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The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

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Fifteen experimental literature data sets on the acute toxicity of substituted nitrobenzenes to algae (Scenedesmus obliquus, Chlorella pyrenoidosa, C. vulgaris), daphnids (Daphnia magna, D. carinata), fish (Cyprinus carpio, Poecilia reticulata), protozoa (Tetrahymena pyriformis), bacteria (Phosphobacterium phosphoreum), and yeast (Saccharomyces cerevisiae) were used to establish quantum chemistry based quantitative structure-activity relationships (QSARs). The logarithm of the octanol/water partition coefficient, log Kow, and the energy of the lowest unoccupied molecular orbital, Elumo, were used as descriptors. Suitable QSAR models (0.65 < r2 < 0.98) to predict acute toxicity of substituted mononitrobenzenes to protozoa, fish, daphnids, yeast, and algae have been derived. The log Kow was a sufficient descriptor for all cases, with the additional Elumo descriptor being required only for algae. The QSARs were found to be valid for neutral substituted mononitrobenzenes with no -OH, -COOH, or -CN substituents attached directly to the ring. From the 100,196 European Inventory of Existing Commercial Substances (EINECS), 497 chemicals were identified that fit the selection criteria for the established QSARs. Based on these results, an advisory tool has been developed that directs users to the appropriate QSAR model to apply for various types of organisms within specified log Kow ranges. Using this tool, it is possible to obtain a good indication of the toxicity of a large set of EINECS chemicals and newly developed substituted mononitrobenzenes to five different organisms without the need for additional experimental testing.