Pharmacogenomics in treatment of depression and psychosis: an update

Dose adjustment of paroxetine based on CYP2D6 activity score inferred metabolizer status in Chinese Han patients with depressive or anxiety disorders: a prospective study and cross-ethnic meta-analysis

BACKGROUND

Understanding the impact of CYP2D6 metabolism on paroxetine, a widely used antidepressant, is essential for precision dosing.

METHODS

We conducted an 8-week, multi-center, single-drug, 2-week wash period prospective cohort study in 921 Chinese Han patients with depressive or anxiety disorders (ChiCTR2000038462). We performed CYP2D6 genotyping (single nucleotide variant and copy number variant) to derive the CYP2D6 activity score and evaluated paroxetine treatment outcomes including steady-state concentration, treatment efficacy, and adverse reaction. CYP2D6 metabolizer status was categorized into poor metabolizers (PMs), intermediate metabolizers (IMs), extensive metabolizers (EMs), and ultrarapid metabolizers (UMs). The influence of CYP2D6 metabolic phenotype on paroxetine treatment outcomes was examined using multiple regression analysis and cross-ethnic meta-analysis. The therapeutic reference range of paroxetine was estimated by receiver operating characteristic (ROC) analyses.

FINDINGS

After adjusting for demographic factors, the steady-state concentrations of paroxetine in PMs, IMs, and UMs were 2.50, 1.12, and 0.39 times that of EMs, with PM and UM effects being statistically significant (multiple linear regression, P = 0.03 and P = 0.04). Sex and ethnicity influenced the comparison between IMs and EMs. Moreover, poor efficacy of paroxetine was associated with UM, and a higher risk of developing adverse reactions was associated with lower CYP2D6 activity score. Lastly, cross-ethnic meta-analysis suggested dose adjustments for PMs, IMs, EMs, and UMs in the East Asian population to be 35%, 40%, 143%, and 241% of the manufacturer's recommended dose, and 62%, 68%, 131%, and 159% in the non-East Asian population.

INTERPRETATION

Our findings advocate for precision dosing based on the CYP2D6 metabolic phenotype, with sex and ethnicity being crucial considerations in this approach.

FUNDING

National Natural Science Foundation of China; Academy of Medical Sciences Research Unit.

Pharmacogenetic testing in primary care could bolster depression treatment: A value proposition

Pharmacogenetic testing could reduce the time to identify a safe and effective medication for depression; however, it is underutilized in practice. Major depression constitutes the most common mental disorder in the US, and while antidepressant therapy can help, the current trial -and error approach can require patients to endure multiple medication trials before finding one that is effective. Tailoring the fit of pharmacogenetic testing with prescribers' needs across a variety of settings could help to establish a generalizable value proposition to improve likelihood of adoption. We conducted a study to explore the value proposition for health systems using pharmacogenetic testing for mental health medications through prescribers' real-world experiences using implementation science concepts and systematic interviews with prescribers and administrators from four health care systems. To identify a value proposition, we organized the themes according to the Triple Aim framework, a leading framework for health care policy which asserts that high-value care should focus on three key metrics: (1) better health care quality and (2) population-level outcomes with (3) reduced per capita costs. Primary care providers whom we interviewed said that they value pharmacogenetic testing because it would provide more information about medications that they can prescribe, expanding their ability to identify medications that best-fit patients and reducing their reliance on referrals to specialists; they said that this capacity would help meet patients' needs for access to mental health care through primary care. At the same time, prescribers expressed differing views about how pharmacogenetic testing can help with quality of care and whether their views about out-of-pocket cost would prevent them from offering it. Thus, implementation should focus on integrating pharmacogenetic testing into primary care and using strategies to support prescribers' interactions with patients.

Influence of CYP2C19 and CYP2D6 on side effects of aripiprazole and risperidone: A systematic review

Variability in hepatic cytochrome P450 (CYP) enzymes such as 2C19 and 2D6 may influence side-effect and efficacy outcomes for antipsychotics. Aripiprazole and risperidone are two commonly prescribed antipsychotics, metabolized primarily through CYP2D6. Here, we aimed to provide an overview of the effect of CYP2C19 and CYP2D6 on side-effects of aripiprazole and risperidone, and expand on existing literature by critically examining methodological issues associated with pharmacogenetic studies. A PRISMA compliant search of six electronic databases (Pubmed, PsychInfo, Embase, Central, Web of Science, and Google Scholar) identified pharmacogenetic studies on aripiprazole and risperidone. 2007 publications were first identified, of which 34 were included. Quality of literature was estimated using Newcastle-Ottowa Quality Assessment Scale (NOS) and revised Cochrane Risk of Bias tool. The average NOS score was 5.8 (range: 3-8) for risperidone literature and 5 for aripiprazole (range: 4-6). All RCTs on aripiprazole were rated as high risk of bias, and four out of six for risperidone literature. Study populations ranged from healthy volunteers to inpatient individuals in psychiatric units and included adult and pediatric samples. All n = 34 studies examined CYP2D6. Only one study genotyped for CYP2C19 and found a positive association with neurological side-effects of risperidone. Most studies did not report any relationship between CYP2D6 and any side-effect outcome. Heterogeneity between and within studies limited the ability to synthesize data and draw definitive conclusions. Studies lacked statistical power due to small sample size, selective genotyping methods, and study design. Large-scale randomized trials with multiple measurements, providing robust evidence on this topic, are suggested.

Rapid Metabolism Underlying Subtherapeutic Serum Levels of Atypical Antipsychotics Preceding Clozapine Treatment: A Retrospective Analysis of Real-World Data

INTRODUCTION

Adequate antipsychotic treatment intensity is required before diagnosing resistant schizophrenia and initiating clozapine treatment. We aimed to investigate potential rapid drug metabolism underlying low dose-adjusted serum concentration (CD) of non-clozapine atypical antipsychotics preceding clozapine treatment.

METHODS

Patients using non-clozapine, atypical antipsychotics (aripiprazole, risperidone, olanzapine, or quetiapine) within 1 year before starting clozapine were included in this study from a therapeutic drug monitoring service in Oslo, Norway, between 2005 and 2023. Patients were assigned into low CD (LCD) and normal CD (NCD) subgroups. Using a reference sample with 147,964 antipsychotic measurements, LCD was defined as CDs below the 25th percentile, while patients with NCD exhibited CDs between the 25th and 75th percentile of the respective reference measurements. Metabolic ratios, doses, and frequency of subtherapeutic levels of non-clozapine antipsychotics were compared between LCD and NCD groups.

RESULTS

Preceding clozapine treatment, 110 out of 272 included patients (40.4%) were identified with LCD. Compared with the NCD group, LCD patients exhibited higher metabolic ratios of olanzapine (1.5-fold; p < 0.001), quetiapine (3.0-fold; p < 0.001), and risperidone (6.0-fold; p < 0.001). Metabolic ratio differences were independent of smoking and CYP2D6 genotype for olanzapine (p = 0.008) and risperidone (p = 0.016), respectively. Despite higher doses of olanzapine (1.25-fold; p = 0.054) and quetiapine (1.6-fold; p = 0.001) in LCD versus NCD patients, faster metabolism among the former was accompanied by higher frequencies of subtherapeutic levels of olanzapine (3.3-fold; p = 0.044) and quetiapine (1.8-fold; p = 0.005).

CONCLUSION

LCD and associated rapid metabolism of non-clozapine antipsychotics is frequent before starting clozapine treatment. For olanzapine and quetiapine, this is associated with significantly increased risk of having subtherapeutic concentrations.

Individualized Pharmacotherapy Utilizing Genetic Biomarkers and Novel In Vitro Systems As Predictive Tools for Optimal Drug Development and Treatment

In the area of drug development and clinical pharmacotherapy, a profound understanding of the pharmacokinetics and potential adverse reactions associated with the drug under investigation is paramount. Essential to this endeavor is a comprehensive understanding about interindividual variations in absorption, distribution, metabolism, and excretion (ADME) genetics and the predictive capabilities of in vitro systems, shedding light on metabolite formation and the risk of adverse drug reactions (ADRs). Both the domains of pharmacogenomics and the advancement of in vitro systems are experiencing rapid expansion. Here we present an update on these burgeoning fields, providing an overview of their current status and illuminating potential future directions. SIGNIFICANCE STATEMENT: There is very rapid development in the area of pharmacogenomics and in vitro systems for predicting drug pharmacokinetics and risk for adverse drug reactions. We provide an update of the current status of pharmacogenomics and developed in vitro systems on these aspects aimed to achieve a better personalized pharmacotherapy.

Proportion of Antipsychotics with CYP2D6 Pharmacogenetic (PGx) Associations Prescribed in an Early Intervention in Psychosis (EIP) Cohort: A Cross-Sectional Study

BACKGROUND

Prescribing drugs for psychosis (antipsychotics) is challenging due to high rates of poor treatment outcomes, which are in part explained by an individual's genetics. Pharmacogenomic (PGx) testing can help clinicians tailor the choice or dose of psychosis drugs to an individual's genetics, particularly psychosis drugs with known variable response due to CYP2D6 gene variants ('CYP2D6-PGx antipsychotics').

AIMS

This study aims to investigate differences between demographic groups prescribed 'CYP2D6-PGx antipsychotics' and estimate the proportion of patients eligible for PGx testing based on current pharmacogenomics guidance.

METHODS

A cross-sectional study took place extracting data from 243 patients' medical records to explore psychosis drug prescribing, including drug transitions. Demographic data such as age, sex, ethnicity, and clinical sub-team were collected and summarised. Descriptive statistics explored the proportion of 'CYP2D6-PGx antipsychotic' prescribing and the nature of transitions. We used logistic regression analysis to investigate associations between demographic variables and prescription of 'CYP2D6-PGx antipsychotic' versus 'non-CYP2D6-PGx antipsychotic'.

RESULTS

Two-thirds (164) of patients had been prescribed a 'CYP2D6-PGx antipsychotic' (aripiprazole, risperidone, haloperidol or zuclopenthixol). Over a fifth (23%) of patients would have met the suggested criteria for PGx testing, following two psychosis drug trials. There were no statistically significant differences between age, sex, or ethnicity in the likelihood of being prescribed a 'CYP2D6-PGx antipsychotic'.

CONCLUSIONS

This study demonstrated high rates of prescribing 'CYP2D6-PGx-antipsychotics' in an EIP cohort, providing a rationale for further exploration of how PGx testing can be implemented in EIP services to personalise the prescribing of drugs for psychosis.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Current level of evidence for improvement of antidepressant efficacy and tolerability by pharmacogenomic-guided treatment: A Systematic review and meta-analysis of randomized controlled clinical trials

The aim of the study was to assess the clinical utility of currently available pharmacogenomic (PGx) tools compared with treatment as usual (TAU), using a meta-analysis of dichotomous and continuous antidepressant efficacy and tolerability data from previously published clinical trials. MEDLINE, clinicaltrial.gov, EU Clinical Trials Register, WHO ICTRP and CENTRAL were systematically searched; of the 962 results originally reviewed, 15 trials were included. Antidepressant efficacy was quantified by relative and absolute changes in symptom severity after eight weeks of treatment and by response and remission rates, while tolerability was estimated by the rate of study discontinuation for any reason. In the PGx-guided patients, symptom severity reduced by an average of 31.0% after eight weeks of treatment, compared to an average reduction of 26.8% in the TAU group. Accordingly, PGx-guided patients experienced a greater reduction in symptom severity of 3.4% (95%CI: 1.6-5.3%), which corresponded to a reduction in the Hamilton Depression score of 0.75 (0.30-1.21), a 37% (15-63%) higher remission rate, and an 18% (5-33%) higher response rate compared with TAU patients, while no difference was observed in discontinuation rate between groups. Notably, the majority of associations lost statistical significance when restricting the dataset to low risk of bias studies, while certain funnel plots suggested a potential publication bias favoring the reporting of statistically significant results. In summary, PGx tools marginally enhance antidepressant efficacy, but not antidepressant tolerability; thus, additional research and advancement of PGx tools are needed to improve integration of PGx in clinical pharmacotherapy of depression.

Therapeutic drug monitoring, liquid biopsies or pharmacogenomics for prediction of human drug metabolism and response

Pharmacokinetics plays a central role in understanding the significant interindividual differences that exist in drug metabolism and response. Effectively addressing these differences requires a multi-faceted approach that encompasses a variety of tools and methods. In this review, we examine three key strategies to achieve this goal, namely pharmacogenomics, therapeutic drug monitoring (TDM) and liquid biopsy-based monitoring of hepatic ADME gene expression and highlight their advantages and limitations. We note that larger cohort studies are needed to validate the utility of liquid biopsy-based assessment of hepatic ADME gene expression, which includes prediction of drug metabolism in the clinical setting. Modern mass spectrometers have improved traditional TDM methods, offering versatility and sensitivity. In addition, the identification of endogenous or dietary markers for CYP metabolic traits offers simpler and more cost-effective alternatives to determine the phenotype. We believe that future pharmacogenomic applications in clinical practice should prioritize the identification of missing heritable factors, using larger, well-characterized patient studies and controlling for confounding factors such as diet, concomitant medication and physical health. The intricate regulation of ADME gene expression implies that large-scale studies combining long-read next-generation sequencing (NGS) of complete genomes with phenotyping of patients taking different medications are essential to identify these missing heritabilities. The continuous integration of such data into AI-driven analytical systems could provide a comprehensive and useful framework. This could lead to the development of highly effective algorithms to improve genetics-based precision treatment by predicting drug metabolism and response, significantly improving clinical outcomes.

Clinical implementation of preemptive pharmacogenomics in psychiatry

BACKGROUND

Pharmacogenomics (PGx) holds promise to revolutionize modern healthcare. Although there are several prospective clinical studies in oncology and cardiology, demonstrating a beneficial effect of PGx-guided treatment in reducing adverse drug reactions, there are very few such studies in psychiatry, none of which spans across all main psychiatric indications, namely schizophrenia, major depressive disorder and bipolar disorder. In this study we aim to investigate the clinical effectiveness of PGx-guided treatment (occurrence of adverse drug reactions, hospitalisations and re-admissions, polypharmacy) and perform a cost analysis of the intervention.

METHODS

We report our findings from a multicenter, large-scale, prospective study of pre-emptive genome-guided treatment named as PREemptive Pharmacogenomic testing for preventing Adverse drug REactions (PREPARE) in a large cohort of psychiatric patients (n = 1076) suffering from schizophrenia, major depressive disorder and bipolar disorder.

FINDINGS

We show that patients with an actionable phenotype belonging to the PGx-guided arm (n = 25) present with 34.1% less adverse drug reactions compared to patients belonging to the control arm (n = 36), 41.2% less hospitalisations (n = 110 in the PGx-guided arm versus n = 187 in the control arm) and 40.5% less re-admissions (n = 19 in the PGx-guided arm versus n = 32 in the control arm), less duration of initial hospitalisations (n = 3305 total days of hospitalisation in the PGx-guided arm from 110 patients, versus n = 6517 in the control arm from 187 patients) and duration of hospitalisation upon readmission (n = 579 total days of hospitalisation upon readmission in the PGx-guided arm, derived from 19 patients, versus n = 928 in the control arm, from 32 patients respectively). It was also shown that in the vast majority of the cases, there was less drug dose administrated per drug in the PGx-guided arm compared to the control arm and less polypharmacy (n = 124 patients prescribed with at least 4 psychiatric drugs in the PGx-guided arm versus n = 143 in the control arm) and smaller average number of co-administered psychiatric drugs (2.19 in the PGx-guided arm versus 2.48 in the control arm. Furthermore, less deaths were reported in the PGx-guided arm (n = 1) compared with the control arm (n = 9). Most importantly, we observed a 48.5% reduction of treatment costs in the PGx-guided arm with a reciprocal slight increase of the quality of life of patients suffering from major depressive disorder (0.935 versus 0.925 QALYs in the PGx-guided and control arm, respectively).

INTERPRETATION

While only a small proportion (∼25%) of the entire study sample had an actionable genotype, PGx-guided treatment can have a beneficial effect in psychiatric patients with a reciprocal reduction of treatment costs. Although some of these findings did not remain significant when all patients were considered, our data indicate that genome-guided psychiatric treatment may be successfully integrated in mainstream healthcare.

FUNDING

European Union Horizon 2020.

Algorithmic assessment reveals functional implications of GABRD gene variants linked to idiopathic generalized epilepsy

OBJECTIVE

The primary objective of this study is to address the challenge posed by the increasing number of variants of unknown clinical significance (VUS) within the GABRD gene, which encodes the δ subunit of γ-Aminobutyric acid type A receptors. The focus is on predicting the most pathogenic GABRD VUS to enhance precision medicine and improve our understanding of relevant pathophysiology.

METHODS

The study employs a combination of in silico algorithms to analyze 82 variants of unknown clinical significance of GABRD gene sourced from the ClinVar database. Initially, separate algorithms based on sequence homology are utilized to assess this variant set. Subsequently, consensus variants predicted as pathogenic undergo further evaluation through a web server employing an algorithm based on structural homology. The resulting 11 variants are then validated using in silico tools that assess variant effects based on genetic and molecular data. The evaluation includes consideration of disease association and protein stability due to amino acid substitutions.

RESULTS

The study identifies specific variants (L111R, R114C, D123N, G150S, and L243P) in the coding region of the GABRD gene, which are predicted as deleterious by multiple algorithms. These variants are evolutionarily conserved, mapped onto the extracellular domain of the δ subunit, and associated with idiopathic generalized epilepsy.

CONCLUSIONS

The findings suggest structural or functional consequences that lead to pathogenicity, offering valuable insights for wet-lab experimentation. Besides, the findings contribute to the validation of clinically significant genetic variants in the GABRD gene, which is critical for epilepsy precision medicine.

Ultrafast and Highly Specific Detection of One-Base Mutated Cell-Free DNA at a Very Low Abundance

Liquid biopsy as well as genotyping plays important roles in guiding the use of tumor-targeted drugs and monitoring the generation of drug resistance. However, current methods, such as next-generation sequencing (NGS) and pyrosequencing, require long analysis time and complicated steps. To achieve ultrafast and highly specific detection of cell-free DNA (cfDNA) from blood, we improved our recently developed FEN1-aided RPA (FARPA), which combined flap endonuclease 1 (FEN1)-catalyzed invasive reactions with recombinase polymerase amplification (RPA) by inactivating the RPA enzymes before invasive reactions, designing short RPA primers, and changing invasive reaction conditions. Using the L858R and T790M mutations as examples, FARPA was sensitive to detect 5 copies of targeted mutants, specific to sense the mutants with an abundance as low as 0.01% from blood, and ultrafast to get results within 40 min. The method was readily expended to genotyping, and 15 min was enough to report the allele species directly from oral swab samples by coupling quick DNA extraction reagents. Validation was carried out by detecting clinical samples, including 20 cfDNA from patients with non-small cell lung cancer (NSCLC) for liquid biopsy and 43 human genomic DNA (gDNA) purified from blood (33) or lysed from oral swabs (10) for genotyping, giving 100% agreement with NGS and pyrosequencing, respectively. Furthermore, a portable battery-driven device with dual-channel fluorescence detection was successfully constructed to facilitate point-of-care testing (POCT) of liquid biopsy and genotyping, providing doctors with a potential tool to achieve genotyping- or mutant-guided personalized medicine at emergency or source-limited regions.

Correlations between Negative Symptoms and Cognitive Deficits in Individuals at First Psychotic Episode or at High Risk of Psychosis: A Systematic Review

The present review aims to identify correlations between negative symptoms (NS) and deficits in neurocognition and social cognition in subjects with first-episode psychosis (FEP) and at-high-risk populations (HR). A systematic search of the literature published between 1 January 2005 and 31 December 2022 was conducted on PubMed, Scopus, and PsycInfo. Out of the 4599 records identified, a total of 32 studies met our inclusion/exclusion criteria. Data on a total of 3086 FEP and 1732 HR were collected. The available evidence shows that NS correlate with executive functioning and theory of mind deficits in FEP subjects, and with deficits in the processing speed, attention and vigilance, and working memory in HR subjects. Visual learning and memory do not correlate with NS in either FEP or HR subjects. More inconsistent findings were retrieved in relation to other cognitive domains in both samples. The available evidence is limited by sample and methodological heterogeneity across studies and was rated as poor or average quality for the majority of included studies in both FEP and CHR populations. Further research based on shared definitions of first-episode psychosis and at-risk states, as well as on more recent conceptualizations of negative symptoms and cognitive impairment, is highly needed.

Novel Approaches to Characterize Individual Drug Metabolism and Advance Precision Medicine

Interindividual variability in drug metabolism can significantly affect drug concentrations in the body and subsequent drug response. Understanding an individual's drug metabolism capacity is important for predicting drug exposure and developing precision medicine strategies. The goal of precision medicine is to individualize drug treatment for patients to maximize efficacy and minimize drug toxicity. While advances in pharmacogenomics have improved our understanding of how genetic variations in drug-metabolizing enzymes (DMEs) affect drug response, nongenetic factors are also known to influence drug metabolism phenotypes. This minireview discusses approaches beyond pharmacogenetic testing to phenotype DMEs-particularly the cytochrome P450 enzymes-in clinical settings. Several phenotyping approaches have been proposed: traditional approaches include phenotyping with exogenous probe substrates and the use of endogenous biomarkers; newer approaches include evaluating circulating noncoding RNAs and liquid biopsy-derived markers relevant to DME expression and function. The goals of this minireview are to 1) provide a high-level overview of traditional and novel approaches to phenotype individual drug metabolism capacity, 2) describe how these approaches are being applied or can be applied to pharmacokinetic studies, and 3) discuss perspectives on future opportunities to advance precision medicine in diverse populations. SIGNIFICANCE STATEMENT: This minireview provides an overview of recent advances in approaches to characterize individual drug metabolism phenotypes in clinical settings. It highlights the integration of existing pharmacokinetic biomarkers with novel approaches; also discussed are current challenges and existing knowledge gaps. The article concludes with perspectives on the future deployment of a liquid biopsy-informed physiologically based pharmacokinetic strategy for patient characterization and precision dosing.

Building a novel self-iterative clinical pharmacist intervention model in psychiatric hospital: Application and evaluation

Clinical pharmacist intervention (CPI) in psychiatric hospital has not been widely developed in China. This study aimed to establish a real-time dynamic self-iterative CPI model in The Affiliated Mental Health Center of Jiangnan University and evaluated its effect. We focused on the running data in 2021 and 2022. The number of interventions, intervention types and intervention strategies were evaluated. In 2021 and 2022, a total of 443 interventions were reported. Among them, 271 (61.17 %) were identified during ward rounds by physicians, pharmacists and nurses. The proportion of CPI through ward rounds and information system gradually decreased while intervention through other ways increased. Meanwhile, there are various of CPI types including adverse drug reactions (26.86 %), therapeutic drug monitoring recommendations (13.32 %), drug usage and dose adjustment (10.61 %) and among them, adverse reactions are the focus of pharmacists' attention. Besides, the intervention strategies of pharmacists mainly concentrated in medication change (18.74 %), medication discontinuation (15.58 %) and dose reduction (12.19 %). In addition, the self-iterative function can continuously optimize the intervention level of clinical pharmacists. Overall, the CPI model established in this study effectively promote pharmacist intervention and accelerate pharmaceutical transformation.

Under the umbrella of depression and Alzheimer's disease physiopathology: Can cannabinoids be a dual-pleiotropic therapy?

Depression and Alzheimer´s disease (AD) are two disorders highly prevalent worldwide. Depression affects more than 300 million people worldwide while AD affects 60-80% of the 55 million cases of dementia. Both diseases are affected by aging with high prevalence in elderly and share not only the main brain affected areas but also several physiopathological mechanisms. Depression disease is already ascribed as a risk factor to the development of AD. Despite the wide diversity of pharmacological treatments currently available in clinical practice for depression management, they remain associated to a slow recovery process and to treatment-resistant depression. On the other hand, AD treatment is essentially based in symptomatology relieve. Thus, the need for new multi-target treatments arises. Herein, we discuss the current state-of-art regarding the contribution of the endocannabinoid system (ECS) in synaptic transmission processes, synapses plasticity and neurogenesis and consequently the use of exogenous cannabinoids in the treatment of depression and on delaying the progression of AD. Besides the well-known imbalance of neurotransmitter levels, including serotonin, noradrenaline, dopamine and glutamate, recent scientific evidence highlights aberrant spine density, neuroinflammation, dysregulation of neurotrophic factor levels and formation of amyloid beta (Aβ) peptides, as the main physiopathological mechanisms compromised in depression and AD. The contribution of the ECS in these mechanisms is herein specified as well as the pleiotropic effects of phytocannabinoids. At the end, it became evident that Cannabinol, Cannabidiol, Cannabigerol, Cannabidivarin and Cannabichromene may act in novel therapeutic targets, presenting high potential in the pharmacotherapy of both diseases.

Longitudinal early epigenomic signatures inform molecular paths of therapy response and remission in depressed patients

Introduction

The etiology of major depressive disorder (MDD) involves the interaction between genes and environment, including treatment. Early molecular signatures for treatment response and remission are relevant in a context of personalized medicine and stratification and reduce the time-to-decision. Therefore, we focused the analyses on patients that responded or remitted following a cognitive intervention of 8 weeks.

Methods

We used data from a randomized controlled trial (RCT) with MDD patients (N = 112) receiving a cognitive intervention. At baseline and 8 weeks, blood for DNA methylation (Illumina Infinium MethylationEPIC 850k BeadChip) was collected, as well as MADRS. First, responders (N = 24; MADRS-reduction of at least 50%) were compared with non-responders (N = 60). Then, we performed longitudinal within-individual analyses, for response (N = 21) and for remission (N = 18; MADRS smaller or equal to 9 and higher than 9 at baseline), respectively, as well as patients with no change in MADRS over time. At 8 weeks the sample comprised 84 individuals; 73 patients had DNA methylation for both time-points. The RnBeads package (R) was used for data cleaning, quality control, and differential DNA-methylation (limma). The within-individual paired longitudinal analysis was performed using Welch's t-test. Subsequently gene-ontology (GO) pathway analyses were performed.

Results

No CpG was genome-wide significant CpG (p < 5 × 10-8). The most significant CpG in the differential methylation analysis comparing response versus non-response was in the IQSEC1 gene (cg01601845; p = 1.53 × 10-6), linked to neurotransmission. The most significant GO-terms were linked to telomeres. The longitudinal response analysis returned 67 GO pathways with a p < 0.05. Two of the three most significant pathways were linked to sodium transport. The analysis for remission returned 46 GO terms with a p-value smaller than 0.05 with pathways linked to phosphatase regulation and synaptic functioning. The analysis with stable patients returned mainly GO-terms linked to basic cellular processes.

Discussion

Our result suggest that DNA methylation can be suitable to capture early signs of treatment response and remission following a cognitive intervention in depression. Despite not being genome-wide significant, the CpG locations and GO-terms returned by our analysis comparing patients with and without cognitive impairment, are in line with prior knowledge on pathways and genes relevant for depression treatment and cognition. Our analysis provides new hypotheses for the understanding of how treatment for depression can act through DNA methylation and induce response and remission.

DRD2, DRD3, and HTR2A Single-Nucleotide Polymorphisms Involvement in High Treatment Resistance to Atypical Antipsychotic Drugs

BACKGROUND

The objective of this study was to investigate the DRD2 rs1800497, rs1799732, rs1801028, DRD3 rs6280, and HTR2A rs6314, rs7997012, and rs6311 single-nucleotide polymorphism (SNP) correlations with resistance to second-generation antipsychotics (SGAs) in a real-world sample of patients with treatment-resistant mental disorders.

METHODS

We divided 129 participants into a high treatment resistance (HTR) group (current treatment with two SGAs, or clozapine, or classic neuroleptics for a failure of previous SGAs trials) and a low treatment resistance (LTR) group (current treatment with one atypical antipsychotic). We used Next-Generation Sequencing on DNA isolated from peripheral blood samples to analyze the polymorphisms. We performed logistic regression to search for predictors of HTR membership.

RESULTS

A diagnosis of schizophrenia significantly predicted the HTR membership compared to other diagnoses. Other predictors were the DRD3 rs6280 C|T (OR = 22.195) and T|T (OR = 18.47) vs. C|C, HTR2A rs7997012 A|G vs. A|A (OR = 6.859) and vs. G|G (OR = 2.879), and DRD2 rs1799732 I|I vs. D|I (OR = 12.079) genotypes.

CONCLUSIONS

A diagnosis of schizophrenia and the DRD2 rs1799732, DRD3 rs6280, and HTR2A rs7997012 genotypes can predict high treatment resistance to SGAs.

The Role of Pharmacogenetics in Personalizing the Antidepressant and Anxiolytic Therapy

Pharmacotherapy for neuropsychiatric disorders, such as anxiety and depression, has been characterized by significant inter-individual variability in drug response and the development of side effects. Pharmacogenetics, as a key part of personalized medicine, aims to optimize therapy according to a patient's individual genetic signature by targeting genetic variations involved in pharmacokinetic or pharmacodynamic processes. Pharmacokinetic variability refers to variations in a drug's absorption, distribution, metabolism, and elimination, whereas pharmacodynamic variability results from variable interactions of an active drug with its target molecules. Pharmacogenetic research on depression and anxiety has focused on genetic polymorphisms affecting metabolizing cytochrome P450 (CYP) and uridine 5'-diphospho-glucuronosyltransferase (UGT) enzymes, P-glycoprotein ATP-binding cassette (ABC) transporters, and monoamine and γ-aminobutyric acid (GABA) metabolic enzymes, transporters, and receptors. Recent pharmacogenetic studies have revealed that more efficient and safer treatments with antidepressants and anxiolytics could be achieved through genotype-guided decisions. However, because pharmacogenetics cannot explain all observed heritable variations in drug response, an emerging field of pharmacoepigenetics investigates how epigenetic mechanisms, which modify gene expression without altering the genetic code, might influence individual responses to drugs. By understanding the epi(genetic) variability of a patient's response to pharmacotherapy, clinicians could select more effective drugs while minimizing the likelihood of adverse reactions and therefore improve the quality of treatment.

Effectiveness of Genotype-Specific Tricyclic Antidepressant Dosing in Patients With Major Depressive Disorder: A Randomized Clinical Trial

Importance

Evidence of the clinical benefit of pharmacogenetics-informed treatment (PIT) with antidepressants is still limited. Especially for tricyclic antidepressants (TCAs), pharmacogenetics may be of interest because therapeutic plasma concentrations are well defined, identification of optimal dosing can be time consuming, and treatment is frequently accompanied by adverse effects.

Objective

To determine whether PIT results in faster attainment of therapeutic TCA plasma concentrations compared with usual treatment in patients with unipolar major depressive disorder (MDD).

Design, Setting, and Participants

This randomized clinical trial compared PIT with usual treatment among 111 patients at 4 centers in the Netherlands. Patients were treated with the TCAs nortriptyline, clomipramine, or imipramine, with clinical follow-up of 7 weeks. Patients were enrolled from June 1, 2018, to January 1, 2022. At inclusion, patients had unipolar nonpsychotic MDD (with a score of ≥19 on the 17-item Hamilton Rating Scale for Depression [HAMD-17]), were aged 18 to 65 years, and were eligible for TCA treatment. Main exclusion criteria were a bipolar or psychotic disorder, substance use disorder, pregnancy, interacting comedications, and concurrent use of psychotropic medications.

Intervention

In the PIT group, the initial TCA dosage was based on CYP2D6 and CYP2C19 genotypes. The control group received usual treatment, which comprised the standard initial TCA dosage.

Main Outcomes and Measures

The primary outcome was days until attainment of a therapeutic TCA plasma concentration. Secondary outcomes were severity of depressive symptoms (measured by HAMD-17 scores) and frequency and severity of adverse effects (measured by Frequency, Intensity, and Burden of Side Effects Rating scores).

Results

Of 125 patients randomized, 111 (mean [SD] age, 41.7 [13.3] years; 69 [62.2%] female) were included in the analysis; of those, 56 were in the PIT group and 55 were in the control group. The PIT group reached therapeutic concentrations faster than the control group (mean [SD], 17.3 [11.2] vs 22.0 [10.2] days; Kaplan-Meier χ21 = 4.30; P = .04). No significant difference in reduction of depressive symptoms was observed. Linear mixed-model analyses showed that the interaction between group and time differed for the frequency (F6,125 = 4.03; P = .001), severity (F6,114 = 3.10; P = .008), and burden (F6,112 = 2.56; P = .02) of adverse effects, suggesting that adverse effects decreased relatively more for those receiving PIT.

Conclusions and Relevance

In this randomized clinical trial, PIT resulted in faster attainment of therapeutic TCA concentrations, with potentially fewer and less severe adverse effects. No effect on depressive symptoms was observed. These findings indicate that pharmacogenetics-informed dosing of TCAs can be safely applied and may be useful in personalizing treatment for patients with MDD.

Trial Registration

ClinicalTrials.gov Identifier: NCT03548675.

The Role of Genetics in the Development and Pharmacotherapy of Depression and Its Impact on Drug Discovery

Complex disorders, such as depression, remain a mystery for scientists. Although genetic factors are considered important for the prediction of one's vulnerability, it is hard to estimate the exact risk for a patient to develop depression, based only on one category of vulnerability criteria. Genetic factors also regulate drug metabolism, and when they are identified in a specific combination, may result in increased drug resistance. A proper understanding of the genetic basis of depression assists in the development of novel promising medications and effective disorder management schemes. This review aims to analyze the recent literature focusing on the correlation between specific genes and the occurrence of depression. Moreover, certain aspects targeting a high drug resistance identified among patients suffering from major depressive disorder were highlighted in this manuscript. An expected direction of future drug discovery campaigns was also discussed.