Predicting ready biodegradability of premanufacture notice chemicals.
Chemical substances other than pesticides, drugs, and food additives are regulated by the U.S. Environmental Protection Agency (U.S. EPA) under the Toxic Substances Control Act (TSCA), but the United States does not require that new substances be tested automatically for such critical properties as biodegradability. The resulting lack of submitted data has fostered the development of estimation methods, and the BioWIN models for predicting biodegradability from chemical structure have played a prominent role in premanufacture notice (PMN) review. Until now, validation efforts have used only the Japanese Ministry of International Trade and Industry (MITI) test data and have not included all models. To assess BioWIN performance with PMN substances, we assembled a database of PMNs for which ready biodegradation data had been submitted over the period 1995 through 2001. The 305 PMN structures are highly varied and pose major challenges to chemical property estimation. Despite the variability of ready biodegradation tests, the use of at least six different test methods, and widely varying quality of submitted data, accuracy of four of six BioWIN models (MITI linear, MITI nonlinear, survey ultimate, survey primary) was in the 80+% range for predicting ready biodegradability. Greater accuracy (>90%) can be achieved by using model estimates only when the four models agree (true for 3/4 of the PMNs). The BioWIN linear and nonlinear probability models did not perform as well even when classification criteria were optimized. The results suggest that the MITI and survey BioWIN models are suitable for use in screening-level applications.
Boethling
Robert S
RS
US Environmental Protection Agency, Office of Pollution Prevention and Toxics, Mail Code 7406, 1200 Pennsylvania Avenue, Northwest, Washington, DC 20004, USA. boethling.bob@epa.gov
Lynch
David G
DG
Thom
Gary C
GC
D001673:Biodegradation, Environmental; D003198:Computer Simulation; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004785:Environmental Pollutants / Q000378:metabolism*; D008954:Models, Biological; D015394:Molecular Structure
D004785:Environmental Pollutants