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Bupropion is hydroxylated to its primary active metabolite hydroxybupropion by cytochrome P450 enzyme CYP2B6. In vitro data suggest the existence of alternative hydroxylation pathways mediated by the highly polymorphic enzyme CYP2C19. However, the impact of its genetic variants on bupropion metabolism in vivo is still under investigation. We report the case of a 28-year-old male Caucasian outpatient suffering from major depressive disorder who did not respond to a treatment with bupropion. Therapeutic drug monitoring revealed very low serum concentrations of both bupropion and hydroxybupropion. Genotyping identified a heterozygous status for the gain-of-function allele with the genotype CYP2C19*1/*17 predicting enhanced enzymatic activity. The present case shows a reduced bupropion efficacy, which may be explained by a reduced active moiety of bupropion and its active metabolite hydroxybupropion, due to alternative hydroxylation pathways mediated by CYP2C19 in an individual with CYP2C19 rapid metabolizer status. The case report thus illustrates the clinical relevance of therapeutic drug monitoring in combination with pharmacogenetics diagnostics for a personalized treatment approach.

Selective serotonin reuptake inhibitors (SSRIs) are first-line antidepressants for the treatment of major depressive disorder (MDD). However, treatment response during an initial therapeutic trial is often poor and is difficult to predict. Heterogeneity of response to SSRIs in depressed patients is partly driven by co-occurring somatic disorders such as coronary artery disease (CAD) and obesity. CAD and obesity may also be associated with metabolic side effects of SSRIs. In this study, we assessed the association of CAD and obesity with treatment response to SSRIs in patients with MDD using a polygenic score (PGS) approach. Additionally, we performed cross-trait meta-analyses to pinpoint genetic variants underpinnings the relationship of CAD and obesity with SSRIs treatment response. First, PGSs were calculated at different p value thresholds (PT) for obesity and CAD. Next, binary logistic regression was applied to evaluate the association of the PGSs to SSRIs treatment response in a discovery sample (ISPC, N = 865), and in a replication cohort (STAR*D, N = 1,878). Finally, a cross-trait GWAS meta-analysis was performed by combining summary statistics. We show that the PGSs for CAD and obesity were inversely associated with SSRIs treatment response. At the most significant thresholds, the PGS for CAD and body mass index accounted 1.3%, and 0.8% of the observed variability in treatment response to SSRIs, respectively. In the cross-trait meta-analyses, we identified (1) 14 genetic loci (including NEGR1, CADM2, PMAIP1, PARK2) that are associated with both obesity and SSRIs treatment response; (2) five genetic loci (LINC01412, PHACTR1, CDKN2B, ATXN2, KCNE2) with effects on CAD and SSRIs treatment response. Our findings implicate that the genetic variants of CAD and obesity are linked to SSRIs treatment response in MDD. A better SSRIs treatment response might be achieved through a stratified allocation of treatment for MDD patients with a genetic risk for obesity or CAD.

Theoretical models of dopamine function stemming from reinforcement learning theory have emphasized the importance of prediction errors, which signal changes in the expectation of impending rewards. Much less is known about the effects of mean reward rates, which may be of motivational significance due to their role in computing the optimal effort put into exploiting reward opportunities. Here, we used a reinforcement learning model to design three functional neuroimaging studies and disentangle the effects of changes in reward expectations and mean reward rates, showing recruitment of specific regions in the brainstem regardless of prediction errors. While changes in reward expectations activated ventral striatal areas as in previous studies, mean reward rates preferentially modulated the substantia nigra/ventral tegmental area, deep layers of the superior colliculi, and a posterior pontomesencephalic region. These brainstem structures may work together to set motivation and attentional efforts levels according to perceived reward opportunities.

Therapeutisches Drug-Monitoring (TDM) umfasst die Quantifizierung und die Interpretation von Serum- oder Plasmakonzentrationen von Arzneistoffen im Blut, um die Pharmakotherapie zu optimieren. TDM ist ein Werkzeug, mit dem sich die hohe interindividuelle Variabilitat der Pharmakokinetik von Patienten identifizieren lasst und es ermoglicht somit eine personalisierte Pharmakotherapie. Im September 2017 veroffentlichte die Arbeitsgruppe Therapeutisches Drug-Monitoring der Arbeitsgemeinschaft fur Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP) ein Update der TDM-Konsensusleitlinien. In diesem Artikel sollen die wesentlichen Inhalte fur die klinische Praxis in der Psychiatrie und Neurologie zusammengefasst werden.

Introduction Drug–drug interactions (DDIs) are an important risk factor for adverse drug reactions. Older, polymedicated patients are particularly affected. Although antithrombotics have been detected as high-risk drugs for DDIs, data on older patients exposed to them are scarce. Methods Baseline data of 365 IDrug study outpatients (≥ 60 years, use of an antithrombotic and one or more additional long-term drug) were analyzed regarding potential drug–drug interactions (pDDIs) with a clinical decision support system. Data included prescription and self-medication drugs. Results The prevalence of having one or more pDDI was 85.2%. The median number of alerts per patient was three (range 0–17). For 58.4% of the patients, potential severe/contraindicated interactions were detected. Antiplatelets and non-steroidal anti-inflammatory drugs (NSAIDs) showed the highest number of average pDDI alert involvements per use (2.9 and 2.2, respectively). For NSAIDs, also the highest average number of severe/contraindicated alert involvements per use (1.2) was observed. 91.8% of all pDDI involvements concerned the 25 most frequently used drug classes. 97.5% of the severe/contraindicated pDDIs were attributed to only nine different potential clinical manifestations. The most common management recommendation for severe/contraindicated pDDIs was to intensify monitoring. Number of drugs was the only detected factor significantly associated with increased number of pDDIs (p

Erratum zu: Nervenarzt 2018 https://doi.org/10.1007/s00115-018-0643-9 Im Originalbeitrag ist Tab. 2 leider fehlerhaft. Fur die Substanzen Carbamazepin und Valproat wurde im Originalbeitrag ein Empfehlungsgrad zur Anwendung des therapeutischen Drug-Monitorings (TDM) von 2 (empfohlen) angegeben. …

Die Pharmakogenetik ist ein wichtiger Baustein in der Individualisierung von Therapien. Eine pharmakogenetische Diagnostik findet jedoch bislang noch wenig Anwendung in der medizinischen Praxis. Eine konsequente Berucksichtigung individueller Patientenfaktoren in der Arzneimitteltherapie kann helfen, diese sicherer und effektiver zu gestalten. Es wird ein kurzer Uberblick uber Strukturen und Auswirkungen von genetischen Varianten auf Arzneimittelwirkungen gegeben. Einige haufig verordnete Medikamente werden exemplarisch herausgegriffen. Des Weiteren wird die Durchfuhrbarkeit von pharmakogenetischer Diagnostik und Dosisanpassungen beschrieben. Die European Medicines Agency (EMA) als europaische Zulassungsbehorde hat bereits uber 70, die U.S. Food and Drug Administration (FDA) sogar uber 150 Arzneimittelinformationen um Angaben zu Biomarkern erweitert. Dies ist ein entscheidender Schritt in Richtung gezielte Therapie. Internationale Leitlinien fur die Dosis- und Therapieanpassung basieren auf einer systematischen Datenauswertung der klinischen Evidenz beispielsweise durch das Clinical Pharmacogenetics Implementation Consortium des Pharmacogenomics Research Network. Wesentlich fur den Erfolg einer Arzneimitteltherapie ist auch die Berucksichtigung individueller Risikofaktoren der Patienten. Eine pharmakogenetische Diagnostik muss von konkreten Dosierungs- und Therapieempfehlungen begleitet werden, um einen Beitrag zur Individualisierung und zur Verbesserung der Sicherheit und Effizienz der Arzneimitteltherapie leisten zu konnen.

The oral anticoagulant phenprocoumon, similar to other vitamin K antagonists, is characterized by pronounced interindividual variability in the doses needed to achieve the desired therapeutic effect. Previous studies assessed the effect of genetic and demographic covariates on empirical dose requirements of phenprocoumon to enable individualized dose prediction. The aim of the present study was to quantify major sources of interindividual variability separately on the pharmacokinetics and pharmacodynamics of phenprocoumon using a population pharmacokinetic–pharmacodynamic model. A single steady-state blood sample was collected from 278 patients and assayed by liquid chromatography–tandem mass spectrometry for phenprocoumon and its metabolites. Genotyping was performed for variants of the cytochrome P450 (CYP) 2C9 (CYP2C9) and vitamin K epoxide reductase complex, subunit 1 (VKORC1) genes. Effects were quantified by international normalized ratio (INR). Data were analyzed simultaneously using NONMEM VII. The model confirmed CYP2C9 and VKORC1 variants as the major predictors of variability in phenprocoumon concentrations and effects, together with body weight, age, comedication with CYP3A modifiers (i.e. inhibitors or inducers) and presence of atrial fibrillation. These covariates explained 50.0 % of the observed variability in the model parameters. Phenprocoumon clearance fractions mediated per CYP2C9 allele were 13.4, 9.5 and 5.7 mL/h for the *1, *2 and *3 variants, respectively. An additional clearance fraction of 5.3 mL/h was independent of CYP2C9 activity. Homozygous VKORC1 wild-type carriers were estimated to have a 2.13-fold higher phenprocoumon exposure requirement than homozygous 1173 C>T carriers to achieve the same effect on INR. The model provides a deeper insight in the separate pharmacokinetic and pharmacodynamic parts of phenprocoumon action. Thus, it provides important information for individualized dose prediction, with the option to include further covariates not studied here with known effects on individual pharmacokinetic or pharmacodynamic processes.

The aim of this study was to investigate pharmacogenetic determinants of skin rash associated with epidermal growth factor receptor (EGFR) inhibitor treatment. A total of 109 prospectively sampled cancer patients, receiving the first treatment with an EGFR inhibitor, were genotyped for functional EGFR polymorphisms and tagging variants in genes involved in receptor downstream signaling. Skin rash was absent in 26 (23.9%) patients and associated with shorter overall survival compared with patients presenting skin rash (P=0.005). The EGFR polymorphisms, 497G/A (P=0.008), and the haplotypes of the promoter variants, EGFR-216G/T and -191C/A (P=0.029), were associated with the appearance of skin rash. In addition, a common haplotype in the PIK3CA gene was associated with skin rash (P=0.045) and overall survival (P=0.009). In conclusion, genetic variation within the EGFR gene and its downstream signaling partner PIK3CA might predict EGFR-inhibitor-related skin rash.

Sexual dysfunction is a common side effect of selective serotonin reuptake inhibitors (SSRIs) like paroxetine in the treatment of depression, imposing a considerable risk on medication adherence and hence therapeutic success. Bupropion, a norepinephrine and dopamine reuptake inhibitor, is recommended as an alternative treatment without adverse effects concerning sexual arousal and libido. We investigated the neural bases of paroxetine-related subjective sexual dysfunction when compared with bupropion and placebo. We scanned 18 healthy, heterosexual males in a randomized, double-blind, within-subject design while watching video clips of erotic and nonerotic content under steady-state conditions after taking 20 mg of paroxetine, 150 mg of bupropion, and placebo for 7 days each. Under paroxetine, ratings of subjective sexual dysfunction increased compared with placebo or bupropion. Activation along the anterior cingulate cortex (ACC), including subgenual, pregenual, and midcingulate cortices, in the ventral striatum and midbrain was decreased when compared with placebo. In contrast, bupropion let subjective ratings and ACC activations unchanged and increased activity of brain regions including posterior midcingulate cortex, mediodorsal thalamus, and extended amygdala relative to placebo and paroxetine. Brain regions that have been related to the processing of motivational (ventral striatum), emotional, and autonomic components of erotic stimulation (anterior cingulate) in previous studies showed reduced responsiveness under paroxetine in our study. Drug effects on these regions may be part of the mechanism underlying SSRI-related sexual dysfunction. Increased activation under bupropion may point to an opposite effect that may relate to the lack of impaired sexual functioning.

The established risk factors for severe sulfonylurea-induced hypoglycemia (SH) include low hemoglobin (Hb)A(1c), advanced age, long duration of diabetes, multimorbidity, and polypharmacy. As the genetically polymorphic cytochrome P450 (CYP), enzyme CYP2C9 is mainly responsible for the hepatic metabolism of sulfonylureas (SUs), we hypothesized that the slow-metabolizer genotypes *2/*2, *2/*3, and *3/*3 might be overrepresented in type 2 diabetic patients with SH.In a prospective population-based case-control study, CYP2C9 allelic variants of 102 diabetic patients with SH were compared with a matched group of 101 SU-treated patients without a history of SH. The 203 Caucasian patients had been treated with the SUs glimepiride, glibenclamide, or gliquidone. SH was defined as a symptomatic event requiring treatment with intravenously administered glucose and was confirmed by a blood glucose measurement of50 mg/dl (2.8 mmol/l). As two control groups, we selected 337 Caucasian diabetic patients receiving antidiabetic drugs per os and 1,988 healthy Caucasian volunteers who had been genotyped earlier.In the univariate analysis, only a low HbA(1c) value (p = 0.0004) was shown as a risk factor for SH. There was no overrepresentation of the CYP2C9 *2/*2, *2/*3, and *3/*3 variants in the SH group (2%) compared with the control group (5%). However, in the control group, patients with CYP2C9 genotypes, predicting slower metabolism of SU drugs, were treated with significantly lower doses (p = 0.027) than were extensive metabolizers, whereas in the patient group with severe hypoglycemia, the dose was the same for all genotype groups.In the present cohort of 102 patients with SH, a low HbA(1c) value was related to the risk for SH. There was no overrepresentation observed of the CYP2C9 slow-metabolizer genotypes in the hypoglycemic patients group, but the drug exposure in the slow-metabolizer genotypes was estimated to be higher in hypoglycemic patients, which might partly have contributed to their risk for SH.

The growing interest in incorporating pharmacogenetics (PGx) into drug development and clinical practice raises several questions: which study designs best reveal relevant pharmacogenetic biomarkers, best clarify specific hypotheses in PGx, and result in the largest gain of clinical evidence in this field? In this review, we present and compare a variety of PGx-related study designs. The type and quality of evidence gained by each category of study design is evaluated, and an appropriate timeline for the integration of pharmacogenetic studies into drug development is proposed. A summary of the pros and cons of the different study designs might help investigators decide how best to incorporate PGx into drug research. Using different scenarios to explain how genetic polymorphisms influence drug action, we illustrate how this knowledge can be translated into individualized drug choices, individualized dosage determination based on pharmacogenetic diagnostics, and other types of monitoring in order to make drug therapies safer and more effective.

Response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably between patients. The International SSRI Pharmacogenomics Consortium (ISPC) was formed with the primary goal of identifying genetic variation that may contribute to response to SSRI treatment of major depressive disorder. A genome-wide association study of 4-week treatment outcomes, measured using the 17-item Hamilton Rating Scale for Depression (HRSD-17), was performed using data from 865 subjects from seven sites. The primary outcomes were percent change in HRSD-17 score and response, defined as at least 50% reduction in HRSD-17. Data from two prior studies, the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, were used for replication, and a meta-analysis of the three studies was performed (N=2394). Although many top association signals in the ISPC analysis map to interesting candidate genes, none were significant at the genome-wide level and the associations were not replicated using PGRN-AMPS and STAR*D data. Top association results in the meta-analysis of response included single-nucleotide polymorphisms (SNPs) in the HPRTP4 (hypoxanthine phosphoribosyltransferase pseudogene 4)/VSTM5 (V-set and transmembrane domain containing 5) region, which approached genome-wide significance (P=5.03E−08) and SNPs 5’ upstream of the neuregulin-1 gene, NRG1 (P=1.20E−06). NRG1 is involved in many aspects of brain development, including neuronal maturation and variations in this gene have been shown to be associated with increased risk for mental disorders, particularly schizophrenia. Replication and functional studies of these findings are warranted.