetic passport app were developed and demonstrated within the project, but there is no evidence of large-scale clinical deployment or regulatory clearance. Additional validation and integration work would be needed for production use.

Who owns the intellectual property?

IP from MSCA-ITN projects typically stays with the host institutions where the research was conducted. With 13 partners across 8 countries, IP is likely distributed. The 3 industry partners in the consortium may hold rights to specific components they co-developed. KU Leuven as coordinator can clarify ownership.

Has any of this been tested with real patient data?

The pharmacogenetic passport app was built for LifeLines-Deep participants, indicating it was tested with real cohort data. The patient stratification tools were designed to work with multi-omics clinical datasets. However, the project does not report results from prospective clinical validation studies.

What diseases does this cover?

The project objective mentions advancing precision medicine across several disease areas without specifying particular conditions. The tools are designed to be disease-agnostic — they analyze biological data patterns to identify patient subgroups regardless of the specific disease, making them applicable across oncology, autoimmune conditions, metabolic diseases, and more.

How does the blockchain component work?

One deliverable describes a decentralized software ecosystem exploring blockchain technology for clinical trial data management. Based on available project data, this focused on understanding how blockchain could improve data integrity and consent tracking in trials, rather than delivering a production-ready blockchain platform.

What is the timeline to adapt these tools for our pipeline?

Based on available project data, the core tools exist as research prototypes. Integration into a commercial pipeline would require additional engineering, validation against your specific datasets, and potentially regulatory review. The project ran from 2019 to 2024, so the tools reflect current state-of-the-art methods.

Consortium

Who built it

TranSYS brings together 13 partners across 8 European countries (Belgium, Germany, Spain, France, Luxembourg, Netherlands, Slovenia, UK), coordinated by KU Leuven — one of Europe's top research universities. The consortium has a 23% industry ratio with 3 SMEs among its partners, which is moderate for an academic training network. The balance of 5 universities and 5 research organizations provides deep scientific credibility, while the 3 industry partners ensure some commercial grounding. For a business looking to engage, KU Leuven is the single entry point, but the distributed expertise means specific tools may sit with different partners across the network.

KATHOLIEKE UNIVERSITEIT LEUVENCoordinator · BE
FUNDACION SECTOR PUBLICO ESTATAL CENTRO NACIONAL INVESTIGACIONES ONCOLOGICAS CARLOS IIIparticipant · ES
LIFEGLIMMER GMBHparticipant · DE
THE GOLDEN HELIX FOUNDATIONparticipant · UK
ACADEMISCH ZIEKENHUIS GRONINGENparticipant · NL
UNIVERSITE DU LUXEMBOURGparticipant · LU
INSTITUT PASTEURparticipant · FR
BIOMAX INFORMATICS AGparticipant · DE
ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAMparticipant · NL
MAX-PLANCK-GESELLSCHAFT ZUR FORDERUNG DER WISSENSCHAFTEN EVparticipant · DE
UNIVERZA V LJUBLJANIparticipant · SI
BARCELONA SUPERCOMPUTING CENTER CENTRO NACIONAL DE SUPERCOMPUTACIONparticipant · ES

How to reach the team

KU Leuven, Belgium — reach out to the TranSYS project office or the Systems Medicine research group

Order a report on this consortium See KATHOLIEKE UNIVERSITEIT LEUVEN profile →

Next steps

Talk to the team behind this work.

Want to explore how TranSYS patient stratification tools could fit your drug development or diagnostics pipeline? SciTransfer can arrange an introduction to the right research team and help you evaluate commercial fit.

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