H2020
Europeo
Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 5 “Unlocking the full potential of new tools, technologies and digital solutions for a healthy society”. To that end, proposals under this topic should aim for delivering results that are directed towards and contributing to several of the following expected outcomes:
Clinicians and other healthcare professionals have access to and/or use validated multi-scale computational models of individual patients for delivering optimised and cost-effective patient management strategies superior to the current standard of care.Healthcare professionals benefit from enhanced knowledge of complex disease onset and progression by recourse to validated, multi-scale and multi-organ models.Clinicians and patients benefit from new, improved personalised diagnostics, medicinal products, devices, and therapeutic strategies tailored to the individual patient patho-physiology.Citizens and patients have access to validated ‘virtual twin’ models enabling the integration of citizen-generated data with medical and other longitudinal health data, and benefi...
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Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination 5 “Unlocking the full potential of new tools, technologies and digital solutions for a healthy society”. To that end, proposals under this topic should aim for delivering results that are directed towards and contributing to several of the following expected outcomes:
Clinicians and other healthcare professionals have access to and/or use validated multi-scale computational models of individual patients for delivering optimised and cost-effective patient management strategies superior to the current standard of care.Healthcare professionals benefit from enhanced knowledge of complex disease onset and progression by recourse to validated, multi-scale and multi-organ models.Clinicians and patients benefit from new, improved personalised diagnostics, medicinal products, devices, and therapeutic strategies tailored to the individual patient patho-physiology.Citizens and patients have access to validated ‘virtual twin’ models enabling the integration of citizen-generated data with medical and other longitudinal health data, and benefit from early detection of disease onset, prediction of disease progression and treatment options, and effective disease management. Scope:This topic will contribute to the consolidation of existing virtual twin models and support research to move towards a more integrated human virtual twin, with the aim to accelerate translational research towards cost-effective development of new health technologies. Furthermore, ‘virtual twin’ patient models hold the potential of transforming clinical processes and healthcare with longitudinal monitoring, making personalised medicine, disease prevention and individualised patient management a reality.
Proposals are expected to contribute to the virtual human twin roadmap and ecosystem supported under the Digital Europe Programme[1], with models aligned and interoperable with those linked to the repository developed thereunder.
The proposals should address all of the following activities:
Develop multi-scale and multi-organ, dynamic, interoperable, modular computational models, capable of accurately simulating the individual patient patho-physiology, spanning different anatomical scales, from the molecular to cell, tissue, organ and systems level, as necessary. Proposals should be multidisciplinary and focus on groups of communicable and/or non-communicable diseases with commonalities within the same or across different medical domains, including co-morbidities. SME(s) participation is encouraged with the aim to strengthen the scientific and technological basis of SME(s) and valorise their innovations towards citizen and patient benefit.Advance the state of the art in multi-scale modelling by employing diverse modelling methodologies, including but not limited to: mechanistic modelling, artificial intelligence, agent-based and network physiology as a means for modelling the healthy state, disease onset, progression, treatment and recovery. Availability of the necessary diverse data types (e.g. data from lab tests, medical imaging, wearables, sensors, medical check-ups, mHealth devices, longitudinal health monitoring etc.) should be demonstrated and the sex/gender dimension should be investigated.Integrate standardised spatiotemporal multi-scale models as a basis for developing personalised ‘virtual twin’ models taking account of patient individual characteristics, medical and health status history for advancing personalised disease management. Proposals should ensure that the development of ‘virtual twin’ models is driven by the end-users/citizens/healthcare professionals needs and their active involvement throughout the development process. Furthermore, applicants should utilise appropriate IT solutions for model visualisation and demonstrate their accessibility and usability for clinical uptake.Validate multi-scale patient-specific models and generate evidence that results can deliver clinically meaningful, real-world observations for the human diseases under study. Applicants should implement proof-of-concept, feasibility studies in relevant end user environments and/or real-world settings, and collect evidence of utility vis-à-vis current clinical practice. Dynamic ‘virtual twin’ models and simulations as clinical decision support tools will need be shown to improve prognosis, medical diagnosis, treatments and health outcomes across the continuum of diseases evolution, including co-morbidities and long-term care as appropriate. An exploitation strategy and a business plan, including regulatory and industrial input, should be developed for accelerating clinical and/or market uptake. The proposals should adhere to the FAIR data[2] principles and adopt data quality standards, GDPR-compliant data sharing, access and data integration procedures based on good practices developed by the European research infrastructures. In relation to the use and interpretation of data, special attention should be paid to systematically assess for bias and/or discrimination (sex/gender, ethnic, minority and vulnerable groups aspects). Proposals are invited to consider adopting recommendations for in-silico models construction and validation.[3]
This topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts, institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities.
All projects funded under this topic are strongly encouraged to participate in networking and joint activities, as appropriate. These networking and joint activities could, for example, involve the participation in joint workshops, the exchange of knowledge, the development and adoption of best practices, or joint communication activities. This could also involve networking and joint activities with projects funded under other clusters and pillars of Horizon Europe, or other EU programmes. Therefore, proposals are expected to include a budget for the attendance to regular joint meetings and may consider covering the costs of any other potential joint activities without the prerequisite to detail concrete joint activities at this stage. The details of these joint activities will be defined during the grant agreement preparation phase. In this regard, the Commission may take on the role of facilitator for networking and exchanges, including with relevant stakeholders.
Applicants envisaging to include clinical studies should provide details of their clinical studies in the dedicated annex using the template provided in the submission system. See definition of clinical studies in the introduction to this work programme part.
[1] https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/digital-2021-deploy-01-twins-health;callCode=DIGITAL-2021-DEPLOY-01 DIGITAL-2021-DEPLOY-01-TWINS-HEALTH
[2] See definition of FAIR data in the introduction to this work programme part.
[3] ISO-paper under development “Recommendations and requirements for predictive computational models in personalized medicine research — Part 1: Guidelines for constructing, verifying and validating models”.
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