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Development of a Harmonized Database of Reported and Predicted Consumer Product Ingredient Information
Citation:
Isaacs, K., K. Phillips, K. Dionisio, D. Biryol, AND P. Price. Development of a Harmonized Database of Reported and Predicted Consumer Product Ingredient Information. 2016 Annual International Society of Exposure Science Meeting, Utrecht, NETHERLANDS, October 09 - 13, 2016.
Impact/Purpose:
Presented at 2016 Annual ISES Meeting held in Utrecht, The Netherlands, from 9-13 October 2016.
Description:
Near-field exposure to chemicals in consumer products has been identified as a significant source of exposure for many chemicals. Quantitative data on product chemical composition and weight fraction is a key parameter for characterizing this exposure. While data on product composition are scarce, recent efforts have obtained such information from publically-available Material Safety Data Sheets (MSDS). To supplement these data, techniques have been developed to predict associated weight fractions based on ordered product ingredient lists (e.g., for personal care products). In addition, quantitative structure property relationship (QSPR) methods have been used to develop models which the functions that an arbitrary chemical might perform in consumer products based on its chemical structure. Here, we describe a new harmonized database to provide reported and predicted weight fractions and chemical functions of chemicals in consumer products. The database contains 1) MSDS-based product ingredient data (new and existing) for over 10,000 products, 2) data collected from publically-available ingredient lists and corresponding weight-fraction predictions for 5,907 products, 3) reported chemical function data for 14,000 chemicals, and 4) QSPR-predicted chemical functional use information for over 8,000 chemicals. The products in the database were mapped to a harmonized set of over 300 consumer product categories suitable for use in human exposure modeling. This product composition database integrates information on the chemical use-category linkages, product composition data, and predictive approaches necessary in estimating near-field human exposures to thousands of chemicals in products in support of risk-based decision-making. This abstract does not necessarily reflect U.S. EPA policy.
