Complex chronic diseases like endometriosis consume 80% of Europe’s healthcare budget. Endometriosis is a painful condition in which the lining of the uterus grows outside the uterus. It affects 200 million women worldwide at the peak of their productive and reproductive years and costs Europe €70bn/year. As with other complex chronic diseases, endometriosis results from multiple combinations of genetic, clinical and environmental factors, making it difficult to diagnose and treat. On average it takes 8 years to diagnose, typically involving 10+ medical consultations. The current diagnostic standard is laparoscopy - an invasive, expensive and painful surgery. Neither non-invasive, accurate early-diagnostic tools nor effective treatments currently exist for endometriosis. As many as 60% of endometriosis cases are undiagnosed. As such, a pressing medical need exists for an affordable and scalable precision diagnostic solution to guide personalised treatments. Building on successful results from our Horizon 2020 FEMaLe project, this EIC Accelerator project will clinically validate the world’s first accurate, cost-effective, scalable and simple to use diagnostic test to predict an individual's risk of developing endometriosis based on the underlying causal mechanisms of the disease. Using these novel insights, this project will demonstrate how the diagnostic can be used to target clinically repurposed drugs to successfully treat specific disease subtypes. Having identified critical disease signatures and potential drug candidates to repurpose (TRL6 achieved), EIC support is essential to certify our novel diagnostic as a Class C IVDR device and prove the efficacy of repurposed drugs to treat endometriosis for a €4bn global market. Overcoming these remaining risky and challenging barriers will enable us to achieve €251m in revenues, €171m in net profits and grow our highly skilled team to 73 FTEs by Y5 post-project, thereby realising our full commercial potential.

Ixion2Complex chronic diseases like endometriosis consume 75% of Europe’s healthcare budget. Endometriosis is a painful condition in which the lining of the uterus grows outside the uterus. It affects 200 million women worldwide at the peak of their productive and reproductive years and costs Europe €70bn per year. As with other complex chronic diseases, endometriosis results from multiple combinations of genetic and clinical factors, making it difficult to diagnose and treat. On average it takes 8 years to diagnose, typically involving +10 medical consultations. The current diagnostic standard is laparoscopy - an invasive, expensive and painful surgery. Neither non-invasive, accurate early-diagnostic tools nor effective treatments currently exist for endometriosis. As much as 60% of endometriosis cases are undiagnosed. As such, a pressing medical need exists for an affordable and scalable precision diagnostic solution to guide personalised treatments. Building on successful results from our Horizon 2020 collaborative project, this EIC Accelerator project will clinically validate the world’s first accurate, cost-effective, scalable and simple to use diagnostic test to predict an individual's risk of developing endometriosis based on the underlying causal mechanisms of the disease. Using these novel insights, this project will demonstrate how the diagnostic can be used to target clinically repurposed drugs to successfully treat specific disease subtypes. Having identified critical disease signatures and potential drug candidates to repurpose (TRL6 achieved), EIC support is essential to certify our novel diagnostic as a Class C IVDR device and prove the efficacy of repurposed drugs to treat endometriosis for a €2.9bn global market. Overcoming these remaining risky and challenging barriers will enable us to achieve €133m in revenues, €80m in net profits and grow our highly skilled team to 85 FTEs by Y5 post-project, thereby realising our full commercial potential.

The framework 'P4 Medicine' (predictive, preventative, personalized, participatory) was developed to detect and prevent disease through close monitoring, deep statistical analysis, biomarker testing, and patient health coaching to best use the limited healthcare resources and produce maximum benefit for all patients. However, we have seen only few feasible examples over the past 10 years. The Finding Endometriosis using Machine Learning (FEMaLe) project will revitalise the concept to develop and demonstrate the Scalable Multi-Omics Platform (SMOP) that converts multi-omic person population datasets into a personalised predictive model to improve intervention along the continuum of care for people with endometriosis. We will design, validate and implement a comprehensive model for the detection and management of people with endometriosis to facilitate shared decision making between the patient and the healthcare provider, enable the delivery of precision medicine, and drive new discoveries in endometriosis treatment to deliver novel therapies and improve quality of life for patients. We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver: 1) mobile health app for people with endometriosis, 2) three clinical decision support (CDS) tools for targeted healthcare providers (risk stratification tool for general practitioners, multi-marker signature tool for gynaecologists, and non-invasive diagnostic tool for radiologist), and 3) computer vision-based software tool for real time augmented reality guided surgery of endometriosis. Health maintenance organisations (HMO) expect to be able to reduce overall cost of treatment by at least 20%, while improving patient outcomes, using CDS tools. The SMOP will be based on open protocol, embedded in all ethical and legal frameworks, to enable tailored and personalised usage to improve the lives of patients across Europe beyond the project period.