The European community requires early stage researchers (ESRs) who can work across the boundaries of traditional disciplines, integrating experimental and in silico approaches to understand and manage complex multifactorial disorders. This training network utilises intervertebral disc degeneration (LDD) leading to low back pain (LBP) as a relevant application for data integration and computational simulations in translational medicine. LBP is the largest cause of morbidity worldwide, yet there remains controversy as to the specific cause leading to poor treatment options and prognosis. LDD is reported to account for 50% of LBP in young adults, but the interplay of factors from genetics, environmental, cellular responses and social and psychological factors is poorly understood. Unfortunately, the integration of such data into a holistic and rational map of degenerative processes and risk factors has not been achieved, requiring creation of professional crosscompetencies, which current training programmes fail to address. Disc4All aims to tackle this issue through collaborative expertise of clinicians; computational physicists and biologists; geneticists; computer scientists; cell and molecular biologists; microbiologists; bioinformaticians; and industrial partners. It provides interdisciplinary training in data curation and integration; experimental and theoretical/computational modelling; computer algorithm development; tool generation; and model and simulation platforms to transparently integrate primary data for enhanced clinical interpretations through models and simulations. Complementary training is offered in dissemination; project management; research integrity; ethics; regulation; policy; business strategy; and public and patient engagement. The Disc4All ESRs will provide a new generation of internationally mobile professionals with unique skill sets for the development of thriving careers in translational research applied to multifactorial disorders.

Our vision consists of revolutionizing dental and orthopedic surgery by introducing a new paradigm, model-based theranostics, which consists of an integrative coupling of therapeutics, diagnostics and numerical simulation in order to optimize the performances of the surgical protocol and to predict its clinical outcome. The success of surgical protocols involving endosseous implants is limited by i) the empirical methods employed to assess implant stability, which is a strong determinant of the surgical outcome, ii) the absence of therapeutic approaches to stimulate osseointegration phenomena and iii) the difficulty of predicting the implant outcome. The aim of UltraSimplant is to develop a radically new unified model-based theranostic concept using innovative ideas in the domain of quantitative ultrasound (QUS). The new concept will combine characterization, simulation and stimulation of osseointegration phenomena, leading to the foundation of a revolutionary approach capable of providing a decision support system to the surgeon, to improve osseointegration in a patient specific manner and to predict the surgical outcome, thus leading to a drastic decrease of the implants failure rate. We will conceive and validate (in vitro, in silico, in vivo and in a clinical trial) a minimum viable product and consisting of a medical device using QUS techniques to assess dental implant stability. A validated model of the evolution of the bone-implant system will take into account the complex multiscale nature of the interface in order to validate in silico the QUS device and to predict the effect of ultrasound stimulation and implant outcome. The model will be used in order to optimize the parameters to be employed in the stimulation. UltraSimplant will first focus on dental implants because of the important failure rate and to the easy access of the implant. In the long term, model-based theranostic approaches will be applied to other implants in orthopedic surgery.

The overall aim of the In Silico World project is to accelerate the uptake of modelling and simulation technologies for the development and regulatory assessment of all kind of medical products. This will be achieved by supporting the trajectory of a number of In Silico Trials solutions through development, validation, regulatory approval, optimisation, and commercial exploitation. These solutions, already developed to different stages, target different medical specialities (endocrinology, orthopaedics, infectiology, neurology, oncology, cardiology), different diseases (osteoporosis, dynapenia-sarcopenia, tuberculosis, multiple sclerosis, mammary carcinoma, arterial stenosis, etc.), and different types of medical products (medicinal products, medical devices, and Advanced Therapeutic Medicinal Products). In parallel the consortium will work with a large multi-stakeholder advisory board to form a community of Practice around In Silico Trials, where academics, industry experts, regulators, clinicians, and patients can develop consensus around Good modelling Practices. As the solutions under development move toward their commercial exploitation, the ISW consortium will make available to the Community of Practice a number of resources (technologies, validation data, first in kind regulatory decisions, technical standardisation plans, good modelling practices, scalability and efficiency-improving solutions, exploitation business models, etc.) that will permanently lower barriers to adoption for any future development.

Cancer patients (2.7M in Europe) with a positive prognosis are exposed to a high incidence of secondary tumours (≈1M). Bone metastases spread to the spine in 30-70% cases, reducing the load bearing capacity of the vertebrae and triggering fracture in 30% cases. Clinicians have only two options: either operate to stabilise the spine, or leave the patient exposed to a high fracture risk. The decision is highly subjective and can either lead to unnecessary surgery, or a fracture significantly affecting the quality of life and cancer treatment. The standard-of-care to classify patients with vertebral metastasis are scoring systems based on radiographic images, with little consideration of the local biomechanics. Current scoring systems are unable to establish an indication for surgery in around 60% of cases. Thus, there is an unmet need to accurately and timely quantify the risk of fracture to improve patient stratification and identify the best personalised treatment. This interdisciplinary project will develop Artificial Intelligence (AI)- and Physiology-based (VPH) biomechanical computational models to stratify patients with spine metastasis who are at high risk of fracture and to identify the best personalised surgical treatment. After rigorous model training with clinical (2000 retrospective cases) and biomechanical (120 ex vivo specimens) data, the new approach will be tested in a multicentric prospective observational study (200 patients). The models will be combined in a decision support system (DSS) enabling clinicians to successfully stratify metastatic patients. The models and the DSS will be designed so as to be suitable for regulatory requirements and future exploitation. METASTRA will propose new guidelines for the stratification and management of metastatic patients. METASTRA approach is expected to cut the uncertain diagnoses from the current 60% down to 20% of cases. This will reduce patient suffering, and allow cutting expenditure by 2.4B€/year.

Despite massive investment in healthcare, huge R&D cost increase and regulatory pathway complexity hamper tremendously commercialisation of new devices & medicines, putting patient populations at risk of not receiving adequate therapy. At the same time, outside healthcare, computer modelling and simulation (CM&S) is precisely recognised to increase speed & agility while reducing costs of development. CM&S can create scientific evidence based on controlled investigations including variability, uncertainty quantification, and satisfying demands for safety, efficacy & improved access. Cardiac modelling has dramatically gained maturity over the last decades, with personalisation to clinical data enabling validation. We selected a number of cardiac devices and medicines where CM&S is mature enough and that represent the most common cardiac pathologies, to demonstrate a standardised and rigorous approach for in-silico clinical trials. SimCardioTest will bring a disruptive innovation by creating an integrated and secure platform standardising & bridging model simulations, in-silico trials, and certification support. This environment will go beyond the state-of-the-art in computational multi-physics & multi-scale personalised cardiac models. Diseased conditions and gender/age differences will be considered to overcome clinical trials limitations such as under-representation of groups (e.g. women, children, low socio-economic status). Advanced big data, visual analytics & artificial intelligence tools will extract the most relevant information. It is critical that Europe demonstrates its capacity to leverage in-silico technology in order to be competitive in healthcare innovation. SimCardioTest exploitation aims at delivering a major economic impact on the European pharmaceutical and cardiac devices industry. It will accelerate development, certification and commercialisation, and will produce a strong societal impact contributing to personalised healthcare.