Physiologically Based Precision Dosing Approach for Drug-Drug-Gene Interactions: A Simvastatin Network Analysis

Drug‐drug interactions (DDIs) and drug‐gene interactions (DGIs) are well known mediators for adverse drug reactions (ADRs), which are among the leading causes of death in many countries. Because physiologically based pharmacokinetic (PBPK) modeling has demonstrated to be a valuable tool to improve pharmacotherapy affected by DDIs or DGIs, it might also be useful for precision dosing in extensive interaction network scenarios. The presented work proposes a novel approach to extend the prediction capabilities of PBPK modeling to complex drug‐drug‐gene interaction (DDGI) scenarios. Here, a whole‐body PBPK network of simvastatin was established, including three polymorphisms (SLCO1B1 (rs4149056), ABCG2 (rs2231142), and CYP3A5 (rs776746)) and four perpetrator drugs (clarithromycin, gemfibrozil, itraconazole, and rifampicin). Exhaustive network simulations were performed and ranked to optimize 10,368 DDGI scenarios based on an exposure marker cost function. The derived dose recommendations were translated in a digital decision support system, which is available at simvastatin.precisiondosing.de. Although the network covers only a fraction of possible simvastatin DDGIs, it provides guidance on how PBPK modeling could be used to individualize pharmacotherapy in the future. Furthermore, the network model is easily extendable to cover additional DDGIs. Overall, the presented work is a first step toward a vision on comprehensive precision dosing based on PBPK models in daily clinical practice, where it could drastically reduce the risk of ADRs

Physiologically-based pharmacokinetic modeling of dextromethorphan to investigate interindividual variability within CYP2D6 activity score groups

This study provides a whole-body physiologically-based pharmacokinetic (PBPK) model of dextromethorphan and its metabolites dextrorphan and dextrorphan O-glucuronide for predicting the effects of cytochrome P450 2D6 (CYP2D6) drug-gene interactions (DGIs) on dextromethorphan pharmacokinetics (PK). Moreover, the effect of interindividual variability (IIV) within CYP2D6 activity score groups on the PK of dextromethorphan and its metabolites was investigated. A parent-metabolite-metabolite PBPK model of dextromethorphan, dextrorphan, and dextrorphan O-glucuronide was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Plasma concentration-time profiles of all three analytes were gathered from published studies and used for model development and model evaluation. The model was evaluated comparing simulated plasma concentration-time profiles, area under the concentration-time curve from the time of the first measurement to the time of the last measurement (AUClast) and maximum concentration (Cmax) values to observed study data. The final PBPK model accurately describes 28 population plasma concentration-time profiles and plasma concentration-time profiles of 72 individuals from four cocktail studies. Moreover, the model predicts CYP2D6 DGI scenarios with six of seven DGI AUClast and seven of seven DGI Cmax ratios within the acceptance criteria. The high IIV in plasma concentrations was analyzed by characterizing the distribution of individually optimized CYP2D6 kcat values stratified by activity score group. Population simulations with sampling from the resulting distributions with calculated log-normal dispersion and mean parameters could explain a large extent of the observed IIV. The model is publicly available alongside comprehensive documentation of model building and model evaluation

Data Digitizing: Accurate and Precise Data Extraction for Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling

In quantitative systems pharmacology (QSP) and physiologically-based pharmacokinetic (PBPK) modeling, data digitizing is a valuable tool to extract numerical information from published data presented as graphs. To quantify their relevance, a literature search revealed a remarkable mean increase of 16% per year in publications citing digitizing software together with QSP or PBPK. Accuracy, precision, confounder influence, and variability were investigated using scaled median symmetric accuracy (ζ), thus finding excellent accuracy (mean ζ = 0.99%). Although significant, no relevant confounders were found (mean ζ ± SD circles = 0.69% ± 0.68% vs. triangles = 1.3% ± 0.62%). Analysis of 181 literature peak plasma concentration values revealed a considerable discrepancy between reported and post hoc digitized data with 85% having ζ > 5%. Our findings suggest that data digitizing is precise and important. However, because the greatest pitfall comes from pre-existing errors, we recommend always making published data available as raw values

Overcome procrastination: Enhancing emotion regulation skills reduce procrastination

AbstractProcrastination is a widespread phenomenon that affects performance in various life domains including academic performance. Recently, it has been argued that procrastination can be conceptualized as a dysfunctional response to undesired affective states. Thus, we aimed to test the hypothesis that the availability of adaptive emotion regulation (ER) skills prevents procrastination.In a first study, cross-sectional analyses indicated that ER skills and procrastination were associated and that these connections were mediated by the ability to tolerate aversive emotions. In a second study, cross lagged panel analyses showed that (1) the ability to modify aversive emotions reduced subsequent procrastination and that (2) procrastination affected the subsequent ability to tolerate aversive emotions. Finally, in a third study, a two-arm randomized control trial (RCT) was conducted. Results indicated that systematic training of the ER skills tolerate and modify aversive emotions reduced procrastination. Thus, in order to overcome procrastination, emotion-focused strategies should be considered

Theta-gamma cross-frequency transcranial alternating current stimulation over the trough impairs cognitive control

Cognitive control is a mental process, which underlies adaptive goal-directed decisions. Previous studies have linked cognitive control to electrophysiological fluctuations in the theta band and theta-gamma cross-frequency coupling (CFC) arising from the cingulate and frontal cortices. Yet, to date the behavioral consequences of different forms of theta-gamma CFC remain elusive. Here, we studied the behavioral effects of the theta-gamma CFC via transcranial alternating current stimulation (tACS) designed to stimulate the frontal and cingulate cortices in humans. Using a double-blind, randomized, repeated measures study design, 24 healthy participants were subjected to three active and one control CFC-tACS conditions. In the active conditions, 80 Hz gamma tACS was coupled to 4 Hz theta tACS. Specifically, in two of the active conditions, short gamma bursts were coupled to the delivered theta cycle to coincide with either its peaks or troughs. In the third active condition, the phase of a theta cycle modulated the amplitude of the gamma oscillation. In the fourth, control protocol, 80 Hz tACS was continuously superimposed over the 4 Hz tACS, therefore lacking any phase-specificity in the CFC. During the 20-minute of stimulation, the participants performed a Go/NoGo monetary reward- and punishment-based instrumental learning task. A Bayesian hierarchical logistic regression analysis revealed that relative to the control, the peak-coupled tACS had no effects on the behavioral performance, whereas the trough-coupled tACS and, to a lesser extent, amplitude-modulated tACS reduced performance in conflicting trials. Our results suggest that cognitive control depends on the phase-specificity of the theta-gamma CFC

Physiologically-Based Pharmacokinetic (PBPK) Modeling of Buprenorphine in Adults, Children and Preterm Neonates

Buprenorphine plays a crucial role in the therapeutic management of pain in adults, adolescents and pediatric subpopulations. However, only few pharmacokinetic studies of buprenorphine in children, particularly neonates, are available as conducting clinical trials in this population is especially challenging. Physiologically-based pharmacokinetic (PBPK) modeling allows the prediction of drug exposure in pediatrics based on age-related physiological differences. The aim of this study was to predict the pharmacokinetics of buprenorphine in pediatrics with PBPK modeling. Moreover, the drug-drug interaction (DDI) potential of buprenorphine with CYP3A4 and P-glycoprotein perpetrator drugs should be elucidated. A PBPK model of buprenorphine and norbuprenorphine in adults has been developed and scaled to children and preterm neonates, accounting for age-related changes. One-hundred-percent of the predicted AUClast values in adults (geometric mean fold error (GMFE): 1.22), 90% of individual AUClast predictions in children (GMFE: 1.54) and 75% in preterm neonates (GMFE: 1.57) met the 2-fold acceptance criterion. Moreover, the adult model was used to simulate DDI scenarios with clarithromycin, itraconazole and rifampicin. We demonstrate the applicability of scaling adult PBPK models to pediatrics for the prediction of individual plasma profiles. The novel PBPK models could be helpful to further investigate buprenorphine pharmacokinetics in various populations, particularly pediatric subgroups

Physiologically Based Pharmacokinetic Modeling to Describe the CYP2D6 Activity Score-Dependent Metabolism of Paroxetine, Atomoxetine and Risperidone

The cytochrome P450 2D6 (CYP2D6) genotype is the single most important determinant of CYP2D6 activity as well as interindividual and interpopulation variability in CYP2D6 activity. Here, the CYP2D6 activity score provides an established tool to categorize the large number of CYP2D6 alleles by activity and facilitates the process of genotype-to-phenotype translation. Compared to the broad traditional phenotype categories, the CYP2D6 activity score additionally serves as a superior scale of CYP2D6 activity due to its finer graduation. Physiologically based pharmacokinetic (PBPK) models have been successfully used to describe and predict the activity score-dependent metabolism of CYP2D6 substrates. This study aimed to describe CYP2D6 drug–gene interactions (DGIs) of important CYP2D6 substrates paroxetine, atomoxetine and risperidone by developing a substrate-independent approach to model their activity score-dependent metabolism. The models were developed in PK-Sim®, using a total of 57 plasma concentration–time profiles, and showed good performance, especially in DGI scenarios where 10/12, 5/5 and 7/7 of DGI AUClast ratios and 9/12, 5/5 and 7/7 of DGI Cmax ratios were within the prediction success limits. Finally, the models were used to predict their compound’s exposure for different CYP2D6 activity scores during steady state. Here, predicted DGI AUCss ratios were 3.4, 13.6 and 2.0 (poor metabolizers; activity score = 0) and 0.2, 0.5 and 0.95 (ultrarapid metabolizers; activity score = 3) for paroxetine, atomoxetine and risperidone active moiety (risperidone + 9-hydroxyrisperidone), respectively

Evaluating the efficacy and cost-effectiveness of web-based indicated prevention of major depression: design of a randomised controlled trial

BACKGROUND: Major depressive disorder (MDD) imposes a considerable disease burden on individuals and societies. Web-based interventions have shown to be effective in reducing depressive symptom severity. However, it is not known whether web-based interventions may also be effective in preventing the onset of MDD. The aim of this study is to evaluate the (cost-) effectiveness of an indicated web-based guided self-help intervention (GET.ON Mood Enhancer Prevention) on the onset of MDD. METHODS/DESIGN: A randomised controlled trial (RCT) will be conducted to compare the (cost-) effectiveness of the GET.ON Mood Enhancer Prevention training with a control condition exclusively receiving online-based psychoeducation on depression. Adults with subthreshold depression (N = 406) will be recruited from the general population and randomised to one of the two conditions. The primary outcome is time to onset of MDD within a 12-months follow-up period. MDD will be assessed according to DSM-IV criteria as assessed by the telephone-administered Structured Clinical Interview for DSM-IV (SCID). Time to onset of MDD will be assessed using life charts. Secondary outcomes include changes on various indicators of depressive symptom severity, anxiety and quality of life from baseline to post-treatment, to a 6-month and a 12-month follow up. Additionally, an economic evaluation using a societal perspective will be conducted to examine the intervention’s cost-effectiveness. DISCUSSION: This is one of the first randomised controlled trials that examines the effect of an indicated guided self-help web-based intervention on the incidence of major depression. If shown to be effective, the intervention will contribute to reducing the disease burden due to MDD in the general population. TRIAL REGISTRATION: German Clinical Trial Registration DRKS00004709

Physiologically Based Pharmacokinetic Modeling of Metoprolol Enantiomers and α-Hydroxymetoprolol to Describe CYP2D6 Drug-Gene Interactions

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug–gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite α-hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5–200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (α-hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration–time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository

Leitfaden Erfolgreiche Kooperationen in der Direktvermarktung entwickeln: Leitfaden REGINA

Der Leitfaden für eine erfolgreiche Kooperation beschreibt, wie sich Landwirte für eine gemeinsame Idee mit anderen Landwirten und Verarbeitern zusammenschließen können, um sich in der Direktvermarktung besser aufzustellen. Gemeinsame Werte und Ziele, die Organisation und das Führen der Gruppe sind wichtige Punkte, um langfristig eine Kooperation zu entwickeln. Ein praktisches Beispiel erklärt Schritt für Schritt, wie das gelingen kann. Zusätzlich werden wichtige Hinweise und Denkanstöße gegeben, damit alles an die eigene individuelle Situation anwendbar wird. Am Ende nützt die Bündelung der gemeinsamen Kräfte nicht nur den Kooperationspartnern, sondern auch dem Kunden an der Hofladentheke. Der Leitfaden richtet sich an Direktvermarkter der Landwirtschaft, aber auch des Ernährungshandwerks. Redaktionsschluss: 31.08.202