# A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe

B. Baune, S. Fromme, M. Aberg, M. Adli, A. Afantitis, I. Akkouh, O.

Andreassen, C. Angulo, S. Barlati, C. Brasso, et al.

## ► To cite this version:

B. Baune, S. Fromme, M. Aberg, M. Adli, A. Afantitis, et al. A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe. European Archives of Psychiatry and Clinical Neuroscience, 2024, 275 (5), pp.1453-1464. 10.1007/s00406-024-01944-3. hal-05387372

HAL Id: hal-05387372

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# A stratified treatment algorithm in psychiatry: a program on stratified pharmacogenomics in severe mental illness (Psych-STRATA): concept, objectives and methodologies of a multidisciplinary project funded by Horizon Europe

B. T. Baune<sup>1,39,40</sup> · S. E. Fromme<sup>1</sup> · M. Aberg<sup>2</sup> · M. Adli<sup>3,24</sup> · A. Afantitis<sup>4</sup> · I. Akkouh<sup>5,6</sup> · O. A. Andreassen<sup>6</sup> ·

C. Angulo<sup>7</sup> · S. Barlati<sup>9,43</sup> · C. Brasso<sup>35</sup> · P. Bucci<sup>11</sup> · M. Budde<sup>36</sup> · P. Buspavanich<sup>38,55</sup> · V. Cavone<sup>8</sup> · K. Demyttenaere<sup>10</sup> ·

C. M. Diaz-Caneja<sup>8</sup> · M. Dierssen<sup>12,14,54</sup> · S. Djurovic<sup>5,6</sup> · M. Driessen<sup>15</sup> · U. W. Ebner-Priemer<sup>16,47</sup> · J. Engelmann<sup>27</sup> ·

S. Englisch<sup>27</sup> · C. Fabbri<sup>46</sup> · P. Fossati<sup>18</sup> · H. Fröhlich<sup>20,42</sup> · S. Gasser<sup>21</sup> · N. Gottlieb<sup>8</sup> · E. Heirman<sup>10</sup> · A. Hofer<sup>21</sup> ·

O. Howes<sup>22</sup> · L. Ilzarbe<sup>50</sup> · H. Jeung-Maarse<sup>15</sup> · L. V. Kessing<sup>19</sup> · T. D. Kockler<sup>16</sup> · M. Landén<sup>25</sup> · L. Levi<sup>26,60</sup> · K. Lieb<sup>27</sup> ·

N. Lorenzon<sup>14</sup> · J. Luykx<sup>48,58,59</sup> · M. Manchia<sup>51,52,53</sup> · M. Martinez de Lagran<sup>14</sup> · A. Minelli<sup>44,45</sup> · C. Moreno<sup>8</sup> · A. Mucci<sup>11</sup> ·

B. Müller-Myhsok<sup>30</sup> · P. Nilsson<sup>31</sup> · C. Okhuijsen-Pfeifer<sup>28</sup> · K. D. Papavasileiou<sup>4</sup> · S. Papiol<sup>36</sup> · A. F. Pardinas<sup>32</sup> ·

P. Paribello<sup>51,52</sup> · C. Pisanu<sup>29</sup> · M. -C. Potier<sup>33</sup> · A. Reif<sup>34</sup> · R. Ricken<sup>3</sup> · S. Ripke<sup>3</sup> · P. Rocca<sup>35</sup> · D. Scherrer<sup>41</sup> ·

C. Schiweck<sup>34</sup> · K. O. Schubert<sup>13,56,57</sup> · T. G. Schulze<sup>36,49</sup> · A. Serretti<sup>17</sup> · A. Squassina<sup>29</sup> · C. Stephan<sup>37</sup> · A. Tsoumanis<sup>4</sup> ·

E. Van der Eycken<sup>7</sup> · E. Vieta<sup>23</sup> · A. Vita<sup>9,43</sup> · J. T. R. Walters<sup>32</sup> · D. Weichert<sup>27</sup> · M. Weiser<sup>26,60</sup> · I. R. Willcocks<sup>32</sup> ·

I. Winter-van Rossum<sup>28</sup> · A. H. Young<sup>8</sup> · M. J. Ziller<sup>1</sup>

Received: 31 July 2024 / Accepted: 20 November 2024

© The Author(s) 2024

## Abstract

Schizophrenia (SCZ), bipolar (BD) and major depression disorder (MDD) are severe psychiatric disorders that are challenging to treat, often leading to treatment resistance (TR). It is crucial to develop effective methods to identify and treat patients at risk of TR at an early stage in a personalized manner, considering their biological basis, their clinical and psychosocial characteristics. Effective translation of theoretical knowledge into clinical practice is essential for achieving this goal. The Psych-STRATA consortium addresses this research gap through a seven-step approach. First, transdiagnostic biosignatures of SCZ, BD and MDD are identified by GWAS and multi-modal omics signatures associated with treatment outcome and TR (steps 1 and 2). In a next step (step 3), a randomized controlled intervention study is conducted to test the efficacy and safety of an early intensified pharmacological treatment. Following this RCT, a combined clinical and omics-based algorithm will be developed to estimate the risk for TR. This algorithm-based tool will be designed for early detection and management of TR (step 4). This algorithm will then be implemented into a framework of shared treatment decision-making with a novel mental health board (step 5). The final focus of the project is based on patient empowerment, dissemination and education (step 6) as well as the development of a software for fast, effective and individualized treatment decisions (step 7). The project has the potential to change the current trial and error treatment approach towards an evidence-based individualized treatment setting that takes TR risk into account at an early stage.

**Keywords** Treatment resistance · Depression schizophrenia · Early detection · Early treatment · Precision psychiatry

## Introduction

A key problem in mental health is that a significant proportion of patients suffering from major mental disorders shows resistance against drug therapy, affecting on average every

third patient [1]. At present, patients showing early signs of treatment resistance do not receive adequate early intensive pharmacological treatment but instead undergo a stepwise trial-and-error approach to first, second and third-line treatments [2, 3]. This roots in three major problems in research and clinical translation: first, we lack effective methods to identify individuals at risk for treatment resistance (TR)

<footnote>Extended author information available on the last page of the article</footnote>
early in the course of illness; second, we lack effective, per-
sonalized treatment strategies grounded in insights into the
biological basis of TR and third, we lack efficient processes
to translate scientific insights about TR into clinical practice
and primary care. There are numerous attempts to define
treatment resistance. In the best known and most established
version for the field of depression, treatment resistance is
defined as the failure of two or more pharmacological treat-
ment attempts of sufficient duration and dosage[4]. This
definition is used in particular by the European Medicines
Agency and the US Food and Drug Administration. This is
a pragmatic and clinical attempt at a definition. The Psych-
Strata consortium is working on this complex topic and will
also strive for research efforts with regard to a new defini-
tion of treatment resistance. However, the focus will be on a
pragmatic and clinical definition.

The Psych-STRATA consortium aims to address these
three major problems and enable evidence-based identifi-
cation and treatment of individuals at risk for TR (Fig. 1)
across the three major psychiatric disorders, schizophrenia
(SCZ), bipolar depression (BD) and major depressive dis-
order (MDD). To this end, the consortium will establish a
comprehensive collection of biological, digital, cognitive
and clinical data, including therapy-response and -outcome
measures across SCZ, BD and MDD. Subsequently, the
consortium will leverage this data resource to derive multi-
layered, transdiagnostic biosignatures associated with TR.
These signatures will be used to build multi-modal machine
learning or artificial intelligence (ML/AI) models to predict
risk of TR and ultimately allocate individual patients to opti-
mal intervention strategies (Fig. 1). We are investigating the
underlying genetic disposition to TR, which forms the basis

of the interrelated and genetically driven transcriptomic
and proteomic findings. Finally, these concepts will con-
verge into interdisciplinary and patient-oriented decision-
making mental health boards. Using a modern participatory
approach, we aim to transfer our results into evidence-based
clinical guidelines and better information for patients at risk.

Overall Objectives

First, we will identify genetically-based biosignatures under-
lying liability to TR in SCZ, BD, MDD as well as transdi-
agnostically (objective 1). These genetic signatures will be
complemented by novel multi-omic biomarkers, biosigna-
tures, and clinical phenotypes underlying treatment outcome
based on multi-omic profiling of longitudinal intervention
cohorts (objective 2). Jointly, the deliverables of these two
objectives will enable a multi-modal machine-learning
model to predict TR risk and treatment outcome (objective
2).

Next, we will compare the efficacy and safety of early
intensive pharmacological treatments to treatment as usual
in SCZ, BD, and MDD patients in patients at risk for TR,
initially defined as first-line treatment failure (objective 3),
in a randomized controlled trial (RCT). The RCT will be
accompanied by in-depth phenotyping during the treatment
process. The retrospective analysis will then enable the
validation and extension of the machine-learning model to
predict risk for TR and treatment outcome (objective 4a).
Moreover, detailed analysis will permit the identification of
novel markers of treatment response as well as the validation
of the polygenic risk score (PRS), and the multi-omic pre-
dictors in the RCT setting. Jointly, these efforts will give rise

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Outline of the Psych-
STRATA overall concept</figcaption></figure>

<figure><img src="image_3.png" /></figure>
to a model to predict the probability of treatment response to early intensive pharmacological treatment after a first-line treatment failure (objective 4b).

Finally, we will develop and implement a novel framework of decision-making incorporating a multi-modal prediction tool and a shared decision-making process between mental health professionals, scientists, and patients, embedded in the context of a mental health board (objective 5a). In parallel, we will translate the results into guidelines for treatment (objective 5b) and disseminate them (objective 6). Figure 2 gives an overview about the main objectives and how they relate to each other. In the following, we present the seven steps in which we plan to pursue our objectives in more detail.

## Psych-STRATA in seven steps

### Step 1: identification of transdiagnostic genetic biosignatures underlying treatment resistance in SCZ, BD and MDD

We will derive genetically based biosignatures (e.g., PRS) associated with transdiagnostic TR in SCZ, BD, and MDD. This objective will also address the clinical need to identify the common biological basis of TR across these disorders and highlight genetics-based predictors for early identification of TR. These constitute essential components of any future clinical decision support system by providing genetic biomarkers for the early detection of individuals at risk for TR, eventually contributing to a substantial reduction in

the trial-and-error period of medication evaluation in each patient.

We will utilize standard, established genome-wide association study (GWAS) and polygenic methods as well as exploiting new approaches in large, harmonized data sets; such as pharmacogenomic allele calling from array data using PyPGx [5]. To this end, the Psych-STRATA consortium brings together the largest collection of treatment resistant individual level genetic data to date, amounting to more than 150,000 cases (90,000 from biobanks).

We will perform within disorder GWAS of TR and refine optimal definitions of TR according to single nucleotide polymorphisms (SNP)-based heritability estimates, genetic correlation of definitions within and across disorders as well as polygenic predictive ability into the independent deeply phenotyped cohorts in step 2. In addition, we will perform cross-disorder GWAS for TR across SCZ, BD, and MDD using standard and cutting-edge methods [6] and will ascertain whether SCZ, BD, and MDD TR risk loci are disorder-specific or act across disorders (TR pleiotropic loci).

We will gain insights into the shared biology of TR by applying statistical fine-mapping to the TR GWAS results as well as downstream analyses at a single cell, tissue, actgene, and pathway level. These models will be used to pinpoint genes, pathways, cell types, and tissues associated with TR using transcriptome and pathway wide association analysis [7]. Methodically we will identify specific genes and biological pathways relevant to TR across disorders through gene prioritization methods, which combine state-of-the-art statistical fine-mapping [8] and co-localization of expression quantitative loci (eQTLs) [8], gene-set enrichment analysis framework [9] as well as functional

<figure><img src="image_4.png" /><figcaption>**Fig. 2** Overview of the main objectives of Psych-STRATA</figcaption></figure>
mapping of with gene-set analysis (GSA)-MiXeR [10]
within and across disorders.

In this step, we will also perform PRS analysis [11, 12]
to assess liability to TR across all cohorts for (1) within-
disorder TR (case/case) for SCZ, BD, MDD and (2) com-
bined TR (case/cases) across diagnoses. We will leverage
emerging methods (e.g., polygenic risk score–continuous
shrinkage (PRS-CS), linkage disequilibrium (LDPRED),
PRS of CHD-associated biomarkers (BioPRS)) [13]
to identify the most powerful approach for assigning a
genetic based biosignature risk score for TR across disor-
ders and additionally consider the potential role of phar-
macogenomic markers in mediating or moderating poly-
genic effects [14, 15] along with polygenic overlap with
metabolic dysfunction [16]. We expect that PRS calcula-
tions will explain rather small but nevertheless signifi-
cant proportions of the cohort variance. We will perform
transcription (TWAS) and pathway activity level (PALAS)
genome wide association studies for TR liability within
and across disorders and leverage the results to compute
tissue specific and multi-tissue gene and pathway level
risk scores for each individual [12]. Additionally, recently
reported analysis on polygenic liability for antipsychotic
dosage and polypharmacy will be implemented [17].

Finally, we will evaluate distinct biosignature scores (PRS, TWAS-PRS, etc.) with respect to their discriminatory power of TR vs non-TR patients individually as well as in combination. Figure 3 gives an overview about the step 1 analysis strategy.

Step 2: identification of multi-modal biosignatures
profiles associated with treatment outcome

In this step, we will first derive multi-omics biosignatures
and biomarkers associated with treatment outcome and TR.
We will conduct proteomic analyses on longitudinal cohorts
of SCZ, BD, and MDD patients to assess treatment effects
on the serum proteome. Using a multi-stage approach, we
will examine over 5400 proteins in ~ 900 serum samples
from TR/non-TR individuals during the discovery phase,
leveraging the Olink Explorer HT panel [18]. This will
elucidate proteomic changes linked to different treatment
responses. Subsequently, we will establish a thoroughly
selected candidate protein panel of up to 380 proteins for
the validation phase. We plan to use the commercially avail-
able kits with 5000 proteins. This will consist of a high-
throughput antibody-based suspension bead-array assay
utilizing antibodies from the Human Protein Atlas, across
approximately 6000 samples of TR/non-TR individuals with
SCZ, BD, and MDD.

In the described datasets, we will aim to identify prot-
eomic biomarkers associated with (1) response to specific
treatments, depending also on sample size considerations,
(2) response irrespective of treatment type, and (3) TR vs
response for all patients and those with two measurement
time points [19]. We will consider the use of both linear and
non-linear methods for detecting possible associations and
the use of strategies to limit multiple-testing burden, and
validate the results of genetic analyses (Step 1). In detail,
we will consider the following: (1) genes with evidence for

<figure><img src="image_5.png" /><figcaption>**Fig. 3** Overview of the analysis strategy of step 1</figcaption></figure>

<figure><img src="image_6.png" /></figure>
association with TR based on TWAS analysis, (2) member-
ship or interaction with TR associated pathways, (3) clas-
sification as druggable gene (e.g., Drugbank, https://go.
drugbank.com/), and (4) proteomics-level pathway analysis.
With the aim to integrate the different layers of data, we
will also integrate genetic and proteomic data to identify
protein quantitative trait loci (pQTL) associated with treat-
ment outcomes.

These insights will then be combined with clinical and psychosocial variables to develop multi-modal predictors that integrate the different key components contributing to treatment outcomes. For this scope, we will evaluate different machine learning and integration strategies (e.g., multi-modal neural networks) [20] to integrate *omics and clinical predictors (e.g., socio-demographics, severity and cause of illness, comorbidities, psychosocial function, clinical digital markers on sleep and activity).

Finally, we will leverage personalized disease models based on induced pluripotent stem cells (iPSCs) from TR/non-TR patients to validate the identified biomarkers/biosignatures [21], identify new biomarkers, and dissect the neurobiological mechanisms contributing to treatment outcomes. RNA-Seq and metabolome profiling of iPSC under baseline and exposure to first line drug treatments considered in Step 3 will be assessed. Finally, these results will be integrated with omics results of the previous steps, to pinpoint mutually validate the TR associated molecular changes/biosignatures.

Step 3: randomized controlled trials on efficacy
and safety of early intensified pharmacological
treatment

The main objective of this step will be to conduct an inno-
vative RCT for MDD, SCZ, and BD patients (strategy in

Fig. 4). The trial will be conducted as an open-label study, with the assessors of the primary outcome measures blinded to treatment. Patients, whose illness did not respond to a first-line therapy in the current disease episode, will be randomized (1:1 randomization) to early intensified pharmacological treatment (EIPT) or treatment as usual (TAU). The treatment phase lasts 6 weeks, but it is an intent-to-treat approach, which means that patients might stop treatment earlier or could be treated longer. Follow-up is carried out up to 6 weeks after the end of the treatment phase.

EIPT will consist of a pharmacological treatment that is
currently recommended for patients with TR by treatment
guidelines (i.e., patients who did not sufficiently respond
to ≥ 2 treatments). In MDD, a switch to another oral anti-
depressant plus esketamine (EIPT) will be compared with
a switch to a second line antidepressant (TAU). In SCZ, a
switch to clozapine (EIPT) will be compared with a switch
to a second-line antipsychotic (TAU). For BD, a switch to
one of the following will be compared: 1. escitalopram, ser-
traline, or venlafaxine plus 2. two of the following: lithium,
lamotrigine, valproate, or quetiapine (EIPT) versus quetiap-
pine plus lithium or valproate, or lamotrigine (TAU).

Treatment outcomes will be measured over a period of six weeks. The primary endpoint is the efficacy so called change in symptom severity (according to standard scales); the secondary endpoints are cognition and multiple patient-centered outcomes such as quality of life, general functioning, and life satisfaction. Side effect profiles and long-term effects will also be assessed until week 12 after treatment. The total follow-up period will be three months, comprising five clinical study appointments; the baseline visit will follow within a week after the screening, and visits 3, 4 and 5 will be conducted at 2 weeks, 6 weeks, and 12 weeks post-baseline, respectively.

<figure><img src="image_7.png" /><figcaption>**Fig. 4** Overview of the randomized controlled treatment (RCT) strategy</figcaption></figure>
In addition to a comprehensive medical history, and numerous clinical self-assessment, and clinician assessment instruments, there are three special features of this RCT, as we will include a comprehensive assessment of cognition, digital markers, and biomarkers (blood and feces). Genomic and proteomic analyses will be performed for integration/ validation of steps 1 and 2 (see the next paragraph). The digital biomarkers will be collected using electronic diaries, mobile sensor technology, and wearables, in some cases via smartphone.

The efficacy of the medication will be measured through the Positive and Negative Symptom Scale (PANSS, SCZ) and Montgomery Åsberg Depression Rating Scale (MADRS, MDD, and BD) and compared between both arms of the trial. The safety will be measured through General Assessment of Side Effects (GASE). A further aim of this step is to identify clinical and digital psychological predictors of treatment response/TR. Other key data of interest are cognitive phenotypes potentially associated with treatment response, and the digital mental health data will be analyzed to identify digital measures associated with treatment outcomes [22, 23].

## Step 4: identification of multi-modal signatures associated with TAU and early intensified treatment response for evidence-based treatment recommendations in clinical practice

This step will integrate and extend the results of step 1–step 3. First, we will leverage the multi-modal data from the RCT in step 3 to validate identified genes, pathways, proteins, pharmacogenomic alleles, and genetic scores predictive of treatment outcome based on steps 1 and 2. More specifically, we will validate biosignature based biomarkers (genetic and proteomic) associated with TR liability and treatment outcome in SCZ, BD, and MDD. We will compute PRS and genetic/multi-omic based biosignatures defined in step 1 and 2, respectively, for all RCT patients at baseline. We will also perform multi-omic integration to validate biomarker candidates from joint step 1, step 2, and RCT cohorts across molecular layers and explore the predictive capacity of multi-omic features with respect to treatment outcome.

Second, we will conduct a further biomarker discovery effort based on the data collected in step 3 to identify transcriptomic or proteomic biomarkers based on their capacity to classify SCZ, BD, and MDD patients into responder/non-responder groups. We will target genes and serum proteins that are associated with 1. response to TAU, 2. response to EIPT, and 3. biomarkers predicting differential response to TAU vs EIPT in each disorder and transdiagnostically. In a complementary effort, we will connect the distinct molecular layers and also identify expression and protein quantitative trait loci (eQTLs and pQTLs) under baseline and

post-treatment conditions [24] to pinpoint eQTLs/pQTLs associated with treatment response.

Finally, we will build on the validated biosignatures from step 1 and 2 as well as the latter results and establish a multi-modal machine-learning model to predict treatment outcome for TAU or EIPT. To this end, we will extend the multi-model machine-learning model developed in step 2 and integrate genetic, transcriptomic, and proteomic profiling of steps 1–3 with deep clinical phenotyping data to build a multi-layered treatment response model for TAU/ EIPT. More specifically, we will combine the following data modalities and evaluate their contribution to overall prediction performance:

* genetic based scores and/or pharmacogenomic alleles associated with TR found in step 1,

* omics based biomarker scores for TR found in step 2,

* omics based individual and aggregated biomarker scores of response to TAU vs EIPT,

* clinical, psychosocial, and neurobiological measures (symptom rating, cognition, etc.), and

* digital mental health features.

These data modalities will be integrated using different early (e.g., canonical correlation analysis, multi-modal autoencoders), intermediate (e.g., multi-modal neural networks) and late (e.g., stacking) integration strategies for multi-modal data. We will evaluate candidate features with respect to added value via nested-cross validation designs.

In addition, patient trajectory modeling and simulation will be implemented in this step, using e.g., the previously published Variational Autoencoder Modular Bayesian Networks (VAMBN) and/or MultiNODE algorithms [25, 26]. We will evaluate statistical dependencies between biomarkers, clinical outcomes, and digital assessments and generate virtual patients to simulate different treatment scenarios. Thus, this step will provide the data and analysis groundwork for the evidence-based treatment recommendation component of the mental-health board in clinical practice (step 5).

## Step 5: development of a treatment decision-making mental health board

In this step, we will focus on three main objectives. The first one is the implementation of a software tool embedding AI/ ML models developed in step 4 for treatment decision support. This software will help clinicians to obtain predictions of treatment outcome for individual patient using their specific characteristics (multi-omic, clinical, and digital data). Model predictions will be explained via methods such as Shapley Additive Explanations (SHAP). Moreover, we will embed interactive features, e.g., mapping of omics-derived

<figure><img src="image_8.png" /></figure>
features to molecular networks. For example, these networks
can include known drug-target interactions, retrieved from
public databases (e.g. Drug Bank), hence pointing to poten-
tial alternative treatments. Finally, we will evaluate this soft-
ware tool regarding its technical correctness and its clinical
utility.

The second objective is the creation of a decision-making
mental health board, which aim will be to conceptualize and
facilitate a shared decision-making (SDM) process between
patients, carers, clinicians, and scientists. We will realize
this in three phases. The first will be the development of a
shared decision-making platform, which we will evaluate by
running focus groups and surveys. We will also analyze and
evaluate the potential impact and obstacles of implement-
ing biosignature-assisted SDM processes. Another important
goal is the implementation of the decision-making mental
health board. It is anticipated that this board will consist of
an interdisciplinary group of scientists, clinicians, primary
care physicians, psychotherapists, patients, and carers. We
will conduct a pilot study utilizing the decision-making men-
tal health board. The outcomes of this pilot study will pro-
vide first experience of the mental health board in national
and international clinical settings.

The last objective of this step is the creation of a frame-
work for implementation of treatment guidelines. For this,
we aim to develop definitive, prospective level evidence
[27]. We will convene an expert working group (clinicians,
researchers, and patient representatives) to review the exist-
ing evidence [28, 29] and submit a consensus report to
the major international guidelines. We will also prepare a
publication.

Step 6: patient empowerment, dissemination, and education

The last step of Psych-STRATA focuses on patient empow-
erment, dissemination, and education in different ways. First,
we will generate opinions maps at the European level regard-
ing the use of digital apps and continuous mental health
assessments among relevant stakeholders. We will integrate
app-based surveys in order to collect participant feedback
from the RCT (step 3). Likewise, we will develop surveys
on the same questions for other stakeholders: participants in
other clinical trials, relatives, clinicians, researchers, policy
makers, and any other relevant group of individuals.

Survey participants will be recruited via channels such
as the GAMIAN-Europe's website, touching base with local
contacts of each patient group embedded in GAMIAN-
Europe, presentations at local and international meetings,
newsletters, and social media. Another important objective
will be the comprehensive evaluation of the novel mental
health board with shared decision-making (step 5), using a
structured survey that will target relevant stakeholders.

The last objective is the dissemination of findings and
education. The World Psychiatric Association (WPA) will
be an important partner in the dissemination and education
part (workshops, surveys, connecting with groups around
the globe). Part of this is setting up and maintaining the
official Psych-STRATA website (https://psych-strata.eu/),
Psych-STRATA Twitter/X account (@psych_strata), and a
LinkedIn account. We are currently creating and updating
communication information material, developing project
factsheets, posters, official presentations, and all types of
dissemination materials including newsletter (https://psych-
strata.eu/newsletter/). Moreover, we will organize workshops
for healthcare providers and disseminate our expert consen-
sus document. Lastly, press releases will be issued when
significant results have been achieved to reach and inform
specialized and/or generalist media on key activities and
results.

Step 7: in silico drug repurposing for identifying potential therapeutics in SCZ, MDD, and BD and development of the Psych-STRATA software as a service (SaaS) platform

This step represents a transformative approach to in silico
drug repurposing, integrating advanced computational
techniques such as cheminformatics, text mining, network
pharmacology, omics data analysis, and network-based drug
repurposing methods. By adopting these methods, Psych-
STRATA aims to introduce a rational approach to drug iden-
tification, improving the precision in identifying potential
drug candidates for conditions like SCZ, MDD, and BD
[30]. Unlike traditional trial-and-error methods, compu-
tational simulations and cheminformatics tools enable the
prediction of drug efficacy, bioavailability, and potential
side effects before clinical trials, thereby streamlining the
drug development process [31]. Moreover, network-based
techniques consider the complex interplay of biological
pathways involved in mental disorders [32], which is cru-
cial for conditions like SCZ, MDD, and BD that often share
common biological pathways. By mapping these pathways
and identifying key nodes or targets, Psych-STRATA can
suggest existing drugs effective across multiple conditions,
aligning with the emerging paradigm of poly-pharmacology
[33]. These methodologies enable the exploration of latent
linkages between different mental disorders, facilitating drug
repurposing by identifying drugs effective for conditions not
initially targeted.

Moreover, the Psych-STRATA SaaS platform repre-
sents a significant methodological advancement, offering
a dedicated platform for seamless integration of ML/AI
into healthcare providers' daily decision-making processes
[34]. This platform aims to provide clinicians with more per-
sonalized, actionable data, enabling real-time, data-driven
decision-making in mental health care, which is often lack-
ing in current systems. By offering patient-specific insights,
particularly crucial for complex mental health disorders like
SCZ, MDD, and BD, the platform enhances the quality of
care while reducing the burden on healthcare systems. The
integration of advanced ML/AI models into clinical practice
streamlines decision-making, making diagnosis and treat-
ment planning quicker and more efficient. Ultimately, by
offering personalized data and enabling real-time decision-
making, the Psych-STRATA platform has the potential to
revolutionize mental health care provision, setting new
standards in the field.

Discussion

The long-term vision of Psych-STRATA is to pave the way
for a shift from the current state-of-the-art trial-and-error
and stepwise treatment paradigms towards evidence-based
treatments tailored and stratified according to the individual
person with SCZ, BD, or MDD at risk for TR early in the
treatment process, and thus preventing further complications
such as chronicity and suicide risk [35].

Psych-STRATA will contribute to a better understanding
of the genetics and molecular biological basis of TR and TR
liability in individuals suffering from SCZ, BD, and MDD.
In particular, this project will deliver (transdiagnostic) genes
and pathways underlying TR as well as molecular genetics
and environmental modulators of treatment outcome, com-
bining molecular, clinical, and digital patient assessments.

Approaches that use artificial intelligence methods and
focus on MRI data in combination with clinical data are also
the subject of current research innovations. Here, too, prom-
ising results are emerging [36–38], for example in affective
disorders and schizophrenia. However, these approaches
are not yet ready to be used in clinical practice [36, 39], as
the necessary validation and assessment of clinical benefit
have not yet been achieved [36]. Blood-based approaches for
predicting treatment success have the advantage that they
are cheaper, faster and more economical than MRI-based
methods and therefore fulfill important criteria for promis-
ing biomarkers. In our view, blood-based approaches are an
important complement to imaging-based approaches. Both
are promising biomarkers which, especially in combina-
tion, may also be indicative for personalized psychiatry. For
this reason, we will also endeavor to establish and intensify
collaborations with neuroimaging research as part of the
project.

The project will provide researchers and mental health
professionals with the ability to detect SCZ, BD, and MDD
patients at risk for TR early in an evidence-based fashion
beyond mere symptom-based criteria. This process will be
based on novel individual biomarkers and most importantly,

on multi-modal predictive models of TR risk and treatment
response by integrating clinical, digital, and multi-omics
information as individual biomarkers are expected to have
limited predictive power [40]. The project will empower
professionals to treat at-risk patients early and more accu-
rately, minimizing the time that treated individuals spend
taking ineffective medications or at high risk of adverse drug
reactions. The establishment of a framework and solid data-
base as well as a mental health decision board will enable a
patient-oriented approach, the translation of early detection
of TR and tailored treatment concepts from primary care
towards specialist care.

The project findings can be directly incorporated into the
development of new evidence-based guidelines for the phar-
macological treatment of patients after an initial treatment
failure, which can be an important contribution to reduce
the transition to TR and the chronicity of mental illness. The
project has a good chance to change the evidence base for
future treatment guidelines for TR, to reduce suffering for
individuals and societal burden in the long run.

**Acknowledgements** The Psych-STRATA project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101057454. BTB received funding from the German Federal Ministry of Health (BMG) [2521FSB004 PROMPT, ERAPERMED2020] and La Marato TV3: SLD044/20/000001 (Convocatoria ERAPERMED2020). MD received funding from PROMPT and La Marato TV3: SLD044/20/000001 (Convocatoria ERAPERMED2020) + Amb el suport del Departament de Salut, Fundació La Marató de TV3, 202212-30. AHY independent research is funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Accepted Author Manuscript version arising from this submission. M-CP was supported with funding from the Investissement d'Avenir (ANR-10-AIHU-06).

**Author contributions** The following authors were responsible for concep-
tion and design of the various steps: James Walter and Stephan
Ripke for step 1, Chiara Fabbri and Michael J. Ziller for step 2, Inge
Winter-van Rossum, and Oliver Howes for step 3, Michael J. Ziller and
Marie-Claude Potier for step 4, Bernhard T. Baune and Holger Fröhlich
for step 5, Sergi Papiol and Erik van der Eyken for step 6, and Antreas
Afantis, Konstantinos Papavasileiou and Bernhard T. Baune for Step 7.
All authors contributed to the material preparation and data collection.
The first draft of the manuscript was written by Bernhard T. Baune and
Sarah E. Fromme. All authors commented on previous versions of the
manuscript. All authors read and approved the final manuscript.

**Funding** Open Access funding enabled and organized by Projekt DEAL. This work of Psych-STRATA project was supported by funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101057454. This work was supported by Departament de Salut, Generalitat de Catalunya, SLD044/20/000001, Mara Dierssen, Fundació la Marató de TV3, 202212-30, Mara Dierssen, NIHR Maudsley Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, Agence

<figure><img src="image_9.png" /></figure>
Nationale de la Recherche, ANR-10-AIHU-06, Marie-Claude Potier, HORIZON EUROPE Framework Programme, 101057454.

**Declarations**

**Conflict of interest** BTB received honoraria from Angelini, AstraZeneca, Biogen, Boehringer Ingelheim, Bristol-Meyers Squibb, Janssen, LivaNova, Lundbeck, Medscape, Neurotorium, Novartis, Otsuka, Pfizer, Recordati, Roche, Rovi, Sanofi, Servier, and Teva. AS is, or has been a consultant to, or has received honoraria or grants unrelated to the present work from Abbott, Abbvie, Angelini, Astra Zeneca, Clinical Data, Boehringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, Servier, and Taliaz. AM received advisory board or consultant fees from Angelini, Pierre Fabre, Rovi Pharma, and Boehringer Ingelheim outside the submitted work. OH has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by Alkermes, Angellini, Auftifony, Biogen, Boehringer Ingelheim, Delix, Eli Lilly, Elysium, Heprates, Global Medical Education, Invicro, Janssen, Karuna, Lundbeck, Merck, Neumora, Neurocrine, Ontrack/Pangea, Otsuka, Sunovion, Teva, Recordati, Roche, Rovi, and Viatris/Mylan. He was previously a part-time employee of Lundbeck A/v. HF received grants from Abbvie, UCB, and Neuway Pharma. PR received honoraria as a consultant for lectures from Angelini, Janssen, Lundbeck, and Otsuka. Furthermore, PR is on Angelini's advisory board. OAA is a consultant to Cortechs, ai and Precision Health.ai, and has received speaker's honoraria from Janssen, Lundbeck, Sunovion, and Otsuka. LVK has been a consultant for Lundbeck and Teva within the preceding three years. AH has received investigator-initiated research funding from and/or participated in advisory/speaker meetings organized by Boehringer Ingelheim, Gedeon Richter, Janssen, Lundbeck, Newron Pharmaceuticals, and Rovi. AV received advisory board, lecture, or consulting fees outside the present work from Angelini, Innova Pharma-Recordati, Janssen Pharmaceuticals, Lundbeck, Otsuka, and Pfizer. CF was a speaker for Janssen. MM received honoraria from Angelini, Lundbeck, Johnson & Johnson, and Fidia Farmaceutici. UE-P reports consultancy for Boehringer Ingelheim and speaker honorarium from Angelini Pharma. AR has received honoraria for lectures and/or advisory boards from Janssen, Boehringer Ingelheim, COMPASS, SAGE/Biogen, LivaNova, Medice, Shire/Takeda, MSD, and cyclerion. In addition, he has received research grants from Medice and Janssen. KDP and AA are employed by NovaMechanics, a cheminformatics company. JTRW was supported by a collaborative research grant from Takeda Pharmaceuticals Ltd. for a project unrelated to work presented here. AFP and JTRW also received grants from Akrivia Health for a project unrelated to this research. EV has received grants and served as a consultant, advisor, or CME speaker for AB-Biotics, AbbVie, Adamed, Alcediag, Angelini, Biogen, Beckley-Psytech, Biohaven, Boehringer Ingelheim, Celon Pharma, Compass, Dainippon Sumitomo Pharma, Ethypharm, Ferrer, Gedeon Richter, GH Research, Glaxo-Smith Kline, HMNC, Idorsia, Johnson & Johnson, Lundbeck, Luye Pharma, Medincell, Merck, Newron, Novartis, Orion Corporation, Organon, Otsuka, Roche, Rovi, Sage, Sanofi-Aventis, Sunovion, Takeda, Teva, and Viatris outside the submitted work. LI has received CME-related honoraria and/or travel support from Angelini, Casen Recordati, Exeltis, Janssen-Cilag, Lundbeck, Rovi, Rubió, and Takeda outside the submitted work. PF served as a consultant or CME speaker for Idorsia, Lundbeck, and Janssen. AR has received honoraria from and/or serves on advisory boards for Medice, Shire/Takeda, SAGE/Biogen, Janssen, Boehringer Ingelheim, and cyclerion. MW serves as a consultant, speaker, and received funding for research from Janssen, Alkermes, Lundbeck, Teva, Newron, Dexcel, MSD, and Minerva. SB received advisory board, lecture, or consulting fees outside the present work from Angelini, Janssen Pharmaceuticals, Lundbeck, and Otsuka. ML has received lecture

honoraria from Lundbeck pharmaceuticals. CB, IW, JL, SEF, KL, JE, DW, CS, COP, PN, PB, M-CP, MA, TDK, MML, and SE declare no conflict of interest. RR received an unrestricted research grant and speaker honoraria from Aristo Pharma. MA received research grants from Berlin University Alliance, Volkswagen Stiftung, Stiftung für Analytische Psychiatrie, and Gilead as well as speaker or consultation honoraria or travel reimbursements from Johnson & Johnson, Aristo Pharma, Gilead, and Neuraxpharm. BMM has a secondary appointment as head of precision psychiatry with HMNC Brain Health. CM has received honoraria as a consultant and/or advisor for lectures from Angelini, Compass, Esteve, Exeltis Janssen, Lundbeck, Neuraxpharm, Nuvelution, Otsuka, Pfizer, Servier, and Sunovion outside the submitted work. CMDC has received honoraria and/or travel support from Angelini, Janssen, and Viatris not related to the submitted work. AHY received paid lectures and advisory boards from Flow Neuroscience, Novartis, Roche, Janssen, Takeda, Noemapharma, Compass, AstraZeneca, Boehringer Ingelheim, Eli Lilly, LivaNova, Lundbeck, Sunovion, Servier, Livanova, Janssen, Allegan, Bionomics, Sumitomo Dainippon Pharma, Sage, and Neurocentrx. KOS received honoraria from Janssen and Lundbeck as well as research funding from Janssen, Lundbeck, and Gilead.

**Ethical approval** The RCT in step 3 has been planned in accordance with the Helsinki Declaration and will be conducted in this way. In addition, ethical approvals have either already been granted or are pending but have been applied for:

Australia: not yet approved.

Austria: SZ: 2023-506602-39-00, BD: 2023-506605-19-00, MDD: 2023-506617-21-00. All approved by the ethical committee of the Medical University of Innsbruck.

Germany: SZ: 2023-506602-39-00 approved by Ethik-Kommission des Landes Berlin, BD: 2023-506605-19-00 approved by Ethik-Kommission der Ärztekammer Westfalen-Lippe und der Universität Münster, MDD: 2023-506617-21-00 approved by Ethik-Kommission der Ärztekammer Nordrhein.

Greece: SZ: 2023-506602-39-00, BD: 2023-506605-19-00, MDD: 2023-506617-21-00 Yet to be submitted; site was added later.

Israel: merged protocol 0293-23-SMC, approved by the ethical committee of The Chaim Sheba Medical Center.

Italy: SZ: 2023-506602-3900, BD: 2023-506605-19-00, MDD: 2023-506617-21-00. All approved by Comitato Etico Territoriale (CET) delle Marche.

Poland: SZ: 2023-506602-3900, BD: 2023-506605-19-00, MDD: 2023-506617-21-00 Yet to be submitted; site was added later.

Spain: SZ: 2023-506602-39-00, BD: 2023-506605-19-00, MDD: 2023-506617-21-00. All approved by CEIm del Hospital Clínic de Barcelona.

United Kingdom: SZ: 2023-506602-39-00 & 23/EM/0266 approved by East Midlands-Nottingham 2 Research Ethics Committee, BD: 2023-506605-19-00 (not yet approved), MDD: 2023-506617-21-00 (not yet approved).

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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Authors and Affiliations

B. T. Baune¹,³⁹,⁴⁰ ID · S. E. Fromme¹ · M. Aberg² · M. Adli³,²⁴ · A. Afantitis⁴ · I. Akkouh⁵,⁶ · O. A. Andreassen⁶ ·

C. Angulo⁷ · S. Barlati⁹,⁴³ · C. Brasso³⁵ · P. Bucci¹¹ · M. Budde³⁶ · P. Buspavanich³⁸,⁵⁵ · V. Cavone⁸ · K. Demyttenaere¹⁰ ·

C. M. Diaz-Caneja⁸ · M. Dierssen¹²,¹⁴,⁵⁴ · S. Djurovic⁵,⁶ · M. Driessen¹⁵ · U. W. Ebner-Priemer¹⁶,⁴⁷ · J. Engelmann²⁷ ·

S. Englisch²⁷ · C. Fabbri⁴⁶ · P. Fossati¹⁸ · H. Fröhlich²⁰,⁴² · S. Gasser²¹ · N. Gottlieb⁸ · E. Heirman¹⁰ · A. Hofer²¹ ·

O. Howes²² · L. Ilzarbe⁵⁰ · H. Jeung-Maarse¹⁵ · L. V. Kessing¹⁹ · T. D. Kockler¹⁶ · M. Landén²⁵ · L. Levi²⁶,⁶⁰ · K. Lieb²⁷ ·

N. Lorenzon¹⁴ · J. Luykx⁴⁸,⁵⁸,⁵⁹ · M. Manchia⁵¹,⁵²,⁵³ · M. Martinez de Lagran¹⁴ · A. Minelli⁴⁴,⁴⁵ · C. Moreno⁸ · A. Mucci¹¹ ·

B. Müller-Myhsok³⁰ · P. Nilsson³¹ · C. Okhuijsen-Pfeifer²⁸ · K. D. Papavasileiou⁴ · S. Papiol³⁶ · A. F. Pardinas³² ·

P. Paribello⁵¹,⁵² · C. Pisanu²⁹ · M. -C. Potier³³ · A. Reif³⁴ · R. Ricken³ · S. Ripke³ · P. Rocca³⁵ · D. Scherrer⁴¹ ·

C. Schiweck³⁴ · K. O. Schubert¹³,⁵⁶,⁵⁷ · T. G. Schulze³⁶,⁴⁹ · A. Serretti¹⁷ · A. Squassina²⁹ · C. Stephan³⁷ · A. Tsoumanis⁴ ·

E. Van der Eycken⁷ · E. Vieta²³ · A. Vita⁹,⁴³ · J. T. R. Walters³² · D. Weichert²⁷ · M. Weiser²⁶,⁶⁰ · I. R. Willcocks³² ·

I. Winter-van Rossum²⁸ · A. H. Young⁸ · M. J. Ziller¹

<figure><img src="image_10.png" /></figure>

B. T. Baune
Bernhard.Baune@uni-muenster.de

¹ Department of Psychiatry, University of Muenster, Muenster, Germany

² Department of Medical Science, Clinical Chemistry and SciLifeLab Affinity Proteomics, Uppsala University, Uppsala, Sweden

3 Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Berlin, Germany

⁴ Department of Chemoinformatics, NovaMechanics MIKE, Piraeus, Greece

5 Department of Medical Genetics, Oslo University Hospital, Oslo, Norway

6 Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo, and Oslo University Hospital Oslo, Oslo, Norway

7 Global Alliance of Mental Illness Advocacy Networks Europe, Brussels, Belgium

8 Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañon, IiSGM, CIBERSAM, ISCIII, School of Medicine, Universidad Complutense, Madrid, Spain

9 Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy

10 KU Leuven and University Psychiatric Center KU Leuven, Leuven, Belgium

11 Department of Mental and Physical Health and Preventive Medicine, University of Campania Luigi Vanvitelli, Naples, Italy

12 Universitat Pompeu Fabra, Barcelona, Spain

13 Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, Australia

14 Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain

15 Department of Psychiatry and Psychotherapy, Ev. Hospital Bethel, Bielefeld University, Bielefeld, Germany

16 Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

17 Department of Medicine and Surgery, Kore University of Enna, Enna, Italy

18 Department of Psychiatry, Paris Brain Institute - Institut du Cerveau (ICM), UMR 7225/UMRS 1127, Sorbonne University/CNRS/INSERM, DMU Neurosciences, Pitié-Salpétrièren, APHP, Paris, France

19 Psychiatric Center Copenhagen, Copenhagen, and Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

20 Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
21 Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Division of Psychiatry I, Medical University of Innsbruck, Innsbruck, Austria

22 Department of Psychosis Studies, King's College London, London, UK

23 Department of Psychiatry and Psychology, Institute of Neuroscience (UBNeuro), Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain

24 Fliedner Klinik Berlin, Berlin, Germany

25 Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

26 Drora and Pinchas Zachai Division of Psychiatry, Sheba Medical Center, Ramat-Gan, Israel

27 Department of Psychiatry and Psychotherapy, University Medical Center, University of Mainz, Mainz, Germany

28 Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands

29 Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy

30 Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany

31 Division of Affinity Proteomics, Department of Protein Science, SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden

32 Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK

33 Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de La Pitié Salpêtrière, Sorbonne Université, Paris, France

34 Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main - Goethe University, Frankfurt am Main, Germany

35 Department of Neuroscience, University of Turin, Turin, Italy

36 Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital Munich, Munich, Germany

37 Kairos GmbH, Bochum, Germany

38 Department of Psychiatry, Psychosomatic and Psychotherapy, Brandenburg Medical School, Neuruppin, Germany

39 Department of Psychiatry, University of Melbourne, Melbourne, Australia

40 Department of Psychiatry, The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia

41 University Clinic of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland

42 Bonn-Aachen International Center for IT (B-It), University of Bonn, Bonn, Germany

43 Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy

44 Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy

45 Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy

46 Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy

47 Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany

48 Department of Psychiatry, Amsterdam University Medical Center, Amsterdam, The Netherlands

49 World Psychiatric Association, Geneva University Psychiatric Hospital, Geneva, Suisse

50 Bipolar and Depressive Disorders Unit, Institute of Neurosciences, Hospital Clinic de Barcelona, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain

51 Unit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy

52 Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy

53 Department of Pharmacology, Dalhousie University, Halifax, Canada

54 Hospital del Mar Research Institute, Barcelona, Spain

55 Gender in Medicine, Institute of Sexology and Sexual Medicine, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Berlin, Germany

56 Division of Mental Health, Northern Adelaide Local Health Network, SA Health, Adelaide, Australia

57 Headspace Adelaide Early Psychosis, Sonder, Adelaide, Australia

58 GGZ inGeest Mental Health Care, Amsterdam, The Netherlands

59 Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands

60 Department of Psychiatry, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel