Prediction of response to drug therapy in psychiatric disorders

Laparoscopic sleeve gastrectomy for premenstrual syndrome symptoms in patients with obesity

BACKGROUND

Premenstrual syndrome (PMS) is a pathological condition characterized by a series of abnormal physical, psychological, and behavioral symptoms. We evaluated the effectiveness of laparoscopic sleeve gastrectomy (LSG) in the treatment of patients with obesity and PMS.

METHODS

In this case-control study, 131 patients with obesity (BMI ≥ 27.5 kg/cm2) diagnosed with moderate-to-severe PMS from March 2018 to March 2022 were prospectively selected to undergo LSG or not at their own discretion. Participants self-reported their PMS severity using the Premenstrual Syndrome Screening Tool. Among them, 68 patients chose LSG surgery, and 63 control group patients were followed up without surgery. Data were recorded at baseline and at 3 months post-treatment. We used a multivariate analysis to assess the improvement in PMS symptoms and associated factors.

RESULTS

Of the 131 patients with obesity and PMS, the improvement rate of PMS in the LSG group was 57.35% (n = 39), while the improvement rate of PMS in the control group was 25.40% (n = 16). Furthermore, our study revealed that surgery is an independent factor affecting the improvement of patients with PMS. Additionally, there was a correlation between alcohol use, T2DM and obesity-related metabolic diseases, and BMI with PMS. The changes in BMI, testosterone, and estradiol(E2) levels may also contribute to the improvement of patients with obesity and PMS.

CONCLUSION

LSG can improve the management of obesity in patients with PMS to some extent. Changes in BMI, testosterone, and E2 may be indicative of improvement in patients with obesity and PMS.

Effects of bilateral repetitive transcranial magnetic stimulation on prospective memory in patients with schizophrenia: A double-blind randomized controlled clinical trial

AIMS

To investigate effects of repetitive transcranial magnetic stimulation (rTMS) on the prospective memory (PM) in patients with schizophrenia (SCZ).

METHODS

Fifty of 71 patients completed this double-blind placebo-controlled randomized trial and compared with 18 healthy controls' (HCs) PM outcomes. Bilateral 20 Hz rTMS to the dorsolateral prefrontal cortex at 90% RMT administered 5 weekdays for 4 weeks for a total of 20 treatments. The Positive and Negative Symptom Scale (PANSS), the Scale for the Assessment of Negative Symptoms (SANS), and PM test were assessed before and after treatment.

RESULTS

Both Event-based PM (EBPM) and Time-based PM (TBPM) scores at baseline were significantly lower in patients with SCZ than that in HCs. After rTMS treatments, the scores of EBPM in patients with SCZ was significantly improved and had no differences from that in HCs, while the scores of TBPM did not improved. The negative symptom scores on PANSS and the scores of almost all subscales and total scores of SANS were significantly improved in both groups.

CONCLUSIONS

Our findings indicated that bilateral high-frequency rTMS treatment can alleviate EBPM but not TBPM in patients with SCZ, as well as improve the negative symptoms.

SIGNIFICANCE

Our results provide one therapeutic option for PM in patients with SCZ.

Genomic Stratification of Clozapine Prescription Patterns Using Schizophrenia Polygenic Scores

BACKGROUND

Treatment-resistant schizophrenia affects approximately 30% of individuals with the disorder. Clozapine is the medication of choice in treatment-resistant schizophrenia, but optimizing administration and dose titration is complex. The identification of factors influencing clozapine prescription and response, including genetics, is of interest in a precision psychiatry framework.

METHODS

We used linear regression models accounting for demographic, pharmacological, and clinical covariates to determine whether a polygenic risk score (PRS) for schizophrenia would be associated with the highest dose recorded during clozapine treatment. Analyses were performed across 2 independent multiancestry samples of individuals from a UK patient monitoring system, CLOZUK2 (n = 3133) and CLOZUK3 (n = 909), and a European sample from a Norwegian therapeutic drug monitoring service (n = 417). In a secondary analysis merging both UK cohorts, logistic regression models were used to estimate the relationship between schizophrenia PRSs and clozapine doses classified as low, standard, or high.

RESULTS

After controlling for relevant covariates, the schizophrenia PRS was correlated with the highest clozapine dose on record for each individual across all samples: CLOZUK2 (β = 12.22, SE = 3.78, p = .001), CLOZUK3 (β = 12.73, SE = 5.99, p = .034), and the Norwegian cohort (β = 46.45, SE = 18.83, p = .014). In a secondary analysis, the schizophrenia PRS was associated with taking clozapine doses >600 mg/day (odds ratio = 1.279, p = .006).

CONCLUSIONS

The schizophrenia PRS was associated with the highest clozapine dose prescribed for an individual in records from 3 independent samples, suggesting that the genetic liability for schizophrenia might index factors associated with therapeutic decisions in cohorts of patients with treatment-resistant schizophrenia.

Current Status of Therapeutic Drug Monitoring in Mental Health Treatment: A Review

Therapeutic drug monitoring (TDM) receives growing interest in different psychiatric clinical settings (emergency, inpatient, and outpatient services). Despite its usefulness, TDM remains underemployed in mental health. This is partly due to the need for evidence about the relationship between drug serum concentration and efficacy and tolerability, both in the general population and even more in subpopulations with atypical pharmacokinetics. This work aims at reviewing the scientific literature published after 2017, when the most recent guidelines about the use of TDM in mental health were written. We found 164 pertinent records that we included in the review. Some promising studies highlighted the possibility of correlating early drug serum concentration and clinical efficacy and safety, especially for antipsychotics, potentially enabling clinicians to make decisions on early laboratory findings and not proceeding by trial and error. About populations with pharmacokinetic peculiarities, the latest studies confirmed very common alterations in drug blood levels in pregnant women, generally with a progressive decrease over pregnancy and a very relevant dose-adjusted concentration increase in the elderly. For adolescents also, several drugs result in having different dose-related concentration values compared to adults. These findings stress the recommendation to use TDM in these populations to ensure a safe and effective treatment. Moreover, the integration of TDM with pharmacogenetic analyses may allow clinicians to adopt precise treatments, addressing therapy on an individual pharmacometabolic basis. Mini-invasive TDM procedures that may be easily performed at home or in a point-of-care are very promising and may represent a turning point toward an extensive real-world TDM application. Although the highlighted recent evidence, research efforts have to be carried on: further studies, especially prospective and fixed-dose, are needed to replicate present findings and provide clearer knowledge on relationships between dose, serum concentration, and efficacy/safety.

Synchronized Auditory Gamma Response to Frontal Transcranial Direct Current Stimulation (tDCS) and its Inter-Individual Variation in Healthy Humans

In schizophrenia, a disorder associated with N-methyl-D-aspartate receptor (NMDAR) hypofunction, auditory cortical plasticity deficits have been indexed by the synchronized electroencephalographic (EEG) auditory steady-state gamma-band (40-Hz) response (ASSR) and the early auditory evoked gamma-band response (aeGBR), both considered to be target engagement biomarkers for NMDAR function, and potentially amenable to treatment by NMDAR modulators. As transcranial direct current stimulation (tDCS) is likely dependent on NMDAR neurotransmission, this preliminary study, conducted in 30 healthy volunteers, assessed the off-line effects of prefrontal anodal tDCS and sham (placebo) treatment on 40-Hz ASSR and aeGBR. Anodal tDCS failed to alter aeGBR but increased both 40-Hz ASSR power, as measured by event-related spectral perturbations (ERSP), and phase locking, as measured by inter-trial phase consistency (ITPC). Inter-individual differences in tDCS-induced increases in ERSP were negatively related to baseline ERSPs. These findings provide tentative support for further study of tDCS as a potential NMDAR neuromodulatory intervention for synchronized auditory gamma response deficits.

Using Stem Cell Models to Explore the Genetics Underlying Psychiatric Disorders: Linking Risk Variants, Genes, and Biology in Brain Disease

There is an urgent and unmet need to advance our ability to translate genetic studies of psychiatric disorders into clinically actionable information, which could transform diagnostics and even one day lead to novel (and potentially presymptomatic) therapeutic interventions. Today, although there are hundreds of significant loci associated with psychiatric disorders, resolving the target gene(s) and pathway(s) impacted by each is a major challenge. Integrating human induced pluripotent stem cell-based approaches with CRISPR-mediated genomic engineering strategies makes it possible to study the impact of patient-specific variants within the cell types of the brain. As the scale and scope of functional genomic studies expands, so does our ability to resolve the complex interplay of the many risk variants linked to psychiatric disorders. In this review, the author discusses some of the technological advances that make it possible to ask exciting questions that are fundamental to our understanding of psychiatric disorders. How do distinct risk variants converge and interact with each other (and the environment) across the diverse cell types that comprise the human brain? Can clinical trajectories and/or therapeutic response be predicted from genetic profiles? Just as critically, by spreading the message that genetic risk for psychiatric disorders is biological and fundamentally no different than for other human conditions, we can dispel the stigma associated with mental illness.

Pharmacogenomics Factors Influencing the Effect of Risperidone on Prolactin Levels in Thai Pediatric Patients With Autism Spectrum Disorder

We investigated the association between genetic variations in pharmacodynamic genes and risperidone-induced increased prolactin levels in children and adolescents with autism spectrum disorder (ASD). In a retrospective study, variants of pharmacodynamic genes were analyzed in 124 ASD patients treated with a risperidone regimen for at least 3 months. To simplify genotype interpretation, we created an algorithm to calculate the dopamine D2 receptor (DRD2) gene genetic risk score. There was no relationship between prolactin levels and single SNPs. However, the H1/H3 diplotype (A2/A2-Cin/Cin-A/G) of DRD2/ankyrin repeat and kinase domain containing 1 (ANKK1) Taq1A, DRD2 -141C indel, and DRD2 -141A>G, which had a genetic risk score of 5.5, was associated with the highest median prolactin levels (23 ng/ml). As the dose-corrected plasma levels of risperidone, 9-OH-risperidone, and the active moiety increased, prolactin levels in patients carrying the H1/H3 diplotype were significantly higher than those of the other diplotypes. DRD2 diplotypes showed significantly high prolactin levels as plasma risperidone levels increased. Lower levels of prolactin were detected in patients who responded to risperidone. This is the first system for describing DRD2 haplotypes using genetic risk scores based on their protein expression. Clinicians should consider using pharmacogenetic-based decision-making in clinical practice to prevent prolactin increase.

Pharmacogenomics: A road ahead for precision medicine in psychiatry

Psychiatric genomics is providing insights into the nature of psychiatric conditions that in time should identify new drug targets and improve patient care. Less attention has been paid to psychiatric pharmacogenomics research, despite its potential to deliver more rapid change in clinical practice and patient outcomes. The pharmacogenomics of treatment response encapsulates both pharmacokinetic ("what the body does to a drug") and pharmacodynamic ("what the drug does to the body") effects. Despite early optimism and substantial research in both these areas, they have to date made little impact on clinical management in psychiatry. A number of bottlenecks have hampered progress, including a lack of large-scale replication studies, inconsistencies in defining valid treatment outcomes across experiments, a failure to routinely incorporate adverse drug reactions and serum metabolite monitoring in study designs, and inadequate investment in the longitudinal data collections required to demonstrate clinical utility. Nonetheless, advances in genomics and health informatics present distinct opportunities for psychiatric pharmacogenomics to enter a new and productive phase of research discovery and translation.

Contrasting Typical and Atypical Antipsychotic Drugs

The beliefs that antipsychotic drugs (APDs) are 1) effective only to treat delusions and hallucinations (positive symptoms), 2) that typical and atypical APDs differ only in ability to cause extrapyramidal side effects, and 3) that their efficacy as antipsychotics is due solely to their dopamine D₂ receptor blockade are outmoded concepts that prevent clinicians from achieving optimal clinical results when prescribing an APD. Atypical APDs are often more effective than typical APDs in treating negative symptoms, cognitive impairment, and mood symptoms as well as reducing the risk for suicide and decreasing aggression. This applies not only to those diagnosed with schizophrenia or schizoaffective disorder but also to bipolar disorder, major depression, and other psychiatric diagnoses. The greater advantage of an atypical APD is not evident in all patients for every atypical APD due, in part, to individual differences in genetic and epigenetic endowment and differences in the pharmacology of the atypical APDs, their mode of action being far more complex than that of the typical APDs. A common misconception is that among the atypical APDs, only clozapine is effective for reducing psychosis in treatment-resistant schizophrenia. Aripiprazole, lurasidone, olanzapine, and risperidone also can be more effective than typical APDs for treatment-resistant schizophrenia; clozapine is uniquely indicated for reducing the risk for suicide. The ability of the atypical APDs to improve cognition and negative symptoms in some patients together with lower propensity to cause tardive dyskinesia (an underappreciated advantage) leads to better overall outcomes. These advantages of the atypical APDs in efficacy and safety are due, in part, to initiation of synaptic plasticity via direct and indirect effects of the atypical APDs on a variety of proteins, especially G proteins, and release of neurotrophins (e.g., brain-derived neurotrophic factor). The typical APDs beneficial effects on psychosis are mainly the result of D₂ receptor blockade, which can be associated with serious side effects and lack of tolerability.

All-optical approaches to studying psychiatric disease

Improvements in all-optical means of monitoring and manipulating neural activity have generated new ways of studying psychiatric disease. The combination of calcium imaging techniques with optogenetics to concurrently record and manipulate neural activity has been used to create new disease models that link distinct circuit abnormalities to specific disease dimensions. These approaches represent a new path towards the development of more effective treatments, as they allow researchers to identify circuit manipulations that normalize pathological network activity. In this review we highlight the utility of all-optical approaches to generate new psychiatric disease models where the specific circuit abnormalities associated with disease symptomology can be assessed in vivo and in response to manipulations designed to normalize disease states. We then outline the principles underlying all-optical interrogations of neural circuits and discuss practical considerations for experimental design.

[Genetic risk factors and their influence on neural development in autism spectrum disorders]

Genetic risk factors and their influence on neural development in autism spectrum disorders Abstract. Abstract. Autism spectrum disorders are etiologically based on genetic and specific gene x biologically relevant environmental risk factors. They are diagnosed based on behavioral characteristics, such as impaired social communication and stereotyped, repetitive behavior and sensory as well as special interests. The genetic background is heterogeneous, i. e., it comprises diverse genetic risk factors across the disorder and high interindividual differences of specific genetic risk factors. Nevertheless, risk factors converge regarding underlying biological mechanisms and shared pathways, which likely cause the autism-specific behavioral characteristics. The current selective literature review summarizes differential genetic risk factors and focuses particularly on mechanisms and pathways currently being discussed by international research. In conclusion, clinically relevant aspects and open translational research questions are presented.

Pattern classification as decision support tool in antipsychotic treatment algorithms

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

Predicting Recovery in Individuals With Serious Mental Illness: Expanding the International Classification of Functioning, Disability, and Health (ICF) Framework

People with psychiatric disabilities experience significant impairment in fulfilling major life roles due to the severity of their mental illness. Recovery for people with serious mental illness (SMI) can be a long, arduous process, impacted by various biological, functional, sociological, and psychological factors which can present as barriers and/or facilitators. The purposes of this study were to: (a) investigate the International Classification of Functioning, Disability, and Health (ICF) framework’s ability to predict recovery in adults with SMI and (b) determine to what extent the ICF constructs in the empirical model explain the variance in recovery. Participants ( N = 192) completed a sociodemographic questionnaire and various measures representing all predictor and outcome variables. Results from hierarchical regression analysis with six sets of predictors entered sequentially (1 = personal factors-demographics, 2 = body functions-mental, 3 = activity-capacity, 4 = environmental factors, 5 = personal factors-characteristics, and 6 = participation-performance) accounted for 75% (large effect) of the variance in recovery. Controlling for all factors, by order of salience, higher levels of significant other support, education, executive function impairment, and social self-efficacy; primary, non-bipolar SMI diagnosis; greater resilience; lower levels of explicit memory-health impairment, affective self-stigma, and cognitive self-stigma; being younger; fewer self-care limitations; less severe psychiatric symptoms; and being unemployed and unmarried were found to significantly predict recovery. Findings support the validation of the ICF framework as a biopsychosocial recovery model and the use of this model in the development of effective recovery-oriented interventions for adults with SMI. Clinical and research implications are discussed.

Regulation of Glutamatergic Activity via Bidirectional Activation of Two Select Receptors as a Novel Approach in Antipsychotic Drug Discovery

Schizophrenia is a mental disorder that affects approximately 1-2% of the population and develops in early adulthood. The disease is characterized by positive, negative, and cognitive symptoms. A large percentage of patients with schizophrenia have a treatment-resistant disease, and the risk of developing adverse effects is high. Many researchers have attempted to introduce new antipsychotic drugs to the clinic, but most of these treatments failed, and the diversity of schizophrenic symptoms is one of the causes of disappointing results. The present review summarizes the results of our latest papers, showing that the simultaneous activation of two receptors with sub-effective doses of their ligands induces similar effects as the highest dose of each compound alone. The treatments were focused on inhibiting the increased glutamate release responsible for schizophrenia arousal, without interacting with dopamine (D₂) receptors. Ligands activating metabotropic receptors for glutamate, GABA_B or muscarinic receptors were used, and the compounds were administered in several different combinations. Some combinations reversed all schizophrenia-related deficits in animal models, but others were active only in select models of schizophrenia symptoms (i.e., cognitive or negative symptoms).

Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review

BACKGROUND

Psychiatric genetics has had limited success in translational efforts. A thorough understanding of the present state of translation in this field will be useful in the facilitation and assessment of future translational progress.

PURPOSE

A narrative literature review was conducted. Combinations of 3 groups of terms were searched in EBSCOhost, Google Scholar, and PubMed. The review occurred in multiple steps, including abstract collection, inclusion/exclusion criteria review, coding, and analysis of included papers.

RESULTS

One hundred and fourteen articles were analyzed for the narrative review. Across those, 4 bottlenecks were noted that, if addressed, may provide insights and help improve and increase translation in the field of psychiatric genetics. These 4 bottlenecks are emphasizing linear translational frameworks, relying on molecular genomic findings, prioritizing certain psychiatric disorders, and publishing more reviews than experiments.

CONCLUSIONS

These entwined bottlenecks are examined with one another. Awareness of these bottlenecks can inform stakeholders who work to translate and/or utilize psychiatric genetic information. Potential solutions include utilizing nonlinear translational frameworks as well as a wider array of psychiatric genetic information (e.g., family history and gene-environment interplay) in this area of research, expanding which psychiatric disorders are considered for translation, and when possible, conducting original research. Researchers are urged to consider how their research is translational in the context of the frameworks, genetic information, and psychiatric disorders discussed in this review. At a broader level, these efforts should be supported with translational efforts in funding and policy shifts.

Pharmacogenetics in psychiatric care, a call for uptake of available applications

In this narrative, we evaluate the role of pharmacogenetics in psychiatry from a pragmatic clinical perspective and address current barriers of clinical implementation of pharmacogenetics. Pharmacogenetics has been successfully implemented to improve drug therapy in several clinical areas, but not psychiatry. Yet, psychotropics account for more than one-third of the drugs for which pharmacogenetic guidelines are available and drug therapy in mental disorders is suboptimal with insufficient effectiveness and frequent adverse events. The limited application of pharmacogenetics in psychiatry is influenced by several factors; e.g. the complexity of psychotropic drug metabolism, possibly impeding the clinical understanding of the benefits of pharmacogenetics. Also, recommendations for most psychotropics classify pharmacogenetic testing only as (potentially) beneficial, not as essential, possibly because life-threatening adverse events are often not involved in these drug-gene interactions. Implementing pharmacogenetics in psychiatry could improve the current practice of time-consuming switching of therapies causing undue delays associated with worse outcomes. We expect pharmacogenetics in psychiatry to expedite with panel-based genotyping, including clinically relevant variants, which will address the complex enzymatic metabolism of psychotropic drugs. Until then, we stress that available pharmacogenetic testing should be seen as an integrated companion, not a competitor, in current clinical psychiatric care.

Investigation of the impact of an ADCY2 polymorphism as a predictive biomarker in bipolar disorder, suicide tendency and response to lithium carbonate therapy: the first report from Iran

High rates of mortality due to both suicide and medical comorbidities in bipolar patients can be decreased through the administration of lithium, which affects the cerebral endothelium as well as neurons. To investigate the role of ADCY2 in risk of bipolar disorder, we genotyped the ADCY2 rs2290910 in bipolar patients and healthy controls using amplification refractory mutation system PCR. This polymorphism was associated with risk of bipolar disorder (odds ratio [OR]: 0.430; 95% CI: 0.296-0.624; p = 0.001). The C allele was more frequent in suicide ideation group compared other groups (OR: 2.7; 95% CI: 1.386-5.302; p = 0.004). The T allele was more frequent in suicide attempt group compared with suicide ideation group (OR: 0.238; 95% CI: 0.111-0.509; p = 0.001).

A Physiological Instability Displayed in Hippocampal Neurons Derived From Lithium-Nonresponsive Bipolar Disorder Patients

BACKGROUND

We recently reported a hyperexcitability phenotype displayed in dentate gyrus granule neurons derived from patients with bipolar disorder (BD) as well as a hyperexcitability that appeared only in CA3 pyramidal hippocampal neurons that were derived from patients with BD who responded to lithium treatment (lithium responders) and not in CA3 pyramidal hippocampal neurons that were derived from patients with BD who did not respond to lithium (nonresponders).

METHODS

Here we used our measurements of currents in neurons derived from 4 control subjects, 3 patients with BD who were lithium responders, and 3 patients with BD who were nonresponders. We changed the conductances of simulated dentate gyrus and CA3 hippocampal neurons according to our measurements to derive a numerical simulation for BD neurons.

RESULTS

The computationally simulated BD dentate gyrus neurons had a hyperexcitability phenotype similar to the experimental results. Only the simulated BD CA3 neurons derived from lithium responder patients were hyperexcitable. Interestingly, our computational model captured a physiological instability intrinsic to hippocampal neurons that were derived from nonresponder patients that we also observed when re-examining our experimental results. This instability was caused by a drastic reduction in the sodium current, accompanied by an increase in the amplitude of several potassium currents. These baseline alterations caused nonresponder BD hippocampal neurons to drastically shift their excitability with small changes to their sodium currents, alternating between hyperexcitable and hypoexcitable states.

CONCLUSIONS

Our computational model of BD hippocampal neurons that was based on our measurements reproduced the experimental phenotypes of hyperexcitability and physiological instability. We hypothesize that the physiological instability phenotype strongly contributes to affective lability in patients with BD.

Potential pharmacogenomic targets in bipolar disorder: considerations for current testing and the development of decision support tools to individualize treatment selection

Background

Treatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations.

Main body

We searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information.

Conclusions

The complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.

Human pluripotent stem cell-derived models and drug screening in CNS precision medicine

Development of effective therapeutics for neurological disorders has historically been challenging partly because of lack of accurate model systems in which to investigate disease etiology and test new therapeutics at the preclinical stage. Human stem cells, particularly patient-derived induced pluripotent stem cells (iPSCs) upon differentiation, have the ability to recapitulate aspects of disease pathophysiology and are increasingly recognized as robust scalable systems for drug discovery. We review advances in deriving cellular models of human central nervous system (CNS) disorders using iPSCs along with strategies for investigating disease-relevant phenotypes, translatable biomarkers, and therapeutic targets. Given their potential to identify novel therapeutic targets and leads, we focus on phenotype-based, small-molecule screens employing human stem cell-derived models. Integrated efforts to assemble patient iPSC-derived cell models with deeply annotated clinicopathological data, along with molecular and drug-response signatures, may aid in the stratification of patients, diagnostics, and clinical trial success, shifting translational science and precision medicine approaches. A number of remaining challenges, including the optimization of cost-effective, large-scale culture of iPSC-derived cell types, incorporation of aging into neuronal models, as well as robustness and automation of phenotypic assays to support quantitative drug efficacy, toxicity, and metabolism testing workflows, are covered. Continued advancement of the field is expected to help fully humanize the process of CNS drug discovery.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Moderators and mediators of antipsychotic response in delusional disorder: Further steps are needed

Delusional disorder (DD) has been traditionally considered a relatively rare and treatment-resistant psychotic disorder. In the last decade, increasing attention has focused on therapeutic outcomes of individuals affected by this disorder. The aim of this paper is to provide a synthesis of the literature addressing two very important questions arising from DD research: (1) For which patients with DD do antipsychotic medications work best (the moderators of response); and (2) What variables best explain the relationship between such treatments and their effectiveness (the mediators of response). We searched PubMed and Google Scholar databases for English, German, French and Spanish language papers published since 2000. We also included a few classic earlier papers addressing this topic. Variables potentially moderating antipsychotic response in DD are gender, reproductive status, age, duration of illness, the presence of comorbidity (especially psychiatric comorbidity) and its treatment, brain structure, and genetics of neurochemical receptors and drug metabolizing enzymes. Antipsychotic and hormonal blood levels during treatment, as well as functional brain changes, are potential mediating variables. Some, but not all, patients with DD benefit from antipsychotic treatment. Understanding the circumstances under which treatment works best can serve to guide optimal management.

Modeling the complex genetic architectures of brain disease

The genetic architecture of each individual comprises common and rare variants that, acting alone and in combination, confer risk of disease. The cell-type-specific and/or context-dependent functional consequences of the risk variants linked to brain disease must be resolved. Coupling human induced pluripotent stem cell (hiPSC)-based technology with CRISPR-based genome engineering facilitates precise isogenic comparisons of variants across genetic backgrounds. Although functional-validation studies are typically performed on one variant in isolation and in one cell type at a time, complex genetic diseases require multiplexed gene perturbations to interrogate combinations of genes and resolve physiologically relevant disease biology. Our aim is to discuss advances at the intersection of genomics, hiPSCs and CRISPR. A better understanding of the molecular mechanisms underlying disease risk will improve genetic diagnosis, drive phenotypic drug discovery and pave the way toward precision medicine.

A randomized double-blind controlled trial to assess the benefits of amisulpride and olanzapine combination treatment versus each monotherapy in acutely ill schizophrenia patients (COMBINE): methods and design

This report presents the rationale and design of a multi-center clinical trial that examines the efficacy and safety of antipsychotic combination treatment in acutely ill schizophrenia patients compared to antipsychotic monotherapy. Antipsychotic combination treatment is common in clinical practice worldwide, despite clinical guidelines generally not recommending such practice due to lacking evidence for its efficacy and safety. Olanzapine has a related chemical structure and comparable receptor-binding profile as clozapine, which demonstrated superior efficacy in combination studies, but has a more unfavorable side-effect profile compared to olanzapine. Amisulpride and olanzapine have shown promising therapeutic efficacy in meta-analyses in monotherapy for people with schizophrenia. Combining amisulpride and olanzapine, complementary receptor-binding properties may enhance efficacy and possibly reduce (or at least not augment) side effects due to the different receptor profiles and metabolization pathways. Accordingly, we hypothesize that patients treated with amisulpride plus olanzapine show greater improvement on the Positive and Negative Syndrome Scale total score after 8 weeks versus either monotherapy. A randomized, double-blind controlled trial is performed at 16 German centers comparing flexibly dosed monotherapy of oral amisulpride (400-800 mg/day), and olanzapine (10-20 mg/day) and amisulpride-olanzapine co-treatment. Sample size was calculated to be n = 101 per treatment arm, assuming an effect size of 0.500 and a two-sided alpha = 0.025 and beta = 0.90. Recruitment for this trial started in June 2012. Until December 2018, 328 patients have been randomized. Trial conduct has been extended to reach the projected sample size. Publication of the study results is expected in 2019 informing an evidence-based recommendation regarding specific antipsychotic combination treatment.

Convergent neurobiological predictors of mood and anxiety symptoms and treatment response

Introduction: Mood and anxiety disorders are leading contributors to the global burden of diseases. Comorbid mood and anxiety disorders have a lifetime prevalence of ~20% globally and increases the risk for suicide, a leading cause of death. Areas covered: In this review, authors highlight recent advances in the understanding of multilevel-neurobiological mechanisms for normal/pathological human affective-functioning. The authors then address the complex interplay between environmental-adversity and molecular-genetic mediators of brain correlates of affective-symptoms. The molecular focus is strategically limited to GTF2i, BDNF, and FKBP5 genes that are, respectively, involved in transcriptional-, neurodevelopmental- and neuroendocrine-pathway mediation of affective-functions. The importance of these genes is illustrated with studies of copy-number-variants, genome-wide association (GWAS), and candidate gene-sequence variant associations with disease etiology. Authors concluded by highlighting the predictive values of integrative neurobiological processing of gene-environment interactions for affective disorder symptom management. Expert opinion: Given the transcriptional, neurodevelopmental and neuroimmune relevance of GTF2i, BDNF, and FKBP5 genes, respectively, authors reviewed the putative roles of these genes in neurobiological mediation of adaptive affective-responses. Authors discussed the importance of studying gene-dosage effects in understanding affective disorder risk biology, and how such targeted neurogenetic studies could guide precision identification of novel pharmacotherapeutic targets and aid in prediction of treatment response.

The Serotonin Syndrome: From Molecular Mechanisms to Clinical Practice

The serotonin syndrome is a medication-induced condition resulting from serotonergic hyperactivity, usually involving antidepressant medications. As the number of patients experiencing medically-treated major depressive disorder increases, so does the population at risk for experiencing serotonin syndrome. Excessive synaptic stimulation of 5-HT_2A receptors results in autonomic and neuromuscular aberrations with potentially life-threatening consequences. In this review, we will outline the molecular basis of the disease and describe how pharmacologic agents that are in common clinical use can interfere with normal serotonergic pathways to result in a potentially fatal outcome. Given that serotonin syndrome can imitate other clinical conditions, an understanding of the molecular context of this condition is essential for its detection and in order to prevent rapid clinical deterioration.

Challenges and Opportunities in Psychiatric Neuroscience

Psychiatry faces a number of challenges as a field. These include the high individual and societal costs of mental illnesses, overlapping and heterogeneous diagnoses, a complete lack of biomarkers, and treatments that, although efficacious for some, leave many without adequate relief. On the other hand, scientific and technical advances present considerable opportunities, especially in genomics, computational and theoretical approaches, and neural circuit technologies. The National Institute of Mental Health is committed to taking advantage of these opportunities to address the challenges of psychiatry, in the service of achieving our mission of transforming the understanding and treatment of mental illnesses.

The identification of biomarkers predicting acute and maintenance lithium treatment response in bipolar disorder: A plea for further research attention

The prediction of acute and maintenance lithium treatment response carries major clinical and neurobiological implications, warranting systematic review. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant review searched major electronic databases from inception until December 2017 for studies documenting a clinical diagnosis of bipolar disorder (BD) made according to the mainstream diagnostic manuals and confirmed by a structured interview. Eligible studies allowed a quantitative comparison of endpoint vs baseline mean values of a given biomarker, regardless of the mood phase of patients with BD, and the disorder was assessed for severity using validated rating tool(s). Owing to the purposely applied stringent selection criteria, 16 acute and 12 maintenance studies could be included. The anticipated publication bias limited the chances of reportable generalizable findings, hindering a side-by-side comparison of different records across varying biomarkers and subsequent meta-analyses. The PRISMA approach was nonetheless preferred; it aimed at enhancing the homogeneity of the included results and minimizing the chances of "apples and oranges" with respect to the present research theme. The present critical review confirms the need for future research to specifically assess either pretreatment and/or posttreatment putative biomarkers of patients with BD and treated with lithium.