About Us - Psych-STRATA

ABOUT THE PROJECT

Concept of the project

Psych-STRATA is a research project funded by the European Commission that focuses on personalised mental health treatment.

The project tackles treatment resistance (TR), a problem affecting a third of patients with major mental disorders who don't respond to drug therapy. These patients often face a trial-and-error treatment approach, which is inefficient and stressful.

The main aim is to address this by identifying at-risk individuals earlier, developing personalised treatment strategies grounded in the biological basis of TR, and efficiently implementing these insights in clinical practice and treatment guidelines.

Psych-STRATA will examine diverse data, including genetic, biological, digital mental health, and clinical information to better understand TR and identify those at risk. Using Europe-wide clinical trial information, the project will determine optimal treatment strategies for those at risk of TR.

The research will aid in the creation of new machine-learning models to predict TR risk and treatment response. Finally, the project aims to integrate these findings into clinical practice, assisting clinicians and patients in making informed treatment decisions.

A Stratified Treatment Algorithm in Psychiatry:

A program on stratified pharmacogenomics in severe mental illness (Psych-STRATA).

Grant agreement No: 101057454.

Started

1 Oct 2022

Completed

30 Sept 2027

Coordinated by

The University of Münster (Westfälische Wilhelms-Universität Münster, WWU)

Total cost

€ 10.8 million

Objectives

Our mission is to pave the way for a shift towards a treatment decision-making process tailored for the patients at risk for treatment resistance. To achieve this, we will:

Understand the genetic basis of TR

We are working to find genetic markers that can identify people at risk of Treatment Resistance (TR), in other

words patients that are not responding well to treatments for schizophrenia, bipolar disorder, and major depressive disorder. We look at genetic and clinical data, study genetic links, analyse genes and cell types, and validate these genetic predictors. This could help spot people at risk early on based on their genetic makeup, which could change how we make future clinical decisions.

Uncover TR
predictors

We aim at finding genes, proteins, and other biological information that
could tell us in advance

We aim at finding genes, proteins, and other biological information
that could tell us in advance

who might not respond well to treatments for schizophrenia, bipolar disorder, and major depressive disorder. We will also check how well these methods work by testing them on patients’ cells. All this will help us predict who might struggle with treatment and find new ways to help them. This work will make our treatment methods better and more personalised.

Evaluate efficacy in a clinical setting

Our goal is to test if stronger medicine given early can help people with schizophrenia, bipolar disorder, and

major depression who did not improve after an initial treatment. These tests will also help us understand if our biomarkers work in real-life settings. The information we gather will not only contribute to our other objectives but also has the potential to change how these mental health conditions are treated, helping many patients across Europe. We’ll see if this approach helps with other symptoms, if it works better taking into account certain biomarkers, and if digital tools can help us measure success. This valuable data will be used in further studies and to create a decision-making tool for treatment.

Predict treatment
response success

We are going to set up ways to predict if intense treatment will work for those whose first treatment did not help,

for different mental health conditions. We will use the data from our earlier tests to do this. Our steps will include studying samples from our tests, finding biological markers linked to treatment results, validating these markers, and creating a machine-learning model to predict treatment response. This will help us understand who is at risk of not responding to treatments. All this information will set the groundwork for the creation of a tool to help decide on the best treatment options.

Develop a decision-making tool

Our goal is to use the findings from our first four objectives to improve real-world treatment. We plan to create

a system that can identify early if a patient (with conditions like schizophrenia, bipolar disorder) might not respond well to treatment, and suggest alternative treatments. We will do this by developing a tool driven by data and setting up a process that puts the patient at the centre of decision-making. The system will be tested in everyday clinical care. The information we gather from our earlier work will be used to see how it can help predict who might not respond well to treatment. We will test this system locally, nationally, and internationally, taking us a step closer to personalised mental health care. We also aim to create a plan for introducing treatment guidelines for early detection and decision-making in severe and resistant cases of schizophrenia, bipolar disorder. There is nothing like this in the field of psychiatry right now, so we believe this work could be a game-changer for treating these conditions. This could also guide us in addressing other mental conditions.

Involve patients & other stakeholders

We aim to share our findings with everyone - from people directly affected by mental illness to policymakers and

healthcare providers. We want to involve everyone in our project, empower patients and their families, and communicate the importance of personalised mental health care. We will also share our project’s goals and results with key groups like researchers, healthcare providers, policymakers, and patients.