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To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry., Competing Interests: Declaration of Competing Interest Bernhard T Baune has consulting roles with the National Health and Medical Research Council, Australia. He has received honoraria from AstraZeneca, Bristol-Myers Squibb, Lundbeck, Pfizer, Servier, Wyeth, Otsuka, Biogen; Research grants from private industries from AstraZeneca, Sanofi-Synthélabo; and research grants from the National Health and Medical Research Council (Australia), DFG (Germany), BMBF (Germany), Horizon Europe (EU); ERAPerMed (EU). He has served on advisory boards for Janssen-Cilag, Lundbeck, Biogen, Otsuka and received research funds from the Fay Fuller Foundation, and James & Diana Ramsay Foundation, Adelaide. Alessandro 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, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, Servier, Taliaz. Chiara Fabbri was a speaker for Janssen. Roos van Westrhenen received research funding from the Royal Dutch medical Association, the Erasmus MC (Koers 2018), the Dutch Kidney Foundation, Baxter and she is the Principal Investigator of PSY-PGx funded by Horizon2020 (www.psy-pgx.nl). She was a consultant for Cipsoft and teaches at PsyFar and Schola Medica. She has served on advisory boards of the EU (Horizon2021) and the British Medical Research Council., (Copyright © 2023 Elsevier B.V. and ECNP. All rights reserved.)

Depression is an invalidating disorder, marked by phenotypic heterogeneity. Clinical assessments for treatment adjustments and data-collection for pharmacological research often rely on subjective representations of functioning. Better phenotyping through digital applications may add unseen information and facilitate disentangling the clinical characteristics and impact of depression and its pharmacological treatment in everyday life. Researchers, physicians, and patients benefit from well-understood digital phenotyping approaches to assess the treatment efficacy and side-effects. This review discusses the current possibilities and pitfalls of wearables and technology for the assessment of the pharmacological treatment of depression. Their applications in the whole spectrum of treatment for depression, including diagnosis, treatment of an episode, and monitoring of relapse risk and prevention are discussed. Multiple aspects are to be considered, including concerns that come with collecting sensitive data and health recordings. Also, privacy and trust are addressed. Available applications range from questionnaire-like apps to objective assessment of behavioural patterns and promises in handling suicidality. Nonetheless, interpretation and integration of this high-resolution information with other phenotyping levels, remains challenging. This review provides a state-of-the-art description of wearables and technology in digital phenotyping for monitoring pharmacological treatment in depression, focusing on the challenges and opportunities of its application in clinical trials and research., Competing Interests: Declaration of Competing Interest EM is CEO and Founder of DocuMental, which is digital platform for mental health, including digital phenotyping tool for depression. All other authors report no conflict of interest., (Copyright © 2022. Published by Elsevier B.V.)

About two-thirds of patients with major depressive disorder (MDD) fail to achieve symptom remission after the initial antidepressant treatment. Despite a role of genetic factors was proven, the specific underpinnings are not fully understood yet. Polygenic risk scores (PRSs), which summarise the additive effect of multiple risk variants across the genome, might provide insights into the underlying genetics. This study aims to investigate the possible association of PRSs for bipolar disorder, MDD, neuroticism, and schizophrenia (SCZ) with antidepressant non-response or non-remission in patients with MDD. PRSs were calculated at eight genome-wide P-thresholds based on publicly available summary statistics of the largest genome-wide association studies. Logistic regressions were performed between PRSs and non-response or non-remission in six European clinical samples, adjusting for age, sex, baseline symptom severity, recruitment sites, and population stratification. Results were meta-analysed across samples, including up to 3,637 individuals. Bonferroni correction was applied. In the meta-analysis, no result was significant after Bonferroni correction. The top result was found for MDD-PRS and non-remission (p = 0.004), with patients in the highest vs. lowest PRS quintile being more likely not to achieve remission (OR=1.5, 95% CI=1.11-1.98, p = 0.007). Nominal associations were also found between MDD-PRS and non-response (p = 0.013), as well as between SCZ-PRS and non-remission (p = 0.035). Although PRSs are still not able to predict non-response or non-remission, our results are in line with previous works; methodological improvements in PRSs calculation may improve their predictive performance and have a meaningful role in precision psychiatry., Competing Interests: Conflicts of Interest B.T. Baune: Advisory Board - Lundbeck, Janssen-Cilag; Consultant - National Health and Medical Research Council, Australia; Grant/Research Support - AstraZeneca, Fay Fuller Foundation, James & Diana Ramsay Foundation, National Health and Medical Research Council, Australia, German Research Council (DFG), Sanofi, Lundbeck; Honoraria - AstraZeneca, Bristol-Myers Squibb, Lundbeck, Pfizer, Servier Laboratories, Wyeth Pharmaceuticals, Takeda, Janssen, LivaNova PLC. K. Domschke is a member of the Steering Committee Neurosciences, Janssen Pharmaceuticals, Inc. P. Ferentinos received grants/research support, consulting fees and/or honoraria within the last three years from Angelini, Boehringer-Ingelheim, Janssen, Medochemie, Vianex, and Servier. S. Kasper received grants/research support, consulting fees and/or honoraria within the last three years from Angelini, AOP Orphan Pharmaceuticals AG, AstraZeneca, Eli Lilly, Janssen, KRKA-Pharma, Lundbeck, Neuraxpharm, Pfizer, Pierre Fabre, Schwabe, and Servier. E. Maron has received grant/research support from Lundbeck, Janssen, Sanofi and GlaxoSmithKline. S. Mendlewicz is a member of the board of the Lundbeck International Neuroscience Foundation and of the advisory board of Servier. S. Montgomery has been a consultant or served on advisory boards for Lundbeck. A. Serretti is or has been a consultant/speaker for Abbott, Abbvie, Angelini, AstraZeneca, Clinical Data, Boehringer, Bristol-Myers Squibb, Eli Lilly, GlaxoSmithKline, Innovapharma, Italfarmaco, Janssen, Lundbeck, Naurex, Pfizer, Polifarma, Sanofi, and Servier. D. Souery has received grant/research support from GlaxoSmithKline and Lundbeck, and he has served as a consultant or on advisory boards for AstraZeneca, Bristol-Myers Squibb, Eli Lilly, Janssen, and Lundbeck. J. Zohar has received grant/research support from Lundbeck, Servier, and Pfizer; he has served as a consultant on the advisory boards for Servier, Pfizer, Solvay, and Actelion; and he has served on speakers’ bureaus for Lundbeck, GSK, Jazz, and Solvay. The other authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. and ECNP. All rights reserved.)

Variation in the expression level and activity of genes involved in drug disposition and action in tissues of pharmacological importance have been increasingly investigated in patients treated with psychotropic drugs. Findings are promising, but reliable predictive biomarkers of response have yet to be identified. Here we conducted a PRISMA-compliant systematic search of PubMed, Scopus and PsycInfo up to 12 September 2020 for studies investigating RNA expression levels in cells or biofluids from patients with major depressive disorder, schizophrenia or bipolar disorder characterized for response to psychotropic drugs (antidepressants, antipsychotics or mood stabilizers) or adverse effects. Among 5497 retrieved studies, 123 (63 on antidepressants, 33 on antipsychotics and 27 on mood stabilizers) met inclusion criteria. Studies were either focused on mRNAs (n = 96), microRNAs (n = 19) or long non-coding RNAs (n = 1), with only a minority investigating both mRNAs and microRNAs levels (n = 7). The most replicated results include genes playing a role in inflammation (antidepressants), neurotransmission (antidepressants and antipsychotics) or mitochondrial function (mood stabilizers). Compared to those investigating response to antidepressants, studies focused on antipsychotics or mood stabilizers more often showed lower sample size and lacked replication. Strengths and limitations of available studies are presented and discussed in light of the specific designs, methodology and clinical characterization of included patients for transcriptomic compared to DNA-based studies. Finally, future directions of transcriptomics of psychopharmacological interventions in psychiatric disorders are discussed., (Copyright © 2021. Published by Elsevier B.V.)

The reasons for variability in treatment response in major depressive disorder (MDD) are not fully understood, but there is accumulating evidence suggesting that therapeutic outcomes of antidepressants can be influenced by genetic factors. In the present study we applied the microarray Illumina platform for whole genome expression profiling in depressive patients treated with escitalopram medication in order to identify genes underlying response to antidepressant treatment. The initial study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1±11.6 years, 68% females) treated with escitalopram 10-20mg/day for 12 weeks, from which 87 patients (55 females) were included in gene expression analyzing. The gene expression profiles were measured on peripheral blood cells at baseline, at week 4 and at the end of treatment (week 12) using BeadChips Illumina. The fold change was used to demonstrate rate of changes in average gene expressions between studied groups. Statistical analyses were performed using the false discovery rate (FDR). The most interesting gene, which showed the predictive effect on treatment outcome by delineating low dose responders and treatment-resistant patients at the beginning of medication, was NLGN2, belonging to a family of neuronal cell surface proteins and involving in synapse formation. In addition, the several gene clusters, related to immune response, signal transduction and neurotrophin pathway, have distinguished responders from non-responders at the week 4 of treatment. After 4 weeks of escitalopram treatment (10mg/day), the YWHAZ gene has showed the highest transcriptional change in responders as compared with non-responders. Finally, at the end of the treatment we noticed that at least three genes (NR2C2, ZNF641, FKBP1A) have been strongly associated with resistance to escitalopram. Thus the results of this study support that exploration of peripheral gene expression is a useful tool in the further identification of novel genetic biomarkers for antidepressant treatment response., (Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.)

Cholecystokinin-tetrapeptide (CCK-4)-induced panic attacks are reportedly attenuated by effective treatment with antipanic antidepressants in patients with panic disorder, but in healthy volunteers such effects are not well studied. The aim of this study was to assess the effect of 6-week treatment with an SSRI escitalopram on CCK-4-induced symptoms in healthy volunteers, who previously responded with a panic attack to CCK-4 challenge. A total of 18 healthy subjects (10 males and eight females, mean age 22.5 ± 5.8) received a 6-week treatment with escitalopram (10 mg/day) and placebo followed by CCK-4 challenge (50 μg) in a double-blind crossover design. The panic rate was 67% after treatment with escitalopram and 56% after treatment with placebo (p = 0.7). Thus, the results showed a significant reduction in CCK-4-induced panic rates without significant differences between escitalopram and placebo conditions. There were no significant effects of either treatment on any other variable of anxiety or cardiovascular indices. Secondary analysis showed no effect of gender or 5-HTTLPR polymorphism on response to CCK-4 challenge. This study demonstrated that in contrast to the findings in patients with panic disorder, in CCK-4-sensitive healthy volunteers the treatment with an antipanic SSRI did not cause a reduction of CCK-4-induced panic attacks beyond the effect of placebo. The mechanisms behind this discrepancy and the reasons of the decrease in sensitivity to CCK-4 challenge on repeated administration remain to be clarified in future studies., (Copyright © 2012 Elsevier B.V. and ECNP. All rights reserved.)

Despite the size, burden and costs of anxiety disorders, many patients remain unrecognised, and the effectiveness of evidence-based interventions in routine clinical practice can be disappointing. The European College of Neuropsychopharmacology (ECNP) has established the ECNP Network Initiative (ECNP-NI) to help meet the goal of extending current understanding of the causes of central nervous system disorders, thereby contributing to improvements in clinical outcomes and reducing the associated societal burden. The Anxiety Disorders Research Network (ADRN) has been adopted within the ECNP-NI: this consensus statement summarises its overall aims and objectives., (2010 Elsevier B.V. and ECNP. All rights reserved.)

Several studies and meta-analyses have implicated a polymorphism in the promoter region of the serotonin transporter (5-HTT) gene, 5-HTTLPR in treatment outcomes of selective serotonin re-uptake inhibitors in patients with major depression. In this study we investigated the impact of 5-HTTLPR and a functional SNP rs25531 on the treatment outcomes to escitalopram in depressive patients. The study sample consisted of 135 outpatients with major depressive disorder (mean age 31.1+/-11.6 years, 68% females) treated with escitalopram 10-20 mg/day for 12 weeks. There were no significant associations between 5-HTT promoter region polymorphisms and response rate or mean change of depressive symptoms during escitalopram treatment. However we showed that patients carrying S allele of 5-HTTLPR may have increased risk for some side effects, including headache, induced by escitalopram medication.