Carcinogenicity of azathioprine: an S-AR investigation

Potential health effects of drinking water disinfection by-products using quantitative structure toxicity relationship

Disinfection by-products (DBPs) are produced as a result of disinfecting water using various treatment methods. Over the years, chlorine has remained the most popular disinfecting agent due to its ability to kill pathogens. However, in 1974, it was discovered that the superchlorination of drinking water resulted in the production of chloroform and other trihalomethanes. Since then hundreds of additional DBPs have been identified, including haloacetic acids and haloacetonitriles with very little or no toxicological data available, thus necessitating the use of additional methods for hazard estimation. Quantitative Structure Toxicity Relationship (QSTR) is one such method and utilizes a computer-based technology to predict the toxicity of a chemical solely from its molecular attributes. The current research was conducted utilizing the TOPKAT/QSTR software package which is comprised of robust, cross-validated QSTR models for assessing mutagenicity, rodent carcinogenicity (female/male; rat/mouse), developmental toxicity, skin sensitization, lowest-observed-adverse-effect level (LOAEL), fathead minnow LC(50), rat oral LD(50) and Daphia magna EC(50). A total of 252 DBPs were analyzed for the likelihood that they would produce tumors and developmental effects using the carcinogenicity and developmental toxicity submodels of TOPKAT. The model predictions were evaluated to identify generalizations between the functional groups (e.g. alcohols, acids, etc.) and specific toxic endpoints. Developmental toxicity was identified as an endpoint common to the majority of aliphatic mono- and dicarboxylic acids, aliphatic halogenated and non-halogenated ketones, and aliphatic haloacetonitriles. In the case of the carcinogenicity submodels, most aliphatic aldehydes were identified as carcinogens only in the female mouse submodel. The majority of the aliphatic and aromatic dicarboxylic acids were identified as carcinogens in the female rat submodel. All other functional groups examined were largely predicted as non-carcinogens in all the cancer submodels (i.e. male/female rats and mice). The QSTR results should aid in the prioritization for evaluation of toxic endpoints in the absence of in vivo bioassays.

Computational predictive programs (expert systems) in toxicology

The increasing number of pollutants in the environment raises the problem of the toxicological risk evaluation of these chemicals. Several so called expert systems (ES) have been claimed to be able to predict toxicity of certain chemical structures. Different approaches are currently used for these ES, based on explicit rules derived from the knowledge of human experts that compiled lists of toxic moieties for instance in the case of programs called HazardExpert and DEREK or relying on statistical approaches, as in the CASE and TOPKAT programs. Here we describe and compare these and other intelligent computer programs because of their utility in obtaining at least a first rough indication of the potential toxic activity of chemicals.

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.