Computational strategy for quantifying human pesticide exposure based upon a saliva measurement

Abstract

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 transcellular 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.

Saliva as a potential matrix for evaluating pharmacologically active dolutegravir concentration in plasma

“…It has been reported that the median unbound fraction of DTG in plasma after 16 weeks of therapy is 0.70% [ 16 ], which is similar to the median ratio of DTG saliva concentration to plasma concentration found in this study. This finding suggests that the secretion of DTG into saliva is governed mainly by passive transcellular diffusion, rather than transcellular active transport, of the unbound drug in plasma [ 17 ]. Similar observations were made in other antiretroviral drugs such as nevirapine [ 18 ], abacavir, darunavir, and raltegravir [ 4 ].…”

Evaluation of non-invasive biomonitoring of 2,4-Dichlorophenoxyacetic acid (2,4-D) in saliva

“…This study is the first to investigate the salivary clearance of 2,4-D, fulfilling a critical research gap. A previously established rat in vitro salivary system was used to investigate the transport mechanism of 2,4-D from plasma into saliva and further predict dose-dependent concentrations by modifying a recently developed computational model assessing pesticide transport into saliva (Smith et al, 2017; Timchalk et al, 2015; Weber et al, 2017). The transport of 2,4-D, a highly ionized organic acid, across the SGEC monolayer was predominantly driven by passive diffusion.…”

“…While the transfer of 2,4-D across SGECs and salivary clearance in rats adds strength to passive diffusion acting as the primary transport mechanism in the salivary clearance of 2,4-D, it is, however, not clear whether passive transcellular transfer or paracellular ultrafiltration predominates. Small (MW ~ ≤ 300 Da), hydrophilic molecules, such as 2,4-D (MW = 221.4 Da), can transfer through tight junctions by the paracellular route (Höld et al, 1996; Timchalk et al, 2015).…”

“…The objective of this study was to evaluate the potential for non-invasive biomonitoring of 2,4-D in saliva. Previously, our group utilized an in vitro rat salivary gland epithelial cell (SGEC) system coupled with computational modeling to successfully predict in vivo salivary transport of a chlorpyrifos metabolite 3,5,6-trichloro-2-pyridinol (TCPy) (Smith et al, 2017; Timchalk et al, 2015; Weber et al, 2017). Here, the SGEC model system was applied to 2,4-D to understand mechanistic aspects of salivary transport and to predict in vivo salivary transport.…”

Tear Fluid as a Matrix for Biomonitoring Environmental and Chemical Exposures

“…Mapping the tear uid metabolome and building models to associate exposures to effects, as has been done for the saliva [164][165][166] , is a critical next step to tear-based biomonitoring.…”