Prediction of biodegradability from chemical structure: Modeling of ready biodegradation test data

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.

[1]  W. Karcher,et al.  Predictions for existing chemicals-a multilateral QSAR project. , 1995, SAR and QSAR in environmental research.

[2]  Vijay K. Gombar,et al.  A Structure-Biodegradability Relationship Model by Discriminant Analysis , 1991 .

[3]  Dragan Gamberger,et al.  Application of Artificial Intelligence in Biodegradation Modelling , 1996 .

[4]  Gerald J. Niemi,et al.  Structural features associated with degradable and persistent chemicals , 1987 .

[5]  Andrew P. Worth,et al.  Environmental Modelling Based on a Structural Fragments Approach , 1996 .

[6]  Mats Tysklind,et al.  Multivariate QSBR modeling of biodehalogenation half‐lives of halogenated aliphatic hydrocarbons , 1995 .

[7]  Fredrik Lindgren,et al.  Prediction of Biodegradablity from Chemical Structure , 1996 .

[8]  M. Alexander,et al.  Nonbiodegradable and other racalcitrant molecules , 1973 .

[9]  P. Garthwaite.  An Interpretation of Partial Least Squares , 1994 .

[10]  D T Gibson,et al.  Oxidative degradation of aromatic hydrocarbons by microorganisms. I. Enzymatic formation of catechol from benzene. , 1968, Biochemistry.

[11]  Rakesh Govind,et al.  Development of quantitative structure‐activity relationships for predicting biodegradation kinetics , 1990 .

[12]  Larry J. Forney,et al.  A Biodegradabelity Evaluation and Simulation System (Bess) Based on Knowledge of Biodegradation Pathways , 1996 .

[13]  S. Wold,et al.  The multivariate calibration problem in chemistry solved by the PLS method , 1983 .

[14]  W. J. G. M. Peijnenburg,et al.  Introduction, Main Conclusions and Recommendations of The Workshop “QSAR Biodegradation II” , 1996 .

[15]  Herbert S. Rosenkranz,et al.  Application of the computer‐automated structure evaluation (CASE) program to the study of structure‐biodegradation relationships of miscellaneous chemicals , 1993 .

[16]  Herbert S. Rosenkranz,et al.  Computer‐Automated predictions of aerobic biodegradation of chemicals , 1995 .

[17]  Robert S. Boethling,et al.  Group contribution method for predicting probability and rate of aerobic biodegradation. , 1994, Environmental science & technology.

[18]  Graham Palmer,et al.  The use of computers with chemical structural information: ICI CROSSBOW system , 1974 .

[19]  Willie J.G.M. Peijnenburg,et al.  On the Usefulness and Reliability of Existing QSBRs for Risk Assessment and Priority Setting , 1996 .