EDITH: Building an Ecosystem for Realizing the Integrated Virtual Human Twin

The use of digital twins in healthcare (DTH) is rapidly increasing . One application area is the personalisation of medical care, where DTH can take the shape of in silico models of organs and organ systems used to test various treatment options, to customise therapy or plan surgery. In the context of the development of medical therapies, they can be used as a tool throughout the entire R&D process to identify knowledge gaps and flaws, obtain a holistic and better understanding of a patient’s disease, design novel strategies, optimise therapies, optimise therapy production, increase safety (by providing additional scrutiny) and shorten the time to market. DTH can be used to improve healthcare organisations by driving efficiency, optimising operational performance and enhancing both patient and caregiver experience. Current solutions labelled as DTH are mostly single-scale, single-organ, single disease systems simply because going beyond this is still too complicated and time consuming, and hence prone to be neither realistic nor reliable. However, the human body is highly entangled: events occurring at one anatomical location at a given time may influence processes occurring at different locations and at different times. Therefore, the number of clinically relevant questions answerable with single-scale, single-organ, single disease models is relatively limited. Integrating several scales and levels of organisations generates huge challenges to modelling. To accelerate the adoption of an integrated Virtual Human Twin, it must first become easier to develop them, even when they need to be multi-scale, multi-organ, and multi-disease. The challenges related to developing a Virtual Human Twin call are too substantial to be handled by any one research group or even collaborative research project. They call for an ecosystem approach. The European Commission’s Coordination and Support Action EDITH (101083771) has as its objective to foster such an ecosystem and develop a roadmap towards the integrated Virtual Human Twin. The first step is an extensive mapping of relevant actors and initiatives, available resources (models, data sets, methods), infrastructures, DT-based solutions and services, as well as detecting technical and non-technical barriers to the uptake of DTH. This will allow to focus on the creation of a functional ecosystem bringing together all relevant stakeholders, including solution developers in academia and industry, technology/resource providers, end-users (particularly healthcare professionals and patients), regulatory agencies and HTA bodies. Leveraging the budding ecosystem, the consortium is working on a roadmap for accelerating the uptake of the DTH-based solutions and their further integration. The roadmap will contain a blueprint of the Virtual Human Twin and will identify the required (technical) developments, including but not limited to interoperability, computability, and integration of health models & data. The previously identified stakeholder needs and implementation barriers will be addressed. Additionally, an analysis of areas of applicability will be conducted, targeting especially applications representing high unmet medical needs and/or high societal benefits or clinical values. Finally, instruments such as funding, policies, standards, and specific recommendations will be specified for short and mid-term, taking into account the current legal, ethical, social and regulatory framework and country-specificities. After going through extensive review by the ecosystem, the first draft of the roadmap has been released (https://doi.org/10.5281/zenodo.8200955) and will be further elaborated through community activities in the coming year. Find out more about EDITH on the official website: https://www.edith-csa.eu *Contents* 00:00 - Introduction 06:00 - EDITH: building an ecosystem for realizing the integrated virtual human twin 41:44 - Questions and Discussions Moderator: James A. Glazier, PhD, Indiana University, Bloomington If you found this video useful, please check out our other videos on computational modeling, infection and immunology: https://youtube.com/playlist?list=PLiEtieOeWbMKh9VcQoinSwODcSZKMTGat Please consider joining our IMAG/MSM WG on Multiscale Modeling and Viral Pandemics: https://www.imagwiki.nibib.nih.gov/content/msm-viral-pandemics-meetings Please also consider joining the Global Alliance for Immune Prediction and Intervention: http://glimprint.org/