Abstract. Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.
Background-Neovasculature within atherosclerotic plaques is believed to be associated with infiltration of inflammatory cells and plaque destabilization. The aim of the present investigation was to determine whether the amount of neovasculature present in advanced carotid plaques can be noninvasively measured by dynamic, contrast-enhanced MRI. Methods and Results-A total of 20 consecutive patients scheduled for carotid endarterectomy were recruited to participate in an MRI study. Images were obtained at 15-second intervals, and a gadolinium contrast agent was injected coincident with the second of 10 images in the sequence. The resulting image intensity within the plaque was tracked over time, and a kinetic model was used to estimate the fractional blood volume. For validation, matched sections from subsequent endarterectomy were stained with ULEX and CD-31 antibody to highlight microvessels. Finally, all microvessels within the matched sections were identified, and their total area was computed as a fraction of the plaque area. Results were obtained from 16 participants, which showed fractional blood volumes ranging from 2% to 41%. These levels were significantly higher than the histological measurements of fractional vascular area. Nevertheless, the 2 measurements were highly correlated, with a correlation coefficient of 0.80 (PϽ0.001). Conclusions-Dynamic contrast-enhanced MRI provides an indication of the extent of neovasculature within carotid atherosclerotic plaque. MRI therefore provides a means for prospectively studying the link between neovasculature and plaque vulnerability. Key Words: magnetic resonance imaging Ⅲ contrast media Ⅲ carotid arteries Ⅲ atherosclerosis R ecent investigations have targeted neovasculature as an important factor contributing to atherosclerotic plaque vulnerability. Zamir and Silver 1 speculated that the presence of neovasculature within coronary artery walls may play a role in the pathogenesis of vascular disease. Kumamoto et al 2 showed that intimal neovasculature arising from the adventitial vasa vasorum was associated with inflammatory infiltrate. A role for neovasculature in the recruitment of leukocytes to the shoulder regions of lipid-rich plaques was proposed by de Boer et al. 3 Because such inflammatory cells are present at the sites of plaque rupture, 4 neovasculature may be a contributor to or a marker for vulnerable plaque. Support for this claim is provided by Mofidi et al, 5 who found higher microvessel counts in carotid endarterectomy specimens from symptomatic patients than from asymptomatic ones. Similarly, McCarthy et al 6 found microvessels in symptomatic patients were larger and more irregular in shape.These studies suggest that an imaging tool capable of measuring the extent of plaque neovasculature could be invaluable for identifying high-risk plaques or assessing the response to plaque-stabilizing therapies. A strong contender for measuring plaque neovasculature is MRI using an intravenously injected contrast-enhancing agent. Contrastenhanced (CE) MRI h...
This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.
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