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  • Title: Development of a human physiologically based pharmacokinetic (PBPK) model for inorganic arsenic and its mono- and di-methylated metabolites.
    Author: El-Masri HA, Kenyon EM.
    Journal: J Pharmacokinet Pharmacodyn; 2008 Feb; 35(1):31-68. PubMed ID: 17943421.
    Abstract:
    A physiologically-based pharmacokinetic (PBPK) model was developed to estimate levels of arsenic and its metabolites in human tissues and urine after oral exposure to arsenate (As(V)), arsenite (As(III)) or organoarsenical pesticides. The model consists of interconnected individual PBPK models for inorganic arsenic (As(V) and As(III)), monomethylarsenic acid (MMA(V)), and, dimethylarsenic acid (DMA(V)). Reduction of MMA(V) and DMA(V) to their respective trivalent forms also occurs in the lung, liver, and kidney including excretion in urine. Each submodel was constructed using flow limited compartments describing the mass balance of the chemicals in GI tract (lumen and tissue), lung, liver, kidney, muscle, skin, heart, and brain. The choice of tissues was based on physiochemical properties of the arsenicals (solubility), exposure routes, target tissues, and sites for metabolism. Metabolism of inorganic arsenic in liver was described as a series of reduction and oxidative methylation steps incorporating the inhibitory influence of metabolites on methylation. The inhibitory effects of As(III) on the methylation of MMA(III) to DMA, and MMA(III) on the methylation of As(III) to MMA were modeled as noncompetitive. To avoid the uncertainty inherent in estimation of many parameters from limited human data, a priori independent parameter estimates were derived using data from diverse experimental systems with priority given to data derived using human cells and tissues. This allowed the limited data for human excretion of arsenicals in urine to be used to estimate only parameters that were most sensitive to this type of data. Recently published urinary excretion data, not previously used in model development, are also used to evaluate model predictions.
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