A quantitative structure-toxicity relationships model for the dermal sensitization guinea pig maximization assay.

We have developed quantitative structure-toxicity relationship (QSTR) models for assessing dermal sensitization using guinea pig maximization test (GPMT) results. The models are derived from 315 carefully evaluated chemicals. There are two models, one for aromatics (excluding one-benzene-ring compounds), and the other for aliphatics and one-benzene-ring compounds. For sensitizers, the models can resolve whether they are weak/moderate or severe sensitizers. The statistical methodology, based on linear discriminant analysis, incorporates an optimum prediction space (OPS) algorithm. This algorithm ensures that the QSTR model will be used only to make predictions on query structures which fall within its domain. Calculation of the similarities between a query structure and the database compounds from which the applicable model was developed are used to validate each skin sensitization assessment. The cross-validated specificity of the equations ranges between 81 and 91%, and the sensitivity between 85 and 95%. For an independent test set, specificity is 79%, and sensitivity 82%.

Assessment of effect levels of chemicals from quantitative structure-activity relationship (QSAR) models. I. Chronic lowest-observed-adverse-effect level (LOAEL).

With the multitude of new chemicals being synthesized and the paucity of long-term test data on chemicals that could be introduced into the environment, innovative approaches must be developed to determine the health and environmental effects of chemicals. Research was conducted to employ quantitative structure-activity relationship (QSAR) techniques to study the feasibility of developing models to estimate the noncarcinogenic toxicity of chemicals that are not addressed in the literature by relevant studies. A database of lowest-observed-adverse effect level (LOAEL) was assembled by extracting toxicity information from 104 U.S. EPA documents, 124 National Cancer Institute/National Toxicology Program (NCI/NTP) reports, and 6 current reports from the literature. A regression model, based on 234 chemicals of diverse structures and chemical classes including both alicyclic and aromatic compounds, was developed to assess the chronic oral LOAELs in rats. The model was incorporated into an automated computer package. Initial testing of this model indicates it has application to a wide range of chemicals. For about 55% of the compounds in the data set, the estimated LOAELs are within a factor of 2 of the observed LOAELs. For over 93%, they are within a factor of 5. Because of the paucity or absence of long-term toxicity data, the public health and risk assessment community could utilize such QSAR models to determine initial estimates of toxicity for the ever-increasing numbers of chemicals that lack complete pertinent data. However, this and other such models should be used only by expert toxicologists who must objectively look at the estimates thus generated in light of the overall weight of evidence of the available toxicologic information of the subject chemical(s).

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.

Carcinogenicity of azathioprine: an S-AR investigation.

Besides its use in the treatment of a variety of presumed autoimmune diseases, azathioprine is given as an immunosuppressant to patients who have had renal transplants. Though epidemiological studies have provided "sufficient" evidence of its carcinogenicity in humans, the carcinogenicity tests in rats and mice are considered to be inconclusive because of limitations in the design and results of these tests (IARC, 1981, 1987). Rosenkranz and Klopman (1991) used the CASE program to identify the structural features responsible for its carcinogenicity. They concluded that this genotoxic chemical was a carcinogen due to the presence of the molecular fragment C"-S-C=. The finding was based on the presence of this biophore fragment in five other compounds, namely: 2-amino-5-nitrothiazole, 2-mercaptobenzothiazole, fenthione, 4,4'-thiodianiline and nithiazide. Recently, Ashby (1992) has expressed concern over the validity of their findings. With the aim of contributing to this debate on the mechanism of carcinogenicity of azathioprine, we have analyzed the structural basis of carcinogenicity of azathioprine and the five support compounds using the carcinogenicity predictor of our toxicity prediction program, TOPKAT. The results, more in line with Ashby's concerns, indicate that no molecular fragment involving the S atom is associated with the carcinogenic properties of these molecules. According to the TOPKAT program the carcinogenicity, if any, of azathioprine is due to the NO2 electrophile because its other major structural features are found to be either associated with non-carcinogenicity or do not discriminate carcinogens from non-carcinogens.

Estimation of maximum tolerated dose for long-term bioassays from acute lethal dose and structure by QSAR.

A quantitative structure-activity relationship (QSAR) model has been developed to estimate maximum tolerated doses (MTD) from structural features of chemicals and the corresponding oral acute lethal doses (LD50) as determined in male rats. The model is based on a set of 269 diverse chemicals which have been tested under the National Cancer Institute/National Toxicology Program (NCI/NTP) protocols. The rat oral LD50 value was the strongest predictor. Additionally, 22 structural descriptors comprising nine substructural MOLSTAC(c) keys, three molecular connectivity indices, and sigma charges on 10 molecular fragments were identified as endpoint predictors. The model explains 76% of the variance and is significant (F = 35.7) at p less than 0.0001 with a standard error of the estimate of 0.40 in the log (1/mol) units used in Hansch-type equations. Cross-validation showed that the difference between the average deleted residual square (0.179) and the model residual square (0.160) was not significant (t = 0.98).

Prediction of probability of carcinogenicity for a set of ongoing NTP bioassays.

Forty-four compounds currently undergoing carcinogenesis bioassay by the National Toxicology Program were submitted to the TOPKAT program for prediction of their potential carcinogenicity. Sixteen compounds could not be handled by TOPKAT. Of the 28 for which predictions were made, 26 (93%) had a confidence level in the estimate of at least moderate. Seventeen were predicted to be carcinogens and 11 non-carcinogens. These results will be compared with the assay results as the assays are completed.

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.