Precision Medicine In Psychiatry Research Articles

Clinical validation of a combinatorial PharmAcogeNomic approach in major Depressive disorder: an Observational prospective RAndomized, participant and rater-blinded, controlled trial (PANDORA trial)

BackgroundMajor depressive disorder (MDD) is a common, chronic, debilitating mood disorder that causes serious functional impairment and significantly decreased quality of life. Pharmacotherapy represents the first-line treatment option; however, only approximately one third of patients respond to the first treatment because of the ineffectiveness or side effects of antidepressants. Precision medicine in psychiatry might offer clinicians the possibility to tailor treatment according to the best possible evidence of efficacy and tolerability for each subject. In this context, our study aims to carry out a clinical validation of a combinatorial pharmacogenomics (PGx) test in an Italian MDD patient cohort with advocacy license independence.MethodsOur study is a prospective participant- and rater-blinded, randomized, controlled clinical observational trial enrolling 300 MDD patients who are referred to psychiatric services to receive a new antidepressant due to the failure of their current treatment and/or the onset of adverse effects. Eligible participants are randomized to the TGTG group (Treated with Genetic Test Guide) or TAU group (Treated as Usual). For all subjects, DNA is collected with a buccal brush. The primary outcome is the reduction in depressive symptomatology. The secondary outcomes involve a range of scales that assess MDD symptoms and social functioning outcomes. The assessment is performed at four timepoints: baseline and 4, 8, and 12 weeks.DiscussionThis project represents the first randomized controlled clinical trial to investigate whether a non-commercial PGx test improves outcomes in an MDD naturalistic cohort. Moreover, the identification of new genetic variants associated with non-response or side effects will improve the efficacy of the test, leading to further cost-saving.Trial registration numberClinicalTrials.gov NCT04615234. Registered on November 4, 2020.

Transdiagnostic biomarker approaches to mental health disorders: Consideration of symptom complexity, comorbidity and context

Depression is a multifaceted disorder characterized by heterogeneous symptom profiles and high rates of comorbidity with other commonly occurring mental illnesses. Considering the burden of mental health disorders and the lack of efficacy of available treatments, there is a need for biomarkers to predict tailored or personalized treatments. However, identifying reliable biomarkers for complex mental illnesses, such as depression, anxiety and PTSD, has been challenging, likely owing to the heterogeneity, comorbidity and differences in experiences and histories of individuals. For these reasons, taking a transdiagnostic approach, which identifies biomarkers that map onto shared symptoms/constructs across disorders could be most effective for informing personalized or precision medicine approaches in psychiatry. Transdiagnostic features of anxiety, depression and anhedonia have been examined in relation to brain activity and connectivity patterns. Neuroendocrine and inflammatory markers, which are altered in depression and other comorbid illness, such as post-traumatic stress disorder (PTSD), might be useful in differentiating transdiagnostic symptom profiles as well as treatment responses. Ultimately, biomarker research that looks beyond diagnostic categories and embraces the complexity of individuals' lives and experiences might be more effective in moving towards precision medicine in psychiatry.

Shared Biological Pathways between Antipsychotics and Omega-3 Fatty Acids: A Key Feature for Schizophrenia Preventive Treatment?

Schizophrenia typically emerges during adolescence, with progression from an ultra-high risk state (UHR) to the first episode of psychosis (FEP) followed by a chronic phase. The detailed pathophysiology of schizophrenia and the factors leading to progression across these stages remain relatively unknown. The current treatment relies on antipsychotics, which are effective for FEP and chronic schizophrenia but ineffective for UHR patients. Antipsychotics modulate dopaminergic and glutamatergic neurotransmission, inflammation, oxidative stress, and membrane lipids pathways. Many of these biological pathways intercommunicate and play a role in schizophrenia pathophysiology. In this context, research of preventive treatment in early stages has explored the antipsychotic effects of omega-3 supplementation in UHR and FEP patients. This review summarizes the action of omega-3 in various biological systems involved in schizophrenia. Similar to antipsychotics, omega-3 supplementation reduces inflammation and oxidative stress, improves myelination, modifies the properties of cell membranes, and influences dopamine and glutamate pathways. Omega-3 supplementation also modulates one-carbon metabolism, the endocannabinoid system, and appears to present neuroprotective properties. Omega-3 has little side effects compared to antipsychotics and may be safely prescribed for UHR patients and as an add-on for FEP patients. This could to lead to more efficacious individualised treatments, thus contributing to precision medicine in psychiatry.

The Ethical Conundrums of “Precision Psychiatry”

The Ethical Conundrums of “Precision Psychiatry”

Precision medicine in psychosis: Translating findings from research into clinical practice

Abstract Body Precision medicine is “an emerging approach for treatment and prevention that takes into account each person’s variability in genes, environment, and lifestyle” [1]. The terminology is increasingly used in psychiatry, and especially in research relevant to the prediction of psychosis onset, response to treatment and functional outcome. While this is an important step-forward for the discipline, at this stage it is very important to promote the translation of research findings into clinical practice, as much as possible. Nowadays the availability of machine learning and artificial intelligence tools, together with advances in data storage and data security, enable the integration of neuroimaging, biological, clinical and cognitive data. By overcoming current limitations in multiple domain data analysis these tools may lead to the identification of reliable diagnostic, prognostic and therapeutic markers in routine clinical care, as well as to the prediction of clinically meaningful outcomes (e.g., psychosis onset, symptomatic and functional outcome, and treatment response). Precision medicine in psychiatry is a developing science, deserving further large-scale research, translational approaches and refinement that, hopefully, will soon be an integral part of every-day clinical practice. However, challenges in pursuing this strategy should not be underestimated, and efforts should be made to constantly advocate for more investments in human and financial resources in psychiatry, and to concentrate on the use of widely available and not too expensive and time-consuming methods.1 Toward Precision Medicine. Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. Washington, DC: National Academies Press; 2011.DisclosureNo significant relationships.

Pharmacogenomic Biomarkers and Their Applications in Psychiatry

Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics in psychiatry that have laid the foundation towards personalized pharmacotherapy and, in a broader sense, precision medicine. We present major pharmacogenomic biomarkers and their applications in a variety of psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder (ADHD), narcolepsy, schizophrenia, and bipolar disorder. In addition, we extend the scope into epilepsy, since antiepileptic drugs are widely used to treat psychiatric disorders, although epilepsy is conventionally considered to be a neurological disorder.

RDoC at 10: changing the discourse for psychopathology.

RDoC at 10: changing the discourse for psychopathology.

Clinical Pharmacological Research for Precision Medicine in Psychiatry with Genomics, Epigenomics and Informatics

Clinical Pharmacological Research for Precision Medicine in Psychiatry with Genomics, Epigenomics and Informatics

Can precision medicine advance psychiatry?

Precision medicine is a new approach that considers differences in genes, environment, and lifestyle in an attempt to tailor treatments for individual patients. Psychiatry, as a discipline, has historically relied on clinical judgement and phenomenology-based diagnostic guidelines and has yet to take full advantage. This editorial provides an insight into the expanding role of precision medicine in psychiatry, both in research and clinical practice. It discusses the application of genetics and subgroup stratification in increasing response rates to therapeutic interventions, mainly focusing on major depressive disorder and schizophrenia. It presents an overview of machine learning techniques and how they are being integrated with traditional research methods within the field. In the context of these developments, while emphasizing the considerable potential for moving toward precision psychiatry, we also acknowledge the inherent challenges.

M17. PREDICTION OF NEUROLEPTIC AND ANTIDEPRESSANT TREATMENT RESPONSES VIA FMRI OF THE EXTENDED REWARD SYSTEM

BackgroundNeuroimaging techniques have been developed as important tools to assess brain dysfunctions that underlie mental disorders. In particular, modern functional magnetic resonance imaging (fMRI) holds the promise to provide neurofunctional biomarkers for improved differential diagnosis and optimized treatment of schizophrenic and affective disorders.MethodsNeurofunctional connectivity MRI of the extended human reward system (Makris et al., 2008) was conducted in a large transnosological cohort of patients suffering from schizophrenia, bipolar disorder or major depressive disorder. Responses to neuroleptic treatments in patients with schizophrenic or bipolar disorder were determined retrospectively, while treatment responses to different antidepressants were directly assessed in a prospective naturalistic clinical study.ResultsResponders to neuroleptic treatment with aripiprazole showed significantly higher reward-related activation in a larger set of brain regions (including ventral striatum, hippocampus, amygdala, pregenual ACC and anteroventral PFC) in comparison to non-responders to aripiprazole. This finding proved to be specific for this neuroleptic treatment when compared to treatments with other atypical or typical neuroleptics. Pre-treatment reward-related activation of the nucleus accumbens, the ventral tegmental area and the amygdala showed a substance-class specific correlation with subsequent antidepressant treatment responses to SSRIs and/or agomelatine.DiscussionThese findings of ongoing studies exemplify the high potential of neuroimaging techniques to foster the development of precision medicine in psychiatry. The identification of neuroimaging markers associated with specific treatment responses may allow the development of “tailored”, i.e. stratified treatment approaches.

Challenges and Future Prospects of Precision Medicine in Psychiatry.

Precision medicine is increasingly recognized as a promising approach to improve disease treatment, taking into consideration the individual clinical and biological characteristics shared by specific subgroups of patients. In specific fields such as oncology and hematology, precision medicine has already started to be implemented in the clinical setting and molecular testing is routinely used to select treatments with higher efficacy and reduced adverse effects. The application of precision medicine in psychiatry is still in its early phases. However, there are already examples of predictive models based on clinical data or combinations of clinical, neuroimaging and biological data. While the power of single clinical predictors would remain inadequate if analyzed only with traditional statistical approaches, these predictors are now increasingly used to impute machine learning models that can have adequate accuracy even in the presence of relatively small sample size. These models have started to be applied to disentangle relevant clinical questions that could lead to a more effective management of psychiatric disorders, such as prediction of response to the mood stabilizer lithium, resistance to antidepressants in major depressive disorder or stratification of the risk and outcome prediction in schizophrenia. In this narrative review, we summarized the most important findings in precision medicine in psychiatry based on studies that constructed machine learning models using clinical, neuroimaging and/or biological data. Limitations and barriers to the implementation of precision psychiatry in the clinical setting, as well as possible solutions and future perspectives, will be presented.

Identifying biological markers for improved precision medicine in psychiatry

Mental disorders represent an increasing personal and financial burden and yet treatment development has stagnated in recent decades. Current disease classifications do not reflect psychobiological mechanisms of psychopathology, nor the complex interplay of genetic and environmental factors, likely contributing to this stagnation. Ten years ago, the longitudinal IMAGEN study was designed to comprehensively incorporate neuroimaging, genetics, and environmental factors to investigate the neural basis of reinforcement-related behaviour in normal adolescent development and psychopathology. In this article, we describe how insights into the psychobiological mechanisms of clinically relevant symptoms obtained by innovative integrative methodologies applied in IMAGEN have informed our current and future research aims. These aims include the identification of symptom groups that are based on shared psychobiological mechanisms and the development of markers that predict disease course and treatment response in clinical groups. These improvements in precision medicine will be achieved, in part, by employing novel methodological tools that refine the biological systems we target. We will also implement our approach in low- and medium-income countries to understand how distinct environmental, socio-economic and cultural conditions influence the development of psychopathology. Together, IMAGEN and related initiatives strive to reduce the burden of mental disorders by developing precision medicine approaches globally.

Successes and Challenges in Precision Medicine in Psychiatry

Successes and Challenges in Precision Medicine in Psychiatry

Improving therapy outcome prediction in major depression using multimodal functional neuroimaging: A pilot study

Mounting evidence emphasizes the usefulness of imaging biomarkers for predicting therapy outcome in major depressive disorder (MDD), in particular building on functional imaging studies of task-based responses to emotional face stimuli and resting state-related connectivity patterns. To explore the possibility that prediction accuracy even in small patient samples would significantly gain from integrating data from different imaging modalities, we acquired functional neuroimaging data both at-rest and during exposure to emotional faces from 21 MDD patients before and 7 weeks after treatment-as-usual, as well as from 20 age- and gender-matched control participants assessed at similar intervals. As expected, MDD patients showed disturbed pre-treatment responses to emotional faces, including left amygdala hyperactivation. Therapeutic outcome correlated with pre-treatment activation, with subgenual cingulate response to emotional faces yielding best results (r values ranging from 0.4 to 0.66). A support vector machine classifier trained on task-based or resting-state data predicted responder status, with the right dorsolateral prefrontal cortex connectivity pattern yielding best accuracy (88.9%). Crucially, combining task-based with resting-state data increased prediction accuracy by 6.5–7.7 percentage points on average. From this pilot study, we conclude that multimodal functional imaging has the potential of improving therapy outcome prediction even in small MDD sample sizes, resulting in about one additional correct classification every 15 patients. The present results inform future studies which are needed to consolidate imaging approaches as a means of establishing precision medicine in psychiatry.

Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models

Schizophrenia and bipolar disorder are severe and complex brain disorders characterized by substantial clinical and biological heterogeneity. However, case-control studies often ignore such heterogeneity through their focus on the average patient, which may be the core reason for a lack of robust biomarkers indicative of an individual's treatment response and outcome. To investigate the degree to which case-control analyses disguise interindividual differences in brain structure among patients with schizophrenia and bipolar disorder and to map the brain alterations linked to these disorders at the level of individual patients. This study used cross-sectional, T1-weighted magnetic resonance imaging data from participants recruited for the Thematically Organized Psychosis study from October 27, 2004, to October 17, 2012. Data were reanalyzed in 2017 and 2018. Patients were recruited from inpatient and outpatient clinics in the Oslo area of Norway, and healthy individuals from the same catchment area were drawn from the national population registry. Interindividual differences in brain structure among patients with schizophrenia and bipolar disorder. Voxel-based morphometry maps were computed, which were used for normative modeling to map the range of interindividual differences in brain structure. This study included 218 patients with schizophrenia spectrum disorders (mean [SD] age, 30 [9.3] years; 126 [57.8%] male), of whom 163 had schizophrenia (mean [SD] age, 31 [8.7] years; 105 [64.4%] male) and 190 had bipolar disorder (mean [SD] age, 34 [11.3] years; 79 [41.6%] male), and 256 healthy individuals (mean [SD] age, 34 [9.5] years; 140 [54.7%] male). At the level of the individual, deviations from the normative model were frequent in both disorders but highly heterogeneous. Overlap of more than 2% among patients was observed in only a few loci, primarily in frontal, temporal, and cerebellar regions. The proportion of alterations was associated with diagnosis and cognitive and clinical characteristics within clinical groups. Patients with schizophrenia, on average, had significantly reduced gray matter in frontal regions, cerebellum, and temporal cortex. In patients with bipolar disorder, mean deviations were primarily present in cerebellar regions. This study found that group-level differences disguised biological heterogeneity and interindividual differences among patients with the same diagnosis. This finding suggests that the idea of the average patient is a noninformative construct in psychiatry that falls apart when mapping abnormalities at the level of the individual patient. This study presents a workable route toward precision medicine in psychiatry.

F142. THE USE OF NEUROIMAGING MARKERS IN STRATIFIED DIAGNOSIS AND THERAPY OF SCHIZOPHRENIC AND AFFECTIVE DISORDERS

BackgroundNeuroimaging techniques have been developed as important tools to investigate brain dysfunctions that underlie mental disorders. In particular, modern functional magnetic resonance imaging (fMRI) holds the promise to provide neurofunctional biomarkers for improved diagnosis, prognosis, and optimized treatment of schizophrenic and affective disorders.MethodsNeurofunctional connectivity MRI using advanced experimental paradigms permits targeted investigation of the functional integrity of brain systems involved in the pathomechanisms of schizophrenic and affective disorders. From these investigations, pathophysiologically relevant neuroimaging biomarkers can be derived for differential diagnosis and tailored treatment selection.ResultsPossible neuroimaging biomarkers will be presented for the prediction of development and clinical course of schizophrenic and affective disorders as well as for the prediction of individual treatment responses. Further, recent neuroimaging findings on possible pathophysiological subtypes of schizophrenic and affective disorders will be discussed.DiscussionThese findings from functional neuroimaging studies may help to foster the development of precision medicine in psychiatry.

Precision Medicine in Psychiatry by Predicting Lithium Treatment Response using Gene Expression Biomarkers and Machine Learning

BackgroundWe tested the hypothesis that transcriptional‐level genes signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who were later were clinically classified as lithium treatment responders using the primary outcome measure of the Clinical Global Impression Scale for Bipolar Disorder‐Severity (CGI‐BP‐S).MethodsGene expression study data was obtained from the Lithium Treatment‐Moderate dose Use Study data accessed from the National Center for Biotechnology Information's Gene Expression Omnibus via accession number GSE4548. Differential gene expression analysis using the CGI‐BP‐S responder status and Gender served as control variables for analysis using the Linear Models for Microarray and RNA‐Seq (limma) package and the Random Forests machine learning algorithm in R for Statistical Computing programming language software.ResultsIn pre‐treatment lithium responders, the following genes were found having a greater than 0.5 fold‐change and differentially expressed indicating a male bias: RBPMS2, SIDT2, CDH23, LILRA5, and KIR2DS5 while the female‐biased genes were identified as: HLA‐H, RPS23, FHL3, RPL10A, NBPF14, PSTPIP2, FAM117B, CHST7, and ABRACL.ConclusionsApplying machine learning methods to gene expression data, we developed a pre‐treatment gender‐specific gene‐expression‐based predictive model selective for lithium treatment responders according to the Clinical Global Impression Scale for Bipolar Disorder‐Severity with an ROC AUC of 0.92 for men and an ROC AUC of 1 for women.Support or Funding InformationNone.

The Present and Future of Precision Medicine in Psychiatry: Focus on Clinical Psychopharmacology of Antidepressants.

Precision medicine is a concept which is recently gaining momentum in all branches of medicine. In particular in psychiatry it is greatly needed given the huge societal costs of psychiatric disorders and given the long time needed to observe benefit from treatments and the response variability. The future will be based on biological determinants, however until such an interesting but still futuristic aim will be reached, at present we may only rely on clinical features to guide our individualized prescription which is currently still frequently based on personal opinion and subjective previous experiences. The aim of this review is to offer an overview of the main aspects to take into consideration when prescribing an antidepressant treatment to reach the best precision medicine using clinical information. More than 40 compounds are available for treating depression and a similar amount of compounds for other psychiatric disorders. The process of matching the profile of the patient with all different profiles of available compounds is therefore quite complex. Our everyday prescribing procedure should take into consideration a number of factors such as the knowledge of the profile of available compounds versus the symptomatology profile of the subject, previous efficacy, medical comorbidities, tolerability profile, individual preferences, and family history. While we are waiting more complex algorithms including biological or genetic measures, it is possible to optimize our current prescription practice by using all available information in order to obtain as much as possible an evidence based precision medicine prescription.

Association between CRP genetic diversity and bipolar disorder comorbid complications

BackgroundChronic low-grade inflammation is believed to contribute, at least in a subset of patients, to the development of bipolar disorder (BD). In this context, the most investigated biological marker is the acute phase response molecule, C-reactive protein (CRP). While the genetic diversity of CRP was amply studied in various pathological settings, little is known in BD.Methods568 BD patients along with 163 healthy controls (HC) were genotyped for the following single-nucleotide polymorphisms (SNPs) on the CRP gene: intron rs1417938 (+ 29) T/A, 3′-UTR rs1130864 (+ 1444) G/A, and downstream rs1205 (+ 1846) (C/T). The statistical analysis was performed using Chi-square testing and consisted of comparisons of allele/genotype frequencies between patients and controls and within patient sub-groups according to BD clinical phenotypes and the presence of thyroid disorders.ResultsWe found that the frequencies of the studied SNPs were similar in BD and HC groups. However, the CRP rs1130864 A allele carrier state was significantly more frequent: (i) in BD patients with thyroid disorders than in those without (pc = 0.046), especially among females (pc = 0.01) and independently of lithium treatment, (ii) in BD patients with rapid cycling than in those without (pc = 0.004).ConclusionsOverall, our findings suggest the possibility that CRP genetic diversity may contribute to the development of auto-immune comorbid disorders and rapid cycling, both proxy of BD severity. Such findings, if replicated, may allow to predict complex clinical presentations of the disease, a possible step towards precision medicine in psychiatry.

The Clinical Challenge of Autoimmune Psychosis: Learning from Anti-NMDA Receptor Autoantibodies.

Schizophrenia is a heterogeneous and complex psychiatric disorder affecting up to 1% of the population worldwide (1). Although the precise development of schizophrenia is not yet fully understood, it is now admitted to be underpinned by the entanglement of genetic, environmental, and immuno-inflammatory factors. Among schizophrenic patients, it is assumed that at least 30% will not respond to conventional antipsychotics (2). These data underlie the importance of precision medicine in psychiatry, in other words, the need to identify subgroups of patients with specific signatures who will benefit from treatment targeting these specific biological pathways. Reviving an area of exploration older than a century, recent and abundant literature emphasized the importance of the immune system in the pathophysiology of schizophrenia [for review, see Ref. (3)]. In psychiatry, the link between psychotic disorders, particularly schizophrenia, and immune system deregulations, including autoimmunity, is an old concept that regained strong support; thanks to the better characterization of brain inflammation-induced psychotic symptoms and autoimmune encephalitis (3). Moreover, recent epidemiological studies evidenced a high prevalence of multiple autoimmune diseases in schizophrenic patients (4). In a recent meta-analysis, autoantibodies against neuronal receptors have indeed been identified in the circulation of patients with neuropsychiatric disorders, constituting, today, one of the hottest topics in psychiatry (5–10). This new era fosters debate on (i) how to explain the increased burden of autoimmunity in schizophrenia, (ii) what could be the precise target(s) and the pathogenic implication(s) of the autoantibodies on the disease onset and development, (iii) how to define patient subgroups carrying such autoantibodies to facilitate their diagnosis, and (iv) how should we treat these patients using appropriate protocols such as immunotherapy (i.e., corticotherapy or plasmapheresis). Several neurological autoimmune diseases are, for instance, efficiently treated once autoantibodies against neurotransmitter receptors and ion channels have been identified (11, 12). The discovery of the autoimmune encephalitis due to anti-N-methyl-d-aspartic acid receptor (NMDAR) has greatly revived the relationship between autoimmunity and psychosis. Indeed, directed against the NMDAR N-methyl-d-aspartate receptors antibodies (NMDAR-Ab), the autoantibodies are directly responsible for the psychotic symptoms and catatonia, followed by profound neurologic deterioration (13, 14). In patients with schizophrenia, the prevalence and clinical significance of circulating NMDAR-Ab remains controversial with detection prevalence rates varying considerably between studies (15–27). Inspite of such imprecisions, defining and isolating seropositive patients, suffering from “autoimmune psychosis,” is a major challenge for appropriate treatments. In this review, we focus our attention on the potential elements possibly helping to define an “autoimmune psychosis” subgroup of schizophrenic patients. Furthermore, we outline some of the specific clinical presentation of these patients that will be of great importance to optimize the diagnostic and subsequent therapies.