Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically-based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra- and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human-population variability in benzene dosimetry.

Trichloroethylene (TCE) is an industrial chemical and an environmental contaminant. TCE and its metabolites may be carcinogenic and affect human health. Physiologically based pharmacokinetic (PBPK) models that differ in compartmentalization are developed for TCE metabolism in humans, and the focus of this investigation is to evaluate alternative models. The two models formulated differ in the compartmentalization of metabolites; more specifically, one model has compartments for all chemicals and the other model has only a generalized body compartment for each the metabolites and contains multiple compartments for the parent, TCE. The models are compared through sensitivity analyses in order to selectively discriminate with regards to model structure. Sensitivities to a parameter of cardiac output (Qcc) are calculated, and the more compartmentalized model predictions for excretion show lower sensitivity to changes in this parameter. Values of Qcc used in the sensitivity analyses are specifically chosen to be applicable to adults of ages into the low 60s. Since information about cardiac output across a population is not often incorporated into a PBPK model, the more compartmentalized ("full") model is probably a more appropriate mathematical description of TCE metabolism, but further study may be necessary to decide which model is a more reasonable option if distributional information about Qcc is used. The study is intended to illustrate how sensitivity analysis can be used in order to make appropriate decisions about model development when considering physiological parameters than vary across the population.

No abstract

Ultra-lightweight, membrane primary mirrors offer a promising future for space telescope technology. However, the advantages of the lightweight structure of the mirrors are restricted by an extremely high susceptibility to microyield. Hence, careful packaging of the membranes is required when transporting mirrors of this type into space. Four packaging models, a cylindrical roll, an umbrella model, a multi-cut model and a single cut model, are presented and compared with each other. Factors such as curvature of the compressed membrane, stability after deployment, and the size of the launch vehicle are considered. All four packaging models appear to be feasible with certain materials and hence warrant physical testing.

No abstract