Biodegradation QSAR models

The fundamentals and background of QSAR modeling and predictions have been detailed previously (e.g., Nendza, 1998). Regarding the different physicochemical bases of transformation processes, different approaches to their estimation have been taken. The intention should always be to use process-related models. Thermodynamic principles underlie the relationships between abiotic one-step reaction rates and physicochemical descriptors of the structures. Mechanistic modeling is much more intricate for multistep biodegradations, where the (varying) rate-limiting... 

From the literature, 64 regression models for specific compound classes were retrieved, of which 35 could be tested with the MITI-I data, but only 7 QSARs were successfully validated. These models were derived with four to eight homologous substances and, because of their specificity, they are suitable for application to corresponding substances only. The number of validated QSAR models for specific compound classes is too low to make predictions solely on this basis in the MITI-I data set they were applicable for estimating the biodegradability of only 3% of the chemicals. 

Degner, P., Nendza, M., and Klein, W., Predictive QSAR models for estimating biodegradation of aromatic compounds, Sci. Total Environ., 109, 253-259, 1991. 

Nirmalakhandan, N.N., Speece, R.E. (1988b) QSAR model for predicting Henry s constant. Environ. Sci. Technol. 22(11), 1349-1357. Novak, J.T., Goldsmith, C.D., Benoit, R.E., O Brien, J.H. (1985) Biodegradation of methanol and tertiary butyl alcohol in subsurface systems. Water Sci. Technol. 17, 71-85. 

Physical - chemical QSAR models are available to predict a range of chemical properties including melting point, boiling point, water solubility, biodegradability, vapor pressure, Henry s law constant, sediment adsorptivity, octanol-water partition coefficient, and half-life in the environment. These and other parameters can be readily predicted by EPISUITE (see Relevant Websites section), and enables batch data entry based on Chemical Abstract Service (CAS) numbers or SMILES notations. 

In addition, a wide range of QSAR models to predict biodegradability or persistence are summarized at the OECD website. QSARs models for... 

Damborsky J, Schultz TW. Comparison of the QSAR models for toxicity and biodegradability of anilines and phenols. Chemosphere 1997 34 429-46. 

Pavan, M. and Worth, A.P. (2008) Review of estimation models for biodegradation. QSAR Comb. Sci., 27, 32-40. 

The data-quality requirements for QSAR models relate to several aspects of the experimental procedure, data transformation and the selection of the appropriate test compounds. Only if the input data of a QSAR meet the highest quality standards may a sound model be derived. Because the accuracy of predictions can never be better than the variability of the respective measurements (usually 20% and more), validity assessment of the activity and effects data is crucial in QSAR derivations. The data should be generated by tests that are methodologically and mechanistically defined. The latter is not trivial for parameters such as biodegradability, soil sorption and ecotoxicity. With regard to the considerable variability of measurements, inter- and also intra-laboratory, the test results, especially when collected from different literature sources, should be critically evaluated with respect to ... 

The compound-specific data required for exposure assessments comprise the 1-octanol/water partition coefficient (log water solubility (S ), vapour pressure (p ), Henry s law constant (H, H ), soil sorption coefficient hydrolysis half-life time, photolysis half-life time and information on biodegradability (OECD, 1993c). These parameters generally relate to steady-state conditions - conditions that are rarely met in the real environment. The experimental data underlying the QSAR models are preferably determined by standardized protocols, but, even then, the absolute values are of variable reliability and precision, which clearly affects the accuracy of the predictions based on the acquired QSARs. The endpoints discussed in the following sections were selected because of their consideration in regulatory evaluation schemes in, for example, the EU (EEC, 1990). 

QSAR models on biodegradation can be categorized with respect to the underlying data set and the statistics used for analysis ... 

From the multitude of QSAR models for estimating biodegradability, only a few provide an adequate level of agreement between calculated and experimental data. Classifications were considered adequate if > 75% of the MITI data could be discriminated into readily degradable and non-readily degradable substances for any arbitrary path-level (for details of this validation study for the individual QSAR models see OECD, 1993b). The reasons for mis-classifications by many models can be ascribed partly to the inconsistent data material, the endpoint inhomogeneity and the selection of limited sets of... 

Table 4.10 Examples of validated QSAR models for estimating biodegradability log COD/log k correlations with various parameters.

Figure 4.10 Flow chart of hierarchic QSAR models for prediciting the biodegradability of alicyclic compounds (Degner, 1991 lUCT, 1992 OECD, 1993b). Reproduced with permission from OECD.

Pavan, M. Worth, A. 2006. Review of QSAR Models for Ready Biodegradation, European Commission Directorate - General Joint Research Centre Institute for Health and Consumer Protection. 

Here we only briefly introduce the prediction of biodegradability by QSAR approach. Readers who are interested in detailed information of this field may check other reviews and articles.Many QSAR models of biodegradation have been proposed which we will simply classify into two main groups descriptor-based models and group contribution models. 

Devillers, J. 1993. Neural modelling of the biodegradability of benzene derivatives. SAR and QSAR in Environ. Res. 1 161-167. 

Okey, R.W. and Stensel, H.D., A QSAR-based biodegradability model a QSBR, Water Res., 30, 2206-2214, 1996. 

The TGD provides recommendations for the use of QSARs to predict long term toxicity to fish (no observed effect concentration [NOEC], 28 days) and to Daphnia (NOEC, 21 days). In particular QSARs are provided for chemicals acting by non-polar narcosis and polar narcosis mechanisms of action. No QSARs have been recommended for substances that act by more specific modes of action. For persistence, the TGD recommends two of the SRC BIOWIN models, namely the BIOWIN2 nonlinear model and the BIOWIN3 survey model for ultimate biodegradation. The exact cutoff points for these models have been calibrated on the basis of the model score for 1,2,4-trichlorobenzene — a substance that is known to be relatively persistent under environmental... 

Structure-activity relationships (SARs) and quantitative structure-activity relationships (QSARs), referred to collectively as QSARs, can be used for the prediction of physicochemical properties, environmental fate parameters (e.g., accumulation and biodegradation), human health effects, and ecotoxicological effects. A SAR is a (qualitative) association between a chemical substructure and the potential of a chemical containing the substructure to exhibit a certain physical or biological effect. A QS AR is a mathematical model that relates a quantitative measure of chemical structure (e.g., a physicochemical property) to a physical property or to a biological effect (e.g., a toxicological endpoint). 

Posthumus R, Traas TP, Peijnenburg W, Hulzebos EM. 2005. External validation of EPIWIN biodegradation models. SAR QSAR Environ Res 16 135-148. 

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