No abstract

Abstract-Two new predictive models for assessing a chemical's biodegradability in the Japanese Ministry of International Trade and Industry (MITI) ready biodegradation test have been developed. The new methods use an approach similar to that in the existing BIOWIN᭧ program, in which the probability of rapid biodegradation is estimated by means of multiple linear or nonlinear regression against counts of 36 chemical substructures (molecular fragments) plus molecular weight (mol wt). The data set used to develop the new models consisted of results (pass/no pass) from the MITI test for 884 discrete organic chemicals. This data set was first divided into randomly selected training and validation sets, and new coefficients were derived for the training set using the BIOWIN fragment library and mol wt as independent variables. Based on these results, the fragment library was then modified by deleting some fragments and adding or refining others, and the new set of independent variables (42 substructures and mol wt) was fit to the MITI data. The resulting linear and nonlinear regression models accurately classified 81% of the chemicals in an independent validation set. Like the established BIOWIN models, the MITI models are intended for use in chemical screening and in setting priorities for further review.

No abstract

Biodegradation data were collected and evaluated for 894 substances with widely varying chemical structures. All data were determined according to the Japanese Ministry of International Trade and Industry (MITI) I test protocol. The MITI I test is a screening test for ready biodegradability and has been described by Organization for Economic Cooperation and Development (OECD) test guideline 301 C and European Union (EU) test guideline C4F. The chemicals were characterized by a set of 127 predefined structural fragments. This data set was used to develop a model for the prediction of the biodegradability of chemicals under standardized OECD and EU ready biodegradation test conditions. Partial least squares (PLS) discriminant analysis was used for the model development. The model was evaluated by means of internal cross-validation and repeated external validation. The importance of various structural fragments and fragment interactions was investigated. The most important fragments include the presence of a long alkyl chain; hydroxy, ester, and acid groups (enhancing biodegradation); and the presence of one or more aromatic rings and halogen substituents (retarding biodegradation). More than 85% of the model predictions were correct for using the complete data set. The not readily biodegradable predictions were slightly better than the readily biodegradable predictions (86 vs 84%). The average percentage of correct predictions from four external validation studies was 83%. Model optimization by including fragment interactions improved the model predicting capabilities to 89%. It can be concluded that the PLS model provides predictions of high reliability for a diverse range of chemical structures. The predictions conform to the concept of readily biodegradable (or not readily biodegradable) as defined by OECD and EU test guidelines.

No abstract