Five use-cases (related to specific applications in cancer, cardiovascular, intensive care, osteoporosis, and brain) have been preselected for early prototyping of the simulation platform, thus allowing EDITH to start working on tangible examples of existing virtual twins. Most of the selected use-cases are currently being tested in a clinical setting in one or more institutions in Europe. EDITH partners responsible for the use-cases represent both technical developers and clinical users. These use-cases cover all the relevant elements of the platform: model types (multi-scale, multi-physics mechanistic models, data-driven models), dataset types (omics, images) and sizes, computational demands (High Performance Computing, cloud), access demands (multiple different users, real-time), possibilities for integration (linking resources across different scales and different application domains), and links to existing repositories. In addition, the SEEK infrastructure, developed by the FAIRDOM initiative, implements the Investigations–Studies–Assays (ISA) structure and it is a well-suited service to bridge between EDITH’s repository and the simulation platform.

• Cancer

  • Cancer care has been identified as one of the major disease areas that will benefit from the European Digital Strategy (EDS). The use case is provided by EDITH’s partner Barcelona Supercomputing Center (BSC), from their prior work in the PerMedCoE (Personalised Medicine Centre of Excellence) project. Their in silico model is based on single-cell sequencing data from patients and quantifies the patient-specific molecular and cellular features that represent real tumours to propose personalised drug treatments. This model will be complemented by the EDITH consortium through microscopy imaging and spatial omics techniques to assess the ability to obtain in silico solutions that recapitulate the main anatomical features of the observed tumours. EDITH will integrate the entire workflow to deliver personalised health forecasts.

• Cardiovascular

  • Cardiovascular diseases are the leading cause of death in the EU. They are also an area where multiple digital resources are already available. Atrial fibrillation is the most common form of cardiac arrhythmia, affecting over 37 million people worldwide. Virtual cohorts that capture variability in atrial anatomy, fibrotic remodelling, and electrical properties are already under investigation and may be used to simulate the effects of different treatment approaches. Atrial fibrillation has a higher incidence in cancer patients. The underlying mechanisms of this association are currently unclear and virtual twins could form an ideal framework for their investigation. Integration of the respective resources and cardiac modelling workflows will be tested by EDITH (Queen Mary University of London, QMUL and École Politechnique Fédérale de Lausanne, EPFL) as a proof-of-concept towards creation of Virtual Human Twins (VHTs). EDITH will provide a pathway to in silico personalised treatment planning.

• Intensive care

  • Intensive care is suffering from increasing demographic and economic pressure worldwide. Patient heterogeneity and metabolic variability are high in critically ill patients, showing the need for personalised solutions to provide safe and effective care for all. Virtual twins based on identifiable and relevant patient-specific, time-varying physiological parameters can be used to design decision support systems to guide healthcare professionals. EDITH’s partner University of Liège (ULIÈGE) is currently co-developing a personalised, risk-based treatment approach providing safe and effective glycaemic control, which is associated with improved survival and recovery in critically ill patients. The computational demands of this model are small, but a challenging real-time interaction between sensors, models, and healthcare professionals is required.

• Osteoporosis

  • Osteoporosis is one of the major causes of mortality among the elderly. With the EU's ageing population, adequate prediction of osteoporosis-related fracture risk is important. The Bologna Biomechanical Computed Tomography (BBCT) is a virtual twin methodology developed by EDITH’s partner University of Bologna (UNIBO). It is a mechanistic model that uses a subject-specific finite element model to predict femoral bone strength. Quantitative Computed Tomography (QCT) scans of the hip region and patient data will inform a patient-specific computer model, which will, in turn, predict the risk of hip fracture. This will be a single-organ model, generated from data at the time of CT acquisition, and eventually lead to a personalised health forecast.

• Brain

  • The human brain can be conceptualised as a complex system organised at different spatial scales. The estimated number of neurons is 86 billion, whereby each neuron can have approximately 10.000 synapses. EDITH’s partner JÜLICH (Forschungszentrum Jülich GMBH) has developed BigBrain, a 3D anatomical model. BigBrain is being used to provide high resolution anatomical data for brain models of patients with epilepsy. These data include both physiological and region connectivities, collectively represented as a virtual brain. A combination of the BigBrain model with heart resources (see cardiovascular use-case) based on very large data sets (TB to PB range) available in the repository will be investigated by EDITH to create a data-driven model. Brain, and the brain stem in particular, influence heart function and this is an exemplary trial case of organ integration towards Virtual Human Twins (VHTs). Thus, it will allow a personalised treatment planning.