The project “Systems biology-driven machine learning in pharmacogenomics to predict antidepressant non-remission in late-life depression” is an international research collaboration between Prof. Dr. Dominik Heider and the University of Toronto (Prof. Dr. Daniel Müller), funded by the DAAD.

Late-onset depression (late-life depression, LLD) is one of the most common mental illnesses in old age (≥ 60 years). This form of depression differs from earlier-onset depression in that it has a stronger association with neurodegenerative and cerebrovascular diseases, making diagnosis and treatment more difficult. A key problem is that over 50% of patients do not respond to antidepressants or relapse, significantly increasing the risk of dementia, stroke and cognitive decline.

The research is based on the hypothesis that genetic risk factors influence the response to antidepressants. The aim of the project is therefore to use machine learning (ML) and systems biology to develop models that can predict at an early stage which patients are at increased risk of treatment failure.

By applying ML techniques to psychiatric pharmacogenomic data, the project could help develop an individually tailored therapy for patients with LLD. In the long term, this should improve treatment efficiency, reduce side effects and reduce the burden of disease for patients and healthcare systems.
 

Contact: Univ.-Prof. Dr. Dominik Heider