A large research consortium of 12 partners, including Euretos, has received a ten million euro grant for predictive personalised treatment in hypertension. High blood pressure, also called hypertension, is a serious medical condition that leads to cardiovascular disease and is a major cause of death worldwide. Although many treatments are available, it is difficult in most countries to keep blood pressure under control. This can result in heart attacks, strokes, kidney diseases and dementia.
The Hypermarker research consortium is developing a clinical decision support tool that will make it easier for clinicians to personalise the treatment for patients with high blood pressure. The consortium consists of world leaders in health data science, patient advocacy and industry. The team is using AI, deep learning methods and patient cohort data from 11 European countries.
“We are developing prediction algorithms that can help clinicians choose the right high blood pressure treatment for individual patients”, project leader Diederick Grobbee from Utrecht University explains. “By measuring and analysing small molecules in the blood that communicate with the body’s systems, you can predict the response to medication.”
Euretos will contribute access to the Euretos AI Platform to aid in the interpretation of the data and the output of the AI algorithms that will be developed as part of the project. The team is currently expected to analyse 4,000 plasma samples from patients with high blood pressure to further improve the prediction algorithms.
Thomas Hankemeier, Professor of Analytical Biosciences at Leiden University and co-project leader of the Hypermarker team, is convinced that the team’s ‘pharmacometabolomics’ approach, which involves predicting a treatment’s effect by analysing metabolites such as amino acids, will be the blueprint for the clinical lab of the future. “This approach will help personalise treatments not only for high blood pressure but also for other diseases because you look at the body’s full physiological state.”
The core objective of Hypermarker is to address an urgent unmet need to improve outcomes for patients with hypertension by effectively and more efficiently using existing pharmaceutical therapies. To achieve this, we will leverage the use of pharmacometabolomics to derive a personalised treatment approach based on the respective patient's metabolomic profile.
The tools will be validated and refined through an innovative randomised clinical trial across 4 countries, supported by patient and public engagement. Artificial intelligence approaches will be used to integrate this information with clinical factors, using deep learning methods to isolate what is most important to determining treatment for each patient.
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