A model, VLOGP, has been developed for assessment of n-octanol/water partition coefficient, log P, of chemicals from their structures. Unlike group contribution methods, VLOGP is based on linear free energy relationship (LFER) approach and employs information-rich electrotopological structure quantifiers derived solely from molecular topology. VLOGP, a robust and cross-validated model derived from accurately measured experimental log P values of 6675 diverse chemicals, has a coefficient of determination, R2, of 0.986 and a standard error of estimate of 0.20. When applied to the training set, the largest deviation observed between experimental and calculated log P was 0.42. VLOGP is different from other log P predictors in that its application domain, called Optimum Prediction Space (OPS), has been quantitatively defined, i.e., structures to which the model should not be applied for predicting log P can be identified. A computer-assisted implementation of this model within HDi's toxicity assessment software package, TOPKAT 3.0, automatically checks whether the submitted structure is inside the OPS or not. VLOGP was applied to a set of 113 chemicals not included in the training set. It was observed that for the structures inside the OPS the average deviation between experimental and model-calculated log P values is 0.27, whereas the corresponding deviation for structures outside the OPS is 1.35. This demonstrates the necessity of identifying the structures to which a model is not applicable before accepting a model-based predicted log P value. For a set of 47 nucleosides, the performance of VLOGP was compared with that of four published log P predictors; a standard deviation of 0.33 was obtained with VLOGP, whereas the standard deviation from other log P predictors ranged between 0.46 and 1.20.

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The ability of a number of prediction systems was examined to determine how well they could predict Salmonella mutagenicity. The prediction systems included two computer-based systems (CASE and TOPKAT), the measurement of a physiochemical parameter (ke) and the use of structural alerts by an expert chemist. The computer-based systems operators and the chemist were supplied with the structures of 100 chemicals that had been tested for mutagenicity in the Salmonella test; the actual chemicals were needed for the physiochemical measurement. None of the participants was provided with the chemical names or Salmonella test results prior to submitting their predictions. The three systems that predicted the mutagenicity from the structure of the chemicals produced equivalent results (71-76% concordance with the Salmonella results); the physiochemical system produced a lower (60-61%) concordance.

The use of structure-activity relationships (SAR) has proven practical for the development of equations which can be used to estimate the above-listed endpoints for a large variety of chemicals. The SAR models predict these endpoints correctly in 85 to 97% of the cases and often surpass in their predictive ability the results obtainable from the equivalent biological assays. These SAR models are being used at several levels: drug, or more generally, chemical discovery; prioritization for testing; regulatory affairs; investigation of detoxification mechanisms; and risk estimation. In the new compound (discovery) use, potential toxic effects of a set of related compounds are investigated before synthesis to select those chemicals with the lesser probabilities of producing toxic effects for further investigation, at considerable savings in research expenditure since fewer compounds need to be synthesized, and the avoidance of blind alleys. Prioritization for testing is used in numerous instances, such as selecting those chemicals in an environment which are most likely to have toxic effects for priority attention. SAR models are used by regulatory agencies to determine the possible toxic effects of chemicals for which data insufficient to render decisions have been submitted, and to gain insight into possible toxicity problems. SAR models are also used to investigate possible metabolites, and toxicity mechanisms due to the ability of making computer-based structural modifications and observing the effects on the modelled toxic endpoints. Risk analysis is a natural outgrowth of several of the above applications, and is particularly useful for SAR models of carcinogenicity. SAR models as alternatives to animal bioassays should be used in the context of other information for the chemicals of concern. Just as bioassays and in vitro methods have their limitations, so do SAR models. These include the sometimes limited data base on which to base an SAR model, the temptation to extrapolate beyond the confines of the model, and the noise inherent in the bioassays on which the models are based. Within these constraints SAR models have a considerable potential in reducing the number of animals used in toxicity testing.