Computational strategy for quantifying human pesticide exposure based upon a saliva measurement - Timchalk, Charles; Weber, Thomas J.; Smith, Jordan N. (Frontiers in Pharmacology, 2015)
Quantitative exposure data is important for evaluating toxicity risk and biomonitoring is a critical tool for evaluating human exposure. Direct personal monitoring provides the most accurate estimation of a subject’s true dose, and non-invasive methods are advocated for quantifying exposure to xenobiotics. In this regard, there is a need to identify chemicals that are cleared in saliva at concentrations that can be quantified to support the implementation of this approach. This manuscript reviews the computational modeling approaches that are coupled to in vivo and in vitro experiments to predict salivary uptake and clearance of xenobiotics and provides additional insight on species-dependent differences in partitioning that are of key importance for extrapolation. The primary mechanism by which xenobiotics leave the blood and enter saliva involves paracellular transport, passive transcellular diffusion, or trancellular active transport with the majority of xenobiotics transferred by passive diffusion. The transcellular or paracellular diffusion of unbound chemicals in plasma to saliva has been computationally modeled using compartmental and physiologically based approaches. Of key importance for determining the plasma:saliva partitioning was the utilization of the Schmitt algorithm that calculates partitioning based upon the tissue composition, pH, chemical pKa and plasma protein-binding. Sensitivity analysis identified that both protein-binding and pKa (for weak acids and bases) have significant impact on determining partitioning and species dependent differences based upon physiological variance. Future strategies are focused on an in vitro salivary acinar cell based system to experimentally determine and computationally predict salivary gland uptake and clearance for xenobiotics. It is envisioned that a combination of salivary biomonitoring and computational modeling will enable the non-invasive measurement of chemical exposures in human populations.