This article is a review of the use of quantitative (and qualitative) structure-activity relationships (QSARs and SARs) by regulatory agencies and authorities to predict acute toxicity, mutagenicity, carcinogenicity, and other health effects. A number of SAR and QSAR applications, by regulatory agencies and authorities, are reviewed. These include the use of simple QSAR analyses, as well as the use of multivariate QSARs, and a number of different expert system approaches.

This article is a review of the use, by regulatory agencies and authorities, of quantitative structure-activity relationships (QSARs) to predict ecologic effects and environmental fate of chemicals. For many years, the U.S. Environmental Protection Agency has been the most prominent regulatory agency using QSARs to predict the ecologic effects and environmental fate of chemicals. However, as increasing numbers of standard QSAR methods are developed and validated to predict ecologic effects and environmental fate of chemicals, it is anticipated that more regulatory agencies and authorities will find them to be acceptable alternatives to chemical testing.

The target lipid model (TLM) has been previously applied to predict the aquatic toxicity of hydrocarbons and other nonionic organic chemicals and for deriving the concentrations above which 95% of species should be protected (HC5 values). Several concerns have been identified with the TLM-derived HC5 when it is applied in a substance risk assessment context. These shortcomings were addressed by expanding the acute and chronic toxicity databases to include more diverse taxonomic groups and increase the number of species. The TLM was recalibrated with these expanded databases, resulting in critical target lipid body burdens and acute-to-chronic ratios that met the required guidelines for using species sensitivity distributions in substance risk assessment. The HC5 equation was further revised to consider covarying model parameters. The calculated HC5 values derived from the revised TLM framework were validated using an independent data set for hydrocarbons comprising 106 chronic values across plants, invertebrates, and fish. Assuming a sum binomial distribution, the 95% confidence limit for a 5% failure is between 0.8 and 9.2%. Eight chronic values fell below the HC5, corresponding to an excursion of 7.5%, which falls within the expected uncertainty bounds. Thus, calculated HC5s derived from the revised TLM framework were found to be consistent with the intended protection goals. Environ Toxicol Chem 2018;37:1579-1593. © 2018 SETAC.

The "Workshop on Regulatory Use of (Q)SARs for Human Health and Environmental Endpoints," organized by the European Chemical Industry Council and the International Council of Chemical Associations, gathered more than 60 human health and environmental experts from industry, academia, and regulatory agencies from around the world. They agreed, especially industry and regulatory authorities, that the workshop initiated great potential for the further development and use of predictive models, that is, quantitative structure-activity relationships [(Q)SARs], for chemicals management in a much broader scope than is currently the case. To increase confidence in (Q)SAR predictions and minimization of their misuse, the workshop aimed to develop proposals for guidance and acceptability criteria. The workshop also described the broad outline of a system that would apply that guidance and acceptability criteria to a (Q)SAR when used for chemical management purposes, including priority setting, risk assessment, and classification and labeling.

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The base-line modeling concept presented in this work is based on the assumption of a maximum bioconcentration factor (BCF) with mitigating factors that reduce the BCF. The maximum bioconcentration potential was described by the multi-compartment partitioning model for passive diffusion. The significance of different mitigating factors associated either with interactions with an organism or bioavailability were investigated. The most important mitigating factor was found to be metabolism. Accordingly, a simulator for fish liver was used in the model, which has been trained to reproduce fish metabolism based on related mammalian metabolic pathways. Other significant mitigating factors, depending on the chemical structure, e.g. molecular size and ionization were also taken into account in the model. The results (r(2)=0.84) obtained for a training set of 511 chemicals demonstrate the usefulness of the BCF base line concept. The predictability of the model was evaluated on the basis of 176 chemicals not used in the model building. The correctness of predictions (abs(logBSF(Obs)-logBCF(Calc))=0.75)) for 59 chemicals included within the model applicability domain was 80%.

Threshold concepts of toxicological concern are based on the possibility of establishing an exposure threshold value for chemicals below which no significant risk is to be expected. The objective of the present study is to address environmental thresholds of no toxicological concern for freshwater systems (ETNCaq) for organic chemicals. We analyzed environmental toxicological databases (acute and chronic endpoints) and substance hazard assessments. Lowest numbers and 95th‐percentile values were derived using data stratification based on mode of action (MOA; 1 = inert chemicals; 2 = less inert chemicals; 3 = reactive chemicals; 4 = specifically acting chemicals). The ETNCaq values were derived by multiplying the lowest 95th percentile values with appropriate application factors; ETNCaq,MOA1–3 is approximately 0.1 μg/L. A preliminary analysis with complete MOA stratification of the databases shows that in the case of MOA1 or MOA2, the ETNCaq value could be even higher than 0.1 μg/L. A significantly lower ETNCaqMOA4 value was observed based on the long‐term toxicity information in the European Centre for the Ecotoxicology and Toxicology of Chemicals database. Application of the ETNCaq value in a tiered risk‐assessment scheme may help chemical producers to set data‐generation priorities and to refine or reduce animal use. It also may help to inform downstream users concerning the relative risk associated with their specific uses and be of value in putting environmental monitoring data into a risk‐assessment perspective.

Abstract-Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.