Immune-mediated inflammatory disease (IMID) includes many common diseases such as inflammatory bowel disease, rheumatoid arthritis, and psoriatic arthritis, affects millions of people worldwide and can cause chronic pain, disability, and reduced quality of life. It also entails a substantial socioeconomic cost. A major problem is that many patients do not respond to medication. One explanation is the enormous complexity of the diseases, where altered gene expression of thousands of genes can change the biological processes in many different cell types and tissues. Gene expression can also vary between patients with the same diagnosis.
Despite the cellular and molecular differences between individuals, patients with the same diagnosis often receive the same treatment in conventional health care. However, this approach ignores the fact that patient’s disease differs on a cellular and molecular level.
In my studies, I am investigating whether it is possible to improve the treatment outcomes of patients by using complex individual gene expression changes in drug selection through the construction of digital twins.
Digital twins are high-resolution models of an individual patient's disease that can be produced in unlimited copies to simulate treatment with thousands of drugs, aiming to find the optimal treatment for that particular patient. The digital twins are constructed through network and AI-based analyses of relevant OMICs data.
