Aiming to present the most recent research on personalised psychiatry and its clinical evidence, this LIVE ECP symposium at the 29^th Annual EPA Virtual Congress 2021 brought together international experts from major areas of personalised psychiatric medicine. Research was presented by the speakers Silvana Galderisi, Dinka Smajlagic, Sinan Gülöksüz and Martina Rojnic Kuzman about current advancements in the field and moderated by Professors Andrea Raballo and Andrea Cipriani. 
 
This symposium demonstrated the potential of bringing findings into clinical practice, and to show the current perspectives and priorities for further research in the area of personalised psychiatry and precision medicine for the treatment of schizophrenia. 

The symposium began with a talk by Professor Silvana Galderisi, Professor of Psychiatry at the University of Campania Luigi Vanvitelli, on how to translate research on precision medicine into clinical practice. She began by discussing that precision medicine in psychiatry is an emerging approach for both treatment and prevention, one that takes into account each person’s unique genes, environment and lifestyle. It is an approach that allows for a more accurate prediction of which treatment and prevention strategies will work best for a patient with a particular disease or disorder.¹ This is in contrast to a one-size-fits-all strategy in which treatments are developed for an average patient with little consideration of individual differences. 
 
Professor Galderisi emphasised that clinical characterisation of a primary psychosis patient should take many different aspects into account;in addition to positive and negative symptoms, neurocognition and social cognition, other factors such as comorbidities, family history and body mass index (BMI) should be also considered¹. Importantly, the vast majority of clinicians endorse the view that primary psychosis management should be personalised, but unfortunately in current clinical contexts, this is hindered by a lack of implementation research in real-world clinical practice.¹
 
When it comes to taking a precision medicine approach, there are three main aims. First, one must make sure that a certain condition is truly present, which is important for differential diagnosis. Secondly, Professor Galderisi highlighted, it is necessary to try and predict condition outcomes, which is needed for prognosis. Finally, one should forecast individual responses too, which is related to predictive models. In a recent meta-analysis, diagnostic models were found to be represented in only 8.2% of cases whereas predictive models in 13.6%.¹ On the other hand, prognostic models were used in 68.2% of cases.¹ Importantly, developing better biomarkers can help to support diagnostic and prognostic models to aid precision medicine, as Professor Galderisi proposed. 
 
As the field stands currently, progress has been made in neuroimaging studies and machine learning techniques that would improve precision medicine further, however these are unlikely to be available for clinical practice until further developments have been made.¹ Nevertheless, Professor Galderisi believes that these tools will aid the matching of the right treatment to the right patient thus helping to close the translational gap between research and clinical practice. 

In the second talk by Dr Dinka Smajlagic, postdoctoral researcher at the University of Oslo, current perspectives on genome-wide association studies (GWAS) were presented. GWAS are studies that aim to associate particular diseases with specific genetic variations by scanning the genomes of many people and identifying genetic markers that can be used to predict the presence of a disease. When considering this data, it is important to highlight that an association test identifies only an association but not causality. Nevertheless, in psychiatry, an increasing number of genetic variants have been identified through the use of large cohort studies and clinical databases. 
 
Before diving in, Dr Smajlagic explained, that successful detection of an association depends on many factors, including sample size, allele frequency, effect size and quality of a given phenotype. To apply any findings clinically, GWAS must be applicable to different populations, and refined candidates for single-nucleotide polymorphisms (SNPs) must be coupled with treatment outcomes.²  When it comes to the field of psychiatric disorders, many loci have been identified in various conditions, however, the most progress has been seen specifically in schizophrenia, with around 30 schizophrenia-associated loci having been identified through GWAS.²  Indeed, Dr Smajlagic presented data from a pivotal multi-stage GWAS, in which a total of 128 independent associations spanning 108 loci were identified that meet genome-wide significance in schizophrenia.² Many of these associations were enriched among genes expressed in the brain, providing biological plausibility for the findings, Dr Smajlagic emphasised. ²
 
Indeed, the power of findings can also be increased by cross-disorder studies in case-control cohorts. In one such study, genetic data from over 100,000 subjects identified 114 genome-wide significant loci pointing to synaptic and neuronal pathways. ³ These loci were mostly shared between bipolar disorder and schizophrenia, whereas some disorder-independent causal variants were also identified. ³
 
Dr Smajlagic finished her talk by reiterating her key points and how studies like these can help to advance our understanding of the genetic background of psychiatric illnesses thus increasing the potential to develop biomarkers and treatment targets in the future. 

In the third talk of the session, Professor Sinan Gülöksüz, Associate Professor of Psychiatry and Neuropsychology at Maastricht University, discussed the different risk profiles for mental disorders. He started with the notion that when the categories for mental disorders start to blur at the edges it becomes neither useful nor valid to diagnose early stage psychological changes through the “schizo”-prism and the application of binary concepts of “risk” and “transition” are also troubling.¹ Through a review of the literature, Professor Gülöksüz presented the findings that categorising individuals as “ultra-high risk” (UHR) or “clinical high risk” (CHR) is unhelpful as it ignores the heterogeneity of these samples and the degree of psychotic experiences. ⁴ 
 
Professor Gülöksüz also pointed out how traditionally, integrated factors like cannabis use and childhood adversity have been used to determine the risk for schizophrenia.⁵ However, research seems to shift this belief and risk is now considered to be determined by a vast number of different things people are exposed to (the exposome) such as winter birth and hearing impairment. ⁶ Importantly, Professor Gülöksüz outlined the development of a so-called Exposome Score (ES) which can distinguish patients from controls with high accuracy regarding risk for developing schizophrenia; indeed, an increase in the score is associated with a gradient increase of schizophrenia risk.⁶ 
 
Tools like the ES thus constitute an index for quantifying environmental predisposition and risk stratification.⁶  In addition, ES may also help in predicting outcomes of schizophrenia and, presently, may have clinical utility in fine-tuning diagnosis and clinical characterisation.⁶  Nevertheless, much work remains to be done to bring the ES into routine daily practice. 

In the final talk, Dr Martina Rojnic Kuzman, specialist psychiatrist at University Hospital Zagreb Centre, aimed to identify the steps that need to be taken to prevent first episode psychosis. To begin with, she outlined the importance of considering both premorbid and prodromal phases where individuals are considered to be in an ultra-high risk (UHR) state for psychosis.⁷ In UHR patients, research indicates that around 40% of patients will develop psychosis in 12 months.⁷ However, Dr Kuzman pointed out that the UHR group is made up of a very heterogenous population, with differences in their level of functioning, basic cognitive symptoms, and level of psychotic symptoms, hence making it difficult to develop a prevention strategy that fits all.⁸
 
Dr Kuzman then highlighted the effectiveness of using biomarkers and other predictive factors to signal the transition to psychosis. The risk for transition to full-blown psychotic disorder is to a large degree predicted by size of psychosis “load,” comorbid depression, cannabis use, cognitive ability, and subjective reports of impairment and coping.⁹ As we develop our understanding of how these predictors are involved in psychosis, she said, we can develop and refine tools to measure them.
 
Finally, Dr Kuzman reflected on the different treatment strategies, and discussed how early intervention services are often the go-to for first-episode patients in Europe, East Asia and North America.¹⁰ However, she pointed out, data from these services indicates high variability in the assessment instruments used, types and duration of intervention, and overall organisation.¹⁰ In addition, needs-based interventions are also being used; by focusing only on the most relevant issues like social relationships or family problems, these treatments are also helpful for patients to prevent transitioning.⁷ Interestingly however, no treatment or combination of treatments has been found to be clearly superior over another in needs-based interventions.¹¹
 
Dr Kuzman emphasised that while significant progress has been made, there is still much work to be done in prevention of first-episode psychosis. In the premorbid phase, she stressed, it would be helpful to promote public health programmes and a healthy lifestyle, alongside with general screening and comprehensive assessments. While in the prodromal phase, early detection and prompt intervention is paramount while ensuring easy access to care. Finally, it remains clear that comprehensive assessment is vital and needs-based interventions are currently the best means for prevention.