Predicting ready biodegradability in the Japanese ministry of international trade and industry test

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.

Designing Small Molecules for Biodegradability

“…Most uses of QACs, and especially the high-volume fabric softeners, lead to their release to municipal wastewater treatment systems. The fabric softener market is dominated by dialkyl quaternaries, in which hydrophobicity is imparted to the molecule by two linear alkyl chains in the C [10][11][12][13][14][15][16][17][18] range. The hydrophobe is generally derived from purified animal fat (tallow) and consists of a mixture chiefly in the C 16 -C 18 (tallow fatty acids) range.…”

The Guidance Document for Using the OECD (Q)SAR Application Toolbox to Develop Chemical Categories According to the OECD Guidance on Grouping Chemicals

“…Details on the model and structural alerts can be found in Tunkel et al (2000) Please note that only the structural alerts have been implemented as a profiler. No predictions on biodegradability are provided as a result of using the profiler.…”

Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

“…Gamberger et al [8], for example, created two different rules, each designed to best predict data from one or the other dataset. The commonly used BIOWIN model package recommended by the EU Risk Ranking Method [14] includes separate linear and non-linear models built from the MITI-I and the BIODEG data [11,18]. It has been reported that due to cross correlations, it is possible to develop a model that fits the training set data well but is not reliable as a predictor for chemicals outside the training set [19].…”