Hulzebos E M, Posthumus R
National Institute of Public Health and Environment, RIVM, Anthonie van Leeuwenhoeklaan 9, P.O. Box 1, 3720 BA Bilthoven, The Netherlands.
SAR QSAR Environ Res. 2003 Aug;14(4):285-316. doi: 10.1080/1062936032000101510.
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.
