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In Silico Model of Endotoxic Shock Mediators (Briefing Charts).

机译:在内毒素休克介质的silico模型中(简报图表)。

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Biologically-based in silico models of pathogen-host interactions are being designed in our lab to predict the time-course of pathogenic infection in humans. Macrophages respond to lipopolysaccharides (LPS), including the release of potent lipid autacoids, causing a cascade of events leading to endotoxic shock. However, animals have been shown to vary in response and susceptibility to E. coli endotoxin: guinea pig > hamster > mouse. To establish a sound basis for interspecies extrapolation, a pathogenesis model is being extended to encompass endotoxic shock. Exposing experimental animals to aerosols of LPS elicits bronchoconstriction, activation of alveolar macrophages, and recruitment of inflammatory cells into airways. These effects have been attributed to a potent lipid autacoid, platelet-activating factor (PAF). Species differences in the biomodulatory effects and mechanisms of PAF are similar to those seen with endotoxin. In guinea pigs, PAF (2 ug/kg IV) causes bronchoconstriction and hypotension in seconds and lethality within 25 minutes. In rats, however, 3 ug/kg of PAF had a negligible impact on heart rate. Therefore, a dynamic model for PAF was developed to link a pathogen's kinetics and host response. The current model focuses on kinetics and receptor binding of PAF and its antagonist ginkgolide B (GB). The kinetic models include plasma, red blood cell, lung, heart, and rapidly and slowly perfused tissues, with IV and inhalation exposure routes, and pathways for binding and elimination of PAF. Kinetic parameters were from the literature. The model was used to simulate experimental exposures to PAF and GB, revealing potential explanations for species differences in sensitivity to PAF. Internal dose metrics were generated and correlated with observed signs of infection and lethality in an attempt to identify the most appropriate metrics for predicting adverse effects. This model of pathogen kinetics and these dose metrics help to elucidate mechanisms of host.

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