Population pharmacokinetic parameters of vancomycin (VCM) in Japanese adult patients infected with methicillin-resistant Staphylococcus aureus (MRSA) were estimated using 1253 items of serum concentration data from 190 patients obtained in routine drug monitoring. The two-compartment linear model was adopted, and VCM clearance (CL) was correlated with the creatinine clearance (CLcr), which was observed or estimated by the Cockcroft-Gault equation. The population pharmacokinetic analysis program NONMEM with first-order conditional estimation method was used. The results showed VCM clearance to be linearly correlated with CLcr (CL [ml/min] = 0.797 x CLcr) when the estimated CLcr was <85 ml/min, but no linear relationship at higher than this level because of the lack of accuracy in the CLcr estimates. The interindividual variability of CL was 38.5%; K12 and K21 were 0.525 hr(-1) and 0.213 hr(-1), respectively. The distribution volume at steady state (V[SS]) was 60.71, with no significant dependence on the actual body weight. The interindividual variability of Vss was 25.4%. The calculated half-life (t1/2,beta) in a typical patient with CLcr of 85 ml/minute was 12.8 hours. Residual variability was 23.7%. These results were compared to those of healthy volunteers, and guidelines for dosage adjustment in VCM therapy are discussed.

A new pharmacodynamic model for the analysis of in vitro bactericidal kinetics was developed based on the logistic growth model, with the bacterial phases divided into two compartments. The model equations are expressed as nonlinear simultaneous differential equations, and the Runge-Kutta-Gill method was adopted to numerically solve the equations in both the simulation and the least squares curve-fitting procedures. The model can describe the initial killing and the regrowth phases and can explain the nonlinear dependence of the killing rate on the drug concentration. The model can also explain the plateau in the bacterial growth curve that is often observed in in vitro experiments. The model was applied to analysis of the in vitro time-killing data of beta-lactam antibiotics, S-4661, meropenem, imipenem, cefpirome, and ceftazidim against three types of bacteria, Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. The results of curve-fitting using the least squares program MULTI (Runge) showed good fits for all types of drugs and bacteria. The relationship between the characteristics of the drug-bacteria interactions and the estimated pharmacodynamic parameters is discussed.

No abstract

The aim of the present study was to determine the influence of severe renal dysfunction (estimated glomerular filtration rate <30 ml/min/1.73 m², including hemodialysis) on the pharmacokinetics and therapeutic effects of febuxostat using a population pharmacokinetic analysis. This study recruited patients with hyperuricemia who were initially treated with allopurinol, but were switched to febuxostat, and it consists of 2 sub-studies: a pharmacokinetic study (26 patients) and retrospective efficacy evaluation study (51 patients). The demographic and clinical data of patients were collected from electronic medical records. Plasma febuxostat concentrations were obtained at each hospital visit. Population pharmacokinetic modeling was performed with NONMEM version 7.2. A total of 128 plasma febuxostat concentrations from 26 patients were used in the population pharmacokinetic analysis. The data were best described by a 1-compartment model with first order absorption. Covariate analysis revealed that renal function did not influence the pharmacokinetics of febuxostat, whereas actual body weight significantly influenced apparent clearance and apparent volume of distribution. The retrospective efficacy analysis showed the favorable therapeutic response of febuxostat switched from allopurinol in patients with moderate to severe renal impairment. No serious adverse event associated with febuxostat was observed irrespective of renal function. The population pharmacokinetic analysis and therapeutic analysis of febuxostat revealed that severe renal dysfunction had no influence on the pharmacokinetic parameters of febuxostat. These results suggest that febuxostat is tolerated well by patients with severe renal impairment.

The intestinal tract is considered the most important reservoir of Pseudomonas aeruginosa in intensive care units (ICUs). Gut colonization by P. aeruginosa underlies the development of invasive infections such as gut-derived sepsis. Intestinal colonization by P. aeruginosa is associated with higher ICU mortality rates. The translocation of endogenous P. aeruginosa from the colonized intestinal tract is an important pathogenic phenomenon. Here we identify bacterial and host proteins associated with bacterial penetration through the intestinal epithelial barrier. We first show by comparative genomic hybridization analysis that the exoS gene, encoding the type III effector protein, ExoS, was specifically detected in a clinical isolate that showed higher virulence in silkworms following midgut injection. We further show using a silkworm oral infection model that exoS is required both for virulence and for bacterial translocation from the midgut to the hemolymph. Using a bacterial two-hybrid screen, we show that the mammalian factor FXYD3, which colocalizes with and regulates the function of Na,K-ATPase, directly binds ExoS. A pulldown assay revealed that ExoS binds to the transmembrane domain of FXYD3, which also interacts with Na,K-ATPase. Na,K-ATPase controls the structure and barrier function of tight junctions in epithelial cells. Collectively, our results suggest that ExoS facilitates P. aeruginosa penetration through the intestinal epithelial barrier by binding to FXYD3 and thereby impairing the defense function of tight junctions against bacterial penetration.Pseudomonas aeruginosa is an opportunistic pathogen that is a major cause of infection-related mortality among individuals with compromised immune systems. Fatality rates among patients infected with P. aeruginosa are higher than those among patients infected with any other opportunistic Gram-negative bacterium (48, 51). The lungs are a major site of P. aeruginosa infection in ill patients; however, a considerable number of such infections occur through direct contamination of the lungs by gastrointestinal flora or through hematogenous spread from the intestine to the lungs (51). In particular, the presence of highly virulent strains of P. aeruginosa within the intestinal tract alone is the main source of sepsis and death among immunocompromised patients, even in the absence of established extraintestinal infection and bacteremia (34,41,51). Furthermore, the lethal effects of intestinal P. aeruginosa are dependent upon its ability to adhere to and disrupt the intestinal epithelial barrier (1).The intestinal tract is considered to be the most important reservoir of P. aeruginosa (2). The rate of mortality of patients in intensive care units (ICUs) suffering from intestinal colonization by P. aeruginosa is significantly higher than that of patients without such colonization (34). The notion that gut colonization by P. aeruginosa sets the stage for the underlying development of invasive infection is supported by reports demonstrating a reduction in rates of...

When inflammation was combined with occlusal trauma, immune complexes were confirmed in more expanding areas than in the area of the I group without occlusal trauma, and loss of attachment at the onset of experimental periodontitis was increased. Damage of collagen fibers by occlusal trauma may elevate the permeability of the antigen through the tissue and result in expansion of the area of immune-complex formation and accelerating inflammatory reaction. The periodontal tissue destruction was thus greater in the T+I group than in the I group.

No abstract

The posterior predictive check (PPC) is a model evaluation tool. It assigns a value (pPPC) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis model is regarded as invalid for the given statistic. Properties of the PPC for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation are examined herein for a particularly simple simulation setting: extensive sampling of a single individual's data arising from simple PK/PD and error models. To test the performance characteristics of the PPC, repeatedly, "real" data are simulated and for a variety of statistics, the PPC is applied to an analysis model, which may (null hypothesis) or may not (alternative hypothesis) be identical to the simulation model. Five models are used here: (PK1) mono-exponential with proportional error, (PK2) biexponential with proportional error, (PK2 epsilon) biexponential with additive error, (PD1) Emax model with additive error under the logit transform, and (PD2) sigmoid Emax model with additive error under the logit transform. Six simulation/analysis settings are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1) evaluate whether the PPC has appropriate type-I error level, whereas the second three (PK2/PK1), (PK2 epsilon/PK2), and (PD2/PD1) evaluate whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed under each model pair according to a stipulated extensive sampling design, the pPPC is computed for a number of statistics in three different ways (each way uses a different approximation to the posterior distribution on the model parameters). We find that in general; (i) The PPC is conservative under the null in the sense that for many statistics, prob(pPPC < or = alpha) < alpha for small alpha. With respect to such statistics, this means that useful models will rarely be regarded incorrectly as invalid. A high correlation of a statistic with the parameter estimates obtained from the same data used to compute the statistic (a measure of statistical "sufficiency") tends to identify the most conservative statistics. (ii) Power is not very great, at least for the alternative models we tested, and it is especially poor with "statistics" that are in part a function of parameters as well as data. Although there is a tendency for nonsufficient statistics (as we have measured this) to have greater power, this is by no means an infallible diagnostic. (iii) No clear advantage for one or another method of approximating the posterior distribution on model parameters is found.