Are some "safer alternatives" hazardous as PBTs? The case study of new flame retardants.

Some brominated flame retardants (BFRs), as PBDEs, are persistent, bioaccumulative, toxic (PBT) and are restricted/prohibited under various legislations. They are replaced by "safer" flame retardants (FRs), such as new BFRs or organophosphorous compounds. However, informations on the PBT behaviour of these substitutes are often lacking. The PBT assessment is required by the REACH regulation and the PBT chemicals should be subjected to authorization. Several new FRs, proposed and already used as safer alternatives to PBDEs, are here screened by the cumulative PBT Index model, implemented in QSARINS (QSAR-Insubria), new software for the development/validation of QSAR models. The results, obtained directly from the chemical structure for the three studied characteristics altogether, were compared with those from the US-EPA PBT Profiler: the two different approaches are in good agreement, supporting the utility of a consensus approach in these screenings. A priority list of the most harmful FRs, predicted in agreement by the two modelling tools, has been proposed, highlighting that some supposed "safer alternatives" are detected as intrinsically hazardous for their PBT properties. This study also shows that the PBT Index could be a valid tool to evaluate appropriate and safer substitutes, a priori from the chemical design, in a benign by design approach, avoiding unnecessary synthesis and tests.

PBT assessment and prioritization by PBT Index and consensus modeling: comparison of screening results from structural models.

The limited availability of comprehensive data for Persistence, Bioaccumulation and Toxicity (PBT) of chemicals is a serious hindrance to the assignment of compounds to the categories of PBT and vPvB; REACH regulation requires authorization for the use of such chemicals, and additionally plans for safer alternatives. In the context of screening and priority-setting tools for PBT-assessment, the cumulative PBT Index model, implemented in QSARINS (QSAR-INSUBRIA), new software tool for the development and validation of multiple linear regression QSAR models, offers a new holistic approach for the identification of chemicals with cumulative PBT properties directly from their molecular structure. In this study the Insubria PBT Index in QSARINS is applied to the screening and prioritization of various data sets, containing a large variety of chemicals of heterogeneous molecular structure, previously screened by various authors by different methods, for their potential PBT behavior. Particular attention is devoted to the model Applicability Domain, using different approaches such as Descriptor Range, Leverage, and Principal Component Analysis (PCA) of the modeling molecular descriptors, in order to discriminate between interpolated and extrapolated predictions. The results of this screening, which is based only on the molecular structure features and is not dependent on single threshold values for P, B and T, are compared with those obtained by the on-line US-EPA PBT Profiler. Good agreement between the various approaches is found, supporting the utility of a consensus approach in priority-setting studies. The main discrepancies are highlighted and commented on. Moreover, a priority list containing the most hazardous compounds identified in agreement between the two tools is drafted. The PBT Index, implemented in QSARINS, which was demonstrated to be a practical, precautionary and reliable screening tool for PBT-behavior directly from the molecular structure, can be usefully applied for focusing experimental studies, and even before chemical synthesis, in a "benign by design" approach of safer alternatives.

The QSPR-THESAURUS: the online platform of the CADASTER project.

The aim of the CADASTER project (CAse Studies on the Development and Application of in Silico Techniques for Environmental Hazard and Risk Assessment) was to exemplify REACH-related hazard assessments for four classes of chemical compound, namely, polybrominated diphenylethers, per and polyfluorinated compounds, (benzo)triazoles, and musks and fragrances. The QSPR-THESAURUS website (http: / /qspr-thesaurus.eu) was established as the project's online platform to upload, store, apply, and also create, models within the project. We overview the main features of the website, such as model upload, experimental design and hazard assessment to support risk assessment, and integration with other web tools, all of which are essential parts of the QSPR-THESAURUS.

QSARINS-chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS.

A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, is included in the updated version of QSARINS, software recently proposed for the development and validation of QSAR models by the genetic algorithm-ordinary least squares method. In this version, a module, named QSARINS-Chem, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). The chemicals are accessible in different ways (CAS, SMILES, names and so forth) and their three-dimensional structure can be visualized. Some of the QSAR models, previously published by our group, have been redeveloped using the free online software for molecular descriptor calculation, PaDEL-Descriptor. The new models can be easily applied for future predictions on chemicals without experimental data, also verifying the applicability domain to new chemicals. The QSAR model reporting format (QMRF) of these models is also here downloadable. Additional chemometric analyses can be done by principal component analysis and multicriteria decision making for screening and ranking chemicals to prioritize the most dangerous.

Assessing predictive uncertainty in comparative toxicity potentials of triazoles.

Comparative toxicity potentials (CTPs) quantify the potential ecotoxicological impacts of chemicals per unit of emission. They are the product of a substance's environmental fate, exposure, and hazardous concentration. When empirical data are lacking, substance properties can be predicted. The goal of the present study was to assess the influence of predictive uncertainty in substance property predictions on the CTPs of triazoles. Physicochemical and toxic properties were predicted with quantitative structure-activity relationships (QSARs), and uncertainty in the predictions was quantified with use of the data underlying the QSARs. Degradation half-lives were based on a probability distribution representing experimental half-lives of triazoles. Uncertainty related to the species' sample size that was present in the prediction of the hazardous aquatic concentration was also included. All parameter uncertainties were treated as probability distributions, and propagated by Monte Carlo simulations. The 90% confidence interval of the CTPs typically spanned nearly 4 orders of magnitude. The CTP uncertainty was mainly determined by uncertainty in soil sorption and soil degradation rates, together with the small number of species sampled. In contrast, uncertainty in species-specific toxicity predictions contributed relatively little. The findings imply that the reliability of CTP predictions for the chemicals studied can be improved particularly by including experimental data for soil sorption and soil degradation, and by developing toxicity QSARs for more species.

Daphnia and fish toxicity of (benzo)triazoles: validated QSAR models, and interspecies quantitative activity-activity modelling.

Due to their chemical properties synthetic triazoles and benzo-triazoles ((B)TAZs) are mainly distributed to the water compartments in the environment, and because of their wide use the potential effects on aquatic organisms are cause of concern. Non testing approaches like those based on quantitative structure-activity relationships (QSARs) are valuable tools to maximize the information contained in existing experimental data and predict missing information while minimizing animal testing. In the present study, externally validated QSAR models for the prediction of acute (B)TAZs toxicity in Daphnia magna and Oncorhynchus mykiss have been developed according to the principles for the validation of QSARs and their acceptability for regulatory purposes, proposed by the Organization for Economic Co-operation and Development (OECD). These models are based on theoretical molecular descriptors, and are statistically robust, externally predictive and characterized by a verifiable structural applicability domain. They have been applied to predict acute toxicity for over 300 (B)TAZs without experimental data, many of which are in the pre-registration list of the REACH regulation. Additionally, a model based on quantitative activity-activity relationships (QAAR) has been developed, which allows for interspecies extrapolation from daphnids to fish. The importance of QSAR/QAAR, especially when dealing with specific chemical classes like (B)TAZs, for screening and prioritization of pollutants under REACH, has been highlighted.

Evaluation of CADASTER QSAR models for the aquatic toxicity of (benzo)triazoles and prioritisation by consensus prediction.

QSAR regression models of the toxicity of triazoles and benzotriazoles ([B]TAZs) to an alga (Pseudokirchneriella subcapitata), Daphnia magna and a fish (Onchorhynchus mykiss), were developed by five partners in the FP7-EU Project, CADASTER. The models were developed by different methods - Ordinary Least Squares (OLS), Partial Least Squares (PLS), Bayesian regularised regression and Associative Neural Network (ASNN) - by using various molecular descriptors (DRAGON, PaDEL-Descriptor and QSPR-THESAURUS web). In addition, different procedures were used for variable selection, validation and applicability domain inspection. The predictions of the models developed, as well as those obtained in a consensus approach by averaging the data predicted from each model, were compared with the results of experimental tests that were performed by two CADASTER partners. The individual and consensus models were able to correctly predict the toxicity classes of the chemicals tested in the CADASTER project, confirming the utility of the QSAR approach. The models were also used for the prediction of aquatic toxicity of over 300 (B)TAZs, many of which are included in the REACH pre-registration list, and were without experimental data. This highlights the importance of QSAR models for the screening and prioritisation of untested chemicals, in order to reduce and focus experimental testing.

QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae.

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.