Projects

This project aims to create a multi-scale patient-specific human virtual twin for patients eligible for CAR T cell therapy, which can be extended to cellular immunotherapies in general.

Over the next four years, PREDICTOM will develop a platform for assessing dementia risks. The platform will enable early detection, allowing timely intervention and preventive treatment.

The overall aim of COMMITMENT is to develop a computational systems medicine framework that allows clinically meaningful stratification of psychotic disorders and the identification of biological processes shared with somatic and neurodegenerative comorbidities.

Recent years have seen an explosion in health data generation from diverse sources. This data has the potential to help advance both research and patient care. However, accessing, integrating, and analyzing it is exceptionally challenging. IDERHA aims to set up an open platform that will facilitate the integration and analysis of diverse types of health data. The platform will link up multiple public and private data sources and implement interoperable tools and services to enable key groups, such as doctors, patients, and researchers, to use the data. To focus their efforts, the IDERHA team will use lung cancer as a use case to design the platform.

The Real4Reg project, coordinated by the German Federal Institute for Drugs and Medical Devices (BfArM), aims to analyze billing data with the help of AI to generate better insights into drug side effects and interactions.

Patients with severe psychiatric disorders such as schizophrenia, bipolar disorder and clinical depression often develop resistance to drug therapy. Even when the early signs of treatment resistance (TR) are detected, patients must undergo lengthy procedures before medical professionals can prescribe adequate pharmaceutical care. The EU-funded Psych-STRATA project intends to analyse extensive clinical, genetic and biological data of psychiatric patients to establish criteria for early detection of TR. Based on the findings, it will propose treatment strategies for patients at risk of TR. The project will further create machine learning models that can predict TR risk and patient response to treatment, thus assisting medical professionals in providing more personalised treatment.

ParKInsonPredict is a joint research project of Technische Universität Dresden and Fraunhofer SCAI aiming to predict disease progression patterns in Parkinson's disease.

REMEDi4ALL aims to make a significant leap forward in drug repurposing. This promising approach to drug development, consisting of identifying, testing, and validating new therapeutic indications for existing medications, is a developing field but faces numerous barriers and systemic inefficiencies. Still, its potential to significantly bring down times and costs of drug development - it focuses on already approved, discontinued, shelved or investigational therapeutics - makes this novel strategy attractive for rare and neglected conditions, cancer, emerging public health threats such as COVID-19 or new drug combinations. It also translates into more sustainable health systems.

ADIS stands for "Early Diagnosis of Alzheimer's Disease by Immune Profiling of Cytotoxic Lymphocytes and Recording of Sleep Disturbances." The project is funded by the EU Joint Programme for Neurodegenerative Diseases Research (JPND). JPND is the largest global research initiative to address neurodegenerative disease challenges. The ADIS project (start: July 2022) will run for three years and has a budget of 1.3 million euros, of which 300,000 euros will go to Fraunhofer SCAI.

The project aims to develop a decentralized, data protection-compliant research platform capable of simulating some of the brain’s complex neurobiological phenomena. As part of the project, researchers will collate various types of information, including data from PET and MRI scans, EEG tests, behavioral studies, lifestyle surveys, and clinical data from thousands of patients and healthy controls. These will be combined with biological information from knowledge databases and made available for research. The resultant digital ‘brain twins’ will enable many researchers to conduct innovative research within a powerful digital infrastructure.