Risperidone-Induced Adverse Drug Reactions and Role of DRD2 (-141 C Ins/Del) and 5HTR2C (-759 C>T) Genetic Polymorphisms in Patients with Schizophrenia

Possible genetic biomarker associated with antipsychotic-induced amenorrhea in female patients with schizophrenia

This study explored the association of pharmacogenomics with antipsychotic-induced amenorrhea in female patients with schizophrenia. A total of 89 female schizophrenia patients aged 18-40 receiving consistent antipsychotics at a consistent dose for more than 3 months were enrolled in this study. Amenorrhea was defined as the absence of menstrual period for 3 months or three periods in a row. Serum levels of prolactin, estradiol, follicle-stimulating hormone, luteinizing hormone, and thyroid-stimulating hormone were measured and Cytochrome P450 2D6, dopamine receptor D2 ( DRD2 ) and estrogen receptor 1 were genotyped. Twenty-two patients with amenorrhea had higher prolactin levels and lower estradiol levels than those without amenorrhea (94.1 vs. 71.5 ng/ml for prolactin; P  = 0.044 and 27.0 vs. 46.7 pg/ml for estradiol; P  = 0.007, respectively). Multiple logistic regression analysis identified DRD2 -141C deletion [odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.01-4.17; P  = 0.049] and drugs increasing prolactin levels (OR = 6.17, 95% CI = 1.28-29.64; P  = 0.023) as significant covariates for antipsychotic-induced amenorrhea. This study suggests that DRD2 -141C deletion is associated with antipsychotic-induced amenorrhea although further studies are needed.

Multilocus Genetic Profile Reflecting Low Dopaminergic Signaling Is Directly Associated with Obesity and Cardiometabolic Disorders Due to Antipsychotic Treatment

Treatment with second-generation antipsychotics (SGAs) can cause obesity and other cardiometabolic disorders linked to D2 receptor (DRD2) and to genotypes affecting dopaminergic (DA) activity, within reward circuits. We explored the relationship of cardiometabolic alterations with single genetic polymorphisms DRD2 rs1799732 (NG_008841.1:g.4750dup -> C), DRD2 rs6277 (NG_008841.1:g.67543C>T), COMT rs4680 (NG_011526.1:g.27009G>A), and VNTR in both DRD4 NC_000011.10 (637269-640706) and DAT1 NC_000005.10 (1392794-1445440), as well as with a multilocus genetic profile score (MLGP). A total of 285 psychiatric patients treated with SGAs for at least three months were selected. Cardiometabolic parameters were classified according to ATP-III and WHO criteria. Blood samples were taken for routinely biochemical assays and PCR genotyping. Obesity (BMI, waist (W)), high diastolic blood pressure (DBP), and hypertriglyceridemia (HTG) were present in those genetic variants related to low dopaminergic activity: InsIns genotype in rs1799732 (BMI: OR: 2.91 [1.42-5.94]), DRD4-VNTR-L allele (W: OR: 1.73 [1.04-2.87]) and 9R9R variant in DAT1-VNTR (W: OR: 2.73 [1.16-6.40]; high DBP: OR: 3.33 [1.54-7.31]; HTG: OR: 4.38 [1.85-10.36]). A low MLGP score indicated a higher risk of suffering cardiometabolic disorders (BMI: OR: 1.23 [1.05-1.45]; W: OR: 1.18 [1.03-1.34]; high DBP: OR: 1.22 [1.06-1.41]; HTG: OR: 1.20 [1.04-1.39]). The MLGP score was more sensitive for detecting the risk of suffering these alterations. Low dopaminergic system function would contribute to increased obesity, BDP, and HTG following long-term SGA treatment.

The Gender-Specific Association of DRD2 Polymorphism with Metabolic Syndrome in Patients with Schizophrenia

Background: Metabolic syndrome is widespread in patients with schizophrenia receiving long-term antipsychotic therapy. Dopamine D2 receptors play an important role in mediating both the therapeutic actions of antipsychotics and their side effects. The present study examined the association of two polymorphisms of the DRD2 gene with metabolic syndrome in patients with schizophrenia. Methods: We examined 517 patients from several regions of Siberia (Russia) with a clinical diagnosis of schizophrenia. Genotyping of two single nucleotide polymorphisms rs1799732 and rs4436578 of the dopamine D2 receptor gene (DRD2) was performed in a population of 471 patients. The results were analyzed using chi-square tests. Results: Functional polymorphism rs1799732 of the DRD2 gene is associated with drug-induced metabolic syndrome in women with schizophrenia. Conclusions: Our results show that the DRD2 gene may be involved in the pathogenesis of metabolic disorders in patients with schizophrenia. Further analysis of possible genetic markers will allow for personalized treatment with minimal side effects and optimal efficacy. This which seems relevant in light of the recent focus on improving the quality of life and ensuring a high level of social adaptation of patients with schizophrenia.

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

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

Etiology and Management of Behavioral Disorder in Adults With Intellectual and Developmental Disabilities

Intellectual disability (ID) encompasses a wide variety of disorders that can severely affect an individual’s cognitive, social, emotional, and physical development, even when identified early in life. Initially, individuals with such disorders had shorter life expectancies. However, medical advances have increased the life expectancy of individuals with ID similar to that of the general population. More attention must be paid to manage diseases affecting the intellectually disabled elderly, such as diabetes, cardiovascular disease, chronic constipation, and behavioral disorders.

Effect of 5-HT2C receptor gene polymorphism (HTR2C-759C/T) on metabolic adverse effects in Thai psychiatric patients treated with risperidone

BACKGROUND

The use of Atypical antipsychotics (AAPs) is related to metabolic disturbances, which put psychiatric patients at risk for cardiovascular morbidity and mortality. Evidence is emerging of genetic risk factors. The HTR2C gene is an essential candidate in pharmacogenetic studies of antipsychotic-induced metabolic effects. Nevertheless, there were inconsistent results among studies.

OBJECTIVE

To investigate the relationship between -759C/T, functional polymorphism of the HTR2C gene and metabolic adverse effects in Thai psychiatric patients treated with risperidone monotherapy.

METHOD

In this cross-sectional study, 108 psychiatric patients treated with risperidone monotherapy for ≥3 months were recruited. Anthropometric measurements and laboratory tests were obtained upon enrollment and history of treatment was reviewed from medical records. Weight gain was defined as an increase ≥7% of baseline weight. Metabolic syndrome was evaluated according to the 2005 International Diabetes Federation (IDF) Asia criteria. The -759C/T, polymorphism was genotyped. The associations between -759C/T polymorphism and metabolic side effects were analyzed. Multiple logistic regression was used for determining potential confounders.

RESULTS

Neither weight gain nor metabolic syndrome was significantly associated with -759C/T allelic and genotype variants of HTR2C. However, T allele of -759C/T polymorphism significantly associated with the hypertension. This association was not affected by possible confounding factors such as gender, risperidone dose, duration of treatment and family history of hypertension.

CONCLUSION

Our findings suggest that psychiatric patients with T allele of -759C/T polymorphism may be at higher risk for hypertension. Further study with prospective design with larger patient groups are needed.

The Burden of Antipsychotic-Induced Weight Gain and Metabolic Syndrome in Children

Antipsychotic medications are critical to child and adolescent psychiatry, from the stabilization of psychotic disorders like schizophrenia, bipolar disorder, and psychotic depression to behavioral treatment of autism spectrum disorder, tic disorders, and pediatric aggression. While effective, these medications carry serious risk of adverse events-most commonly, weight gain and cardiometabolic abnormalities. Negative metabolic consequences affect up to 60% of patients and present a major obstacle to long-term treatment. Since antipsychotics are often chronically prescribed beginning in childhood, cardiometabolic risk accumulates. An increased susceptibility to antipsychotic-induced weight gain (AIWG) has been repeatedly documented in children, particularly rapid weight gain. Associated cardiometabolic abnormalities include central obesity, insulin resistance, dyslipidemia, and systemic inflammation. Lifestyle interventions and medications such as metformin have been proposed to reduce risk but remain limited in efficacy. Furthermore, antipsychotic medications touted to be weight-neutral in adults can cause substantial weight gain in children. A better understanding of the biological underpinnings of AIWG could inform targeted and potentially more fruitful treatments; however, little is known about the underlying mechanism. As yet, modest genetic studies have nominated a few risk genes that explain only a small percentage of the risk. Recent investigations have begun to explore novel potential mechanisms of AIWG, including a role for gut microbiota and microbial metabolites. This article reviews the problem of AIWG and AP metabolic side effects in pediatric populations, proposed mechanisms underlying this serious side effect, and strategies to mitigate adverse impact. We suggest future directions for research efforts that may advance the field and lead to improved clinical interventions.

Association of Genetic Variants of Dopamine and Serotonin In Schizophrenia

BACKGROUND

Several studies indicated that antipsychotic treatment response and side effect manifestation can be different due to inter-individual variability in genetic variations.

AIM OF THE STUDY

Here we perform a case-control study to explore a potential association between schizophrenia and variants within the antipsychotic drug molecular targets (DRD1, DRD2, DRD3, HTR2A, HTR6) and metabolizing enzymes (CYP2D6, COMT) genes in Armenian population including also analysis of their possible relationship with disease clinical symptoms.

METHODS

A total of 18 SNPs was studied in patients with schizophrenia (n = 78) and healthy control subjects (n = 77) using MassARRAY genotyping.

RESULTS

We found that two studied genetic variants, namely DRD2 rs4436578*C and HTR2A rs6314*A are underrepresented in the group of patients compared to healthy subjects. After the correction for multiple testing, the rs4436578*C variant remained significant while the rs6314*A reported borderline significance. No significant differences in minor allele frequencies for other studied variants were identified. Also, a relationship between the genotypes and age of onset as well as disease duration has been detected.

CONCLUSIONS

The DRD2 rs4436578*C genetic variant might have protective role against schizophrenia, at least in Armenians.

Progress in Genetic Polymorphisms Related to Lipid Disturbances Induced by Atypical Antipsychotic Drugs

Metabolic side effects such as weight gain and disturbed lipid metabolism are often observed in the treatment of atypical antipsychotic drugs (AAPDs), which contribute to an excessive prevalence of metabolic syndrome among schizophrenic patients. Great individual differences are observed but the underlying mechanisms are still uncertain. Research on pharmacogenomics indicates that gene polymorphisms involved in the pathways controlling food intake and lipid metabolism may play a significant role. In this review, relevant genes (HTR2C, DRD2, LEP, NPY, MC4R, BDNF, MC4R, CNR1, INSIG2, ADRA2A) and genetic polymorphisms related to metabolic side effects of AAPDs especially dyslipidemia were summarized. Apart from clinical studies, in vitro and in vivo evidence is also analyzed to support related theories. The association of central and peripheral mechanisms is emphasized, enabling the possibility of using peripheral gene expression to predict the central status. Novel methodological development of pharmacogenomics is in urgent need, so as to provide references for individualized medication and further to shed some light on the mechanisms underlying AAPD-induced lipid disturbances.

Reverse translation of adverse event reports paves the way for de-risking preclinical off-targets

The Food and Drug Administration Adverse Event Reporting System (FAERS) remains the primary source for post-marketing pharmacovigilance. The system is largely un-curated, unstandardized, and lacks a method for linking drugs to the chemical structures of their active ingredients, increasing noise and artefactual trends. To address these problems, we mapped drugs to their ingredients and used natural language processing to classify and correlate drug events. Our analysis exposed key idiosyncrasies in FAERS, for example reports of thalidomide causing a deadly ADR when used against myeloma, a likely result of the disease itself; multiplications of the same report, unjustifiably increasing its importance; correlation of reported ADRs with public events, regulatory announcements, and with publications. Comparing the pharmacological, pharmacokinetic, and clinical ADR profiles of methylphenidate, aripiprazole, and risperidone, and of kinase drugs targeting the VEGF receptor, demonstrates how underlying molecular mechanisms can emerge from ADR co-analysis. The precautions and methods we describe may enable investigators to avoid confounding chemistry-based associations and reporting biases in FAERS, and illustrate how comparative analysis of ADRs can reveal underlying mechanisms.

DOI:http://dx.doi.org/10.7554/eLife.25818.001

New treatments are tested in clinical trials before they are licensed for use in patients, but until the drugs are available for prescribing it’s not always possible to identify every side effect. When the drugs enter the clinic, they might be prescribed to patients with multiple medical conditions, or combined with other treatments. The drugs may also be taken for longer periods of time than tested in trials. It is therefore common for new adverse reactions to emerge after a drug is in widespread use.

The FDA Adverse Event Reporting System (FAERS) is a surveillance system used in the United States for reporting drug side effects after new treatments have been licensed. Healthcare professionals and patients can submit reports to the database, logging the adverse drug reactions that they have experienced.

FAERS currently contains over 8.5 million entries, and is growing all the time. However, Maciejewski et al. show that the database has several shortcomings that are reducing its usefulness. For instance, on average any given drug will have 16 different names in the system; this makes it challenging to group all of the reported side effects so that trends and patterns can be correctly seen.

To address this first problem, Maciejewski et al. grouped together drugs according to their active ingredients, rather than their name. This made it much easier to account for subsequent, and more crucial conflating factors such as multiple reports for the same adverse event and patient, or cases where adverse reactions were confused with the diseases that the drugs are trying to treat. For example, diabetes was listed as a side effect for drugs used to treat diabetes.

Building on this cleaned-up dataset, Maciejewski et al. monitored how adverse event signals evolve over time and uncovered biases that were hard to see otherwise. For example, side-effects were reported more often when drugs were in the news. More strikingly, this bias affected not only the drug in question, but also other drugs that acted in the same way or on the same molecular target.

The computational method developed by Maciejewski et al. allows the data in FAERS to be combined and corrected, making easier to evaluate the safety of different medicines. The link between adverse side effects and the molecular targets of the drug, via the ingredient’s chemical structure, furthermore makes it possible to analyze such clinical data reliably by using chemical and genetic information. In the future, this method could also help to identify previously unknown side effects and the biological mechanisms behind them. This could help researchers to develop new drugs with improved side effect profiles.

DOI:http://dx.doi.org/10.7554/eLife.25818.002