Application of QSARs and VFARs to the rapid risk assessment process at US EPA.

With continued development of new chemicals and genetically engineered microbes as potential agents for terrorism and industrial development, there is a great need for the continued development and application of quantitative structure activity relationships (QSARs) and virulence factor activity relationships (VFARs). Development and application of QSARs and VFARs will facilitate efficient and streamlined use of dwindling resources and assessment of risks associated with exposures to chemical and biological agents. To facilitate the continued development of QSARs and VFARs at US Environmental Protection Agency, a two day workshop was organized June 20-21, 2006, in Cincinnati, OH, USA. This article summarizes the workshop report by highlighting the importance of continued QSAR research, the current state of VFAR science, and the guidance provided to the National Homeland Security Research Center and National Risk Management Research Laboratory by an expert panel for the continued use and development of computational approaches.

Prediction of the health effects of polychlorinated biphenyls (PCBs) and their metabolites using quantitative structure-activity relationship (QSAR).

Polychlorinated biphenyls (PCBs) are a group of 209 persistent environmental contaminants that are slightly different but structurally related. PCBs are known to induce a variety of health effects and often have been toxicologically tested as complex commercial mixtures (Aroclors) but environmental exposure occurs separately to a small number of specific congeners. Recently, the Third National Report on Human Exposures to Environmental Chemicals, an assessment of exposure data of the National Health and Nutrition Examination Survey (NHANES), identified 35 individual PCB congeners in the U.S. population. These types of findings necessitate the toxicity evaluation of individual congeners but adequate toxicity data for most individual PCB congeners are not available. Due to this, a quantitative structure-activity relationship (QSAR) approach was used to assess the potential mutagenesis and carcinogenesis of individual congeners and their possible metabolites. The predictions were analyzed to define the underlying generalizations between the parent PCBs, their metabolites, and some important toxicological endpoints. This analysis reveals that (1) mono and di-chlorinated PCBs and their metabolites can be potential mutagens; (2) PCB benzoquinone metabolites could be carcinogenic but the weight of evidence is poor. These results support the hypothesis that environmental exposure to some PCBs and/or their metabolites could produce mutagenicity and/or carcinogenicity. Hence, these data should be considered as priority toxicological testing data needs. As with all computational toxicology analytical findings, these conclusions must yield to empirical data as they become available.

Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction.

The performance of the rat chronic lowest observed adverse effect level (LOAEL, the lowest exposure level at which there are biologically significant increases in the severity of adverse effects) model in Toxicity Prediction by Komputer Assisted Technology (TOPKAT), a commercial quantitative structure-activity relationship software package, was tested on a database of chemicals that are of interest to the U.S. EPA's Office of Pesticide Programs. The testing was repeated on a database of chemicals from three U.S. EPA sources that report peer-reviewed LOAELs. The results of this study were also contrasted with the results of the testing performed during TOPKAT's model-building process.

Application of QSTRs in the selection of a surrogate toxicity value for a chemical of concern.

As part of the EPA's mission to protect the environment, chemicals of concern (CoCs) at Superfund or other hazardous waste sites are cleaned up based on their potential toxicity to humans and the surrounding ecosystem. Oftentimes, there is a lack of experimental toxicity data to assess the health effects for a CoC in the literature. This research describes a method using Quantitative Structure Toxicity Relationships (QSTRs) for identifying a surrogate chemical for any given CoC. The toxicity data of the surrogate chemical can then be used to rank hazardous waste-site chemicals prior to cleanup decisions. A commercial QSTR model, TOPKAT, was used to establish structural and descriptor similarity between the CoC and the compounds in the QSTR model database using the Oral Rat Chronic LOAEL model. All database chemicals within a similarity distance of < or = 0.200 from the CoC are considered as potential surrogates. If the CoC fails to satisfy model considerations for the LOAEL model, no surrogate is suggested. Potential surrogates that have toxicity data on Integrated Risk Information System (IRIS), Health Effects Assessment Summary Tables (HEAST), or National Center for Environmental Assessment (NCEA) provisional toxicity value list become candidate surrogates. If more than one candidate surrogate is identified, the chemical with the most conservative RfD is suggested as the surrogate. The procedure was applied to determine an appropriate surrogate for dichlorobenzophenone (DCBP), a metabolite of chlorobenzilate, dichlorodiphenyltrichloroethane, and dicofol. Forty-seven potential surrogates were identified that were within the similarity distance of < or = 0.200, of which only five chemicals had an RfD on IRIS, HEAST, or on the NCEA provisional toxicity value list. Among the five potential surrogates, chlorobenzilate with an RfD of 2 x 10(-2) mg/kg-day was chosen as a surrogate for DCBP as it had the most conservative toxicity value. This compared well with surrogate selection using available metabolic information for DCBP and its metabolites or parent compounds in the literature and the provisional toxicity value of 3 x 10(-2) mg/kg-day that NCEA developed using a subchronic study.

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.