Schizophrenia (SCZ) is a mental disorder with one of the most unfavourable long-term outcomes, ranking within the top ten medical causes of disability worldwide. SCZ symptoms are managed with different types of medications (antipsychotic treatment), reducing the clinical severity and improving the functional and social outcomes of these patients.

However, several factors limit the efficacy of antipsychotics: i) about 30% of patients are affected by treatment-resistant SCZ (TRS) and do not respond to standard antipsychotic medications; and ii) antipsychotic-induced side effects (AISE) manifest in a remarkable fraction of SCZ patients, with a gradient of severity that ranges from mild to life-threatening. TRS/AISE remain a challenge in psychiatry due to a lack of clinically useful tools for prediction.

The omiCSFit project aims to lay the groundwork for developing support tools for clinicians for the early prediction of TRS- and AISE-related outcomes in SCZ patients. By utilising unique cerebrospinal fluid (CSF) and serum samples of SCZ patients, combined with state-of-the-art multiomics approaches and advanced statistical techniques, the project will define individualised drug-specific pharmacogenomic profiles for the early prediction of TRS/AISE risk.

This project will deliver a pilot version of a pharmacogenomic profiler tool for personalised prediction of TRS and AISE in SCZ. Such a tool will be able to support clinicians during drug treatment selection, with a direct positive impact on the quality of life of patients.