Imagine trying to find a needle in a haystack, but the haystack grows by 25 petabytes every year. This tool acts like a high-powered magnet that organizes messy biological data so scientists can quickly spot which drugs will actually work for specific patients. It removes the need for complex coding, making it as easy as using a standard cloud app.
Oncology drug discovery suffers from extremely high failure rates and massive costs, often exceeding €2bn per drug. Scientists are overwhelmed by petabytes of data and lack user-friendly tools to identify which patients will actually respond to a treatment.
An AI-powered cloud platform (mCUBE) featuring data mapping pipelines and modules for target discovery, biomarker identification, and clinical data harmonization.
If you are a drug discovery firm dealing with high attrition rates where only 1 in 5 patients respond to treatment — this project developed an AI-augmented platform that doubles the clinical trial success rate by using molecular analysis to prioritize candidates.
If you are a precision medicine startup dealing with the high cost of €2bn to bring a compound to market — this project developed a multi-omic analysis pipeline that identifies biomarkers and selects the right preclinical models to reduce waste.
If you are a health data provider dealing with clinical data that is too time-consuming to harmonize manually — this project developed a cancer-agnostic clinical data harmonization module that uses AI to automate the process.
The project initially utilizes a SaaS business model, which is planned to transition into a drug discovery model focused on licensing IP assets.
Yes, the platform is designed as a cloud-based solution for industrial partners and is capable of integrating and harmonizing omic data at scale.
The business strategy involves identifying and validating new drug candidates to license IP assets to pharma companies, with a target deal of €25M by 2025.
It provides a user-friendly cloud-based platform that replaces the need for manual programming or difficult collaborations with bioinformaticians.
Based on available project data, the company expects to sign a €25M deal with a biopharma partner by 2025.
The project is led by a single SME, EPIGENE LABS SAS from France. With a 100% industry ratio and no university or research partners in the formal consortium, the project is heavily driven by commercial viability and direct industrial application.
Contact EPIGENE LABS SAS in France
Contact us to explore licensing opportunities for AI-driven oncology platforms.
