Genetics-Based Population Pharmacokinetics and Pharmacodynamics of Risperidone in a Psychiatric Cohort.

BACKGROUND: High interindividual variability in plasma concentrations of risperidone and its active metabolite, 9-hydroxyrisperidone, may lead to suboptimal drug concentration. OBJECTIVE: Using a population pharmacokinetic approach, we aimed to characterize the genetic and non-genetic sources of variability affecting risperidone and 9-hydroxyrisperidone pharmacokinetics, and relate them to common side effects. METHODS: Overall, 150 psychiatric patients (178 observations) treated with risperidone were genotyped for common polymorphisms in NR1/2, POR, PPARα, ABCB1, CYP2D6 and CYP3A genes. Plasma risperidone and 9-hydroxyrisperidone were measured, and clinical data and common clinical chemistry parameters were collected. Drug and metabolite concentrations were analyzed using non-linear mixed effect modeling (NONMEM(®)). Correlations between trough concentrations of the active moiety (risperidone plus 9-hydroxyrisperidone) and common side effects were assessed using logistic regression and linear mixed modeling. RESULTS: The cytochrome P450 (CYP) 2D6 phenotype explained 52% of interindividual variability in risperidone pharmacokinetics. The area under the concentration-time curve (AUC) of the active moiety was found to be 28% higher in CYP2D6 poor metabolizers compared with intermediate, extensive and ultrarapid metabolizers. No other genetic markers were found to significantly affect risperidone concentrations. 9-hydroxyrisperidone elimination was decreased by 26% with doubling of age. A correlation between trough predicted concentration of the active moiety and neurologic symptoms was found (p = 0.03), suggesting that a concentration >40 ng/mL should be targeted only in cases of insufficient, or absence of, response. CONCLUSIONS: Genetic polymorphisms of CYP2D6 play an important role in risperidone, 9-hydroxyrisperidone and active moiety plasma concentration variability, which were associated with common side effects. These results highlight the importance of a personalized dosage adjustment during risperidone treatment

Prediction of antipsychotics efficacy based on a polygenic risk score: a real-world cohort study.

Background: Response to antipsychotics is subject to a wide interindividual variability, due to genetic and non-genetic factors. Several single nucleotide polymorphisms (SNPs) have been associated with response to antipsychotics in genome-wide association studies (GWAS). Polygenic risk scores (PRS) are a powerful tool to aggregate into a single measure the small effects of multiple risk alleles. Materials and methods: We studied the association between a PRS composed of SNPs associated with response to antipsychotics in GWAS studies (PRS <sub>response</sub> ) in a real-world sample of patients (N = 460) with different diagnoses (schizophrenia spectrum, bipolar, depressive, neurocognitive, substance use disorders and miscellaneous). Two other PRSs composed of SNPs previously associated with risk of schizophrenia (PRS <sub>schizophrenia1</sub> and PRS <sub>schizophrenia2</sub> ) were also tested for their association with response to treatment. Results: PRS <sub>response</sub> was significantly associated with response to antipsychotics considering the whole cohort (OR = 1.14, CI = 1.03-1.26, p = 0.010), the subgroup of patients with schizophrenia, schizoaffective disorder or bipolar disorder (OR = 1.18, CI = 1.02-1.37, p = 0.022, N = 235), with schizophrenia or schizoaffective disorder (OR = 1.24, CI = 1.04-1.47, p = 0.01, N = 176) and with schizophrenia (OR = 1.27, CI = 1.04-1.55, p = 0.01, N = 149). Sensitivity and specificity were sub-optimal (schizophrenia 62%, 61%; schizophrenia spectrum 56%, 55%; schizophrenia spectrum plus bipolar disorder 60%, 56%; all patients 63%, 58%, respectively). PRS <sub>schizophrenia1</sub> and PRS <sub>schizophrenia2</sub> were not significantly associated with response to treatment. Conclusion: PRS <sub>response</sub> defined from GWAS studies is significantly associated with response to antipsychotics in a real-world cohort; however, the results of the sensitivity-specificity analysis preclude its use as a predictive tool in clinical practice

Seasonal influences on first-episode admission in affective and non-affective psychosis

Background: Since bipolar affective disorder has been recorded, clinicians treating patients with this disorder have noted the cyclic nature of episodes, particularly an increase in mania in the spring and summer months and depression during winter. Objective: The aim of this study was to investigate seasonality in symptom onset and service admissions over a period of 10 years in a group of patients (n= 359) with first-episode (FE) mania (n= 133), FE schizoaffective disorder (n= 49) and FE schizophrenia (n= 177). Method: Patients were recruited if they were between 15 and 28 years of age and if they resided in the geographical mental health service catchment area. The number of patients experiencing symptom onset and service admission over each month and season was recorded. Results: In terms of seasonality of time of service admission, the results indicate a high overall seasonality (particularly in men), which was observed in both the schizoaffective and the bipolar groups. In terms of seasonality of symptom onset, the results indicate that seasonality remains in the male bipolar group, but other groups have no seasonal trend. Conclusions: This provides further evidence that systems mediating the entrainment of biological rhythms to the environment may be more pronounced in BPAD than in schizoaffective disorder and schizophrenia. These results may help facilitate the preparedness of mental heath services for patients at different times of the yea

Matrix effects on the optical response of silver nanoclusters

We report absorption spectra for Ag7, Ag9, and Ag11 in an argon matrix grown at a temperature of 28 K and compare them with previous spectra of the same species measured in matrices of argon grown at lower temperatures as well as in neon matrices. We discuss the discrepancies in the light of the matrix crystallinity and show that this leads to an understanding of the influence of the matrix on the optical response of small clusters

Association of genetic risk scores with body mass index in Swiss psychiatric cohorts.

OBJECTIVE: Weight gain is associated with psychiatric disorders and/or with psychotropic drug treatments. We analyzed in three psychiatric cohorts under psychotropic treatment the association of weighted genetic risk scores (w-GRSs) with BMI by integrating BMI-related polymorphisms from the candidate-gene approach and Genome-Wide Association Studies (GWAS). MATERIALS AND METHODS: w-GRS of 32 polymorphisms associated previously with BMI in general population GWAS and 20 polymorphisms associated with antipsychotics-induced weight gain were investigated in three independent psychiatric samples. RESULTS: w-GRS of 32 polymorphisms were significantly associated with BMI in the psychiatric sample 1 (n=425) and were replicated in another sample (n=177). Those at the percentile 95 (p95) of the score had 2.26 and 2.99 kg/m higher predicted BMI compared with individuals at the percentile 5 (p5) in sample 1 and in sample 3 (P=0.009 and 0.04, respectively). When combining all samples together (n=750), a significant difference of 1.89 kg/m predicted BMI was found between p95 and p5 individuals at 12 months of treatment. Stronger associations were found among men (difference: 2.91 kg/m of predicted BMI between p95 and p5, P=0.0002), whereas no association was found among women. w-GRS of 20 polymorphisms was not associated with BMI. The w-GRS of 52 polymorphisms and the clinical variables (age, sex, treatment) explained 1.99 and 3.15%, respectively, of BMI variability. CONCLUSION: The present study replicated in psychiatric cohorts previously identified BMI risk variants obtained in GWAS analyses from population-based samples. Sex-specific analysis should be considered in further analysis

Sensorimotor Induction of Auditory Misattribution in Early Psychosis.

Dysfunction of sensorimotor predictive processing is thought to underlie abnormalities in self-monitoring producing passivity symptoms in psychosis. Experimentally induced sensorimotor conflict can produce a failure in bodily self-monitoring (presence hallucination [PH]), yet it is unclear how this is related to auditory self-monitoring and psychosis symptoms. Here we show that the induction of sensorimotor conflict in early psychosis patients induces PH and impacts auditory-verbal self-monitoring. Participants manipulated a haptic robotic system inducing a bodily sensorimotor conflict. In experiment 1, the PH was measured. In experiment 2, an auditory-verbal self-monitoring task was performed during the conflict. Fifty-one participants (31 early psychosis patients, 20 matched controls) participated in the experiments. The PH was present in all participants. Psychosis patients with passivity experiences (PE+) had reduced accuracy in auditory-verbal self-other discrimination during sensorimotor stimulation, but only when sensorimotor stimulation involved a spatiotemporal conflict (F(2, 44) = 6.68, P = .002). These results show a strong link between robotically controlled alterations in sensorimotor processing and auditory misattribution in psychosis and provide evidence for the role of sensorimotor processes in altered self-monitoring in psychosis

Prediction of early weight gain during psychotropic treatment using a combinatorial model with clinical and genetic markers.

Psychotropic drugs can induce significant (>5%) weight gain (WG) already after 1 month of treatment, which is a good predictor for major WG at 3 and 12 months. The large interindividual variability of drug-induced WG can be explained in part by genetic and clinical factors. The aim of this study was to determine whether extensive analysis of genes, in addition to clinical factors, can improve prediction of patients at risk for more than 5% WG at 1 month of treatment. Data were obtained from a 1-year naturalistic longitudinal study, with weight monitoring during weight-inducing psychotropic treatment. A total of 248 Caucasian psychiatric patients, with at least baseline and 1-month weight measures, and with compliance ascertained were included. Results were tested for replication in a second cohort including 32 patients. Age and baseline BMI were associated significantly with strong WG. The area under the curve (AUC) of the final model including genetic (18 genes) and clinical variables was significantly greater than that of the model including clinical variables only (AUCfinal: 0.92, AUCclinical: 0.75, P<0.0001). Predicted accuracy increased by 17% with genetic markers (Accuracyfinal: 87%), indicating that six patients must be genotyped to avoid one misclassified patient. The validity of the final model was confirmed in a replication cohort. Patients predicted before treatment as having more than 5% WG after 1 month of treatment had 4.4% more WG over 1 year than patients predicted to have up to 5% WG (P≤0.0001). These results may help to implement genetic testing before starting psychotropic drug treatment to identify patients at risk of important WG

Effect of Quetiapine, from Low to High Dose, on Weight and Metabolic Traits: Results from a Prospective Cohort Study.

The atypical antipsychotic quetiapine is known to induce weight gain and other metabolic complications. The underlying mechanisms are multifactorial and poorly understood with almost no information on the effect of dosage. Concerns were thus raised with the rise in low-dose quetiapine off-label prescription (i. e.,<150 mg/day). In this study, we evaluated the influence of quetiapine dose for 474 patients included in PsyMetab and PsyClin studies on weight and metabolic parameter evolution. Weight, blood pressure, lipid, and glucose profiles were evaluated during a follow-up period of 3 months after treatment initiation. Significant dose-dependent metabolic alterations were observed. The daily dose was found to influence weight gain and increase the risk of undergoing clinically relevant weight gain (≥7% from baseline). It was also associated with a change in plasma levels of cholesterol (total cholesterol, LDL cholesterol, and HDL cholesterol) as well as with increased odds of developing hypertriglyceridemia, as well as total and LDL hypercholesterolemia. No impact of a dose increase on blood pressure and plasma glucose level was observed. The dose-dependent effect highlighted for weight gain and lipid alterations emphasizes the importance of prescribing the minimal effective dose. However, as the effect size of a dose increase on metabolic worsening is low, the potential harm of low-dose quetiapine should not be dismissed. Prescriptions must be carefully evaluated and regularly questioned in light of side effect onset

Second-Generation Antipsychotics in Adolescent Psychiatric Patients: Metabolic Effects and Impact of an Early Weight Change to Predict Longer Term Weight Gain.

To examine the metabolic profile of adolescents at baseline and to determine the potential predictive power of a 1-month weight gain (WG) on weight changes during longer term second-generation antipsychotic (SGA) treatment. A retrospective chart review study, including patients between 13 and 18 years of age and treated with SGA, was conducted. Available data at baseline, 1, 3, and 12 months of treatment were recorded. Four hundred fifty-six patients were included, with a median age of 15 years. Ten percent of the patients were obese (>95th percentile) and abdominal obesity (>90th percentile) was observed in 12% of patients. In a subgroup of 42 patients with both baseline, 1, and 3-month weight data available, WG >4.5% after 1 month was found to be the best predictor (sensitivity: 100; specificity: 66; area under the curve: 83) for a WG >15% after 3 months. After adjusting for potential confounders, a threshold of WG >4% was found as being the best predictor. A worrisome prevalence of metabolic disorders was observed in an adolescent psychiatric cohort. In such patients, a WG >4% during the first month of treatment should raise concerns about weight controlling strategies. Further research is needed to confirm the present results and to determine the impact of a 1-month WG on a 1-year weight change