Just-in-Time Retail Distribution:A Systems Perspective on Cross-Docking

Cross-docking is a just-in-time strategy for distribution logistics. It is aimed at reducing inventory levels and distribution lead times by creating a seamless flow of products from suppliers to customers. Prior supply chain literature has argued that creating such a seamless product flows requires a holistic view on cross-docking management, aimed at synchronizing cross-docking operations at the distribution center with its inbound and outbound network logistics. This paper provides an in-depth case study illustrating how cross-docking operations can be managed more holistically in a retail distribution context. A discrete event simulation model has been developed to understand and improve the cross-docking operations of a large grocery retailer in The Netherlands. The model is used to quantitatively evaluate two proposed changes that exploit opportunities in the design and control of the retailer’s distribution network. An extensive real-world data set is used as input to the model. Overall, the case and simulation results show that a holistic cross-docking management approach can indeed improve system-wide performance, which further stresses the importance of making cross-dock operational decisions making and network decisions together

Impact of disease, drug and patient adherence on the effectiveness of antiviral therapy in pediatric HIV

INTRODUCTION: Maintaining effective antiretroviral treatment for life is a major problem, in both resource-limited and resource-rich countries. Despite the progresses observed in paediatric antiretroviral therapy, approximately 12% of children still experience treatment failure due to drug resistance, inadequate dosing and poor adherence. This review is aimed at exploring the current status of antiretroviral therapy in children with focus on the interaction between disease, drug pharmacokinetics and patient behaviour, all of which are strongly interconnected and determine treatment outcome. AREAS COVERED: An overview is provided of the viral characteristics and available drug combinations aimed at the prevention of resistance. In this context, the role of patient adherence is scrutinised. A detailed assessment of the factors affecting adherence is presented together with the main strategies to enhance treatment response in children. EXPERT OPINION: Using modelling and simulation, a framework for assessing the forgiveness of non-adherence for specific antiretroviral regimens in children is proposed in which information on pharmacokinetics (PK), pharmacokinetic-pharmacodynamic (PKPD) relationships and viral dynamics are integrated. This approach represents an opportunity for the simplification of dosing regimens taking into account the interaction between these factors. Based on clinical trial simulations, we envisage the possibility to assess the impact of variable adherence to antiretroviral drug combinations in HIV-infected children

Summary data of potency and parameter information from semi-mechanistic PKPD modeling of prolactin release following administration of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride in rats

We provide the reader with relevant data related to our recently published paper, comparing two mathematical models to describe prolactin turnover in rats following one or two doses of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride, “A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D2 receptor antagonists in rats” (Taneja et al., 2016) [1]. All information is tabulated. Summary level data on the in vitro potencies and the physicochemical properties is presented in Table 1. Model parameters required to explore the precursor pool model are presented in Table 2. In Table 3, estimated parameter comparisons for both models are presented, when separate potencies are estimated for risperidone and paliperidone, as compared to a common potency for both drugs. In Table 4, parameter estimates are compared when the drug effect is parameterized in terms of drug concentration or receptor occupancy

The role of population PK-PD modelling in paediatric clinical research

Pharmacolog

Pharmacokinetic Modeling of Non-Linear Brain Distribution of Fluvoxamine in the Rat

Introduction. A pharmacokinetic (PK) model is proposed for estimation of total and free brain concentrations of fluvoxamine. Materials and methods. Rats with arterial and venous cannulas and a microdialysis probe in the frontal cortex received intravenous infusions of 1, 3.7 or 7.3 mg.kg j1 of fluvoxamine. Analysis. With increasing dose a disproportional increase in brain concentrations was observed. Th

Dietary trehalose enhances virulence of epidemic Clostridium difficile

Molecular basis of bacterial pathogenesis, virulence factors and antibiotic resistanc

Extensions to the Visual Predictive Check to facilitate model performance evaluation

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example

Not-in-trial simulation I: Bridging cardiovascular risk from clinical trials to real-life conditions

Aims The assessment of heart rate-corrected QT (QTc) interval prolongation relies on the evidence of drug effects in healthy subjects. This study demonstrates the relevance of pharmacokinetic-pharmacodynamic (PKPD) relationships to characterize drug-induced QTc interval prolongation and explore the discrepancies between clinical trials and real-life conditions. Methods d,l-Sotalol data from healthy subjects and from the Rotterdam Study cohort were used to assess treatment response in a phase I setting and in a real-life conditions, respectively. Using modelling and simulation, drug effects at therapeutic doses were predicted in both populations. Results Inclusion criteria were shown to restrict the representativeness of the trial population in comparison to real-life conditions. A significant part of the typical patient population was excluded from trials due to weight and baseline QTc interval criteria. Relative risk was significantly different between sotalol users with and without heart failure, hypertension, diabetes and myocardial infarction (P < 0.01). Although drug effects do cause an increase in the relative risk of QTc interval prolongation, the presence of diabetes represented an increase from 4.0 [95% confidence interval (CI) 2.7-5.8] to 6.5 (95% CI 1.6-27.1), whilst for myocardial infarction it increased from 3.4 (95% CI 2.3-5.13) to 15.5 (95% CI 4.9-49.3). Conclusions Our findings show that drug effects on QTc interval do not explain the observed QTc values in the population. The prevalence of high QTc values in the real-life population can be assigned to co-morbidities and concomitant medications. These findings substantiate the need to account for these factors when evaluating the cardiovascular risk of medicinal products