Pharmacogenomics: A road ahead for precision medicine in psychiatry

Antipsychotic and pharmacogenomic effects on cross-sectional symptom severity and cognitive ability in schizophrenia

BACKGROUND

People with schizophrenia differ in the type and severity of symptoms experienced, as well as their response to medication. A better understanding of the factors that influence this heterogeneity is necessary for the development of individualised patient care. Here, we sought to investigate the relationships between phenotypic severity and both medication and pharmacogenomic variables in a cross-sectional sample of people with schizophrenia or schizoaffective disorder depressed type.

METHODS

Confirmatory factor analysis derived five dimensions relating to current symptom severity (positive symptoms, negative symptoms of diminished expressivity, negative symptoms of reduced motivation and pleasure, depression and suicide) and cognitive ability in participants prescribed with antipsychotic medication. Linear models were fit to test for associations between medication and pharmacogenomic variables with dimension scores in the full sample (N = 585), and in a sub-sample of participants prescribed clozapine (N = 215).

FINDINGS

Lower cognitive ability was associated with higher chlorpromazine-equivalent daily antipsychotic dose (β = -0.12; 95% CI, -0.19 to -0.05; p = 0.001) and with the prescription of clozapine (β = -0.498; 95% CI, -0.65 to -0.35; p = 3 × 10-10) and anticholinergic medication (β = -0.345; 95% CI, -0.55 to -0.14; p = 8 × 10-4). We also found associations between pharmacogenomics-inferred cytochrome P450 (CYP) enzyme activity and symptom dimensions. Increased genotype-predicted CYP2C19 and CYP3A5 activity were associated with reduced severity of the positive (β = -0.108; 95% CI, -0.19 to -0.03; p = 0.009) and both negative symptom dimensions (β = -0.113; 95% CI, -0.19 to -0.03; p = 0.007; β = -0.106; 95% CI, -0.19 to -0.02; p = 0.012), respectively. Faster predicted CYP1A2 activity was associated with higher cognitive dimension scores in people taking clozapine (β = 0.17; 95% CI, 0.05-0.29; p = 0.005).

INTERPRETATION

Our results confirm the importance of taking account of medication history (and particularly antipsychotic type and dose) in assessing potential correlates of cognitive impairment or poor functioning in patients with schizophrenia. We also highlight the potential for pharmacogenomic variation to be a useful tool to help guide drug prescription, although these findings require further validation.

FUNDING

Medical Research Council (MR/Y004094/1) and The National Center for Mental Health, funded by the Welsh Government through Health and Care Research Wales. SKL was funded by a PhD studentship from Mental Health Research UK (MHRUK). DBK, JTRW, MCOD and AFP were supported by the European Union's Horizon 2020 research and innovation programme under grant agreement 964874.

Genomics of schizophrenia, bipolar disorder and major depressive disorder

Schizophrenia, bipolar disorder and major depressive disorder - which are the most common adult disorders requiring psychiatric care - contribute substantially to premature mortality and morbidity globally. Treatments for these disorders are suboptimal, there are no diagnostic pathologies or biomarkers and their pathophysiologies are poorly understood. Novel therapeutic and diagnostic approaches are thus badly needed. Given the high heritability of psychiatric disorders, psychiatry has potentially much to gain from the application of genomics to identify molecular risk mechanisms and to improve diagnosis. Recent large-scale, genome-wide association studies and sequencing studies, together with advances in functional genomics, have begun to illuminate the genetic architectures of schizophrenia, bipolar disorder and major depressive disorder and to identify potential biological mechanisms. Genomic findings also point to the aetiological relationships between different diagnoses and to the relationships between adult psychiatric disorders and childhood neurodevelopmental conditions.

Nomograms based on clinical factors to predict abnormal metabolism of psychotropic drugs

Interindividual variability in drug metabolism serves a critical role in the occurrence of adverse drug reactions. Factors such as age, sex, body mass index (BMI) and liver and renal function can influence the metabolism of antipsychotic medications. To the best of our knowledge, however, clinical prediction models based on these factors for estimating drug-metabolizing capacity have not yet been developed. Between January 2022 and September 2023, 185 adult patients (aged ≥18 years) who did not have cancer and were not critically ill, with or without comorbidities such diabetes, hypertension and liver and kidney diseases, who underwent pharmacogenetic testing at The First Hospital of Jilin University (Changchun, China) were enrolled. Clinical data were collected, and the participants were divided into training and validation cohorts. Logistic regression was performed to identify significant risk factors, which were incorporated into multivariable models to construct nomograms predicting psychotropic drug metabolism. A total of eight clinical indicators (BMI, hypertension, alkaline phosphatase, aspartate aminotransferase, cholinesterase, albumin to globulin ratio, urea, and uric acid) were significantly associated with psychotropic drug metabolism (all P<0.05). Based on these indicators, along with age and sex, prediction models for psychotropic drug metabolism were developed. The areas under the receiver operating characteristic curves for haloperidol, olanzapine, paroxetine, mirtazapine/venlafaxine and oxazepam/lorazepam in the validation dataset were 0.767, 0.767, 0.705, 0.740 and 0.789, respectively, indicating the models had moderate diagnostic efficiency. Nomograms were constructed to demonstrate the contribution of each indicator to drug metabolism capacity. To the best of our knowledge, the present study is the first to develop predictive models for psychotropic drug metabolism. These models offer clinicians practical tools to identify patients with impaired drug-metabolizing capacity, thereby enabling more precise and personalized medication management.

Adjunctive ketamine vs. buprenorphine in co-occurring major depressive disorder and opioid use disorder: a randomized, double-blind clinical trial assessing anxiety symptom severity and craving intensity

BACKGROUND

The concomitant presence of major depressive disorder (MDD) and opioid use disorder (OUD) poses a formidable clinical challenge, warranting effective interventions that address both psychiatric and addictive components.

AIMS

This study sought to compare the efficacy of adjunctive ketamine and buprenorphine in mitigating anxiety symptom severity and craving intensity in individuals with co-occurring MDD and OUD.

METHODS

A randomized, double-blind clinical trial was conducted, involving individuals meeting diagnostic criteria for both MDD and OUD. Participants were randomly assigned to receive adjunctive ketamine or buprenorphine, in conjunction with standard psychiatric and addiction treatments. Anxiety symptom severity and craving intensity were assessed using Hamilton Anxiety Rating Scale (HAM-A), and the Opioid Craving Scale after 2 h, 24 h, and 7 days.

RESULTS

The findings revealed distinct treatment trajectories, with ketamine demonstrating rapid and substantial reduction in anxiety symptom severity within hours of administration, accompanied by a pronounced decline in opioid craving intensity. In contrast, buprenorphine was associated with a more gradual but sustained improvement in anxiety symptoms over several days, paralleled by a modest initial reduction in opioid craving, followed by persistent attenuation.

CONCLUSIONS

In conclusion, this randomized clinical trial provides evidence supporting the efficacy of adjunctive Ketamine and Buprenorphine in reducing anxiety symptoms and craving intensity in patients with co-occurring MDD and OUD.

TRIAL REGISTRATION

IRCT20211214053411N1.

The Public Knowledge of Precision Medicine and Genomic Research: A Survey in the Aosta Valley

Background: Precision medicine (PM) considers the genetic variability of individuals to identify tailored diagnosis and treatments. It relies on the possibility of gathering the widest possible health data and genetic information from individuals to obtain a broad pool of comparative data. To achieve this goal, the Region of Valle d'Aosta, since 2019, has co-financed the research center CMP3VdA, aiming to sequence 5000 genomes of patients with neurodevelopmental, neurodegenerative, oncological, and organ transplantation diseases, and to investigate the genetic variability of the resident population. Methods: This paper presents the results of an online survey of 472 (328F) respondents regarding willingness to participate in the genomic project and awareness, attitudes, and concerns about PM. Results: The main results show that the vast majority (92.6%) would be willing to participate-a higher percentage than in previous studies. Age, education, and prior experience in the healthcare sector are significant factors influencing the awareness of PM. Additionally, subgroups organized by age, gender, and religiosity show significant differences with respect to participants' reasons for participating in research and which types of biological samples they would be willing to donate. Conclusions: Our findings can serve as a guide for stakeholders-particularly policymakers-to target institutional communication and achieve maximum participation in genomic research projects.

How Real-World Data Can Facilitate the Development of Precision Medicine Treatment in Psychiatry

Precision medicine has the ambition to improve treatment response and clinical outcomes through patient stratification and holds great potential for the treatment of mental disorders. However, several important factors are needed to transform current practice into a precision psychiatry framework. Most important are 1) the generation of accessible large real-world training and test data including genomic data integrated from multiple sources, 2) the development and validation of advanced analytical tools for stratification and prediction, and 3) the development of clinically useful management platforms for patient monitoring that can be integrated into health care systems in real-life settings. This narrative review summarizes strategies for obtaining the key elements-well-powered samples from large biobanks integrated with electronic health records and health registry data using novel artificial intelligence algorithms-to predict outcomes in severe mental disorders and translate these models into clinical management and treatment approaches. Key elements are massive mental health data and novel artificial intelligence algorithms. For the clinical translation of these strategies, we discuss a precision medicine platform for improved management of mental disorders. We use cases to illustrate how precision medicine interventions could be brought into psychiatry to improve the clinical outcomes of mental disorders.

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Efficacy and safety of pharmacogenomic-guided antidepressant prescribing in patients with depression: an umbrella review and updated meta-analysis

Aim

To determine the efficacy and safety of pharmacogenomics (PGx)-guided antidepressant prescribing in patients with depression through an umbrella review and updated meta-analysis.

Methods

A comprehensive systematic search was conducted on PsycINFO, PubMed, Embase and the Cochrane databases. The pooled effect sizes of randomized controlled trials (RCTs) were expressed as mean differences for continuous data and risk ratios for noncontinuous data.

Results

Patients who received PGx-guided medications were 41% to 78% more likely to achieve remission and 20% to 49% more likely to respond to antidepressants than patients receiving treatment-as-usual (TAU).

Conclusion

PGx-guided antidepressant prescribing improves the treatment of depression. However, the significance and magnitude of the benefit varies widely between studies and different PGx testing panels.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42022321324.

QPGx-CARES: Qatar pharmacogenetics clinical applications and research enhancement strategies

Pharmacogenetic (PGx)-informed medication prescription is a cutting-edge genomic application in contemporary medicine, offering the potential to overcome the conventional "trial-and-error" approach in drug prescription. The ability to use an individual's genetic profile to predict drug responses allows for personalized drug and dosage selection, thereby enhancing the safety and efficacy of treatments. However, despite significant scientific and clinical advancements in PGx, its integration into routine healthcare practices remains limited. To address this gap, the Qatar Genome Program (QGP) has embarked on an ambitious initiative known as QPGx-CARES (Qatar Pharmacogenetics Clinical Applications and Research Enhancement Strategies), which aims to set a roadmap for optimizing PGx research and clinical implementation on a national scale. The goal of QPGx-CARES initiative is to integrate PGx testing into clinical settings with the aim of improving patient health outcomes. In 2022, QGP initiated several implementation projects in various clinical settings. These projects aimed to evaluate the clinical utility of PGx testing, gather valuable insights into the effective dissemination of PGx data to healthcare professionals and patients, and identify the gaps and the challenges for wider adoption. QPGx-CARES strategy aimed to integrate evidence-based PGx findings into clinical practice, focusing on implementing PGx testing for cardiovascular medications, supported by robust scientific evidence. The current initiative sets a precedent for the nationwide implementation of precision medicine across diverse clinical domains.

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

Role of Transporters and Enzymes in Metabolism and Distribution of 4-Chlorokynurenine (AV-101)

4-Chlorokynurenine (4-Cl-KYN, AV-101) is a prodrug of a NMDA receptor antagonist and is in clinical development for potential CNS indications. We sought to further understand the distribution and metabolism of 4-Cl-KYN, as this information might provide a strategy to enhance the clinical development of this drug. We used excretion studies in rats, in vitro transporter assays, and pharmacogenetic analysis of clinical trial data to determine how 4-Cl-KYN and metabolites are distributed. Our data indicated that a novel acetylated metabolite (N-acetyl-4-Cl-KYN) did not affect the uptake of 4-Cl-KYN across the blood-brain barrier via LAT1. 4-Cl-KYN and its metabolites were found to be renally excreted in rodents. In addition, we found that N-acetyl-4-Cl-KYN inhibited renal and hepatic transporters involved in excretion. Thus, this metabolite has the potential to limit the excretion of a range of compounds. Our pharmacogenetic analysis found that a SNP in N-acetyltransferase 8 (NAT8, rs13538) was linked to levels of N-acetyl-4-Cl-KYN relative to 4-Cl-KYN found in the plasma and that a SNP in SLC7A5 (rs28582913) was associated with the plasma levels of the active metabolite, 7-Cl-KYNA. Thus, we have a pharmacogenetics-based association for plasma drug level that could aid in the drug development of 4-Cl-KYN and have investigated the interaction of a novel metabolite with drug transporters.

Effectiveness of pharmacogenomics on the response and remission of treatment-resistant depression: a meta-analysis of randomised controlled trials

Background

Pharmacogenomics (PGx) is a promising tool to realise tailored drug therapy for depression.

Aims

To investigate the treatment efficacy of PGx for treatment-resistant depression (TRD) compared with treatment as usual.

Methods

A systematic search was conducted in PubMed, Embase, the Cochrane Library, Web of Science and PsycINFO to identify relevant studies published from inception to 15 April 2023. Two-arm randomised controlled trials (RCTs) exploring the efficacy of PGx-guided versus unguided treatment for TRD were included. The risk of bias in the included studies was evaluated using the Cochrane risk of bias assessment tool. The overall quality of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.

Results

Seven RCTs (n=3003) comparing PGx-guided (n=1492) and unguided (n=1511) groups were identified and analysed. PGx-guided treatment was superior to treatment as usual in response (relative risk (RR)=1.31; 95% confidence interval (95% CI): 1.15 to 1.49; p<0.001) and remission (RR=1.40; 95% CI: 1.09 to 1.80; p=0.009) improvements. Effect sizes for acceptability (RR=0.90; 95% CI: 0.80 to 1.02; p=0.100) and side effect burden (RR=0.58; 95% CI: 0.29 to 1.15; p=0.120) between the two groups were not statistically different. The overall quality of evidence was rated from 'very low' (25%) to 'low' (75%) based on the GRADE criteria.

Conclusions

PGx-guided treatment has shown a small overall effect in improving the response and remission rates for patients with TRD. However, these results should be interpreted cautiously because of the few included studies and the low quality of evidence. Further high-quality clinical trials are warranted to confirm the findings.

PROSPERO registration number

CRD42022340182.

Pharmacogenomics of Dementia: Personalizing the Treatment of Cognitive and Neuropsychiatric Symptoms

Dementia is a syndrome of global and progressive deterioration of cognitive skills, especially memory, learning, abstract thinking, and orientation, usually affecting the elderly. The most common forms are Alzheimer's disease, vascular dementia, and other (frontotemporal, Lewy body disease) dementias. The etiology of these multifactorial disorders involves complex interactions of various environmental and (epi)genetic factors and requires multiple forms of pharmacological intervention, including anti-dementia drugs for cognitive impairment, antidepressants, antipsychotics, anxiolytics and sedatives for behavioral and psychological symptoms of dementia, and other drugs for comorbid disorders. The pharmacotherapy of dementia patients has been characterized by a significant interindividual variability in drug response and the development of adverse drug effects. The therapeutic response to currently available drugs is partially effective in only some individuals, with side effects, drug interactions, intolerance, and non-compliance occurring in the majority of dementia patients. Therefore, understanding the genetic basis of a patient's response to pharmacotherapy might help clinicians select the most effective treatment for dementia while minimizing the likelihood of adverse reactions and drug interactions. Recent advances in pharmacogenomics may contribute to the individualization and optimization of dementia pharmacotherapy by increasing its efficacy and safety via a prediction of clinical outcomes. Thus, it can significantly improve the quality of life in dementia patients.

On the utilization of the induced pluripotent stem cell (iPSC) model to study substance use disorders: A scoping review protocol

INTRODUCTION

Induced pluripotent stem cells (iPSCs) are cells derived from somatic cells via reprogramming techniques. The iPSC approach has been increasingly used in neuropsychiatric research in the last decade. Though substance use disorders (SUDs) are a commonly occurring psychiatric disorder, the application of iPSC model in addiction research has been limited. No comprehensive review has been reported. We conducted a scoping review to collate existing evidence on the iPSC technologies applied to SUD research. We aim to identify current knowledge gaps and limitations in order to advance the use of iPSCs in the SUD field.

METHODS AND ANALYSIS

We employed a scoping review using the methodological framework first created by Arksey and O'Malley and further updated by Levac et al. and the Joanna Briggs Institute (JBI). We adopted the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Protocols (PRISMA-P) to report items for the protocol. We searched evidence from four electronic databases: PubMed®, Embase®, Web of Science™, and Scopus®. Primary research, systematic reviews, and meta-analyses were included and limited to studies published in English, at the time from 2007 to March 2022. This is an "ongoing" scoping review. Searched studies will be independently screened, selected, and extracted by two reviewers. Disagreement will be solved by the third reviewer and discussion. Extracted data will be analyzed in descriptive and quantitative approaches, then summarized and presented in appropriate formats. Results will be reported following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline and disseminated through a peer-reviewed publication and conference presentations.

CONCLUSION

To our best knowledge, this is the first comprehensive scoping review of iPSC methods specifically applied to a broad range of addictive drugs/substances that lead to SUDs or misuse behavior.

REGISTRATION

This protocol is registered on Zenodo repository (https://zenodo.org/) with doi:10.5281/zenodo.7915252.

Polygenic overlap with body-mass index improves prediction of treatment-resistant schizophrenia

Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further, polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment.

Identification of signaling pathways modifying human dopaminergic neuron development using a pluripotent stem cell-based high-throughput screening automated system: purinergic pathways as a proof-of-principle

Introduction: Alteration in the development, maturation, and projection of dopaminergic neurons has been proposed to be associated with several neurological and psychiatric disorders. Therefore, understanding the signals modulating the genesis of human dopaminergic neurons is crucial to elucidate disease etiology and develop effective countermeasures. Methods: In this study, we developed a screening model using human pluripotent stem cells to identify the modulators of dopaminergic neuron genesis. We set up a differentiation protocol to obtained floorplate midbrain progenitors competent to produce dopaminergic neurons and seeded them in a 384-well screening plate in a fully automated manner. Results and Discussion: These progenitors were treated with a collection of small molecules to identify the compounds increasing dopaminergic neuron production. As a proof-of-principle, we screened a library of compounds targeting purine- and adenosine-dependent pathways and identified an adenosine receptor 3 agonist as a candidate molecule to increase dopaminergic neuron production under physiological conditions and in cells invalidated for the HPRT1 gene. This screening model can provide important insights into the etiology of various diseases affecting the dopaminergic circuit development and plasticity and be used to identify therapeutic molecules for these diseases.

Pharmacokinetics and pharmacogenomics of clozapine in an ancestrally diverse sample: a longitudinal analysis and genome-wide association study using UK clinical monitoring data

BACKGROUND

The antipsychotic, clozapine, is the only licensed drug against the treatment-resistant symptoms that affect 20-30% of people with schizophrenia. Clozapine is markedly underprescribed, partly because of concerns about its narrow therapeutic range and adverse drug reaction profile. Both concerns are linked to drug metabolism, which varies across populations globally and is partly genetically determined. Our study aimed to use a cross-ancestry genome-wide association study (GWAS) design to investigate variations in clozapine metabolism within and between genetically inferred ancestral backgrounds, to discover genomic associations to clozapine plasma concentrations, and to assess the effects of pharmacogenomic predictors across different ancestries.

METHODS

In this GWAS, we analysed data from the UK Zaponex Treatment Access System clozapine monitoring service as part of the CLOZUK study. We included all available individuals with clozapine pharmacokinetic assays requested by their clinicians. We excluded people younger than 18 years, or whose records contained clerical errors, or with blood drawn 6-24 h after dose, a clozapine or norclozapine concentration less than 50 ng/mL, a clozapine concentration of more than 2000 ng/mL, a clozapine-to-norclozapine ratio outside of the 0·5-3·0 interval, or a clozapine dose of more than 900 mg/day. Using genomic information, we identified five biogeographical ancestries: European, sub-Saharan African, north African, southwest Asian, and east Asian. We did pharmacokinetic modelling, a GWAS, and a polygenic risk score association analysis using longitudinal regression analysis with three primary outcome variables: two metabolite plasma concentrations (clozapine and norclozapine) and the clozapine-to-norclozapine ratio.

FINDINGS

19 096 pharmacokinetic assays were available for 4760 individuals in the CLOZUK study. After data quality control, 4495 individuals (3268 [72·7%] male and 1227 [27·3%] female; mean age 42·19 years [range 18-85]) linked to 16 068 assays were included in this study. We found a faster average clozapine metabolism in people of sub-Saharan African ancestry than in those of European ancestry. By contrast, individuals with east Asian or southwest Asian ancestry were more likely to be slow clozapine metabolisers than those with European ancestry. Eight pharmacogenomic loci were identified in the GWAS, seven with significant effects in non-European groups. Polygenic scores generated from these loci were associated with clozapine outcome variables in the whole sample and within individual ancestries; the maximum variance explained was 7·26% for the metabolic ratio.

INTERPRETATION

Longitudinal cross-ancestry GWAS can discover pharmacogenomic markers of clozapine metabolism that, individually or as polygenic scores, have consistent effects across ancestries. Our findings suggest that ancestral differences in clozapine metabolism could be considered for optimising clozapine prescription protocols for diverse populations.

FUNDING

UK Academy of Medical Sciences, UK Medical Research Council, and European Commission.

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.

A paradigm shift in pharmacogenomics: From candidate polymorphisms to comprehensive sequencing

Genetic factors have long been recognized as important determinants of interindividual variability in drug efficacy and toxicity. However, despite the increasing number of established gene-drug associations, candidate polymorphisms can only explain a fraction of the genetically encoded functional variability in drug disposition. Advancements in genetic profiling methods now allow to analyse the landscape of human pharmacogenetic variations comprehensively, which opens new opportunities to identify novel factors that could explain the "missing heritability." Here, we provide an updated overview of the landscape of pharmacogenomic variability based on recent analyses of population-scale sequencing projects. We then summarize the current state-of-the-art how the functional consequences of variants with unknown effects can be quantitatively estimated while discussing challenges and peculiarities that are specific to pharmacogenes. In the last sections, we discuss the importance of considering ethnogeographic diversity to provide equitable benefits of pharmacogenomics and summarize current roadblocks for the implementation of sequencing-based guidance of clinical decision-making. Based on the current state of the field, we conclude that testing is likely to gradually shift from the interrogation of selected candidate polymorphisms to comprehensive sequencing, which allows to consider the full spectrum of pharmacogenomic variations for a true personalization of genomic prescribing.

Advances in Pharmacy Practice: A Look towards the Future

This review looks at the factors that may influence practice in the future. Transformation could occur at 3 levels. Firstly, the traditional profession of the pharmacist as a dispenser of medicines is expanding. Secondly, the pharmacist’s activities are progressing into new healthcare fields. Thirdly, other changes are stimulated by global developments. This review may be helpful for pharmacy and healthcare leaders looking at the future configuration and aims of their pharmacy services.

Multidisciplinary Consulting Team for Complicated Cases of Neurodevelopmental and Neurobehavioral Disorders: Assessing the Opportunities and Challenges of Integrating Pharmacogenomics into a Team Setting

Neurodevelopmental disorders have steadily increased in incidence in the United States. Over the past decade, there have been significant changes in clinical diagnoses and treatments some of which are due to the increasing adoption of pharmacogenomics (PGx) by clinicians. In this pilot study, a multidisciplinary team at the Arkansas Children’s Hospital North West consulted on 27 patients referred for difficult-to-manage neurodevelopmental and/or neurobehavioral disorders. The 27 patients were evaluated by the team using records review, team discussion, and pharmacogenetic testing. OneOme RightMed^® (Minneapolis, MN, USA) and the Arkansas Children’s Hospital comprehensive PGx test were used for drug prescribing guidance. Of the 27 patients’ predicted phenotypes, the normal metabolizer was 11 (40.8%) for CYP2C19 and 16 (59.3%) for CYP2D6. For the neurodevelopmental disorders, the most common comorbid conditions included attention-deficit hyperactivity disorder (66.7%), anxiety disorder (59.3%), and autism (40.7%). Following the team assessment and PGx testing, 66.7% of the patients had actionable medication recommendations. This included continuing current therapy, suggesting an appropriate alternative medication, starting a new therapy, or adding adjunct therapy (based on their current medication use). Moreover, 25.9% of patients phenoconverted to a CYP2D6 poor metabolizer. This retrospective chart review pilot study highlights the value of a multidisciplinary treatment approach to deliver precision healthcare by improving physician clinical decisions and potentially impacting patient outcomes. It also shows the feasibility to implement PGx testing in neurodevelopmental/neurobehavioral disorders.

Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia

IMPORTANCE

About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.

OBJECTIVE

To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.

DESIGN, SETTING, AND PARTICIPANTS

Two case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).

MAIN OUTCOMES AND MEASURES

GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.

RESULTS

The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41-0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (r2 = 2.03%; P = .001), which was replicated in the STRATA-G incidence sample (r2 = 1.09%; P = .04).

CONCLUSIONS AND RELEVANCE

In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.