Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders

Frequencies of CYP2C19 and CYP2D6 gene variants in a German inpatient sample with mood and anxiety disorders

OBJECTIVES

Previous results demonstrated that CYP2D6 and CYP2C19 gene variants affect serum concentrations of antidepressants. We implemented a PGx service determining gene variants in CYP2D6 and CYP2C19 in our clinical routine care and report on our first patient cohort.

METHODS

We analysed CYP2D6 and CYP2C19 allele, genotype, and phenotype frequencies, and actionable pharmacogenetic variants in this German psychiatric inpatient cohort. Two-tailed z-test was used to investigate for differences in CYP2D6 and CYP2C19 phenotypes and actionable/non-actionable genetic variant frequencies between our cohort and reference cohorts.

RESULTS

Out of the 154 patients included, 44.8% of patients were classified as CYP2D6 normal metabolizer, 38.3% as intermediate metabolizers, 8.4% as poor metabolizers, and 2.6% as ultrarapid metabolizers. As for CYP2C19, 40.9% of patients were classified as normal metabolizers, 19.5% as intermediate metabolizers, 2.6% as poor metabolizers, 31.2% as rapid metabolizers, and 5.8% as ultrarapid metabolizers. Approximately, 80% of patients had at least one actionable PGx variant.

CONCLUSION

There is a high prevalence of actionable PGx variants in psychiatric inpatients which may affect treatment response. Physicians should refer to PGx-informed dosing guidelines in carriers of these variants. Pre-emptive PGx testing in general may facilitate precision medicine also for other drugs metabolised by CYP2D6 and/or CYP2C19.

The HPA Axis as Target for Depression

Major depressive disorder (MDD) is a stress-related mental disorder with a lifetime prevalence of 20% and, thus, is one of the most prevalent mental health disorders worldwide. Many studies with a large number of patients support the notion that abnormalities of the hypothalamus-pituitaryadrenal (HPA) axis are crucial for the development of MDD. Therefore, a number of strategies and drugs have been investigated to target different components of the HPA axis: 1) corticotrophinreleasing hormone (CRH) 1 receptor antagonists; 2) vasopressin V1B receptor antagonists, 3) glucocorticoid receptor antagonists, and 4) FKBP5 antagonists. Until now, V1B receptor antagonists and GR antagonists have provided the most promising results. Preclinical data also support antagonists of FKBP5, which seem to be partly responsible for the effects exerted by ketamine. However, as HPA axis alterations occur only in a subset of patients, specific treatment approaches that target only single components of the HPA axis will be effective only in this subset of patients. Companion tests that measure the function of the HPA axis and identify patients with an impaired HPA axis, such as the dexamethasone-corticotrophin-releasing hormone (dex-CRH) test or the molecular dexamethasonesuppression (mDST) test, may match the patient with an effective treatment to enable patient-tailored treatments in terms of a precision medicine approach.

Ulotaront: a TAAR1/5-HT1A agonist in clinical development for the treatment of schizophrenia

INTRODUCTION

Current antipsychotics are postsynaptic dopamine-2(D₂) receptor blockers, which often, but not always, effectively improve acute psychotic symptoms and prevent relapse in schizophrenia and other severe mental disorders, but are associated with various side effects, including parkinsonism, akathisia, sedation/somnolence, and cardiometabolic alterations. Furthermore, the efficacy of current antipsychotics for negative and cognitive symptoms in schizophrenia is limited. Ulotaront is a novel trace-amine-associated receptor-1(TAAR1) agonist with serotonin-1A receptor agonist activity, and without postsynaptic D2-receptor antagonism. Phase 2 clinical data for ulotaront in patients with acutely exacerbated schizophrenia are promising regarding the potential improvement in positive, negative, and depressive symptoms.

AREAS COVERED

An overview of the pharmacokinetic and pharmacodynamic properties of ulotaront is given. Summary of clinical efficacy and safety/tolerability from Phase 1/2-trials, and of ongoing Phase 3-trials, is also given.

EXPERT OPINION

Ulotaront is a promising agent for the treatment of schizophrenia with an apparent benign safety profile, which might provide a much-needed new and different treatment option for various domains of schizophrenia. Data from larger Phase 3-trials, including for relapse prevention, schizophrenia subdomains, and in adolescents, are awaited. If ongoing Phase 3-trials in adults are successful, further research on combination regimens with existing antipsychotics, and in treatment-resistant schizophrenia as well as in mood disorders would be desirable.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology, radiology, and dermatology. However, the use of AI in mental health care and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental health care providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. While published research on AI in neuropsychiatry is rather limited, there is a growing number of successful examples of AI's use with electronic health records, brain imaging, sensor-based monitoring systems, and social media platforms to predict, classify, or subgroup mental illnesses as well as problems such as suicidality. This article is the product of a study group held at the American College of Neuropsychopharmacology conference in 2019. It provides an overview of AI approaches in mental health care, seeking to help with clinical diagnosis, prognosis, and treatment, as well as clinical and technological challenges, focusing on multiple illustrative publications. Although AI could help redefine mental illnesses more objectively, identify them at a prodromal stage, personalize treatments, and empower patients in their own care, it must address issues of bias, privacy, transparency, and other ethical concerns. These aspirations reflect human wisdom, which is more strongly associated than intelligence with individual and societal well-being. Thus, the future AI or artificial wisdom could provide technology that enables more compassionate and ethically sound care to diverse groups of people.

Pathophysiology of Major Depression by Clinical Stages

The comprehension of the pathophysiology of the major depressive disorder (MDD) is essential to the strengthening of precision psychiatry. In order to determine the relationship between the pathophysiology of the MDD and its clinical progression, analyzed by severity of the depressive symptoms and sleep quality, we conducted a study assessing different peripheral molecular biomarkers, including the levels of plasma C-reactive protein (CRP), serum mature brain-derived neurotrophic factor (mBDNF), serum cortisol (SC), and salivary cortisol awakening response (CAR), of patients with MDD (n = 58) and a control group of healthy volunteers (n = 62). Patients with the first episode of MDD (n = 30) had significantly higher levels of CAR and SC than controls (n = 32) and similar levels of mBDNF of controls. Patients with treatment-resistant depression (TRD, n = 28) presented significantly lower levels of SC and CAR, and higher levels of mBDNF and CRP than controls (n = 30). An increased severity of depressive symptoms and worse sleep quality were correlated with levels low of SC and CAR, and with high levels of mBDNF. These results point out a strong relationship between the stages clinical of MDD and changes in a range of relevant biological markers. This can assist in the development of precision psychiatry and future research on the biological tests for depression.

How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection

Decision support systems embodying machine learning models offer the promise of an improved standard of care for major depressive disorder, but little is known about how clinicians' treatment decisions will be influenced by machine learning recommendations and explanations. We used a within-subject factorial experiment to present 220 clinicians with patient vignettes, each with or without a machine-learning (ML) recommendation and one of the multiple forms of explanation. We found that interacting with ML recommendations did not significantly improve clinicians' treatment selection accuracy, assessed as concordance with expert psychopharmacologist consensus, compared to baseline scenarios in which clinicians made treatment decisions independently. Interacting with incorrect recommendations paired with explanations that included limited but easily interpretable information did lead to a significant reduction in treatment selection accuracy compared to baseline questions. These results suggest that incorrect ML recommendations may adversely impact clinician treatment selections and that explanations are insufficient for addressing overreliance on imperfect ML algorithms. More generally, our findings challenge the common assumption that clinicians interacting with ML tools will perform better than either clinicians or ML algorithms individually.

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Reading, Conducting, and Developing Systematic Review and Individual Patient Data Meta-Analyses in Psychiatry for Treatment Issues

Introduction: Individual participant data meta-analyses (IPD-MAs) include the raw data from relevant randomised clinical trials (RCTs) and involve secondary analyses of the data. Performed since the late 1990s, ~50 such meta-analyses have been carried out in psychiatry, mostly in the field of treatment. IPD-MAs are particularly relevant for three objectives: (1) evaluation of the average effect of an intervention by combining effects from all included trials, (2) evaluation of the heterogeneity of an intervention effect and sub-group analyses to approach personalised psychiatry, (3) mediation analysis or surrogacy evaluation to replace a clinical (final) endpoint for the evaluation of new treatments with intermediate or surrogate endpoints. The objective is to describe the interest and the steps of an IPD-MA method applied to the field of psychiatric therapeutic research. Method: The method is described in three steps. First, the identification of the relevant trials with an explicit description of the inclusion/exclusion criteria for the RCT to be incorporated in the IPD-MA and a definition of the intervention, the population, the context and the relevant points (outcomes or moderators). Second, the data management with the standardisation of collected variables and the evaluation and the assessment of the risk-of-bias for each included trial and of the global risk. Third, the statistical analyses and their interpretations, depending on the objective of the meta-analysis. All steps are illustrated with examples in psychiatry for treatment issues, excluding study protocols. Conclusion: The meta-analysis of individual patient data is challenging. Only strong collaborations between all stakeholders can make such a process efficient. An "ecosystem" that includes all stakeholders (questions of interest prioritised by the community, funders, trialists, journal editors, institutions, …) is required. International medical societies can play a central role in favouring the emergence of such communities.

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg, novel sensors) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Perhaps these innovative technologies will lead to a paradigm shift from disintegrated and data-poor behavioral science to cohesive and data-rich science that permits improved translation from bench to bedside. The 4 main advances influencing the scientific priorities of a recent NIH Office of Behavioral and Social Sciences Research strategic plan include the following: integration of neuroscience into behavioral and social sciences, transformational advances in measurement science, digital intervention platforms, and large-scale population cohorts and data integration. This paper reviews these opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments. Furthermore, the effects of such advancements necessitate discussion and modification of training as well as ethical and legal mandates for neuropsychological research and praxes.

Eliciting Willingness and Beliefs towards Participation in Genetic Psychiatric Testing in Black/African American Mothers at Risk for Depression

Black/African American women are at high risk for depression, yet are underrepresented in psychiatric genetic research for depression prevention and treatment. Little is known about the factors that influence participation in genetic testing for Black/African American women at risk. The purpose of this study was to elicit the beliefs that underlie participation in genetic testing for depression in Black/African American mothers, a subgroup at high risk. Willingness to participate in genetic testing procedures was also determined. A qualitative, descriptive design was employed. Exactly 19 mothers aged 21–42 completed open-ended questionnaires. Directed content and descriptive analyses of the text were conducted based on the Theory of Planned Behavior. Salient beliefs included: behavioral advantages—diagnosing/detecting depression (31.6%), finding cure/treatment (21.1%); disadvantages—not finding follow-up treatment/help (21.1%); salient referents, who approves—family members (47.4%), agencies/organizations (26.3%); who disapproves—church associates (21.1%). Control beliefs included: barriers—unpleasant/difficult testing procedures (42.1%), limited knowledge about the purpose of testing (26.3%); facilitator—a convenient location (21.1%). Most mothers (89.5%) indicated willingness to participate in testing. Interventions can target families, address barriers, emphasize future benefits, and use convenient locations and community-based participatory research methods. Policies can address social determinants of participation to increase inclusion of these mothers in psychiatric genetic research.

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.

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.

Metabolic signature in nucleus accumbens for anti-depressant-like effects of acetyl-L-carnitine

Emerging evidence suggests that hierarchical status provides vulnerability to develop stress-induced depression. Energy metabolic changes in the nucleus accumbens (NAc) were recently related to hierarchical status and vulnerability to develop depression-like behavior. Acetyl-L-carnitine (LAC), a mitochondria-boosting supplement, has shown promising antidepressant-like effects opening therapeutic opportunities for restoring energy balance in depressed patients. We investigated the metabolic impact in the NAc of antidepressant LAC treatment in chronically-stressed mice using ¹H-magnetic resonance spectroscopy (¹H-MRS). High rank, but not low rank, mice, as assessed with the tube test, showed behavioral vulnerability to stress, supporting a higher susceptibility of high social rank mice to develop depressive-like behaviors. High rank mice also showed reduced levels of several energy-related metabolites in the NAc that were counteracted by LAC treatment. Therefore, we reveal a metabolic signature in the NAc for antidepressant-like effects of LAC in vulnerable mice characterized by restoration of stress-induced neuroenergetics alterations and lipid function.

A Deep Learning Approach for Missing Data Imputation of Rating Scales Assessing Attention-Deficit Hyperactivity Disorder

A variety of tools and methods have been used to measure behavioral symptoms of attention-deficit/hyperactivity disorder (ADHD). Missing data is a major concern in ADHD behavioral studies. This study used a deep learning method to impute missing data in ADHD rating scales and evaluated the ability of the imputed dataset (i.e., the imputed data replacing the original missing values) to distinguish youths with ADHD from youths without ADHD. The data were collected from 1220 youths, 799 of whom had an ADHD diagnosis, and 421 were typically developing (TD) youths without ADHD, recruited in Northern Taiwan. Participants were assessed using the Conners' Continuous Performance Test, the Chinese versions of the Conners' rating scale-revised: short form for parent and teacher reports, and the Swanson, Nolan, and Pelham, version IV scale for parent and teacher reports. We used deep learning, with information from the original complete dataset (referred to as the reference dataset), to perform missing data imputation and generate an imputation order according to the imputed accuracy of each question. We evaluated the effectiveness of imputation using support vector machine to classify the ADHD and TD groups in the imputed dataset. The imputed dataset can classify ADHD vs. TD up to 89% accuracy, which did not differ from the classification accuracy (89%) using the reference dataset. Most of the behaviors related to oppositional behaviors rated by teachers and hyperactivity/impulsivity rated by both parents and teachers showed high discriminatory accuracy to distinguish ADHD from non-ADHD. Our findings support a deep learning solution for missing data imputation without introducing bias to the data.

Fetal alcohol spectrum disorders from childhood to adulthood: a Swedish population-based naturalistic cohort study of adoptees from Eastern Europe

BACKGROUND

Fetal alcohol spectrum disorders (FASD) are a global health concern. To further understand FASD in adulthood is a major public health interest.

OBJECTIVE

To describe the clinical characteristics of young adults with FASD adopted from orphanages to a socially more favourable and stable rearing environment as children.

DESIGN

Prospective observational cohort study SETTING: Western Sweden PARTICIPANTS: A population-based cohort of 37 adoptees diagnosed with FASD in childhood.

OUTCOME MEASURES

Assessment consisted of clinical evaluations of social, medical, psychiatric, neuropsychological, adaptive and ophthalmological status by a physician, ophthalmologist, orthoptist and psychologist.

RESULTS

Out of 37 adoptees with FASD, 36 (15 females) were evaluated at a median age of 22 years (range 18-28) and a mean follow-up time of 15.5 years (range 13-17). Twenty (56%) were dependent on social support. Sexual victimisation was reported by nine (26%). In 21 individuals with fetal alcohol syndrome, growth restriction in height and head circumference of approximately -1.8 SD persisted into adulthood. Of 32 examined, 22 (69%) had gross motor coordination abnormalities. High blood pressure was measured in nine (28%). Ophthalmological abnormalities were found in 29 of 30 (97%). A median IQ of 86 in childhood had declined significantly to 71 by adulthood (mean difference: 15.5; 95% CI 9.5-21.4). Psychiatric disorders were diagnosed in 88%, most commonly attention deficit hyperactivity disorder (70%). Three or more disorders were diagnosed in 48%, and 21% had attempted suicide. The median Clinical Global Impression-Severity score was 6 = 'severely ill'.

CONCLUSION

Major cognitive impairments, psychiatric morbidity, facial dysmorphology, growth restriction and ophthalmological abnormalities accompanies FASD in adulthood. Recognition of FASD in childhood warrants habilitation across the lifespan.

Patient Dossier: Healthcare queries over distributed resources

As with many other aspects of the modern world, in healthcare, the explosion of data and resources opens new opportunities for the development of added-value services. Still, a number of specific conditions on this domain greatly hinders these developments, including ethical and legal issues, fragmentation of the relevant data in different locations, and a level of (meta)data complexity that requires great expertise across technical, clinical, and biological domains. We propose the Patient Dossier paradigm as a way to organize new innovative healthcare services that sorts the current limitations. The Patient Dossier conceptual framework identifies the different issues and suggests how they can be tackled in a safe, efficient, and responsible way while opening options for independent development for different players in the healthcare sector. An initial implementation of the Patient Dossier concepts in the Rbbt framework is available as open-source at https://github.com/mikisvaz and https://github.com/Rbbt-Workflows.

Machine learning methods for developing precision treatment rules with observational data

Clinical trials have identified a variety of predictor variables for use in precision treatment protocols, ranging from clinical biomarkers and symptom profiles to self-report measures of various sorts. Although such variables are informative collectively, none has proven sufficiently powerful to guide optimal treatment selection individually. This has prompted growing interest in the development of composite precision treatment rules (PTRs) that are constructed by combining information across a range of predictors. But this work has been hampered by the generally small samples in randomized clinical trials and the use of suboptimal analysis methods to analyze the resulting data. In this paper, we propose to address the sample size problem by: working with large observational electronic medical record databases rather than controlled clinical trials to develop preliminary PTRs; validating these preliminary PTRs in subsequent pragmatic trials; and using ensemble machine learning methods rather than individual algorithms to carry out statistical analyses to develop the PTRs. The major challenges in this proposed approach are that treatment are not randomly assigned in observational databases and that these databases often lack measures of key prescriptive predictors and mental disorder treatment outcomes. We proposed a tiered case-cohort design approach that uses innovative methods for measuring and balancing baseline covariates and estimating PTRs to address these challenges.

Is the HPA Axis as Target for Depression Outdated, or Is There a New Hope?

Major depressive disorder (MDD) is a very common stress-related mental disorder that carries a huge burden for affected patients and the society. It is associated with a high mortality that derives from suicidality and the development of serious medical conditions such as heart diseases, diabetes, and stroke. Although a range of effective antidepressants are available, more than 50% of the patients do not respond to the first treatment they are prescribed and around 30% fail to respond even after several treatment attempts. The heterogeneous condition of MDD, the lack of biomarkers matching patients with the right treatments and the situation that almost all available drugs are only targeting the serotonin, norepinephrine, or dopamine signaling, without regulating other potentially dysregulated systems may explain the insufficient treatment status. The hypothalamic-pituitary-adrenal (HPA) axis is one of these other systems, there is numerous and robust evidence that it is implicated in MDD and other stress-related conditions, but up to date there is no specific drug targeting HPA axis components that is approved and no test that is routinely used in the clinical setting identifying patients for such a specific treatment. Is there still hope after these many years for a breakthrough of agents targeting the HPA axis? This review will cover tests detecting altered HPA axis function and the specific treatment options such as glucocorticoid receptor (GR) antagonists, corticotropin-releasing hormone 1 (CRH₁) receptor antagonists, tryptophan 2,3-dioxygenase (TDO) inhibitors and FK506 binding protein 5 (FKBP5) receptor antagonists.