Optimizing the aquatic toxicity assessment under REACH through an integrated testing strategy (ITS)

An Integrated Testing Strategy (ITS) to assess the environmental compatibility of wood protection techniques

To quantify the possible impact of different wood protection techniques on the aquatic environment, we applied a tiered Integrated Testing Strategy (ITS) on leachates obtained from untreated (UTW) Norway spruce (Picea abies), specimens treated with a copper-ethanolamine-based preservative solution, complying with the Use Class 3 (UC3), and specimens thermally modified (TM). Different maturation times in water were tested to verify whether toxicant leaching is time-dependent. Tier I tests, addressing acute effects on Aliivibrio fischeri, Raphidocelis subcapitata, and Daphnia magna, evidenced that TM toxicity was comparable or even lower than in UTW. Conversely, UC3 significantly affected all species compared to UTW, also after 30 days of maturation in water, and was not considered an environmentally acceptable wood preservation solution. Tier II (effects on early-life stages of Lymnea auricularia) and III (chronic effects on D. magna and L. auricularia) performed on UTW and TM confirmed the latter as an environmentally acceptable treatment, with increasing maturation times resulting in decreased adverse effects. The ITS allowed for rapid and reliable identification of potentially harmful effects due to preservation treatments, addressed the choice for a less impacting solution, and can be effective for manufacturers in identifying more environmentally friendly solutions while developing their products.

Modelling lethality and teratogenicity of zebrafish (Danio rerio) due to β-lactam antibiotics employing the QSTR approach

Nowadays, β-lactam antibiotics are one of the most consumed OTC (over-the-counter) medicines in the world. Its frequent use against several infectious diseases leads to the development of antibiotic resistance. Another unavoidable risk factor of β-lactam antibiotics is environmental toxicity. Numerous terrestrial as well as aquatic species have suffered due to the excessive use of these pharmaceuticals. In this present study, we have performed a toxicity assessment employing a novel in silico technique like quantitative structure-toxicity relationships (QSTRs) to explore toxicity against zebrafish (Danio rerio). We have developed single as well as inter-endpoint QSTR models for the β-lactam compounds to explore important structural attributes responsible for their toxicity, employing median lethal (LC50) and median teratogenic concentration (TC50) as the endpoints. We have shown how an inter-endpoint model can extrapolate unavailable endpoint values with the help of other available endpoint values. To verify the models' robustness, predictivity, and goodness-of-fit, several universally popular metrics for both internal and external validation were extensively employed in model validation (single endpoint models: r2 = 0.631 - 0.75, Q2F1 = 0.607 - 0.684; inter-endpoint models: r2 = 0.768 - 0.84, Q2F1 = 0.678 - 0.76). Again, these models were engaged in the prediction of these two responses for a true external set of β-lactam molecules without response values to prove the reproducibility of these models.

QSAR assessment of aquatic toxicity potential of diverse agrochemicals

The fast-increasing number of commercially produced chemicals challenges the experimental ecotoxicity assessment methods, which are costly, time-consuming, and dependent on the sacrifice of animals. In this regard, Quantitative Structure-Property/Activity Relationships (QSPR/QSAR) have led the way in developing ecotoxicity assessment models. In this study, QSAR models have been developed using the pEC50 values of 82 diverse agrochemicals or agro-molecules against a planktonic crustacean Daphnia magna with easily interpretable 2D descriptors. Moreover, a link among octanol-water partition coefficient (KOW), bio-concentration factor (BCF), and critical body residue (CBR) has been addressed, and their imputation for the prediction of the toxicity endpoint (EC50) has been done with an objective of the advanced exploration of several ecotoxicological parameters for toxic chemicals. The developed partial least squares (PLS) models were validated rigorously and proved to be robust, sound, and immensely well-predictive. The final Daphnia toxicity model derived from experimental derived properties along with computed descriptors emerged better in statistical quality and predictivity than those obtained solely from computed descriptors. Additionally, the pEC50 and other important properties (log KOW, log BCF, and log CBR) for a set of external agro-molecules, not employed in model development, were predicted to show the predictive ability of the models.

Comparison of seven in silico tools for evaluating of daphnia and fish acute toxicity: case study on Chinese Priority Controlled Chemicals and new chemicals

BACKGROUND

A number of predictive models for aquatic toxicity are available, however, the accuracy and extent of easy to use of these in silico tools in risk assessment still need further studied. This study evaluated the performance of seven in silico tools to daphnia and fish: ECOSAR, T.E.S.T., Danish QSAR Database, VEGA, KATE, Read Across and Trent Analysis. 37 Priority Controlled Chemicals in China (PCCs) and 92 New Chemicals (NCs) were used as validation dataset.

RESULTS

In the quantitative evaluation to PCCs with the criteria of 10-fold difference between experimental value and estimated value, the accuracies of VEGA is the highest among all of the models, both in prediction of daphnia and fish acute toxicity, with accuracies of 100% and 90% after considering AD, respectively. The performance of KATE, ECOSAR and T.E.S.T. is similar, with accuracies are slightly lower than VEGA. The accuracy of Danish Q.D. is the lowest among the above tools with which QSAR is the main mechanism. The performance of Read Across and Trent Analysis is lowest among all of the tested in silico tools. The predictive ability of models to NCs was lower than that of PCCs possibly because never appeared in training set of the models, and ECOSAR perform best than other in silico tools.

CONCLUSION

QSAR based in silico tools had the greater prediction accuracy than category approach (Read Across and Trent Analysis) in predicting the acute toxicity of daphnia and fish. Category approach (Read Across and Trent Analysis) requires expert knowledge to be utilized effectively. ECOSAR performs well in both PCCs and NCs, and the application shoud be promoted in both risk assessment and priority activities. We suggest that distribution of multiple data and water solubility should be considered when developing in silico models. Both more intelligent in silico tools and testing are necessary to identify hazards of Chemicals.

Environmental hazard and risk assessment of thiochemicals. Application of integrated testing and intelligent assessment strategies (ITS) to fulfil the REACH requirements for aquatic toxicity

REACH requires information on hazardous properties of substances to be generated avoiding animal testing where possible. It is the objective of the present case study with thiochemicals to extract as much information as possible from available experimental data with fish, daphnia and algae and to fill data gaps for analogues to be registered under REACH in 2018. Based on considerations of chemical similarity and common mode of action (MOA) the data gaps regarding the aquatic toxicity of the thiochemicals were largely closed by trend analysis ("category approach") and read-across within the same group, for example, thioglycolates or mercaptopropionates. Among 16 thiochemicals to be registered by 2018 there are only 2 substances with sufficient data. 36 data gaps for 14 thiochemicals were identified. Most of the required data (>60%) could be estimated by in silico methods. Only 14 tests (6 algae, 6 daphnia, 1 limit fish test and 1 acute fish test) were proposed. When the results of these tests are available it has to be discussed whether 2 further fish (limit) tests are required. For two substances (exposure-based) waiving was suggested. The relatively high toxicity of the thiochemicals is manifested in low predicted no-effect concentrations (PNECs). Only preliminary predicted environmental concentrations (PECs) could be derived for the thiochemicals for which a risk assessment has to be performed (production rate >10 t/y). The preliminary PEC/PNEC ratios indicate no risk for the aquatic compartment at the production site. PECs due to down-stream use must not exceed the estimated PNECs.

Rapid assessment of estrogenic compounds by CXCL-test illustrated by the screening of the UV-filter derivative benzophenones

CXCL-test is a method that uses the estrogen-dependent secretion of the natural endogenous chemokine CXCL12 to evaluate the estrogenic activity of molecules. CXCL12 chemokine is involved in the estrogen dependent proliferation of breast cancer cells. Its measure is an indicator of cell proliferation and is used as an alternative test to classical proliferation test. Here we aimed to optimize this test, first to increase the number of tested molecules in a single assay and then to decrease the number of intermediate steps. The optimized CXCL-test was finally used for the evaluation of the estrogenic potency of emerging chemical pollutants: the UV filter benzophenones (BPs). The effect of BPs on CXCL12 secretion was also validated by real time quantitative RT-PCR. The optimized CXCL-test allowed a fast and direct assessment of estrogenic potency of molecules. The estrogenic activities of benzophenones were characterized and divided in two groups. The first one contains weak estrogenic compounds (BP, BP1, BP2, BP3, 234BP and 2344'BP). The second one contains medium estrogenic compounds (4BP, 44'BP, BP8, THB).

Classification of baseline toxicants for QSAR predictions to replace fish acute toxicity studies

Fish acute toxicity studies are required for environmental hazard and risk assessment of chemicals by national and international legislations such as REACH, the regulations of plant protection products and biocidal products, or the GHS (globally harmonised system) for classification and labelling of chemicals. Alternative methods like QSARs (quantitative structure-activity relationships) can replace many ecotoxicity tests. However, complete substitution of in vivo animal tests by in silico methods may not be realistic. For the so-called baseline toxicants, it is possible to predict the fish acute toxicity with sufficient accuracy from log K_ow and, hence, valid QSARs can replace in vivo testing. In contrast, excess toxicants and chemicals not reliably classified as baseline toxicants require further in silico, in vitro or in vivo assessments. Thus, the critical task is to discriminate between baseline and excess toxicants. For fish acute toxicity, we derived a scheme based on structural alerts and physicochemical property thresholds to classify chemicals as either baseline toxicants (=predictable by QSARs) or as potential excess toxicants (=not predictable by baseline QSARs). The step-wise approach identifies baseline toxicants (true negatives) in a precautionary way to avoid false negative predictions. Therefore, a certain fraction of false positives can be tolerated, i.e. baseline toxicants without specific effects that may be tested instead of predicted. Application of the classification scheme to a new heterogeneous dataset for diverse fish species results in 40% baseline toxicants, 24% excess toxicants and 36% compounds not classified. Thus, we can conclude that replacing about half of the fish acute toxicity tests by QSAR predictions is realistic to be achieved in the short-term. The long-term goals are classification criteria also for further groups of toxicants and to replace as many in vivo fish acute toxicity tests as possible with valid QSAR predictions.

Modelling the aquatic toxicity of ionic liquids by means of VolSurf in silico descriptors

VolSurf+ in silico physicochemical descriptors for both the cationic and the anionic counterparts of ionic liquids (ILs) have been derived. These descriptors, suitable for molecular modelling of IL structures which, due to their amphiphilic nature, interact strongly with biological matrices, can be related to aquatic toxicity by means of a partial least squares statistical model. This model gives an insight into the relationships between structural physicochemical properties and aquatic toxicity as well as a satisfactory quantitative structure-property correlation, allowing prediction of aquatic toxicity scores of ILs.