Benjamin W. Blake

Assessment of developmental toxicity potential of chemicals by quantitative structure-toxicity relationship models

Statistically significant quantitative structure-toxicity relationship (QSTR) models have been developed for assessing developmental toxicity potential (DTP) of chemicals. Three submodels, one each for aliphatic, heteroaromatic and carboaromatic compounds, have been cross-validated to ascertain their robustness. The specificities of the models range from 86% to 97%, and their sensitivities between 86% and 89%. For convenient computer-assisted application, the models are installed in a toxicity assessment software package, TOPKAT, which has been recently enhanced with algorithms to identify whether or not a query structure is inside the optimum prediction space (OPS) of a QSTR model. Different functionalities of the TOPKAT program have been explained by assessing the DTP of a number of compounds not used in the model training sets. The DTP of 18 existing drugs was assessed using these models; the DT assay results were available for 5 of these. Three of these 5 molecules were identified to be inside the OPS and their TOPKAT assessment matched their experimental assignment.

A structure-activity prediction model of carcinogenicity based on NCI/NTP assays and food additives.

Structure-activity relationships (SARs) in chemistry represent a set of techniques by which biological effects and physical-chemical properties can be modelled for a set of chemicals. These methods have been applied to the design of pharmaceuticals, pesticides, and herbicides, among other desired endpoints. SAR applications for such endpoints have been mostly popularized by Hansch et al. (Hansch, 1979) in the US. It was not until recently that SAR methods have been applied to toxicity endpoints. As far as carcinogenicity endpoints are concerned, the efforts have involved but a few investigators, notably Wishnok (Wishnok, 1976; Wishnok, 1978), Jurs and his associates (Jurs, 1979; Chou, 1979; Yuan, 1980; Yuta, 1981 ), and the present author and his collaborators (Enslein, 1982; Enslein, 1983; Enslein, 1984a). In the following sections, we will describe the most recent models of carcinogenicity that we have developed. Estimates from these models can be used for decision-making for carcinogenic risk assessment, setting testing priorities, and aiding in the development of new chemical entities. These estimates should not be used in a vacuum, but in the context of other information available on the specific chemicals.

Salmonella mutagenicity and rodent carcinogenicity: quantitative structure-activity relationships.

Based on a compilation of 222 reports of rodent nominal lifetime carcinogenicity bioassays by the NCI/NTP on the one hand, and corresponding Salmonella mutagenicity bioassays (Ames tests) on the other, Ashby and Tennant (1988) have divided the carcinogens and non-carcinogens into genotoxic (Ames test positive) and non-genotoxic (Ames test negative) groups and discussed structural characteristics common to each of these groups. The Ames test alone was deemed to be adequate for the identification of genotoxicity because other short-term bioassays, and even combinations, or batteries, appeared to offer no significant advantages. From the results of this study it is possible to achieve (1) a division of the carcinogens into the same genotoxic and non-genotoxic groups, and (2) a division of the non-genotoxic compounds into the same carcinogenic and non-carcinogenic groups, solely on the basis of structure-activity relationships, with a classification accuracy of approx. 95%. (1) An equation comprising 8 sigma molecular charge descriptors, 2 molecular connectivity indices (MCIs), 2 kappa molecular shape descriptors and one MOLSTAC substructure descriptor achieved discrimination between genotoxic and non-genotoxic carcinogens with an accuracy of 94.5%. (2) Another equation comprising 8 sigma molecular charge descriptors, 3 MCIs, one kappa shape descriptor and 12 substructural descriptors achieved discrimination between non-genotoxic carcinogens and non-genotoxic non-carcinogens with an accuracy of 95.2%. These SAR models are suitable for the distinction between (1) genotoxic and non-genotoxic carcinogens and (2) carcinogenic and non-carcinogenic non-genotoxins, both in the absence of animal bioassay data.

Prediction of Rat Oral LD50 From Daphnia Magna LC50 and Chemical Structure

A structure-activity model (QSAR) of rat oral LD50 toxicity based on Daphnia magna LC50 values and structural parameters has been developed. Even though the two species represent widely different animals, it is possible to achieve reasonably good predictions of the mammalian endpoint. A regression equation based on 147 diverse chemicals for which both endpoints were available has a correlation coefficient square of 0.75. The independent parameters consisted of molecular connectivity indexes, both simple and valence adjusted, and substructural keys acting as covariates for the different series of compounds in the data base. 50% of the compounds can be predicted within a factor of 1.7 and 95% within a factor of 6 of the actual values. These results demonstrate that it is possible to develop i nterspecies QSAR equations for toxicological and, possibly, efficacy endpoints. The same principles can be used to model different routes of administration.