Projects

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

A Digital Twin database to improve the treatment of Parkinson's disease

Funding: IFAB call for projects

Enabling technology: Advanced Modeling and Simulation, Artificial Intelligence, Big Data Analytics, Digital Twin

Sustainable Development Goals

Precision medicine is expanding into the treatment of Parkinson’s disease through deep phenotyping, genetics, and the use of other omics data. This approach generates large datasets, which require a dedicated database to identify patient subgroups that respond better to specific treatments. Since original data is often difficult to obtain, a Digital Twin approach will be used to generate large series of synthetic “digital patients” that statistically resemble real patients.

The project uses a synthetic database of digital twins built from publicly available data (e.g., clinical profile, biomarkers, genetics), integrated with new diagnostic tools for Parkinson’s 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.

Objectives

The goal of this project is to generate “preliminary evidence” on how precision medicine can help identify patients who could benefit from specific therapies, creating an impact on the medical-scientific community to encourage engagement with larger databases and prospective studies.

The project also has a broader social impact, with several intergovernmental bodies emphasizing the need for efficient information flows. 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 anonymization methods have proven insufficient for achieving these goals, both in terms of protecting privacy and preserving analytical usefulness.

Initial challenge

Parkinson’s disease is on the rise in Europe. Applying an effective precision medicine paradigm could impact clinicians’ treatment decisions, patient satisfaction, and healthcare system efficiency, thereby contributing to economic savings. In addition, original data is extremely difficult to obtain and is often scattered.

Solution

Synthetic data is increasingly seen as a superior alternative to anonymization. The proposed project will be among the first to demonstrate its potential in a complex, practical setting.

Successful implementation will lead to greater awareness and adoption of synthetic data technology by society. This will result in greater data mobility, but also fairer, more transparent use of data thanks to the elimination of bias.

The time and cost of data-driven projects currently prevent small businesses from competing. The technology developed in this project will democratize the use of data, allowing companies of all sizes and across all sectors to compete on a level playing field.

Benefits

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. In line with global initiatives for secure data sharing, the project overcomes the limitations of traditional anonymization, balancing privacy with data usefulness. It promotes democratic access to information, reduces bias in data, and drives innovation across various sectors, potentially influencing privacy and data-sharing policies at a global level.

IFAB’s role

IFAB took part in the project as a funding body, selecting it as part of its investment lines in research, innovation, and technology transfer. IFAB’s contribution consisted of recognizing the project’s scientific and practical value and providing financial support, making it possible to carry out the research activities conducted by the technical partners. This involvement confirms IFAB’s mission as an accelerator of high-impact initiatives for the local area and productive system.