PERsonalised medicine for SAIDs

Systemic autoinflammatory diseases (SAIDs) are a growing group of rare, frequently hereditary diseases of the immune system. Mainly affected are children who show ill-defined symptoms such as fluctuating degrees of fever, as well as abdominal, articular, and cutaneous signs, making a clinical diagnosis difficult. These afflictions are a substantial burden to the quality of life, both for the affected children and their parents. If not correctly diagnosed and treated in time, severe complications may arise. Therefore, in the last few decades, intensive research has been carried out into the detection and treatment of these diseases and a number of genetic factors have been found which can help to detect the diseases timely and accurately and treat them in a targeted manner. For the Patient, however, such a process can take several years and in many cases the elucidation of the cause of disease and a personalised treatment is still not possible. The PerSAIDs project tries to find new ways to close this diagnostic gap. Methods are to be developed that allow the attending physician to recognize all SAID patients early and correctly and to treat them effectively and without side effects. In order to achieve this goal, the most important European patient registries are available to the consortium. The most modern molecular analysis methods are used to characterize the diseases, the results of which are analyzed using methods of artificial intelligence and systems biology. The aim is to reduce the complexity of the diseases, decipher the underlying causes and open up new therapeutic paths. Automated systems are to be built with the involvement of all relevant players (doctors, scientists, industry), which should make it easier for the treating physicians to make correct and timely decisions in routine diagnostics. To ensure compliance with the highest ethical, legal and social standards, the project is accompanied by an ELSI (Ethical, Legal and Social Implications) working group. Six research groups from five different countries are working on the project, which is led by Prof Isabella Ceccherini at the Instituto Gaslini in Genoa (IT).

Systemic autoinflammatory diseases (SAIDs) are rare disorders in which the body's immune system becomes overactive and causes inflammation. Some of these diseases are caused by a single known genetic change, and for these patients, personalized treatments tailored to their genetic profile are already available. However, most SAID patients (around 70-80%) do not have a clearly identified genetic cause. These "undefined" SAIDs could also benefit from personalized medicine, but more research is needed to understand them better. The PerSAIDs project aimed to improve the diagnosis, classification, and treatment of these diseases by combining clinical information with advanced biological data, such as genetic, molecular, and protein measurements. Researchers collected and analyzed large amounts of data from patients, developed secure systems to store and share this information, and ensured that data could be reused for future research while protecting patient privacy. The project created a shared database that combines clinical and biological information from different research centers. It generated new biological datasets and stored them for future studies. Artificial intelligence and machine learning tools were developed to help doctors identify specific diseases and improve diagnosis. Researchers also discovered new genetic and molecular markers linked to these conditions and identified important biological pathways that may play a role in disease development. In addition, the project engaged researchers, clinicians, patient organizations, and other stakeholders through meetings, training activities, and scientific conferences. Overall, PerSAIDs advanced the use of personalized medicine for autoinflammatory diseases by combining cutting-edge biological research with practical AI-based tools that can help improve diagnosis, disease understanding, and patient care in the future.