Abstract-Some regulatory programs rely on quantitative structure-activity relationship (QSAR) models to predict toxic effects to biota. Many currently existing QSAR models can predict the effects of a wide range of substances to biota, particularly aquatic biota. The difficulty for regulatory programs is in choosing the appropriate QSAR model or models for application in their new and existing substances programs. We evaluated model performance of six QSAR modeling packages: Ecological Structure Activity Relationship (ECOSAR), TOPKAT, a Probabilistic Neural Network (PNN), a Computational Neural Network (CNN), the QSAR components of the Assessment Tools for the Evaluation of Risk (ASTER) system, and the Optimized Approach Based on Structural Indices Set (OASIS) system. Using a testing data set of 130 substances that had not been included in the training data sets of the QSAR models under consideration, we compared model predictions for 96-h median lethal concentrations (LC50s) to fathead minnows to the corresponding measured toxicity values available in the AQUIRE database. The testing data set was heavily weighted with neutral organics of low molecular weight and functionality. Many of the testing data set substances also had a nonpolar narcosis mode of action and/or were chlorinated. A variety of statistical measures (correlation coefficient, slope and intercept from a linear regression analysis, mean absolute and squared difference between log prediction and log measured toxicity, and the percentage of predictions within factors of 2, 5, 10, 100, and 1,000 of measured toxicity values) indicated that the PNN model had the best model performance for the full testing data set of 130 substances. The rank order of the remainder of the models depended on the statistical measure employed. TOPKAT also had excellent model performance for substances within its optimum prediction space. Only 37% of the substances in the testing data set, however, fell within this optimum prediction space.

It has been asserted that, when screening chemicals for bioaccumulation potential, molecular size cutoff criteria (or indicators) can be applied above which no, or limited, bioaccumulation is expected. The suggested molecular size values have increased over time as more measurements have become available. Most of the proposed criteria have been derived from unevaluated fish bioconcentration factor (BCF) data, and less than 5% of existing organic substances have measured BCFs. We critically review the proposed criteria, first by considering other factors that may also contribute to reduced bioaccumulation for larger molecules, namely, reduced bioavailability in the water column, reduced rate of uptake corresponding to reduced diffusion rates, and the effects of biotransformation and growth dilution. An evaluated BCF and bioaccumulation factor (BAF) database for more than 700 substances and dietary uptake efficiency data are compared against proposed cutoff values. We examine errors associated with interpreting BCF data, particularly for developing molecular size criteria of bioaccumulation potential. Reduced bioaccumulation that is often associated with larger molecular size can be explained by factors other than molecular size, and there is evidence of absorption of molecules exceeding the proposed cutoff criteria. The available data do not support strict cutoff criteria, indicating that the proposed values are incorrect. Rather than assessing bioaccumulation using specific chemical properties in isolation, holistic methods that account for competing rates of uptake and elimination in an organism are recommended. An integrated testing strategy is suggested to improve knowledge of the absorption and bioaccumulation of large substances. Integr Environ Assess Manag 2010;6:210-224. ß 2009 SETAC

Published literature indicates that oil spill dispersion by chemical dispersants will enhance biodegradation because of the increase in interfacial area. However, some of the literature is contradictory concerning whether the use of surfactants will enhance or temporarily inhibit biodegradation, suggesting that more than one mechanism is at work. We set out to study the correlation between the area of dispersed oil droplets and the rate and extent of microbial oil degradation using sorbitan surfactants. We varied the surfactant blend hydrophile-lipophile balance (HLB) and treat level in a statistically designed experiment. Both dispersed area and percent oil degraded at a given time were shown to depend on surfactant HLB and treat level, but to different degrees. The difference was accounted for by demonstrating that percent oil degraded depended on both dispersed area and percent sorbitan in the dispersant treat. The quantitative finding that both dispersed area and surfactant chemistry control microbial growth and oil biodegradation explains the apparent contradiction that some good dispersants enhance, while others temporarily inhibit, degradation. Corexit 9500 dispersant was observed to have a positive influence on biodegradation of oil on water.

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999), Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa, or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.