A population pharmacokinetic model for sertraline in women during the perinatal period-A contribution from the ConcePTION project.

AIMS: Sertraline is frequently prescribed for mental health conditions in both pregnant and breastfeeding women. According to the limited available data, only small amounts of sertraline are transferred into human milk, yet with a large amount of unexplained interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model to describe the pharmacokinetics of sertraline during the perinatal period and explain interindividual variability. METHODS: Pregnant women treated with sertraline were enrolled in the multicenter prospective cohort SSRI-Breast Milk study. A popPK model for sertraline maternal plasma and breast milk concentrations was developed and allowed estimating the milk-to-plasma ratio (MPR). An additional fetal compartment allowed cord blood concentrations to be described. Several covariates were tested for significance. Ultimately, model-based simulations allowed infant drug exposure through placenta and breast milk under various conditions to be predicted. RESULTS: Thirty-eight women treated with sertraline were included in the study and provided 89 maternal plasma, 29 cord blood and 107 breast milk samples. Sertraline clearance was reduced by 42% in CYP2C19 poor metabolizers compared to other phenotypes. Doubling milk fat content increased the MPR by 95%. Simulations suggested a median daily infant dosage of 6.9 µg kg-1 after a 50 mg maternal daily dose, representing 0.95% of the weight-adjusted maternal dose. Median cord blood concentrations could range from 3.29 to 33.23 ng mL-1 after maternal daily doses between 25 and 150 mg. CONCLUSIONS: Infant exposure to sertraline, influenced by CYP2C19 phenotype and breast milk fat content, remains low, providing reassurance regarding the use of sertraline during pregnancy and breastfeeding.

Optimisation of pharmacotherapy in psychiatry through therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests: Focus on antipsychotics.

BACKGROUND: For psychotic disorders (i.e. schizophrenia), pharmacotherapy plays a key role in controlling acute and long-term symptoms. To find the optimal individual dose and dosage strategy, specialised tools are used. Three tools have been proven useful to personalise drug treatments: therapeutic drug monitoring (TDM) of drug levels, pharmacogenetic testing (PG), and molecular neuroimaging. METHODS: In these Guidelines, we provide an in-depth review of pharmacokinetics, pharmacodynamics, and pharmacogenetics for 45 antipsychotics. Over 30 international experts in psychiatry selected studies that have measured drug concentrations in the blood (TDM), gene polymorphisms of enzymes involved in drug metabolism, or receptor/transporter occupancies in the brain (positron emission tomography (PET)). RESULTS: Study results strongly support the use of TDM and the cytochrome P450 (CYP) genotyping and/or phenotyping to guide drug therapies. Evidence-based target ranges are available for titrating drug doses that are often supported by PET findings. CONCLUSION: All three tools discussed in these Guidelines are essential for drug treatment. TDM goes well beyond typical indications such as unclear compliance and polypharmacy. Despite its enormous potential to optimise treatment effects, minimise side effects and ultimately reduce the global burden of diseases, personalised drug treatment has not yet become the standard of care in psychiatry.

Therapeutic Drug Monitoring of Olanzapine: Effects of Clinical Factors on Plasma Concentrations in Psychiatric Patients.

BACKGROUND: Therapeutic drug monitoring (TDM) is strongly recommended for olanzapine due to its high pharmacokinetic variability. This study aimed to investigate the impact of various clinical factors on olanzapine plasma concentrations in patients with psychiatric disorders. METHODS: The study used TDM data from the PsyMetab cohort, including 547 daily dose-normalized, steady-state, olanzapine plasma concentrations (C:D ratios) from 248 patients. Both intrinsic factors (eg, sex, age, body weight) and extrinsic factors (eg, smoking status, comedications, hospitalization) were examined. Univariate and multivariable, linear, mixed-effects models were employed, with a stepwise selection procedure based on Akaike information criterion to identify the relevant covariates. RESULTS: In the multivariable model (based on 440 observations with a complete data set), several significant findings emerged. Olanzapine C:D ratios were significantly lower in smokers (ß = -0.65, P < 0.001), valproate users (ß = -0.53, P = 0.002), and inpatients (ß = -0.20, P = 0.025). Furthermore, the C:D ratios decreased significantly as the time since the last dose increased (ß = -0.040, P < 0.001). The male sex had a significant main effect on olanzapine C:D ratios (ß = -2.80, P < 0.001), with significant interactions with age (ß = 0.025, P < 0.001) and body weight (ß = 0.017, P = 0.011). The selected covariates explained 30.3% of the variation in C:D ratios, with smoking status accounting for 7.7% and sex contributing 6.9%. The overall variation explained by both the fixed and random parts of the model was 67.4%. The model facilitated the prediction of olanzapine C:D ratios based on sex, age, and body weight. CONCLUSIONS: The clinical factors examined in this study, including sex, age, body weight, smoking status, and valproate comedication, remarkably influence olanzapine C:D ratios. Considering these factors, in addition to TDM and the clinical situation, could be important for dose adjustment.

Self-reported caffeine consumption miss-matched consumption measured by plasma levels of caffeine and its metabolites: results from two population-based studies.

IMPORTANCE AND OBJECTIVE: Self-reported caffeine consumption has been widely used in research while it may be subject to bias. We sought to investigate the associations between self-reported caffeine consumption and plasma levels of caffeine and its two main metabolites (paraxanthine and theophylline) in the community. METHODS: Data from two population-based studies (SKIPOGH1 and 2 (N = 1246) and CoLaus|PsyCoLaus (N = 4461)) conducted in Switzerland were used. Self-reported caffeine consumption was assessed using questionnaires. Plasma levels of caffeine and its metabolites were quantified by ultra-high performance liquid chromatography coupled to a tandem quadrupole mass spectrometer. RESULTS: In both studies, mean log plasma levels of caffeine and its two metabolites were over 6.48 (plasma levels = 652 ng/ml) when no caffeine consumption was reported. Subsequently, nonlinear associations between log plasma levels and self-reported caffeine consumption were observed in SKIPOGH, with a change of the slope at 3-5 cups of espresso per day in SKIPOGH1 but not SKIPOGH2. In CoLaus|PsyCoLaus, increased daily consumption of caffeinated beverages was associated with increased log plasma levels with a change of the slope at 3 cups. In both studies, declared caffeine consumption higher than 3-5 cups per day was not associated with higher plasma levels of caffeine and its metabolites. CONCLUSION: Self-reports of no or low caffeine consumption and consumption of more than 3-5 cups of coffee should be interpreted with caution, with possible under- or over-estimation. Quantifying plasma levels of caffeine and its metabolites may contribute to a better estimation of caffeine intake.

Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis.

Patients suffering from mental disorders are at high risk of developing cardiovascular diseases, leading to a reduction in life expectancy. Genetic variants can display greater influence on cardiometabolic features in psychiatric cohorts compared to the general population. The difference is possibly due to an intricate interaction between the mental disorder or the medications used to treat it and metabolic regulations. Previous genome wide association studies (GWAS) on antipsychotic-induced weight gain included a low number of participants and/or were restricted to patients taking one specific antipsychotic. We conducted a GWAS of the evolution of body mass index (BMI) during early (i.e., ≤ 6) months of treatment with psychotropic medications inducing metabolic disturbances (i.e., antipsychotics, mood stabilizers and some antidepressants) in 1135 patients from the PsyMetab cohort. Six highly correlated BMI phenotypes (i.e., BMI change and BMI slope after distinct durations of psychotropic treatment) were considered in the analyses. Our results showed that four novel loci were associated with altered BMI upon treatment at genome-wide significance (p < 5 × 10-8): rs7736552 (near MAN2A1), rs11074029 (in SLCO3A1), rs117496040 (near DEFB1) and rs7647863 (in IQSEC1). Associations between the four loci and alternative BMI-change phenotypes showed consistent effects. Replication analyses in 1622 UK Biobank participants under psychotropic treatment showed a consistent association between rs7736552 and BMI slope (p = 0.017). These findings provide new insights into metabolic side effects induced by psychotropic drugs and underline the need for future studies to replicate these associations in larger cohorts.

The psychosis metabolic risk calculator (PsyMetRiC) for young people with psychosis: International external validation and site-specific recalibration in two independent European samples.

Background: Cardiometabolic dysfunction is common in young people with psychosis. Recently, the Psychosis Metabolic Risk Calculator (PsyMetRiC) was developed and externally validated in the UK, predicting up-to six-year risk of metabolic syndrome (MetS) from routinely collected data. The full-model includes age, sex, ethnicity, body-mass index, smoking status, prescription of metabolically-active antipsychotic medication, high-density lipoprotein, and triglyceride concentrations; the partial-model excludes biochemical predictors. Methods: To move toward a future internationally-useful tool, we externally validated PsyMetRiC in two independent European samples. We used data from the PsyMetab (Lausanne, Switzerland) and PAFIP (Cantabria, Spain) cohorts, including participants aged 16-35y without MetS at baseline who had 1-6y follow-up. Predictive performance was assessed primarily via discrimination (C-statistic), calibration (calibration plots), and decision curve analysis. Site-specific recalibration was considered. Findings: We included 1024 participants (PsyMetab n=558, male=62%, outcome prevalence=19%, mean follow-up=2.48y; PAFIP n=466, male=65%, outcome prevalence=14%, mean follow-up=2.59y). Discrimination was better in the full- compared with partial-model (PsyMetab=full-model C=0.73, 95% C.I., 0.68-0.79, partial-model C=0.68, 95% C.I., 0.62-0.74; PAFIP=full-model C=0.72, 95% C.I., 0.66-0.78; partial-model C=0.66, 95% C.I., 0.60-0.71). As expected, calibration plots revealed varying degrees of miscalibration, which recovered following site-specific recalibration. PsyMetRiC showed net benefit in both new cohorts, more so after recalibration. Interpretation: The study provides evidence of PsyMetRiC's generalizability in Western Europe, although further local and international validation studies are required. In future, PsyMetRiC could help clinicians internationally to identify young people with psychosis who are at higher cardiometabolic risk, so interventions can be directed effectively to reduce long-term morbidity and mortality. Funding: NIHR Cambridge Biomedical Research Centre (BRC-1215-20014); The Wellcome Trust (201486/Z/16/Z); Swiss National Research Foundation (320030-120686, 324730- 144064, and 320030-173211); The Carlos III Health Institute (CM20/00015, FIS00/3095, PI020499, PI050427, and PI060507); IDIVAL (INT/A21/10 and INT/A20/04); The Andalusian Regional Government (A1-0055-2020 and A1-0005-2021); SENY Fundacion Research (2005-0308007); Fundacion Marques de Valdecilla (A/02/07, API07/011); Ministry of Economy and Competitiveness and the European Fund for Regional Development (SAF2016-76046-R and SAF2013-46292-R).For the Spanish and French translation of the abstract see Supplementary Materials section.

Case Report: Opioid Use Disorder Associated With Low/Moderate Dose of Loperamide in an Intellectual Disability Patient With CYP3A and P-Glycoprotein Reduced Activity.

Loperamide is an over-the-counter antidiarrheal for which increasing cases of abuse or misuse are described. We report the onset of opioid use disorder associated with low to moderate doses of loperamide in an intellectual disability patient without previous history of substance use disorder (SUD). Our patient presented strongly reduced activities of CYP3A and P-glycoprotein, which are mainly involved in loperamide metabolism and transport. We suggest that this led to an increase in bioavailability, systemic exposure, and brain penetration thus allowing loperamide to act on the central nervous system and contributing to the development of SUD. Slow release oral morphine (SROM) was chosen as opioid agonist treatment, which successfully contained loperamide use and globally improved her clinical condition. This situation highlights the need for caution and awareness when prescribing loperamide, particularly in vulnerable patients with few cognitive resources to understand the risks of self-medication and little insight into its effects.

Insomnia disorders are associated with increased cardiometabolic disturbances and death risks from cardiovascular diseases in psychiatric patients treated with weight-gain-inducing psychotropic drugs: results from a Swiss cohort.

STUDY OBJECTIVES: Insomnia disorders as well as cardiometabolic disorders are highly prevalent in the psychiatric population compared to the general population. We aimed to investigate their association and evolution over time in a Swiss psychiatric cohort. METHODS: Data for 2861 patients (8954 observations) were obtained from two prospective cohorts (PsyMetab and PsyClin) with metabolic parameters monitored routinely during psychotropic treatment. Insomnia disorders were based on the presence of ICD-10 "F51.0" diagnosis (non-organic insomnia), the prescription of sedatives before bedtime or the discharge letter. Metabolic syndrome was defined using the International Diabetes Federation definition, while the 10-year risk of cardiovascular event or death was assessed using the Framingham Risk Score and the Systematic Coronary Risk Estimation, respectively. RESULTS: Insomnia disorders were observed in 30% of the cohort, who were older, predominantly female, used more psychotropic drugs carrying risk of high weight gain (olanzapine, clozapine, valproate) and were more prone to suffer from schizoaffective or bipolar disorders. Multivariate analyses showed that patients with high body mass index (OR = 2.02, 95%CI [1.51-2.72] for each ten-kg/m2 increase), central obesity (OR = 2.20, [1.63-2.96]), hypertension (OR = 1.86, [1.23-2.81]), hyperglycemia (OR = 3.70, [2.16-6.33]), high density lipoprotein hypocholesterolemia in women (OR = 1.51, [1.17-1.95]), metabolic syndrome (OR = 1.84, [1.16-2.92]) and higher 10-year risk of death from cardiovascular diseases (OR = 1.34, [1.17-1.53]) were more likely to have insomnia disorders. Time and insomnia disorders were associated with a deterioration of cardiometabolic parameters. CONCLUSIONS: Insomnia disorders are significantly associated with metabolic worsening and risk of death from cardiovascular diseases in psychiatric patients.

Olanzapine-associated dose-dependent alterations for weight and metabolic parameters in a prospective cohort.

Metabolic abnormalities have been associated with olanzapine treatment. We assessed if olanzapine has dose-dependent effects on metabolic parameters with changes for weight, blood pressure, lipid and glucose profiles being modelled using linear mixed-effects models. The risk of metabolic abnormalities including early weight gain (EWG) (≥5% during first month) was assessed using mixed-effects logistic regression models. In 392 olanzapine-treated patients (median age 38.0 years, interquartile range [IQR] = 26.0-53.3, median dose 10.0 mg/day, IQR = 5.0-10.0 for a median follow-up duration of 40.0 days, IQR = 20.7-112.2), weight gain was not associated with olanzapine dose (p = 0.61) although it was larger for doses versus ≤10 mg/day (2.54 ± 5.55 vs. 1.61 ± 4.51% respectively, p = 0.01). Treatment duration and co-prescription of >2 antipsychotics, antidepressants, benzodiazepines and/or antihypertensive agents were associated with larger weight gain (p < 0.05). Lower doses were associated with increase in total and HDL cholesterol and systolic and diastolic blood pressure (p < 0.05), whereas higher doses were associated with glucose increases (p = 0.01). Patients receiving >10 mg/day were at higher EWG risk (odds risk: 2.15, 1.57-2.97). EWG might be prominent in high-dose olanzapine-treated patients with treatment duration and co-prescription of other medications being weight gain moderators. The lack of major dose-dependent patterns for weight gain emphasizes that olanzapine-treated patients are at weight gain risk regardless of the dose.

Associations Between High Plasma Methylxanthine Levels, Sleep Disorders and Polygenic Risk Scores of Caffeine Consumption or Sleep Duration in a Swiss Psychiatric Cohort.

Objective: We first sought to examine the relationship between plasma levels of methylxanthines (caffeine and its metabolites) and sleep disorders, and secondarily between polygenic risk scores (PRS) of caffeine consumption or sleep duration with methylxanthine plasma levels and/or sleep disorders in a psychiatric cohort. Methods: Plasma levels of methylxanthines were quantified by ultra-high performance liquid chromatography/tandem mass spectrometry. In inpatients, sleep disorder diagnosis was defined using ICD-10 "F51.0," sedative drug intake before bedtime, or hospital discharge letters, while a subgroup of sedative drugs was used for outpatients. The PRS of coffee consumption and sleep duration were constructed using publicly available GWAS results from the UKBiobank. Results: 1,747 observations (1,060 patients) were included (50.3% of observations with sleep disorders). Multivariate analyses adjusted for age, sex, body mass index, setting of care and psychiatric diagnoses showed that patients in the highest decile of plasma levels of methylxanthines had more than double the risk for sleep disorders compared to the lowest decile (OR = 2.13, p = 0.004). PRS of caffeine consumption was associated with plasma levels of caffeine, paraxanthine, theophylline and with their sum (ß = 0.1; 0.11; 0.09; and 0.1, pcorrected = 0.01; 0.02; 0.02; and 0.01, respectively) but not with sleep disorders. A trend was found between the PRS of sleep duration and paraxanthine levels (ß = 0.13, pcorrected = 0.09). Discussion: Very high caffeine consumption is associated with sleep disorders in psychiatric in- and outpatients. Future prospective studies should aim to determine the benefit of reducing caffeine consumption in high caffeine-consuming patients suffering from sleep disorders.

Sorafenib N-Oxide Is an Inhibitor of Human Hepatic CYP3A4.

The multi-kinase inhibitor sorafenib (SOR) is clinically important in the treatment of hepatocellular and renal cancers and undergoes CYP3A4-dependent oxidation in liver to the pharmacologically active N-oxide metabolite (SNO). There have been reports that kinase inhibitors such as SOR may precipitate pharmacokinetic interactions with coadministered drugs that compete for CYP3A4-mediated biotransformation, but these occur non-uniformly in patients. Clinical evidence also indicates that SNO accumulates in serum of some patients during prolonged SOR therapy. In this study undertaken in hepatic microsomes from individual donors, we assessed the possibility that SNO might contribute to pharmacokinetic interactions mediated by SOR. Enzyme kinetics of CYP3A4-mediated midazolam 1'-hydroxylation in individual human hepatic microsomes were analyzed by non-linear regression and appropriate replots. Thus, SNO and SOR were linear-mixed inhibitors of microsomal CYP3A4 activity (Kis 15 ± 4 and 33 ± 14 µM, respectively). To assess these findings, further molecular docking studies of SOR and SNO with the 1TQN crystal structure of CYP3A4 were undertaken. SNO elicited a larger number of interactions with key amino acid residues located in substrate recognition sequences of the enzyme. In the optimal docking pose, the N-oxide moiety of SNO was also found to interact directly with the heme moiety of CYP3A4. These findings suggest that SNO could contribute to pharmacokinetic interactions involving SOR, perhaps in individuals who produce high circulating concentrations of the metabolite.

Genetic immune and inflammatory markers associated with diabetes in solid organ transplant recipients.

New-onset diabetes mellitus after transplantation (NODAT) is a complication following solid organ transplantation (SOT) and may be related to immune or inflammatory responses. We investigated whether single nucleotide polymorphisms (SNPs) within 158 immune- or inflammation-related genes contribute to NODAT in SOT recipients. The association between 263 SNPs and NODAT was investigated in a discovery sample of SOT recipients from the Swiss Transplant Cohort Study (STCS, n1  = 696). Positive results were tested in a first STCS replication sample (n2  = 489) and SNPs remaining significant after multiple test corrections were tested in a second SOT replication sample (n3  = 156). Associations with diabetic traits were further tested in several large general population-based samples (n > 480 000). Only SP110 rs2114592C>T remained associated with NODAT in the STCS replication sample. Carriers of rs2114592-TT had 9.9 times (95% confidence interval [CI]: 3.22-30.5, P = .00006) higher risk for NODAT in the combined STCS samples (n = 1184). rs2114592C>T was further associated with NODAT in the second SOT sample (odds ratio: 4.8, 95% CI: 1.55-14.6, P = .006). On the other hand, SP110 rs2114592C>T was not associated with diabetic traits in population-based samples, suggesting a specific gene-environment interaction, possibly due to the use of specific medications (ie, immunosuppressants) in transplant patients and/or to the illness that may unmask the gene effect.

Genetic and clinic predictors of new onset diabetes mellitus after transplantation.

New Onset Diabetes after Transplantation (NODAT) is a frequent complication after solid organ transplantation, with higher incidence during the first year. Several clinical and genetic factors have been described as risk factors of Type 2 Diabetes (T2DM). Additionally, T2DM shares some genetic factors with NODAT. We investigated if three genetic risk scores (w-GRS) and clinical factors were associated with NODAT and how they predicted NODAT development 1 year after transplantation. In both main (n = 725) and replication (n = 156) samples the clinical risk score was significantly associated with NODAT (ORmain: 1.60 [1.36-1.90], p = 3.72*10-8 and ORreplication: 2.14 [1.39-3.41], p = 0.0008, respectively). Two w-GRS were significantly associated with NODAT in the main sample (ORw-GRS 2:1.09 [1.04-1.15], p = 0.001 and ORw-GRS 3:1.14 [1.01-1.29], p = 0.03) and a similar ORw-GRS 2 was found in the replication sample, although it did not reach significance probably due to a power issue. Despite the low OR of w-GRS on NODAT compared to clinical covariates, when integrating w-GRS 2 and w-GRS 3 in the clinical model, the Area under the Receiver Operating Characteristics curve (AUROC), specificity, sensitivity and accuracy were 0.69, 0.71, 0.58 and 0.68, respectively, with significant Likelihood Ratio test discrimination index (p-value 0.0004), performing better in NODAT discrimination than the clinical model alone. Twenty-five patients needed to be genotyped in order to detect one misclassified case that would have developed NODAT 1 year after transplantation if using only clinical covariates. To our knowledge, this is the first study extensively examining genetic risk scores contributing to NODAT development.

Association Between Plasma Caffeine and Other Methylxanthines and Metabolic Parameters in a Psychiatric Population Treated With Psychotropic Drugs Inducing Metabolic Disturbances.

Importance: Multiple studies conducted in the general population identified an association between self-reported coffee consumption and plasma lipid levels. To date, no study assessed whether and which plasma methylxanthines (caffeine and/or its metabolites, i.e., paraxanthine, theophylline, and theobromine) are associated with plasma lipids. In psychiatric patients, an important coffee consumption is often reported and many psychotropic drugs can induce a rapid and substantial increase of plasma lipid levels. Objective: To determine whether plasma methylxanthines are associated with metabolic parameters in psychiatric patients receiving treatments known to induce metabolic disturbances. Design, Setting, and Participants: Data were obtained from a prospective study including 630 patients with metabolic parameters [i.e., body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), non-high-density lipoprotein cholesterol (non-HDL-C), and fasting triglycerides (TG)] monitored routinely during psychotropic treatment. Exposures: Plasma methylxanthines levels. Main Outcomes and Measures: Metabolic variables including BMI and plasma lipid levels. Results: Multivariate analyses indicated that BMI, TC, HDL-C, and non-HDL-C increased significantly with increasing total methylxanthines (p corrected ≤ 0.05). In addition, compared to patients with plasma caffeine concentration in the lowest quartile, those with caffeine concentration in the highest quartile were twice more prone to suffer from non-HDL hypercholesterolemia (p corrected = 0.05), five times more likely to suffer from hypertriglyceridemia (p corrected = 0.01) and four times more susceptible to be overweight (p corrected = 0.01). Conclusions and Relevance: This study showed that plasma caffeine and other methylxanthines are associated with worsening of metabolic parameters in patients receiving psychotropic treatments known to induce metabolic disturbances. It emphasizes that important caffeine consumption could be considered as an additional environmental risk factor for metabolic worsening in patients receiving such treatments.

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

BACKGROUND: 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. AIM: 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. METHODS: 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. RESULTS: 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). CONCLUSION: These results may help to implement genetic testing before starting psychotropic drug treatment to identify patients at risk of important WG.

Weighted Genetic Risk Scores and Prediction of Weight Gain in Solid Organ Transplant Populations.

BACKGROUND: Polygenic obesity in Solid Organ Transplant (SOT) populations is considered a risk factor for the development of metabolic abnormalities and graft survival. Few studies to date have studied the genetics of weight gain in SOT recipients. We aimed to determine whether weighted genetic risk scores (w-GRS) integrating genetic polymorphisms from GWAS studies (SNP group#1 and SNP group#2) and from Candidate Gene studies (SNP group#3) influence BMI in SOT populations and if they predict ≥10% weight gain (WG) one year after transplantation. To do so, two samples (nA = 995, nB = 156) were obtained from naturalistic studies and three w-GRS were constructed and tested for association with BMI over time. Prediction of 10% WG at one year after transplantation was assessed with models containing genetic and clinical factors. RESULTS: w-GRS were associated with BMI in sample A and B combined (BMI increased by 0.14 and 0.11 units per additional risk allele in SNP group#1 and #2, respectively, p-values<0.008). w-GRS of SNP group#3 showed an effect of 0.01 kg/m2 per additional risk allele when combining sample A and B (p-value 0.04). Models with genetic factors performed better than models without in predicting 10% WG at one year after transplantation. CONCLUSIONS: This is the first study in SOT evaluating extensively the association of w-GRS with BMI and the influence of clinical and genetic factors on 10% of WG one year after transplantation, showing the importance of integrating genetic factors in the final model. Genetics of obesity among SOT recipients remains an important issue and can contribute to treatment personalization and prediction of WG after transplantation.

Population Genetic-Based Pharmacokinetic Modeling of Methadone and its Relationship with the QTc Interval in Opioid-Dependent Patients.

BACKGROUND AND OBJECTIVES: Methadone is a µ-opioid agonist widely used for the treatment of pain, and for detoxification or maintenance treatment in opioid addiction. It has been shown to exhibit large pharmacokinetic variability and concentration-QTc relationships. In this study we investigated the relative influence of genetic polymorphism and other variables on the dose concentration-QTc relationship. PATIENTS AND METHODS: A population model for methadone enantiomers in 251 opioid-dependent patients was developed using non-linear mixed effect modeling (NONMEM®). Various models were tested to characterize the pharmacokinetics of (R)- and (S)-methadone and the pharmacokinetic-pharmacodynamic relationship, while including demographics, physiological conditions, co-medications, and genetic variants as covariates. Model-based simulations were performed to assess the relative increase in QTc with dose upon stratification according to genetic polymorphisms involved in methadone disposition. RESULTS: A two-compartment model with first-order absorption and lag time provided the best model fit for (R)- and (S)-methadone pharmacokinetics. (S)-methadone clearance was influenced by cytochrome P450 (CYP) 2B6 activity, ABCB1 3435C>T, and α-1 acid glycoprotein level, while (R)-methadone clearance was influenced by CYP2B6 activity, POR*28, and CYP3A4*22. A linear model described the methadone concentration-QTc relationship, with a mean QTc increase of 9.9 ms and 19.2 ms per 1000 ng/ml of (R)- and (S)-methadone, respectively. Simulations with different methadone doses up to 240 mg/day showed that <8 % of patients presented with a QTc interval above 450 ms; however, this might reach 12 to 18 % for (R)- and (S)-methadone, respectively, in patients with a genetic status associated with a decreased methadone elimination at doses exceeding 240 mg/day. CONCLUSION: Risk factor assessment, electrocardiogram monitoring, and therapeutic drug monitoring are beneficial to optimize treatment in methadone patients, especially for those who have low levels despite high methadone doses, or who are at risk of overdosing.

Variability of response to methadone: genome-wide DNA methylation analysis in two independent cohorts.

AIM: Methadone maintenance treatment is characterized by large interindividual dose variability. The aim of this study was to evaluate whether DNA methylations are associated with daily dose of methadone. MATERIALS & METHODS: Subjects stabilized at high (n = 12) or low (n = 12) methadone doses were selected from two independent cohorts (French and Swiss). DNA methylation patterns were analyzed using HumanMethylation450 BeadChips. RESULTS: In total, 584 differentially methylated sites were identified in the French cohort corresponding to 352 genes. Of these, 26 were replicated in the Swiss cohort. The methylation status of 13 genes varied similarly in both cohorts and calcium signaling pathway was significantly enriched. CONCLUSION: Our results indicate that differentially methylated sites are associated with methadone daily dose and give insights into the molecular pathways underlying this interindividual dose variability.

Contribution of CYP2B6 alleles in explaining extreme (S)-methadone plasma levels: a CYP2B6 gene resequencing study.

BACKGROUND: (S)-Methadone, metabolized mainly by CYP2B6, shows a wide interindividual variability in its pharmacokinetics and pharmacodynamics. METHODS: Resequencing of the CYP2B6 gene was performed in 12 and 35 selected individuals with high (S)-methadone plasma exposure and low (S)-methadone plasma exposure, respectively, from a previously described cohort of 276 patients undergoing methadone maintenance treatment. Selected genetic polymorphisms were then analyzed in the complete cohort. RESULTS: The rs35303484 (*11; c136A>G; M46V) polymorphism was overrepresented in the high (S)-methadone level group, whereas the rs3745274 (*9; c516G>T; Q172H), rs2279344 (c822+183G>A), and rs8192719 (c1294+53C>T) polymorphisms were underrepresented in the low (S)-methadone level group, suggesting an association with decreased CYP2B6 activity. Conversely, the rs3211371 (*5; c1459C>T; R487C) polymorphism was overrepresented in the low-level group, indicating an increased CYP2B6 activity. A higher allele frequency was found in the high-level group compared with the low-level group for rs3745274 (*9; c516G>T; Q172H), rs2279343 (*4; c785A>G; K262R) (together representing CYP2B6*6), rs8192719 (c1294+53C>T), and rs2279344 (c822+183G>A), suggesting their involvement in decreased CYP2B6 activity. These results should be replicated in larger independent cohorts. CONCLUSION: Known genetic polymorphisms in CYP2B6 contribute toward explaining extreme (S)-methadone plasma levels observed in a cohort of patients following methadone maintenance treatment.