Environmental QSAR

Mudder s (1981) work, which was a pioneer effort in the area of environmental QSARs, was based on real biodegradation data and used 25 molecular descriptors as described by Hansch and Leo. 

Devillers, J. and Lipnick, R.L. (1990). Practical Applications of Regression Analysis in Environmental QSAR Studies. In Practical Applications of Quantitative Structure-Activity Relationships (QSAR) in Environmental Chemistry and Toxicology (Karcher, W. and Devillers, J., eds.), Kluwer, Dordrecht (The Netherlands), pp. 129-144. 

Eriksson, L., Johansson, E., Miiller, M. and Wold, S. (1997). Cluster-Based Design in Environmental QSAR. Quant.Struct.-Act.Relat., 16,383-390. 

Validation is one of the most difficult aspects of environmental QSAR development due to the comparatively small size of the database. Cross-validation has been useful in validating the effectiveness of the model. In this method, one compound is removed from the database, the equation is recalculated, and the toxicity of the omitted compound is estimated. The process is repeated for all compounds in the dataset and the results are tabulated. In this manner, a calculation of the accuracy of prediction of continuous data and the rate of misclassification for categorial data can be compiled. A more useful estimate of the validity of the QSAR model is its ability to predict the toxicity of new compounds. Generally, this is difficult to accomplish in a statistically significant way due to the slow accumulation of new data that meet the criteria used in the modeling process and the associated expense. 

The plan of this chapter is as follows After briefly introducing some basic concepts in environmental QSARs, the characteristic use of QSARs in aquatic toxicology will be discussed and their advantages and limitations will be presented by way of various examples. 

Figure 23.2 Molecular descriptors used in environmental QSAR.

Devillers J, Lipnick RL. Practical applications of regression analysis in environmental QSAR studies. In Karcher W, Devillers J, editors, Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology. Dordrecht Kluwer Academic, 1990. p. 129 13. 

Devillers J. A decade of research in environmental QSAR. SAR QSAR Environ Res 2003 14 1-6. 

Our long term interest in environmental QSAR (quantitative structure-activity relationships) is to develop general, nonempirical model(s) for predicting environmental distributions and toxicities of organic pollutants based only on Information encoded in their structural formulas. This nonempirical approach was selected because it has two major advantages over empirical approaches. First, the determination of nonempirical parameters is faster and less expensive than measurement of empirical parameters and can be performed almost anywhere office, laboratory, home, field, etc., while measurements commonly require laboratory facilities with specialized equipment and qualified and experienced personnel. The second advantage is the higher... 

Figure 3 Design of a training set in environmental QSAR studies...

Most physicochemical properties and biological activities are multidimensional in essence. Their study in terms of structure-property or structure-activity relationships requires the examination of multidimensional spaces which are hardly perceivable by humans. Under these conditions, numerous linear and nonlinear methods are routinely used in environmental QSAR for data reduction and graphical display (Figure 2). In the same way, even if in most cases linear statistical methods allow the derivation of powerful QSAR models, it has been shown that numerous environmental phenomena are better simulated by means of nonlinear statistical tools such as artificial neural networks. ... 

Use of other molecular descriptors (alone or in combination) for deriving QSAR models is also common in environmental chemistiy. However, it is interesting to note that only two-dimensional descriptors are employed (Figure 4). Indeed, the number of QSAR models designed with three-dimensional descriptors is very scarce in environmental sciences. Thus, for example, in the whole environmental QSAR literature, fewer than 10 publications deal with CoMFA (e.g., Briens and co-workers, Dearden and Stott ) while this approach is now widely used in drug design (see Comparative Molecular Field Aiuilysis (CoMFA)). 

Under circiim.stances in which the molecular descriptors are highly intercorrelated (e.g., molecular connectivity indices), there are statistical limitations with respect to the use of a classical multiple regression analysis. Such data sets can be satisfactorily treated by the application of principal components regression (PCR) and partial least squares (PLS) methods.Numerous environmental QSAR model.s use... 

Equation (7) can appear very appealing because of the size of the training set, the apparent quality of its statistical parameters, and also because, basically, the connectivity indices can describe all the organic molecules. However, this model has an important shortcoming. Indeed, the calculation procedure of the x index does not take into account the nature of the atoms. Thus, the same x values are obtained for groups of molecules that contain different atoms, while they have different sorption behaviors (Table 2). Numerous environmental QSAR equations derived from simple connectivity indices suffer from this problem. This is particularly dramatic when the sets contain chemicals with heteroatoms, since no distinction is made between them and yet they induce different properties and activities. 

---