Use of category approaches, read-across and (Q)SAR: general considerations.

Read-across has generated much attention since it may be used as an alternative approach for addressing the information requirements under regulatory programmes, notably the EU's REACH regulation. Read-across approaches are conceptually accepted by ECHA and Member State Authorities (MS) but difficulties remain in applying them consistently in practice. Technical guidance is available and there are a plethora of models and tools that can assist in the development of categories and read-across, but guidance on how to practically apply categorisation approaches is still missing. This paper was prepared following an ECETOC (European Centre for Ecotoxicology and Toxicology) Task Force that had the objective of summarising guidance and tools available, reviewing their practical utility and considering what technical recommendations and learnings could be shared more widely to refine and inform on the current use of read-across. The full insights are recorded in ECETOC Technical Report TR No. 116. The focus of this present paper is to describe some of the technical and practical considerations when applying read-across under REACH. Since many of the deliberations helped identify the issues for discussion at a recent ECHA/Cefic LRI workshop on "read-across", summary outcomes from this workshop are captured where appropriate for completeness.

On the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity.

The new EU legislation for managing chemicals called REACH aims to fill in gaps in toxicity information that exist for the chemicals listed on the European Inventory of Existing Chemical Substances (EINECS). REACH advocates the use of alternatives to animal experimentation including, amongst others, (quantitative) structure-activity relationship models [(Q)SARs] to help fill in the toxicity data gaps. The aim of the present study was to provide a science-based estimate of the number of EINECS compounds that can be covered by (Q)SAR models for acute toxicity. Using the ECOSAR software, 54% of the 100196 EINECS chemicals were classified into 49 classes that can be potentially covered by (Q)SAR models. The largest proportion of the classified compounds (40% of the EINECS list) falls into the classes of non-polar and polar narcotics. Compounds that were not classified include, for example, fish oils, botanical and animal extracts, and crude oil distillates. With rapid improvements in analytical tools, the number of EINECS compounds for which toxicity evaluations may be based on (Q)SAR approaches may be extended by further developing the method recently developed for the safety assessment of natural flavor complexes used as ingredients in food. This method is based on identification of the individual components in a mixture, and judgment of the safety of these identified individual compounds using toxicity information on structurally similar congeners in the respective classes. Such (Q)SAR approaches may be applied to an additional 2938 EINECS compounds, representing botanical and animal extracts, leading to a total estimate of 57% of the EINECS compounds for which (Q)SAR-based approaches may assist in their safety assessment. It is concluded that, despite the fact that individual (Q)SARs may often each cover only a limited number, i.e. less than 1%, of the EINECS compounds, the potential for applying (Q)SAR approaches for safety assessment of EINECS compounds may prove to be significant.

Disruption of thyroid hormone-mediated Xenopus laevis tadpole tail tip regression by hexabromocyclododecane (HBCD) and 2,2',3,3',4,4',5,5',6-nona brominated diphenyl ether (BDE206).

Thyroid hormone regulates amphibian metamorphosis, including the transformation of a tadpole into a froglet and regression of the tail. Xenopus laevis tadpole tail tips in organ culture (ex vivo) undergo regression when exposed to 3,3',5-triiodo-l-thyronine (T(3)) and interference by chemicals with this process was utilized as a bioassay to detect thyroid hormone disruption. In the present study the bioassay was further validated by investigating its response to compound induced T(3)-antagonism and - potentiation. Tadpole tail tips were exposed to two brominated flame retardants (BFRs) in presence or absence of T(3) at its EC(50) (20 nM). T(3)-induced tail tip regression was antagonized by 2,2',3,3',4,4',5,5',6-nona brominated diphenyl ether (BDE206) and potentiated by hexabromocyclododecane (HBCD) in a concentration dependent manner, which was consistent with results obtained with a in vitro T(3)-dependent proliferation bioassay termed the T-screen. Neither compound induced any effect in the absence of T(3). The results indicate that studying possible hormone disrupting effects of agonistic, antagonistic or potentiating compounds should include combined exposure with the natural hormone at around its EC(50) concentration. The results obtained with the tail tip exposures were in accordance with the T-screen predictions, and occurred at BFR-concentrations that were only 5-50 times those of T(3). The bioassay proved to be suitable not only for detecting T(3)-agonism, but also for antagonism and potentiation.

Quantum chemistry based quantitative structure-activity relationships for modeling the (sub)acute toxicity of substituted mononitrobenzenes in aquatic systems.

Fifteen experimental literature data sets on the acute toxicity of substituted nitrobenzenes to algae (Scenedesmus obliquus, Chlorella pyrenoidosa, C. vulgaris), daphnids (Daphnia magna, D. carinata), fish (Cyprinus carpio, Poecilia reticulata), protozoa (Tetrahymena pyriformis), bacteria (Phosphobacterium phosphoreum), and yeast (Saccharomyces cerevisiae) were used to establish quantum chemistry based quantitative structure-activity relationships (QSARs). The logarithm of the octanol/water partition coefficient, log Kow, and the energy of the lowest unoccupied molecular orbital, Elumo, were used as descriptors. Suitable QSAR models (0.65 < r2 < 0.98) to predict acute toxicity of substituted mononitrobenzenes to protozoa, fish, daphnids, yeast, and algae have been derived. The log Kow was a sufficient descriptor for all cases, with the additional Elumo descriptor being required only for algae. The QSARs were found to be valid for neutral substituted mononitrobenzenes with no -OH, -COOH, or -CN substituents attached directly to the ring. From the 100,196 European Inventory of Existing Commercial Substances (EINECS), 497 chemicals were identified that fit the selection criteria for the established QSARs. Based on these results, an advisory tool has been developed that directs users to the appropriate QSAR model to apply for various types of organisms within specified log Kow ranges. Using this tool, it is possible to obtain a good indication of the toxicity of a large set of EINECS chemicals and newly developed substituted mononitrobenzenes to five different organisms without the need for additional experimental testing.

Quantitative structure-activity relationship modeling of the toxicity of organothiophosphate pesticides to Daphnia magna and Cyprinus carpio.

Within the REACH regulatory framework in the EU, quantitative structure-activity relationships (QSAR) models are expected to help reduce the number of animals used for experimental testing. The objective of this study was to develop QSAR models to describe the acute toxicity of organothiophosphate pesticides to aquatic organisms. Literature data sets for acute toxicity data of organothiophosphates to fish and one data set from experiments with 15 organothiophosphates on Daphniamagna performed in the present study were used to establish QSARs based on quantum mechanically derived molecular descriptors. The logarithm of the octanol/water partition coefficient, logK(ow,) the energy of the lowest unoccupied molecular orbital, E(lumo), and the energy of the highest occupied molecular orbital, E(homo) were used as descriptors. Additionally, it was investigated if toxicity data for the invertebrate D. magna could be used to build a QSAR model to predict toxicity to fish. Suitable QSAR models (0.80

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity.

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

QSAR models for predicting in vivo aquatic toxicity of chlorinated alkanes to fish.

Quantitative structure-activity relationship (QSAR) models are expected to play a crucial role in reducing the number of animals to be used for toxicity testing resulting from the adoption of the new European Union chemical control system called Registration, Evaluation, and Authorization of Chemicals (REACH). The objective of the present study was to generate in vitro acute toxicity data that could be used to develop a QSAR model to describe acute in vivo toxicity of chlorinated alkanes. Cytotoxicity of a series of chlorinated alkanes to Chinese hamster ovary (CHO) cells was observed at concentrations similar to those that have been shown previously to be toxic to fish. Strong correlations exist between the acute in vitro toxicity of the chlorinated alkanes and (i) hydrophobicity [modeled by the calculated log K ow (octanol-water partition coefficient); r (2) = 0.883 and r int (2) = 0.854] and (ii) in vivo acute toxicity to fish ( r (2) = 0.758). A QSAR model has been developed to predict in vivo acute toxicity to fish, based on the in vitro data and even on in silico log K ow data only. The developed QSAR model is applicable to chlorinated alkanes with up to 10 carbon atoms, up to eight chlorine atoms, and log K ow values lying within the range from 1.71 to 5.70. Out of the 100204 compounds on the European Inventory of Existing Chemicals (EINECS), our QSAR model covers 77 (0.1%) of them. Our findings demonstrate that in vitro experiments and even in silico calculations can replace animal experiments in the prediction of the acute toxicity of chlorinated alkanes.