Digital Twin simulations and precision medicine for foster better understanding and management of Parkinson Disease

A Digital Twin database to improve treatment of Parkinson disease
Thematic area: Health, Projects
Financing: IFAB call for projects
Enabling Technology: Advanced Modeling and Simulation, Artificial Intelligence, Big Data Analytics, Digital Twin

Precision medicine is extending to the treatment of Parkinson Disease through deep phenotyping, genetics, and the usage of other -omics data. This approach generates large datasets, which require a specific database to identify subgroups of patients who respond better to specific treatments. Because original data are often difficult to obtain, a Digital Twin approach will be used to generate large sets of synthetic “digital patients” that statistically resemble real patients.

The project utilizes a synthetic Digital Twin database obtained from public domain data (e.g., clinical profile, biomarkers, genetics) supplemented with novel diagnostics for Parkinson Disease (e.g., gastrointestinal symptoms, gut microbiome, and in vitro pathology on iPSC-derived neurons) obtained from a small prospective study designed to explore the practical aspects of data collection.


The goal of this project is to generate “preliminary evidence” on how precision medicine can be useful in identifying patients who may benefit from specific therapies, generating an impact on the scientific medical community to engage for larger database and prospective studies.

The project also has a broader societal impact, with several intergovernmental bodies emphasizing the need for efficient information flow. This is demonstrated by the European Commission’s Towards Common Data Spaces initiative and recent G7 efforts to establish global standards for data sharing. Traditional methods of anonymization have proven insufficient to achieve these goals, both in terms of protecting privacy and preserving analytical utility.

The initial challenge

The prevalence of Parkinson Disease is increasing in Europe. The application of an effective precision medicine paradigm could have an impact on physicians’ treatment decisions, patient satisfaction, and health system efficiency, thereby contributing to cost savings. Furthermore, original data are extremely difficult to obtain and are often scattered.

The solution

Synthetic data are increasingly perceived as a superior alternative to anonymization. The proposed project will be one of the first to demonstrate its potential in a complex practical context.

Successful deployment will lead to increased societal awareness and adoption of synthetic data technology. This will cause greater mobility of data, but will also result in fairer and more transparent use of data through the elimination of bias.

The time and cost of data-driven projects currently do not allow smaller companies to compete. The technology developed in this project will democratize the use of data, allowing companies of all sizes and in all industries to compete on a level playing field.


The project offers a personalized approach to patient treatment, using data-omics to create synthetic models or Digital Twins. This not only improves treatment effectiveness, but also protects patient privacy. Aligning with global initiatives for secure data sharing, the project overcomes the limitations of traditional anonymization, balancing privacy with data utility. It promotes democratic access to information, reduces bias in data, and stimulates innovation in various fields, potentially influencing privacy policies and data sharing globally.


For further information, please contact:

Sustainable Development Goals


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