h Population pharmacokinetic analyses can be applied to predict optimized dosages for individual patients. The aim of this study was to compare the prediction performance of the published population pharmacokinetic models for meropenem in critically ill patients. We coded the published population pharmacokinetic models with covariate relationships into dosing software to predict unbound meropenem concentrations measured in a separate cohort of critically ill patients. The agreements between the observed and predicted concentrations were evaluated with Bland-Altman plots. The absolute and relative bias and precision of the models were determined. The clinical implications of the results were evaluated according to whether dose adjustments were required from the predictions to achieve a meropenem concentration of >2 mg/liter throughout the dosing interval. A total of 157 free meropenem concentrations from 56 patients were analyzed. Eight published population pharmacokinetic models were compared. The models showed an absolute bias in predicting the unbound meropenem concentrations from a mean percent difference (95% confidence interval [CI]) of ؊108.5% (؊119.9% to ؊97.3%) to 19.9% (7.3% to 32.7%), while absolute precision ranged from ؊249.1% (؊263.4% to ؊234.8%) to 31.9% (17.6% to 46.2%) and ؊178.9% (؊196.9% to ؊160.9%) to 175.0% (157.0% to 193.0%). A dose change was required in 44% to 64% of the concentration results. Seven of the eight equations evaluated underpredicted free meropenem concentrations. In conclusion, the overall accuracy of these models supports their inclusion in dosing software and application for individualizing meropenem doses in critically ill patients to increase the likelihood of achievement of optimal antibiotic exposures. Meropenem, a carbapenem antibiotic with broad-spectrum activities against both Gram-positive and Gram-negative bacteria, is commonly used in critically ill patients with life-threatening infections. Vital to the success of this treatment is early and appropriate antibiotic therapy. Selecting the correct dose is as important, but this process is highly challenging in critically ill patients because of the variable and difficult-to-predict pharmacokinetics in these patients (1, 2). Dose optimization of meropenem should be considered imperative because suboptimal antibiotic exposures might jeopardize the clinical outcomes and potentially increase the emergence of antibiotic resistance (3).Meropenem is a time-dependent antibiotic: its clinical and microbiological efficacy is related to the percentage of the dosing interval in which the free drug concentration remains above the MIC of the pathogenic organism (fT ϾMIC ) (4, 5). The in vitro bactericidal activity of carbapenems is optimal at an fT ϾMIC of Ն40%; however, a target fT ϾMIC of 100% has been suggested in critically ill patients (6). Population pharmacokinetic models that quantify the effect of demographic, pathophysiological, and other drug-related factors on drug disposition should be considered valuable in the crit...

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i Bayesian methods for voriconazole therapeutic drug monitoring (TDM) have been reported previously, but there are only sparse reports comparing the accuracy and precision of predictions of published models. Furthermore, the comparative accuracy of linear, mixed linear and nonlinear, or entirely nonlinear models may be of high clinical relevance. In this study, models were coded into individually designed optimum dosing strategies (ID-ODS) with voriconazole concentration data analyzed using inverse Bayesian modeling. The data used were from two independent data sets, patients with proven or suspected invasive fungal infections (n ‫؍‬ 57) and hematopoietic stem cell transplant recipients (n ‫؍‬ 10). Observed voriconazole concentrations were predicted whereby for each concentration value, the data available to that point were used to predict that value. The mean prediction error (ME) and mean squared prediction error (MSE) and their 95% confidence intervals (95% CI) were calculated to measure absolute bias and precision, while ⌬ME and ⌬MSE and their 95% CI were used to measure relative bias and precision, respectively. A total of 519 voriconazole concentrations were analyzed using three models. Voriconazole is a triazole antifungal that exhibits broad-spectrum activity and is a first-line agent for the treatment of Candida sp. infections (1), invasive aspergillosis (2), and other serious fungal infections. With increasing numbers of at-risk immunocompromised populations, such as those undergoing solid-organ transplantation or those with HIV infections, the incidence of invasive mycoses is on the rise (3, 4). Despite the advent of newer antifungals, Aspergillus sp. and Candida sp. infections have exhibited high mortality rates of 60% and 30%, respectively (5, 6).Recent published studies of voriconazole have shed light on the clinical relevance of therapeutic drug monitoring (TDM) for optimization of dosing based on voriconazole's highly variable pharmacokinetics (PK) and the resultant poor predictability of plasma concentrations (7,8). Subtherapeutic concentrations have been linked to higher failure rates in patients with life-threatening invasive fungal infections, and supratherapeutic concentrations are associated with neurological and hepatic toxicity (9-19). Voriconazole is primarily metabolized by CYP2C19, which commonly exhibits genetic polymorphism, leading to variable PK and leaving certain populations susceptible to decreased metabolism and increased plasma concentrations of voriconazole (20-23). Patients of Asian descent with polymorphisms in CYP2C19 have up to a 20% incidence of being poor metabolizers while this value is up to 5% for Caucasian and African American individuals (24). Poor metabolizers can have a PK exposure up to four times higher than that of homozygous comparators.Nonlinearity in voriconazole PK relating to saturable clearance mechanisms has been reported (8), which together with its extensive variability makes dosing profoundly challenging, especially when higher doses are used. Conve...

The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software.

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