Pharmacogenomics is the study of genetic variability affecting an individual’s response to a drug. Its use allows personalized medicine and reduction in ‘trial and error’ prescribing leading to more efficacious, safer and cost-effective drug therapy. The U-PGx consortium will investigate a pre-emptive genotyping approach (that is: multiple pharmacogenomic variants are collected prospectively and embedded into the patients’ electronic record) of a panel of important pharmacogenomic variants as a new model of personalised medicine. To meet this goal we combine existing pharmacogenomics guidelines and novel health IT solutions. Implementation will be conducted at a large scale in seven existing European health care environments and accounts for the diversity in health system organisations and settings. Feasibility, health outcome and cost-effectiveness will be investigated. We will formulate European strategies for improving clinical implementation of pharmacogenomics based on the findings of this project.

Obtaining functional information on living organs non-invasively across different size scales is a tremendous challenge in medical imaging research, as diseases start locally at the cellular level deep into organs before expressing large-scale and observable symptoms. The unique complexity of the human brain adds another level of difficulty for neuroimaging. The cerebrovascular system consists of a multiscale network of blood vessels. Interaction between neurons and this vascular system, the so-called neurovascular coupling, is a major foundation of brain function leading to constant adaptation of the local cerebral blood flow to local metabolic demand. Its alteration is intimately linked to cerebral dysfunction. Current brain imaging modalities are essential for evaluating cerebrovascular diseases in patients but are restricted to millimetric resolution and fail to capture most of blood flow dynamics. Here, we propose to revolutionize the field of neuroimaging by introducing a groundbreaking technology called functional Ultrasound Localization Microscopy (fULM) capable of monitoring transcranially the whole human brain vasculature and function down to microscopic resolution. Beyond opening a complete paradigm shift in brain angiography (at least two orders of magnitude increase in spatial resolution), fULM will also be able to map the functional brain response during task-evoked and spontaneous activity at microscopic levels. We will address major technical challenges of ultrasound imaging, develop advanced neurocomputational analysis methods, validate our methods in preclinical models of cerebrovascular diseases and perform a First-In-Human study. Fundamental understanding of brain hemodynamics and neurovascular coupling as well as early clinical diagnosis of neurovascular abnormalities and evaluation of drug efficacy would tremendously benefit from such capabilities revealing both the brain vasculature and neurofunctional activity down to microscopic resolutions.

SHERPA will empower interventional radiologists (IR), an overburdened group of specialists who use medical imaging and image-guided devices to perform complex, high-risk interventions. Since trust in technology is rooted in relationships – not in a technical specification or feature, AI-powered assistive technologies will be delivered as a seamless, trusted companion (a ‘sherpa’) across the workflow for two clinical domains (Neurology and Oncology), validated with IR and patients through seven clinical studies, and made available to other medical specialities through a framework methodology and outreach to the entire IR community. The automated workflows and their benefits at user, patient and system levels will be evaluated for two clinical applications: 1. Minimally invasive neurovascular interventions (brain aneurysms) and 2. Minimally invasive interventional oncology (liver tumour ablations). Fueled by cutting-edge advances in AI and robotics yet profusely human-centric, workflow automation will minimise the risk of errors and inconsistencies, ensuring the accuracy and reliability of clinical decisions and boosting IR confidence and job satisfaction. As repetitive, time-consuming tasks can be performed automatically, the IR will gain the much-needed relief to focus on the intervention and the interaction with the patient; the expertise threshold will be decreased, improving workload distribution and team dynamics. Through generalising principles and methodological framework, the insights from SHERPA will extend their benefits to other medical specialties dealing with complex workflows and critical decision-making.