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ECOSAR and DEREKfW predictions for the (eco)toxicological effects of circa 70 substances were compared with experimental data for risk assessment purposes. These and other (quantitative) structure-activity relationships ((Q)SARs) programs will play an important role in future chemical policies, such as in the European Union and The Netherlands, to reduce animal testing and costs and to speed up the number of risk assessments for hazardous chemicals. The two programs, ECOSAR and DEREKfW, were selected because they are easy to use and transparent in their predictions. They predict to which chemical class a substance belongs and also predict some (eco)toxicological properties. ECOSAR categorised 87% of the chemicals correctly in chemical classes. With regard to predicting ecotoxicity, criteria were drawn up for the reliability of the QSARs provided by ECOSAR. Application of these criteria had the result that half of the regression lines from ECOSAR were considered unreliable beforehand. It turned out, however, that the "unreliable" regression lines predicted similar accurately as the "reliable" lines, although much less chemicals were available for validating the "unreliable" QSARs. The overall accurate prediction of toxicity by ECOSAR was 67%. DEREKfW categorised 90% of the chemicals correctly in chemical classes, while 10% of the structural fragments needed a more detailed description. The accuracy of prediction was around 60% for sensitisation, 75% for genotoxicity and carcinogenicity for a limited number of chemicals. Irritation and reproductive toxicity were predicted poorly. Finally, it should be stressed that regulators and industries need to agree on the acceptability criteria relating to false negative and false positive (Q)SAR predictions. This to prevent unnecessary animal testing when regulators do not sufficiently rely on (Q)SAR predictions or to prevent too much faith in (Q)SAR predictions which will then may cause an insufficient protection of man and the environment. Therefore, if the regulatory trend is that (Q)SARs have to be applied more and more systematically in the risk assessment process, their validity and the available tools have to be explored further.

The BIOWIN biodegradation models were evaluated for their suitability for regulatory purposes. BIOWIN includes the linear and non-linear BIODEG and MITI models for estimating the probability of rapid aerobic biodegradation and an expert survey model for primary and ultimate biodegradation estimation. Experimental biodegradation data for 110 newly notified substances were compared with the estimations of the different models. The models were applied separately and in combinations to determine which model(s) showed the best performance. The results of this study were compared with the results of other validation studies and other biodegradation models. The BIOWIN models predict not-readily biodegradable substances with high accuracy in contrast to ready biodegradability. In view of the high environmental concern of persistent chemicals and in view of the large number of not-readily biodegradable chemicals compared to the readily ones, a model is preferred that gives a minimum of false positives without a corresponding high percentage false negatives. A combination of the BIOWIN models (BIOWIN2 or BIOWIN6) showed the highest predictive value for not-readily biodegradability. However, the highest score for overall predictivity with lowest percentage false predictions was achieved by applying BIOWIN3 (pass level 2.75) and BIOWIN6.

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At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.