# Biomarkers of treatment-resistant schizophrenia: A systematic review

Claudia Pisanu, Giovanni Severino, Alessandra Minelli, Mara Dierssen, Marie-Claude Potier, Chiara Fabbri, Alessandro Serretti, Massimo Gennarelli, Bernhard Baune, Alessio Squassina

► To cite this version:

Claudia Pisanu, Giovanni Severino, Alessandra Minelli, Mara Dierssen, Marie-Claude Potier, et al. Biomarkers of treatment-resistant schizophrenia: A systematic review. Neuroscience Applied, 2024, 3, pp.104059. 10.1016/j.nsa.2024.104059. hal-05387930

HAL Id: hal-05387930

https://hal.science/hal-05387930v1

Submitted on 28 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Biomarkers of treatment-resistant schizophrenia: A systematic review

<figure><img src="image_2.png" /></figure>

Claudia Pisanu<sup>a</sup>, Giovanni Severino<sup>a</sup>, Alessandra Minelli<sup>b,c</sup>, Mara Dierssen<sup>d,e</sup>, Marie-Claude Potier<sup>f</sup>, Chiara Fabbri<sup>g</sup>, Alessandro Serretti<sup>g,h</sup>, Massimo Gennarelli<sup>b,c</sup>, European College of Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Network<sup>1</sup>, Bernhard T. Baune<sup>i,j,k</sup>, Alessio Squassina<sup>a,l,*</sup>

## ARTICLE INFO

Handling Editor: Prof. A. Meyer-Lindenberg

### Keywords:

Biomarkers

Precision medicine

Personalized medicine

Precision psychiatry

GWAS

Inflammation

Interleukin 6

IL-6

Methylation

RNA

## ABSTRACT

Treatment-resistant schizophrenia (TRS) is associated with great disability, functional impairment, and substantial socioeconomic costs. While clozapine is indicated in patients with TRS, its use is restricted to patients who have not responded to at least 2 other antipsychotics, thus implying a series of empirical trials of different drugs before receiving effective treatment. In this scenario, the identification of reliable biological markers to predict the risk for TRS before starting pharmacological treatments might significantly improve the management of TRS in its early stages. We conducted a systematic review on PubMed, Scopus and Web of Science to identify studies investigating peripheral biological markers of TRS. A total of 75 articles were included. These studies mostly investigated the association between TRS and genetic markers (n = 42, of which 16 with a genome-wide and 25 with a candidate-gene design) and protein/metabolite markers (n = 23), while only a minority of studies investigated RNA markers (n = 5), methylation levels (n = 4), gut microbiota profiles (n = 1), or more than one type of marker (n = 3). The elucidation of peripheral biomarkers of TRS is challenging due to the large heterogeneity across studies in terms of clinical definition of TRS, the relatively small sample size of many studies, as well as the lack of powered studies integrating data at a multi-omic level. Nonetheless, available studies suggest TRS to be a trait with a significant heritability and point to inflammation and cytokine imbalance as the most promising pathways involved in this complex phenotype.

## 1. Introduction

Schizophrenia (SZ) is a severe psychiatric disorder that affects cognitive, behavioral and emotional functioning (Correll and Howes,

2021) and is associated with a substantial socioeconomic burden (Vigo et al., 2016). Pharmacological treatments represent the mainstay intervention for SZ, but only one third of patients adequately treated with first-line antipsychotic treatments respond sufficiently, while the

<footnote><sup>a</sup> Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy</footnote>

<footnote><sup>b</sup> Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy</footnote>

<footnote><sup>c</sup> Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy</footnote>

<footnote><sup>d</sup> Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain</footnote>

<footnote><sup>e</sup> Universitat Pompeu Fabra (UPF), Barcelona, Spain</footnote>

<footnote><sup>f</sup> Paris Brain Institute ICM, Salpetriere Hospital, Paris, France</footnote>

<footnote><sup>g</sup> Department of Biomedical and NeuroMotor Sciences, University of Bologna, Italy</footnote>

<footnote><sup>h</sup> Department of Medicine and Surgery, Kore University of Enna, Enna, Italy</footnote>

<footnote><sup>i</sup> Department of Psychiatry, University of Münster, Germany</footnote>

<footnote><sup>j</sup> Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia</footnote>

<footnote><sup>k</sup> The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia</footnote>

<footnote><sup>l</sup> Department of Psychiatry, Dalhousie University, Halifax, NS, Canada</footnote>

<footnote>* Corresponding author. Laboratory of Pharmacogenomics, Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy. Sp 8 Sestu-Monserrato, Km 0, Monserrato, Italy.</footnote>

<footnote>E-mail address: squassina@unica.it (A. Squassina).</footnote>

<footnote n="1">¹ European College of Neuropsychopharmacology (ECNP) Pharmacogenomics & Transcriptomics Network list of collaborators: Bernhard T Baune, Alessandra Minelli, Alessandro Serretti, Mara Dierssen, Eduard Maron, Marie-Claude Potier, Massimo Gennarelli, Roos van Westrhenen, Alessio Squassina, David Stacey, Divja Mehta, Joost G E Janzing, Chiara Fabbri, Pietro Lio', Filip Rybakowski, Claudia Pisanu.</footnote>
other two thirds only show partial or no response (Siskind et al., 2022). A suboptimal clinical response represents an important obstacle to the improvement of the real-world functioning and quality of life of patients with SZ, which are relevant aims in the management of this disorder (Luykx et al., 2023). Up to 34% patients with SZ show treatment-resistant schizophrenia (TRS), which can be defined as the persistence of substantial symptoms that have not responded to at least two antipsychotic trials with an adequate dose, duration and adherence (Howes et al., 2017; Potkin et al., 2020). This definition is broader compared to the first operational criteria provided by Kane and colleagues, which included 3 or more periods of treatment with at least 2 neuroleptic agents of different classes (1000 mg/day of chlorpromazine equivalents) for at least 6 weeks, no period of good functioning within the preceding 5 years and severe psychopathology according to the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impression (CGI) scores (Kane et al., 1988). Both the broad definition as well as modified versions of Kane's criteria are widely used. However, in the last thirty years, several other definitions of TRS have been proposed. These definitions show relevant differences as regards the number of failed antipsychotic trials, the definition of adequate dose or duration, the assessment of functioning, severity of disease and adherence, thus increasing the challenges associated with the identification of biomarkers of this complex trait. This heterogeneity, as well as the complex biological architecture of TRS, limit our ability to maximize the power and informativity of studies exploring biological markers of this phenotype.

Different genetic and environmental factors might interact to determine the large interindividual variability in response to treatment with antipsychotics, but the complex interplay of these factors is still largely elusive. Nonetheless, data from family and genetic studies suggest that TRS is a trait with detectable heritability (Nucifora et al., 2019; Pardinas et al., 2022). Clozapine is the only antipsychotic recommended by several expert guidelines (Shimomura et al., 2021) and approved for TRS in many countries including US, Canada and European countries (Correll et al., 2022; Fenton and Kang, 2023). However, this drug is still largely underutilized due to potentially severe adverse effects as well as monitoring requirements. In fact, while around 30% of patients with SZ are considered treatment-resistant, clozapine has been estimated to account for only 5% of antipsychotic prescriptions in the United States (Stroup et al., 2014; Sultan et al., 2017). In addition, guidelines currently restrict the use of clozapine to patients who have not responded to at least 2 other antipsychotics, thus implying a series of empirical trials of different drugs before receiving effective treatment. In this scenario, the identification of clinical and biological reliable markers to predict the risk for treatment resistance before starting pharmacological treatments might significantly improve the management of TRS in its early stages. While precision medicine in psychiatry is still in its infancy, this approach is increasingly recognized as a promising strategy to improve clinical management by taking into consideration the individual demographic, clinical, lifestyle and biological characteristics shared by specific subgroups of patients (Manchia et al., 2020). The implementation of this approach might allow to anticipate the use of intensive pharmacological treatments such as clozapine in the early phases of TRS, thus potentially contributing to decrease the substantial socioeconomic costs associated with this condition. Besides being exposed to potential detrimental effects of inefficacious treatments, patients with TRS generally show a more severe disease course, higher risk of suicide and lower quality of life (Correll and Howes, 2021). Clinical characteristics previously associated with worse response to treatment in SZ include family history of psychosis, early onset of disease, longer duration of untreated psychosis, longer illness duration and more severe negative symptoms (Correll and Howes, 2021). However, the clinical predictors alone do not explain a sufficient proportion of variance in the risk of TRS (Carbon and Correll, 2014), and the collection of this information is not trivial and requires a long-term follow-up, thus calling for an urgent need to identify reliable biological

markers of TRS. The biological characteristics investigated as potential biomarkers of TRS are numerous. Among these, based on the observed high heritability of SZ, many studies focused on genetic markers. To this regard, large genome-wide association studies (GWAS) generated by international consortia and population-based studies (Pardinas et al., 2022), as well as novel analytical methods based on the estimation of the cumulative effect of variants (O'Connell et al., 2023), recently allowed to shift from a candidate-gene to a genome-wide approach. More recently, studies also started to explore the role of dynamic measures, such as circulating proteins or differences in levels of transcripts and methylation levels.

The aim of this article is to provide a systematic review of studies exploring the potential utility and predictive value of biological markers of TRS with a specific focus on peripheral biomarkers, being these easily accessible and potentially of high translational value. The majority of the studies that will be discussed in this review explored the association between TRS and genetic or protein markers, while a smaller number of studies explored the role of other features such as transcripts or epigenetic markers. Whenever data are available, we will discuss which markers or systems have been validated in independent cohorts.

## 2. Material and methods

### 2.1. Systematic search of the literature and eligibility criteria

A systematic search was conducted on PubMed, Scopus and Web of Science from inception to June 1st, 2023. Details of the protocol were registered on PROSPERO, registration number CRD42023416865. The search was conducted using combinations of the terms ("treatment-resistant schizophrenia" OR "resistant schizophrenia" OR "refractory schizophrenia") AND (genet* OR methyl* OR epigen* OR transcript* OR RNA OR marker* OR biomarker* OR proteom* OR metabol*). The filter "English language" was activated. References of relevant articles were reviewed for additional studies. We included studies evaluating the association between molecular markers (e.g. genetic variants, epigenetic markers, transcripts, proteins, metabolites) measured in plasma, serum, blood cells or stools and TRS (defined using any recognized criteria) compared with treatment-responsive schizophrenia, in participants with a diagnosis of SZ (as diagnosed using any recognized diagnostic criteria, e.g. in accordance to any version of The Diagnostic and Statistical Manual of Mental Disorders, Research Diagnostic Criteria, or the International Classification of Diseases). *In-silico* studies, i.e., studies in which biomarkers were not measured but imputed from other types of data such as genetic data, were also included. We excluded a) preclinical studies, b) reviews, c) case reports, d) in-vitro studies (e.g. studies on cell lines), e) studies conducted in post-mortem brain samples, f) neuroimaging studies, g) studies in which no biomarker was measured or in which no statistical analysis to evaluate its association with TRS was conducted.

### 2.2. Study selection

A systematic review following the PRISMA guidelines (http://www.prisma-statement.org/) was conducted (Fig. 1). The screening of eligible articles was carried out independently by two authors (CP and GS). First, the screening of titles and abstracts of all retrieved publications was carried out. In a second step, the full text of potentially relevant articles was evaluated. Any disagreement was resolved through discussion with a third author (AS). From each selected study the following information was collected: authors, year of publication, study design, participant characteristics, diagnosis and criteria used to define the diagnosis, criteria used to define TRS, assessed biological markers, type of cells/biofluids, method of assessment, data related to statistical analysis and main findings. Quality assessment of selected studies was conducted using a modified version (Korakakis et al., 2018) of the Downs and Black checklist (Downs and Black, 1998). The checklist includes 27 questions
<figure><img src="image_3.png" /><figcaption>Fig. 1. PRISMA diagram.</figcaption></figure>

that evaluate the overall quality of reporting (ten items), external validity (three items), internal validity - study bias (seven items), internal validity - confounding and selection bias (six items) and power of the study (one item). Since the large majority of studies included in our review were observational studies with a cross-sectional design, we excluded questions related to adverse events (#8), follow-up (#9, #17 and #26), blind (#14 and #15), randomization (#23 and #24), leading to a maximum score of 19 points.

## 3. Results

Our search retrieved a total of 2075 articles (Fig. 1). After exclusion of duplicates (n = 984) and inclusion of articles identified through references (n = 8), a total of 1099 articles were reviewed. Of these, 942 articles were excluded after evaluation of title and abstract. For 157 articles identified as potentially relevant, the full text was evaluated. A total of 73 articles were excluded because the outcome was not TRS, 3 because no statistical analysis on molecular markers was conducted, 3 because they included no data (e.g. commentaries), 1 because the number of patients with TRS was missing and 1 because data were already included in another article (Fig. 1). A total of 75 articles met the inclusion criteria and were therefore included in this review. Included articles investigated the association between TRS and genetic markers (n = 42, of which 16 with a genome-wide (Pardinas et al., 2022; O'Connell et al., 2023; Ajnakina et al., 2017; Cheng et al., 2023; Facial and Costas, 2023; Frank et al., 2015; Gasse et al., 2019; Koga et al., 2017; Kowalec et al., 2021; Legge et al., 2020; Li and Meltzer, 2014; Martin and Mowry, 2016; Ruderfer et al., 2016; Talarico et al., 2022; Werner et al., 2020; Wimberley et al., 2017; Zoghbi et al., 2021) and 25 with a candidate-gene design (Bilic et al., 2014; Erdos et al., 1995; Escamilla et al., 2018; Goldberger et al., 2005; Hajj et al., 2019; Hotta et al., 2011; Ji et al., 2008a, 2008b; Jia et al., 2011; Kogure et al., 2021; Miyazawa et al., 2022; Mouaffak et al., 2011a, 2011b, 2011c; Nakata et al., 2021; Nunes et al., 2019; Rodrigues-Silva et al., 2020; Sagud et al., 2018; Takao et al., 2006; Takebayashi et al., 2011; Teo et al., 2012; Terzic et al., 2015a, 2015b, 2016; van de Bilt et al., 2015), Table 1 and Supplementary Table 1), RNA markers (n = 5, of which 2 genome-wide and

3 candidate-based, Table 2 (Alacam et al., 2016; Chau et al., 2018; Funahashi et al., 2023; Moretti et al., 2018; Perez-Rodriguez et al., 2023)), methylation levels (n = 4, of which 3 genome-wide and 1 candidate-based, Table 3 (Bani-Fatemi et al., 2014; De Luca et al., 2023; Hannon et al., 2021; Lu et al., 2023)), protein or metabolite levels (n = 23, all of which with a candidate based design except one in-silico study performing a proteome-wide association study, PWAS) (Huang et al., 2022a, 2022b; Al-Dujaili et al., 2021; Bousman et al., 2019; Buosi et al., 2021; Chen et al., 2023; Enache et al., 2021; Eratne et al., 2022; Horsdal et al., 2017; Kudo et al., 2020; Labonte et al., 2022; Leboyer et al., 2021; Lee et al., 2011; Lin et al., 1998; Maes et al., 2002; Medina-Hernandez et al., 2007; Moustafa et al., 2020; Noto et al., 2015; Osimo et al., 2023; Squassina et al., 2020; Wei et al., 2023; Yang et al., 2023; Yesilkaya et al., 2021) (Table 4) or gut microbiota profiles (Manchia et al., 2021) (Table 5). Three studies investigated more than one type of marker (n = 2 genetic markers and protein levels, n = 1 genetic markers and methylation levels). The characteristics of the included articles are presented in the following sections. For sections containing a high number of studies (genetic and protein markers) the discussion was focused on studies with a genome-wide design, or reporting targets or systems replicated among different cohorts or for which additional evidence supporting their potential involvement in treatment response is available.

### 3.1. Genetic biomarkers of TRS

The characteristics of genome-wide and candidate gene studies investigating the potential role of genetic biomarkers in TRS are shown in Table 1 and Supplementary Table 1, respectively. Quality scores ranged from 10 to 17 for genome-wide studies as well as for candidate-gene studies (Supplementary Table 2). The largest genome-wide study aiming to characterize the contribution of common genetic variants to TRS was recently published by Pardinas and colleagues (Pardinas et al., 2022). This study conducted 2 GWASs in which matched sets of controls were compared with either patients with TRS (based on clozapine prescription after failure of at least 2 trials of antipsychotics, n = 10501) or SZ without TRS (n = 20325), leveraging data from large
<table><caption>**Table 1**
Studies with a genome-wide design investigating the association between genetic markers and treatment-resistant schizophrenia.</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define response or remission</th><th>Sample</th><th>Assessed variables</th><th>DNA source</th><th>Measurement method or analysis</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Cheng</td><td>2023</td><td>Clozapine prescription after failure of at least two trials of AP</td><td>GWAS for TRS, psychiatric disorders, immune and metabolic disorders, gut microbiota</td><td>NA</td><td>Peripheral blood</td><td>Pleiotropic analyses (MR, LDSC)</td><td>Associations between TRS and ADHD</td><td>(Cheng et al. (2023))</td></tr><tr><td>Facal</td><td>2023</td><td>Clozapine prescription</td><td>Discovery sample: PGC3-SZ<br/>Target sample: 91 patients with TRS, 336 without TRS</td><td>Age, sex</td><td>NA</td><td>PRS</td><td>Significant association in a meta-analysis including five studies (OR = 1.09, p = 0.003)</td><td>(Facal and Costas (2023))</td></tr><tr><td>O'Connell</td><td>2023</td><td>Clozapine prescription after failure of at least two trials of AP</td><td>TRS GWAS: 10,501 patients with TRS, 20325 without TRS (Pardinas et al., 2022)<br/>BMI GWAS 1: 795,640 individuals; BMI GWAS 2: 28,681 children</td><td>NA</td><td>Peripheral blood</td><td>cFDR</td><td>Association between MAP2K1 and ZDBF2 loci and TRS<br/>The polygenic prediction based on the cFDR analysis explained more variance in TRS compared to the original GWAS</td><td>(O'Connell et al. (2023))</td></tr><tr><td>Pardiñas</td><td>2022</td><td>CLOZUK1 and CLOZUK2 cohorts: clozapine prescription after failure of at least two trials of AP<br/>Other cohorts: various (most used: clozapine prescription)</td><td>Discovery sample: CLOZUK1 and CLOZUK2 (10,501 patients with TRS and 24,542 HC). PGC SZ: 20,325 patients without TRS and 30,122 HC<br/>Target sample: CardiffCOGS (315 patients with and 502 without TRS) and STRATA-G (71 patients with and 492 without TRS)</td><td>Sex</td><td>Peripheral blood</td><td>GWAS arrays, PRS</td><td>No significant SNP associate with TRS after multiple testing correction. The TRS PRS based on the GWAS cohorts was significantly associated with history of taking clozapine in the two PRS cohorts, p = 0.001 and p = 0.04)</td><td>(Pardiñas et al. (2022))</td></tr><tr><td>Talarico</td><td>2022</td><td>IPAP criteria</td><td>Discovery sample: PGC2-SZ<br/>Target sample: 63 patients with TRS, 11 without TRS</td><td>Age, sex, clinical variables</td><td>Peripheral blood</td><td>PRS</td><td>Individuals on the top three deciles of SZ PRS distribution showed higher odds of being refractory to AP than those on the bottom three deciles</td><td>(Talarico et al. (2022))</td></tr><tr><td>Kowalec</td><td>2021</td><td>Definition 1: clozapine prescription; definition 2: clozapine prescription or AP polypharmacy for ≥90 days</td><td>Discovery sample: PGC1-SZ SZ, BD, MDD, ASD, IQ, educational attainment<br/>Target sample: 4170 patients with SZ (1261 met TRS definition 1 and 2997 TRS definition 2)</td><td>Age, sex, education, clinical variables</td><td>NA</td><td>PRS, CNV, rare exonic burden</td><td>No significant association</td><td>(Kowalec et al. (2021))</td></tr><tr><td>Zoghbi</td><td>2021</td><td>Kane criteria</td><td>112 patients with TRS, 228 without TRS, 4929 HC</td><td>Age, sex, ethnicity, education, clinical variables</td><td>Peripheral blood</td><td>Whole genome/exome sequencing</td><td>Individuals with severe TRS had a high burden of rare loss-of-function (OR: 1.91) and damaging missense variants in intolerant genes (OR = 2.9)</td><td>(Zoghbi et al. (2021))</td></tr><tr><td>Legge</td><td>2020</td><td>Clozapine treatment or rated negatively for OPCRIT item 89 ("psychotic symptoms respond to neuroleptics")</td><td>Discovery sample: 11,260 cases and 24,542 controls (Pardiñas et al., 2018)<br/>Target sample: CardiffCOGS (561 patients with TRP, 509 without TRP)</td><td>Sex, smoking, clinical variables</td><td>Peripheral blood</td><td>PRS, CNV</td><td>No association between SZ PRS or large CNV and TRS</td><td>(Legge et al. (2020))</td></tr><tr><td>Werner</td><td>2020</td><td>Howes criteria</td><td>Discovery sample: PGC1-SZ<br/>Target sample: 108 patients with TRS, 213 without TRS</td><td>Age, sex, BMI, clinical variables</td><td>Peripheral blood</td><td>PRS</td><td>Association between higher SZ PRS and TRS (p = 0.003, PPV = 61.5%, NPV = 71.7%)</td><td>(Werner et al. (2020))</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define response or remission</th><th>Sample</th><th>Assessed variables</th><th>DNA source</th><th>Measurement method or analysis</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Gasse</td><td>2019</td><td>Clozapine initiation or hospitalization due to SZ during AP treatment within 18 months, after ≥2 different AP monotherapies (at least 6 weeks each)</td><td>Discovery sample: PGC1-SZ<br/>Target sample: individuals from the Danish national register: 593 with TRS, 3882 without TRS</td><td>Sex, urbanicity, clinical variabiles</td><td>Dried blood spot samples</td><td>PRS</td><td>One SD increase in the SZ PRS was associated with TRS (adj HR: 1.11). The effect of SZ PRS on the risk of TRS varied across urbanicity levels and was highest for patients born in the capital areas</td><td>(Gasse et al. (2019))</td></tr><tr><td>Ajnakina</td><td>2017</td><td>Little or no symptomatic improvement to two consecutive treatments of adequate dose and duration with AP</td><td>Discovery sample: PGC2-SZ<br/>Target sample: 34 patients with TRS, 84 without TRS, European origin</td><td>NA</td><td>NA</td><td>PRS</td><td>A SZ PRS for was significantly associated with TRS (R<sup>2</sup> = 0.06 p = 0.026)</td><td>(Ajnakina et al. (2017))</td></tr><tr><td>Koga</td><td>2017</td><td>APA guidelines</td><td>31 patients with TRS, 53 without TRS</td><td>Sex, clinical variables</td><td>Peripheral blood</td><td>GWAS arrays</td><td>No significant association</td><td>(Koga et al. (2017))</td></tr><tr><td>Wimberley</td><td>2017</td><td>Clozapine initiation or hospitalization due to SZ during AP treatment within 18 months, after ≥2 different AP monotherapies (at least 6 weeks each)</td><td>Discovery sample: PGC1-SZ<br/>Target sample: individuals from the Danish national register: 181 with TRS, 681 without TRS</td><td>Sex, geographical area at birth, clinical variables</td><td>Dried blood spot samples</td><td>PRS</td><td>No significant association between the SZ PRS and TRS (adj HR = 1.13)</td><td>(Wimberley et al. (2017))</td></tr><tr><td>Martin</td><td>2016</td><td>Two or more among delusions, hallucinations, disorganization, and negative symptoms; moderate or severe current GAF, continuous course of illness; moderate to severe disease severity</td><td>Discovery sample: PGC1-SZ<br/>Target sample: 227 patients with TRS, 385 without TRS</td><td>Sex, clinical variables</td><td>LCL</td><td>GWAS arrays, qRT-PCR</td><td>Significant association between total genome-wide duplication burden and TRS (OR 1.04, p = 0.007)</td><td>(Martin and Mowry (2016))</td></tr><tr><td>Ruderfer</td><td>2016</td><td>Clozapine treatment</td><td>531 patients with TRS, 2002 without TRS</td><td>NA</td><td>NA</td><td>Exome sequencing</td><td>Association between TRS and an excess of rare disruptive variants in gene targets of AP (347 genes, p = 0.007) or implicated in AP efficacy (91 genes, p = 0.003)</td><td>(Ruderfer et al. (2016))</td></tr><tr><td>Frank</td><td>2015</td><td>Clozapine treatment</td><td>Discovery sample: PGC1-SZ<br/>Target sample: 434 patients with TRS, 370 without TRS</td><td>Age, clinical variables</td><td>NA</td><td>PRS</td><td>Association between higher SZ PRS and TRS. Highest PRS in patients characterized by non-response to clozapine, early age at onset and poor social functioning</td><td>(Frank et al. (2015))</td></tr><tr><td>Li</td><td>2014</td><td>NA</td><td>Discovery sample: 79 patients with TRS, 95 without TRS<br/>Validation sample: 70 patients with TRS, 125 without TRS</td><td>Age, sex, clinical variables</td><td>Peripheral blood</td><td>GWAS arrays, qRT-PCR</td><td>No significant hit after multiple testing correction. rs2237457 showed the lowest p value in an allelic association test (p = 5.5 × 10<sup>-6</sup>), with a weaker result in the replication sample, but in the same direction</td><td>(Li and Meltzer (2014))</td></tr></tbody></table>

**Abbreviations.** ADHD, attention-deficit hyperactivity disorder; adj, adjusted; AP, antipsychotics; ASD, autism spectrum disorder; BD, bipolar disorder; BMI, body mass index; CardiffCOGS, Cardiff Cognition in Schizophrenia; cFDR, conditional FDR; CNV, copy number variation; CPZ-eq, chlorpromazine-equivalent; GAF, Global Assessment of Functioning; GWAS, genome-wide association study; HC, healthy controls; HR, hazard ratio; IPAP, International Psychopharmacology Algorithm Project; LCL, lymphoblastoid cell lines; LDSC, linkage disequilibrium score regression; MDD, major depressive disorder; MR, mendelian randomization; NA, not available; NPV, negative predictive value; OR, odds ratio; PPV, positive predictive value; PRS, polygenic risk score; qRT-PCR, Quantitative Reverse Transcription Polymerase Chain Reaction; SD, standard deviation; SZ, schizophrenia; SNP, single nucleotide polymorphism; STRATA-G, Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances; TRP, treatment-resistant psychoses; TRS, treatment-resistant schizophrenia.

consortium-based studies such as CLOZUK and Psychiatric Genomics Consortium (PGC). The differences estimated between the effect sizes for allelic associations between the two GWASs were considered to reflect genetic signals of TRS rather than SZ itself. In addition, a polygenic risk score (PRS) validation analysis was conducted using 2 independent cohorts of patients with and without TRS. While no genome-wide significant association was identified at variant level, TRS showed a SNP-based heritability of 1–4%. In addition, the TRS PRS was significantly

associated with history of taking clozapine in the validation samples (Pardinas et al., 2022). Using linkage-disequilibrium score regression (LDSC), this study also found TRS to be negatively genetically correlated with cognitive ability and educational attainment. Previous GWASs conducted in much smaller samples did not report genome-wide significant findings either (Koga et al., 2017; Li and Meltzer, 2014). Data from the GWAS by Pardinas and colleagues laid the basis for subsequent studies that applied in-silico analytical approaches to explore the
<table><caption>**Table 2**
Studies investigating the association between RNA markers and treatment-resistant schizophrenia.</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define response or remission</th><th>Sample</th><th>Assessed variables</th><th>RNA Source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Funahashi</td><td>2023</td><td>No response to more than two sufficient doses of antipsychotics (≥600 mg CPZ-eq for more than 4 weeks), including at least one atypical AP</td><td>Discovery sample: 9 patients with TRS and 9 HC, Japanese origin Validation sample: 50 patients without TRS and 50 HC</td><td>Age, sex, clinical variables</td><td>Plasma exosomes</td><td>3D-Gene Human, miRNA Oligo chips, qRT-PCR</td><td>Genome-wide miRNA levels</td><td>13 up- and 18 nominally downregulated miRNAs in patients with TRS compared with HC. hsa-miR-675-3p levels were higher in patients with TRS than HC (p = 0.04), and lower in patients without TRS than HC (p &lt; 0.001)</td><td>(Funahashi et al. (2023))</td></tr><tr><td>Perez-Rodriguez</td><td>2023</td><td>NA</td><td>21 patients with TRS, 19 without TRS, 13 patients with a first psychotic episode (4 with TRS and 9 without TRS), 43 HC</td><td>Age, sex, smoking, clinical variables</td><td>Whole blood</td><td>NGS</td><td>Genome-wide miRNA levels</td><td>6 miRNAs were upregulated (miR-3127-3p, miR-504-5p, miR-3605-3p, miR-6747-3p, miR-3615, miR-1343-3p) and 10 downregulated (miR-145-5p, miR-500a-3p, miR-210-3p, miR-199a-5p, miR-548ay-3p, miR-548ac, miR-296-5p, miR-660-5p, miR-30 b-5p and miR-223-3p) in patients with compared with those without TRS</td><td>(Perez-Rodriguez et al. (2023))</td></tr><tr><td>Chau</td><td>2018</td><td>Howes criteria</td><td>71 patients with TRS, 30 with recent-onset SZ, 57 HC</td><td>Age, sex, ethnicity, smoking, clinical variables</td><td>Whole blood, plasma</td><td>qRT-PCR</td><td>SELENBP1 mRNA and protein expression</td><td>Lower SELENBP1 protein levels in patients with recent-onset SZ (p = 0.04) but not in patients with TRS compared with HC. No difference in mRNA levels between patients with and without TRS</td><td>(Chau et al. (2018))</td></tr><tr><td>Moretti</td><td>2018</td><td>Modified IPAP criteria</td><td>162 patients (78 with TRS), 94 HC</td><td>Age, sex, clinical variables</td><td>Whole blood</td><td>Microfluidic cards</td><td>13 mRNAs</td><td>No differences between patients with and without TRS. AKT1 and DICER1 were upregulated in TRS compared with HC</td><td>(Moretti et al. (2018))</td></tr><tr><td>Alacam</td><td>2016</td><td>Kane criteria</td><td>18 patients with TRS, 19 with responsive SZ</td><td>Age, sex, clinical variables</td><td>Plasma</td><td>qRT-PCR</td><td>29 miRNAs related to SZ</td><td>Levels of miR-301a-3p, miR-195-5p and miR-181 b-3p were nominally increased in TRS compared to responsive SZ</td><td>(Alacam et al. (2016))</td></tr></tbody></table>

**Abbreviations.** AP, antipsychotics; BMI, body mass index; CPZ-eq, chlorpromazine-equivalent; HC, healthy controls; miRNA, microRNA; NGS, next generation sequencing; qRT-PCR, Quantitative Reverse Transcription Polymerase Chain Reaction; Ref, reference; SZ, schizophrenia; TRS, treatment-resistant schizophrenia.
<table><caption>**Table 3**
Studies investigating the association between methylation markers and treatment-resistant schizophrenia.</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define TRS</th><th>Sample</th><th>Assessed variables</th><th>DNA source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>De Luca</td><td>2023</td><td>APA criteria</td><td>43 patients with TRS and 66 without TRS</td><td>Age, sex</td><td>Peripheral blood</td><td>Infinium 450 K</td><td>Genome-wide</td><td>No significant hit after multiple testing correction. The CpG with the highest significance was cg25009327 (ZNF425) which was hypermethylated in patients with TRS</td><td>(De Luca et al. (2023))</td></tr><tr><td>Lu</td><td>2023</td><td>Conley and Kelly criteria</td><td>Discovery sample: 8 patients with TRS, 8 without TRS, 16 HC, Han-Chinese ethnicity Validation sample: 40 patients with TRS, 40 without TRS, 40 HC</td><td>Age, sex, BMI, smoking, education, clinical variables</td><td>Peripheral blood</td><td>Infinium 850 K</td><td>Genome-wide</td><td>13 DMPs between patients with and without TRS. A methylation risk score model including six probes ((LOC404266, LOXL2, CERK, CHMP7 and SLC17A9) selected for validation showed AUC of 0.95, accuracy of 88.3%, sensitivity of 91.7%, and specificity of 80% in discriminating patients with and without TRS</td><td>(Lu et al. (2023))</td></tr><tr><td>Hannon</td><td>2021</td><td>Treatment with clozapine</td><td>399 patients with TRS, 636 without TRS</td><td>Age, sex, smoking, clinical variables</td><td>Peripheral blood</td><td>Infinium 450 K and 850 K</td><td>Genome-wide</td><td>Seven DMPs (cg25643253, cg26276667, cg13039199, cg14836555, cg02596819, cg17470397, cg07828169) were associated with clozapine exposure after multiple testing correction</td><td>(Hannon et al. (2021))</td></tr><tr><td>Bani-Fatemi</td><td>2014</td><td>APA criteria</td><td>6 patients with TRS, 6 without TRS</td><td>NA</td><td>NA</td><td>HumanOmni 2.5-8, Infinium 450 K</td><td>SNPs and methylation of 14 5-HT receptors</td><td>No significant association</td><td>(Bani-Fatemi et al. (2014))</td></tr></tbody></table>

**Abbreviations.** AP, antipsychotics; AUC, area under the curve; DMP, differentially methylated position; BMI, body mass index; HC, healthy controls; miRNA, microRNA; PANSS, Positive and Negative Syndrome Scale; qRT-PCR, Quantitative Reverse Transcription Polymerase Chain Reaction; Ref, reference; SCZ, schizophrenia; TRS, treatment-resistant schizophrenia.

pleiotropy between TRS and other traits. O'Connell and colleagues used the conditional false discovery rate (condFDR) method to investigate the pleiotropy between TRS and body mass index (BMI) (O'Connell et al., 2023), which is particularly relevant given the relationship between treatment with antipsychotics and metabolic dysfunctions. This study found the polygenic prediction based on the condFDR analysis to explain more variance in TRS compared to the original GWAS and identified 2 loci significantly associated with TRS after conditioning on BMI. These loci were located on chromosomes 2 and 15, with rs7560232 and rs3087660 as lead single nucleotide polymorphisms (SNPs), respectively. The SNP on chromosome 15 was validated when conditioning on an additional BMI cohort and found to be a significant expression quantitative trait locus (eQTL) for the MAP2K1 gene in the cerebellum and in the adipose subcutaneous tissue (O'Connell et al., 2023). Another recent study tested pleiotropy between TRS and a wide range of conditions (psychiatric, immune and metabolic disorders) as well as gut microbiota profiles, using LDSC and two-sample Mendelian randomization (MR) (Cheng et al., 2023). The study reported genetic correlations between TRS and 4 disorders (obesity, intestinal infectious diseases, attention-deficit hyperactivity disorder and SZ), while two-sample MR showed a positive causal association between attention-deficit hyperactivity disorder and TRS which was consistent across MR methods; on the other hand, SZ and genus Barnesiella levels were causally associated with TRS but without consistency across MR approaches (Cheng et al., 2023).

Most of the other GWASs evaluated the association between TRS and SZ PRS. The study recently conducted by Fagal and Costas did not identify a significant association between the SZ PRS and TRS in a relative small validation sample (91 cases and 336 controls without TRS); however, a significant association was detected through a meta-analysis including this and 4 previous studies (Fagal and Costas, 2023). Consistently, other studies, most of which used different versions of the PGC GWAS to generate the PRS, identified a significant association between SZ PRS and TRS (Moretti et al., 2018; Werner et al., 2020;

Ajnakina et al., 2017; Frank et al., 2015; Gasse et al., 2019). Interestingly, in some of these studies differences in the association were observed based on clinical or demographic characteristics of the included patients. For example, Frank and colleagues observed the highest PRS for SZ in patients non-responders to clozapine, with poor premorbid social functioning and early disease onset (Frank et al., 2015). When these clinical variables were included in the regression model, they reduced the effect of the association between the PRS and TRS to non-significant values (Frank et al., 2015), although the pleiotropic effect of PRS on clinical features may explain this finding.

In a larger sample from the Danish national register (593 individuals with TRS and 3882 without TRS) conducted by Gasse and colleagues (Gasse et al., 2019), the effect of the SZ PRS on TRS was found to vary across urbanicity levels and to be highest for individuals born in the capital area (Gasse et al., 2019). However, negative associations between SZ PRS and TRS were also reported, possibly due to differences in the criteria used to defined TRS or the method used to compute the PRS (Legge et al., 2020; Wimberley et al., 2017).

Finally, other studies focused on the role of rare variants as markers of TRS. Martin and Mowry reported a significant association between TRS and genome-wide duplication burden in a study including 227 patients with TRS and 385 without TRS (Martin and Mowry, 2016). More recently, Zoghbi and colleagues conducted whole genome sequencing in a sample including 112 patients with TRS (according to Kane criteria) and 228 without TRS, reporting a high burden of rare loss-of-function variants in patients with TRS (Zoghbi et al., 2021). Another study focused on rare variants located in genes relevant for response to antipsychotics, reporting a high burden of disruptive variants in patients with TRS in a study including 531 patients with TRS (based on clozapine exposure) and 2002 without TRS from the Swedish Hospital Discharge Register (Ruderfer et al., 2016).

Candidate-gene studies mostly focused on neurotransmitter systems such as monoaminergic (Hajj et al., 2019; Escamilla et al., 2018; Sagud et al., 2018; Nunes et al., 2019; Terzic et al., 2015a, 2015b, 2016; Bilic
<table><caption>**Table 4**
Studies investigating protein levels, metabolite levels and other tests in blood.</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define TRS</th><th>Sample</th><th>Assessed variables</th><th>Source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Yang</td><td>2023</td><td>Poor effect of treatment with two AP (≥600 mg/day CPZ-eq) for >6 months, total PANSS score ≥70 and a score of ≥3 for each of the eight items of PANSS</td><td>31 patients with TRS and 49 with chronic stable SZ hospitalized continuously for at least 2 years (all men); 41 matched HC</td><td>Age, sex, BMI, education, smoking, clinical variables</td><td>Serum, plasma</td><td>Different methods</td><td>Serum IL-1α, IL-6, TNF-α, IFN-γ levels: plasma MDA, Mn-SOD, CuZn-SOD, T-SOD, CAT, and GSH-Px levels</td><td>CAT activities (OR = 1.58, P = 0.046) and log IL-6 levels (OR = 0.10, p < 0.001) were significantly associated with TRS</td><td>(Yang et al. (2023))</td></tr><tr><td>Chen</td><td>2023</td><td>Howes criteria</td><td>52 patients with TRS, 47 without TRS, 56 HC</td><td>Age, sex, BMI, smoking, education, clinical variables</td><td>Plasma</td><td>ELISA</td><td>Pro-inflammatory and anti-inflammatory markers</td><td>IRS/CIRS and M1pTh1pTh17/Th2pTreg ratios were significantly higher in patients with TRS compared with patients without TRS and HC</td><td>(Chen et al. (2023))</td></tr><tr><td>Osimo</td><td>2023</td><td>Treatment with clozapine</td><td>Discovery sample: 785 patients (58 with TRS at FUP), validation sample: 1110 patients (102 with TRS at FUP)</td><td>Age, sex, ethnicity, smoking</td><td>Peripheral blood</td><td>Different methods</td><td>Triglycerides, lymphocyte and neutrophil counts, alkaline phosphatase, glucose</td><td>Two models predicted clozapine use (MOZART, C statistic: 0.63; LASSO, C statistic: 0.64)</td><td>(Osimo et al. (2023))</td></tr><tr><td>Wei</td><td>2023</td><td>Treatment with clozapine after at least two failed trials of AP</td><td>GWAS from CLOZUK1, CLOZUK2 and PGC, 10,501 patients with TRS and 20,325 without TRS</td><td>Age, sex, cognitive status</td><td>NA</td><td>In-silico analysis (PWAS)</td><td>Genome-wide protein levels</td><td>CPT2 (PWAS-ROS/MAP, p = 0.042 and PPWAS-Banner, p = 0.003) and APOL2 (PWAS-ROS/MAP, p = 0.005 and PWAS-Banner, p = 0.008) were associated with TRS</td><td>(Wei et al. (2023))</td></tr><tr><td>Huang</td><td>2022</td><td>Howes criteria</td><td>53 patients with TRS, 46 without TRS and 66 HC</td><td>Age, sex, BMI, smoking, clinical variables</td><td>Saliva</td><td>LC-MS-MS</td><td>KYNA</td><td>Higher salivary KYNA levels in patients with TRS compared with patients without TRS (p = 0.003)</td><td>(Huang et al. (2022b))</td></tr><tr><td>Huang</td><td>2022</td><td>Howes criteria</td><td>54 patients with TRS, 49 without TRS and 62 HC</td><td>Age, sex, BMI, smoking, clinical variables</td><td>Serum</td><td>LC-MS-MS</td><td>Metabolites of the TRP pathway</td><td>Higher 5-HT/TRP ratio in patients with TRD compared with patients without TRS (p = 0.009)</td><td>(Huang et al. (2022a))</td></tr><tr><td>Labonte</td><td>2022</td><td>TRS: patients who did not respond to APs but to clozapine monotherapy URS: patients who did not respond to any AP including clozapine</td><td>22 patients with TRS, 29 without TRS, 105 patients with URS</td><td>Age, sex, smoking, clinical variables</td><td>Whole blood</td><td>Different methods</td><td>CBC inflammatory markers</td><td>At admission, PLR and platelet count decreased with ascending order of TRS. At discharge, NLR, neutrophil count and WBC count increased with ascending order of TRS. NLR and neutrophil count showed a decrease in patients responders to APs and an increase in patients with URS</td><td>(Labonte et al. (2022))</td></tr><tr><td>Eratne</td><td>2022</td><td>Howes criteria</td><td>82 patients with TRS on clozapine, 37 first-degree relatives, 13 patients not treated with clozapine and 59 HC</td><td>Age, sex, BMI, smoking, clinical variables</td><td>Plasma</td><td>Simoa technology</td><td>NfL</td><td>No significant association</td><td>(Eratne et al. (2022))</td></tr><tr><td>Yesilkaya</td><td>2021</td><td>Howes criteria</td><td>65 patients with TRS, 62 without TRS, 62 HC</td><td>Age, sex, BMI, smoking, clinical variables</td><td>Serum, plasma</td><td>Different methods</td><td>Glucose, LDL, HDL, triglyceride, serum IGF-1 levels</td><td>Lower plasma LDL and higher serum IGF-1 levels in patients</td><td>(Yesilkaya et al. (2021))</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 4 (continued)</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define TRS</th><th>Sample</th><th>Assessed variables</th><th>Source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Enache</td><td>2021</td><td>Treatment with at least two APs for more than 4 weeks each, above the British National Formulary minimum therapeutic dose; CGI-SCH score &gt;3; PANSS score &ge;70; compliance rating scale score &gt;3</td><td>94 patients with SZ or related psychoses of which 47 with TRS and 47 without TRS</td><td>smoking, education, clinical variables</td><td>Plasma</td><td>Immunoassays, ELISA, turbidimetric methods</td><td>cholesterol, insulin, GH, IGF-1</td><td>with TRS compared with patients without TRS</td><td>(Enache et al. (2021))</td></tr><tr><td>Al-Dujaili</td><td>2021</td><td>Modified Conley and Kelly criteria</td><td>60 patients with TRS, 55 without TRS and partial response, 43 HC</td><td>Age, sex, BMI, smoking, education, clinical variables</td><td>Serum</td><td>ELISA</td><td>CCL11, DKK1, HMGB1, IL-10, IL-6</td><td>Levels of IL-8 adjusted for age, sex, BMI and smoking status were associated with TRS (OR = 24.7, adj p = 0.014)</td><td>(Al-Dujaili et al. (2021))</td></tr><tr><td>Leboyer</td><td>2021</td><td>Treatment with clozapine</td><td>39 patients with TRS, 15 with UTRS, 141 without TRS</td><td>Age, sex, BMI, smoking, education, clinical variables</td><td>Serum, plasma</td><td>Electrochemiluminescence assay, ELISA</td><td>Levels of a large panel of inflammatory markers</td><td>Increased IL-6 and CCL11 levels were associated with TRS</td><td>(Leboyer et al. (2021))</td></tr><tr><td>Buosi</td><td>2021</td><td>Kane and IPAP criteria</td><td>27 patients with TRS, 26 patients without TRS, 36 HC</td><td>Age, sex, clinical variables</td><td>Plasma, hemolysate</td><td>Spectrophotometry</td><td>CAT, SOD, GPx, GSH-t, MDA, TEAC</td><td>Higher levels of IL-12/IL-23p40, IL-17 A, IL-6, IL-10, IFNγ, and B2M in patients with TRS or UTRS compared with patients without TRS</td><td>(Buosi et al. (2021))</td></tr><tr><td>Kudo</td><td>2020</td><td>No or little response to &ge;2 AP (&gt;600 mg/d CPZ-eq for &ge;4 weeks), (at least 1 s-generation AP), GAF scores never &ge;40; or intolerance to at least 2 s-generation AP (uncontrolled EPS)</td><td>249 patients with SZ, of which 22 had TRS, 257 HC</td><td>Age, sex, smoking, clinical variables</td><td>Plasma</td><td>ELISA</td><td>MMP-9</td><td>No significant association</td><td>(Kudo et al. (2020))</td></tr><tr><td>Squassina</td><td>2020</td><td>Kane criteria</td><td>20 patients with TRS, 21 without TRS, 36 HC</td><td>Age, sex, BMI, smoking, clinical variables</td><td>Lymphocytes, plasma</td><td>ELISA, qFISH</td><td>TL, CRP, TNFα</td><td>Shorter TL in patients with TRS (adj p = 0.001), but not in patients without TRS compared with HC</td><td>(Squassina et al. (2020))</td></tr><tr><td>Moustafa</td><td>2020</td><td>Non-response to two episodes of treatment (each for at least two months), with two different AP at adequate doses, without a decrease in symptom severity (CGI-S)</td><td>60 patients with TRS, 55 partial responders, 43 HC</td><td>Age, sex, BMI, education, clinical variables</td><td>Serum</td><td>ELISA</td><td>IL-6, IL-10, MOR, KOR, endomorphin 2, b-endorphin</td><td>b-endorphin, EM2, MOR and IL-6 levels were significantly higher in patients with TRS compared with partial responders</td><td>(Moustafa et al. (2020))</td></tr><tr><td>Bousman</td><td>2019</td><td>Howes criteria</td><td>63 patients with TRS treated with clozapine, 30 with recent onset SZ, 88 HC</td><td>Age, sex, ethnicity, smoking, clinical variables</td><td>Erythrocytes</td><td>Western blot, global polyubiquitination assay</td><td>Markers of the ubiquitin proteasome system</td><td>Higher erythrocytes levels of ubiquitinated proteins in patients with TRS compared with those with recent onset SZ (adj p &lt; 0.001, AUC = 65.5%) and HC (adj p &lt; 0.001, AUC = 69.4%).</td><td>(Bousman et al. (2019))</td></tr></tbody></table>

(continued on next page)
<table><caption>Table 4 (continued)</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define TRS</th><th>Sample</th><th>Assessed variables</th><th>Source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Horsdal</td><td>2017</td><td>Clozapine initiation or hospital admission due to schizophrenia after having received at least 2 prior antipsychotic monotherapy trials of adequate duration</td><td>390 individuals, from a Danish population-based study, with a CRP measurement at diagnosis, of which 52 with TRS during a 2-year follow-up and 338 without TRS</td><td>Age, sex, clinical variables</td><td>NA</td><td>NA</td><td>CRP</td><td>Ubiquitination activity was decreased exclusively in erythrocytes from patients with TRS compared with HC (adj p &lt; 0.001, AUC = 65.5%)<br/>Nonsignificant higher median CRP level (4.0 vs. 3.1 mg/L) in patients with TRS</td><td>(Horsdal et al. (2017))</td></tr><tr><td>Noto</td><td>2015</td><td>IPAP criteria</td><td>31 patients with TRS, 23 without TRS, 118 HC</td><td>Age, sex, ethnicity, smoking, clinical variables</td><td>Serum</td><td>ELISA</td><td>15 markers of inflammation</td><td>Increased levels of sTNF-R1, sTNF-R2 and MCP-1 in patients with TRS</td><td>(Noto et al. (2015))</td></tr><tr><td>Lee</td><td>2011</td><td>Kane criteria</td><td>74 patients with TRS, 85 without TRS, 55 HC</td><td>Age, sex, BMI, clinical variables</td><td>Plasma</td><td>HPLC, RIA</td><td>TRP, TRP/LNAA ratio, cortisol</td><td>Plasma TRP levels were lower in patients with TRS compared with patients without TRS and HC. TRP/LNAA ratios were lower exclusively in patients with TRS compared with HC<br/>Higher concentrations of lipoperoxides by-products and neuron specific enolase in patients with TRS compared with patients without TRS and HC</td><td>(Lee et al. (2011))</td></tr><tr><td>Medina-Hernandez</td><td>2007</td><td>Keefe, Brenner and Merlo criteria</td><td>13 patients with TRS, 13 without TRS, 13 HC, all men</td><td>Age, smoking, clinical variables</td><td>Serum</td><td>Spectrophotometry, RIA</td><td>Lipid peroxidation</td><td>Higher concentrations of lipoperoxides by-products and neuron specific enolase in patients with TRS compared with patients without TRS and HC<br/>No significant association</td><td>(Medina-Hernandez et al. (2007))</td></tr><tr><td>Maes</td><td>2002</td><td>Kane criteria</td><td>17 patients with TRS, 14 without TRS, 7 HC</td><td>Age, sex, clinical variables</td><td>Serum</td><td>ELISA</td><td>IL-8, IL-10, LIF-R, sCD8,</td><td>No significant association</td><td>(Maes et al. (2002))</td></tr><tr><td>Lin</td><td>1998</td><td>Kane criteria</td><td>15 patients with TRS, 12 without TRS, 15 HC</td><td>Age, sex, clinical variables</td><td>Serum</td><td>ELISA</td><td>IL-6, sIL-6R, gp130, CC16</td><td>Higher serum IL-6 in patients with TRS (6.68 ± 7.31) but also in those without TRS (2.72 ± 3.62 pg/ml) compared with HC (0.39 ± 0.65 pg/ml)</td><td>(Lin et al. (1998))</td></tr></tbody></table>

**Abbreviations.** 5-HT, 6-hydroxytryptophan; AP, antipsychotics; AUC, area under the curve; BMI, body mass index; BPRS, Brief Psychiatric Rating Scale; CAT, catalase; CBC, complete blood count; CGI-S, Clinical Global Impression Severity; CGI-SCH, Clinical Global Impression Scale; CK, cytokines, CPZ-eq, chlorpromazine-equivalent; CRP, C-reactive protein; DKK1, Dickkopf-related protein; ELISA, enzyme-linked immunosorbent assays; EM2, endomorphin-2; EPS, extrapyramidal symptoms; FUP, follow-up; GPx, glutathione peroxidase; GSH-t, total glutathione; HC, healthy controls; HMGB1, high mobility group protein 1; HPLC, high-performance liquid chromatography; IL-6, interleukin 6; IL-8, interleukin 8; IL-10, interleukin 10; IPAP, International Psychopharmacology Algorithm Project; IRS/CIRS, immune inflammatory response system/compensatory immune-regulatory system; KOR, kappa-opioid receptor; KYN, kynurenine; KYNA, kynurenine acid; miRNA, LC-MS-MS, Liquid chromatography-tandem mass spectrometry; LIF-R, leukemia inhibitory factor receptor; LNAA, large neutral amino acid; MCP-1, monocyte chemoattractant protein 1; MDA, malondialdehyde; MIP-1, macrophage inflammatory protein 1 alpha; MMP-9, Matrix metalloproteinase-9; MOR, mu-opioid receptor; NfL, neurofilament light chain protein; NLR, neutrophil to lymphocyte ratio; OR, odds ratio; PANSS, Positive and Negative Syndrome Scale; PLR, platelet to lymphocyte ratio; PWAS, proteome-wide association study; qRT-PCR, Quantitative Reverse Transcription Polymerase Chain Reaction; QUIN, quinolinate; Ref, reference; RIA, radioimmunoassay; SCZ, schizophrenia; SOD, superoxide dismutase; sCD8, soluble CD8; sTNF, soluble TNF receptor; TEAC, trolox-equivalent antioxidant capacity; TL, telomere length; TRP, tryptophan; TRS, treatment-resistant schizophrenia; URS, ultra-resistant schizophrenia.
<table><caption>**Table 5**
Studies investigating gut microbiota composition in patients with treatment-resistant schizophrenia.</caption><thead><tr><th>First author</th><th>Year</th><th>Criteria to define TRS</th><th>Sample</th><th>Assessed variables</th><th>Source</th><th>Measurement method</th><th>Targets</th><th>Main findings</th><th>Ref</th></tr></thead><tbody><tr><td>Manchia</td><td>2021</td><td>Kane criteria</td><td>18 patients with TRS, 20 without TRS, 20 HC</td><td>Age, sex, BMI, smoking, diet, clinical variables</td><td>Stool</td><td>NGS of the 16 S rRNA V3-V4 region</td><td>Gut microbiota composition</td><td>The phyla Candidatus Saccharibacteria and Tenericutes, and the genera Actinomyces and Porphyromonas were found in TRS but not in R patients, whereas the Flavobacteriaceae and Enterococcaceae families, and the Flintibacter butyricus species were absent in patients with TRS</td><td>(Manchia et al. (2021))</td></tr></tbody></table>

**Abbreviations.** BMI, body mass index; HC, healthy controls; NGS, next generation sequencing; Ref, reference; TRS, treatment-resistant schizophrenia.

et al., 2014; Ji et al., 2008a, 2008b) or GABAergic transmission (Kogure et al., 2021; Miyazawa et al., 2022). Other studies focused on genes previously suggested to be involved in SZ such as DISC1 (Hotta et al., 2011; Mouaffak et al., 2011b) or involved in the metabolism of anti-psychotics such as CYP2C19 and CYP2D6 (Rodrigues-Silva et al., 2020; van de Bilt et al., 2015). Among the most replicated results, the rs4680 Met allele of the catechol-o-methyltransferase (COMT) gene, which decreases the activity of the enzyme, was associated with TRS in different studies (Hajj et al., 2019; Kogure et al., 2021; Sagud et al., 2018). Some of these studies investigated potential sex differences in the association between the Met allele and TRS with conflicting results (Hajj et al., 2019; Sagud et al., 2018). Importantly, other studies did not replicate the association between variants in the COMT gene and TRS (Terzic et al., 2016) or found the association to be not significant after including in the model clinical variables such as younger age of illness onset (Escamilla et al., 2018).

Overall, available results from studies evaluating the association between common genetic variants and TRS support the existence of a modest but significant SNP-based heritability of this phenotype and the potential utility of SZ PRS in predicting the risk of TRS. In addition, a potentially relevant role might be played by rare but disruptive genetic variants.

### 3.2. RNA biomarkers of TRS

We retrieved 5 studies comparing microRNA (miRNA) or messenger RNA (mRNA) levels between patients with and without TRS (Table 2), of which 2 with a genome-wide and 3 with a candidate target design. Quality scores ranged from 13 to 16 (Supplementary Table 2). One study did not report the criteria used to define TRS, while the other studies used different criteria (Table 2). The 2 studies with a genome-wide design measured levels of miRNAs either in whole blood (Perez-Rodriguez et al., 2023) or plasma exosomes (Funahashi et al., 2023). No overlap was observed between the miRNAs identified as differentially expressed in TRS by the two studies. However, besides differences in the source of miRNAs, also differences in the design of the studies might explain this inconsistency. In fact, while in the study from Perez-Rodriguez and colleagues patients with TRS were compared with patients without TRS (Perez-Rodriguez et al., 2023), in the one from Funahashi and colleagues two samples of patients with and without TRS were compared with controls (Funahashi et al., 2023). Among the most important findings, this study reported miR-675-3p to be overexpressed in patients with TRS and underexpressed in patients without TRS compared with non-psychiatric controls. Studies focused on mRNA levels suggested that patients with TRS may present increased peripheral whole blood levels of AKT3 and DICER1 (Moretti et al., 2018) compared to controls, while protein but not mRNA levels of SELENBP1 were found to be lower in patients with recent-onset SZ but not in patients with TRS compared with controls (Chau et al., 2018). Overall, evidence from studies investigating the role of transcripts as markers of

TRS is sparse and include findings that still lack replication in larger and independent cohorts.

### 3.3. Methylation biomarkers of TRS

We retrieved 4 studies comparing peripheral blood methylation levels between patients with and without TRS, of which 3 with a genome-wide design and 1 focused on methylation as well as genetic variants of genes encoding for serotonin (5-HT) receptors (Table 3). Quality scores ranged from 9 to 16 (Supplementary Table 2). Two studies defined TRS according to the APA criteria (Bani-Fatemi et al., 2014; De Luca et al., 2023), 1 based on Conley and Kelly criteria (Lu et al., 2023) and 1 based on treatment with clozapine (Hannon et al., 2021). The largest study to date was conducted by Hannon and colleagues, who published a meta-analysis of blood DNA methylation profiles from 4483 patients with SZ or psychosis from 7 independent cohorts (Hannon et al., 2021). For a subsample of these participants information on medication records allowed to define TRS based on clozapine exposure, leading to identify 399 patients with TRS and 636 without TRS. While the authors reported a set of 7 differentially methylated sites associated with clozapine exposure (Hannon et al., 2021), the criterion used to define TRS does not allow to draw definitive conclusion as regards to the differences observed in methylation levels might be associated with the TRS status or rather being a consequence of exposure to clozapine. In addition, the authors reported that 17% of the SZ-associated differentially methylated positions (DMP) identified in the main meta-analysis are influenced by TRS and might reflect either differences induced by exposure to clozapine or TRS (Hannon et al., 2021). A recent study from Lu and colleagues measured genome-wide blood methylation levels in a discovery cohort of 8 patients with TRS based on the Conley and Kelly criteria and 8 without TRS, all of Han-Chinese origin and an independent validation sample including 40 patients with TRS, 40 without TRS and 40 non-psychiatric controls (Lu et al., 2023). This study reported 13 DMPs between patients with and without TRS, 6 of which were located within gene coding regions (LOC404266, LOXL2, CEPK, CHMP7 and SLC17A9) and were selected for validation. In the validation sample, the 6 methylation probes showed good discrimination between TRS and non-TRS patients, with an area under the curve (AUC) ranging from 0.83 to 0.92, accuracy from 77.8% to 87.3%, sensitivity from 80% to 90%, and specificity from 65.6% to 85% (Lu et al., 2023). Another study from De Luca and colleagues measured genome-wide methylation levels in peripheral blood from 43 patients with and 66 without TRS (De Luca et al., 2023). While this study reported no significant hits based on a genome-wide significant threshold, the CpG with the highest significance was cg25009327, located within the ZNF425 gene, which was found to be hypermethylated in patients with TRS compared with patients with responsive SZ (De Luca et al., 2023). Overall, while the number of studies that investigated DNA methylation is still limited, available evidence supports a potential utility of these markers to differentiate between patients with and without TRS.
### 3.4. Proteins levels, metabolite levels and other tests in blood

We retrieved 23 studies comparing protein, metabolite or other markers measured in blood such as complete blood count between patients with and without TRS (Table 4). Studies obtained quality scores ranging from 13 to 19 (Supplementary Table 2) and the majority focused on markers of inflammation. Increased serum levels of IL-6 in patients with TRS are the most replicated result (Al-Dujaili et al., 2021; Leboyer et al., 2021; Moustafa et al., 2020; Yang et al., 2023), while increased levels of other inflammatory markers such as interleukin 8 (IL-8) still lack replication in different independent studies (Table 4). C-reactive protein (CRP), a widely investigated marker of inflammation, does not seem to be associated with TRS, as reported in a relatively large population-based Danish study including a total of 390 individuals with CRP measure at diagnosis, and a 2-year follow-up to assess the development of TRS (Horsdal et al., 2017). Consistently, other smaller studies with a cross-sectional design did not observe significant differences in CRP levels between patients with and without TRS (Enache et al., 2021; Squassina et al., 2020). Among studies focused on sets of inflammatory markers, a recent investigation by Cheng and colleagues evaluated the association between an inflammatory index and treatment resistance in a sample including 52 patients with TRS and 47 without TRS (Cheng et al., 2023). The authors constructed the immune-inflammatory response system/compensatory immune-regulatory reflex system (IRS/CIRS) score, based on the ratio between plasma levels of 10 and 8 pro- and anti-inflammatory cytokines and receptors, respectively. Besides being associated with TRS, a higher IRS/CIRS ratio was also associated with disease severity based on the Positive and Negative Syndrome Scale (PANSS) score and with larger lateral ventricle volume (Cheng et al., 2023). Another study conducted by Leboyer and colleagues tested a large panel of serum and plasma inflammatory markers in a sample including 39 patients with TRS and 156 without TRS (Leboyer et al., 2021). In this study, TRS was associated with an inflammatory signature including pro-inflammatory markers mainly belonging to the T helper 17 pathway (Leboyer et al., 2021). The potential involvement of inflammation in TRS is also supported by studies evaluating complete blood count markers such as white blood cell and specifically neutrophil counts (Labonte et al., 2022; Osimo et al., 2023). A smaller number of studies focused on metabolites of the tryptophan (TRP) pathway (Huang et al., 2022a, 2022b; Lee et al., 2011), reporting lower plasma TRP levels (Lee et al., 2011) or higher serum 5-HT/TRP ratio (Huang et al., 2022a) in patients with TRS compared with responsive SZ. Overall, an association between TRS and increased inflammation seems plausible based on the available data, with more robust results reported for IL-6 as well as for models considering integrated sets of inflammatory and anti-inflammatory markers.

### 3.5. Gut microbiota composition

Finally, our search retrieved a single study that evaluated the association between gut microbiota profiles and TRS (Table 5). Manchia and colleagues sequenced the 16 S rRNA V3–V4 region to characterize the gut microbiota profiles of 18 patients with TRS (according to Kane criteria), 20 without TRS and 20 controls (Manchia et al., 2021). This study showed a distinct signature of gut microbiota composition in patients with SZ who had a clinical history of response or resistance to antipsychotic treatments. Specifically, the phyla Candidatus Saccharibacteria and Tenericutes, and the genera Actynomyces and Porphyromonas were exclusively found in patients with TRS (Manchia et al., 2021). Interestingly, the Porphyromonas exert pro-inflammatory actions at the level of the intestine, increasing the permeability of the gut blood barrier, and creating a substantial dysbiosis (Nakajima et al., 2015). Conversely, the families Flavobacteriaceae and Enterococcaceae, and the species Flintibacter butyricus were absent in TRS (Manchia et al., 2021). While no other study evaluated gut microbiota profiles in patients with and without TRS, a study previously described in section

3.1 used a different but related approach. As previously described, using two-sample MR, Cheng and colleagues reported a potential association between TRS and genus Barnesiella levels, although this finding did not show consistency across MR methods (Cheng et al., 2023). While these results are promising, the absence of other studies investigating gut microbiota profiles in patients with and without TRS calls for replication in independent cohorts.

## 4. Discussion

This systematic review provides a comprehensive and updated picture of the studies evaluating peripheral biomarkers of TRS. Around half of the included studies focused on genetic biomarkers, with a recent shift from candidate-gene to genome-wide studies. This scenario is expected based on the higher availability of large, well-powered datasets collected by international consortia or population-based studies. Nonetheless, the identification of genetic variants associated with TRS at a genome-wide significant threshold has proved to be challenging. For instance, the largest study conducted by Pardiñas and colleagues found that TRS has only a modest despite significant SNP-based heritability, and did not identify any variant significant at the genome-wide level (Pardiñas et al., 2022). Since many of the available datasets were not originally designed to investigate TRS, or did not include information to define this phenotype, this study adopted an interaction design and conducted 2 GWASs in which matched sets of controls were compared with either patients with SZ with or without TRS. The differences estimated between the effect sizes for allelic associations between the 2 GWAS were considered to reflect TRS rather than SZ. While this approach is only sensitive to relatively large differences in effect sizes between studies, it allowed to conduct a GWAS even in the absence of a powered and harmonized case-case dataset. In addition, a validation analysis conducted using 2 independent cohorts of patients with and without TRS showed a significant association of the TRS PRS constructed in the GWAS cohorts (in which TRS was with defined as treatment with clozapine after failure of at least 2 trials of antipsychotics) with history of taking clozapine in the validation cohorts. The polygenic effects implicated in TRS were confirmed by the PRS analyses performed in other studies presented in our review, as discussed in Section 3.1, even when the PRS was constructed based on variants associated with SZ, and not specifically with TRS. In the study by Pardiñas and colleagues, the validation of the PRS was obtained despite differences in the characteristics across the samples, including the use of data prescription information as a proxy for TRS. The finding is encouraging considering the large heterogeneity in the criteria used to define TRS across the studies included in this review. Indeed, especially for genome-wide investigations, a growing number of studies used clozapine exposure as a proxy for TRS, to take advantage of datasets provided by registries or consortia. While this choice allows to maximize sample size, it might increase heterogeneity, considering that clozapine prescription patterns may vary across countries and that this definition of TRS does not provide any information as regards to relevant aspects such as presence of contraindications to clozapine, treatment adherence, or levels of psychopathology and functioning. In particular, it cannot be excluded that studies in which the definition of TRS is only based on clozapine prescription, also include patients with pseudo resistance, i.e. a condition in which the lack of clinical response is due to e.g. lack of adherence, insufficient plasma levels, inadequate dosage or duration of treatment (Potkin et al., 2020). In addition, the definition of TRS based on treatment with clozapine might be particularly challenging when evaluating dynamic measures (e.g., gene expression, methylation), since it does not allow to dissect changes related to TRS from those related to clozapine exposure. Nonetheless, at the present time, the use of a stringent definition of TRS would not allow to achieve a sufficient sample size since individuals with TRS are underrepresented in research studies due e.g. to poor health and limited capacity to consent (Hofer et al., 2017). Larger samples are likely needed for reliable identification of genetic
markers, as demonstrated by the improved prediction obtained during the last decade for schizophrenia diagnosis. Initial studies in the range of 10,000 subjects were marginally informative, but the latest analysis on over 70,000 subjects identified biomarkers close to clinical relevance (Trubetskoy et al., 2022).

A high number of studies evaluated the correlation of TRS with circulating protein or metabolite markers, with only a minority of studies focusing on other dynamic measures such as transcripts, methylation levels or gut microbiota profiles. Studies investigating protein levels mostly focused on pro- and anti-inflammatory markers, based on the hypothesis of an activated immune-inflammatory system in TRS (Leboyer et al., 2021). The most encouraging results suggesting that cytokine imbalance might represent a biomarker for TRS were provided for IL-6 (Al-Dujaili et al., 2021; Leboyer et al., 2021; Moustafa et al., 2020; Yang et al., 2023), while studies evaluating other markers of inflammation provided conflicting results. Nonetheless, promising findings were also provided by studies exploring panels of inflammatory markers rather than single proteins. Among these studies, Leboyer and colleagues found TRS to be associated with a signature of inflammatory markers mainly related to the differentiation of the T helper 17 pathway (Leboyer et al., 2021). A more recent study by Chen and colleagues suggested that patients with TRS show an elevated IRS/CIRS ratio based on plasma levels of 10 and 8 pro- and anti-inflammatory cytokines and receptors, respectively (Chen et al., 2023). Specifically, IRS activation implicates increased levels of pro-inflammatory cytokines produced by M1 macrophages, T helper (TH) 1 and Th-2 cells, while CIRS activation is related to anti-inflammatory markers produced by Th-2 and T regulatory cells. In this study, the IRS/CIRS ratio was also positively correlated with lateral ventricle volume and higher PANSS score in patients with TRS (Chen et al., 2023). While the impact of a cytokine imbalance in TRS has not been largely explored, peripheral cytokines may compromise the integrity of the blood-brain barrier (Novellino et al., 2020), leading to neuroinflammation and alterations of synaptic plasticity and neuronal remodeling.

The identification of reliable biomarkers of TRS would allow to identify these patients much earlier in the illness course, thus reducing the delay in clozapine initiation, which has been shown to be associated with poorer treatment outcomes (Shah et al., 2018). In this sense, while this was not the focus of our article, in order to better evaluate the benefit-risk ratio of this drug, a research priority in the field would also be to identify reliable biomarkers of clozapine adverse effects.

Studies included in our review have an observational design and are therefore more prone to bias and confounding. While we adopted systematic procedures during the search and screening of articles, and we conducted an evaluation of the quality of included studies, we cannot exclude that the design of the included studies might impact on the results discussed in this review. Notably, our systematic review shows that only a very limited number of studies, all with a candidate-gene design, tried to integrate different types of biomarkers such as genetic variants with e.g. transcript or protein levels. Moreover, there is currently a lack of studies integrating clinical and multi-omic measures to identify models with higher predictive value. These studies, if provided in a well-powered sample size, might leverage machine learning models to identify multi-level signatures of TRS, ultimately leading us closer to the understanding of the neurobiological architecture of TRS.

## Funding sources

Bernhard T Baune, Claudia Pisanu, Mara Dierssen, Marie-Claude Potier, Chiara Fabbri, Alessandro Serretti, Alessio Squassina and Alessandra Minelli received support from Psych-STRATA, a project funded from the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101057454. Massimo Gennar-elli received support by the Italian Ministry of Health under Grant [Ricerca Corrente 2022].

## Declaration of competing interest

CF was a speaker for Janssen. Prof. Serretti 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, Boheringer, Bristol Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italianfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, Servier, Taliaz. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The presented work is part of the ECNP Network "Pharmacogenomics and Transcriptomics" which is supported by the European College of Neuropsychopharmacology (ECNP).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nsa.2024.104059.

## References

* Ajnakina, O., Lally, J., Di Forti, M., Lopez-Morinigo, J., David, A.S., Dazzan, P., Pariante, C., Mondelli, V., Gaughran, F., Coleman, J., Murray, R., Breen, G., Vassos, E., 2017. Polygenic RISK score for schizophrenia IS associated with treatment resiStant schizophrenia during the first five years after first contact with mental health services. Eur. Neuropsychopharmacol 27, S189.

* Al-Dujaili, A.H., Mousa, R.F., Al-Hakeim, H.K., Maes, M., 2021. High mobility group protein 1 and dickkopf-related protein 1 in schizophrenia and treatment-resistant schizophrenia: associations with interleukin-6, symptom domains, and neurocognitive impairments. Schizophr. Bull. 47, 530-541.

* Alacam, H., Akgun, S., Akca, H., Ozturk, O., Kabukcu, B.B., Herken, H., 2016. miR-181b-5p, miR-195-5p and miR-301a-3p are related with treatment resistance in schizophrenia. Psychiatr. Res. 245, 200-206.

* Bani-Fatemi, A., Ruzicka, W.B., De Luca, V., 2014. Potential and direct methylation of 5-HT receptors in treatment resistant schizophrenia. Eur. Neuropsychopharmacol 24, S562.

* Bilic, P., Jukic, V., Vilibic, M., Savic, A., Bozina, N., 2014. Treatment-resistant schizophrenia and DAT and SERT polymorphisms. Gene 543, 125-132.

* Bousman, C.A., Luza, S., Mancuso, S.G., Kang, D., Opazo, C.M., Mostaid, M.S., Cropley, V., McGorry, P., Shannon Weickert, C., Pantelis, C., Bush, A.I., Everall, I.P., 2019. Elevated ubiquitinated proteins in brain and blood of individuals with schizophrenia. Sci. Rep. 9, 2307.

* Buosi, P., Borghi, F.A., Lopes, A.M., Facincani, I.D.S., Fernandes-Ferreira, R., Oliveira-Brancati, C.I.F., do Carmo, T.S., Souza, D.R.S., da Silva, D.G.H., de Almeida, E.A., de Araujo Filho, G.M., 2021. Oxidative stress biomarkers in treatment-responsive and treatment-resistant schizophrenia patients. Trends Psychiatry Psychother 43, 278-285.

* Carbon, M., Correll, C.U., 2014. Clinical predictors of therapeutic response to antipsychotics in schizophrenia. Dialogues Clin. Neurosci. 16, 505-524.

* Chau, E.J., Mostaid, M.S., Cropley, V., McGorry, P., Pantelis, C., Bousman, C.A., Everall, I.P., 2018. Downregulation of plasma SELENBP1 protein in patients with recent-onset schizophrenia. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 85, 1-6.

* Chen, W., Gou, M., Wang, L., Li, N., Li, W., Tong, J., Zhou, Y., Xie, T., Yu, T., Feng, W., Li, Y., Chen, S., Tian, B., Tan, S., Wang, Z., Pan, S., Luo, X., Zhang, P., Huang, J., Tian, L., Li, C.R., Tan, Y., 2023. Inflammatory disequilibrium and lateral ventricular enlargement in treatment-resistant schizophrenia. Eur. Neuropsychopharmacol 72, 18-29.

* Cheng, B., Cheng, S., Li, C., Wei, W., Liu, L., Meng, P., Yang, X., Jia, Y., Wen, Y., Zhang, F., 2023. Treatment resistance in schizophrenia is associated with attention deficit/hyperactivity disorder and gut microbiota: a genetic correlation and mendelian randomization study. Neuropsychobiology 82, 24-32.

* Correll, C.U., Howes, O.D., 2021. Treatment-resistant schizophrenia: definition, predictors, and therapy options. J. Clin. Psychiatry 82.

* Correll, C.U., Agid, O., Crespo-Facorro, B., de Bartolomeis, A., Fagiolini, A., Seppala, N., Howes, O.D., 2022. A guideline and checklist for initiating and managing clozapine treatment in patients with treatment-resistant schizophrenia. CNS Drugs 36, 659-679.

* De Luca, V., Chaudhary, Z., Al-Chalabi, N., Qian, J., Borlido, C., Gerretsen, P., Graff, A., Remington, G., Chintoh, A., 2023. Genome-wide methylation analysis of treatment resistant schizophrenia. J. Neural. Transm. 130, 165-169.

* Downs, S.H., Black, N., 1998. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J. Epidemiol. Community Health 52, 377-384.

* Enache, D., Nikkheslat, N., Fathalla, D., Morgan, B.P., Lewis, S., Drake, R., Deakin, B., Walters, J., Lawrie, S.M., Egerton, A., MacCabe, J.H., Mondelli, V., 2021. Peripheral
immune markers and antipsychotic non-response in psychosis. *Schizophr. Res.* 230, 1–8.

Eratne, D., Janelidze, S., Malpas, C.B., Loi, S., Walterfang, M., Merritt, A., Diouf, I., Blennow, K., Zetterberg, H., Cilia, B., Wannan, C., Bousman, C., Everall, I., Zalesky, A., Jayaram, M., Thomas, N., Berkovic, S.F., Hansson, O., Velakoulis, D., Pantelis, C., Santillo, A., Mi, N.D.S.G., 2022. Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives. Aust. N. Z. J. Psychiatr. 56, 1295–1305.

Erdos, J., Charney, D., Heninger, G., Vankammen, D.P., Gelernter, J., 1995. Apoe Alleles and Refractory Schizophrenia 57, 1096.

Escamilla, R., Camarena, B., Saracco-Alvarez, R., Fresan, A., Hernandez, S., Aguilar-Garcia, A., 2018. Association study between COMT, DRD2, and DRD3 gene variants and antipsychotic treatment response in Mexican patients with schizophrenia. *Neuropsychiatric Dis. Treat.* 14, 2981–2987.

Facal, F., Costas, J., 2023. Polygenic risk scores for schizophrenia and treatment resistance: new data, systematic review and meta-analysis. *Schizophr. Res.* 252, 189–197.

Fenton, C., Kang, C., 2023. Clozapine is the approved option in treatment-resistant schizophrenia and requires careful management. *Drugs Ther. Perspect.* 39, 107–113.

Frank, J., Lang, M., Witt, S.H., Strohmaier, J., Rujescu, D., Cichon, S., Degenhardt, F., Nothen, M.M., Collier, D.A., Ripke, S., Naber, D., Rietschel, M., 2015. Identification of increased genetic risk scores for schizophrenia in treatment-resistant patients. *Mol. Psychiatr.* 20, 150–151.

Funahashi, Y., Yoshino, Y., Iga, J.I., Ueno, S.I., 2023. Impact of clozapine on the expression of miR-675-3p in plasma exosomes derived from patients with schizophrenia. *World J. Biol. Psychiatr.* 24, 303–313.

Gasse, C., Wimberley, T., Wang, Y., Mors, O., Borglum, A., Als, T.D., Werge, T., Nordofto, M., Hougaard, D.M., Horsdal, H.T., 2019. Schizophrenia polygenic risk scores, urbanicity and treatment-resistant schizophrenia. *Schizophr. Res.* 212, 79–85.

Goldberger, C., Gourion, D., Leroy, S., Schurhoff, F., Bourdel, M.C., Leboyer, M., Krebs, M.O., 2005. Population-based and family-based association study of 5'UTR polymorphism of the reelin gene and schizophrenia. *Am. J. Med. Genet. B Neuro-psychiatr. Genet.* 137B, 51–55.

Hajj, A., Obeid, S., Sahyoun, S., Haddad, C., Azar, J., Rabbaa Khabbaz, L., Hallit, S., 2019. Clinical and genetic factors associated with resistance to treatment in patients with schizophrenia: a case-control study. *Int. J. Mol. Sci.* 20.

Hannon, E., Dempster, E.L., Mansell, G., Burrage, J., Bass, N., Bohlken, M.M., Corvin, A., Curtis, C.J., Dempster, D., Di Forti, M., Dinan, T.G., Donohoe, G., Gaughran, F., Gill, M., Gillespie, A., Gunasinghe, C., Hulshoff, H.E., Hultman, C.M., Johansson, V., Kahn, R.S., Kaprio, J., Kenis, G., Kowalec, K., MacCabe, J., McDonald, C., McQuillin, A., Morris, D.W., Murphy, K.C., Mustard, C.J., Nenadic, I., O'Donovan, M. C., Quattrone, D., Richards, A.L., Rutten, B.P., St Clair, D., Therman, S., Toullopoulou, T., Van Os, J., Waddington, J.L., C. Wellcome Trust Case Control, consortium, C., Sullivan, P., Vassos, E., Breen, G., Collier, D.A., Murray, R.M., Schalkwyk, L.S., Mill, J., 2021. DNA methylation meta-analysis reveals cellular alterations in psychosis and markers of treatment-resistant schizophrenia. *Elife* 10.

Hofer, A., Radner, V., Edlinger, M., Kemmler, G., Rettenbacher, M.A., Fleischhacker, W. W., 2017. Why do individuals with schizophrenia drop out of observational clinical trials? *Psychiatr. Res.* 256, 1–5.

Horsdal, H.T., Wimberley, T., Benros, M.E., Gasse, C., 2017. C-reactive protein levels and treatment resistance in Schizophrenia-A Danish population-based cohort study. *Hum. Psychopharmacol.* 32.

Hotta, Y., Ohnuma, T., Hanzawa, R., Shibata, N., Maeshima, H., Baba, H., Hatano, T., Takebayashi, Y., Kitazawa, M., Higa, M., Suzuki, T., Arai, H., 2011. Association study between Disrupted-in-Schizophrenia-1 (DISC1) and Japanese patients with treatment-resistant schizophrenia (TRS). *Prog. Neuro-Psychopharmacol. Biol. Psychiatry* 35, 636–639.

Howes, O.D., McCutcheon, R., Agid, O., de Bartolomeis, A., van Beveren, N.J., Birnbaum, M.L., Bloomfield, M.A., Bressan, R.A., Buchanan, R.W., Carpenter, W.T., Castle, D.J., Citrone, L., Daskalakis, Z.J., Davidson, M., Drake, R.J., Dursun, S., Ebdrup, B.H., Elkis, H., Falkai, P., Fleischacker, W.W., Gadelha, A., Gaughran, F., Glenthoj, B.Y., Graff-Guerrero, A., Hallak, J.E., Honer, W.G., Kennedy, J., Kinon, B. J., Lawrie, S.M., Lee, J., Leweke, F.M., MacCabe, J.H., McNabb, C.B., Meltzer, H., Moller, H.J., Nakajima, S., Pantelis, C., Reis Marques, T., Remington, G., Rossell, S. L., Russell, B.R., Siu, C.O., Suzuki, T., Sommer, I.E., Taylor, D., Thomas, N., Ucok, A., Umbricht, D., Walters, J.T., Kane, J., Correll, C.U., 2017. Treatment-resistant schizophrenia: treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. *Am. J. Psychiatr.* 174, 216–229.

Huang, J., Zhang, P., Zhou, Y., Tong, J., Cui, Y., Tan, S., Wang, Z., Yang, F., Kochunov, P., Tian, B., Tian, L., Hong, L.E., Tan, Y., 2022a. Serum kynurenine metabolites might not be associated with risk factors of treatment-resistant schizophrenia. *J. Psychiatr. Res.* 145, 339–346.

Huang, J., Tong, J., Zhang, P., Zhou, Y., Li, Y., Tan, S., Wang, Z., Yang, F., Kochunov, P., Chiappelli, J., Tian, B., Tian, L., Hong, L.E., Tan, Y., 2022b. Elevated salivary kynurenine acid levels related to enlarged choroid plexus and severity of clinical phenotypes in treatment-resistant schizophrenia. *Brain Behav. Immun.* 106, 32–39.

Ji, X., Takahashi, N., Branko, A., Ishihara, R., Nagai, T., Mouri, A., Saito, S., Maeno, N., Inada, T., Ozaki, N., 2008a. An association between serotonin receptor 3B gene (HTR3B) and treatment-resistant schizophrenia (TRS) in a Japanese population. *Nagoya J. Med. Sci.* 70, 11–17.

Ji, X., Takahashi, N., Saito, S., Ishihara, R., Maeno, N., Inada, T., Ozaki, N., 2008b. Relationship between three serotonin receptor subtypes (HTR3A, HTR2A and HTR4) and treatment-resistant schizophrenia in the Japanese population. *Neurosci. Lett.* 435, 95–98.

Jia, P., Jayathilake, K., Zhao, Z., Meltzer, H.Y., 2011. Association of FAS, a TNF-alpha receptor gene, with treatment resistant schizophrenia. *Schizophr. Res.* 129, 211–212.

Kane, J., Honigfeld, G., Singer, J., Meltzer, H., 1988. Clozapine for the treatment-resistant schizophrenia. A double-blind comparison with chlorpromazine. *Arch. Gen. Psychiatr.* 45, 789–796.

Koga, A., Bani-Fatemi, A., Hettige, N., Borlido, C., Zai, C., Strauss, J., Gerretsen, P., Graff, A., Remington, G., De Luca, V., 2017. GWAS analysis of treatment resistant schizophrenia: interaction effect of childhood trauma. *Pharmacogenomics* 18, 663–671.

Kogure, M., Kanahara, N., Miyazawa, A., Oishi, K., Nakata, Y., Oda, Y., Iyo, M., 2021. Interacting roles of COMT and GAD1 genes in patients with treatment-resistant schizophrenia: a genetic association study of schizophrenia patients and healthy controls. *J. Mol. Neurosci.* 71, 2575–2582.

Korakakis, V., Whiteley, R., Tzavara, A., Malliaropoulos, N., 2018. The effectiveness of extracorporeal shockwave therapy in common lower limb conditions: a systematic review including quantification of patient-rated pain reduction. *Br. J. Sports Med.* 52, 387–407.

Kowalec, K., Lu, Y., Sariaslan, A., Song, J., Ploner, A., Dalman, C., Hultman, C.M., Larsson, H., Lichtenstein, P., Sullivan, P.F., 2021. Increased schizophrenia family history burden and reduced premorbid IQ in treatment-resistant schizophrenia: a Swedish National Register and Genomic Study. *Mol. Psychiatr.* 26, 4487–4495.

Kudo, N., Yamamori, H., Ishima, T., Nemoto, K., Yasuda, Y., Fujimoto, M., Azechi, H., Niitsu, T., Numata, S., Ikeda, M., Iyo, M., Ohmori, T., Fukunaga, M., Watanabe, Y., Hashimoto, K., Hashimoto, R., 2020. Plasma levels of matrix metalloproteinase-9 (MMP-9) are associated with cognitive performance in patients with schizophrenia. *Neuropharmacol. Rep.* 40, 150–156.

Labonte, C., Zhand, N., Park, A., Harvey, P.D., 2022. Complete blood count inflammatory markers in treatment-resistant schizophrenia: evidence of association between treatment responsiveness and levels of inflammation. *Psychiatr. Res.* 308, 114382.

Leboyer, M., Godin, O., Terro, E., Boukouaci, W., Lu, C.L., Andre, M., Aouizerate, B., Berna, F., Barau, C., Capdevielle, D., Claus-Kobayashi, J., Chereau, I., T., D.A., Dubertret, C., Dubreucq, J., Fond, G., Laouamri, H., Leignier, S., Lancon, C., Lorpra, M.P., Mallet, J., Le Corvoisier, P., Misdrahi, D., Passieroux, C., Rey, C., Pignon, B., Urbach, M., Szoke, A., Schurhoff, F., Tamouza, R., Groups, F.-S., 2021. Immune signatures of treatment-resistant schizophrenia: a FondaMental academic centers of expertise for schizophrenia (FACE-SZ) study. *Schizophr Bull Open 2* sgab012.

Lee, M., Jayathilake, K., Dai, J., Meltzer, H.Y., 2011. Decreased plasma tryptophan and tryptophan/large neutral amino acid ratio in patients with neuroleptic-resistant schizophrenia: relationship to plasma cortisol concentration. *Psychiatr. Res.* 185, 328–333.

Legge, S.E., Dennison, C.A., Pardinas, A.F., Rees, E., Lynham, A.J., Hopkins, L., Bates, L., Kirov, G., Owen, M.J., O'Donovan, M.C., Walters, J.T.R., 2020. Clinical indicators of treatment-resistant psychosis. *Br. J. Psychiatry* 216, 259–266.

Li, J., Meltzer, H.Y., 2014. A genetic locus in 7p12.2 associated with treatment resistant schizophrenia. *Schizophr. Res.* 159, 333–339.

Lin, A., Kenis, G., Bignotti, S., Tura, G.J., De Jong, R., Bosmans, E., Pioli, R., Altamura, C., Scharpe, S., Maes, M., 1998. The inflammatory response system in treatment-resistant schizophrenia: increased serum interleukin-6. *Schizophr. Res.* 32, 9–15.

Lu, A.K., Lin, J.J., Tseng, H.H., Wang, X.Y., Jang, F.L., Chen, P.S., Huang, C.C., Hsieh, S., Lin, S.H., 2023. DNA methylation signature aberration as potential biomarkers in treatment-resistant schizophrenia: constructing a methylation risk score using a machine learning method. *J. Psychiatr. Res.* 157, 57–65.

Luyx, J.J., Gonzalez-Diaz, J.M., Guu, T.W., van der Horst, M.Z., van Dellen, E., Boks, M. P., Guloksuz, S., DeLisi, L.E., Sommer, I.E., Cummins, R., Shiers, D., Lee, J., Every-Palmer, S., Mhalla, A., Chadly, Z., Chan, S.K.W., Cotes, R.O., Takahashi, S., Benros, M.E., Wagner, E., Correll, C.U., Hasan, A., Siskind, D., Endres, D., MacCabe, J., Tiihonen, J., 2023. An international research agenda for clozapine-resistant schizophrenia. *Lancet Psychiatr.* 10, 644–652.

Maes, M., Bocchio Chiavetto, L., Bignotti, S., Battisa Tura, G.J., Pioli, R., Boin, F., Kenis, G., Bosmans, E., de Jongh, R., Altamura, C.A., 2002. Increased serum interleukin-8 and interleukin-10 in schizophrenic patients resistant to treatment with neuroleptics and the stimulatory effects of clozapine on serum leukemia inhibitory factor receptor. *Schizophr. Res.* 54, 281–291.

Manchia, M., Pisanu, C., Squassina, A., Carpiniello, B., 2020. Challenges and future prospects of precision medicine in psychiatry. *Pharmgenomics Pers Med.* 13, 127–140.

Manchia, M., Fontana, A., Panebianco, C., Paribello, P., Arzedi, C., Cossu, E., Garzilli, M., Montis, M.A., Mura, A., Pisanu, C., Congiu, D., Copetti, M., Pinna, F., Pazienza, V., Squassina, A., Carpiniello, B., 2021. Involvement of gut microbiota in schizophrenia and treatment resistance to antipsychotics. *Biomedicines* 9.

Martin, A.K., Mowry, B., 2016. Increased rare duplication burden genomewide in patients with treatment-resistant schizophrenia. *Psychol. Med.* 46, 469–476.

Medina-Hernandez, V., Ramos-Loyo, J., Luquin, S., Sanchez, L.F., Garcia-Estrada, J., Navarro-Ruiz, A., 2007. Increased lipid peroxidation and neuron specific enolase in treatment refractory schizophrenics. *J. Psychiatr. Res.* 41, 652–658.

Miyazawa, A., Kanahara, N., Kogure, M., Otsuka, I., Okazaki, S., Watanabe, Y., Yamasaki, F., Nakata, Y., Oda, Y., Hishimoto, A., Iyo, M., 2022. A preliminary genetic association study of GAD1 and GABAB receptor genes in patients with treatment-resistant schizophrenia. *Mol. Biol. Rep.* 49, 2015–2024.

Moretti, P.N., Ota, V.K., Gouvea, E.S., Pedrini, M., Santoro, M.L., Talarico, F., Spindola, L.M., Carvalho, C.M., Noto, C., Xavier, G., Brietzke, E., Gadelha, A., Bressan, R., Mari, J., Belangero, S., 2018. Accessing gene expression in treatment-resistant schizophrenia. *Mol. Neurobiol.* 55, 7000–7008.
Mouaffak, F., Kebir, O., Bellon, A., Gourevitch, R., Tordjman, S., Viala, A., Millet, B., Jaafari, N., Olie, J.P., Krebs, M.O., 2011a. Association of an UCP4 (SLC25A27) haplotype with ultra-resistant schizophrenia. Pharmacogenomics 12, 185-193.

Mouaffak, F., Kebir, O., Chayet, M., Tordjman, S., Vacheron, M.N., Millet, B., Jaafari, N., Bellon, A., Olie, J.P., Krebs, M.O., 2011b. Association of Disruption of Schizophrenia 1 (DISC1) misense variants with ultra-resistant schizophrenia. Pharmacogenomics J. 11, 267-273.

Mouaffak, F., Kebir, O., Picard, V., Bonhomme-Faivre, L., Millet, B., Olie, J.P., Reynaud, M., Krebs, M.O., Benyamina, A., 2011c. Ultra-resistant schizophrenia is not associated with the multidrug-resistant resistant transporter 1 (MDR1) gene rs1045642 variant. J. Clin. Psychopharmacol. 31, 236-238.

Moustafa, S.R., Al-Rawi, K.F., Stoyanov, D., Al-Dujaili, H.A., Supasithurong, T., Al-Hakeem, H.K., Maes, M., 2020. The endogenous opioid system in schizophrenia and treatment resistant schizophrenia: increased plasma endomorphin 2 and kappa and mu opioid receptors are associated with interleukin-6. Diagnostics 10.

Nakajima, M., Arimatsu, K., Kato, T., Matsuda, Y., Minagawa, T., Takahashi, N., Ohno, H., Yamazaki, K., 2015. Oral administration of P. Gingivalis induces dysbiosis of gut microbiota and impaired barrier function leading to dissemination of enterobacteria to the liver. PLoS One 10, e0134234.

Nakata, Y., Kanahara, N., Kimura, A., Niitsu, T., Komatsu, R., Hoda, Y., Nakamura, M., Ishikawa, M., Hasegawa, T., Kamata, Y., Yamauchi, A., Inazumi, K., Kimura, H., Shiko, Y., Kawasaki, Y., Iyo, M., 2021. Oxytocin system dysfunction in patients with treatment-resistant schizophrenia: alterations of blood oxytocin levels and effect of a genetic variant of OXTR. J. Psychiatr. Res. 138, 219-227.

Noto, C., Maes, M., Ota, V.K., Teixeira, A.L., Bressan, R.A., Gadelha, A., Brietzke, E., 2015. High predictive value of immune-inflammatory biomarkers for schizophrenia diagnosis and association with treatment resistance. World J. Biol. Psychiatr. 16, 422-429.

Novellino, F., Saccà, V., Donato, A., Zaffino, P., Spadea, M.F., Vismara, M., Arcidiacono, B., Malara, N., Presta, I., Donato, G., 2020. Innate immunity: a common denominator between neurodegenerative and neuropsychiatric diseases. Int. J. Mol. Sci. 21, 2019.

Nucifora Jr., F.C., Woznica, E., Lee, B.J., Cascella, N., Sawa, A., 2019. Treatment resistant schizophrenia: clinical, biological, and therapeutic perspectives. Neurobiol. Dis. 131, 104257.

Nunes, F.D.D., Pinto, J.A.F., Freitas, P.H.B., Santos, L.L., Machado, R.M., 2019. Frequency of ANKK1, DRD2, DRD3 polymorphisms in refractory schizophrenia patients. Genot. Mol. Res. 18, GMR18389.

O'Connell, K.S., Koch, E., Lenk, H.C., Akkouh, I.A., Hindley, G., Jaholkowski, P., Smith, R.L., Holen, B., Shadrin, A.A., Frei, O., Smeland, O.B., Steen, N.E., Dale, A.M., Molden, E., Djurovic, S., Andreassen, O.A., 2023. Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia. Psychiatr. Res. 325, 115217.

Osimo, E.F., Perry, B.I., Mallikarjun, P., Pritchard, M., Lewis, J., Katunda, A., Murray, G., Perez, J., Jones, P.B., Cardinal, R.N., Howes, O.D., Upthegrove, R., Khandaker, G., 2023. Predicting treatment resistance from first-episode psychosis using routinely collected clinical information. Nat Ment Health 1, 25-35.

Pardinas, A.F., Smart, S.E., Willcocks, I.R., Holmans, P.A., Dennison, C.A., Lynham, A.J., Legge, S.E., Baune, W.T., Bigdeli, T.B., Cairns, M.J., Corvin, A., Fanous, A.H., Frank, J., Kelly, B., McQuillin, A., Melle, I., Mortensen, P.B., Mowry, B.J., Pato, C.N., Periyasamy, S., Rietschel, M., Rujescu, K., Simonsen, C., St Clair, D., Tooney, P., Wu, J.Q., Andreassen, O.A., Kowalec, K., Sullivan, P.F., Murray, R.M., Owen, M.J., MacCabe, J.H., O'Donovan, M.C., Walters, J.T.R., Genetics Workstream of the Schizophrenia Treatment, R. Therapeutic Advances, C., the Schizophrenia Working Group of the Psychiatric Genomics, C., Ajnakina, O., Alameda, L., Barnes, T.R.E., Berardi, D., Bonora, G., Camporesi, S., Cleusix, M., Conus, P., Crespo-Facorro, B., D'Andrea, G., Demjaha, A., Do, K.Q., Doody, G.A., Eap, C.B., Ferchiou, A., Di Forti, M., Guidi, L., Homman, L., Jenni, R., Joyan, E.M., Kassoumeri, L., Khadimali, I., Lastrina, O., Muratori, R., Nooyen, H., O'Neill, F.A., Pignon, B., Restellini, R., Richard, J.R., Schurhoff, F., Spaniel, F., Szoke, A., Tarricone, I., Tortelli, A., Ucok, A., Vazquez-Bourgon, J., 2022. Interaction testing and polygenic risk scoring to estimate the association of common genetic variants with treatment resistance in schizophrenia. JAMA Psychiatr. 79, 260-269.

Perez-Rodriguez, D., Penedo, M.A., Rivera-Baltanas, T., Pena-Centeno, T., Burkhardt, S., Fischer, A., Prieto-Gonzalez, J.M., Olivares, J.M., Lopez-Fernandez, H., Agis-Balboa, R.C., 2023. MiRNA differences related to treatment-resistant schizophrenia. Int. J. Mol. Sci. 24.

Potkin, S.G., Kane, J.M., Correll, C.U., Lindenmayer, J.P., Agid, O., Marder, S.R., Olfson, M., Howes, O.D., 2020. The neurobiology of treatment-resistant schizophrenia: paths to antipsychotic resistance and a roadmap for future research. NJP Schizophren 6, 1.

Rodrigues-Silva, C., Semedo, A.T., Neri, H., Vianello, R.P., Galaviz-Hernandez, C., Sosa-Macias, M., de Brito, R.B., Ghedini, P.C., 2020. The CYP2C19*2 and CYP2C19*17 polymorphisms influence responses to clozapine for the treatment of schizophrenia. Neuropsychiatric Dis. Treat. 16, 427-432.

Ruderfer, D.M., Charney, A.W., Readhead, B., Kidd, B.A., Kahler, A.K., Kenny, P.J., Keiser, M.J., Moran, J.L., Hultman, C.M., Scott, S.A., Sullivan, P.F., Purcell, S.M., Dudley, J.T., Sklar, P., 2016. Polygenic overlap between schizophrenia risk and antipsychotic response: a genomic medicine approach. Lancet Schiatr. 3, 350-357.

Sagud, M., Tudor, L., Uzun, S., Perkovic, M.N., Zivkovic, M., Konjevod, M., Kozumplik, O., Uksan Cusa, B., Svob Strac, D., Radios, I., Mimica, N., Mihaljevic Peles, A., Nedic Erjavec, G., Pivac, N., 2018. Haplotypic and genotypic association of catechol-O-methyltransferase rs4680 and rs4818 polymorphisms and treatment resistance in schizophrenia. Front. Pharmacol. 9, 705.

Shah, P., Iwata, Y., Plitman, E., Brown, E.E., Caravaggio, F., Kim, J., Nakajima, S., Hahn, M., Remington, G., Gerretsen, P., Graff-Guerrero, A., 2018. The impact of

delay in clozapine initiation on treatment outcomes in patients with treatment-resistant schizophrenia: a systematic review. Psychiatr. Res. 268, 114-122.

Shimomura, Y., Kikuchi, Y., Suzuki, T., Uchida, H., Mimura, M., Takeuchi, H., 2021. Antipsychotic treatment strategies for acute phase and treatment resistance in schizophrenia: a systematic review of the guidelines and algorithms. Schizophr. Res. 236, 142-155.

Siskind, D., Orr, S., Sinha, S., Yu, O., Jrijbail, W., Warren, N., MacCabe, J.H., Smart, S.E., Kisely, S., 2022. Rates of treatment-resistant schizophrenia from first-episode cohorts: systematic review and meta-analysis. Br. J. Psychiatry 220, 115-120.

Quassina, A., Manchia, M., Pisanu, C., Ardau, B., Arzedi, C., Bocchetta, A., Caria, P., Cocco, C., Congiu, D., Cossu, E., Dettori, T., Frau, D.V., Garzilli, M., Manca, E., Meloni, A., Montis, M.A., Mura, A., Nieddu, M., Noli, P., Paribello, P., Pinna, F., Robledo, R., Severino, G., Sogos, V., Del Zompo, M., Ferri, G.L., Chillotti, C., Vanni, R., Carpiniello, B., 2020. Telomere attrition and inflammatory load in severe psychiatric disorders and in response to psychotropic medications. Neuropsychopharmacology 45, 2229-2238.

Stroup, T.S., Gerhard, T., Crystal, S., Huang, C., Olfson, M., 2014. Geographic and clinical variation in clozapine use in the United States. Psychiatr. Serv. 65, 186-192.

Sultan, R.S., Olfson, M., Correll, C.U., Duncan, E.J., 2017. Evaluating the effect of the changes in FDA guidelines for clozapine monitoring. J. Clin. Psychiatry 78, e933-e939.

Takao, T., Tachikawa, M., Kawanishi, Y., Katano, T., Sen, B., Homma, M., Kohda, Y., Mizukami, K., Asada, T., 2006. Association of treatment-resistant schizophrenia with the G2677A/T and C3435T polymorphisms in the ATP-binding cassette subfamily B member 1 gene. Psychiatr. Genet. 16, 47-48.

Takebayashi, Y., Ohnuma, T., Hanzawa, R., Shibata, N., Maeshima, H., Baba, H., Hatano, T., Hotta, Y., Kitazawa, M., Higa, M., Arai, H., 2011. No genetic association between SLC7A10 and Japanese patients with schizophrenia. Prog. Neuro-Schizophrenial. Biol. Psychiatry 35, 1965-1968.

Talarico, F., Costa, G.O., Ota, V.K., Santoro, M.L., Noto, C., Gadelha, A., Bressan, R., Azevedo, H., Belangero, S.I., 2022. Systems-level analysis of genetic variants reveals functional and spatiotemporal context in treatment-resistant schizophrenia. Mol. Neurobiol. 59, 3170-3182.

Teo, C., Zai, C., Borlido, C., Tomasetti, C., Strauss, J., Shinkai, T., Le Foll, B., Wong, A., Kennedy, J.L., De Luca, V., 2012. Analysis of treatment-resistant schizophrenia and 384 markers from candidate genes. Pharmacogenetics Genom. 22, 807-811.

Terzic, T., Kastelic, M., Dolzan, V., Plesnicar, B.K., 2015a. Genetic variability testing of neurodevelopmental genes in schizophrenic patients. J. Mol. Neurosci. 56, 205-211.

Terzic, T., Kastelic, M., Dolzan, V., Plesnicar, B.K., 2015b. Influence of 5-HT1A and 5-HTTLPR genetic variants on the schizophrenia symptoms and occurrence of treatment-resistant schizophrenia. Neuropsychiatric Dis. Treat. 11, 453-459.

Terzic, T., Kastelic, M., Dolzan, V., Plesnicar, B.K., 2016. Genetic polymorphisms in dopaminergic system and treatment-resistant schizophrenia. Psychiatr. Danub. 28, 127-131.

Trubetskoy, V., Pardinas, A.F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T.B., Bryo, J., Chen, C.Y., Dennison, C.A., Hall, L.S., Lam, M., Watanabe, K., Frei, O., Ge, T., Harwood, J.C., Koopmans, F., Magnusson, S.E., Richards, A.L., Sidorenko, J., Wu, Y., Zeng, J., Grove, J., Kim, M., Li, Z., Voloudakis, G., Zhang, W., Adams, M., Agart, I., Atkinson, E.G., Agbero, E., Al Eissa, M., Albus, M., Alexander, M., Alizadeh, B.Z., Alptekin, K., Als, T.D., Amin, F., Arolt, V., Arrojo, M., Athanasius, L., Azevedo, M.H., Bacanu, S.A., Bass, N.J., Begemann, M., Belliveau, R.A., Bene, J., Benyamin, B., Bergen, S.E., Blasi, G., Bobes, J., Bonassi, S., Braun, A., Bressan, R.A., Bromet, E.J., Bruggeman, R., Buckley, P.F., Buckner, R.L., Bybjerg-Grauholm, J., Cahn, W., Cairns, M.J., Calkins, M.E., Carr, V.J., Castle, D., Catts, S.V., Chambert, K. D., Chan, R.C.K., Chaumette, B., Cheng, W., Cheung, E.F.C., Chong, S.A., Cohen, D., Consoli, A., Cordeiro, Q., Costas, J., Curtis, C., Davidson, M., Davis, K.L., de Haan, L., Degenhardt, F., DelLisi, L.E., Demontis, D., Dickerson, F., Dikeos, D., Dinan, T., Djurovic, S., Duan, J., Ducci, G., Dudbridge, F., Eriksson, J.G., Fananas, L., Faraone, S.V., Fiorentino, A., Forstner, A., Frank, J., Freimer, N.B., Fromer, M., Frustaci, A., Gadelha, A., Genovese, G., Gershon, E.S., Giannitelli, M., Giegling, I., Giusti-Rodriguez, P., Godard, S., Goldstein, J.I., Gonzalez Perez, A., Gonzalez-Pinto, A., Gopal, S., Gratten, J., Green, M.F., Greenwood, T.A., Guillin, O., Guloksuz, S., Gur, R.E., Gur, R.C., Gutierrez, B., Hahn, E., Hakonarson, H., Guloutyan, V., Hartmann, A.M., Harvey, C., Harvey, C., Henskens, F.A., Herms, S., Hoffmann, P., Howrigan, D.P., Ikeda, M., Iyegbe, C., Joa, I., Julia, A., Kahler, A.K., Kam-Thong, T., Kamatani, Y., Karachanak-Yankova, S., Kebir, O., Keller, M.C., Kelly, B.J., Khrunin, A., Kim, S.W., Klovins, J., Kondratiev, N., Konte, B., Kraft, J., Kubo, M., Kucinskas, V., Kucinskiene, Z.A., Kusumawardhani, A., Kuzelova-Ptackova, H., Landi, S., Lazzeroni, L.C., Lee, P.H., Lege, S.E., Lehrer, D.S., Lencer, R., Lerer, B., Li, M., Lieberman, J., Light, G.A., Limborska, S., Liu, C.M., Lonqnqvist, J., Loughland, C.M., Lubinski, J., Luykx, J.J., Lynham, A., Macek Jr., M., Mackinnon, A., Magnusson, P.K.E., Maher, B.S., Maier, W., Malaspina, D., Mallet, J., Marder, S.R., Marsal, S., Martin, A.R., Martorell, L., Mattheisen, M., McCarley, R.W., McDonald, C., McGrath, J.J., Medeiros, H., Meier, S., Melegh, B., Melle, I., Mesholam-Gately, R.I., Metspalu, A., Michie, P.T., Milani, L., Milanova, V., Mitjans, M., Molden, E., Molina, E., Molto, M.D., Mondelli, V., Moreno, C., Morley, C.P., Muntane, G., Murphy, K.C., Myin-Germeys, I., Nenadic, I., Nestadt, G., Nikitina-Zake, L., Noto, C., Nuechterlein, K.H., O'Brien, N.L., O'Neill, F.A., Oh, S.Y., Olinc, A., Ota, V.K., Pantelis, C., Papadimitriou, G.N., Parellada, M., Paunio, T., Pellegrino, R., Periyasamy, S., Perkins, D.O., Pfuhlmann, B., Pietilainen, O., Pimm, J., Porteous, D., Powell, J., Quattrone, D., Quested, D., Radant, A.D., Rampino, A., Rapaport, M.H., Rautanen, A., Reichenberg, A., Roe, C., Roffman, J.L., Roth, J., Rothermundt, M., Rutten, B.P.F., Saker-Delye, S., Salomaa, V., Sanjuan, J., Santoro, M.L., Savitz, A., Schall, U., Scott, R.J., Seidman, L.J., Sharp, S.I., Shi, J., Siever, L.J., Sigurdsson, E., Sim, K., Skarabis, N., Slominsky, P., So, H.C., Sobell, J.L., Soderman, E., Stain, H.J., Steen, N.E., Steixner-Kumar, A.A., Stogmann, E., Stone, W.
S., Straub, R.E., Streit, F., Strengman, E., Stroup, T.S., Subramaniam, M., Sugar, C.A., Suvisaari, J., Svrakic, D.M., Swerdlow, N.R., Szatkiewicz, J.P., Ta, T.M.T., Takahashi, A., Terao, C., Thibaut, F., Toncheva, D., Tooney, P.A., Torretta, S., Tosato, S., Tura, G.B., Turetsky, B.I., Ucok, A., Vaaler, A., van Amelsvoort, T., van Winkel, R., Veijola, J., Waddington, J., Walter, H., Waterreus, A., Webb, B.T., Weiser, M., Williams, N.M., Witt, S.H., Wormley, B.K., Wu, J.Q., Xu, Z., Yolken, R., Zai, C.C., Zhou, W., Zhu, F., Zimprich, F., Atbasoglu, E.C., Ayub, M., Benner, C., Bertolino, A., Black, D.W., Bray, N.J., Breen, G., Buccola, N.G., Byerley, W.F., Chen, W.J., Cloninger, C.R., Crespo-Facorro, B., Donohoe, G., Freedman, R., Galletly, C., Gandal, M.J., Gennarelli, M., Hougaard, D.M., Hwu, H.G., Jablensky, A. V., McCarroll, S.A., Moran, J.L., Mors, O., Mortensen, P.B., Muller-Myhsok, B., Neil, A.L., Nordentoft, M., Pato, M.T., Petryshen, T.L., Pirinen, M., Pulver, A.E., Schulze, T.G., Silverman, J.M., Smoller, J.W., Stahl, E.A., Tsuang, D.W., Vilella, E., Wang, S.H., Xu, S., Indonesia Schizophrenia, C., PsychEncode, Psychosis Endophenotypes International, C., Syn, G.O.C., Adolfsson, R., Arango, C., Baune, B. T., Belangero, S.I., Borglum, A.D., Braff, D., Bramon, E., Buxbaum, J.D., Campion, D., Cervilla, J.A., Cichon, S., Collier, D.A., Corvin, A., Curtis, D., Forti, M.D., Domenici, E., Ehrenreich, H., Escott-Price, V., Esko, T., Fanous, A.H., Gareeva, A., Gawlik, M., Gejman, P.V., Gill, M., Glatt, S.J., Golimbet, V., Hong, K.S., Hultman, C. M., Hyman, S.E., Iwata, N., Jonsson, E.G., Kahn, R.S., Kennedy, J.L., Khusnutdinova, E., Kirov, G., Knowles, J.A., Krebs, M.O., Laurent-Levinson, C., Lee, J., Lencz, T., Levinson, D.F., Li, Q.S., Liu, J., Malhotra, A.K., Malhotra, D., McIntosh, A., McQuillin, A., Menezes, P.R., Morgan, V.A., Morris, D.W., Mowry, B.J., Murray, R.M., Nimgaonkar, V., Nothen, M.M., Ophoff, R.A., Paciga, S.A., Palotie, A., Pato, C.N., Qin, S., Rietschel, M., Riley, B.P., Rivera, M., Rujescu, D., Saka, M.C., Sanders, A.R., Schwab, S.G., Serretti, A., Sham, P.C., Shi, Y., St Clair, D., Stefansson, H., Stefansson, K., Tsuang, M.T., van Os, J., Vawter, M.P., Weinberger, D. R., Werge, T., Wildenauer, D.B., Yu, X., Yue, W., Holmans, P.A., Pocklington, A.J., Roussos, P., Vassos, E., Verhage, M., Visscher, P.M., Yang, J., Posthuma, D., Andreassen, O.A., Kendler, K.S., Owen, M.J., Wray, N.R., Daly, M.J., Huang, H.,

Neale, B.M., Sullivan, P.F., Ripke, S., Walters, J.T.R., O'Donovan, M.C., Schizophrenia Working, C., 2022. Group of the Psychiatric Genomics, Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature 604, 502–508.

van de Bilt, M.T., Prado, C.M., Ojopi, E.P., Sousa, R.T., Loch, A.A., Zanetti, M.V., Talib, L. L., Gattaz, W.F., 2015. Cytochrome P450 genotypes are not associated with refractoriness to antipsychotic treatment. Schizophr. Res. 168, 587–588.

Vigo, D., Thornicroft, G., Atun, R., 2016. Estimating the true global burden of mental illness. Lancet Psychiatr. 3, 171–178.

Wei, W., Zhang, H., Cheng, B., Qin, X., He, D., Zhang, N., Zhao, Y., Cai, Q., Shi, S., Chu, X., Wen, Y., Liu, H., Jia, Y., Zhang, F., 2023. Identification of novel functional brain proteins for treatment-resistant schizophrenia: based on a proteome-wide association study. Eur. Psychiatr. 66, e33.

Werner, M.C.F., Wirgenes, K.V., Haram, M., Bettella, F., Lunding, S.H., Rodevand, L., Hjell, G., Agartz, I., Djurovic, S., Melle, I., Andreassen, O.A., Steen, N.E., 2020. Indicated association between polygenic risk score and treatment-resistance in a naturalistic sample of patients with schizophrenia spectrum disorders. Schizophr. Res. 218, 55–62.

Wimberley, T., Gasse, C., Meier, S.M., Agerbo, E., MacCabe, J.H., Horsdal, H.T., 2017. Polygenic risk score for schizophrenia and treatment-resistant schizophrenia. Schizophr. Bull. 43, 1064–1069.

Yang, H., Zhang, J., Yang, M., Xu, L., Chen, W., Sun, Y., Zhang, X., 2023. Catalase and interleukin-6 serum elevation in a prediction of treatment-resistance in male schizophrenia patients. Asian J. Psychiatr. 79, 103400.

Yesilkaya, U.H., Gica, S., Ilnem, M.C., Sen, M., Ipekcioglu, D., 2021. Evaluation of IGF-1 as a novel theranostic biomarker for schizophrenia. J. Psychiatr. Res. 140, 172–179.

Zoghbi, A.W., Dhindsa, R.S., Goldberg, T.E., Mehralizade, A., Motelow, J.E., Wang, X., Alkelai, A., Harms, M.B., Lieberman, J.A., Markx, S., Goldstein, D.B., 2021. High-impact rare genetic variants in severe schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 118.