A scalable strategy for personalised treatment of immune-mediated inflammatory diseases

Immune Mediated Inflammatory Diseases (IMIDs) have a high world wide prevalence, and particularly affect women. A key problem is that many IMID patients do not respond to treatment. This causes enormous suffering and costs.

One explanation is the daunting complexity of IMIDs, which may involve thousands of genes that are variably expressed in different cell types. There is a wide gap between this complexity and modern health care. Bridging that gap may involve challenges that lie close to, or beyond the limits of current knowledge.

We hypothesise that it may be possible by constructing Digital Twins of IMID patients.

These are high-resolution models of individual patients, which can be produced in unlimited copies for in silico treatment with thousands of drugs, in order to find the drug that is optimal for the patient. The twins will be constructed by network- and AI-based analyses of genome-wide single cell and bulk data.

We will develop and validate the methods in a mouse model of one IMID and then translate the methods to clinical studies of IMID patients.

Our ultimate aim is that each IMID patient will has her/his own digital twin that is used and modified for personalised medicine until cured.

The project is based on multi-disciplinary collaborations within the Swedish Digital Twin Consortium (SDTC .se), as well as with leading international collaborators