Use of knowledge bases and QSARs to estimate the relative ecological risk of agrichemicals: a problem formulation exercise

Prioritization of pesticide environmental transformation products in drinking water supplies

Receiving waters within catchments may be exposed to many different transformation products following the application of pesticides. As environmental waters are abstracted for drinking water treatment these compounds may pose a risk to human health. This paper describes a prioritization approach for identifying the most important transformation products in drinking water sources. The approach can be applied to different geographical areas that have suitable pesticide usage data. The risk based approach incorporates data on pesticide usage and toxicity as well as transformation product formation, mobility, and persistence. The application of the approach is illustrated for two geographical areas that have good quality pesticide usage data: Great Britain and California. The transformation products with the highest risk index and a complete experimentally derived data set for Great Britain were 3,5,6-trichloro-2-pyridinol, thifensulfuron acid, and kresoxim-methyl acid and for California were carbendazim, aldicarb sulfoxide, and RP30228.

Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity

Quantitative structure-activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR's utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model's potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe.

Comparative evaluation of chemical and environmental online and CD-ROM databases

In a constantly expanding world of chemical and environmental information sources, the need for their evaluation gains more and more importance. This paper presents a comparative evaluation of datasources of online databases and databases on CD-ROM (called CD-ROMs in this paper) in the field of environmental chemicals. The approach is based on research results gained in the years 1996/1997. The authors are aware that changes in the database industry may lead to different results. Before the actual evaluation process can be carried out, two major procedures are necessary, namely, the selection of sets of datasources and the definition of evaluation criteria. In order to perform the difficult task of an evaluation based on several criteria, a general order relation has to be introduced. Methods of partially ordered set theory are applied, and the results are visualized by the technique of Hasse diagrams. On the basis of these evaluation results, the datasources are grouped and then evaluated. It will be shown that there are groups of datasources with quite specific property profiles, and only two groups turn out to be relatively better than the others.