No abstract

No abstract

Exposure to inorganic arsenic (iAs) remains a global public health problem. Urinary arsenicals are the current gold-standard for estimating both iAs exposure and iAs metabolism. However, the distribution of these arsenicals may differ between the urine and target organs. Instead, plasma arsenicals may better represent internal dose and capture target organ exposure to arsenicals. Drinking water iAs, plasma and urinary arsenicals were quantified individuals living in the Zimapan and Lagunera regions of Mexico. The relationship between drinking water iAs and plasma arsenicals was examined using both Spearman correlations and multivariable linear regression models. In addition, the distribution of arsenicals in plasma and urine was examined and the association between plasma and urinary arsenicals was assessed using both Spearman correlations and multivariable linear regression models. Levels of iAs in drinking water were significantly associated with plasma arsenicals in unadjusted and adjusted analyses and the strength of these associations was similar to that of drinking water iAs and urinary arsenicals. These results suggest that plasma arsenicals are reliable biomarkers of iAs exposure via drinking water. However, there were notable differences between the profiles of arsenicals in the plasma and the urine. Key differences between the proportions of arsenicals in plasma and urine may indicate that urine and plasma arsenicals reflect different aspects of iAs toxicokinetics, including metabolism and excretion..

A kinetic model describing the hepatic methylation of arsenite ([As[III]) was developed on the basis of limited data from in vitro mechanistic studies. The model structure is as follows: sequential enzymic methylation of arsenite to its monomethylated (MMA) and dimethylated (DMA) products by first-order and Michaelis-Menten kinetics, respectively; uncompetitive inhibition of the formation of DMA by As(III); and first-order reversible binding of As(III), MMA and DMA to cytosolic proteins. Numerical sensitivity analysis was used to evaluate systematically the impact of changes in input parameters on model responses. Sensitivity analysis was used to investigate the possibility of designing experiments for robust testing of the uncompetitive inhibition hypothesis, and for further refining the model. Based on the sensitivity analysis, the MMA concentration is the most important response on which to focus. The parameters Vmax and ki can be reliably estimated by using the same concentration time-course data at intermediate initial arsenite concentrations of 1–5μM at 30 ± 5 minutes. Km must be estimated independently of Vmax, since the two parameters are highly correlated at all times, and the optimal experimental conditions would include lower initial concentrations of arsenite (0.1–0.5μM) and earlier time-points (about 8–18 minutes). The use of initial arsenite concentrations much above 5μM would not yield additional useful information, because the sensitivity coefficients for MMA, protein-bound MMA, DMA and protein-bound DMA tend to become extremely small or exhibit erratic trends. Overall trends in the sensitivity analysis indicated the desirability of performing measurements at times shorter than 60 minutes. This work demonstrates that physiological modelling and sensitivity analysis can be efficient tools for experimental planning and hypothesis testing when applied in the earliest phases of kinetic model development, thus allowing more-efficient and more-directed experimentation, and minimising the use of laboratory animals.

No abstract