气候变化领域集成服务门户

High-throughput screening (HTS) models are being developed and applied to prioritize chemicals for more comprehensive exposure and risk assessment. Dermal pathways are possible exposure routes to humans for thousands of chemicals found in personal care products and the indoor environment. HTS exposure models rely on skin permeability coefficient (K-p; cm/h) models for exposure predictions. An initial database of approximately 1000 entries for empirically-based Kp data was compiled from the literature and a subset of 480 data points for 245 organic chemicals derived from testing with human skin only and using only water as a vehicle was selected. The selected dataset includes chemicals with log octanol-water partition coefficients (K-ow) ranging from -6.8 to 7.6 (median = 1.8; 95% of the data range from -2.5 to 4.6) and molecular weight (MW) ranging from 18 to 765 g/mol (median = 180); only 3%> 500 g/mol. Approximately 53% of the chemicals in the database have functional groups which are ionizable in the pH range of 6 to 7.4, with 31% being appreciably ionized. The compiled log Kp values ranged from -5.8 to 0.1 cm/h (median = -2.6). The selected subset of the Kp data was then used to evaluate eight representative Kp models that can be readily applied for HTS assessments, i.e., parameterized with K--ow and MW. The analysis indicates that a version of the SKINPERM model performs the best against the selected dataset. Comparisons of representative Kp models against model input parameter property ranges (sensitivity analysis) and against chemical datasets requiring human health assessment were conducted to identify regions of chemical properties that should be tested to address uncertainty in K-p models and HTS exposure assessments. (C) 2016 Elsevier Ltd. All rights reserved.