DigiTREAT

Rheumatoid Arthritis (RA) is an autoimmune disease leading to joint destruction. Despite the existence of 14 approved drugs, up to 40% of patients do not respond adequately to treatment, and no reliable biomarkers are available to predict therapeutic responses. This emphasises the need for new approaches to improve treatment personalisation. Recent advances have brought medical digital twins (MDTs) to the forefront, as they can integrate and analyse complex, heterogeneous data. MDTs could enable the development of personalised care by offering testable hypotheses. The project DigiTREAT seeks to develop the first medical digital twin for RA (RA-DT), building on recent bioinformatics and computational biology advancements. The DigiTREAT project aims to create patient-specific models that provide insights into disease heterogeneity and treatment responses. Key objectives of DigiTREAT include: 1) Developing a multiscale, multicellular model to simulate the interaction between immune cells in the blood and resident cells in the joints, aiming to identify biomarkers predicting disease progression and treatment response. 2) Testing predictions in a mouse model of RA (collagen-induced arthritis, CIA) using advanced spatial omics technologies. 3) Validating the model’s predictions and biomarkers through single-cell RNA sequencing (scRNAseq) in blood samples from a cohort of RA patients treated with three primary RA drugs (etanercept, tocilizumab, and rituximab). 4) Linking predicted biomarkers to clinical symptoms and systemic disease manifestations in patients. The RA-DT aims to provide personalised treatment recommendations by integrating patient data and predicted treatment responses. A virtual environment will be developed to host the RA-DT, allowing for data visualisation, simulation, and connection with clinical data, making it accessible in clinical and preclinical settings.