Computational drug repurposing for mental disorders using population-wide Electronic Health Records and Biobanks

Mental disorders present a significant global health challenge, markedly contributing to disability and diminished quality of life worldwide.

Presently, treatment options are sparse, and drug discovery efforts are hindered by ineffective understanding of underlying disease mechanisms.Our objective is to identify and repurpose existing drugs to treat mental disorders, using advanced computational methods applied to real-world data from electronic health records (EHRs) and nationwide healthcare registers and biobanks across several countries, coupled with state-of-the-art, secure, and efficient analytical infrastructure.We propose a four-step strategy:Identification: Use Danish EHRs to identify drugs with potential collateral benefits for mental disorders.Prioritization: Prioritize drug-disease associations using genetic, transcriptomic, proteomic, metabolic pathway, and drug-protein interaction data.Replication: Replicate the candidate associations using national EHRs from Norway, Sweden, Iceland, and Estonia.Biomarker Approach: Identify biomarker signatures of patient subgroups with distinct drug-disease effects.Our team integrates expertise from psychiatry, epidemiology, pharmaco-epidemiology, pharmaco-genomics, genetics, psychopharmacology, machine learning, and bioinformatics.

The goal of our project is to advance personalized psychiatry, and further the transitioning from group-level treatments towards tailored, individualized therapies.