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Predicting dermal penetration for Expocast chemicals using in silico approaches – should dermal metabolism be considered?
Citation:
Evans, M., P. Bozard, M. Hollister, A. Kinney, K. Lewis, AND A. Nikas. Predicting dermal penetration for Expocast chemicals using in silico approaches – should dermal metabolism be considered? Society of Toxicology, Baltimore, Maryland, March 12 - 16, 2017.
Impact/Purpose:
The US Environmental Protection Agency has the responsibility to determine health risk due to exposure to thousands of chemicals, due to their potential to become environmental contaminants. In addition, there are limited financial resources to perform traditional toxicological screens for all potentially toxic chemicals.An integrative modeling framework will become essential in this predictive toxicology paradigm, one that integrates exposure, dose, and toxicity. A working prototype has been developed by NERL and consists of a HT stochastic model combining exposure, and combines multiple exposure routes into a faster HT-SHEDS. the current PBPK model used in HT-SHEDS has a very simple dermal exposure description.The relative importance of dermal exposure to total internal dose in unknown for almost all chemicals, yet dermal exposure has the potential to be one of the most common modes of exposure. The goal of the work described within this IRP is to improve the description of dermal exposure within this framework.
Description:
There are thousands of consumer product chemicals to which humans may be exposed to via direct (e.g. product use) or indirect (e.g. contact with contaminated media) pathways. The US EPA has developed a research program known as ExpoCast to predict exposures to give real-world context to chemicals being tested within the Toxcast high-throughput screening (HTS) program. The mechanistic human exposure and pharmacokinetic models being developed under ExpoCast require prediction of dermal absorption for large numbers of chemicals with sparse kinetic data. Dermal metabolism may contribute to clearing chemicals from the skin and therefore can prevent them from entering the general circulation. A three compartment model was derived using mass balance combining Fick’s Diffusion Law and Michaelis-Menten metabolism. A literature search for a compound with metabolism data provided the phthalate MEHP, a by-product of the plasticizer DEHP. Both parent compound (MEHP) and metabolite (5-OH-MEHP) time course data were needed to calibrate our model. An optimization algorithm was written in Matlab© and used to solve for parent chemical coefficients and metabolism constants for the metabolite. The numerical code written used central and forward differences to solve for diffusion. This code was used to estimate metabolic maximum velocity (Vmax) and affinity (Km) constants using the 5-OH-MEHP data. The experimental data was found to be more sensitive to Vmax determination. Due to individual metabolic variability observed in the dataset, our preliminary values for Vmax and Km have large confidence intervals. Additional phthalate metabolism data would increase our confidence with final predictions. In summary, this effort indicates the importance of including metabolism data in dermal modeling and points to a current data gap in dermal absorption kinetics. (This abstract does not reflect EPA policy)